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  <channel>
    <title>Northflank Blog</title>
    <link>https://northflank.com/blog</link>
    <description>Code. Build. Deploy. Release. Repeat. The fullstack cloud platform. Regain the initiative and ship smarter. Reduce stress, time and cost.</description>
    <language>en</language>
    <lastBuildDate>2026-05-08T15:00:00.000Z</lastBuildDate>
    <item>
  <title>Top OpenComputer alternatives for AI agent sandboxes in 2026</title>
  <link>https://northflank.com/blog/opencomputer-alternatives</link>
  <pubDate>2026-05-08T15:00:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the top OpenComputer alternatives for AI agent sandbox infrastructure in 2026: Northflank, E2B, Fly.io Sprites, Runloop, Modal, and CodeSandbox.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/opencomputer_alternatives_1e4aea2063.png" alt="Top OpenComputer alternatives for AI agent sandboxes in 2026" /><InfoBox className="BodyStyle">

## TL;DR: OpenComputer alternatives at a glance

OpenComputer provides persistent KVM-based Linux VMs with hibernation and checkpoint support for AI agent workflows. It is open-source and actively developed, but managed-cloud only, with no BYOC and no GPU support. Teams looking for alternatives with broader deployment options, GPU access, or a more complete production stack will find the platforms below worth evaluating.

- [**Northflank**](https://northflank.com/) is the strongest alternative for production deployments. It provides microVM sandboxes (Kata Containers and Firecracker) and gVisor isolation, both ephemeral and persistent environments, GPU support, self-serve BYOC into AWS, GCP, Azure, and bare-metal, and the full infrastructure stack alongside sandboxes: databases, APIs, CI/CD, and observability.
- **E2B** provides Firecracker microVM isolation with Python and TypeScript SDKs, with a 24-hour session limit on Pro.
- **Fly.io Sprites** provides persistent Firecracker VMs with a 100GB NVMe filesystem and idle-based billing.
- **Runloop** provides microVM-isolated Devboxes with built-in benchmarking against SWE-Bench, suspend/resume, snapshot branching, and VPC deployment on Enterprise.
- **Modal** is a Python-first serverless platform with gVisor isolation, GPU support, and autoscaling.

</InfoBox>

## What to look out for in OpenComputer alternatives

Not all sandbox platforms are built for the same use case. When evaluating alternatives, the following dimensions determine whether a platform fits production agent infrastructure.

- **Isolation model**: Full KVM VMs, microVMs (Firecracker, Kata Containers), and gVisor offer different trade-offs between boot time and isolation strength. Shared-kernel containers are weaker for truly untrusted code.
- **Session persistence:** Some platforms impose hard session time limits. Agents that maintain state across user sessions or multi-day workflows need a platform without artificial cutoffs.
- **BYOC support:** For regulated industries or teams with data residency requirements, workloads must stay inside the company's own cloud account. Most platforms in this space are managed-only.
- **GPU availability:** Agents that run inference, fine-tuning, or compute-intensive tasks need GPU access on the same platform as sandbox execution.
- **Platform completeness:** Sandboxes alone are rarely enough. Production agent platforms typically also need databases, background workers, CI/CD, and observability in the same control plane.
- **Pricing transparency:** Billing models vary significantly across platforms. Some charge for provisioned resources; others charge for active usage only. Cost at scale can differ by 5x or more between providers.

## What are the top alternatives to OpenComputer?

The platforms below cover the main use cases for persistent VM and microVM sandbox infrastructure: production agent deployments, fast SDK integration, long-running coding environments, enterprise agent infrastructure with benchmarking, ML-heavy workloads, and snapshot-first workflows.

### 1. Northflank

[Northflank](https://northflank.com/) provides microVM-backed sandbox infrastructure alongside a full production stack: databases, APIs, workers, CI/CD pipelines, GPU workloads, and observability, all running either on Northflank's managed cloud or inside your own VPC.

[Sandboxes on Northflank](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank) boot in under a second using Kata Containers, Firecracker, or gVisor depending on the workload's isolation requirements. Each isolation technology offers different trade-offs between boot time and isolation strength, giving teams flexibility to match the runtime to their threat model. For a technical comparison, see the guides on [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor) and [Firecracker vs gVisor](https://northflank.com/blog/firecracker-vs-gvisor).

A key architectural differentiator is self-serve BYOC. Northflank supports deployment into AWS, GCP, Azure, Oracle, CoreWeave, Civo, bare-metal, and on-premises without requiring a sales call. This is particularly relevant for regulated industries and any deployment where data residency is a hard requirement. For setup details, see [deploying sandboxes in your cloud](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud).

Northflank also supports on-demand GPU workloads running alongside sandboxes in the same platform. A range of GPUs including L4, A100 (40GB and 80GB), H100, H200, and others are available without quota requests. See [GPU workloads on Northflank](https://northflank.com/docs/v1/application/gpu-workloads/gpus-on-northflank) for full hardware details.

- Both ephemeral and persistent sandbox environments with no forced session time limits
- Multi-tenant microVM isolation via Kata Containers, Firecracker, and gVisor
- Self-serve BYOC across AWS, GCP, Azure, Oracle, CoreWeave, Civo, bare-metal, and on-premises
- On-demand GPUs (L4, A100, H100, H200) without quota requests
- Full workload runtime: APIs, workers, databases, CI/CD, and observability in one control plane
- API, CLI, and SSH access
- In production since 2021 across startups, public companies, and government deployments. SOC 2 Type 2 certified.

For API-driven sandbox creation, see [creating sandboxes with the SDK](https://northflank.com/docs/v1/application/sandboxes/create-sandbox-with-sdk). For a full product overview, see the [Northflank sandboxes page](https://northflank.com/product/sandboxes).

**Best for:** Teams that need production-grade microVM isolation, unlimited session lengths, self-serve BYOC, GPU workloads, or a complete infrastructure stack beyond just sandboxes.

**Pricing (PaaS):** CPU at $0.01667/vCPU-hour, memory at $0.00833/GB-hour, billed per second. H100 at $2.74/hour. Full details on the [Northflank pricing page](https://northflank.com/pricing).

<InfoBox className="BodyStyle">

**Get started with sandboxes on Northflank**

- [Sandboxes on Northflank](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank): architecture overview and core sandbox concepts
- [Deploy sandboxes on Northflank](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-on-northflank): step-by-step deployment guide
- [Deploy sandboxes in your cloud](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud): run sandboxes inside your own VPC
- [GPUs on Northflank](https://northflank.com/docs/v1/application/gpu-workloads/gpus-on-northflank): GPU workload overview and supported hardware
- [Deploy GPUs on Northflank cloud](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-on-northflank-cloud): step-by-step GPU deployment guide
- [Deploy GPUs in your own cloud](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-in-your-own-cloud): GPU workloads inside your own VPC

[Get started (self-serve)](https://app.northflank.com/signup), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) if you have specific infrastructure or compliance requirements.

</InfoBox>

### 2. E2B

E2B provides sandbox infrastructure for AI agents with Python and TypeScript SDKs and Firecracker microVM isolation. The SDK supports integration with LangChain, OpenAI, and Anthropic tooling.

- Firecracker microVM isolation with a dedicated kernel per sandbox
- Python and TypeScript SDKs with AI framework integrations
- Pause and resume: state is preserved with no compute cost while paused; storage included free
- Default 2 vCPUs / 1GB RAM, configurable up to 8 vCPUs / 8 GiB on Pro
- No GPU support
- BYOC available for enterprise customers only, not self-serve
- 24-hour session limit on Pro

**Best for:** Teams building AI coding agents or code interpreter experiences who need SDK integrations and sessions under 24 hours.

### 3. Fly.io Sprites

Fly.io Sprites provides stateful sandbox environments for AI coding agents. Each Sprite is a persistent Linux VM running on a Firecracker microVM with hardware-level isolation.

- Firecracker microVM isolation
- 100GB NVMe-backed filesystem that persists across sessions without explicit snapshotting
- Checkpoint and restore in approximately 300ms, capturing the full VM state
- Up to 8 CPUs and 16GB RAM per Sprite
- No compute charge when idle; billing stops when the Sprite is inactive
- No GPU support
- No BYOC; all environments run on Fly.io's managed infrastructure
- $30 in trial credits available

Sprites are designed for individual developer workflows and coding agent use cases. They do not provide multi-tenant orchestration APIs or broader platform features such as databases, CI/CD, or observability.

**Best for:** Individual developers building coding agents who want persistent environments with idle-based billing and checkpoint/restore. Teams already operating on Fly.io.

### 4. Runloop

Runloop provides microVM-isolated Devboxes for AI coding agents. Devboxes run on a custom bare-metal hypervisor with two layers of isolation: a VM layer and a container layer. The platform supports running agents against SWE-Bench Verified, SWE-Smith, R2E-Gym, and other public benchmarks directly from the platform with no setup required.

- Two-layer isolation (VM + container) on a custom bare-metal hypervisor
- Suspend and resume: compute billing stops on suspension, storage continues
- Snapshot and branch from Devbox disk state
- Repo Connections for automatic build environment inference from Git repositories
- Both arm64 and x86 architecture support
- SSH, CLI, and IDE connections to running Devboxes
- No GPU support

**Best for:** Teams building AI coding agents that need persistent, isolated Devboxes with suspend/resume and snapshot branching for stateful agentic workflows.

### 5. Modal

Modal is a Python-first serverless compute platform. Modal Sandboxes run on gVisor, which intercepts Linux system calls in user space rather than providing a dedicated VM kernel per workload.

- gVisor isolation (user-space kernel interception, not hardware-level microVM)
- GPU support across H100, A100 80GB, A100 40GB, L4, and others
- Persistent storage via Volumes at $0.09/GiB/month (1 TiB/month free)
- Session timeout default of 5 minutes, configurable up to 24 hours; longer workflows use filesystem snapshots
- Environments defined through Modal's Python SDK, not arbitrary container images
- No BYOC; managed infrastructure only

**Best for:** Python-first ML teams running inference, training, or data pipelines who need sandboxing integrated with GPU compute in one platform.

### 6. CodeSandbox

CodeSandbox, now part of Together AI, provides microVM-based sandbox environments with snapshot and forking as first-class primitives. Named checkpoints can be forked into multiple independent sandboxes or restored in under two seconds.

- microVM isolation with snapshot and fork support
- No platform-imposed session time limit on any plan
- Dev Container images and standard environment formats supported
- No GPU compute available
- No BYOC outside of enterprise dedicated cluster arrangements
- Scales to 250 concurrent VMs on the Scale plan, custom on Enterprise

CodeSandbox is web-focused in its feature set and integrations, suited more toward development and educational use cases than production agent infrastructure at scale.

**Best for:** Teams that need snapshot and forking as a core workflow primitive, web-focused coding agents, and educational platforms.

## OpenComputer alternatives pricing comparison

*Pricing as of May 2026. Verify current rates on each platform's pricing page before making cost decisions.*

### Compute pricing (PaaS)

| Platform | CPU | Memory | Storage | GPU | Billing model |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | $0.01667/vCPU-hr | $0.00833/GB-hr | $0.15/GB-month | L4: $0.80/hr, A100 40GB: $1.42/hr, A100 80GB: $1.76/hr, H100: $2.74/hr, H200: $3.14/hr | Per second |
| **E2B** | $0.0504/vCPU-hr | $0.0162/GiB-hr | 10–20GB included free | No GPU | Per second |
| **Fly.io Sprites** | $0.07/CPU-hr | $0.04375/GB-hr | Hot NVMe: $0.000683/GB-hr; Object: $0.000027/GB-hr | No GPU | Per second, actual cgroup usage. No charge when idle |
| **Runloop** | $0.108/CPU-hr | $0.0252/GB-hr | $0.00034236/GB-hr | No GPU | Per second |
| **Modal Sandboxes** | $0.1419/physical core-hr (= 2 vCPU) | $0.0242/GiB-hr | $0.09/GiB-month (1 TiB free) | L4: $0.80/hr, A100 40GB: $2.10/hr, A100 80GB: $2.50/hr, H100: $3.95/hr | Per second |
| **CodeSandbox** | Pico (2 cores, 1GB): $0.0743/hr. Nano (2 cores, 4GB): $0.1486/hr | Bundled with VM tier | Included | No GPU | Credit-based |

## Which OpenComputer alternative should you choose?

| Platform | Choose if... |
| --- | --- |
| **Northflank** | You need production-grade microVM isolation, unlimited sessions, self-serve BYOC, GPU workloads, or a full infrastructure stack in one place |
| **E2B** | You need SDK integrations with major AI frameworks and sessions under 24 hours. |
| **Fly.io Sprites** | You want persistent VMs with idle-based billing and checkpoint/restore for coding agents |
| **Runloop** | You need sandbox environments with integrated evaluation tooling and suspend/resume for agentic workflows |
| **Modal** | Your workloads are Python-first and ML-heavy with GPU compute requirements |
| **CodeSandbox** | Snapshot and forking are central to your workflow and your use case is web-focused |

Northflank is the only platform here that covers production microVM isolation, self-serve BYOC, GPU support, unlimited sessions, and a full platform stack in one place. For teams building multi-tenant AI platforms or agent infrastructure that needs to scale under compliance requirements, it is the platform worth evaluating first. See the [Northflank AI sandbox pricing guide](https://northflank.com/blog/ai-sandbox-pricing) for a detailed cost breakdown.

## Frequently asked questions about OpenComputer alternatives

### Does OpenComputer support BYOC or GPU workloads?

No. OpenComputer runs on managed infrastructure only and has no GPU compute capability. Teams that need BYOC deployment or GPU workloads should evaluate [Northflank](https://northflank.com/product/bring-your-own-cloud), which supports both on a self-serve basis.

### Which platform is cheapest for AI sandboxes at scale?

On PaaS, Northflank has the lowest published CPU rate at $0.01667/vCPU-hour among the platforms in this comparison with transparent pricing. Cost at scale varies significantly depending on workload spec, concurrency, and whether BYOC is an option. See the [AI sandbox pricing guide](https://northflank.com/blog/ai-sandbox-pricing) for a detailed cost breakdown across providers.

### Which platforms support persistent sandbox environments?

Northflank supports both ephemeral and persistent environments with no forced time limits. Fly.io Sprites maintains a persistent 100GB NVMe filesystem across sessions with idle-based billing. E2B supports pause and resume with state preserved at no compute cost. Runloop supports suspend and resume with no compute charge while suspended, alongside snapshot and branch from Devbox disk state. CodeSandbox supports persistence via snapshots with VM restore in under two seconds. Modal supports snapshot-based state preservation across sessions up to 24 hours.

### What isolation model does OpenComputer use compared to its alternatives?

OpenComputer uses full KVM-based virtual machines, giving each sandbox a dedicated kernel, memory, and disk. Northflank supports Kata Containers, Firecracker, and gVisor, giving teams the option to choose between microVM-level hardware isolation and user-space kernel interception depending on their workload. E2B and Fly.io Sprites use Firecracker microVMs, providing a dedicated kernel per sandbox. Runloop uses two layers of isolation: a VM layer and a container layer. Modal uses gVisor, which intercepts system calls in user space without a dedicated VM per workload. For a deeper comparison, see the Northflank guide on [microVM vs gVisor](https://northflank.com/blog/microvm-vs-gvisor).]]>
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  <title>Multi-tenant SaaS platform deployment in 2026: a production guide</title>
  <link>https://northflank.com/blog/multi-tenant-saas-platform-deployment</link>
  <pubDate>2026-05-07T15:45:00.000Z</pubDate>
  <description>
    <![CDATA[A production guide to multi-tenant SaaS platform deployment in 2026. Covers tenant provisioning, database strategy, CI/CD, Kubernetes isolation, BYOC, and per-tenant observability.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/multi_tenant_saas_platform_deployment_133aab1ac9.png" alt="Multi-tenant SaaS platform deployment in 2026: a production guide" />Multi-tenant SaaS platform deployment is where most engineering teams lose weeks they did not plan for. Getting the architecture right is one problem. Running it in production across dozens of tenants, varying compliance requirements, and a CI/CD pipeline that has to update all of them without downtime is another.

This guide covers the deployment side: provisioning, database strategy, CI/CD, Kubernetes isolation, bring your own cloud (BYOC), and per-tenant observability. It assumes you have already chosen your architecture model and need to ship it.

<InfoBox className="BodyStyle">

## TL;DR: multi-tenant SaaS platform deployment in 2026

- Multi-tenant SaaS platform deployment covers automated tenant provisioning, infrastructure isolation, database deployment strategy, CI/CD across tenants, and per-tenant observability. This is the operational layer most teams underestimate until they are already in production.
- The deployment model you choose determines your cost structure, compliance ceiling, and operational complexity. Most production SaaS platforms use a hybrid approach: shared infrastructure for standard tenants, dedicated for enterprise.
- Automated tenant provisioning is the single most important investment to make before you scale. Manual provisioning works early on, but becomes a blocker as your tenant count grows.
- [Northflank](https://northflank.com/) automates the hardest parts of multi-tenant SaaS deployment: tenant provisioning, namespace isolation, mTLS, managed databases, and BYOC into your own VPC. Workloads are isolated between tenants with nothing shared between them, making it suitable for SaaS vendors whose customers have strict data isolation and compliance requirements.

</InfoBox>

## What is multi-tenant SaaS platform deployment?

Multi-tenant SaaS platform deployment is the process of running a multi-tenant application in production across cloud infrastructure, with automated systems managing tenant lifecycle, isolation, updates, and observability. It is distinct from multi-tenant architecture (the design decisions) and multi-tenant development (the application code).

In practice, deployment covers:

- how new tenants are provisioned when a customer signs up
- how their data and workloads are isolated from other tenants at the network, compute, and database layers
- how updates are rolled out across all tenants without downtime
- how resource consumption is tracked and attributed per tenant

These are operational problems that do not surface during development but become the primary engineering constraint as a SaaS platform scales. For background on multitenancy as a concept, see [What is multitenancy? Meaning, architecture, benefits and risks](https://northflank.com/blog/what-is-multitenancy).

## Quick reference: multi-tenant SaaS deployment models in 2026

The deployment model you choose before going to production determines how you scale, what compliance tiers you can support, and how much operational overhead you carry. Most production SaaS platforms run a hybrid model: standard customers on shared infrastructure, enterprise customers on partitioned or dedicated deployments, tiered by pricing.

| Model | Best for | Cost efficiency | Isolation | Operational complexity |
| --- | --- | --- | --- | --- |
| Shared infrastructure | Early-stage SaaS, internal tools | High | Logical | Low |
| Partitioned (namespace/VPC per tenant) | Growing SaaS | Medium | Network + compute | Medium |
| Dedicated (cluster per tenant) | Enterprise, compliance | Low | Dedicated | High |
| Hybrid | Mixed customer tiers | Variable | Flexible | Medium-high |

For a detailed breakdown of each model, see [What are the main deployment models for multi-tenant cloud systems?](https://northflank.com/blog/multi-tenant-cloud-deployment#what-are-the-main-deployment-models-for-multitenant-cloud-systems).

## How do you set up automated tenant provisioning in production?

Manual tenant provisioning does not scale. As your tenant count grows, the operational burden becomes a blocker: inconsistent configurations, slow onboarding, and no audit trail for what was provisioned when.

Automated provisioning covers the full onboarding sequence:

- namespace creation with network policies and resource quotas
- database provisioning (new schema, new instance, or row-level scoping depending on your model)
- secrets creation and scoping
- DNS and subdomain configuration
- RBAC assignment

Each step needs to be idempotent, meaning if it runs twice, the result should be the same as if it ran once.

<InfoBox className="BodyStyle">

[Northflank](https://northflank.com/) automates this when you create a new project. A namespaced environment is provisioned with Cilium-based network policies, RBAC scoped to project members, encrypted secrets storage, and isolated networking with automatic mTLS. Workloads are isolated between tenants with nothing shared between them, making it suitable for vendors whose customers have strict data isolation and compliance requirements.

For workloads needing stronger isolation, Kata Containers and Firecracker provide microVM-based isolation, while gVisor intercepts system calls at the kernel boundary. See [Kubernetes multi-tenancy: a 2026 guide to secure shared infrastructure](https://northflank.com/blog/kubernetes-multi-tenancy) for a full breakdown. For teams deploying into their own cloud, [BYOC](https://northflank.com/product/bring-your-own-cloud) extends provisioning into the customer's VPC across AWS, GCP, Azure, CoreWeave, Civo, and Oracle.

Northflank is self-serve. [Get started for free](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) if you want to talk through your setup with an engineer.

</InfoBox>

## What database deployment strategy should you use for multi-tenant SaaS?

The database model is the decision that is hardest to change after you have customers. The three patterns are:

| Pattern | Cost per tenant | Isolation | Migration complexity | Best for |
| --- | --- | --- | --- | --- |
| Shared schema | Low | Logical | Low | High-volume SMB SaaS |
| Schema-per-tenant | Medium | Logical (schema boundary) | Medium | Mid-market SaaS |
| Database-per-tenant | High | Dedicated | High | Enterprise, compliance tiers |
- **Shared schema:** all tenants share the same tables, isolated by a tenant identifier column. The risk is a missing filter clause exposing one tenant's data to another.
- **Schema-per-tenant:** each tenant gets their own schema within a shared database instance. The most practical middle ground for mid-market SaaS on PostgreSQL.
- **Database-per-tenant:** each tenant gets a separate database instance. Right for enterprise customers with compliance requirements or performance SLAs, but each tenant requires its own connection pool, backup schedule, and migration run.

Most mature SaaS platforms use a hybrid of all three, tiered by customer pricing. Northflank's managed databases (PostgreSQL, MongoDB, MySQL, Redis, RabbitMQ, MinIO) are deployed within isolated projects, giving each tenant their own database environment. See [pricing](https://northflank.com/pricing) for resource costs.

## How do you handle CI/CD for a multi-tenant SaaS platform?

Deploying updates across all tenants without downtime is the core CI/CD challenge. A broken deployment that affects all customers at once is the blast radius problem in its worst form.

The production approach involves:

- **Canary rollouts:** deploy to a small cohort of tenants first, validate metrics, then progressively roll out with automated rollback triggers if error rates spike
- **Tenant-specific feature flags:** enable new functionality per tenant before full rollout, giving enterprise customers advance testing windows before production changes
- **Parallel schema migrations:** for database-per-tenant and schema-per-tenant models, migrate a subset of tenant databases concurrently, halt on failure, and maintain per-tenant migration state so partial runs can resume

Northflank's [release pipelines](https://northflank.com/product/deployments) support GitOps workflows and per-pull-request environments for testing changes before they reach any tenant.

## How do you deploy a multi-tenant SaaS platform into a customer's own cloud?

Enterprise customers in regulated industries frequently require software to run inside their own cloud account. This requirement typically comes from HIPAA, PCI-DSS, FedRAMP, GDPR data residency rules, or internal security policies.

The standard architecture separates two planes:

- **Control plane:** stays in the vendor's account and handles deployment orchestration and update delivery
- **Application plane:** services, databases, and compute that run inside the customer's VPC, connected to the control plane through a secure, least-privilege mechanism specific to each cloud provider

Supporting a handful of customer VPC deployments manually is feasible. Supporting many requires automation that extends your standard provisioning and monitoring into each customer environment without a dedicated engineering team per customer.

<InfoBox className="BodyStyle">

Northflank's [BYOC](https://northflank.com/product/bring-your-own-cloud) model handles this through a single control plane across AWS (EKS), GCP (GKE), Azure (AKS), CoreWeave, Civo, and Oracle. Each deployment includes namespace isolation, mTLS, encrypted secrets, and audit trails. Northflank is SOC 2 Type 2 compliant. See [SaaS deployment in customer environments](https://northflank.com/blog/saas-deployment-in-customer-environment) and the [BYOC features page](https://northflank.com/features/bring-your-own-cloud) for full details.

</InfoBox>

## How do you monitor and track costs per tenant in a multi-tenant SaaS platform?

Aggregate metrics hide tenant-specific problems. A healthy p99 latency across all tenants can mask one enterprise customer experiencing slow response times. Per-tenant observability is not optional in production.

Per-tenant monitoring covers:

- tagging all logs, metrics, and traces with a tenant identifier
- per-tenant dashboards tracking query latency, compute consumption, API usage, and error rates
- identifying heavy tenants whose usage requires throttling or placement on dedicated infrastructure
- per-tenant resource tracking to support usage-based pricing or internal chargeback models

Northflank provides centralized logs, CPU and memory metrics, and audit trails across all projects out of the box.

## Frequently asked questions about multi-tenant SaaS platform deployment in 2026

### What is the difference between multi-tenant and single-tenant SaaS deployment?

In single-tenant SaaS, each customer runs on their own dedicated infrastructure instance. In multi-tenant SaaS, multiple customers share the same infrastructure with logical or dedicated isolation between them. Multi-tenancy is more cost-efficient to operate at scale but requires more deliberate engineering around isolation, provisioning, and observability.

### What is the noisy neighbour problem in multi-tenant SaaS?

The noisy neighbour problem occurs when one tenant's workload consumes a disproportionate share of shared resources, degrading performance for other tenants on the same infrastructure. It is addressed through per-tenant resource quotas, rate limiting, and dedicated infrastructure placement for enterprise tenants with performance SLAs.

### How do you handle zero-downtime deployments in a multi-tenant SaaS platform?

The standard approach is canary rollouts: deploy to a small cohort of tenants first, validate that error rates and latency stay within acceptable bounds, then progressively roll out to the remaining tenant pool with automated rollback triggers. Tenant-specific feature flags give you an additional layer of control, letting you enable changes per tenant independently of the deployment itself.

## Deploy your multi-tenant SaaS platform on Northflank

Northflank handles the infrastructure complexity of multi-tenant SaaS deployment so your engineering team does not have to build it from scratch. From automated tenant provisioning to BYOC deployment inside customer VPCs, it covers the operational layer end to end.

[Get started for free](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to talk through your deployment requirements with an engineer.]]>
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  <title>Enterprise AI coding agent deployment in 2026</title>
  <link>https://northflank.com/blog/enterprise-ai-coding-agent-deployment</link>
  <pubDate>2026-05-07T14:30:00.000Z</pubDate>
  <description>
    <![CDATA[Enterprise AI coding agent deployment requires secure infrastructure, sandbox isolation, audit logging, SSO, RBAC, and BYOC controls to move AI agents from pilot to production safely.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Enterprise_AI_coding_agent_deployment_in_2026_438c031053.png" alt="Enterprise AI coding agent deployment in 2026" /><InfoBox className="BodyStyle">

## TL;DR: enterprise AI coding agent deployment in 2026

- 88% of enterprise AI agent pilots never reach production. Gartner predicts over 40% of agentic AI projects will be canceled by 2027 due to unclear business value and inadequate risk controls, not model quality.
- Enterprise deployment requires seven non-negotiable controls: SSO integration, SIEM-connected audit logging, secret scanning on agent PRs, PR policy gates, license governance, sandbox isolation for agent execution, and incident response runbooks.
- The infrastructure layer, compute isolation, RBAC, network controls, and data residency are separate from the AI coding tool itself. Most enterprise deployments fail because they treat tool selection as the deployment decision and skip the infrastructure layer.
- [Northflank](https://northflank.com/) provides the execution infrastructure for enterprise AI coding agent deployment: microVM sandbox isolation, self-serve BYOC into your own cloud or on-premises, RBAC, audit logging, SSO, and GPU workloads in one control plane.

> [Northflank](https://northflank.com/) is a full-stack cloud platform that provides the execution infrastructure enterprises need to deploy AI coding agents safely in production. [MicroVM sandbox](https://northflank.com/product/sandboxes) isolation, [BYOC](https://northflank.com/product/bring-your-own-cloud) into AWS, GCP, Azure, and on-premises, RBAC, audit logging, SSO, and [GPU workloads](https://northflank.com/product/gpu-paas). [Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30).
> 
</InfoBox>

Enterprise AI coding agent adoption is widespread. Getting agents from pilot to production is not. 88% of agent pilots never reach production. The blocker is rarely the agent itself. It is the deployment infrastructure: isolation, governance, compliance controls, and data residency that enterprise security teams require before any agent touches production code.

This article covers what enterprise AI coding agent deployment actually requires, where most deployments stall, and how to build the infrastructure layer that gets agents from pilot to production.

## Why 88% of enterprise AI coding agent pilots never reach production

The production gap is not a model quality problem. By April 2026, Claude Code, OpenAI Codex, Google Jules, Cursor, Amazon Kiro, and Windsurf all produce strong code. What separates deployments that reach scale from the ones that get pulled after a quarter is whether the identity, logging, code review, and incident controls around the agent are in place from day one.

Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls. McKinsey research shows that while nearly two-thirds of enterprises have experimented with AI agents, fewer than 10% have scaled them to deliver measurable value, with poor data quality and governance cited as the primary barriers. None of these are model-quality problems. They are scoping, ownership, and governance problems. The enterprise security and compliance review that every AI coding agent deployment must pass does not ask which model scores highest on SWE-bench. It asks whether the agent survives contact with Okta, Splunk, and the code review policy.

## What enterprise AI coding agent deployment requires

These are the controls every enterprise deployment must clear before an AI coding agent reaches general availability. Skipping anyone creates a gap that blocks the rollout at the next audit cycle.

### 1. Identity and SSO

Every agent session must map to a named human identity. Without this, access reviews, offboarding, and audit trails do not work. Every mature AI coding agent, including Claude Code, Codex, Cursor, and GitHub Copilot, supports SAML SSO and SCIM provisioning against Okta, Entra ID, and Google Workspace. Configure SSO before anything else. Every agent request needs to be attributable to a specific person when the audit team asks.

### 2. Audit logging connected to SIEM

Agent activity must be centrally logged and queryable. This means every file access, every shell command, every PR creation, and every API call the agent makes should flow into the enterprise SIEM. Log retention must meet the compliance framework's requirements. For SOC 2 Type 2, auditors need demonstrable evidence that controls operated consistently across the audit period, not just at a point in time.

### 3. Secret scanning on agent PRs

AI coding agents commit code with credentials in it more often than human developers. Every PR created by an agent must run secret scanning before merge. This is not optional. Configure pre-receive hooks or required status checks in GitHub, GitLab, or Bitbucket that block merges when secrets are detected. Do not rely on agents to avoid this problem. Enforce it at the infrastructure level.

### 4. PR policy gates

Agent PRs must go through the same review gates as human PRs, with no pilot exemptions. Required checks include owner review, coverage thresholds, lint, SAST, and secret detection. Make these mandatory and tie any override to a named role. Log every bypass. Label agent PRs with the tool and session ID (for example, `agent:claude-code`) so security operations can pivot from a PR to the originating session in the SIEM.

### 5. Sandbox isolation for agent execution

Agents that execute shell commands, install packages, read files, or make network requests at runtime need isolated execution environments. Without sandbox isolation, a misconfigured agent can access the host system, other teams' infrastructure, or sensitive data stores. MicroVM isolation with a dedicated kernel per agent workload is the right baseline for production deployments handling proprietary code.

### 6. License governance

AI coding agents generate code that may contain snippets matching open-source licensed material. Enterprise legal teams require a policy covering what licenses are acceptable in agent-generated code, a scanning mechanism for detecting problematic licenses before merge, and a remediation process when issues are found.

### 7. Incident response runbooks

When an agent causes a production incident, the enterprise needs a documented process covering who gets paged, how agent access is revoked, how the affected code is identified and rolled back, and how the incident is reported to auditors. Teams that deploy agents without runbooks discover their gaps at the worst possible moment.

## The four-phase rollout that reaches production

### Phase 1: pilot with a single team

Deploy the AI coding agent to one team with above-average security maturity. Configure SSO and basic logging. Instrument PR gates. Run for four to six weeks and measure PR throughput, defect rate, and security findings. Establish a baseline before expanding.

### Phase 2: infrastructure hardening

Before expanding to additional teams, close the infrastructure gaps identified in the pilot. Wire audit logging to SIEM. Configure sandbox isolation for agent execution. Implement secret scanning as a required check. Define the license governance policy. Build the incident response runbooks. Do not expand until these controls are in place.

### Phase 3: controlled expansion

Roll out to two or three additional teams with active monitoring. Track agent-authored PR volume per team, security finding rates, and any anomalies in agent network activity. Use this phase to validate that the infrastructure controls work at slightly higher volume before general availability.

### Phase 4: general availability with governance

Open to all eligible teams with documented governance: approved agents, approved models, approved use cases, and documented escalation paths. Assign an AI agent owner or agentic ops lead responsible for the program. 56% of enterprises that successfully scale AI agent programs name a dedicated owner. Ownership maturity correlates strongly with reaching the production threshold.

## The infrastructure layer: where most deployments fall short

Most enterprise AI coding agent deployments treat tool selection as the deployment decision. They pick Claude Code or Cursor, configure SSO, and consider the deployment done. The infrastructure layer is where production deployments diverge from pilots.

The infrastructure layer handles where agents run, not what they do. It covers compute isolation so agent execution is hardware-separated from other workloads, network controls so agents cannot make arbitrary outbound requests, data residency so proprietary code never leaves the enterprise's own infrastructure, and audit logging at the execution level rather than just at the tool level.

AI coding tools provide governance within their own perimeter. Cursor's Sandbox Mode and hooks apply to Cursor. GitHub Copilot's audit logs cover Copilot sessions. When an enterprise runs multiple agents across multiple tools, a platform-level infrastructure layer provides consistent governance across all of them, regardless of which tool is running.

## How Northflank provides enterprise AI coding agent deployment infrastructure

[Northflank](https://northflank.com/product/sandboxes) provides the execution infrastructure layer that enterprise AI coding agent deployments require. AI coding agents run inside [microVM-backed sandbox](https://northflank.com/product/sandboxes) environments using Kata Containers with Cloud Hypervisor, Firecracker, and gVisor applied per workload. Each agent execution runs in its own microVM with a dedicated kernel, providing hardware-enforced isolation between agents, between teams, and between customer workloads.

![northflank-home-page.png](https://assets.northflank.com/northflank_home_page_a457933045.png)

[RBAC at the organisation](https://northflank.com/enterprise), project, and environment level controls who can provision and access agent environments. SAML and OIDC-based SSO with automatic role assignment integrates with Okta, Entra ID, and Google Workspace. Full audit logging across all platform actions is exported to SIEM. Network policies apply at the environment level with default-deny egress and whitelisted endpoints. [GPU workloads](https://northflank.com/product/gpu-paas) (H100, H200, A100, L4, L40S, B200) run alongside agent sandbox environments for teams running local model inference.

For enterprises with data residency requirements, [BYOC](https://northflank.com/product/bring-your-own-cloud) is self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, and bare-metal. Agent execution runs inside the enterprise's own VPC. Code never leaves the enterprise's own infrastructure boundary. SOC 2 Type 2 certification covers managed cloud and BYOC deployments.

<InfoBox className="BodyStyle">

[Get started on Northflank](https://app.northflank.com/signup) (self-serve, no demo required). Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to walk through your enterprise AI coding agent deployment requirements.

</InfoBox>

## FAQ: enterprise AI coding agent deployment

### Why do most enterprise AI coding agent pilots fail to reach production?

Forrester's root-cause analysis of agent deployments with negative ROI at 12 months attributes failures to unclear success criteria (41%), insufficient tool or data access (33%), and drift in evaluation coverage (26%). None are model quality problems. The most common infrastructure gap is missing governance controls: SSO not configured, audit logs not connected to SIEM, PR gates not enforced, and no sandbox isolation for agent execution.

### What is the difference between an AI coding tool and deployment infrastructure?

The AI coding tool handles agent logic, model inference, and code generation. Deployment infrastructure handles where the agent runs, who can access the environment, what network traffic is allowed, and whether all activity is logged. Tool selection and infrastructure are separate decisions. Most enterprise deployments fail because they treat them as the same decision.

### Do AI coding agents need sandbox isolation in enterprise environments?

Yes, for any agent that executes shell commands, installs packages, or makes network requests at runtime. Without sandbox isolation, a misconfigured agent can access the host system, other teams' environments, or sensitive data. MicroVM isolation with a dedicated kernel per agent workload enforces a hardware boundary around agent execution.

### How do you handle data residency for enterprise AI coding agent deployment?

Deploy agents on infrastructure where code never leaves the enterprise's own VPC. BYOC deployment on Northflank runs agent execution inside the enterprise's own AWS, GCP, Azure, or on-premises infrastructure. The enterprise retains full data sovereignty. Code does not route through Northflank's managed infrastructure.

### What audit logging is required for enterprise AI coding agent compliance?

Every agent file access, shell command, PR creation, and API call should be logged with a timestamp and user identity and exported to the enterprise SIEM. Log retention must meet the compliance framework's requirements. For SOC 2 Type 2, auditors require demonstrable evidence that controls operated consistently across the audit period.

## Conclusion

Enterprise AI coding agent deployment is an infrastructure problem as much as a tooling problem. The agents are capable. The governance and execution infrastructure is where most deployments stall. SSO, audit logging, PR gates, sandbox isolation, secret scanning, license governance, and incident response runbooks are not optional. They are the controls that determine whether a pilot becomes a production program or gets pulled at the next security review.

[Northflank](https://northflank.com/) provides the execution infrastructure layer that makes enterprise AI coding agent deployment production-ready: [microVM sandbox](https://northflank.com/product/sandboxes) isolation, [self-serve BYOC](https://northflank.com/product/bring-your-own-cloud) for data residency, RBAC, audit logging, SSO, and [GPU workloads](https://northflank.com/product/gpu-paas) in one control plane.

<InfoBox className="BodyStyle">

[Sign up for free on Northflank](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to see how Northflank handles enterprise AI coding agent deployment infrastructure.

</InfoBox>

## Related articles

- [**Enterprise vibe coding: how to deploy AI-generated apps safely**](https://northflank.com/blog/enterprise-vibe-coding-how-to-deploy-ai-generated-apps-safely): Covers governance, security, and compliance controls for enterprise vibe coding at scale.
- [**Best enterprise-safe platforms for running and hosting AI apps in 2026**](https://northflank.com/blog/best-enterprise-safe-platforms-for-running-and-hosting-ai-apps): A comparison of platforms covering SOC 2, HIPAA, BYOC, sandbox isolation, and GPU support for enterprise AI app deployment.
- [**Best platforms for untrusted code execution in 2026**](https://northflank.com/blog/best-platforms-for-untrusted-code-execution): Isolation model selection, multi-tenant design, and network controls for platforms running AI-generated code.]]>
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  <title>Enterprise AI remote coding environments in 2026</title>
  <link>https://northflank.com/blog/enterprise-ai-remote-coding-environments</link>
  <pubDate>2026-05-07T12:15:00.000Z</pubDate>
  <description>
    <![CDATA[Enterprise AI remote coding environments run AI coding agents in secure cloud sandboxes with RBAC, audit logs, BYOC, network controls, and GPU support for compliant AI development.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Enterprise_AI_remote_coding_environments_5fdb1527a8.png" alt="Enterprise AI remote coding environments in 2026" /><InfoBox className="BodyStyle">

## TL;DR: enterprise AI remote coding environments in 2026

- An enterprise AI remote coding environment runs AI coding agents in cloud infrastructure rather than on developer machines, providing compute, isolation, network controls, and audit trails that local environments cannot.
- The shift from local to remote AI coding environments is driven by security risk, compliance requirements, and the compute demands of running multiple parallel agents.
- Enterprise requirements include sandbox isolation, RBAC, SSO, audit logging, BYOC for data residency, network controls, and GPU access for agents running local model inference.
- The landscape splits into two layers: the AI coding tools that handle agent logic and model inference, and the execution infrastructure that provides the isolation, governance, and compliance controls.

> [Northflank](https://northflank.com/) provides the execution infrastructure layer for enterprise AI remote coding environments: [microVM sandbox isolation](https://northflank.com/product/sandboxes), self-serve [BYOC](https://northflank.com/product/bring-your-own-cloud) into AWS, GCP, Azure, and on-premises, RBAC, audit logging, SSO, and [GPU workloads](https://northflank.com/product/gpu-paas) in one control plane. [Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30).
> 
</InfoBox>

AI coding agents have moved from developer tools to critical enterprise infrastructure. About 65 to 70 percent of enterprise code is now written by AI. The question enterprises are now asking is not whether to use AI coding agents but where they run, what they can access, and whether their activity is auditable and compliant.

Most AI coding tools default to local execution on developer machines. That model breaks at enterprise scale: no audit trail, no network controls, agents accessing sensitive infrastructure through unmanaged devices, and no path to data residency compliance. Remote coding environments solve this by running agent execution in governed cloud infrastructure instead.

## What is an enterprise AI remote coding environment?

A remote coding environment is a cloud-based workspace where development tasks run on remote infrastructure rather than a developer's local machine. For AI coding agents specifically, the remote environment is where the agent executes: running shell commands, reading and writing files, calling APIs, executing tests, and submitting pull requests.

This distinction matters at enterprise scale. When an AI agent runs locally, it has access to whatever the developer's machine can reach: credentials in environment files, internal network services, SSH keys, and other sensitive context. When it runs remotely, access is defined by the environment's configuration, network policies, and access controls, not the developer's local setup.

## Why local AI coding environments do not work for enterprises

Most AI coding tools default to local execution. This model works for individual developers on trusted devices. It creates several problems at enterprise scale.

1. **Security and IP exposure:** Local execution means proprietary source code, internal credentials, and sensitive business logic pass through the developer's machine and potentially through the AI provider's cloud infrastructure. Enterprises in financial services, healthcare, and government routinely block cloud-based AI coding tools because the data-sharing model is incompatible with their compliance posture.
2. **No audit trail:** When an AI agent runs locally, the enterprise has no centralized record of what the agent accessed, what code it generated, or what commands it executed. SOC 2 Type 2 audits and security incident investigations require this visibility.
3. **Unmanaged compute:** Running multiple parallel AI coding agents is compute-intensive. Developer laptops are not provisioned for this workload. Remote environments provide on-demand compute that scales with the number of agents running in parallel.
4. **No network controls:** Local AI agents can make arbitrary outbound network requests. Remote environments apply default-deny egress policies, whitelist specific endpoints, and log all network activity.
5. **No environment standardization:** Local developer environments drift over time. Remote coding environments are provisioned from a template, ensuring every agent runs in an identical, reproducible environment with defined dependencies, tooling versions, and access policies.

## What enterprise AI remote coding environments require

These are the controls that enterprise security and compliance teams require when AI coding agents run in production development workflows.

- **Sandbox isolation:** Each agent execution runs in an isolated environment with its own filesystem, network namespace, and process space. For multi-tenant deployments, microVM isolation with a dedicated kernel per workload is the right baseline.
- **RBAC and access controls:** Different teams, projects, and environments need different access levels. Developers should be able to provision agent environments without accessing other teams' codebases or infrastructure.
- **Audit logging:** Every agent action, every file access, every network request, and every code generation event should be logged with a timestamp and identity for SOC 2 Type 2 compliance and security incident investigation.
- **SSO integration:** Agents should authenticate through the same SAML or OIDC-based identity infrastructure as human developers.
- **Network controls:** Agents operate under defined policies covering which external endpoints they can reach, which internal services they can access, and what traffic is blocked by default.
- **BYOC and data residency:** Enterprises with data residency requirements need agent execution inside their own VPC, on-premises, or bare-metal. Code should never leave the enterprise's own infrastructure boundary.
- **GPU access:** Enterprises running local model inference alongside coding agents need GPU compute available in the same environment.
- **Ephemeral and persistent environments:** Some agent tasks run ephemerally and tear down on completion. Others maintain state across sessions for longer-running projects.

## The enterprise AI remote coding environment landscape

The landscape splits into two distinct layers: the AI coding tools that handle agent logic and model inference, and the execution infrastructure that provides isolation, governance, and compliance controls. Most enterprises need both.

### AI coding tools with remote execution

**Claude Code** runs in cloud-based remote environments and supports background agent tasks that complete asynchronously. It handles complex multi-file changes, repository understanding, and long-horizon coding tasks. Enterprise deployment requires a separate infrastructure layer for compliance controls.

**GitHub Copilot Workspace** runs agent tasks in GitHub's cloud infrastructure, integrated directly with pull requests and GitHub Actions. It covers the full GitHub workflow natively, but execution happens on GitHub's managed infrastructure with no BYOC option.

**Cursor** provides IDE-native AI coding with Sandbox Mode for agent isolation and hooks for policy enforcement. SOC 2 Type 2 certified. Governance applies only within Cursor itself. Teams running Claude Code or other agents in parallel need a separate governance layer.

**Mistral Vibe**, launched in April 2026 with Medium 3.5, runs coding sessions asynchronously and in parallel in cloud environments. Agents can receive tasks via CLI, run multiple jobs simultaneously, and deliver results as pull requests. Integrates with GitHub, Jira, Slack, and Teams.

**Coder** provides self-hosted remote development environments using Terraform-provisioned workspaces. Supports air-gapped and on-premises deployments. Used by enterprises in finance, government, and defense. Focused on developer workspaces rather than agent execution infrastructure specifically.

### Execution infrastructure

The execution infrastructure layer is what makes AI coding agent environments enterprise-safe. It handles compute isolation, RBAC, audit logging, network policies, and data residency independently of which AI coding tool runs on top.

[**Northflank**](https://northflank.com/) provides the execution infrastructure layer with production-grade enterprise controls. MicroVM sandbox isolation using Kata Containers with Cloud Hypervisor, Firecracker, and gVisor per workload. [Self-serve BYOC](https://northflank.com/product/bring-your-own-cloud) into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, and bare-metal. RBAC at the organisation, project, and environment level. SAML and OIDC-based SSO with automatic role assignment. Full audit logging is exportable for SIEM integration. [GPU workloads](https://northflank.com/product/gpu-paas) (H100, H200, A100, L4, L40S, B200) alongside agent sandbox environments. SOC 2 Type 2 certified across managed cloud and BYOC deployments. No enterprise sales process required.

## How the two layers work together

AI coding tools and execution infrastructure are not alternatives. They work in combination. An enterprise might run Claude Code agents inside Northflank-provisioned microVM sandbox environments, with BYOC deployment keeping all execution inside the enterprise's own AWS VPC, RBAC controlling which teams can provision agent environments, and audit logs exporting to the enterprise's SIEM.

The AI coding tool handles what the agent does. The execution infrastructure handles where it runs, who can access it, what it can reach, and whether the activity is logged.

| Layer | What it handles | Examples |
| --- | --- | --- |
| **AI coding tools** | Agent logic, model inference, code generation, repository understanding | Claude Code, GitHub Copilot, Cursor, Mistral Vibe |
| **Execution infrastructure** | Compute isolation, RBAC, audit logging, network controls, BYOC, data residency | Northflank |

## Northflank as an enterprise AI remote coding environment infrastructure

[Northflank](https://northflank.com/product/sandboxes) provides the execution infrastructure that enterprises need to run AI coding agents safely at scale. Connect a Git repository, provision an agent environment in minutes, and Northflank handles the microVM isolation, networking, secrets management, and observability. AI coding agents from any provider run inside isolated Firecracker or Kata Container microVMs with dedicated kernels, hardware-enforced boundaries between agent workloads, and no shared kernel state between tenants.

![northflank-home-page.png](https://assets.northflank.com/northflank_home_page_a457933045.png)

For enterprise teams with data residency requirements, [BYOC](https://northflank.com/product/bring-your-own-cloud) is self-serve. Northflank deploys the platform into the enterprise's existing AWS, GCP, Azure, or on-premises infrastructure and manages orchestration and microVM lifecycle on the enterprise's hardware. Agent execution runs inside the enterprise's own VPC. Code never leaves the enterprise's own infrastructure boundary. The enterprise retains full data sovereignty without building the execution infrastructure themselves.

<InfoBox className="BodyStyle">

[Get started on Northflank](https://app.northflank.com/signup) (self-serve, no demo required). Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to walk through your enterprise AI coding environment requirements.

</InfoBox>

## FAQ: enterprise AI remote coding environments

### What is the difference between a local and remote AI coding environment?

A local AI coding environment runs agent execution on the developer's machine using local compute, local credentials, and local network access. A remote AI coding environment runs agent execution in cloud infrastructure with defined compute resources, network policies, access controls, and audit logging. Remote environments provide the governance and isolation controls that enterprise compliance frameworks require.

### Why do enterprises need sandbox isolation for AI coding agents?

AI coding agents execute shell commands, install packages, read and write files, and make network requests at runtime. Without sandbox isolation, a misconfigured or compromised agent can access the host system, other tenants' environments, or sensitive infrastructure. MicroVM isolation gives each agent its own dedicated kernel, enforcing a hardware boundary around agent execution.

### Can AI coding agents run in air-gapped enterprise environments?

Yes, with the right infrastructure. Northflank supports air-gapped and on-premises deployments where agent execution has no dependency on any public cloud or internet connectivity. Agents need to be configured to use internally hosted models rather than cloud-based inference APIs.

### How do you audit AI coding agent activity in an enterprise environment?

Audit logging at the platform level captures every agent execution event, file access, network request, and environment change with a timestamp and user identity. Northflank's audit logs are exportable for SIEM integration. For SOC 2 Type 2 compliance, this provides the demonstrable audit trail that auditors require.

### How does BYOC work for enterprise AI coding environments on Northflank?

BYOC deploys Northflank's platform into the enterprise's existing AWS, GCP, Azure, or on-premises infrastructure, self-serve. Northflank manages orchestration and microVM lifecycle on the enterprise's infrastructure. Agent execution runs inside the enterprise's own VPC. Data never leaves the enterprise's own infrastructure boundary.

## Conclusion

Enterprise AI remote coding environments require two layers working together: the AI coding tools that handle agent logic and model inference, and the execution infrastructure that provides isolation, governance, and compliance controls. Most enterprises have the tools. The infrastructure layer is where most deployments fall short.

[Northflank](https://northflank.com/) provides that infrastructure layer out of the box with [self-serve BYOC](https://northflank.com/product/bring-your-own-cloud), [microVM sandbox](https://northflank.com/product/sandboxes) isolation, RBAC, audit logging, SSO, and [GPU workloads](https://northflank.com/product/gpu-paas) in one control plane. AI coding agents from any provider run inside it with the enterprise compliance posture that regulated industries and security teams require.

<InfoBox className="BodyStyle">

[Sign up for free on Northflank](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to see how Northflank handles enterprise AI remote coding environment infrastructure.

</InfoBox>

## Related articles

- [**Enterprise vibe coding: how to deploy AI-generated apps safely**](https://northflank.com/blog/enterprise-vibe-coding-how-to-deploy-ai-generated-apps-safely): Covers the governance, security, and compliance controls required for enterprise vibe coding at scale.
- [**Best enterprise-safe platforms for running and hosting AI apps in 2026**](https://northflank.com/blog/best-enterprise-safe-platforms-for-running-and-hosting-ai-apps): A comparison of platforms covering SOC 2, HIPAA, BYOC, sandbox isolation, and GPU support for enterprise AI app deployment.
- [**Sandboxes on Kubernetes: isolation options and how to run them in production**](https://northflank.com/blog/sandboxes-on-kubernetes): Covers isolation options for AI agent workloads on Kubernetes, including Kata Containers and gVisor.
- [**What is sandbox infrastructure?**](https://northflank.com/blog/what-is-sandbox-infrastructure): The full stack required to run isolated workloads safely at scale, covering isolation technology, orchestration, and lifecycle management.]]>
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</item><item>
  <title>Top AI companies in 2026: models, infrastructure, and tooling</title>
  <link>https://northflank.com/blog/top-ai-companies</link>
  <pubDate>2026-05-06T15:45:00.000Z</pubDate>
  <description>
    <![CDATA[Top AI companies in 2026: Northflank, NVIDIA, OpenAI, Anthropic, Mistral, and more. A breakdown by category across the AI stack.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/top_ai_companies_04cbad315b.png" alt="Top AI companies in 2026: models, infrastructure, and tooling" />Top AI companies in 2026 operate across distinct layers of a shared stack: hardware, foundation models, deployment infrastructure, data intelligence, and application tooling.

This article breaks down eight of the most significant AI companies by category, covering what each one provides and where it fits in the stack. The goal is to help engineers and technical teams understand the landscape and make better decisions about tooling, vendors, and architecture.

<InfoBox className="BodyStyle">

## TL;DR: top AI companies in 2026 at a glance

- [Northflank](https://northflank.com/) is the deployment infrastructure layer for AI engineering teams. It runs AI services, agents, GPU workloads, and microVM-backed sandboxes for secure agentic code execution, in your cloud or its own. If you are building an AI product and need a platform that handles deployment, orchestration, sandboxing, and GPUs without a dedicated DevOps team, Northflank is built for that.
- Most engineers building AI products in 2026 need at least three of these layers: a model provider, a data platform, and a deployment platform. The infrastructure layer is where most teams underinvest early and pay for it later.
- NVIDIA supplies the compute that nearly every other company on this list runs on. OpenAI, Anthropic, and Mistral AI build the models that sit on top of it.
- Databricks handles data pipelines and AI governance at scale. Hugging Face is where most engineers start when evaluating open-weight models. ElevenLabs covers voice AI for teams building conversation or audio products.

</InfoBox>

## Quick reference: top AI companies in 2026

The table below organises each company by category and location to help you quickly identify which part of the AI stack they occupy.

| Company | Category | HQ | Founded |
| --- | --- | --- | --- |
| Northflank | Deployment infrastructure, sandboxes, GPU workloads | London, UK | 2019 |
| NVIDIA | AI hardware, GPU architecture, software ecosystem | Santa Clara, USA | 1993 |
| OpenAI | Foundation models, AI APIs, consumer AI | San Francisco, USA | 2015 |
| Anthropic | Foundation models, AI safety research | San Francisco, USA | 2021 |
| Mistral AI | Open-weight and proprietary LLMs | Paris, France | 2023 |
| Databricks | Data intelligence platform, lakehouse architecture | San Francisco, USA | 2013 |
| Hugging Face | Open-source model hub, ML collaboration platform | New York, USA | 2016 |
| ElevenLabs | Voice AI, text-to-speech, voice agents | London, UK | 2022 |

## What are the top AI companies in 2026?

The companies below are selected to cover the breadth of the AI stack rather than a single category. Each entry covers what the company provides, its key products, and its relevance to engineering teams building AI systems in production.

### 1. Northflank

**Category:** Deployment infrastructure, AI sandboxes, GPU workloads

[Northflank](https://northflank.com/) is a London-based deployment platform for engineering teams running AI workloads, services, databases, and background jobs. Founded in 2019, it provides a control plane for Kubernetes-based infrastructure, running either on Northflank's managed cloud or inside a customer's own VPC.

![northflank-home-page.png](https://assets.northflank.com/northflank_home_page_a457933045.png)

[Sandboxes](https://northflank.com/product/sandboxes) run inside microVM-backed containers using Kata Containers, Firecracker, or gVisor, giving each workload its own kernel instance and preventing container escape. They spin up in 1 to 2 seconds and support both ephemeral and persistent environments, making them suitable for LLM-generated code execution, AI agents, and multi-tenant platforms. See the [sandbox documentation](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank) for full details.

Northflank supports [GPU workloads](https://northflank.com/product/gpu-paas) on its managed cloud and in your own cloud account. Supported hardware includes NVIDIA H100 ($2.74/hr) and B200 ($5.87/hr). The platform handles spot instance orchestration, GPU timeslicing, custom autoscaling, multi-read-write storage for model loading, and Jupyter notebook support. Full configuration options are covered in the [GPU workloads documentation](https://northflank.com/docs/v1/application/gpu-workloads/gpus-on-northflank).

Most enterprise customers deploy Northflank inside their own VPC. [BYOC](https://northflank.com/product/bring-your-own-cloud) support covers AWS (EKS), GCP (GKE), Azure (AKS), CoreWeave, Civo, and Oracle, and is self-serve rather than requiring a professional services engagement. Northflank is SOC 2 Type 2 compliant. See the [BYOC features page](https://northflank.com/features/bring-your-own-cloud) for a full breakdown.

<InfoBox className="BodyStyle">

Beyond AI workloads, Northflank covers the full deployment lifecycle: Git-connected CI/CD, release pipelines, preview environments, managed databases (PostgreSQL, MongoDB, MySQL, Redis, RabbitMQ, MinIO), secrets management, RBAC, and GitOps. [Pricing](https://northflank.com/pricing) starts at $0.01667/vCPU/hour and $0.00833/GB/hour, with GPU pricing and a [cost calculator](https://northflank.com/pricing#calculator) on the pricing page.

One engineering team of two used Northflank to run 10,000+ AI training jobs and half a million inference runs per day across nine clusters, 40+ microservices, and 250+ concurrent GPUs on AWS, GCP, and Azure, without a dedicated DevOps hire. Read the [Weights case study](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s) to see how they did it.

Northflank is self-serve. [Get started for free](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) if you want to talk through your infrastructure requirements first.

</InfoBox>

### 2. NVIDIA

**Category:** AI hardware, GPU architecture, software ecosystem

NVIDIA is the semiconductor company that supplies the compute underpinning almost all AI training and inference. Its GPUs are the standard hardware layer for large language model development across research labs, cloud providers, and enterprise deployments.

Current GPU architectures include Hopper (H100) and Blackwell (B200, GB200). Beyond hardware, NVIDIA's software ecosystem includes CUDA-X (GPU-accelerated libraries), NIM microservices (model inference), Dynamo (inference engine), and the NGC catalog (GPU-optimised containers and models).

Engineers evaluating GPU infrastructure will encounter NVIDIA hardware on almost every cloud platform. CUDA compatibility is a practical consideration when selecting frameworks, containers, and deployment targets. Northflank runs [NVIDIA GPUs](https://northflank.com/cloud/gpus) on its managed cloud and supports deploying them inside your own cloud account.

### 3. OpenAI

**Category:** Foundation models, AI APIs, consumer AI

OpenAI is a San Francisco-based AI research organisation structured as a public benefit corporation. It develops the GPT family of large language models, DALL-E for image generation, Whisper for speech-to-text, and Codex for coding tasks.

The current model lineup includes GPT-5.5, the o-series reasoning models, GPT-4o for multimodal tasks across text, image, and audio, and GPT-4o Mini for cost-sensitive applications. All models are accessible via REST API. OpenAI also offers a Realtime API for low-latency voice AI and Codex for agentic coding workflows.

For engineering teams, OpenAI's API is the most widely integrated LLM endpoint in the ecosystem. A broad model selection across price points and extensive third-party tooling support make it a common starting point when evaluating which model to build on.

For teams considering open-source alternatives alongside proprietary models, Northflank's [complete guide to open-source LLM deployment](https://northflank.com/blog/open-source-llms-the-complete-developers-guide-to-deployment) covers the tradeoffs and deployment options.

### 4. Anthropic

**Category:** Foundation models, AI safety research

Anthropic is a San Francisco-based AI safety company and public benefit corporation, founded in 2021. Its stated focus is developing reliable, interpretable, and steerable AI systems.

The Claude model family includes Opus (frontier reasoning), Sonnet (balanced performance and cost), and Haiku (fast and lightweight). Models are available via the Claude API, Claude.ai for consumer and team use, and Claude Code for terminal-based agentic coding workflows. Claude is also available through AWS Bedrock and Google Cloud Vertex AI.

Anthropic's published safety research includes Constitutional AI, interpretability research into model internals, and the Responsible Scaling Policy. For engineering teams, Claude models are notable for strong performance on long-context tasks, coding, and document analysis. Anthropic's safety research is relevant to teams deploying AI in regulated industries where output reliability and auditability matter.

For a direct comparison of Claude Code against other coding tools, see Northflank's [Claude Code vs OpenAI Codex breakdown](https://northflank.com/blog/claude-code-vs-openai-codex).

### 5. Mistral AI

**Category:** Open-weight and proprietary large language models

Mistral AI is a Paris-based AI company founded in April 2023. It develops open-weight and proprietary large language models with a focus on efficiency and European data sovereignty.

The model portfolio includes Mistral Large, Mistral Medium, Mistral Small, Codestral, Mistral NeMo, Ministral, Voxtral, and Document AI. Models are accessible via La Plateforme (developer API), Mistral AI Studio (enterprise platform), and Le Chat (consumer and enterprise assistant), with self-hosted and private cloud deployment options available.

Mistral's MoE architecture activates only a relevant subset of expert layers per token, reducing inference cost while preserving capability. Apache 2.0 models are deployable on your own infrastructure without per-token API costs, and teams with European data residency requirements benefit from Mistral's EU-based infrastructure and GDPR compliance.

Mistral models are deployable on GPU platforms such as Northflank's [GPU workloads](https://northflank.com/product/gpu-paas). For a broader look at deploying open-source models in production, see Northflank's [engineer's guide to open-source AI models](https://northflank.com/blog/an-engineers-guide-to-open-source-ai-models).

### 6. Databricks

**Category:** Data intelligence platform, lakehouse architecture

Databricks is a San Francisco-based data and AI company founded in 2013. It invented the lakehouse architecture, combining features of a data warehouse and data lake into a unified platform for managing structured and unstructured data.

The Data Intelligence Platform covers data engineering, SQL analytics, ML and AI (MLflow, Agent Bricks), governance (Unity Catalog), and business intelligence. Its open-source contributions include Apache Spark, Delta Lake, and MLflow, all widely used independently of the commercial platform.

For data and AI engineering teams, Databricks is the standard platform when the workload requires managing large-scale data pipelines alongside model training, experiment tracking, and agent deployment in a governed environment.

### 7. Hugging Face

**Category:** Open-source model hub and ML collaboration platform

Hugging Face is a New York-based AI company that operates the largest open-source model repository and ML collaboration platform available. The Hub is the de facto distribution layer for open-weight AI models, where most major releases including Llama, Mistral, Qwen, and DeepSeek are distributed with versioning, model cards, evaluation results, and licence information.

The software library ecosystem includes Transformers, Diffusers, Datasets, and Tokenizers. The Inference Providers API gives developers access to models from multiple AI providers through a single unified API, and Inference Endpoints allows teams to deploy any Hub model on dedicated infrastructure. Enterprise tier includes private model hosting, SSO, audit logs, and dedicated deployment regions.

For engineering teams, Hugging Face is the starting point for discovering, evaluating, and pulling open-weight models into a deployment pipeline. Teams running open-weight models on GPU infrastructure such as Northflank's [GPU workloads](https://northflank.com/product/gpu-paas) will typically source model weights from the Hub. For a practical example, see Northflank's guide to [self-hosting DeepSeek V3](https://northflank.com/blog/deploy-self-host-deep-seek-v3-1-on-northflank).

### 8. ElevenLabs

**Category:** Voice AI, text-to-speech, voice agents

ElevenLabs is a London and New York-based voice AI company providing voice generation, cloning, and agent products accessible via API and a consumer platform.

The core product set covers text-to-speech generation, speech-to-text transcription (batch and real-time), voice cloning, voice design, voice changing, audio dubbing for content localisation, sound effects generation, and AI music generation.

The conversational AI agent platform supports customer support, outbound calling, lead qualification, and AI receptionist workflows. The Voice Library is a two-sided marketplace where creators can upload and license voice clones. All capabilities are available via API, with enterprise access including SAML SSO, audit logs, and a trust and compliance centre.

ElevenLabs is relevant to engineering teams building voice interfaces, TTS pipelines, real-time conversation systems, or content localisation workflows.

## Frequently asked questions about top AI companies in 2026

### What are the top AI companies in 2026?

The top AI companies in 2026 span several distinct categories. Northflank is the deployment infrastructure platform for AI engineering teams, covering GPU workloads, microVM-backed sandboxes for secure agentic code execution, CI/CD, managed databases, and BYOC deployment inside your own VPC. NVIDIA dominates AI hardware. OpenAI and Anthropic lead on proprietary foundation models. Mistral AI is the leading European open-weight model provider. Databricks is the standard platform for data and AI engineering at scale. Hugging Face is the primary distribution layer for open-source models. ElevenLabs leads voice AI.

### What is the difference between an AI model company and an AI infrastructure company?

An AI model company builds and trains large language models and makes them accessible via API or direct deployment. OpenAI, Anthropic, and Mistral are model companies. Northflank is an infrastructure company: it does not train models, but provides the runtime environment for services, agents, sandboxes, and GPU workloads that use those models.

### Which top AI companies are UK or European-based?

Northflank (London, UK), ElevenLabs (London and New York), and Mistral AI (Paris, France) are the three European companies on this list. For teams with European data residency requirements or GDPR compliance constraints, both Mistral and Northflank's [BYOC deployment options](https://northflank.com/product/bring-your-own-cloud) are relevant starting points.

### What is the best platform for deploying AI models and agents in production?

Northflank covers the full stack AI engineering teams need in production: GPU workloads on managed cloud or BYOC, microVM-backed sandboxes for secure agentic code execution, CI/CD, managed databases, and secrets management, all deployable inside your own VPC. See the [Northflank docs](https://northflank.com/docs) for a technical breakdown or the [enterprise page](https://northflank.com/enterprise) for large-scale deployment details.

### How do I run open-source AI models like Mistral or Llama in production?

Pull model weights from the Hugging Face Hub, deploy a serving framework such as vLLM, TGI, or Ollama as a container, and run it on a GPU-enabled platform. Northflank supports this workflow via [GPU workloads](https://northflank.com/product/gpu-paas), with on-demand H100 and B200 support, spot instance orchestration, and BYOC deployment. The [GPU workloads documentation](https://northflank.com/docs/v1/application/gpu-workloads/gpus-on-northflank) covers configuration and optimisation.

## Deploy your AI stack on Northflank

If you are building with any of the platforms on this list, whether that is serving a Mistral model, running an ElevenLabs voice agent, or executing LLM-generated code in a secure sandbox, Northflank provides the deployment infrastructure to run it in production.

[Get started for free](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to talk through your setup with an engineer.]]>
  </content:encoded>
</item><item>
  <title>AI Sandbox pricing comparison (2026)</title>
  <link>https://northflank.com/blog/ai-sandbox-pricing</link>
  <pubDate>2026-05-05T16:24:00.000Z</pubDate>
  <description>
    <![CDATA[A complete AI sandbox pricing comparison for 2026. PaaS rates, BYOC costs, GPU access, billing models, and a cost breakdown to find the cheapest AI sandbox provider at scale.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/ai_sandbox_pricing_1_ebc69785c3.png" alt="AI Sandbox pricing comparison (2026)" />Finding the cheapest AI sandbox provider requires more than comparing headline CPU rates. The most affordable sandbox provider for your use case depends on billing model, idle behaviour, deployment model, and whether GPU access is required.

This article covers PaaS rates, billing model differences, GPU pricing, BYOC costs, and a cost comparison at scale (using 200 concurrent sandboxes as a worked example).

<InfoBox className="BodyStyle">

## TL;DR: AI sandbox pricing at a glance

- AI sandbox pricing and cost comparison is harder than it looks. Billing models differ across sandbox providers, hidden costs exist, and headline rates rarely reflect what teams actually pay at scale.
- Northflank has the lowest published PaaS CPU rate in this comparison at $0.01667/vCPU-hr, billed per second. At scale, the gap between platforms widens significantly (for instance, at 200 concurrent sandboxes, total costs range from $7,200 to over $35,000 depending on the platform).
- Most platforms in this comparison do not provide GPU access within sandboxed environments. Northflank and Modal are the two platforms here that support GPU workloads in sandboxes and publish GPU rates.
- BYOC (Bring Your Own Cloud) with self-serve access and publicly available pricing is available only on Northflank. Other platforms that offer BYOC require a sales process and do not publish rates.

</InfoBox>

## What are AI sandboxes?

AI sandboxes are isolated execution environments used to run untrusted or AI-generated code safely, without risking the host system or other tenants. They are used across AI agent workflows, code execution products, reinforcement learning pipelines, and multi-tenant platforms where user-submitted code needs to run in isolation.

> Northflank sandboxes use microVM-based isolation (Kata Containers and gVisor depending on the underlying infrastructure), supporting both ephemeral and persistent environments in managed cloud or your own VPC.
> 

## What does AI sandbox pricing look like across platforms?

The table below shows PaaS pricing where you use each platform's hardware directly. The rates vary more than they appear to at first glance, partly because billing models differ. The section that follows explains those differences in detail.

*Pricing as of May 2026. Verify current rates on each platform's pricing page before making cost decisions.*

| Platform | CPU | Memory | Storage | GPU | Billing model |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | $0.01667/vCPU-hr | $0.00833/GB-hr | $0.15/GB-month | Yes (see GPU section) | Per second |
| **E2B** | $0.0504/vCPU-hr | $0.0162/GiB-hr | 10–20GB free | No (CPU only) | Per second |
| **Daytona** | $0.0504/vCPU-hr | $0.0162/GiB-hr | $0.000108/GiB-hr | No | Per second |
| **Modal** | $0.1419/physical core-hr (2 vCPU) | $0.0242/GiB-hr | $0.09/GiB-month | Yes (see GPU section) | Per second |
| **Fly.io Sprites** | $0.07/CPU-hr | $0.04375/GB-hr | NVMe (active), object storage (idle) | No (CPU only) | Per second, no charge when idle |
| **Vercel Sandbox** | $0.128/vCPU-hr (active CPU only) | $0.0212/GB-hr (provisioned) | $0.023/GB-month (snapshots) | No (CPU only) | Active CPU only. Persistent sandboxes in beta |
| **Blaxel** | Bundled with memory tier | XS: $0.0828/hr, S: $0.1656/hr, M: $0.3312/hr, L: $0.6624/hr, XL: $1.3248/hr | $0.12/GB-month | No (CPU only) | Per second |
| **Runloop** | $0.108/CPU-hr | $0.0252/GB-hr | $0.00034236/GB-hr | No (CPU only) | Per second |
| **Cloudflare Sandbox** | $0.072/vCPU-hr (active CPU only) | Provisioned | Provisioned | No (CPU only) | Active CPU only. Requires $5/month Workers Paid plan. Workers and Durable Objects charges apply additionally |

Two notes on specific platforms: Modal sandbox CPU rate is approximately 3x Modal's standard compute rate, so sandbox compute is priced differently from standard Modal Functions. Vercel Sandbox memory is billed on provisioned resources for the full sandbox duration, not active usage only.

## How do AI sandbox billing models work?

The billing model a platform uses matters more than the headline CPU rate. Two platforms with similar per-hour rates can produce very different bills depending on when the meter starts and stops, and which resources are included in the base price.

### Per-second active CPU billing

Some platforms only charge for the time the sandbox is actively using the CPU. Fly.io Sprites, for instance, uses this model with no charge when the sandbox is idle. The distinction matters for agentic workloads where sandboxes often spend significant time waiting on I/O between active execution bursts.

### Active CPU vs provisioned memory

Vercel Sandbox, for instance, bills Active CPU only (time spent waiting for network requests, database queries, or API calls does not count toward CPU charges). However, memory is billed based on provisioned resources for the full duration the sandbox runs, not active usage. A sandbox waiting on an LLM API call pays for provisioned memory but not CPU during that wait.

### Memory-tier pricing

Blaxel, for instance, does not publish separate CPU and memory rates. Pricing is based on memory tier: XS (2GB) at $0.0828/hr, S (4GB) at $0.1656/hr, M (8GB) at $0.3312/hr, L (16GB) at $0.6624/hr, and XL (32GB) at $1.3248/hr, all billed per second. CPU is bundled into the tier. This simplifies cost estimation when memory requirements are fixed but makes direct comparison with per-vCPU rates from other platforms harder.

### Physical core vs vCPU

Modal, for instance, bills per physical core for sandbox compute, where one physical core equals 2 vCPU. The sandbox CPU rate of $0.1419/physical core-hr is higher than it appears when compared to per-vCPU rates from other platforms. Modal's sandbox CPU rate is also approximately 3x its standard compute rate, meaning sandbox compute is priced differently from standard Modal Functions. Always convert to a common unit before comparing headline numbers across platforms.

### Layered cost structures

Cloudflare Sandbox, for instance, has pricing determined by the underlying Containers platform. Compute is billed at active CPU rates, but you also pay for Workers requests and Durable Objects on top of container costs, and a $5/month Workers Paid plan is required before any sandbox usage. The total cost of a Cloudflare Sandbox deployment is the sum of multiple billing dimensions, not a single rate.

## What is the cheapest AI sandbox provider?

Finding the most affordable AI sandbox provider requires looking beyond the headline CPU rate. Billing models, idle behaviour, and deployment model all affect what teams actually pay at scale, making a direct cost comparison essential.

On PaaS, Northflank has the lowest published CPU rate at $0.01667/vCPU-hr, billed per second. This is significantly lower than E2B at $0.0504/vCPU-hr, Daytona at $0.0504/vCPU-hr, Fly.io Sprites at $0.07/CPU-hr, and Modal at $0.1419/physical core-hr (2 vCPU equivalent). At 200 concurrent sandboxes on the same workload specification, Northflank PaaS costs $7,200 versus $16,819 on E2B and Daytona, $24,491 on Modal, and over $35,000 on Fly.io Sprites.

For teams evaluating low-cost AI sandbox infrastructure at scale, BYOC changes the calculation further. Northflank is the only platform in this comparison offering self-serve BYOC with publicly available pricing. Teams running sandboxes inside their own cloud account pay their cloud provider directly plus a Northflank management fee, bringing the total cost for 200 sandboxes down to $2,060 compared to $7,200 on Northflank PaaS.

The most cost-effective AI sandbox option at any given scale depends on workload pattern, idle behaviour, and whether GPU access is required. For CPU-only workloads at scale, Northflank is consistently the lowest cost option in this comparison across both PaaS and BYOC.

## Which platforms provide GPU access in sandboxes?

Most platforms in this comparison do not provide GPU access within sandboxed environments, as covered in detail in [GPU sandboxes explained](https://northflank.com/blog/gpu-sandboxes). Northflank and Modal are the two platforms that do and publish GPU rates.

| Platform | L4 | A100 40GB | A100 80GB | H100 | H200 |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | $0.80/hr | $1.42/hr | $1.76/hr | $2.74/hr | $3.14/hr |
| **Modal** | $0.80/hr | $2.10/hr | $2.50/hr | $3.95/hr | $4.54/hr |

Northflank's GPU pricing covers GPU, CPU, and RAM as a combined rate per hour for GPU workloads. Modal charges GPU, CPU, and memory as separate line items, all billed per second.

## How does AI sandbox pricing compare at scale?

Headline rates tell a partial story. The cost difference between platforms becomes much more significant at volume. The table below shows total cost for 200 concurrent sandboxes on a PaaS deployment model, based on an nf-compute-100-4 plan on an m7i.2xlarge infrastructure node.

*Pricing as of May 2026. Verify current rates on each platform's pricing page before making cost decisions.*

| Provider | Sandbox vendor cost |
| --- | --- |
| **Northflank** | $7,200.00 |
| **E2B** | $16,819.20 |
| **Daytona** | $16,819.20 |
| **Modal** | $24,491.50 |
| **Runloop** | $30,484.80 |
| **Vercel Sandbox** | $31,068.80 |
| **Fly Sprites** | $35,770.00 |

For teams running sandboxes inside their own cloud account, BYOC changes the cost structure significantly. On Northflank BYOC, the same 200 sandboxes cost $2,060 in total, compared to $7,200 on Northflank PaaS. Northflank is the only platform in this comparison offering self-serve BYOC with publicly available pricing.

## Which platforms support BYOC for AI sandboxes?

For teams running sandboxes at scale, BYOC can change the cost structure significantly. Rather than paying a per-sandbox vendor rate, teams pay their cloud provider directly and a platform management fee. The table below covers BYOC availability and terms across platforms in this comparison.

| Platform | BYOC available | Clouds supported | Access model | Pricing model |
| --- | --- | --- | --- | --- |
| **Northflank** | Yes, self-serve | AWS, GCP, Azure, Oracle, CoreWeave, Civo, bare-metal, on-premises | Self-serve | Cloud bill + $0.01389/vCPU-hr and $0.00139/GB-hr management fee |
| **E2B** | Yes, limited | AWS, GCP | Enterprise only, contact sales | Not publicly disclosed |
| **Runloop** | Yes | Custom VPC | Enterprise plan, contact sales | Custom |
| **Modal** | No | Managed only | — | — |
| **Fly.io Sprites** | No | Managed only | — | — |
| **Vercel Sandbox** | No | Managed only (iad1 only) | — | — |
| **Cloudflare Sandbox** | No | Managed only | — | — |
| **Blaxel** | Custom | Private network connectivity | Contact sales | Custom |

Northflank is the only platform in this comparison with self-serve BYOC and publicly available pricing. All other BYOC options require a sales process, with no published rates.

<InfoBox className="BodyStyle">

**Get started with sandboxes on Northflank**

- [Northflank pricing](https://northflank.com/pricing): full pricing breakdown for compute, GPU, and BYOC with an interactive calculator
- [Sandboxes on Northflank](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank): architecture overview and core sandbox concepts
- [Deploy sandboxes on Northflank](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-on-northflank): step-by-step deployment guide
- [Deploy sandboxes in your cloud](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud): run sandboxes inside your own VPC
- [GPUs on Northflank](https://northflank.com/docs/v1/application/gpu-workloads/gpus-on-northflank): GPU workload overview and supported types
- [Deploy GPUs on Northflank cloud](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-on-northflank-cloud): step-by-step GPU deployment guide
- [Deploy GPUs in your own cloud](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-in-your-own-cloud): GPU workloads inside your own VPC

[Get started (self-serve)](https://app.northflank.com/signup), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) if you have specific infrastructure or compliance requirements.

</InfoBox>

## What affects AI sandbox cost beyond the headline rate?

With the tables and billing model differences in context, a few patterns are worth calling out for teams modelling real-world costs.

- **Idle behaviour:** Fly.io Sprites charges nothing when idle, with the filesystem preserved. E2B allows indefinite pause with only storage costs accruing while paused. How a platform handles idle time significantly affects cost for agent workloads that remain provisioned between sessions.
- **The Modal physical core distinction:** Modal's $0.1419/physical core-hr equates to approximately $0.071/vCPU-hr, which is still higher than E2B's $0.0504/vCPU-hr but not as high as the headline number suggests. The sandbox rate is also 3x Modal's standard compute rate, which matters if you are comparing sandbox pricing against other Modal workloads on the same account.
- **Cloudflare's layered cost structure:** The $0.072/vCPU-hr active CPU rate is not the total cost. Workers requests, Durable Objects, and the $5/month base plan all contribute. Teams building on Cloudflare Sandbox should model the full cost across all billing dimensions before comparing against single-rate platforms.

## Frequently asked questions about AI sandbox pricing

### Which is the cheapest AI sandbox platform?

Northflank is the cheapest AI sandbox provider in this comparison on both PaaS and BYOC. On PaaS, Northflank has the lowest published CPU rate at $0.01667/vCPU-hr, billed per second. E2B and Daytona are next at $0.0504/vCPU-hr, followed by Fly.io Sprites at $0.07/CPU-hr and Modal at $0.1419/physical core-hr (2 vCPU equivalent). On BYOC, Northflank is the only platform in this comparison with self-serve access and publicly available pricing, making it the lowest cost option at scale across both deployment models.

### Do AI sandbox platforms charge when sandboxes are idle?

It depends on the platform. Fly.io Sprites uses per-second active billing with no charge when the sandbox is idle. E2B allows indefinite pause with only storage costs accruing while paused. Vercel charges active CPU only but bills provisioned memory for the full duration. Always confirm whether a platform bills for the full sandbox duration or only active compute time before modelling costs for your workload.

### Which AI sandbox platforms support GPU workloads?

Northflank and Modal both support GPU workloads within sandboxed environments and publish GPU pricing. Most other platforms in this comparison do not provide GPU access in sandboxes. See [GPU sandboxes explained](https://northflank.com/blog/gpu-sandboxes) for a technical breakdown of why most platforms are CPU-only and how GPU sandbox isolation works.

### What is BYOC and how does it affect sandbox pricing?

BYOC (Bring Your Own Cloud) allows teams to run sandbox infrastructure inside their own cloud account. Instead of paying a per-sandbox vendor rate, teams pay their cloud provider directly and a management fee to the platform. At scale, this can reduce total cost significantly. Northflank is the only platform in this comparison offering self-serve BYOC with publicly available pricing. All other BYOC options require a sales process.

### Why does Modal sandbox pricing differ from Modal's standard compute pricing?

Modal prices sandbox compute at approximately 3x its standard compute rate. Sandbox CPU is $0.00003942/core/sec versus $0.0000131/core/sec for standard compute. Memory is also priced differently for sandboxes. The GPU rate for sandboxes follows Modal's standard GPU pricing. This means sandboxes on Modal cost more per unit of compute than standard Modal Functions.

## Related articles on AI sandbox platforms and pricing

The following articles cover topics referenced in this piece in more depth.

- [GPU sandboxes explained](https://northflank.com/blog/gpu-sandboxes): why most sandbox platforms are CPU-only, how GPU sandbox isolation works, and which platforms support GPU workloads
- [Top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes): platforms that support bring-your-own-cloud deployment with comparison across access model, clouds supported, and pricing
- [Best persistent sandbox platforms](https://northflank.com/blog/best-persistent-sandbox-platforms): comparison of platforms that support persistent sandbox environments and how they handle idle state
- [E2B vs Modal vs Fly.io Sprites](https://northflank.com/blog/e2b-vs-modal-vs-fly-io-sprites): detailed comparison across isolation model, GPU support, persistence, and BYOC
- [Top AI sandbox platforms for code execution](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution): broader platform overview including isolation models, startup times, and use cases
- [Best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents): ranked comparison of sandbox platforms for agent workloads
- [Ephemeral sandbox environments](https://northflank.com/blog/ephemeral-sandbox-environments): when to use ephemeral vs persistent sandbox design patterns and the cost implications of each
- [Sandbox providers](https://northflank.com/blog/sandbox-providers): overview of the AI sandbox provider landscape]]>
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</item><item>
  <title>GPU sandboxes: isolation models and platform support in 2026</title>
  <link>https://northflank.com/blog/gpu-sandboxes</link>
  <pubDate>2026-05-04T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[GPU sandboxes isolate GPU workloads using microVM passthrough or gVisor. Most sandbox providers are CPU-only. This explains why, and covers platforms that support GPU sandboxing.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/gpu_sandboxes_423e2a2d32.png" alt="GPU sandboxes: isolation models and platform support in 2026" /><InfoBox className="BodyStyle">

## TL;DR: what you need to know about GPU sandboxes

- A GPU sandbox is an isolated execution environment where a workload gets access to a GPU with a hardware or syscall-level boundary separating it from the host and other tenants. Most sandbox platforms do not support GPU workloads at all.
- GPU isolation requires hardware-level PCIe device passthrough, IOMMU configuration, and a VMM that supports passing devices through into a VM. Most sandbox platforms use Firecracker, which does not support GPU passthrough, making them CPU-only by design.
- [Northflank](https://northflank.com/) is one of the few platforms that supports sandboxing both CPU and GPU workloads. Where nested virtualization is available, Northflank uses microVM-based isolation (KVM / Kata Containers) to run GPU workloads, with the GPU passed through into the sandboxed runtime. This is the same execution model as CPU workloads, with strong isolation guarantees.
- Where nested virtualization is unavailable, Northflank falls back to gVisor. GPU workloads still run inside the sandboxed environment, but the isolation boundary is at the syscall level rather than hardware virtualization. The deployment model is the same in both cases.

</InfoBox>

GPU sandboxes are isolated execution environments that give untrusted workloads access to a GPU while enforcing hardware or syscall-level boundaries between the workload, the host system, and other tenants.

Most sandbox platforms today are CPU-only, and the reason comes down to how GPU hardware virtualization works at the PCIe level.

This article explains the technical constraints that make GPU sandboxing harder than CPU sandboxing, how the nested virtualization requirement shapes which isolation model applies, and which platforms support GPU workloads in sandboxed environments today.

## What is a GPU sandbox?

A GPU sandbox is an isolated execution environment that provides a workload with access to a GPU while preventing it from affecting the host system or other tenants.

A bare-metal GPU instance gives a single tenant unrestricted access with no isolation boundary. A GPU-enabled container shares the host kernel and NVIDIA driver namespace with everything else on that host.

A GPU sandbox adds an isolation layer using hardware virtualization or syscall interception, which is what makes multi-tenant GPU execution safe.

## Why is GPU sandboxing harder than CPU sandboxing?

CPU sandboxing and GPU sandboxing solve different problems at different layers of the stack. Understanding the distinction explains why most sandbox platforms support only CPU workloads.

### CPU isolation is a kernel problem

CPU workload isolation requires memory isolation, syscall boundaries, and process separation. Both microVMs, which give each workload its own kernel via hardware virtualization, and gVisor, which intercepts system calls in user space, solve this problem well. The problem is well-understood, and multiple viable solutions exist.

### GPU isolation is a hardware problem

GPUs are PCIe devices. To sandbox a GPU workload you need: an IOMMU (Intel VT-d or AMD-Vi) to enforce DMA isolation, preventing one tenant's GPU from reading or writing another tenant's memory over the PCIe bus; VFIO binding to detach the GPU from the host driver and assign it to a specific VM; IOMMU group separation so devices sharing a group are assigned together; and a VMM that supports PCIe device passthrough. On multi-GPU systems with NVLink fabrics, nv-fabricmanager adds further host-level isolation requirements.

### Why Firecracker does not support GPU passthrough

Firecracker excludes GPU passthrough as a design decision to minimize its attack surface. It implements only six emulated devices: virtio-net, virtio-block, virtio-balloon, virtio-vsock, serial console, and a minimal keyboard controller.

Because E2B, Fly.io Sprites, and Vercel Sandbox all use Firecracker for isolation, none of them can sandbox GPU workloads. VMMs that support PCIe device passthrough are required for GPU access inside a sandboxed VM. Northflank uses Kata Containers for microVM-based workloads.

See [what is AWS Firecracker](https://northflank.com/blog/what-is-aws-firecracker), [Firecracker vs gVisor](https://northflank.com/blog/firecracker-vs-gvisor), and the [guide to Cloud Hypervisor](https://northflank.com/blog/guide-to-cloud-hypervisor).

## What is the nested virtualization constraint for GPU sandboxes?

Nested virtualization determines which isolation path is available for GPU sandboxes. MicroVM-based isolation requires KVM to be present, and most cloud VMs do not expose this since the host hypervisor does not pass the KVM interface through to VMs running on top of it.

Without nested virtualization, hardware-level GPU passthrough through microVMs is not available, and platforms need to use an alternative isolation approach.

Northflank handles this constraint differently depending on the underlying infrastructure.

### Path 1: microVM with GPU passthrough (nested virtualization available)

When nested virtualization is available, Northflank uses microVM-based isolation to run GPU workloads. The GPU is passed through into the sandboxed runtime, and the workload runs inside a hardware-isolated VM with its own kernel. This is the same execution model used for CPU workloads, with strong isolation guarantees. See [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor) and [microVM vs gVisor](https://northflank.com/blog/microvm-vs-gvisor).

### Path 2: gVisor (nested virtualization unavailable)

When nested virtualization is unavailable, Northflank falls back to gVisor. GPU workloads run inside the sandboxed environment with access to the GPU, but the isolation boundary sits at the syscall level rather than at hardware virtualization. The deployment model is identical across both paths: the same APIs, the same workload definitions, the same platform. What changes is the isolation mechanism applied underneath.

## How Northflank runs GPU sandboxes

[Northflank](https://northflank.com/) supports both CPU and GPU workloads in isolated sandbox environments, with the isolation model adapting to the underlying infrastructure as described above.

Sandboxes start in approximately 1 to 2 seconds and support both ephemeral and persistent environments. GPU workloads run on on-demand NVIDIA GPUs including L4, A100 (40GB and 80GB), H100, and H200, with self-service provisioning and no quota requests required.

The same platform also runs APIs, background workers, databases, and GPU inference alongside sandboxes, so teams are not managing separate tooling for each workload type.

BYOC ([Bring Your Own Cloud](https://northflank.com/product/bring-your-own-cloud)) deployment is available self-serve across AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, and on-premises. Teams with data residency requirements or compliance mandates can deploy GPU sandboxes inside their own VPC using the same isolation model as the managed cloud. Northflank has been running production workloads across startups, public companies, and government deployments since 2021.

<InfoBox className="BodyStyle">

**Get started with GPU sandboxes on Northflank**

- [Sandboxes on Northflank](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank): architecture overview and core sandbox concepts
- [Deploy sandboxes on Northflank](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-on-northflank): step-by-step deployment guide
- [Deploy sandboxes in your cloud](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud): run sandboxes inside your own VPC
- [GPUs on Northflank](https://northflank.com/docs/v1/application/gpu-workloads/gpus-on-northflank): GPU workload overview and supported GPU types
- [Deploy GPUs on Northflank cloud](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-on-northflank-cloud): step-by-step GPU deployment guide
- [Deploy GPUs in your own cloud](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-in-your-own-cloud): GPU workloads inside your own VPC

[Get started (self-serve)](https://app.northflank.com/signup), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) if you have specific infrastructure or compliance requirements.

</InfoBox>

## Which sandbox platforms support GPU workloads?

The table below covers GPU support within the sandboxed execution environment specifically.

| Platform | GPU support | Isolation model | Bring Your Own Cloud (BYOC) | Persistent environments |
| --- | --- | --- | --- | --- |
| **Northflank** | Yes | Kata Containers (microVM) / gVisor | Yes, self-serve across multiple clouds | Yes (ephemeral and persistent) |
| **Modal** | Yes | gVisor | No, managed only | Partial (snapshotting available) |
| **E2B** | No (CPU only) | Firecracker microVMs | Enterprise only (AWS, GCP) | Yes (pause/resume, indefinite retention) |
| **Fly.io Sprites** | No (CPU only) | Firecracker microVMs | No, managed only | Yes (persistent NVMe filesystem) |
| **Vercel Sandbox** | No (CPU only) | Firecracker microVMs | No, managed only | Beta |
| **Blaxel** | No (CPU only) | Firecracker microVMs | Custom (contact sales) | Yes (standby with state preserved) |

For deeper platform comparisons, see [E2B vs Modal vs Fly.io Sprites](https://northflank.com/blog/e2b-vs-modal-vs-fly-io-sprites), [E2B vs Modal](https://northflank.com/blog/e2b-vs-modal), and [top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes).

## When do you need a GPU sandbox?

Not every GPU workload requires a sandboxed environment. A single-tenant inference service or a dedicated training job on reserved hardware does not need the isolation layer.

GPU sandboxes are relevant when the execution environment is shared, and workloads are untrusted or user-submitted:

- Platforms where multiple tenants submit GPU-accelerated code, and each workload needs isolation from the others
- AI agents calling local inference or embedding generation rather than an external API
- Platforms running user-submitted training jobs or [reinforcement learning](https://northflank.com/blog/reinforcement-learning-agents-in-secure-sandboxes) reward evaluations at scale
- [Code execution products](https://northflank.com/blog/code-execution-environment-for-autonomous-agents) that allow users to attach GPUs to notebooks or execution environments

## Pricing comparison for sandbox platforms

Pricing at scale differs significantly across platforms. The table below shows the total cost for 200 concurrent sandboxes across PaaS and BYOC deployment models, based on an nf-compute-100-4 plan on an m7i.2xlarge infrastructure node. *Pricing as of May 2026. Verify current rates on each platform's pricing page before making cost decisions.*

| Model | Provider | Cloud cost | Sandbox vendor cost | Total |
| --- | --- | --- | --- | --- |
| **PaaS** | Northflank | — | $7,200.00 | $7,200.00 |
| **PaaS** | E2B | — | $16,819.20 | $16,819.20 |
| **PaaS** | Modal | — | $24,491.50 | $24,491.50 |
| **PaaS** | Fly Sprites | — | $35,770.00 | $35,770.00 |
| **PaaS** | Vercel Sandbox | — | $31,068.80 | $31,068.80 |
| **BYOC (0.2 overcommit)** | Northflank | $1,500.00 | $560.00 | $2,060.00 |
| **BYOC** | E2B | $1,500.00 | $10,000.00 | $11,500.00 |

The BYOC row for Northflank reflects a request modifier of 0.2. On BYOC plans, Northflank applies an overcommit so more sandboxes run on the same hardware. For example, with a modifier of 0.2, each sandbox requests 20% of its plan resources as a guaranteed minimum but can burst to the full limit when capacity is available, fitting 40 sandboxes per node instead of 8. This comparison covers CPU sandbox workloads. GPU workload costs depend on GPU type and usage pattern. See the [Northflank pricing page](https://northflank.com/pricing) for current GPU rates.

## Frequently asked questions about GPU sandbox isolation

### Why do most sandbox providers not support GPU workloads?

Most sandbox providers use Firecracker microVMs, which do not support GPU passthrough. GPU passthrough requires VFIO binding, IOMMU configuration, and a VMM that supports PCIe device passthrough. Firecracker's minimal virtio device set does not include these capabilities.

### What is the difference between gVisor and microVM isolation for GPU workloads?

MicroVM isolation passes the GPU into a hardware-isolated VM; the workload runs its own kernel with a hardware boundary separating it from the host. gVisor intercepts the NVIDIA device calls in user space and proxies them to the host driver. Both provide GPU access inside a sandboxed environment; the isolation boundary differs.

### Does nested virtualization affect which isolation model applies?

Yes. MicroVM-based GPU passthrough requires KVM, which requires bare metal or a host with nested virtualization enabled. Where nested virtualization is unavailable, gVisor is the fallback. The deployment interface is identical in both cases.

### Can GPU sandboxes run inside my own cloud account?

Northflank supports self-serve BYOC deployment across AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, and on-premises. GPU sandboxes can run inside your own VPC using the same isolation model as the managed cloud.

### What GPU types are available for sandboxed workloads on Northflank?

Northflank provides on-demand access to NVIDIA L4, A100 (40GB and 80GB), H100, H200, and additional GPU types with self-service provisioning and no quota requests. See the [Northflank GPU documentation](https://northflank.com/docs/v1/application/gpu-workloads/gpus-on-northflank) for the current list.

## Related articles on GPU sandboxes and sandbox infrastructure

The following articles cover topics referenced in this piece in more depth.

- [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): in-depth comparison of the three primary container isolation technologies
- [What is a microVM?](https://northflank.com/blog/what-is-a-microvm): microVM architecture and how it differs from containers and full VMs
- [What is gVisor?](https://northflank.com/blog/what-is-gvisor): gVisor's syscall interception model, execution modes, and use cases
- [E2B vs Modal vs Fly.io Sprites](https://northflank.com/blog/e2b-vs-modal-vs-fly-io-sprites): sandbox platform comparison across isolation model, GPU support, persistence, and BYOC
- [Self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes): running AI sandbox infrastructure on your own cloud or on-premises
- [Top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes): sandbox platforms that support bring-your-own-cloud deployment
- [Reinforcement learning agents in secure sandboxes](https://northflank.com/blog/reinforcement-learning-agents-in-secure-sandboxes): sandboxed environments for RL reward function evaluation at scale
- [Best platforms for running untrusted code](https://northflank.com/blog/best-platforms-for-untrusted-code-execution): platforms designed for safely executing untrusted workloads]]>
  </content:encoded>
</item><item>
  <title>Best Vercel alternatives in 2026</title>
  <link>https://northflank.com/blog/best-vercel-alternatives-for-scalable-deployments</link>
  <pubDate>2026-05-04T15:03:00.000Z</pubDate>
  <description>
    <![CDATA[Best Vercel alternatives in 2026: Northflank, Netlify, AWS Amplify, Cloud Run, Heroku, Render, and more, compared by workload support, databases, and infrastructure model.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/best_vercel_alternatives_for_scalable_deployments_a948360e0d.png" alt="Best Vercel alternatives in 2026" /><InfoBox className="BodyStyle">

## TL;DR: What are the best Vercel alternatives in 2026?

Vercel is a cloud platform for frontend deployments, designed around static sites, server-side rendering, and serverless functions. Teams typically look for alternatives when they need backend services, managed databases, containerised workloads, or more infrastructure control alongside their frontend hosting.

1. **[Northflank](https://northflank.com/)**: Best for teams that need frontend and backend deployments, managed databases, CI/CD, and [BYOC support](https://northflank.com/features/bring-your-own-cloud) from one platform.
2. **Netlify**: Best for JAMstack projects and static frontends with serverless function support.
3. **AWS Amplify**: Best for teams in the AWS ecosystem that need full-stack hosting with AWS service integrations.
4. **Google Cloud Run**: Best for teams that need to deploy any containerised workload on a managed serverless runtime.
5. **Heroku**: Best for teams needing multi-language PaaS deployments with a broad add-on ecosystem.
6. **Render**: Best for teams that want a managed platform covering static sites, web services, databases, and cron jobs.
7. **DigitalOcean App Platform**: Best for teams needing straightforward PaaS deployments with predictable pricing.
8. **Cloudflare Pages and Workers**: Best for frontend-heavy applications requiring edge delivery with serverless function support.
9. **Microsoft Azure Static Web Apps**: Best for Azure-centric teams needing static frontend hosting with Azure Functions integration.
10. **Firebase Hosting**: Best for client-heavy frontends already using Firebase services for authentication and data.

The right alternative depends on your workload types, whether you need backend services and databases alongside your frontend, your infrastructure requirements, and your preferred cloud provider.

</InfoBox>

## Why consider alternatives to Vercel?

Vercel is a cloud platform built for frontend developers, focused on static sites, server-side rendering via Next.js, and short-lived serverless functions. It was created by the team behind Next.js and provides first-class Next.js hosting with automated builds and global delivery.

Teams consider alternatives for the following reasons.

- **No support for long-running backend services:** Vercel's runtime is designed for serverless functions with short execution windows. Running persistent backend APIs, WebSocket servers, or background workers requires separate infrastructure.
- **No managed databases:** Vercel does not provide managed databases natively. Teams must connect external database providers.
- **Serverless runtime constraints:** Vercel imposes limits on execution time, memory, and outbound network connections for serverless functions. Workloads that exceed these constraints cannot run on the platform.
- **Infrastructure control:** Teams with data residency, compliance, or networking requirements may need more control over the underlying infrastructure than Vercel provides.
- **Vendor lock-in:** Vercel-specific features such as Edge Functions and Next.js incremental static regeneration behaviour are not portable. Migrating to a different platform later requires re-architecting these parts of an application.

## What to consider when choosing a Vercel alternative

- **Workload types supported**: Does the platform run static sites, backend APIs, containers, databases, cron jobs, and microservices, or is it limited to frontend and serverless only?

- **CI/CD integration**: Does the platform trigger builds and deployments automatically from GitHub, GitLab, or Bitbucket, and does it support preview environments for pull requests?

- **Infrastructure model**: Does the platform run on its own managed infrastructure, or does it support deployment to your own cloud account? Teams with compliance or cost requirements may need BYOC or self-hosted options.

- **Scalability**: Can the platform scale workloads automatically in response to traffic, and does it support horizontal scaling for containerised services?

- **Pricing model**: Evaluate whether the platform's pricing model (usage-based, fixed plans, or per-seat) suits your expected workload volume and team size.

## The 10 best Vercel alternatives in 2026

### 1. Northflank

[Northflank](https://northflank.com/) is a developer platform that provides CI/CD pipelines, managed deployments, managed databases, preview environments, GPU workload support, and [Bring Your Own Cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) from a single control plane. Unlike Vercel, Northflank supports the full application stack: frontend services, backend APIs, containerised workloads, managed databases, and scheduled jobs can all run on the same platform.

BYOC support allows teams to run Northflank's orchestration layer on their own AWS, GCP, Azure, Oracle, or Civo infrastructure, so workloads are not tied to a single provider. CI/CD pipelines trigger from GitHub, GitLab, or Bitbucket commits, and preview environments spin up on pull requests including databases and microservices.

Northflank is a polyglot platform and supports any containerised workload. Teams with Next.js applications that also require persistent backend services, managed databases, or GPU workloads are a primary use case. Teams whose requirements are limited to static sites or Next.js deployments without backend complexity may find Vercel's native Next.js integration more direct for that specific use case.

Northflank operates at 99.99% historical uptime. For customers on enterprise agreements, this uptime is guaranteed under an SLA with service credits if not met.

![](https://assets.northflank.com/northflank_s_home_page_a1226caa06.png)

**Key capabilities:**
- [Built-in CI/CD pipelines](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) with Git-based triggers supporting GitHub, GitLab, and Bitbucket.
- [Preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) including databases and microservices, triggered by pull requests.
- [BYOC support](https://northflank.com/features/bring-your-own-cloud) across AWS, GCP, Azure, Oracle, and Civo.
- Managed databases including [PostgreSQL](https://northflank.com/dbaas/managed-postgresql), [MySQL](https://northflank.com/dbaas/managed-mysql), [MongoDB](https://northflank.com/dbaas/mongodb-on-northflank), and [Redis](https://northflank.com/dbaas/managed-redis).
- [GPU workload support](https://northflank.com/product/gpu-paas) for inference, model serving, and AI training.
- [Secure sandboxes and microVMs](https://northflank.com/product/sandboxes) for running untrusted or AI-generated code.
- [Secrets management](https://northflank.com/docs/v1/application/secure/manage-secret-groups), [RBAC](https://northflank.com/docs/v1/application/secure/use-role-based-access-control), and [audit logging](https://northflank.com/docs/v1/application/observe/audit-logs).
- Usage-based [pricing](https://northflank.com/pricing) based on compute and storage consumption.

**Best for:** Teams that need frontend and backend deployments, managed databases, CI/CD, and multi-cloud or on-premises flexibility from a single platform.

*See [how to deploy Next.js on Northflank](https://northflank.com/stacks/deploy-next) and how [Weights scales to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s).*

<InfoBox className="BodyStyle">

Get started with a [free plan](https://app.northflank.com/signup), follow the [getting started guide](https://northflank.com/docs/v1/application/getting-started/introduction-to-northflank), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo) if you have specific infrastructure or compliance requirements. See the [pricing page](https://northflank.com/pricing) for full details on compute, database, and GPU workload costs.

</InfoBox>

### 2. Netlify

Netlify is a cloud platform for deploying static sites, single-page applications, and JAMstack projects. It connects to a Git repository and builds and deploys automatically on push, with instant cache invalidation and preview URLs for pull requests.

Netlify provides serverless function support through Netlify Functions and Edge Functions. It delivers content through a global CDN. It does not support running persistent backend servers or managed databases natively. Teams that need backend services alongside their frontend will need to connect external providers.

![](https://assets.northflank.com/netlify_s_home_page_bb21edc82e.png)

**Key capabilities:**
- Git-based workflow with automatic builds and pull request preview deployments.
- Serverless functions and edge functions for dynamic functionality.
- Global CDN for static content delivery.
- Form handling and split testing features.

**Best for:** JAMstack development, static frontends, and projects that need serverless function support without persistent backend services.

**Considerations:** Netlify does not provide managed databases or persistent backend services. Teams with complex backend or database requirements will need separate infrastructure.

### 3. AWS Amplify

AWS Amplify is Amazon's service for deploying and hosting web and mobile applications. Amplify Hosting supports static and server-side rendered applications and connects to a Git repository for automated builds. It uses AWS CloudFront for content delivery.

Amplify integrates with AWS services including Lambda for functions, AppSync for GraphQL APIs, DynamoDB and Aurora for databases, and Cognito for authentication. This makes it a full-stack option for teams already using AWS, though the breadth of AWS services introduces pricing complexity that requires monitoring.

![](https://assets.northflank.com/AWS_Amplify_bd0927bcba.png)

**Key capabilities:**
- Amplify Hosting for static and server-side rendered applications.
- Integration with AWS Lambda, AppSync, DynamoDB, Aurora, and Cognito.
- Built-in CI/CD with pull request preview environments.
- Custom domains and SSL.

**Best for:** Full-stack applications already leveraging the AWS ecosystem that need frontend hosting integrated with AWS backend services.

**Considerations:** Deep AWS integration requires familiarity with AWS service pricing and configuration. Teams without AWS experience may find the setup more involved than dedicated frontend platforms.

### 4. Google Cloud Run

Google Cloud Run is a managed serverless runtime that deploys any Docker container image. Unlike Vercel's restricted serverless runtime, Cloud Run supports any containerised workload including REST APIs, web servers, and background workers. It scales container instances automatically based on traffic and scales down to zero when idle.

Cloud Run integrates with GCP services including Cloud SQL for databases, Cloud Build for CI/CD, and other Google Cloud services.

![](https://assets.northflank.com/cloudrun_home_page_d71aaa51c7.png)

**Key capabilities:**
- Deploys any Docker container image in a managed serverless environment.
- Automatic scaling including scale-to-zero.
- Integration with GCP services including Cloud SQL and Cloud Build.
- Usage-based pricing with a free tier.

**Best for:** Teams that need to deploy custom containerised backends, APIs, or any workload that exceeds Vercel's serverless function constraints.

**Considerations:** Cloud Run requires Docker image management and GCP project configuration. It covers containerised workloads but does not provide a full CI/CD pipeline, managed databases, or preview environments natively.

### 5. Heroku

Heroku is a PaaS platform that supports multiple programming languages and runtime environments via buildpacks. It deploys applications from a Git push and manages the underlying infrastructure, covering backend APIs, web applications, and worker processes across languages including Node.js, Python, Ruby, Go, PHP, and others.

Heroku provides a broad add-on ecosystem for third-party databases, caching, monitoring, and other services. It supports pipelines and review apps for preview environments. Heroku removed its free tier for production dynos in 2022; pricing is based on dyno size and type.

![](https://assets.northflank.com/heroku_s_home_page_100d751e93.png)

**Key capabilities:**
- Multi-language support via buildpacks.
- Git-based deployment workflow.
- Pipelines and review apps for preview environments.
- Add-on ecosystem for databases, caching, and monitoring.

**Best for:** Teams needing multi-language PaaS deployments with a mature add-on ecosystem and straightforward backend hosting.

**Considerations:** Heroku does not have a free production tier. Scaling is based on dyno size rather than serverless auto-scaling. Teams with complex Kubernetes or multi-cloud requirements should evaluate whether Heroku covers their infrastructure needs.

*See [top Heroku alternatives](https://northflank.com/blog/top-heroku-alternatives) and [how to migrate from Heroku to Northflank](https://northflank.com/docs/v1/application/migrate-from-heroku).*

### 6. Render

Render is a cloud platform that provides hosting for static sites, web services, managed PostgreSQL databases, Redis instances, cron jobs, and background workers. It deploys from GitHub or GitLab repositories using Docker or buildpacks and provides automatic builds on each commit.

Render provides managed PostgreSQL databases natively, which is a capability Vercel does not provide. It includes free SSL, a built-in load balancer, and private networking between services.

![](https://assets.northflank.com/render_s_home_page_eb2eb796c7.png)

**Key capabilities:**
- Hosting for static sites, web services, databases, cron jobs, and background workers.
- Managed PostgreSQL and Redis.
- Automatic builds from GitHub and GitLab.
- Private networking between services.

**Best for:** Full-stack projects that need a managed platform covering frontend, backend services, and databases without configuring separate infrastructure.

**Considerations:** Render abstracts infrastructure management, which limits control over networking and runtime configuration for teams with specific infrastructure requirements.

### 7. DigitalOcean App Platform

DigitalOcean App Platform is a managed PaaS service built on DigitalOcean's cloud infrastructure. It deploys static sites, Docker images, and source code in multiple languages, and connects to GitHub for automated builds and deployments. It supports attaching DigitalOcean managed databases.

App Platform provides automatic SSL, custom domains, and vertical and horizontal scaling options. Pricing uses fixed tiers based on resource size.

![](https://assets.northflank.com/Digitalocean_app_platform_s_home_page_700cfd80e3.png)

**Key capabilities:**
- Deploys static sites, Docker containers, and multi-language source code.
- Integration with GitHub for automated builds.
- Supports attaching DigitalOcean managed databases.
- Fixed pricing tiers with automatic SSL and custom domains.

**Best for:** Teams needing straightforward PaaS deployments with predictable pricing and support for backend workloads alongside static content.

**Considerations:** App Platform does not provide the same edge network performance as Vercel or Cloudflare for static content delivery. Advanced networking and multi-cloud options are more limited than hyperscaler alternatives.

*See [how to send logs to DigitalOcean Spaces from Northflank](https://northflank.com/guides/send-logs-to-a-digitalocean-space-from-northflank) for an example of integrating Northflank with DigitalOcean infrastructure.*

### 8. Cloudflare Pages and Workers

Cloudflare Pages is a JAMstack hosting platform that builds and deploys static sites and single-page applications from a Git repository. Cloudflare Workers extends this with serverless JavaScript and TypeScript functions that run on Cloudflare's edge network, enabling API proxies, authentication, and dynamic content handling close to users.

Cloudflare Pages and Workers are suited to frontend-heavy applications where edge delivery performance is a priority. Workers run in a V8 isolate-based runtime rather than Node.js, which means not all Node.js libraries are compatible. Persistent backend server workloads and managed databases are not supported on this platform natively.

![](https://assets.northflank.com/Cloudflare_Pages_home_page_2a7b843018.png)

**Key capabilities:**
- JAMstack hosting on Cloudflare's global edge network.
- Cloudflare Workers for serverless edge functions.
- Git-based automatic builds and pull request previews.
- Generous free tier for static content.

**Best for:** Frontend applications where global edge delivery performance is the primary requirement, with limited or no persistent backend services.

**Considerations:** Cloudflare Workers use a V8 isolate runtime rather than Node.js, which limits library compatibility. Persistent backend services and databases are not supported on the platform natively.

### 9. Microsoft Azure Static Web Apps

Azure Static Web Apps is Microsoft's managed service for hosting static frontends with Azure Functions integration for dynamic API routes. It provides global static content delivery, automated CI/CD through GitHub and Azure DevOps, and pull request staging environments.

Azure Static Web Apps integrates with other Azure services including Cosmos DB, Azure SQL, and Azure Active Directory for authentication.

![](https://assets.northflank.com/Microsoft_Azure_Static_Web_Apps_home_page_c7de64c83f.png)

**Key capabilities:**
- Global hosting for static frontends with Azure Functions for API routes.
- CI/CD integration with GitHub and Azure DevOps.
- Pull request staging environments.
- Integration with Azure services including Cosmos DB and Azure Active Directory.

**Best for:** Azure-centric teams that need static frontend hosting with serverless API support and integration with existing Azure services.

**Considerations:** Azure Static Web Apps is most effective for teams already using Azure. Teams unfamiliar with Azure's service configuration and pricing model may find setup more involved than dedicated frontend platforms.

*See [how to integrate your Azure account with Northflank](https://northflank.com/docs/v1/application/cloud-providers/azure-on-northflank) for hybrid deployments.*

### 10. Firebase Hosting

Firebase Hosting is part of Google Firebase and provides managed hosting for static sites and single-page applications. It delivers content through Google's CDN and integrates with Cloud Functions and Cloud Run for dynamic backend routes. It includes automatic SSL, custom domain support, and versioning with rollback support.

Firebase Hosting integrates with other Firebase services including Firestore, Firebase Authentication, and Firebase Storage, making it well-suited for applications that already use Firebase as their backend platform.

![](https://assets.northflank.com/firebase_home_page_3e98d01021.png)

**Key capabilities:**
- Static site and single-page application hosting on Google's CDN.
- Integration with Cloud Functions and Cloud Run for dynamic routes.
- Automatic SSL and custom domain support.
- Versioning and rollback support.

**Best for:** Client-heavy frontend applications that use Firebase for authentication, data, or storage, and need minimal custom backend infrastructure.

**Considerations:** Firebase Hosting is designed for static and client-heavy applications. Substantial custom backend workloads or complex query requirements typically require additional GCP services.

*See [how to deploy Supabase on Northflank](https://northflank.com/stacks/deploy-supabase) as an open-source Firebase alternative.*

## Comparison table: Vercel alternatives at a glance

| **Platform** | **Workload support** | **Managed databases** | **CI/CD and previews** | **Infrastructure model** |
| --- | --- | --- | --- | --- |
| **Northflank** | Containers, APIs, databases, cron jobs, GPU workloads | Yes (PostgreSQL, MySQL, MongoDB, Redis) | Yes, including full-stack preview environments | Northflank cloud or BYOC (AWS, GCP, Azure, Oracle, Civo) |
| **Netlify** | Static sites and serverless functions | No | Yes, Git-based with pull request previews | Managed cloud |
| **AWS Amplify** | Static and SSR frontends, AWS Lambda functions | Via AWS (DynamoDB, Aurora) | Yes, with pull request previews | AWS cloud |
| **Google Cloud Run** | Any containerised workload | Via GCP (Cloud SQL) | Via Cloud Build | GCP cloud |
| **Heroku** | Multi-language web apps and workers | Via add-ons | Yes, with review apps | Managed cloud |
| **Render** | Static sites, web services, databases, cron jobs | Yes (PostgreSQL, Redis) | Yes, Git-based | Managed cloud |
| **DigitalOcean App Platform** | Static sites, Docker containers, multi-language apps | Via DigitalOcean managed databases | Yes, GitHub integration | DigitalOcean cloud |
| **Cloudflare Pages and Workers** | Static sites and edge functions | No | Yes, Git-based with pull request previews | Cloudflare edge network |
| **Azure Static Web Apps** | Static frontends and Azure Functions | Via Azure services | Yes, GitHub and Azure DevOps | Azure cloud |
| **Firebase Hosting** | Static sites and SPAs | Via Firebase/GCP | Yes, Firebase CLI and CI integration | Google cloud |

## How to choose the right Vercel alternative

The right alternative depends primarily on what workload types you need to run alongside your frontend.

For teams that need the full application stack — frontend, backend APIs, managed databases, and background jobs — from one platform, Northflank and Render both cover this. Northflank adds BYOC support, managed Kubernetes, GPU workloads, and enterprise SLA options for teams with more complex infrastructure requirements.

For teams whose workloads are primarily static sites and serverless functions, Netlify and Cloudflare Pages are direct alternatives. Cloudflare is the stronger choice where global edge delivery performance is the primary concern.

For teams embedded in a specific cloud provider ecosystem, AWS Amplify, Google Cloud Run, Firebase Hosting, and Azure Static Web Apps each provide deep integrations with their respective platforms.

For teams needing multi-language backend deployments with a broad add-on ecosystem, Heroku and DigitalOcean App Platform are straightforward options with predictable pricing.

## Frequently asked questions about Vercel alternatives

### What are the main limitations of Vercel?

Vercel is designed for frontend deployments and serverless functions. It imposes execution time and memory limits on serverless functions, does not support persistent backend services or managed databases natively, and ties teams to Vercel's infrastructure with limited BYOC or on-premises options. Teams that need backend services, databases, or containerised workloads alongside their frontend typically look for alternatives.

### Can Northflank host Next.js applications?

Yes. Northflank supports any containerised application including Next.js. See [how to deploy Next.js on Northflank](https://northflank.com/stacks/deploy-next). Northflank also supports running backend services, managed databases, and CI/CD pipelines in the same platform, which is the primary advantage over Vercel's more narrowly scoped environment.

### What is the best free alternative to Vercel?

Netlify, Cloudflare Pages, and Render all provide free tiers for static site hosting. Northflank provides a [free Sandbox tier](https://app.northflank.com/signup) for deploying and testing services and databases. Firebase Hosting provides a free tier with usage-based pricing beyond the free quota.

### What is BYOC and why does it matter for Vercel alternatives?

BYOC (Bring Your Own Cloud) means running a platform's orchestration layer on your own cloud account rather than the platform's managed infrastructure. It gives teams control over data residency, networking, and infrastructure costs. Northflank supports BYOC across AWS, GCP, Azure, Oracle, and Civo. Vercel does not provide a BYOC option.

### What is the difference between a static site host and a full-stack platform?

A static site host serves pre-built HTML, CSS, and JavaScript files through a CDN and may support short-lived serverless functions for dynamic functionality. A full-stack platform supports static frontends alongside persistent backend services, managed databases, containers, and background workers. Vercel and Netlify are static site hosts with serverless function support. Northflank, Render, and Heroku are full-stack platforms.

### Does Northflank provide an SLA?

Northflank operates at 99.99% historical uptime. For customers on enterprise agreements, this uptime is guaranteed under an SLA with service credits if not met. See the [Northflank enterprise page](https://northflank.com/enterprise) for details.]]>
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</item><item>
  <title>Firecracker vs Cloud Hypervisor</title>
  <link>https://northflank.com/blog/firecracker-vs-cloud-hypervisor</link>
  <pubDate>2026-05-01T14:00:00.000Z</pubDate>
  <description>
    <![CDATA[Firecracker and Cloud Hypervisor are both Rust-based VMMs using KVM for microVM isolation. Learn how they compare on features, performance, guest OS support, and when to use each.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/firecracker_vs_cloud_hypervisor_78b948f97f.png" alt="Firecracker vs Cloud Hypervisor" />Firecracker and Cloud Hypervisor are both open-source Virtual Machine Monitors written in Rust that use KVM to create lightweight VMs for cloud workloads. They share the same underlying philosophy (minimal device models, small attack surfaces, and fast boot times) but differ in scope, feature set, guest OS support, and the use cases they are designed for.

This article compares the two on architecture, performance, features, operational complexity, and when each is the right choice.

<InfoBox className="BodyStyle">

**What is Northflank?**

[Northflank](https://northflank.com/) is a full-stack cloud platform that uses Cloud Hypervisor as its primary VMM for microVM-backed sandboxes via Kata Containers, with Firecracker and gVisor applied depending on workload requirements. 

In production since 2021 across startups, public companies, and government deployments. [Get started (self-serve)](https://app.northflank.com/signup) or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) for specific infrastructure or compliance requirements.

</InfoBox>

## TL;DR: Firecracker vs Cloud Hypervisor

|  | Firecracker | Cloud Hypervisor |
| --- | --- | --- |
| **Type** | VMM (microVM monitor) | VMM |
| **Developer** | Amazon Web Services | Linux Foundation (multi-org) |
| **Language** | Rust | Rust |
| **Hypervisor backend** | KVM only | KVM and MSHV |
| **Boot time** | User space in as little as 125ms | Boot to userspace in less than 100ms |
| **Memory overhead** | Less than 5 MiB per microVM | Low (minimal footprint) |
| **Guest OS** | Linux, OSv | 64-bit Linux, Windows 10, Windows Server 2019 |
| **Live migration** | Not supported | Supported |
| **GPU passthrough** | Not supported | Supported via VFIO |
| **CPU/memory hotplug** | Not supported | Supported |
| **Device model** | Five virtio devices only | Broader virtio device set, no legacy devices |
| **Jailer** | Yes (companion security process) | Seccomp filtering |
| **Rate limiting** | Built-in per microVM | Not built-in |
| **Kata Containers support** | Yes | Yes (primary backend) |
| **Best for** | High-density serverless, custom platforms | Feature-rich cloud workloads, GPU, Windows guests |

## What is Firecracker?

Firecracker is an open-source VMM built by Amazon Web Services, released in 2018 under the Apache 2.0 licence. It was purpose-built for multi-tenant serverless and container workloads, specifically to power AWS Lambda and AWS Fargate. It started from crosvm, the Chromium OS VMM written in Rust, and has diverged significantly since.

Firecracker's design philosophy is strict minimalism. It implements only five devices: virtio-net, virtio-block, virtio-vsock, a serial console, and a minimal keyboard controller. There is no graphics, no USB, no BIOS, no ACPI. This minimal device model keeps the attack surface small and boot times fast. Each microVM initiates user-space code in as little as 125ms, with less than 5 MiB of memory overhead per instance. It supports up to 150 microVMs per second per host in benchmarks.

Firecracker runs on 64-bit Intel, AMD, and Arm CPUs with hardware virtualisation support, using KVM as its hypervisor backend. It supports Linux and OSv guests. It includes a built-in rate limiter per microVM for granular control of network and storage resources, and a RESTful API for VM lifecycle management. A companion process called the jailer provides an additional security layer by further isolating the Firecracker process using Linux namespaces and cgroups in case the virtualisation barrier is compromised.

See [What is AWS Firecracker?](https://northflank.com/blog/what-is-aws-firecracker) for a full technical breakdown.

## What is Cloud Hypervisor?

Cloud Hypervisor is an open-source VMM written in Rust, governed under the Linux Foundation and based on the rust-vmm crates. It runs on KVM and Microsoft Hypervisor (MSHV) and targets modern cloud workloads with a broader feature set than Firecracker while maintaining a minimal, auditable codebase.

Where Firecracker draws a hard line at five devices, Cloud Hypervisor supports a wider range of paravirtualised virtio devices without legacy hardware emulation. Its capabilities include boot to userspace in less than 100ms, live migration, CPU and memory hotplug, PCI hotplug, GPU passthrough via VFIO, and a REST API for programmatic VM lifecycle management. It supports x86-64 and AArch64 architectures and runs 64-bit Linux and Windows 10/Windows Server 2019 guests.

Cloud Hypervisor is the primary VMM backend for Kata Containers in production deployments. It is supported by organisations including Alibaba, AMD, Ampere, ARM, ByteDance, Intel, Microsoft, SAP, and Tencent Cloud.

For a full technical breakdown of Cloud Hypervisor's architecture and capabilities, see the [guide to Cloud Hypervisor](https://northflank.com/blog/guide-to-cloud-hypervisor).

## How do Firecracker and Cloud Hypervisor architectures compare?

Both VMMs share the same core approach: Rust implementation, KVM-based hardware virtualisation, virtio paravirtualised devices, and a focus on modern cloud workloads without legacy hardware emulation. They diverge significantly in scope.

Firecracker optimises for the absolute minimum. Five devices, no Windows support, no live migration, no GPU passthrough, no hotplug. Every feature that does not directly serve its target use case (high-density serverless and container workloads) is excluded. That constraint is a feature: fewer code paths mean fewer vulnerabilities and lower overhead.

Cloud Hypervisor optimises for modern cloud workloads broadly defined. It supports live migration, GPU passthrough, Windows guests, hotplug, and a wider virtio device set while still keeping legacy hardware out. It is more capable than Firecracker but has a larger codebase as a result.

Both use a RESTful API for VM management. Both are supported as VMM backends in Kata Containers. Both use KVM as their primary hypervisor. Cloud Hypervisor additionally supports MSHV, extending its reach to environments where KVM is not available but Microsoft Hypervisor is.

## Performance (Firecracker vs Cloud Hypervisor)

Both VMMs target fast boot and low memory overhead, but measuring them head-to-head requires care because the figures come from different benchmarking conditions.

Firecracker's official documentation states user-space initiation in as little as 125ms. Cloud Hypervisor's official documentation states boot to userspace in less than 100ms. These figures reflect each project's own benchmarks under their own conditions and should not be read as a definitive claim that Cloud Hypervisor is faster than Firecracker in all environments. Real-world performance depends on host hardware, kernel configuration, image size, and workload characteristics.

At runtime, both deliver near-native performance for their guest workloads since the guest kernel handles syscalls directly without an interception layer. Firecracker's rate limiter provides built-in per-microVM throttling for network and storage, which Cloud Hypervisor does not have built-in.

For very high workload density, Firecracker's less-than-5-MiB memory overhead per microVM and support for up to 150 microVM creations per second per host in benchmarks make it well-suited to environments where packing thousands of microVMs on a single host matters.

## Security model (Firecracker vs Cloud Hypervisor)

Both VMMs use KVM hardware virtualisation to enforce isolation and are implemented in Rust, which eliminates a class of memory corruption vulnerabilities common in C-based VMMs.

Firecracker adds a companion process called the jailer that provides a second line of defence. The jailer further isolates the Firecracker process using Linux namespaces and cgroups, so that if the virtualisation boundary were compromised, the attacker's access to the host remains limited. Cloud Hypervisor uses seccomp filtering to restrict which host syscalls the VMM process can make.

Both approaches reduce the host attack surface beyond KVM alone. The choice between them is not meaningfully a security differentiator for most use cases, both are production-hardened and used at scale.

## Feature comparison (Firecracker vs Cloud Hypervisor)

- **Live migration:** Cloud Hypervisor supports live VM migration from one host to another without interruption. Firecracker does not support live migration. For workloads that need to move between hosts without downtime (long-running services, stateful workloads), Cloud Hypervisor is the right choice.
- **GPU passthrough:** Cloud Hypervisor supports GPU passthrough via VFIO, allowing VMs to access physical GPU hardware directly. Firecracker does not support GPU passthrough. For isolated GPU workloads, Cloud Hypervisor is required.
- **Windows guests:** Cloud Hypervisor supports Windows 10 and Windows Server 2019 as guest operating systems. Firecracker supports Linux and OSv guests only. For teams running mixed Linux and Windows workloads, Cloud Hypervisor is the only option.
- **CPU and memory hotplug:** Cloud Hypervisor supports adding CPU and memory to a running VM without rebooting. Firecracker does not. For long-running workloads that need to scale resources dynamically, Cloud Hypervisor has an advantage.
- **Rate limiting:** Firecracker includes a built-in rate limiter per microVM for granular network and storage throttling configurable via its API. Cloud Hypervisor does not include built-in rate limiting.
- **MSHV support:** Cloud Hypervisor runs on Microsoft Hypervisor in addition to KVM, extending its reach to environments where KVM is unavailable. Firecracker is KVM-only.

## Kubernetes and container integration (Firecracker vs Cloud Hypervisor)

Neither Firecracker nor Cloud Hypervisor integrates with Kubernetes directly. Both require Kata Containers as the orchestration layer that bridges them to the Container Runtime Interface. You configure Kata with your chosen VMM backend via RuntimeClass, and Kata handles VM provisioning, guest kernel boot, and networking transparently from Kubernetes' perspective. See [What are Kata Containers?](https://northflank.com/blog/what-are-kata-containers) for how that integration works.

Firecracker also integrates with containerd via the firecracker-containerd project, providing an alternative path for container workloads outside Kubernetes.

## When should you use Firecracker vs Cloud Hypervisor?

**Use Firecracker when:**

- You are building a custom serverless or FaaS platform and need maximum density and simplicity
- Your workloads run on Linux or OSv with no requirements for GPU passthrough, live migration, or Windows guests
- Built-in per-microVM rate limiting matters for your resource management model
- You want the smallest possible attack surface and codebase
- You have the infrastructure expertise to manage Firecracker directly or are using it through Kata Containers

**Use Cloud Hypervisor when:**

- You need live migration for long-running or stateful workloads
- Your workloads require GPU passthrough for isolated GPU compute
- You need to run Windows guests alongside Linux workloads
- CPU or memory hotplug is a requirement
- You are running on an environment where MSHV is available but KVM is not
- You want a broader feature set while still maintaining a modern, auditable codebase

For most teams running microVM isolation in Kubernetes via Kata Containers, Cloud Hypervisor is the default and covers the majority of workload requirements. Firecracker is the right choice when density, simplicity, and rate limiting matter more than feature breadth.

## How does Northflank use Firecracker and Cloud Hypervisor?

Northflank's [sandbox infrastructure](https://northflank.com/product/sandboxes) uses Kata Containers with Cloud Hypervisor as its primary VMM, with Firecracker applied for workloads that benefit from its minimal device model and gVisor applied where syscall-interception isolation is sufficient or where nested virtualisation is unavailable. The platform has been in production since 2021 across startups, public companies, and government deployments.

Sandboxes spin up in approximately 1 to 2 seconds, with compute pricing starting at $0.01667 per vCPU per hour and $0.00833 per GB of memory per hour. See the [pricing page](https://northflank.com/pricing) for full details.

Northflank supports both ephemeral and persistent sandbox environments on managed cloud or inside your own VPC, self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal via [bring your own cloud](https://northflank.com/product/bring-your-own-cloud).

<InfoBox className="BodyStyle">

**Get started with Northflank sandboxes**

- [Sandboxes on Northflank: overview and concepts](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank): architecture overview and core sandbox concepts
- [Deploy sandboxes on Northflank: step-by-step deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-on-northflank): step-by-step deployment guide
- [Deploy sandboxes in your cloud: BYOC deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud): run sandboxes inside your own VPC
- [Create a sandbox with the SDK: programmatic sandbox creation](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank#create-sandboxes-with-the-sdk): programmatic sandbox creation via the Northflank JS client

[Get started (self-serve)](https://app.northflank.com/signup), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) if you have specific infrastructure or compliance requirements.

</InfoBox>

## Frequently asked questions about Firecracker vs Cloud Hypervisor

### Is Firecracker faster than Cloud Hypervisor?

Both VMMs target sub-200ms boot times to userspace under their own benchmarking conditions. Firecracker documents user-space initiation in as little as 125ms. Cloud Hypervisor documents boot to userspace in less than 100ms. These figures come from different benchmarks and should not be read as an absolute ranking. Real-world performance depends on host hardware, kernel size, and workload configuration.

### Can Firecracker run Windows guests?

No. Firecracker supports Linux and OSv guests. Cloud Hypervisor supports 64-bit Linux and Windows 10/Windows Server 2019 guests.

### Do Firecracker and Cloud Hypervisor both work with Kata Containers?

Yes. Both Firecracker and Cloud Hypervisor are supported VMM backends for Kata Containers. Cloud Hypervisor is the default backend in most Kata Containers production deployments. Firecracker is available as an alternative backend.

### Does Firecracker support live migration?

No. Firecracker does not support live VM migration. Cloud Hypervisor supports live migration from one host to another without interruption.

### What is the jailer in Firecracker?

The jailer is a companion process that provides a second line of defence for Firecracker microVMs. It further isolates the Firecracker process using Linux namespaces and cgroups, limiting what an attacker can access if the virtualisation barrier were compromised.

### Does Firecracker or Cloud Hypervisor have a smaller attack surface?

Firecracker's device model is more constrained, five devices versus Cloud Hypervisor's broader virtio device set, which means a smaller codebase and fewer potential attack vectors in the VMM itself. Both are implemented in Rust and are production-hardened. For most use cases, the difference is not the deciding factor.

## Related articles on Firecracker, Cloud Hypervisor, and sandboxes

- [What is AWS Firecracker?](https://northflank.com/blog/what-is-aws-firecracker): a full technical breakdown of Firecracker's architecture, device model, and jailer
- [What are Kata Containers?](https://northflank.com/blog/what-are-kata-containers): how Kata Containers orchestrates Firecracker and Cloud Hypervisor for Kubernetes workloads
- [What is a microVM?](https://northflank.com/blog/what-is-a-microvm): how microVMs work and how both VMMs implement them
- [What is KVM?](https://northflank.com/blog/what-is-kvm): the hardware virtualisation layer both VMMs build on
- [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): a three-way comparison of the leading isolation technologies
- [Cloud Hypervisor vs gVisor](https://northflank.com/blog/cloud-hypervisor-vs-gvisor): how Cloud Hypervisor compares to syscall-interception isolation
- [Firecracker vs gVisor](https://northflank.com/blog/firecracker-vs-gvisor): how Firecracker compares to syscall-interception isolation
- [Firecracker vs Docker](https://northflank.com/blog/firecracker-vs-docker): how microVM isolation compares to standard container isolation]]>
  </content:encoded>
</item><item>
  <title>Best PaaS platforms for AI-generated and vibe-coded apps in 2026</title>
  <link>https://northflank.com/blog/best-paas-platforms-for-ai-generated-and-vibe-coded-apps</link>
  <pubDate>2026-05-01T14:00:00.000Z</pubDate>
  <description>
    <![CDATA[Best PaaS platforms for vibe-coded apps in 2026: compare Northflank, Vercel, Render, and Railway on managed databases, secrets management, preview environments, sandboxes, and BYOC.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Best_developer_experience_Paa_S_2025_34cda1a773.png" alt="Best PaaS platforms for AI-generated and vibe-coded apps in 2026" /><InfoBox className="BodyStyle">

### TL;DR: best PaaS platforms for AI-generated and vibe-coded apps in 2026

Most vibe-coded apps never make it to production. The code generation problem is solved. The deployment gap is where momentum dies. A PaaS removes that gap, but not all of them cover what production actually requires.

- [**Northflank**](https://northflank.com/) – A full-stack PaaS for vibe-coded apps. Managed databases, secrets groups, [preview environments](https://northflank.com/product/preview-environments) per pull request, CI/CD, [sandbox isolation](https://northflank.com/product/sandboxes) for AI-generated code execution, [GPU workloads](https://northflank.com/product/gpu-paas), and [self-serve BYOC](https://northflank.com/product/bring-your-own-cloud) into your own cloud. No infrastructure knowledge required.
- **Vercel** – Best for Next.js and React frontends where the backend is minimal or handled by external APIs. Serverless-only, no native managed databases.
- **Render** – Best for straightforward full-stack apps with a single service and database, where managed-only infrastructure is acceptable.
- **Railway** – Best for teams that want the fastest path from code to a deployed app with a database using template-based setup.

> [Northflank](https://northflank.com/) is a full-stack cloud platform built for teams that need more than a prototype host. Managed databases, secrets management, preview environments, CI/CD, sandbox isolation for AI-generated code, GPU workloads, and self-serve BYOC into AWS, GCP, Azure, and on-premises. [Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30).
> 
</InfoBox>

Vibe coding gets an app to localhost fast. A PaaS is what keeps it running reliably after the first deploy. The gap between built-in hosting in vibe coding tools and a PaaS is managed databases, secrets management, preview environments, autoscaling, and the controls that real users and compliance frameworks require.

This article covers what a PaaS for vibe coding needs to handle, where built-in hosting in vibe coding tools falls short, and which platforms cover the full stack.

## What is a PaaS for vibe-coded apps?

A Platform-as-a-Service abstracts the infrastructure layer so builders define what to run rather than how to run it. Connect a Git repository, configure a service, and the platform handles building, deploying, scaling, TLS, and health checks. For vibe coders, this means deployment becomes an extension of building rather than a separate discipline requiring DevOps knowledge.

The distinction between built-in hosting in vibe coding tools and a PaaS is what happens after the first deploy. Built-in hosting in vibe coding tools gets your app live with a URL while a PaaS handles what comes next: managed databases that provision in minutes with scoped credentials, secrets injected at runtime so credentials never appear in source code, preview environments that spin up per pull request and tear down on merge, autoscaling that adjusts to traffic without manual intervention, and observability that tells you what is happening inside the app.

## Why does built-in hosting in vibe coding tools fall short for production?

Built-in hosting from Lovable, Bolt, and Replit works for the first deploy. The constraints surface quickly as apps grow. Data lives on the platform's infrastructure with no clear path to owning it. Database connections require external providers and manual credential management. Environment separation between development and production is limited or nonexistent. Pricing scales unpredictably as usage grows. Compliance requirements that enterprise customers or regulated industries introduce cannot be met on shared managed infrastructure.

The same applies to platforms like Vercel for full-stack workloads. Vercel is optimized for frontend and serverless. Long-running services, background workers, persistent database connections, and stateful AI workloads need external providers or workarounds. For apps that start as a Next.js frontend and grow into a full-stack system with a database, background jobs, and AI execution, a single-purpose PaaS creates friction that a full-stack platform removes.

## What a PaaS for vibe-coded apps needs to cover

These are the capabilities that separate a PaaS from built-in hosting in vibe coding tools.

- **Managed databases:** Your app needs somewhere to store data with automated backups, scaling, and connection management. A PaaS that provisions PostgreSQL, MySQL, MongoDB, or Redis as a first-class addon and automatically injects connection credentials removes the hardest part of full-stack deployment.
- **Secrets management:** API keys, database passwords, and third-party credentials must never appear in source code or build logs. A PaaS that stores secrets in a managed secrets store and injects them at runtime prevents the credential exposure that is the most common production security failure in vibe-coded apps.
- [**Preview environments**](https://northflank.com/product/preview-environments): Every pull request should spin up an isolated copy of the app with its own database instance and tear it down on merge. This lets builders test changes against real data without affecting production.
- **CI/CD from Git:** Push to a branch, the platform builds and deploys automatically. No manual deployment steps between writing code and seeing it live.
- **Autoscaling:** Traffic is unpredictable. The platform should scale services up to handle load and down to reduce cost without manual intervention or pre-provisioning.
- [**Sandbox isolation for AI-generated code**](https://northflank.com/product/sandboxes): Apps that execute AI-generated or user-submitted code at runtime need microVM isolation. Standard container execution shares the host kernel and is not sufficient for code you do not fully trust.
- **Observability:** Real-time logs and performance metrics should be built in so builders can see what is happening inside the app without adding a separate monitoring tool.
- [**BYOC for compliance and cost**](https://northflank.com/product/bring-your-own-cloud): When apps grow into production systems with enterprise customers, compliance requirements, or significant cloud spend, the ability to deploy into your own AWS, GCP, or Azure account keeps data inside your own infrastructure and eliminates vendor lock-in.

## Best PaaS platforms for vibe-coded apps in 2026

### Northflank

[Northflank](https://northflank.com/product/deployments) is a full-stack cloud platform that covers the full production PaaS requirement for vibe-coded apps. Connect a Git repository, and Northflank detects the framework, builds the app, and deploys it with TLS, environment variables, and health checks configured automatically. Managed databases (PostgreSQL, MySQL, MongoDB, Redis, MinIO, RabbitMQ) provision in minutes with scoped credentials injected through secret groups. Credentials never appear in code or logs.

![northflank-home-page.png](https://assets.northflank.com/northflank_home_page_a457933045.png)

[Preview environments](https://northflank.com/product/preview-environments) spin up per pull request with isolated database instances and tear down on merge. Background workers, scheduled jobs, and build pipelines run in the same control plane as the main application. For apps that execute AI-generated or user-submitted code at runtime, Northflank's [sandbox infrastructure](https://northflank.com/product/sandboxes) runs microVM-backed execution using Kata Containers, Firecracker, and gVisor. [GPU workloads](https://northflank.com/product/gpu-paas) (H100, A100, L4, and more) run alongside services and databases in the same control plane. [BYOC is self-serve](https://northflank.com/product/bring-your-own-cloud) into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, and bare-metal for apps that need to run inside existing infrastructure.

**Best for:** Vibe coders whose apps have grown beyond prototypes and need managed databases, secrets management, preview environments, and the option to run inside their own cloud account.

**Pricing:** Free tier includes two services, one database, and two cron jobs. Paid compute from $0.01667/vCPU-hour and $0.00833/GB-hour, billed per second.

### Vercel

Vercel is optimized for Next.js and React frontends. Git integration handles CI/CD automatically, preview deployments spin up per pull request, and the edge network provides fast global delivery. For vibe-coded apps that are primarily frontend with minimal backend, Vercel provides the cleanest deployment experience in the category.

The ceiling for full-stack vibe coding is backend scope. Long-running services, background workers, and stateful database connections need external providers. Database connections come via Marketplace integrations (Neon, Supabase, others) rather than native managed addons. For apps that outgrow a serverless model, the platform requires adding external tooling.

**Best for:** Vibe-coded Next.js or React apps where the backend is minimal or handled by external APIs.

**Pricing:** Free tier available. Pro from $20/user/month.

### Render

Render provides managed PostgreSQL, Redis, background workers, static sites, and preview environments from a Git repository. Setup is minimal, and the operational model is simpler than AWS. For straightforward full-stack vibe-coded apps with a service and a database, Render covers the production baseline well.

Render is managed-only with no BYOC option and charges separately for each service and database instance, which adds up faster than usage-based alternatives for apps with multiple services. Preview environment support requires a Professional plan.

**Best for:** Vibe-coded full-stack apps that need managed databases and a simpler operational model than AWS, where managed-only infrastructure is acceptable.

**Pricing:** Services from $7/month. Managed Postgres from $7/month.

### Railway

Railway provides fast, template-based deployment with managed databases (PostgreSQL, MySQL, Redis, MongoDB) and resource-based pricing. Templates cover common stacks. Git integration handles CI/CD automatically. For vibe coders who want the fastest path from code to a deployed app with a database, Railway reduces friction.

Resource-based pricing becomes unpredictable for apps with variable traffic or multiple services. Preview environments are available but less mature than on Northflank or Vercel. RBAC and SSO require enterprise plan commitments.

**Best for:** Vibe coders who want the fastest path to a deployed app with a database using template-based setup.

**Pricing:** Hobby from $5/month plus usage. Pro from $20/month.

## Which PaaS is right for your vibe-coded app?

The right choice depends on where your app is in its lifecycle and what it needs to run reliably in production.

If the app is a Next.js frontend with minimal backend, Vercel is the right default. If you need a service and a database with minimal setup, Render or Railway gets you there fast. If the app has grown into a full-stack system with background workers, multiple databases, AI code execution, GPU workloads, or compliance requirements, [Northflank](https://northflank.com/) covers the full production stack in one platform without requiring external tooling for each capability.

| Platform | Managed databases | Secrets management | Preview environments | Sandboxes | GPU support | BYOC |
| --- | --- | --- | --- | --- | --- | --- |
| **Northflank** | Yes (6+ types) | Yes, built-in | Yes, with isolated DBs | Yes (Firecracker, Kata, gVisor) | Yes (H100, A100, and more) | Yes, self-serve |
| **Vercel** | Via Marketplace | Environment variables | Yes | Yes | No | No |
| **Render** | Postgres, Redis | Environment variables | Yes (Professional plan+) | No | No | No |
| **Railway** | Postgres, MySQL, Redis, MongoDB | Environment variables | Yes (PR environments) | No | No | Yes, enterprise-only |

## FAQ: PaaS for vibe-coded apps

### What is the difference between a PaaS and built-in hosting from vibe coding tools?

Built-in hosting from Lovable, Bolt, or Replit deploys your app to the tool's own infrastructure. It works for prototypes. A PaaS is a separate deployment platform that takes your code from any source and handles the infrastructure underneath it. A PaaS gives you portability, control over your data, environment separation, and the ability to add managed databases, secrets management, and compliance controls as your app grows.

### How do I add a database to a vibe-coded app on a PaaS?

On a PaaS with native managed database support like Northflank, you add a database addon from the dashboard or CLI, and the platform provisions the database, creates scoped credentials, and injects the connection string as an environment variable automatically. On platforms without native databases, you provision a database from an external provider and configure the connection manually.

### What is secrets management, and why does it matter for vibe-coded apps?

Secrets management stores API keys, database passwords, and third-party credentials in a secure secrets store rather than in source code or environment files committed to a repository. AI coding tools regularly include credentials in generated code. A PaaS with built-in secrets management intercepts this before credentials reach production by injecting them at build and runtime from a separate secure store.

### When does a vibe-coded app need sandbox isolation?

Any vibe-coded app that executes code at runtime, including AI coding assistant features, code interpreter functionality, agentic workflows, or any feature that runs user-submitted input as code, needs sandbox isolation. Without microVM isolation, a single bad execution can compromise the host application. This matters most for apps that let users run code or that embed LLM-generated tool calls.

### What does BYOC mean for a vibe-coded app on a PaaS?

[BYOC (Bring Your Own Cloud)](https://northflank.com/product/bring-your-own-cloud) means the PaaS deploys your app into your own AWS, GCP, Azure, or on-premises infrastructure rather than the vendor's shared managed environment. Your data stays inside your own VPC. This matters when apps grow into production systems with enterprise customers who require data residency controls, or when you want to use existing cloud credits or negotiated pricing.

### Can I move from a prototype PaaS to a production PaaS without rebuilding?

Yes. Moving to a production PaaS typically means connecting a Git repository, provisioning a managed database, migrating existing data, and configuring secrets. The application code does not need to change. The migration is an infrastructure operation, not a code rewrite.

## Conclusion

Vibe coding gets an app to a working prototype fast. A PaaS is what keeps it running reliably after the first deploy. The gap between built-in hosting in vibe coding tools and a PaaS is managed databases, secrets management, preview environments, autoscaling, observability, and the controls that enterprise customers and compliance frameworks require.

Northflank covers the full production stack for vibe-coded apps in one platform, with the option to run inside your own cloud account as the app grows. Vercel, Render, and Railway cover specific use cases well but require external tooling as complexity increases.

<InfoBox className="BodyStyle">

[Sign up for free on Northflank](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to deploy your vibe-coded app to production.

</InfoBox>

## Related articles

- [**Best deployment platforms for vibe coders in 2026**](https://northflank.com/blog/best-deployment-platforms-vibe-coders): A comparison of Northflank, Vercel, Render, Railway, and Fly.io on databases, secrets management, preview environments, and full-stack scope.
- [**How to deploy vibe-coded apps**](https://northflank.com/blog/how-to-deploy-vibe-coded-apps): A step-by-step walkthrough of taking a vibe-coded app from localhost to a live HTTPS URL on Northflank.
- [**Enterprise vibe coding: how to deploy AI-generated apps safely**](https://northflank.com/blog/enterprise-vibe-coding-deployment): Covers the governance, security, and compliance controls required for enterprise vibe coding at scale.
- [**Top managed database services in 2026**](https://northflank.com/blog/top-managed-database-services): Managed Postgres, MySQL, Redis, MongoDB, and more for apps that need a database alongside their PaaS.]]>
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  <title>Cloud Hypervisor vs gVisor</title>
  <link>https://northflank.com/blog/cloud-hypervisor-vs-gvisor</link>
  <pubDate>2026-04-30T14:30:00.000Z</pubDate>
  <description>
    <![CDATA[Cloud Hypervisor is a Rust-based VMM providing hardware-enforced isolation via KVM. gVisor intercepts syscalls in user space. Learn how they compare and when to use each.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/cloud_hypervisor_vs_gvisor_99083255d1.png" alt="Cloud Hypervisor vs gVisor" />Cloud Hypervisor and gVisor both contribute to the same goal: running workloads with stronger isolation than standard containers provide. They take fundamentally different approaches to get there, and understanding the difference matters before choosing between them or deciding to use both.

This article compares Cloud Hypervisor and gVisor on architecture, isolation model, performance, infrastructure requirements, and use case fit.

<InfoBox className="BodyStyle">

**What is Northflank?**

[Northflank](https://northflank.com/) is a full-stack cloud platform that uses Cloud Hypervisor as its primary VMM for microVM-backed sandboxes, with gVisor applied where syscall-interception isolation is sufficient or where nested virtualisation is unavailable. In production since 2021 across startups, public companies, and government deployments. [Get started (self-serve)](https://app.northflank.com/signup) or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) for specific infrastructure or compliance requirements.

</InfoBox>

## TL;DR: Cloud Hypervisor vs gVisor

|  | Cloud Hypervisor | gVisor |
| --- | --- | --- |
| **Type** | Virtual Machine Monitor (VMM) | Application kernel (syscall interception) |
| **Isolation model** | Hardware-level (KVM or MSHV) | Syscall interception (user-space kernel) |
| **Kernel** | Dedicated guest kernel per VM | No dedicated kernel (Sentry handles syscalls) |
| **Hardware virtualisation required** | Yes (KVM or MSHV) | No (Systrap) / Optional (KVM mode) |
| **Boot time** | Less than 100ms to userspace | Milliseconds |
| **Guest OS support** | 64-bit Linux, Windows 10, Windows Server 2019 | Linux only |
| **Live migration** | Supported | Not applicable |
| **GPU passthrough** | Supported via VFIO | Not supported |
| **Kubernetes integration** | Via Kata Containers / RuntimeClass | Via RuntimeClass (runsc) |
| **Operational complexity** | Higher (requires Kata for container workflows) | Lower (drop-in OCI runtime) |
| **Best for** | Hardware-enforced isolation, GPU workloads, Windows guests | Enhanced container security, no nested virtualisation |

## What is Cloud Hypervisor?

Cloud Hypervisor is an open-source Virtual Machine Monitor written in Rust that runs on top of KVM and Microsoft Hypervisor (MSHV). It is governed under the Linux Foundation and based on the rust-vmm crates. The project focuses on running modern cloud workloads with minimal hardware emulation, targeting low latency, low memory overhead, and a small attack surface.

Unlike QEMU, which supports a wide range of hardware for general-purpose virtualisation, Cloud Hypervisor exclusively targets modern cloud workloads. It uses paravirtualised devices (virtio) throughout and requires no legacy device support. It supports x86-64 and AArch64 architectures and runs 64-bit Linux and Windows 10/Windows Server 2019 guests.

Key capabilities include: boot to userspace in less than 100ms, live migration, CPU and memory hotplug, GPU passthrough via VFIO, and a REST API for programmatic VM lifecycle management. See [What is a microVM?](https://northflank.com/blog/what-is-a-microvm) and [What are Kata Containers?](https://northflank.com/blog/what-are-kata-containers) for context on how it fits in the stack.

For a full technical breakdown of Cloud Hypervisor's architecture and capabilities, see the [guide to Cloud Hypervisor](https://northflank.com/blog/guide-to-cloud-hypervisor).

## What is gVisor?

gVisor is an open-source application kernel developed by Google that sandboxes containers by intercepting system calls in user space. Its core component, the Sentry, handles syscalls on behalf of the sandboxed workload without passing them to the host kernel. The Sentry is written in Go, a memory-safe language.

gVisor is not a VMM and not a VM. It does not boot a dedicated guest kernel per workload. In its default Systrap mode, it requires no hardware virtualisation. In KVM mode, it uses virtualisation hardware for address space isolation, but the sandbox retains a process model rather than booting a full guest OS. It ships an OCI-compatible runtime called `runsc` that integrates directly with Docker, containerd, and Kubernetes. See [What is gVisor?](https://northflank.com/blog/what-is-gvisor) for a full technical breakdown.

## What to know before comparing Cloud Hypervisor and gVisor

Cloud Hypervisor is a VMM. It creates and manages virtual machines. On its own, it does not integrate with container tooling and needs Kata Containers to act as the orchestration layer that bridges it to Kubernetes and Docker workflows. gVisor is an OCI-compatible container runtime that drops into existing container workflows directly.

This means the comparison is not purely symmetrical. When people ask "Cloud Hypervisor vs gVisor", they are usually asking: should I use hardware-enforced VM isolation via Cloud Hypervisor and Kata Containers, or syscall-interception isolation via gVisor, for my containerised workloads? That is the question this article answers.

## How do Cloud Hypervisor and gVisor isolation models differ?

Cloud Hypervisor enforces isolation at the hardware level. Each workload boots its own dedicated Linux kernel inside a VM boundary enforced by KVM or MSHV hardware. To escape, an attacker must first compromise the guest kernel, then escape the hypervisor layer enforced by CPU hardware. Those are two separate hardware-enforced barriers.

gVisor enforces isolation at the syscall level. The Sentry intercepts syscalls and handles them in user space, so the workload never reaches the host kernel directly. In Systrap mode, there is no hardware-enforced boundary. In KVM mode, virtualisation hardware is used for address space isolation, but the sandbox does not provide a dedicated guest kernel per workload.

For actively adversarial workloads, Cloud Hypervisor via Kata Containers provides the stronger isolation guarantee. For workloads that need meaningfully stronger isolation than standard containers without the overhead or KVM requirements of microVMs, gVisor is a practical middle ground.

See how the isolation models compare:

![Container isolation (3).png](https://assets.northflank.com/Container_isolation_3_fce9c02898.png)

## Performance and overhead (Cloud Hypervisor vs gVisor)

- **Boot time:** Cloud Hypervisor boots to userspace in less than 100ms. Kata Containers adds orchestration overhead on top, putting end-to-end sandbox startup in the 150 to 300ms range, depending on configuration. gVisor starts in milliseconds with no kernel boot. For high-frequency, short-lived workloads, gVisor's startup advantage is real.
- **I/O overhead:** gVisor's syscall interception adds latency on I/O-heavy workloads. Benchmarks suggest 10 to 30% slower than native containers depending on workload type. Cloud Hypervisor workloads run near-native I/O because the guest kernel handles syscalls directly without an interception layer. For databases, high-throughput file processing, or network-intensive workloads, Cloud Hypervisor has a performance advantage.
- **CPU-bound workloads:** For CPU-bound workloads with low syscall frequency, gVisor's overhead is minimal. The syscall tax primarily affects high-frequency syscall workloads.
- **GPU passthrough:** Cloud Hypervisor supports GPU passthrough via VFIO, making it suitable for GPU workloads that need hardware-level isolation. gVisor does not support GPU passthrough.
- **Windows guests:** Cloud Hypervisor supports Windows 10 and Windows Server 2019 as guest operating systems. gVisor runs Linux workloads only.

## Infrastructure requirements (Cloud Hypervisor vs gVisor)

**Cloud Hypervisor requires KVM or MSHV.** The host must support Intel VT-x or AMD-V with KVM available, or run on a host with Microsoft Hypervisor support. On cloud instances, the provider must support nested virtualisation for that instance type. Running it in Kubernetes also requires Kata Containers for orchestration. See [What is KVM?](https://northflank.com/blog/what-is-kvm) for the hardware layer details.

**gVisor's Systrap mode requires no hardware virtualisation.** It runs on any Linux host. This makes it the practical choice when nested virtualisation is unavailable. Its KVM mode optionally uses virtualisation hardware but does not require it.

For Kubernetes integration, both use RuntimeClass. Kata Containers provides the runtime handler for Cloud Hypervisor-backed workloads. gVisor provides `runsc` as its handler. Both can run alongside standard container pods on the same cluster without control plane changes.

## Syscall compatibility (Cloud Hypervisor vs gVisor)

Cloud Hypervisor runs a full Linux guest kernel per workload. Syscall compatibility is not a concern; any Linux workload runs inside a Cloud Hypervisor VM without modification. Windows guest support adds further compatibility for teams running mixed workloads.

gVisor's Sentry re-implements Linux system interfaces but does not cover every syscall. Applications that depend on less common or recently added syscalls may not behave correctly under gVisor. Testing your specific workload before deploying to production matters. For workloads with unusual syscall requirements, Cloud Hypervisor is the safer choice.

## When should you use Cloud Hypervisor vs gVisor?

**Use Cloud Hypervisor (via Kata Containers) when:**

- Your threat model involves actively adversarial workloads requiring hardware-enforced isolation
- You are running untrusted code at scale (AI-generated outputs, customer-submitted scripts, multi-tenant platforms)
- Your workloads are I/O-heavy and near-native performance is a requirement
- You need GPU passthrough for isolated GPU workloads
- You need to run Windows guests
- Your workloads have syscall requirements gVisor does not cover
- KVM or MSHV is available on your host

**Use gVisor when:**

- Nested virtualisation is unavailable on your host
- You want stronger isolation than standard containers without microVM overhead or operational complexity
- Your workloads start and stop frequently and millisecond startup matters
- Your workloads are CPU-bound with low syscall frequency
- You want a simpler integration path with existing Docker and Kubernetes workflows
- Defence-in-depth is the goal rather than maximum isolation strength

## Can you use Cloud Hypervisor and gVisor together?

Yes. Cloud Hypervisor and gVisor complement rather than compete. Northflank uses Cloud Hypervisor as the primary VMM for microVM-backed workloads via Kata Containers, with gVisor applied where syscall-interception isolation is sufficient or where nested virtualisation is unavailable on the host. The isolation technology is applied based on workload requirements rather than a single approach for everything.

## How does Northflank use Cloud Hypervisor and gVisor?

Northflank's [sandbox infrastructure](https://northflank.com/product/sandboxes) uses Kata Containers with Cloud Hypervisor as its primary VMM, with gVisor applied for workloads where syscall-interception isolation is sufficient or where nested virtualisation is unavailable. Firecracker is also applied for workloads that benefit from its minimal device model. The platform has been in production since 2021 across startups, public companies, and government deployments.

Sandboxes spin up in approximately 1 to 2 seconds, with compute pricing starting at $0.01667 per vCPU per hour and $0.00833 per GB of memory per hour. See the [pricing page](https://northflank.com/pricing) for full details.

Northflank supports both ephemeral and persistent sandbox environments on managed cloud or inside your own VPC, self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal via [bring your own cloud](https://northflank.com/product/bring-your-own-cloud).

<InfoBox className="BodyStyle">

**Get started with Northflank sandboxes**

- [Sandboxes on Northflank: overview and concepts](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank): architecture overview and core sandbox concepts
- [Deploy sandboxes on Northflank: step-by-step deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-on-northflank): step-by-step deployment guide
- [Deploy sandboxes in your cloud: BYOC deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud): run sandboxes inside your own VPC
- [Create a sandbox with the SDK: programmatic sandbox creation](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank#create-sandboxes-with-the-sdk): programmatic sandbox creation via the Northflank JS client

[Get started (self-serve)](https://app.northflank.com/signup), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) if you have specific infrastructure or compliance requirements.

</InfoBox>

## Frequently asked questions about Cloud Hypervisor vs gVisor

### Is Cloud Hypervisor the same as a microVM?

No. Cloud Hypervisor is a VMM (the software that creates and manages virtual machines). A microVM is the lightweight virtual machine it creates. Cloud Hypervisor is to a microVM what QEMU is to a traditional VM. See [What is a microVM?](https://northflank.com/blog/what-is-a-microvm) for the distinction.

### Does Cloud Hypervisor work without KVM?

Cloud Hypervisor runs on KVM or Microsoft Hypervisor (MSHV). It requires one of these hypervisor backends. It does not run on hosts without hardware virtualisation support the way gVisor's Systrap mode does.

### Can gVisor replace Cloud Hypervisor?

It depends on your threat model and infrastructure. For workloads where syscall-interception isolation is sufficient and hardware virtualisation is unavailable, gVisor is a practical alternative. For actively adversarial workloads where hardware-enforced boundaries are required, or for GPU and Windows workloads, Cloud Hypervisor via Kata Containers provides capabilities gVisor does not.

### Does Cloud Hypervisor integrate with Kubernetes directly?

No. Cloud Hypervisor is a VMM and requires an orchestration layer to integrate with Kubernetes. Kata Containers provides that layer via the Container Runtime Interface. See [What are Kata Containers?](https://northflank.com/blog/what-are-kata-containers) for how that integration works.

### What is the difference between Cloud Hypervisor and Firecracker?

Both are Rust-based VMMs that use KVM for hardware isolation. Cloud Hypervisor targets a broader range of cloud workloads with features including live migration, GPU passthrough, Windows guest support, CPU and memory hotplug, and a REST API. Firecracker prioritises a minimal device model for maximum simplicity and density. Both are supported as VMM backends in Kata Containers. See [What is AWS Firecracker?](https://northflank.com/blog/what-is-aws-firecracker) for Firecracker's architecture.

### What is the difference between Cloud Hypervisor and QEMU?

Both are VMMs that use KVM. QEMU supports a wide range of hardware architectures and legacy devices for general-purpose virtualisation. Cloud Hypervisor exclusively targets modern cloud workloads using paravirtualised virtio devices, no legacy hardware, and 64-bit guests only. Cloud Hypervisor's narrower scope results in a smaller codebase and attack surface.

## Related articles on Cloud Hypervisor, gVisor, and sandboxes

- [Guide to Cloud Hypervisor](https://northflank.com/blog/guide-to-cloud-hypervisor): a full technical guide to Cloud Hypervisor's architecture, capabilities, and how to run it in production
- [What are Kata Containers?](https://northflank.com/blog/what-are-kata-containers): how Kata Containers orchestrates Cloud Hypervisor for Kubernetes workloads
- [What is gVisor?](https://northflank.com/blog/what-is-gvisor): how gVisor works, its components, execution platforms, and limitations
- [What is a microVM?](https://northflank.com/blog/what-is-a-microvm): how microVMs work and how Cloud Hypervisor implements them
- [What is KVM?](https://northflank.com/blog/what-is-kvm): the hardware virtualisation layer Cloud Hypervisor builds on
- [Kata Containers vs gVisor](https://northflank.com/blog/kata-containers-vs-gvisor): how Kata Containers and gVisor compare for Kubernetes workload isolation
- [MicroVM vs gVisor](https://northflank.com/blog/microvm-vs-gvisor): the broader comparison of hardware-enforced and syscall-interception isolation
- [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): a three-way comparison of the leading isolation technologies
- [What is AWS Firecracker?](https://northflank.com/blog/what-is-aws-firecracker): how Firecracker compares to Cloud Hypervisor as a VMM backend
- [Firecracker vs gVisor](https://northflank.com/blog/firecracker-vs-gvisor): a focused comparison of Firecracker against gVisor]]>
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</item><item>
  <title>Enterprise vibe coding: how to deploy AI-generated apps safely</title>
  <link>https://northflank.com/blog/enterprise-vibe-coding-how-to-deploy-ai-generated-apps-safely</link>
  <pubDate>2026-04-30T03:30:00.000Z</pubDate>
  <description>
    <![CDATA[Enterprise vibe coding security guide covering deployment risks, governance gaps, and how infrastructure controls like RBAC, secrets, and sandboxing make AI-generated apps safe in production.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/database_hosting_fba4c3c2f1.png" alt="Enterprise vibe coding: how to deploy AI-generated apps safely" /><InfoBox className="BodyStyle">

## TL;DR: enterprise vibe coding deployment

- Enterprise vibe coding requires the same deployment controls as any production application: secrets management, scoped database credentials, environment isolation, RBAC, audit logging, and sandbox execution for apps that run AI-generated code at runtime.
- The security risks in vibe-coded apps are concentrated at deployment, not in the generated code itself. Hardcoded credentials, admin database access, no environment isolation, and no access controls on deployed URLs are the most common failure modes.
- At scale in large enterprises, the governance gap compounds: dozens of employees ship AI-generated apps to production without IT visibility, creating an audit surface that security teams cannot see or control.
- [Northflank](https://northflank.com/) provides the deployment infrastructure that makes enterprise vibe coding safe: secrets management, managed databases, sandbox isolation, RBAC, audit logs, preview environments, and BYOC into your own cloud or on-premises.

> [Northflank](https://northflank.com/) is a full-stack cloud platform that handles the infrastructure enterprises need to deploy vibe-coded apps safely. Secrets management, managed databases, microVM sandbox isolation for AI-generated code execution, RBAC, audit logs, preview environments, and self-serve BYOC into AWS, GCP, Azure, and on-premises. [Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30).


</InfoBox>

Enterprise vibe coding is already happening. Employees are generating and deploying internal apps with AI, often without involving engineering or security teams.
The bottleneck is no longer writing code. It is controlling what gets deployed, what data it touches, and whether it creates a security incident the moment it goes live.

This article explains where enterprise vibe coding creates real risk, why the problem sits at the deployment layer, and what infrastructure is required to make it safe at scale.

## Why enterprise vibe coding creates security risk

AI coding tools generate working applications quickly. They do not generate secure deployment configurations by default.

AI-generated code does introduce vulnerabilities. But those vulnerabilities only become critical when the app is deployed with production access, real data, and no isolation.

The deployment gap is where incidents happen. A prototype is low risk. The same app deployed with hardcoded credentials, admin database access, and no access controls is not.

Non-technical employees generate code, deploy it, and move on. The platform they use, and whether it enforces security controls by default, determines whether that app stays internal or becomes a security incident.

## What the enterprise governance gap looks like

Consumer vibe coding tools are built for speed, not governance. They lack the controls enterprises rely on: staging environments, security reviews, RBAC, SSO, and audit trails.

At scale, this creates a governance gap. Vibe coding is not a single-app problem. It becomes a **distributed shadow engineering layer**, where dozens of employees can build and deploy apps outside IT and security visibility.

The result is an expanding attack surface: apps touching production data with unclear ownership, shared credentials in repositories, and internal tools exposed without proper access controls. Security teams cannot govern what they cannot see.

The only viable response is not restriction, but infrastructure-level control: a deployment layer that enforces security defaults, centralizes visibility, and makes safe deployment the path of least resistance.

Enterprise vibe coding is ultimately a shift in who can ship software. That makes the deployment layer the new control plane for risk.

## What secure enterprise vibe coding deployment requires

These are the controls that must exist at the deployment layer for vibe-coded apps to be safe in enterprise environments.

- **Secrets management:** API keys, database credentials, and environment variables must be stored in a secrets manager and injected at runtime. They must never appear in source code, build logs, or repositories. AI coding tools regularly include credentials in generated code. The deployment platform must intercept this before it reaches production.
- **Scoped database credentials:** Vibe-coded apps default to whatever database access is easiest to configure, which is typically admin access. Production apps should connect with the minimum permissions required, not shared admin accounts.
- **Environment isolation:** Development and production environments must be separated. Testing changes on a live application that other employees or customers depend on is a common failure mode for vibe-coded internal tools.
- **RBAC and access controls:** Deployed apps should only be accessible to the intended audience. IT and security teams need role-based access controls at the project and environment level to enforce least-privilege access and satisfy audit requirements.
- **Audit logging:** Every deployment, every secret access, every environment change needs to be logged with a timestamp and a user identity. SOC 2 Type 2 audits require demonstrable audit trails. Enterprise security incidents require forensic evidence of what happened and when.
- **SSO integration:** Enterprise teams require SAML or OIDC-based SSO for centralized identity management. Apps deployed outside the SSO perimeter are invisible to the identity provider and cannot be governed.
- **Sandbox execution for AI-generated code:** Any app that executes code at runtime, including AI coding assistants, code interpreter features, and agentic workflows, needs microVM isolation so execution cannot affect the host system or other users. Standard container isolation shares the host kernel and is not sufficient.
- **Preview environments:** Every change needs to be testable in an isolated environment before it reaches production. Preview environments that spin up per pull request and tear down on merge make this the default rather than the exception.

## How Northflank provides enterprise deployment infrastructure for vibe-coded apps

[Northflank](https://northflank.com/product/deployments) provides the deployment infrastructure that applies these controls by default, without requiring vibe coders to understand the infrastructure layer or IT teams to manually review every deployment.

Connect a Git repository, and Northflank detects the framework, builds the application, and deploys it with TLS, health checks, and environment isolation configured automatically. Secrets are stored in secret groups and injected at build and runtime, never exposed in logs or code. Managed databases (PostgreSQL, MySQL, MongoDB, Redis) provision in minutes with scoped credentials injected through the same mechanism. For apps that execute AI-generated or user-submitted code at runtime, Northflank's [sandbox infrastructure](https://northflank.com/product/sandboxes) runs microVM-backed execution using Kata Containers, Firecracker, and gVisor, so execution is contained at the hardware level.

![northflank-home-page.png](https://assets.northflank.com/northflank_home_page_a457933045.png)

For enterprise IT and security teams, Northflank provides the organizational visibility that governance requires. RBAC at the organisation, project, and environment level means every deployment is tied to a user identity, every secret access is logged, and every environment is visible to the security team. Non-technical employees get self-service. The security team gets oversight and a full audit trail. SAML and OIDC-based SSO with automatic role assignment from identity provider groups integrates with existing enterprise identity infrastructure.

[BYOC](https://northflank.com/product/bring-your-own-cloud) is self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, and bare-metal. For enterprises with data residency requirements or existing cloud commitments, vibe-coded apps run inside your own infrastructure with your data never leaving your own VPC.

<InfoBox className="BodyStyle">

For a step-by-step walkthrough of the full deployment process, see [How to deploy vibe-coded apps to production on Northflank](https://northflank.com/blog/how-to-deploy-vibe-coded-apps).

</InfoBox>

> [Get started on Northflank](https://app.northflank.com/signup) (self-serve, no demo required). Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to walk through your enterprise deployment requirements.
> 

## Enterprise vibe coding deployment checklist

Before any vibe-coded app reaches production in an enterprise environment, verify the following:

- Secrets and credentials are stored in a secrets manager, not in source code or environment files committed to a repository.
- Database connections use scoped credentials with minimum required permissions, not admin accounts.
- The app is deployed behind SSO and is not accessible via a public URL without authentication.
- RBAC controls are in place, limiting access to the intended audience.
- Development and production environments are separated with no shared state.
- A preview environment has been used to test changes before they reached production.
- Audit logging is enabled, and logs are retained for the duration required by your compliance framework.
- Any code execution features use sandbox isolation, not standard container execution.
- The deployment is visible to the IT or security team through the centralized control plane.

## FAQ: enterprise vibe coding deployment

### Why are vibe-coded apps more likely to have security vulnerabilities?

AI coding tools generate code that works functionally but frequently includes security flaws. Georgetown CSET found XSS vulnerabilities in 86% of AI-generated code samples tested across five major LLMs. AI-assisted commits expose secrets at twice the rate of human-written code. The tools optimize for generating code that runs, not for generating code that is secure. Human review is still required for security-sensitive logic.

### What is the most common security failure in enterprise vibe coding?

Hardcoded credentials are the most common and highest-impact failure mode. AI tools regularly include API keys, database passwords, and access tokens directly in generated code or configuration files. When that code is pushed to a repository, the credentials are exposed to anyone with repository access, and often to public search engines if the repository is public.

### How do you give enterprise employees self-service vibe coding without losing security control?

Use a deployment platform that applies security controls by default rather than relying on individual builders to configure them. Secrets management, RBAC, SSO integration, and audit logging should be configured at the platform level so they apply to every deployment regardless of who built the app. Non-technical employees get self-service. IT and security teams get visibility and control without reviewing every deployment manually.

### When does a vibe-coded app need sandbox execution?

Any app that executes code at runtime rather than just running pre-written application logic needs sandbox isolation. This includes apps with AI coding assistant features, code interpreter functionality, agentic workflows, or any feature that executes user-submitted input as code. Without microVM isolation, a single bad execution can compromise the host application and expose other users' data.

### Can vibe-coded apps meet enterprise compliance requirements?

Yes, if they are deployed with the right infrastructure controls. SOC 2 Type 2, HIPAA, and other compliance frameworks require secrets management, RBAC, audit logging, and access controls. These controls need to exist at the deployment platform level, not in the generated code itself. Platforms like Northflank apply them by default and provide the audit trails that compliance reviews require.

### How does BYOC help enterprises deploying vibe-coded apps?

BYOC (Bring Your Own Cloud) deploys the platform into your existing AWS, GCP, Azure, or on-premises infrastructure. Your data never leaves your own VPC. For enterprises with data residency requirements or existing cloud commitments, BYOC means vibe-coded apps benefit from enterprise-grade deployment infrastructure without routing data through a third-party vendor's systems.

## Conclusion

Enterprise vibe coding is not a security problem that can be solved at the code level. The vulnerability classes appearing in AI-generated code are real, but the production incidents happen at the deployment layer: hardcoded credentials, admin database access, apps deployed to public URLs without authentication, and no audit trail for what got deployed or who deployed it.

The infrastructure layer is where the security gap has to be closed. Secrets management, RBAC, audit logging, SSO, sandbox execution for runtime code, and IT visibility over all deployments are not optional at enterprise scale. Northflank provides all of it by default, on managed cloud or inside your own infrastructure, without requiring vibe coders to understand what they are deploying on top of.

<InfoBox className="BodyStyle">

[Sign up for free on Northflank](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to see how Northflank handles enterprise vibe coding deployment for your organization.

</InfoBox>

## Related articles

- [**How non-technical employees can build and ship internal apps with AI, securely**](https://northflank.com/blog/non-technical-employees-build-internal-apps): Covers the full deployment workflow for AI-generated apps, including secrets management, sandbox execution, and enterprise visibility.
- [**Best deployment platforms for vibe coders in 2026**](https://northflank.com/blog/best-deployment-platforms-vibe-coders): A comparison of Northflank, Vercel, Render, Railway, and Fly.io on databases, secrets management, preview environments, and full-stack scope.
- [**Best enterprise-safe platforms for running and hosting AI apps in 2026**](https://northflank.com/blog/enterprise-safe-ai-hosting-platforms): Covers SOC 2, HIPAA, BYOC, sandbox isolation, and GPU support for enterprise AI app deployment.
- [**How to deploy vibe-coded apps**](https://northflank.com/blog/how-to-deploy-vibe-coded-apps): A step-by-step walkthrough of taking a vibe-coded app from localhost to a live HTTPS URL on Northflank.]]>
  </content:encoded>
</item><item>
  <title>Best CI/CD tools in 2026</title>
  <link>https://northflank.com/blog/best-ci-cd-tools</link>
  <pubDate>2026-04-29T14:15:00.000Z</pubDate>
  <description>
    <![CDATA[14 best CI/CD tools in 2026 compared: GitHub Actions, Jenkins, GitLab CI, CircleCI, Northflank, Harness, and more, by use case, execution environment, and scope.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/best_ci_cd_tools_1_b36afff489.png" alt="Best CI/CD tools in 2026" /><InfoBox className="BodyStyle">

## TL;DR: What are the best CI/CD tools in 2026?

CI/CD (continuous integration and continuous delivery) tools automate the build, test, and deployment stages of the software delivery pipeline. Here are some of the top 14 options for 2026 that we’ll cover in this article:

1. [**Northflank**](https://northflank.com/): CI/CD with integrated deployments, managed databases, preview environments, and [BYOC support](https://northflank.com/features/bring-your-own-cloud) from one platform.
2. **GitHub Actions**: Native CI/CD for GitHub repositories with workflow automation and a marketplace of reusable actions.
3. **GitLab CI/CD**: CI/CD integrated into the GitLab DevOps platform, available cloud-hosted and self-hosted.
4. **CircleCI**: Cloud-native CI/CD with Docker, VM, and ARM execution environments, caching, and parallelism.
5. **Jenkins**: Self-hosted, open-source CI/CD server with a large plugin ecosystem and full pipeline customisation.
6. **Travis CI**: CI/CD with YAML-based configuration, supporting GitHub, GitLab, and Bitbucket.
7. **Azure DevOps**: Microsoft's DevOps suite covering repositories, pipelines, boards, and package management.
8. **Harness**: DevOps platform with CI, CD, feature management, infrastructure as code, and cloud cost management modules.
9. **Bitbucket Pipelines**: CI/CD built into Bitbucket Cloud with Atlassian ecosystem integration.
10. **TeamCity**: JetBrains' CI/CD server with Kotlin DSL pipeline definitions and test analytics.
11. **Argo CD**: GitOps continuous delivery controller for Kubernetes clusters.
12. **Spinnaker**: Multi-cloud continuous delivery platform with canary and blue/green deployment strategies.
13. **AWS CodePipeline**: Amazon's managed CI/CD service for AWS infrastructure.
14. **Google Cloud Build**: Google's managed container build and CI/CD service for GCP workloads.

</InfoBox>

## What is CI/CD?

CI/CD stands for continuous integration and continuous delivery (or continuous deployment). It is a set of practices that automate how code moves from development to production.

**Continuous integration (CI)** is the practice of automatically building and testing code every time a change is committed to a shared repository. The goal is to catch integration errors early by running automated tests on every commit.

[**Continuous delivery**](https://northflank.com/blog/continuous-delivery) ensures code is always in a deployable state. Every change that passes CI is packaged and staged, but a human still decides when to deploy to production.

[**Continuous deployment**](https://northflank.com/blog/continuous-deployment) removes that final manual step. Every change that passes all automated quality gates is deployed to production without human intervention.

Together, these practices allow teams to release more frequently, catch bugs earlier, and maintain a consistent delivery process as codebases grow.

## What are CI/CD tools used for?

CI/CD tools connect to a version control system, trigger automated workflows on code changes, and manage the movement of code through build, test, and deployment stages. Teams use them to:

- Compile code and create deployable artefacts on every commit.
- Run unit tests, integration tests, and security scans automatically.
- Deploy code to staging or production environments with consistent processes.
- Define complex workflows with dependencies, approval gates, and parallel stages.
- Track deployment status and roll back to previous versions when needed.

## What should teams consider when choosing a CI/CD tool?

Not all CI/CD tools cover the same scope. The following questions help narrow down the right fit.

- **Do you need CI/CD only, or a complete delivery platform?** Some tools handle only build and test automation. Others include deployment orchestration, infrastructure management, preview environments, and managed databases in one platform.

- **What execution environments does your pipeline require?** Check whether the tool supports Docker containers, VMs, ARM, bare metal, or Kubernetes-native execution.

- **Cloud-managed or self-hosted?** Cloud-managed tools handle updates, scaling, and runner maintenance. Self-hosted tools give more control over the runtime environment, networking, and data residency but require operational overhead.

- **How complex are your deployment workflows?** Basic pipelines work with most tools. Multi-environment deployments, canary releases, approval gates, and rollback controls require more capable platforms.

- **What are your security and compliance requirements?** Evaluate secrets management, RBAC, audit logging, and support for private cloud or air-gapped deployments.

- **What version control systems does your team use?** GitHub Actions only works with GitHub. GitLab CI is most effective on the GitLab platform. Tools like Northflank, CircleCI, and others integrate with GitHub, GitLab, and Bitbucket.

## What are the best CI/CD tools in 2026?

The following sections cover each tool by what it provides, how it fits into a CI/CD workflow, and which teams it suits.

### 1. Northflank

[Northflank](https://northflank.com/) provides CI/CD pipelines alongside managed deployments, managed databases, preview environments, and Kubernetes orchestration from a single control plane. It covers the full delivery lifecycle from build to production, rather than CI/CD only.

Builds trigger automatically from GitHub, GitLab, or Bitbucket commits. Path rules allow builds to trigger only when specific directories change. Continuous deployment keeps environments up to date with the latest validated builds. See [how to build and deploy your code on Northflank](https://northflank.com/docs/v1/application/getting-started/build-and-deploy-your-code) to get started.

Preview environments spin up automatically from pull requests, including databases and microservices, giving teams an isolated environment that mirrors production configuration for every branch. Release flows support approval gates, staged promotion across environments, and rollback to previous releases. See [how to manage CI/CD on Northflank](https://northflank.com/docs/v1/application/release/manage-ci-cd) for pipeline configuration details.

[BYOC support](https://northflank.com/features/bring-your-own-cloud) allows Northflank's orchestration layer to run on teams' own AWS, GCP, Azure, Oracle, Civo, or bare-metal infrastructure, so workloads are not tied to a single provider.

![northflank-home-page.png](https://assets.northflank.com/northflank_home_page_a457933045.png)

**Key capabilities:**

- [Built-in CI/CD pipelines](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) with Git-based triggers supporting GitHub, GitLab, and Bitbucket.
- Path rules and commit message filters for selective build triggers. ([See how](https://northflank.com/docs/v1/application/build/build-code-from-a-git-repository#trigger-a-build-on-changes-to-specific-files-or-directories))
- [Preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) including databases and microservices, triggered by pull requests.
- [Release flows](https://northflank.com/docs/v1/application/release/configure-a-release-flow) with approval gates, staged promotion, and rollback controls.
- [BYOC support](https://northflank.com/features/bring-your-own-cloud) across AWS, GCP, Azure, Oracle, Civo, and bare-metal infrastructure.
- Managed databases including [PostgreSQL](https://northflank.com/dbaas/managed-postgresql), [MySQL](https://northflank.com/dbaas/managed-mysql), [MongoDB](https://northflank.com/dbaas/mongodb-on-northflank), and [Redis](https://northflank.com/dbaas/managed-redis).
- [GPU workload support](https://northflank.com/product/gpu-paas) for inference, model serving, and AI training jobs.
- [Secrets management](https://northflank.com/docs/v1/application/secure/manage-secret-groups), [RBAC](https://northflank.com/docs/v1/application/secure/use-role-based-access-control), and [audit logging](https://northflank.com/docs/v1/application/observe/audit-logs).
- Usage-based [pricing](https://northflank.com/pricing) based on compute and storage, not per-seat.

**Best for:** Teams that need CI/CD integrated with deployment infrastructure, managed databases, and preview environments. Organisations running Kubernetes without managing cluster administration directly. Teams with multi-cloud or BYOC requirements, or AI/ML workloads requiring GPU support.

*See how [Weights scales to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s) and how [Clock manages 30,000 deployments with 100% uptime](https://northflank.com/blog/scaling-30-000-deployments-with-100-uptime-how-clock-uses-northflank-to-simplify-infrastructure).*

<InfoBox className="BodyStyle">

Get started with a [free plan](https://app.northflank.com/signup), follow the [getting started guide](https://northflank.com/docs/v1/application/getting-started/introduction-to-northflank), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo) if you have specific pipeline or infrastructure requirements. See the [pricing page](https://northflank.com/pricing) for full details on compute, database, and GPU workload costs.

</InfoBox>

### 2. GitHub Actions

GitHub Actions provides CI/CD directly within GitHub repositories. Workflows are defined in YAML files stored in the repository under `.github/workflows/` and trigger on GitHub events such as pushes, pull requests, or scheduled runs. A marketplace of reusable actions covers common build, test, and deployment tasks.

GitHub Actions supports matrix builds for testing across multiple language versions or operating systems, and provides both GitHub-hosted runners and self-hosted runners for teams that need custom build environments.

GitHub Actions handles CI/CD but does not include deployment infrastructure, preview environments, or release orchestration. Teams typically pair it with platforms like Northflank for managing multi-environment deployments and rollbacks. See [how to use GitHub Actions with Northflank](https://northflank.com/docs/v1/application/infrastructure-as-code/use-github-actions-with-northflank) for integration details.

![Github actions home page.png](https://assets.northflank.com/Github_actions_home_page_6093a76be8.png)

**Key capabilities:**

- Native GitHub integration with workflow triggers on GitHub events.
- Matrix builds for testing across multiple environments.
- Self-hosted runners for custom build infrastructure.
- Marketplace of reusable actions for common tasks.

**Best for:** Teams with code hosted on GitHub who need integrated CI/CD within their existing GitHub workflow.

*Read more: [GitHub Actions vs Jenkins](https://northflank.com/blog/github-actions-vs-jenkins), [CircleCI vs GitHub Actions](https://northflank.com/blog/circleci-vs-github-actions), [GitHub Actions alternatives](https://northflank.com/blog/github-actions-alternatives)*

### 3. GitLab CI/CD

GitLab CI/CD is the CI/CD component of the GitLab DevOps platform, which includes version control, issue tracking, code review, container registry, and security scanning in one interface. Pipelines are defined in `.gitlab-ci.yml` files and run on GitLab-hosted runners or self-hosted runners.

GitLab CI/CD integrates with GitLab's container registry and Kubernetes integration, and includes built-in security scanning tools in higher-tier plans.

![new gitlab cicd home page.png](https://assets.northflank.com/new_gitlab_cicd_home_page_6db2ffa6b1.png)

**Key capabilities:**

- YAML-based pipeline configuration integrated into the GitLab UI.
- Integrated container registry and Kubernetes deployment support.
- Security scanning and compliance features available in higher-tier plans.

**Best for:** Teams using GitLab for source control who want CI/CD integrated into their existing platform.

*Read more: [Best GitLab alternatives](https://northflank.com/blog/best-gitlab-alternatives)*

### 4. CircleCI

CircleCI is a CI/CD platform where pipelines are defined in `.circleci/config.yml` files and run in Docker containers, VMs, or on ARM hardware. CircleCI Orbs provide reusable configuration packages for common workflows. It supports GitHub, GitLab, and Bitbucket as source control integrations.

CircleCI provides SSH access to running builds for debugging, configurable resource classes per job, and autoscaling for self-hosted runner fleets.

CircleCI handles CI/CD but does not include application hosting, managed databases, or preview environments. Teams typically pair it with deployment platforms for the full delivery pipeline.

![circleci home page.png](https://assets.northflank.com/circleci_home_page_5010422a55.png)

**Key capabilities:**

- YAML-based pipeline configuration with Docker, VM, and ARM execution environments.
- Caching and test parallelism.
- SSH access to running builds for debugging.
- Orbs for reusable pipeline configuration.
- Cloud-hosted and self-hosted runner support.

**Best for:** Teams that need configurable pipelines with multiple execution environment options and strong caching and parallelism support.

*Read more: [CircleCI vs Jenkins](https://northflank.com/blog/circleci-vs-jenkins), [CircleCI vs GitHub Actions](https://northflank.com/blog/circleci-vs-github-actions), [Top CircleCI alternatives](https://northflank.com/blog/top-circleci-alternatives), [Travis CI vs CircleCI](https://northflank.com/blog/travis-ci-versus-circleci)*

### 5. Jenkins

Jenkins is a self-hosted, open-source CI/CD automation server. Pipelines are defined in Jenkinsfiles using Groovy-based declarative or scripted syntax. Jenkins has a large plugin ecosystem covering integrations with version control systems, cloud providers, testing frameworks, and deployment targets.

Jenkins uses a controller-agent architecture that supports distributed builds across multiple agent nodes. It requires teams to provision, maintain, and update the Jenkins infrastructure themselves.

Jenkins offers complete control over the CI/CD environment but requires dedicated operational resources for maintenance, plugin management, and security updates.

![jenkins x home page.png](https://assets.northflank.com/jenkins_x_home_page_ea832a2d5d.png)

**Key capabilities:**

- Declarative and scripted pipeline syntax via Jenkinsfiles.
- Controller-agent architecture for distributed builds.
- Large plugin ecosystem covering a wide range of integrations.
- Free and open source.

**Best for:** Organisations with DevOps expertise that need full control over CI/CD infrastructure and have existing Jenkins investments.

*Read more: [Jenkins alternatives](https://northflank.com/blog/jenkins-alternatives-2026), [CircleCI vs Jenkins](https://northflank.com/blog/circleci-vs-jenkins), [GitHub Actions vs Jenkins](https://northflank.com/blog/github-actions-vs-jenkins)*

### 6. Travis CI

Travis CI provides CI/CD with YAML-based configuration. Adding a `.travis.yml` file to a repository and enabling it in Travis CI triggers builds automatically on commits. Travis CI supports cloud-hosted builds and self-hosted deployments via Travis CI Server, and integrates with GitHub, GitLab, and Bitbucket.

Travis CI supports multi-language and multi-OS builds and parallel job execution.

![travis ci.png](https://assets.northflank.com/travis_ci_1225c7977f.png)

**Key capabilities:**

- YAML-based pipeline configuration.
- Multi-language and multi-OS build support.
- Parallel job execution.
- Cloud-hosted builds and self-hosted runner support via Travis CI Server.

**Best for:** Teams that need straightforward YAML-based CI/CD with cloud and self-hosted options.

*Read more: [Top Travis CI alternatives](https://northflank.com/blog/travis-ci-alternatives), [Travis CI vs CircleCI](https://northflank.com/blog/travis-ci-versus-circleci)*

### 7. Azure DevOps

Azure DevOps is Microsoft's DevOps suite that includes Azure Repos (Git repositories), Azure Pipelines (CI/CD), Azure Boards (project tracking), Azure Artifacts (package management), and Azure Test Plans. Azure Pipelines supports multiple languages, platforms, and cloud providers, and provides both cloud-hosted and self-hosted agents.

Azure DevOps integrates with the broader Microsoft ecosystem including Azure cloud services, Active Directory, and Visual Studio.

![azure devops home page-min.png](https://assets.northflank.com/azure_devops_home_page_min_b0a5f3378f.png)

**Key capabilities:**

- Integrated suite covering repos, CI/CD pipelines, project tracking, and package management.
- Multi-language and multi-platform pipeline support.
- Cloud-hosted and self-hosted agents.
- Integration with Azure cloud services and the Microsoft ecosystem.

**Best for:** Organisations using Microsoft technologies or requiring an integrated DevOps suite that covers source control, CI/CD, and project management.

*Read more: [Top Azure DevOps alternatives](https://northflank.com/blog/azure-devops-alternatives)*

### 8. Harness

Harness is a DevOps platform that provides CI, CD, feature management and experimentation, infrastructure as code management, cloud cost management, and security testing as separate modules. The CD module supports deployment pipelines with approval gates, canary and blue/green deployment strategies, rollback controls, and audit logging.

Harness supports deployment to Kubernetes clusters, cloud providers, and on-premises infrastructure, and integrates with GitHub, GitLab, Bitbucket, and Jenkins.

![harness.png](https://assets.northflank.com/harness_6ed883f12e.png)

**Key capabilities:**

- CI and CD pipeline modules with YAML-based configuration.
- Approval gates, deployment policies, and audit logging.
- Canary and blue/green deployment strategy support.
- Feature management and experimentation module.
- Infrastructure as code management and cloud cost management modules.

**Best for:** Enterprises needing deployment pipelines with approval gates, compliance controls, and a broad DevOps platform covering CI, CD, feature flags, and cost management.

*Read more: [Top Harness alternatives](https://northflank.com/blog/top-harness-alternatives)*

### 9. Bitbucket Pipelines

Bitbucket Pipelines provides CI/CD built directly into Bitbucket Cloud. Pipelines are configured in bitbucket-pipelines.yml files stored in the repository. Bitbucket Pipes provide reusable components for common deployment tasks. Bitbucket Pipelines integrates with the broader Atlassian ecosystem including Jira and Confluence.

![Bitbucket pipelines.png](https://assets.northflank.com/Bitbucket_pipelines_a8b6d63a24.png)

**Key capabilities:**

- CI/CD integrated into Bitbucket Cloud.
- Docker-native execution environments.
- Bitbucket Pipes for reusable deployment components.
- Atlassian ecosystem integration with Jira and Confluence.

**Best for:** Teams using Bitbucket Cloud and other Atlassian products who want CI/CD without configuring a separate tool.

*Read more: [Bitbucket Pipelines alternatives](https://northflank.com/blog/bitbucket-pipelines-alternatives)*

### 10. TeamCity

TeamCity is JetBrains' CI/CD server, available cloud-hosted (TeamCity Cloud) and self-hosted. Pipelines can be defined through the web interface or using Kotlin DSL stored in the repository. TeamCity includes test analytics with flaky test detection, build chain dependencies for defining relationships between builds, and support for parallel testing.

![TeamCity.png](https://assets.northflank.com/Team_City_8dd2dcf76b.png)

**Key capabilities:**

- Kotlin DSL for pipeline definitions stored as code.
- Test analytics with flaky test detection.
- Build chain dependencies for multi-project builds.
- Available cloud-hosted (TeamCity Cloud) and self-hosted.

**Best for:** Teams that prefer Kotlin DSL for pipeline configuration and need test analytics including flaky test detection.

### 11. Argo CD

Argo CD is a GitOps continuous delivery controller for Kubernetes. It monitors a Git repository and continuously reconciles the state of a Kubernetes cluster to match the desired state defined in the repository. Changes committed to Git are applied to the cluster automatically, making Git the single source of truth for cluster state.

Argo CD supports multi-cluster deployments and provides drift detection that alerts when the live cluster state diverges from the desired state in Git.

![argocd home page.png](https://assets.northflank.com/argocd_home_page_59fa1af37c.png)

**Key capabilities:**

- GitOps-based continuous delivery for Kubernetes clusters.
- Continuous reconciliation between Git state and cluster state.
- Multi-cluster support.
- Drift detection and automated remediation.

**Best for:** Teams running Kubernetes who want Git-driven deployments with continuous cluster reconciliation.

*Read more: [Argo CD alternatives](https://northflank.com/blog/argo-cd-alternatives-northflank-developer-platform-git-ops-self-service)*

### 12. Spinnaker

Spinnaker is an open-source, multi-cloud continuous delivery platform. It supports deployment strategies including canary analysis, blue/green deployments, and multi-region rollouts, with automated verification at each stage using monitoring integrations.

![spinnaker home page.png](https://assets.northflank.com/spinnaker_home_page_3a22825196.png)

**Key capabilities:**

- Multi-cloud deployment support.
- Canary, blue/green, and multi-region deployment strategies.
- Pipeline templates for reusable delivery workflows.
- Integration with monitoring tools for deployment verification.

**Best for:** Organisations deploying across multiple cloud providers with complex release strategies and automated verification requirements.

*Read more: [Best Spinnaker alternatives](https://northflank.com/blog/spinnaker-alternatives)*

### 13. AWS CodePipeline

AWS CodePipeline is Amazon's managed CI/CD service that orchestrates build, test, and deployment stages using AWS services including CodeBuild, CodeDeploy, and Lambda. It integrates with AWS's IAM security model and CloudFormation for infrastructure changes.

![AWS Codepipeline.png](https://assets.northflank.com/AWS_Codepipeline_50156bd820.png)

**Key capabilities:**

- Native integration with AWS services (CodeBuild, CodeDeploy, Lambda, CloudFormation).
- Managed service with no servers to maintain.
- IAM-based access control.
- Visual pipeline builder in the AWS console.

**Best for:** Teams deploying primarily to AWS infrastructure who want a managed CI/CD service with native AWS service integrations.

### 14. Google Cloud Build

Google Cloud Build is Google's managed CI/CD service that builds container images and integrates with GKE, Cloud Run, and other GCP services. Build configurations are defined in YAML or use a Dockerfile. Cloud Build includes vulnerability scanning for container images.

![google-cloud-build.png](https://assets.northflank.com/google_cloud_build_017c1f9100.png)

**Key capabilities:**

- Container image builds integrated with GCP services.
- Native integration with GKE and Cloud Run.
- Vulnerability scanning for container images.
- YAML and Dockerfile-based build configuration.

**Best for:** Teams deploying to Google Cloud Platform who need container builds integrated with GCP services.

## How to choose the right CI/CD tool

The right CI/CD tool depends on the scope of what your team needs from the platform, your infrastructure, and your version control system.

For teams that need CI/CD only, GitHub Actions, CircleCI, GitLab CI/CD, and Travis CI all cover build, test, and basic deployment automation. The right choice among these depends on which version control platform you use and whether you need cloud-hosted runners, self-hosted runners, or a specific execution environment.

For teams that need CI/CD combined with deployment infrastructure, managed databases, and preview environments from one platform, Northflank covers the full delivery lifecycle without requiring separate tools.

For teams with complex deployment governance requirements, including approval gates, canary releases, and compliance audit trails, Harness and Spinnaker provide advanced deployment orchestration. For teams running Kubernetes who want GitOps-based delivery, Argo CD is the dedicated tool for that model.

For teams embedded in AWS or GCP, CodePipeline and Cloud Build provide managed CI/CD with native integrations for those platforms.

| Tool | Type | Best for |
| --- | --- | --- |
| **Northflank** | Full delivery platform | CI/CD, deployments, preview environments, managed databases, and BYOC from one platform |
| **GitHub Actions** | CI/CD (GitHub-native) | Teams on GitHub needing integrated CI/CD |
| **GitLab CI/CD** | CI/CD (GitLab-native) | Teams on GitLab needing integrated CI/CD |
| **CircleCI** | CI/CD | Configurable pipelines with Docker, VM, and ARM execution |
| **Jenkins** | CI/CD (self-hosted) | Teams needing full control over CI/CD infrastructure |
| **Travis CI** | CI/CD | Straightforward YAML-based CI/CD |
| **Azure DevOps** | DevOps suite | Microsoft ecosystem teams needing repos, CI/CD, and project tracking |
| **Harness** | DevOps platform | Enterprise deployment governance and compliance |
| **Bitbucket Pipelines** | CI/CD (Bitbucket-native) | Teams on Bitbucket and Atlassian products |
| **TeamCity** | CI/CD server | Teams preferring Kotlin DSL and test analytics |
| **Argo CD** | GitOps CD controller | Kubernetes teams wanting Git-driven cluster reconciliation |
| **Spinnaker** | Multi-cloud CD | Multi-cloud deployments with canary and blue/green strategies |
| **AWS CodePipeline** | Managed CI/CD (AWS) | Teams deploying to AWS with native service integrations |
| **Google Cloud Build** | Managed CI/CD (GCP) | Teams deploying to GCP needing container builds |


## Frequently asked questions about CI/CD tools

### What is a CI/CD tool?

A CI/CD tool is a platform that automates the build, test, and deployment stages of the software delivery pipeline. It connects to a version control system, triggers automated workflows on code changes, and manages the movement of code from commit through to production.

### What is the difference between CI and CD?

Continuous integration (CI) covers automated build and test on every commit. Continuous delivery (CD) ensures code is always in a deployable state, with a manual step to release. Continuous deployment extends this by removing the manual step, so every passing build is released to production automatically. See the [continuous delivery](https://northflank.com/blog/continuous-delivery) and [continuous deployment](https://northflank.com/blog/continuous-deployment) articles for full explanations of each.

### What is the most widely used CI/CD tool?

According to the JetBrains State of Developer Ecosystem Report 2025, GitHub Actions is the most widely used CI/CD tool in organisational contexts at 33% adoption, followed by Jenkins at 28% and GitLab CI at 19%.

### Do I need a separate deployment tool alongside my CI/CD tool?

It depends on the tool. Platforms like GitHub Actions, CircleCI, and Travis CI handle CI/CD but do not include deployment infrastructure, managed databases, or preview environments. Teams using these tools typically pair them with a deployment platform. Northflank covers both CI/CD and deployment infrastructure from one platform, removing the need for separate tools.

### What is a CI/CD pipeline?

A CI/CD pipeline is the sequence of automated stages a code change passes through from commit to production. Typical stages include build, automated testing, staging deployment, post-deploy validation, and production deployment. Each stage acts as a quality gate. If a stage fails, the pipeline stops and the change does not progress.

### What is GitOps and how does it relate to CI/CD?

GitOps is a practice in which Git is used as the single source of truth for infrastructure and application configuration. A GitOps controller such as Argo CD watches a Git repository and applies changes to the target Kubernetes cluster automatically when the repository state changes. GitOps is one implementation of continuous deployment, specifically for Kubernetes workloads.

## Related articles

- [Continuous delivery explained](https://northflank.com/blog/continuous-delivery): What continuous delivery is, how it differs from continuous deployment, and when each model applies.
- [Continuous deployment explained](https://northflank.com/blog/continuous-deployment): How continuous deployment pipelines work, deployment strategies, and what teams need before adopting it.
- [GitHub Actions alternatives](https://northflank.com/blog/github-actions-alternatives): Northflank, CircleCI, GitLab CI, Buildkite, Travis CI, and Harness compared by capability and use case.
- [Jenkins alternatives](https://northflank.com/blog/jenkins-alternatives-2026): Options for teams moving off Jenkins, including cloud-native and managed alternatives.
- [Top CircleCI alternatives](https://northflank.com/blog/top-circleci-alternatives): How CircleCI compares to Northflank, GitHub Actions, GitLab CI, and others.
- [Best GitLab alternatives](https://northflank.com/blog/best-gitlab-alternatives): Alternatives to GitLab for teams evaluating their DevOps platform.
- [Top Harness alternatives](https://northflank.com/blog/top-harness-alternatives): Options for teams evaluating or moving off Harness.
- [Travis CI alternatives](https://northflank.com/blog/travis-ci-alternatives): Alternatives to Travis CI for teams with more advanced CI/CD requirements.
- [Azure DevOps alternatives](https://northflank.com/blog/azure-devops-alternatives): Alternatives to Azure DevOps for teams outside the Microsoft ecosystem.
- [Bitbucket Pipelines alternatives](https://northflank.com/blog/bitbucket-pipelines-alternatives): Options for teams moving off Bitbucket Pipelines.
- [Argo CD alternatives](https://northflank.com/blog/argo-cd-alternatives-northflank-developer-platform-git-ops-self-service): Alternatives to Argo CD for Kubernetes-native delivery.]]>
  </content:encoded>
</item><item>
  <title>Kata Containers vs gVisor</title>
  <link>https://northflank.com/blog/kata-containers-vs-gvisor</link>
  <pubDate>2026-04-29T14:00:00.000Z</pubDate>
  <description>
    <![CDATA[Kata Containers provides hardware-enforced isolation via KVM. gVisor intercepts syscalls in user space. Learn how they compare on isolation strength, performance, and when to use each.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/kata_containers_vs_gvisor_bb03db6066.png" alt="Kata Containers vs gVisor" />Kata Containers and gVisor both address the shared-kernel problem that standard containers leave unsolved. They take different architectural approaches, make different tradeoffs on isolation strength and overhead, and are suited to different environments and threat models.

This article compares Kata Containers and gVisor on architecture, isolation strength, performance, infrastructure requirements, and use case fit, and covers how Northflank runs both in production.

<InfoBox className="BodyStyle">

**What is Northflank?**

[Northflank](https://northflank.com/) is a full-stack cloud platform that runs Kata Containers and gVisor in production alongside Firecracker and Cloud Hypervisor, applying the right isolation technology based on workload requirements. In production since 2021 across startups, public companies, and government deployments. [Get started (self-serve)](https://app.northflank.com/signup) or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) for specific infrastructure or compliance requirements.

</InfoBox>

## TL;DR: Kata Containers vs gVisor

|  | Kata Containers | gVisor |
| --- | --- | --- |
| **Type** | Container runtime with VM-level isolation | Container runtime with syscall interception |
| **Isolation model** | Hardware-level (KVM via VMM) | Syscall interception (user-space kernel) |
| **Kernel** | Dedicated guest kernel per workload | No dedicated kernel (Sentry handles syscalls) |
| **Hardware virtualisation required** | Yes (KVM) | No (Systrap) / Optional (KVM mode) |
| **Boot time** | ~150ms to ~300ms depending on VMM and configuration | Milliseconds |
| **I/O overhead** | Near-native | Syscall tax on I/O-heavy workloads |
| **Syscall compatibility** | Full Linux | Most syscalls, some gaps |
| **Kubernetes integration** | Native via CRI / RuntimeClass | Via RuntimeClass (runsc) |
| **Operational complexity** | Higher (VMM backend required) | Lower (drop-in OCI runtime) |
| **Best for** | Adversarial multi-tenant workloads, untrusted code | Enhanced container security, no nested virtualisation |

## What is Kata Containers?

Kata Containers is an open-source container runtime that runs workloads inside lightweight virtual machines, integrating natively with Kubernetes via the Container Runtime Interface. It is maintained under the OpenInfra Foundation and supports Cloud Hypervisor (default), Firecracker, and QEMU as VMM backends.

Each workload gets its own dedicated Linux guest kernel, enforced by hardware virtualisation via [KVM](https://northflank.com/blog/what-is-kvm). Kata is not itself a VMM; it is the orchestration layer that makes microVMs work with container tooling. From Kubernetes' perspective, a Kata-backed pod looks like a standard container. The isolation underneath is hardware-enforced. See [What are Kata Containers?](https://northflank.com/blog/what-are-kata-containers) for a full technical breakdown.

## What is gVisor?

gVisor is an open-source application kernel developed by Google that sandboxes containers by intercepting system calls in user space. Its core component, the Sentry, handles syscalls on behalf of the sandboxed workload without passing them to the host kernel. The Sentry is written in Go, a memory-safe language.

gVisor is not a VM. It does not boot a dedicated guest kernel per workload. In its default Systrap mode, it requires no hardware virtualisation. In KVM mode, it uses virtualisation hardware for address space isolation, but the sandbox retains a process model rather than booting a full guest OS. See [What is gVisor?](https://northflank.com/blog/what-is-gvisor) for a full technical breakdown.

## How do the isolation models differ between Kata Containers and gVisor?

This is the core tradeoff between the two technologies.

Kata Containers enforces isolation at the hardware level. Each workload boots its own Linux kernel inside a KVM boundary enforced by CPU hardware. To escape, an attacker must first compromise the guest kernel, then escape the KVM hypervisor layer. Those are two separate hardware-enforced barriers.

gVisor enforces isolation at the syscall level. The Sentry intercepts syscalls and handles them in user space, so the workload never reaches the host kernel directly. In Systrap mode, there is no hardware-enforced boundary. In KVM mode, virtualisation hardware is used for address space isolation, but the sandbox does not provide a dedicated guest kernel per workload.

For actively adversarial workloads where an attacker is specifically trying to escape the sandbox, Kata Containers provides the stronger guarantee. For workloads that need meaningfully better isolation than standard containers without the overhead or KVM requirements of microVMs, gVisor is a practical middle ground.

See how the two approaches compare against standard containers:

![Container isolation (3).png](https://assets.northflank.com/Container_isolation_3_fce9c02898.png)

## Performance and overhead (Kata Containers vs gVisor)

- **Boot time:** Kata Containers boots a guest kernel per workload, adding 150 to 300ms depending on VMM and configuration. gVisor starts in milliseconds with no kernel boot. For high-frequency, short-lived workloads, gVisor's startup advantage is meaningful.
- **I/O overhead:** gVisor's syscall interception adds latency on I/O-heavy workloads. Benchmarks suggest 10 to 30% slower than native containers depending on workload type. Kata Containers runs near-native I/O because the guest kernel handles syscalls directly. For databases, high-throughput file processing, or network-intensive workloads, Kata has a performance advantage.
- **CPU-bound workloads:** For CPU-bound workloads with infrequent syscalls, gVisor's overhead is minimal. The syscall tax only materialises on high-frequency syscall workloads.
- **Memory overhead:** Kata Containers carries the overhead of a VMM process and guest kernel per workload, though modern VMM designs keep this low. gVisor's Sentry adds per-sandbox overhead that is low but non-zero. Both are significantly lighter than traditional VMs.

## Infrastructure requirements (Kata Containers vs gVisor)

**Kata Containers requires KVM.** The host must support Intel VT-x or AMD-V with KVM available. On cloud instances, the provider must support nested virtualisation for that instance type. Not all providers or instance types support this.

**gVisor's Systrap mode requires no hardware virtualisation.** It runs on any Linux host. This makes it the practical choice when nested virtualisation is unavailable, for example on certain cloud instance types or constrained environments. Its KVM mode optionally uses virtualisation hardware but does not require it.

For Kubernetes integration, both use RuntimeClass. Kata provides its runtime handler via `kata-runtime`. gVisor provides `runsc`. Both can run alongside standard container pods on the same cluster without changes to the control plane. See [What is KVM?](https://northflank.com/blog/what-is-kvm) for more on the hardware requirements.

## Syscall compatibility (Kata Containers vs gVisor)

Kata Containers runs a full Linux guest kernel per workload. Syscall compatibility is not a concern; any workload that runs on Linux runs inside a Kata VM without modification.

gVisor's Sentry re-implements Linux system interfaces but does not cover every syscall. Applications that depend on less common or recently added syscalls may not behave correctly. Testing your specific workload under gVisor before deploying to production is important. For workloads with unusual syscall requirements, Kata is the safer choice.

## Operational complexity (Kata Containers vs gVisor)

Kata Containers requires more operational investment. A VMM backend must be configured and maintained, nested virtualisation must be available on the host, and Kata adds a layer of complexity on top of standard Kubernetes operations. For teams that need microVM isolation without building the orchestration layer themselves, platforms like Northflank abstract this complexity.

gVisor is closer to a drop-in replacement for the standard container runtime. You configure `runsc` as a RuntimeClass handler, reference it in your pod spec, and existing container images work without modification. The integration path is simpler than Kata.

## When should you use Kata Containers vs gVisor?

**Use Kata Containers when:**

- Your threat model involves actively adversarial workloads where hardware-enforced isolation is required
- You are running untrusted code at scale (AI-generated outputs, customer-submitted scripts, multi-tenant platforms)
- Your workloads are I/O-heavy, and near-native performance is a requirement
- Your workloads have syscall requirements that gVisor does not cover
- KVM and nested virtualisation are available on your host

**Use gVisor when:**

- Nested virtualisation is unavailable on your host
- You want meaningfully stronger isolation than standard containers without microVM overhead
- Your workloads start and stop frequently, and millisecond startup matters
- Your workloads are CPU-bound with low syscall frequency
- You want a simpler integration path with existing Docker and Kubernetes workflows

See [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor) for a three-way comparison that also covers Firecracker directly.

## How does Northflank run Kata Containers and gVisor?

Northflank uses Kata Containers with Cloud Hypervisor as its primary approach for microVM isolation, with gVisor applied where syscall-interception isolation is sufficient or where nested virtualisation is unavailable. Firecracker is applied for workloads that benefit from its minimal device model. The platform has been in production since 2021 across startups, public companies, and government deployments.

Sandboxes spin up in approximately 1 to 2 seconds, with compute pricing starting at $0.01667 per vCPU per hour and $0.00833 per GB of memory per hour. See the [pricing page](https://northflank.com/pricing) for full details.

Northflank supports both ephemeral and persistent sandbox environments on managed cloud or inside your own VPC, self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal via [bring your own cloud](https://northflank.com/product/bring-your-own-cloud).

<InfoBox className="BodyStyle">

**Get started with Northflank sandboxes**

- [Sandboxes on Northflank: overview and concepts](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank): architecture overview and core sandbox concepts
- [Deploy sandboxes on Northflank: step-by-step deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-on-northflank): step-by-step deployment guide
- [Deploy sandboxes in your cloud: BYOC deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud): run sandboxes inside your own VPC
- [Create a sandbox with the SDK: programmatic sandbox creation](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank#create-sandboxes-with-the-sdk): programmatic sandbox creation via the Northflank JS client

[Get started (self-serve)](https://app.northflank.com/signup), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) if you have specific infrastructure or compliance requirements.

</InfoBox>

## Frequently asked questions about Kata Containers vs gVisor

### Which provides stronger isolation, Kata Containers or gVisor?

Kata Containers provides stronger isolation. Each workload boots a dedicated Linux guest kernel inside a KVM-enforced hardware boundary. For an attacker to escape, they must compromise the guest kernel and then escape the hypervisor layer. gVisor's Systrap mode does not have a hardware-enforced boundary. Its KVM mode uses virtualisation hardware for address space isolation but does not boot a dedicated guest kernel per workload.

### Does gVisor require KVM?

No. gVisor's Systrap mode runs on any Linux host without KVM support. Its KVM mode optionally uses virtualisation hardware for better performance but does not require it. Kata Containers requires KVM on the host.

### Can Kata Containers and gVisor run on the same Kubernetes cluster?

Yes. Both integrate via RuntimeClass. You can run Kata-backed pods, gVisor-backed pods, and standard container pods on the same cluster simultaneously by assigning different RuntimeClasses to different workloads.

### Is gVisor faster than Kata Containers?

For startup time, yes. gVisor starts in milliseconds with no kernel boot. Kata Containers takes 150 to 300ms, depending on VMM and configuration. For I/O-heavy workloads at runtime, Kata is faster because syscall interception in gVisor adds latency that does not exist in a microVM. For CPU-bound workloads, the runtime difference is minimal.

### Do Kata Containers work with existing Docker images?

Yes. Kata Containers is OCI-compatible. Existing container images run inside Kata VMs without modification. The runtime changes. The image format does not.

### What is the difference between Kata Containers and Firecracker?

Firecracker is a VMM that creates microVMs. Kata Containers is an orchestration framework that can use Firecracker as one of its VMM backends. Kata adds the CRI integration, VM lifecycle management, and Kubernetes compatibility that Firecracker alone does not provide. See [What are Kata Containers?](https://northflank.com/blog/what-are-kata-containers) and [What is AWS Firecracker?](https://northflank.com/blog/what-is-aws-firecracker) for full breakdowns.

## Related articles on Kata Containers, gVisor, and sandboxes

- [What are Kata Containers?](https://northflank.com/blog/what-are-kata-containers): a full technical breakdown of Kata Containers' architecture, VMM backends, and Kubernetes integration
- [What is gVisor?](https://northflank.com/blog/what-is-gvisor): how gVisor works, its components, execution platforms, and limitations
- [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): a three-way comparison of the leading isolation technologies
- [MicroVM vs gVisor](https://northflank.com/blog/microvm-vs-gvisor): how the microVM model as a category compares to gVisor's syscall-interception approach
- [What is a microVM?](https://northflank.com/blog/what-is-a-microvm): how microVMs work and which technologies implement them
- [What is KVM?](https://northflank.com/blog/what-is-kvm): the hardware virtualisation layer Kata Containers builds on
- [Firecracker vs gVisor](https://northflank.com/blog/firecracker-vs-gvisor): a focused comparison of Firecracker specifically against gVisor
- [Containers vs virtual machines](https://northflank.com/blog/containers-vs-virtual-machines): the broader isolation landscape in context]]>
  </content:encoded>
</item><item>
  <title>Best enterprise-safe platforms for running and hosting AI apps in 2026</title>
  <link>https://northflank.com/blog/best-enterprise-safe-platforms-for-running-and-hosting-ai-apps</link>
  <pubDate>2026-04-29T13:45:00.000Z</pubDate>
  <description>
    <![CDATA[Compare enterprise AI hosting platforms and learn how to deploy AI apps securely with SOC 2, BYOC, sandbox isolation, RBAC, and audit-ready infrastructure for regulated environments.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/how_non_technical_employees_can_build_and_ship_internal_apps_1_bd256f63f2.png" alt="Best enterprise-safe platforms for running and hosting AI apps in 2026" />> *Enterprise-safe deployment for AI apps means more than a live URL. It means compliance certifications that cover your deployment model, execution isolation for AI-generated code, data that never leaves your infrastructure, and audit trails that satisfy security reviews.*
> 

Most platforms that host AI apps are built for speed and developer experience. The enterprise requirements, SOC 2 Type 2, HIPAA, BYOC deployment, RBAC, secrets management, and sandbox isolation for AI-generated code, are where most of them fall short. 

This article covers the platforms built to meet those requirements and what each one actually provides at the infrastructure layer.

<InfoBox className="BodyStyle">

## TL;DR: best enterprise-safe platforms for hosting AI apps in 2026

Enterprise AI apps face a different set of deployment requirements than standard web applications. The platform handling your deployment is a third-party data processor under GDPR. Execution of AI-generated code without isolation creates security risk. Shared infrastructure without RBAC creates audit gaps.

1. [**Northflank**](https://northflank.com/) – Full-stack cloud platform with SOC 2 Type 2, managed cloud or self-serve [BYOC](https://northflank.com/product/bring-your-own-cloud) into AWS, GCP, Azure, and on-premises, [microVM sandbox](https://northflank.com/product/sandboxes) isolation for AI-generated code, RBAC, audit logs, SSO, managed databases, preview environments, and [GPU workloads](https://northflank.com/product/gpu-paas). The strongest option for enterprise teams that need the full stack running inside their own infrastructure.
2. **AWS** – Broadest compliance certification set available. Best for enterprises already on AWS that need FedRAMP, HIPAA, and deep MLOps tooling alongside deployment.
3. **Render** – SOC 2 Type 2, HIPAA BAA available on enterprise, private networking, managed databases, and preview environments. Best for teams that need a simpler managed platform with enterprise compliance and do not need BYOC.
4. **Railway** – SOC 2 Type 2, managed databases, Git-based deployment, and preview environments. RBAC, SSO, audit logs, HIPAA BAA, and BYOC are available but require enterprise plan commitments.
5. **Vercel** – SOC 2 Type 2, enterprise SSO, and audit logs on enterprise plans. Best for AI apps with a Next.js frontend where serverless execution is sufficient, and the backend complexity is minimal.

> [Northflank](https://northflank.com/) provides the full enterprise infrastructure stack for AI apps: SOC 2 Type 2, managed cloud or BYOC into your own cloud or on-premises, microVM sandbox isolation (Kata Containers, Firecracker, gVisor), RBAC, audit logs, managed databases, preview environments, and GPU workloads in one control plane. [Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30).
> 
</InfoBox>

## What should you look for in an enterprise-safe AI hosting platform?

These are the dimensions that matter most when deploying AI apps in regulated or security-conscious enterprise environments.

- **Compliance certifications:** SOC 2 Type 2 is the baseline. Verify that certifications cover the deployment model you plan to use, not just the vendor's managed cloud. HIPAA with a BAA is required for healthcare data. FedRAMP is required for US government.
- **BYOC and data residency:** Managed platforms send your data to the vendor's infrastructure. Enterprise teams with data residency requirements need execution inside their own VPC, on-premises, or bare-metal. Verify whether BYOC is self-serve or requires an enterprise sales process.
- **Sandbox isolation for AI-generated code:** AI apps that execute code at runtime need microVM isolation so execution cannot affect the host system or other tenants. Standard container isolation shares the host kernel and is not sufficient for untrusted code execution.
- **RBAC and access controls:** Granular role-based access controls at the project and environment level determine whether your security team can enforce least-privilege access and satisfy audit requirements.
- **Audit logging:** SOC 2 Type 2 audits require demonstrable audit trails. Verify what the platform logs, how long logs are retained, and whether they can be exported to your SIEM.
- **SSO integration:** Enterprise teams require SAML or OIDC-based SSO. Platforms that support only username and password will not pass procurement.
- **GPU and AI workload support:** Enterprise AI apps often require GPU inference or fine-tuning alongside standard services. A platform that handles both in the same control plane reduces operational complexity.

## Best enterprise-safe platforms for hosting AI apps in 2026

### 1. Northflank

[Northflank](https://northflank.com/product/deployments) is a full-stack cloud platform with enterprise features built in from day one. SOC 2 Type 2 certification covers managed cloud and BYOC deployments. BYOC is self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, and bare-metal with no enterprise sales process required. Data stays inside your own infrastructure.

![northflank-home-page.png](https://assets.northflank.com/northflank_home_page_a457933045.png)

For AI apps that execute code at runtime, Northflank's [sandbox infrastructure](https://northflank.com/product/sandboxes) runs microVM-backed execution using Kata Containers with Cloud Hypervisor, Firecracker, and gVisor per workload. Every sandbox runs in its own microVM with a dedicated kernel. AI-generated code, user-submitted scripts, and LLM tool calls execute inside hardware-enforced isolation that cannot affect the host application or other tenants. GPU workloads (H100, H200, A100, L4, L40S) run alongside services, databases, and sandboxes in the same control plane.

**Key features:**

- **SOC 2 Type 2 certified:** Covers managed cloud and BYOC deployments. Trust center at trust.northflank.com.
- **Self-serve BYOC:** AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, bare-metal. No enterprise sales required.
- **Sandbox isolation:** Kata Containers, Firecracker, and gVisor applied per workload. Every sandbox runs in its own microVM.
- **RBAC:** Role-based access at organisation, project, and environment levels. API roles with scoped permissions. MFA enforcement.
- **SSO:** SAML and OIDC-based SSO with automatic role assignment from identity provider groups.
- **Audit logging:** Full audit trail across all platform actions. Exportable for SIEM integration.
- **Managed databases:** PostgreSQL, MySQL, MongoDB, Redis, MinIO, and RabbitMQ with scoped credentials injected automatically.
- **GPU workloads:** H100, H200, A100, L4, L40S, B200, and TPUs with all-inclusive pricing.
- **Preview environments:** Isolated app, database, and sandbox instances per pull request, torn down on merge.

**Best for:** Enterprise teams building AI apps with code execution, regulated industries where data cannot leave physical infrastructure, and platform engineering teams that need the full stack without a lengthy enterprise sales process.

**Pricing:** $0.01667/vCPU-hour, $0.00833/GB-hour, H100 GPU at $2.74/hour all-inclusive. BYOC deployments bill against your own cloud account.

<InfoBox className="BodyStyle">

[Get started on Northflank](https://app.northflank.com/signup) (self-serve, no demo required). Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to walk through your enterprise requirements.

</InfoBox>

### 2. AWS

AWS provides the broadest compliance certification set on this list: SOC 2, ISO 27001, HIPAA, FedRAMP, PCI-DSS, and more. For enterprise AI apps that need FedRAMP or the full AWS compliance catalog, it is often the only option. SageMaker covers managed MLOps, and Bedrock provides managed foundation model access with private invocation that does not use customer data for training.

The operational overhead is significant. Deploying a complete AI app stack on AWS requires substantial infrastructure engineering expertise across networking, IAM, ECS or EKS, RDS, and secrets management. For teams with existing AWS infrastructure and dedicated platform engineering capacity, AWS provides the deepest compliance posture available.

**Best for:** Large enterprises that need FedRAMP, HIPAA, or the full AWS compliance catalog and have the engineering capacity to manage the infrastructure layer themselves.

**Pricing:** Usage-based across all services. Variable and requires careful cost modeling.

### 3. Render

Render is a managed cloud platform with SOC 2 Type 2, HIPAA BAA available on the enterprise plan, private networking, managed PostgreSQL and Redis, background workers, and preview environments. AI apps deploy from a Git repository with minimal configuration. Private networking isolates services from public internet exposure by default.

Render is managed-only with no BYOC option. For enterprises with data residency requirements, that is a hard constraint. For teams where managed infrastructure is acceptable and SOC 2 with a simpler operational model than AWS is the priority, Render covers the baseline well.

**Best for:** Enterprise teams that need SOC 2, private networking, and managed databases without AWS complexity, where managed-only infrastructure is acceptable.

**Pricing:** Services from $7/month. Managed Postgres from $7/month. Enterprise plans available

### 4. Railway

Railway provides SOC 2 Type 2, managed databases (PostgreSQL, MySQL, Redis, MongoDB), Git-based deployment, preview environments, and private networking. Deployment is fast with minimal configuration. RBAC, SSO, and 18-month audit log retention are available on enterprise plans. HIPAA BAAs are available from $1,000/month minimum spend. BYOC is available on enterprise plans for teams that need execution inside their own infrastructure.

The enterprise constraints are worth understanding. RBAC, SSO, and extended audit logs require a minimum $2,000/month enterprise commitment. For enterprises with straightforward deployment requirements and SOC 2 as the primary compliance need, Railway provides a fast path to production. For regulated workloads that need HIPAA or BYOC, those features are available but gated behind the enterprise tier.

**Best for:** Enterprise teams with straightforward deployment requirements where SOC 2 is the primary compliance need.

**Pricing:** Hobby from $5/month plus usage. Pro from $20/month. Enterprise from $1,000/month minimum spend. BYOC and HIPAA BAA from $1,000/month.

### 5. Vercel

Vercel holds SOC 2 Type 2, ISO 27001, and HIPAA BAA on enterprise plans, and provides enterprise SSO, audit logs, and RBAC on enterprise plans. For AI apps with a Next.js or React frontend, it provides the most optimized deployment experience in the category. The AI SDK integrates with Vercel's edge runtime for streaming LLM responses and preview deployments spin up per pull request.

The constraint is backend scope. Vercel is optimized for serverless functions and static frontends. Long-running AI workloads, background workers, stateful agents, and GPU requirements need external providers. There is no BYOC option.

**Best for:** Enterprise teams building AI apps with Next.js frontends where serverless execution is sufficient and backend complexity is minimal.

**Pricing:** Pro from $20/user/month. Enterprise custom.

## Which platform should you choose?

If your AI app executes code at runtime, processes sensitive data, requires GPU workloads, or must run inside your own VPC, Northflank is the only option here that covers all of those requirements with self-serve BYOC and microVM sandbox isolation. AWS covers the same requirements with significantly more operational overhead. Render and Railway cover SOC 2 and managed infrastructure for teams without data residency mandates. Vercel fits AI apps where the frontend is the primary workload and serverless execution is sufficient.

| Platform | SOC 2 Type 2 | BYOC | Sandbox isolation | GPU support | Managed databases | SSO |
| --- | --- | --- | --- | --- | --- | --- |
| **Northflank** | Yes | Yes, self-serve | Yes (Kata, Firecracker, gVisor) | Yes (H100, A100, and more) | Yes (6+ types) | Yes (SAML, OIDC) |
| **AWS** | Yes | Native | Manual configuration | Yes (EC2 GPU instances) | Yes (RDS, ElastiCache) | Yes (IAM Identity Center) |
| **Render** | Yes | No | No | No | Yes (Postgres, Redis) | Yes (enterprise) |
| **Railway** | Yes | Yes, enterprise only | No | No | Yes (Postgres, MySQL, Redis, MongoDB) | Yes (enterprise) |
| **Vercel** | Yes | No | No | No | Via Marketplace only | Yes (enterprise) |

## FAQ: enterprise-safe platforms for AI app hosting

### What compliance certifications should I require from an AI app hosting platform?

SOC 2 Type 2 is the baseline for B2B enterprise deployments. HIPAA with a Business Associate Agreement is required for healthcare data. FedRAMP is required for US government. Verify that certifications cover the deployment model you plan to use, since some vendors hold certifications for managed cloud but not for BYOC or on-premises deployments.

### Why does sandbox isolation matter for enterprise AI apps?

AI apps that execute code at runtime, including code interpreters, agentic workflows, and LLM tool calls, run code that was not written or reviewed by a developer. Without microVM isolation, that code runs with the same privileges as the application and has access to the same network and filesystem. A single bad execution can compromise the host application or expose other tenants' data. Northflank's sandbox infrastructure runs each execution in its own microVM with a dedicated kernel, enforcing a hardware boundary around untrusted code.

### Does a managed hosting platform count as a third-party data processor?

Yes. If your AI app processes personal data and execution runs on the vendor's infrastructure, the vendor is a third-party data processor under GDPR. This requires a Data Processing Agreement and can complicate compliance audits. Teams with strict data residency requirements need execution inside their own infrastructure via BYOC or on-premises deployment.

### What is the difference between BYOC and managed hosting for enterprise AI apps?

Managed hosting runs your app on the vendor's infrastructure. The vendor controls the physical hardware and network. BYOC deploys the vendor's platform into your own cloud account, on-premises, or bare-metal. Your data stays inside your own infrastructure. For regulated industries with data residency requirements, BYOC is often the only compliant option.

### Can I run GPU workloads and sandboxes on the same platform as my application services?

On Northflank, yes. Services, managed databases, GPU workloads, and microVM sandboxes all run in the same control plane. For the other platforms on this list, GPU workloads require a separate provider or significant additional configuration.

## Conclusion

Enterprise-safe hosting for AI apps requires more than a SOC 2 badge. It requires execution isolation for AI-generated code, data that stays inside your own infrastructure when compliance demands it, RBAC and audit logging that satisfy security reviews, and a platform that covers GPU workloads, managed databases, and sandboxes in the same control plane.

Northflank covers all of it with self-serve BYOC and microVM isolation built in from day one. AWS covers it with more operational complexity. Render and Railway cover the baseline for teams where managed infrastructure is acceptable. Vercel fits AI apps where the frontend is the primary workload and serverless execution is sufficient.

<InfoBox className="BodyStyle">

[Sign up for free on Northflank](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to walk through your enterprise requirements.

</InfoBox>

## Related articles

- [**Best enterprise AI sandbox platforms in 2026**](https://northflank.com/blog/best-enterprise-ai-sandbox-platforms): A comparison of sandbox platforms covering SOC 2, HIPAA, BYOC, and microVM isolation for enterprise code execution workloads.
- [**How non-technical employees can build and ship internal apps with AI, securely**](https://northflank.com/blog/how-non-technical-employees-can-build-and-ship-internal-apps-with-ai-securely): Covers the full deployment workflow for AI-generated apps, including secrets management, sandbox execution, and enterprise visibility.
- [**Best platforms for untrusted code execution in 2026**](https://northflank.com/blog/best-platforms-for-untrusted-code-execution): Isolation model selection, multi-tenant design, and network controls for platforms running AI-generated or user-submitted code.
- [**Best BYOC sandbox platforms in 2026**](https://northflank.com/blog/best-byoc-sandbox-platforms): Platforms that support running execution inside your own cloud account for teams with data residency requirements.]]>
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</item><item>
  <title>MicroVM vs gVisor</title>
  <link>https://northflank.com/blog/microvm-vs-gvisor</link>
  <pubDate>2026-04-28T15:00:00.000Z</pubDate>
  <description>
    <![CDATA[MicroVMs provide hardware-enforced isolation via KVM. gVisor intercepts syscalls in user space. Learn how they compare on isolation strength, overhead, and when to use each.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/microvm_vs_gvisor_4a1d9ff526.png" alt="MicroVM vs gVisor" />MicroVMs and gVisor both address the same fundamental problem: standard containers share the host kernel, and that shared kernel is the attack surface. The two technologies take different architectural approaches to reducing that risk, and choosing between them comes down to your threat model, infrastructure, and workload characteristics.

This article covers how each approach works, how they compare across isolation strength, overhead, and operational complexity, and when each is the right choice.

<InfoBox className="BodyStyle">

**What is Northflank?**

[Northflank](https://northflank.com/) is a full-stack cloud platform that runs both microVM-backed sandboxes and gVisor in production, applying the right isolation technology based on workload requirements. In production since 2021 across startups, public companies, and government deployments. [Get started (self-serve)](https://app.northflank.com/signup) or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) for specific infrastructure or compliance requirements.

</InfoBox>

## TL;DR: MicroVM vs gVisor

|  | MicroVM | gVisor |
| --- | --- | --- |
| **Isolation model** | Hardware-level (KVM) | Syscall interception (user-space kernel) |
| **Kernel** | Dedicated guest kernel per workload | No dedicated kernel (Sentry handles syscalls) |
| **Hardware virtualisation required** | Yes (KVM) | No (Systrap) / Optional (KVM mode) |
| **Boot time** | ~125ms to ~300ms depending on VMM | Milliseconds |
| **Memory overhead** | Less than 5 to 10 MiB per workload | Low |
| **I/O overhead** | Near-native | Syscall tax on I/O-heavy workloads |
| **Syscall compatibility** | Full Linux | Most syscalls, some gaps |
| **Kubernetes integration** | Via Kata Containers / RuntimeClass | Via RuntimeClass (runsc) |
| **Best for** | Actively adversarial workloads, multi-tenant platforms | Enhanced container security, no nested virtualisation |

## What is a microVM?

A microVM is a lightweight virtual machine that gives each workload its own dedicated Linux kernel, enforced by hardware virtualisation via KVM. Unlike a traditional VM, a microVM strips the device model to the minimum needed for cloud workloads, keeping memory overhead in single-digit MiB and boot times in the low hundreds of milliseconds.

The key technologies that implement microVMs are Firecracker (built by AWS in Rust), Cloud Hypervisor (maintained by the Linux Foundation), and QEMU with a microVM machine type. Kata Containers is the orchestration framework that makes microVMs work natively with Kubernetes via the CRI. See [What is a microVM?](https://northflank.com/blog/what-is-a-microvm) for a full technical breakdown, and [What is KVM?](https://northflank.com/blog/what-is-kvm) for the hardware layer they build on.

## What is gVisor?

gVisor is an open-source application kernel developed by Google that sandboxes containers by intercepting system calls in user space. Its core component, the Sentry, handles syscalls on behalf of the sandboxed workload without passing them to the host kernel. The host kernel's attack surface is reduced because the workload never talks to it directly.

gVisor is not a VM. It does not boot a dedicated guest kernel per workload and does not require hardware virtualisation in its default Systrap mode. In KVM mode, it uses virtualisation hardware for address space isolation, but the sandbox retains a process model rather than booting a full guest OS. See [What is gVisor?](https://northflank.com/blog/what-is-gvisor) for a full technical breakdown.

## How do the isolation models of gVisor and microVM differ?

This is the most important distinction to understand before making a decision.

### microVM isolation model

A microVM enforces isolation at the hardware level. Each workload boots its own Linux kernel inside a KVM boundary enforced by CPU hardware (Intel VT-x or AMD-V). For an attacker to escape, they must first compromise the guest kernel, then escape the KVM hypervisor layer. Those are two separate, hardware-enforced barriers.

### gVisor isolation model

gVisor enforces isolation at the syscall level. The Sentry intercepts syscalls and handles them in user space, so the workload never reaches the host kernel directly. The Sentry is written in Go, a memory-safe language, which eliminates a class of memory corruption vulnerabilities common in C-based kernels.

However, the sandbox does not have a hardware-enforced boundary in Systrap mode. In KVM mode, virtualisation hardware is used for address space isolation, but the sandbox still retains a process model rather than a dedicated guest kernel per workload.

For actively adversarial workloads, microVMs provide the stronger guarantee. For workloads that need meaningfully better isolation than standard containers without the overhead or infrastructure requirements of microVMs, gVisor is a practical middle ground.

See how both approaches compare against standard containers:

![Container isolation (3).png](https://assets.northflank.com/Container_isolation_3_fce9c02898.png)

## Performance and overhead (microVM vs gVisor)

The two approaches make different performance tradeoffs.

- **Boot time:** MicroVMs boot a guest kernel, which takes approximately 125ms to 300ms depending on VMM and configuration. gVisor starts in milliseconds with no kernel boot required. For workloads that spin up frequently or need to start instantly, gVisor has an advantage.
- **I/O overhead:** gVisor’s syscall interception adds latency, especially on I/O-heavy workloads (often 10–30% slower). MicroVMs avoid syscall interception but incur a small overhead from virtualised device I/O (virtio). Generally, MicroVMs perform better for high-throughput I/O, while gVisor is better suited for compute-heavy tasks with infrequent syscalls.
- **Memory overhead:** Both are lightweight relative to traditional VMs. Firecracker targets less than 5 MiB of memory overhead per instance in benchmarks. gVisor's Sentry process adds low but non-zero overhead per sandbox. For very high workload density, the differences are worth testing against your specific workload.
- **CPU-bound workloads:** For workloads that are primarily CPU-bound with infrequent syscalls, gVisor's overhead is minimal. The syscall tax only materialises on workloads with high syscall frequency.

## Infrastructure requirements (microVM vs gVisor)

- **MicroVMs require KVM.** The host must support Intel VT-x or AMD-V and have KVM available. On cloud instances, this means the provider must support nested virtualisation for that instance type. Not all providers or instance types support this. Running microVMs in Kubernetes also requires Kata Containers for orchestration, which adds operational complexity.
- **gVisor's Systrap mode requires no hardware virtualisation.** It runs on any Linux host with no KVM requirement. This makes it the practical choice when nested virtualisation is unavailable, for example, on certain cloud instance types or environments where the host does not expose virtualisation extensions. Its KVM mode optionally uses virtualisation hardware but does not require it.

For teams already running Kubernetes, both integrate via RuntimeClass. Kata Containers provides the RuntimeClass handler for microVMs. gVisor provides `runsc` as its RuntimeClass handler. Both can run alongside standard container pods on the same cluster.

## Syscall compatibility (microVM vs gVisor)

MicroVMs run a full Linux guest kernel, so syscall compatibility is not a concern. Any workload that runs on Linux runs inside a microVM without modification.

gVisor's Sentry re-implements Linux system interfaces but does not cover every syscall. Applications that depend on less common or recently added syscalls may not work correctly. Testing your specific workload under gVisor before deploying to production matters. For most common workloads, compatibility is not an issue. For workloads with unusual syscall requirements, a microVM is the safer choice.

## When should you use a microVM?

- **Your threat model involves actively adversarial workloads.** Multi-tenant platforms where different customers or users execute arbitrary code on shared infrastructure need hardware-enforced isolation. A microVM is the stronger guarantee.
- **You are running AI agent or LLM-generated code at scale.** Untrusted code execution where the output cannot be audited before it runs needs the strongest available isolation boundary.
- **Your workloads are I/O-heavy.** Databases, high-throughput file processing, and network-intensive workloads see less overhead with microVMs than with gVisor's syscall interception.
- **Your workloads have unusual syscall requirements.** If your application depends on syscalls that gVisor does not implement, a microVM is the reliable path.
- **KVM and nested virtualisation are available on your host.**

See [What is a microVM?](https://northflank.com/blog/what-is-a-microvm), [What is AWS Firecracker?](https://northflank.com/blog/what-is-aws-firecracker), and [What are Kata Containers?](https://northflank.com/blog/what-are-kata-containers) for deeper technical context.

## When should you use gVisor?

- **Nested virtualisation is unavailable on your host.** gVisor's Systrap mode runs on any Linux host without KVM support.
- **You want stronger container isolation without VM overhead.** For workloads where container isolation is insufficient but you do not need hardware-enforced boundaries, gVisor is a practical middle ground.
- **Your workloads start and stop frequently and boot time matters.** Millisecond startup versus 150 to 300ms is meaningful for high-frequency, short-lived workloads.
- **Your workloads are not I/O-heavy.** CPU-bound workloads see minimal overhead from gVisor's syscall interception.
- **You need defence-in-depth alongside other security controls.** gVisor layers well with other isolation mechanisms as one part of a broader security strategy.

See [What is gVisor?](https://northflank.com/blog/what-is-gvisor) for a full explanation of how it works and its limitations.

## Can you use both microVM and gVisor?

Yes. MicroVMs and gVisor are complementary rather than competing. Production platforms often use both, applying the isolation technology based on workload requirements rather than using a single approach for everything.

Northflank applies this model in production. Kata Containers with Cloud Hypervisor is used as the primary approach for microVM isolation, with Firecracker applied for workloads that benefit from its minimal device model, and gVisor applied where syscall-interception isolation is sufficient or where nested virtualisation is unavailable. The isolation technology is applied based on workload requirements.

## How does Northflank run microVMs and gVisor?

Northflank's [sandbox infrastructure](https://northflank.com/product/sandboxes) uses Kata Containers with Cloud Hypervisor as its primary VMM, with Firecracker and gVisor applied depending on workload requirements. The platform has been in production since 2021 across startups, public companies, and government deployments. Sandboxes spin up in approximately 1 to 2 seconds, with compute pricing starting at $0.01667 per vCPU per hour and $0.00833 per GB of memory per hour. See the [pricing page](https://northflank.com/pricing) for full details.

Northflank supports both ephemeral and persistent sandbox environments on managed cloud or inside your own VPC, self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal via [bring your own cloud](https://northflank.com/product/bring-your-own-cloud).

<InfoBox className="BodyStyle">

**Get started with Northflank sandboxes**

- [Sandboxes on Northflank: overview and concepts](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank): architecture overview and core sandbox concepts
- [Deploy sandboxes on Northflank: step-by-step deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-on-northflank): step-by-step deployment guide
- [Deploy sandboxes in your cloud: BYOC deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud): run sandboxes inside your own VPC
- [Create a sandbox with the SDK: programmatic sandbox creation](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank#create-sandboxes-with-the-sdk): programmatic sandbox creation via the Northflank JS client

[Get started (self-serve)](https://app.northflank.com/signup), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) if you have specific infrastructure or compliance requirements.

</InfoBox>

## Frequently asked questions about microVM vs gVisor

### Is gVisor safer than a microVM?

Not in general. MicroVMs provide hardware-enforced isolation via KVM, giving each workload a dedicated guest kernel that is significantly harder to escape than a software-based sandbox. gVisor provides meaningful isolation by reducing the host kernel's attack surface through syscall interception, but does not provide the same hardware-enforced boundary in Systrap mode. For actively adversarial workloads, microVMs provide the stronger guarantee.

### Does gVisor use a VM?

No. gVisor does not boot a dedicated guest kernel or emulate hardware per workload. In KVM mode, it uses virtualisation hardware for address space isolation, but the sandbox retains a process model. It is an application kernel that intercepts syscalls, not a virtual machine.

### Can gVisor replace Firecracker?

It depends on your threat model. For workloads where syscall-interception isolation is sufficient and KVM is unavailable, gVisor is a practical alternative. For actively adversarial multi-tenant workloads where hardware-enforced boundaries are required, Firecracker or another microVM technology provides stronger isolation. See [Firecracker vs gVisor](https://northflank.com/blog/firecracker-vs-gvisor) for a detailed comparison.

### Do microVMs work without KVM?

No. MicroVMs require KVM, and the host CPU must support Intel VT-x or AMD-V. Without KVM support on the host, microVMs cannot run. gVisor's Systrap mode is the practical alternative in environments where KVM is unavailable.

### How do microVMs integrate with Kubernetes?

Via Kata Containers, which implements the Container Runtime Interface so Kubernetes can schedule workloads into microVMs via RuntimeClass. See [What are Kata Containers?](https://northflank.com/blog/what-are-kata-containers) for a full breakdown.

## Related articles on microVMs, gVisor, and sandboxes

- [What is a microVM?](https://northflank.com/blog/what-is-a-microvm): how microVMs work, which technologies implement them, and when to use them
- [What is gVisor?](https://northflank.com/blog/what-is-gvisor): how gVisor works, its components, and its limitations
- [Firecracker vs gVisor](https://northflank.com/blog/firecracker-vs-gvisor): a focused comparison of Firecracker specifically against gVisor
- [What are Kata Containers?](https://northflank.com/blog/what-are-kata-containers): how Kata Containers orchestrates microVMs for Kubernetes
- [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): how all three leading isolation technologies compare
- [What is AWS Firecracker?](https://northflank.com/blog/what-is-aws-firecracker): a full technical breakdown of Firecracker's architecture
- [What is KVM?](https://northflank.com/blog/what-is-kvm): the hardware virtualisation layer microVMs build on
- [How to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents): isolation architectures for AI agent execution environments
- [Containers vs virtual machines](https://northflank.com/blog/containers-vs-virtual-machines): the broader isolation landscape in context]]>
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</item><item>
  <title>How non-technical employees can build and ship internal apps with AI, securely</title>
  <link>https://northflank.com/blog/how-non-technical-employees-can-build-and-ship-internal-apps-with-ai-securely</link>
  <pubDate>2026-04-28T14:00:00.000Z</pubDate>
  <description>
    <![CDATA[How non-technical employees can build and ship internal apps with AI securely: what to build, where the security risks live, and how to deploy safely without writing infrastructure code.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/how_non_technical_employees_can_build_and_ship_internal_apps_84b3e81114.png" alt="How non-technical employees can build and ship internal apps with AI, securely" />> Non-technical employees can now generate working internal apps from natural language descriptions using AI coding tools like Claude Code, Codex, Cursor, and many others. The code generation problem is largely solved. The deployment and security gap is not.
> 

In 2026, non-technical employees can build internal apps with AI from natural language prompts. A product manager with years of Excel data, a designer without engineering support, a program manager copying data between systems.

AI tools have changed what is possible. The bottleneck is no longer writing code, but deploying internal tools securely, connecting them to real data, and ensuring they do not introduce risk in production. In large enterprises, this problem is compounded: dozens or hundreds of employees may be experimenting with AI-generated apps simultaneously, and the IT or engineering team has no visibility into what is running, who built it, or what data it touches. This article covers what teams are building with AI, where the real security risks lie, and how to deploy AI-generated apps securely using platforms like [Northflank](https://northflank.com/).

<InfoBox className="BodyStyle">

## TL;DR: building and shipping internal apps with AI

- AI coding tools like Lovable, Bolt, and Claude Code let non-technical employees generate working internal apps from natural language descriptions. No coding background required.
- The security risks are not in the generated code. They emerge at deployment: hardcoded credentials, admin database access, no environment isolation, no access controls on the deployed URL, and no sandboxing for AI-generated code that executes at runtime.
- Secure deployment requires secrets management, scoped database credentials, environment isolation, TLS by default, access controls, and sandbox execution for any app that runs user-submitted or AI-generated code.
- Most non-technical builders do not know these controls exist. The right platform applies them by default without requiring the builder to understand the infrastructure layer.

> [Northflank](https://northflank.com/) is a full-stack cloud platform that handles the infrastructure non-technical teams need to ship internal apps securely. Secrets management, managed databases, environment isolation, TLS, preview environments, [sandboxes](https://northflank.com/product/sandboxes) for AI-generated code execution, and access controls configured by default. No infrastructure code required. [Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30).
> 
</InfoBox>

## What non-technical employees are building with AI in 2026

The range of internal apps non-technical teams build with AI tools has expanded significantly. Ops teams and finance managers generate dashboards that pull from internal databases and display real-time metrics without waiting on BI team backlogs. HR teams and program managers build workflow automation tools that pull from one system, transform data, and push to another. Teams build internal request forms, approval workflows, and customer-facing intake tools connected to existing databases. Analysts build internal search interfaces on top of company documentation and knowledge bases. Data processing scripts that previously required a developer to write get generated in minutes.

What all of these have in common is that they move beyond a prototype the moment they connect to real data or allow user interaction. That is where the security questions start, and where most non-technical builders have no frame of reference for what controls are needed.

## Where the security risk actually lives

The code generation step is not where AI-generated internal apps create risk. A prototype is safe until it touches real data and gets deployed somewhere accessible. The risks emerge at deployment.

Non-technical builders often hardcode API keys, database connection strings, and passwords directly into application code because they do not know about environment variables or secrets management. When that code is pushed to a repository or deployed to a shared platform, those credentials are exposed. An internal app that connects to a production database with admin credentials can read and write far more than it needs to, and a bug becomes a data breach. An app deployed to a public URL with no authentication exposes personal data, financial figures, and internal metrics to anyone who finds the link.

A separate risk emerges for apps that execute code at runtime. AI coding assistants embedded in internal tools, code interpreter features, and agentic workflows all involve executing code that was not written or reviewed by a developer. Without [sandbox isolation](https://northflank.com/product/sandboxes), that code runs with the same privileges as the application itself and has access to the same network and filesystem. The execution boundary needs to be enforced at the infrastructure level, not assumed from the code.

In large enterprises, the scale of this problem is significantly harder to manage. When many employees across different teams are building and deploying AI-generated apps independently, the attack surface grows with every new tool that goes live. Without a centralized deployment platform, IT and engineering teams have no way to audit what apps are running, what credentials they use, or what data they access. A single misconfigured app in one department can expose data that belongs to another.

## What secure internal app deployment requires

Shipping an AI-generated internal app securely requires the same infrastructure controls that any production application needs. The difference is that non-technical builders need those controls to be available without requiring them to understand the infrastructure layer.

- **Secrets management:** API keys, database credentials, and environment variables should be stored in a secrets manager and injected at runtime. They should never appear in code or logs.
- **Scoped database credentials:** Internal apps should connect with the minimum permissions they need, not shared admin accounts.
- **Environment isolation:** Development and production should be separated so that testing changes do not affect live data or live users.
- **Access controls:** Internal tools should only be accessible to the intended audience, not anyone with the URL.
- **TLS by default:** Any app handling company data should serve over HTTPS automatically, not as a manual configuration step.
- **Sandbox execution:** Any app that executes AI-generated or user-submitted code at runtime needs isolated execution environments. Without sandbox isolation, one bad execution can affect the host system and other users.
- **Preview environments:** Every change should be testable in an isolated environment before it affects production.

## How Northflank provides this infrastructure without infrastructure code

[Northflank](https://northflank.com/product/deployments) gives non-technical teams a deployment platform that handles all of these controls without requiring them to understand the infrastructure layer underneath. If you built your app with Claude Code, it can push the code to a GitHub repository for you. From there, connect the repository to Northflank, and the platform detects the framework, builds the app, and deploys it with TLS and health checks configured automatically.

![northflank-home-page.png](https://assets.northflank.com/northflank_home_page_a457933045.png)

Secrets are stored in secret groups and injected at build and runtime, never exposed in logs or code. Managed databases (PostgreSQL, MySQL, MongoDB, Redis) provision in minutes and connect via scoped credentials injected through the same mechanism. For apps that execute AI-generated or user-submitted code, Northflank's [sandbox infrastructure](https://northflank.com/product/sandboxes) runs isolated microVM environments so execution cannot affect the host application or other users. Preview environments spin up per pull request and tear down on merge. For teams that need execution inside their own cloud account, [BYOC](https://northflank.com/product/bring-your-own-cloud) is self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal. Your data never leaves your own VPC.

For enterprise teams, Northflank also provides the organizational visibility that IT and engineering teams need when non-technical employees are building and shipping independently. RBAC at the project and environment level means every deployment is tied to a user, every secret access is logged, and every environment is visible to the people responsible for security. Non-technical employees get self-service. The IT or engineering team gets oversight.

<InfoBox className="BodyStyle">

For a step-by-step walkthrough of the full deployment process, see [How to deploy vibe-coded apps to production on Northflank](https://northflank.com/blog/how-to-deploy-vibe-coded-apps).

</InfoBox>

> [Get started on Northflank](https://app.northflank.com/signup) (self-serve, no demo required). Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) with an engineer to walk through your requirements.
> 

## Conclusion

The code generation problem is largely solved. Non-technical employees can describe what they need and have working code in minutes. The infrastructure gap is what prevents those apps from shipping securely, the sandbox gap is what prevents apps that execute AI-generated code from being safe to run, and the visibility gap is what prevents enterprise IT teams from knowing what is running and who built it.
[Northflank](https://northflank.com/) closes all three. Secrets management, managed databases, sandbox execution, environment isolation, TLS, RBAC, and preview environments configured by default, without writing infrastructure code.

<InfoBox className="BodyStyle">

[Sign up for free](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to see how Northflank handles secure deployment for your team's internal apps.

</InfoBox>

## FAQ: non-technical employees building internal apps with AI

### **Is it safe to build internal apps with AI?**

With the right infrastructure in place, yes. The risks come from deployment without proper controls: hardcoded credentials, no access controls, no environment isolation, and no sandbox execution for AI-generated code. Platforms like Northflank handle these by default, removing most risks without requiring the builder to understand the underlying infrastructure.

### What AI tools do non-technical employees use to build internal apps?

Lovable, Bolt, and v0 generate full-stack applications from natural language descriptions. Claude Code and Cursor are better suited for users who want to iterate on generated code directly. For simpler internal tools with direct database connections, Retool and Superblocks provide visual builders with AI assistance. The right tool depends on how much custom logic the app requires.

### How do you handle database connections securely for AI-generated apps?

Store database connection strings in a secrets manager and inject them as environment variables at runtime. Never hardcode credentials in application code. Use scoped database users with minimum permissions, not admin accounts. Northflank handles this through secret groups that inject credentials automatically at build and runtime.

### When do you need sandbox execution for an internal app?

Any app that executes code at runtime rather than just running pre-written application code needs sandbox execution. This includes apps with AI coding assistant features, code interpreter functionality, agentic workflows, or any feature that executes user-submitted input as code. Without isolation, a single bad execution can compromise the host application.

### Can non-technical employees deploy to our own cloud account?

Yes, with [Northflank BYOC](https://northflank.com/product/bring-your-own-cloud). Deploy into your existing AWS, GCP, Azure, or on-premises infrastructure, self-serve. The managed deployment experience is identical to Northflank's managed cloud, but your apps and data run inside your own infrastructure.

## Related articles

- [**How to deploy vibe-coded apps**](https://northflank.com/blog/how-to-deploy-vibe-coded-apps): A step-by-step walkthrough of taking a vibe-coded app from localhost to a live HTTPS URL on Northflank.
- [**Best deployment platforms for vibe coders in 2026**](https://northflank.com/blog/best-deployment-platforms-for-vibe-coders): A comparison of Northflank, Vercel, Render, Railway, and Fly.io for teams shipping AI-generated apps without infrastructure overhead.
- [**Top internal developer portals in 2026**](https://northflank.com/blog/top-internal-developer-portals): How platform teams give developers and non-technical employees self-service access to infrastructure without managing Kubernetes directly.
- [**What is sandbox infrastructure?**](https://northflank.com/blog/what-is-sandbox-infrastructure): The full stack required to run isolated workloads safely at scale, covering isolation technology, orchestration, and lifecycle management.]]>
  </content:encoded>
</item><item>
  <title>March &amp; April 2026 | Changelog</title>
  <link>https://northflank.com/changelog/march-and-april-2026</link>
  <pubDate>2026-04-27T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[We’ve made improvements and releases across performance, workflows, security, observability, and core infrastructure, including a faster UI for large teams, cross-project builds, stronger BYOC and sandbox support.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Group_1410119490_0caa51e974.png" alt="March &amp; April 2026 | Changelog" />## Faster UI for large teams

Resources are now fetched on-demand and paginated rather than loading everything at startup. Projects, services, addons, jobs, secret groups, volumes, and domains all use localised subscriptions. Teams with large numbers of resources will see significantly faster load times and lower memory usage across the dashboard. Table pagination controls now show a loading indicator during fetches.

## Cross-project build services

Build services can now be referenced from any project within the same team. A new **Cross-Project Access** setting on each build service controls which projects can use it. Services and jobs can trigger builds from a build service in a different project, and the UI includes a cross-project build selector. The format `<project-id>/<build-service-id>` is fully backwards-compatible with existing configurations.

## Templates & Pipelines

**Template teardown.** Templates now support an attached teardown spec, a companion template that runs when the environment is destroyed. It integrates with the full node editor and schema validation, and surfaces its own run history with backlinks to the originating run.

**Introducing Preview Blueprint teardown:**

- Teardowns now run when a preview environment is deleted, before any resources are removed
- Teardowns correctly clean up partially-created environments, node results are stored incrementally as the run progresses
- Teardown runs no longer run concurrently with regular runs on the same environment
- Argument overrides can be submitted for preview environment teardowns
- An escape hatch allows skipping teardown when manual intervention has already cleaned up resources

**Import Backup template node.** A new node for importing an addon backup is available in the template editor.

**Create build service in template context.** Templates can now create a build service as part of the flow, automatically inserting it before any dependent start-build nodes.

**File injection into OpenTofu runs.** Secret files are now injected into OpenTofu runs avoiding logging environment variables

**VCS trigger: PR label rule enable checkbox.** PR label trigger rules now have an enable/disable checkbox, consistent with other trigger rule types.

**External addon environment tracking.** External addons created inside preview environment blueprints now carry the environment ID, enabling correct cleanup on teardown.

**Delete external addons with environments.** External addons can now be deleted as part of environment teardown.

**Template editor UX improvements.** Template nodes can now be added to a workflow via a new ‘add node’ menu, as an alternative to drag-and-drop. Performance has also been improved when viewing large template runs.

**Fixed: template `concat` wrapping arguments in an array.** The `concat` function was incorrectly nesting its arguments, breaking string concatenation expressions.

**Fixed: nested workflows and blueprints not resolving project ID refs.**

**Fixed: global secret references in nested templates.** The ref resolver was incorrectly replacing child template refs when they shared the same name as parent refs.

**Fixed: only deployment services and jobs appear as targets in release workflow nodes.** Combined/build services and combined jobs were incorrectly appearing in the target selector.

**Preview runs remain visible after environment deletion.**

## External Addons

**Permission check before creation.** Northflank now verifies upfront that the selected provider integration has the required IAM permissions and shows a summary table before the run starts.

**Aurora RDS support.** The external addon form shows the correct configuration fields when an Aurora engine is selected for RDS addons.

**VPC/Subnet selection for RDS.** RDS addons can now specify which VPC and subnet to deploy into, rather than defaulting to the account's default VPC.

**Advanced config mode.** A toggle exposes the full addon spec in a code editor for direct editing.

## Bring your own Addon (BYOA)

**Improved irrecoverable error handling.** All Helm errors now transition the addon into an error state and surface details, rather than silently failing.

## OpenTofu

**OpenTofu destroy node.** A new teardown node type runs `tofu destroy` against resources provisioned by a corresponding OpenTofu node in the same template.

**Akamai OpenTofu provider.** Akamai is now available as an OpenTofu provider.

## BYOC

**Sandbox security: simplified setup.** MicroVMs (kata) and gVisor can now be enabled with a single checkbox in the cluster creation form. Secure runtime options can also be enabled on existing clusters post-creation without recreating them, the controller handles installing the required components.

**gVisor on ARM.** Northflank with gVisor is now also supported on ARM-based node pools.

**Networking settings in the UI.** A new Networking section in BYOC cluster Advanced Options allows configuring the overlay network and CIDR from the UI.

**Overlay network for AWS.** BYOC clusters on AWS now support configuring an overlay network with a custom CIDR.

**ARM on Oracle.** Added support for ARM compute shapes on Oracle BYOC clusters.

**AWS networking validation.** Pre-flight checks now more reliably detect incompatible AWS subnets before a cluster is created, reducing failed cluster creations caused by networking misconfigurations.

**BYOC node filtering.** The BYOC node list can now be filtered.

**Cluster observe dashboard updates.** The cluster observe node list and metrics overlays have been updated with improved responsive layout.

**Cluster error UI.** Errored clusters now show a clear error message with a support link. GCP provisioning errors (quota exceeded, stockout, constraint violations) surface as structured, readable messages.

## Compute & Deployments

**Addon replica scale-down.** Addon replicas can now be scaled down without destroying and recreating the addon.

**Faster pod startup.** For PaaS and BYOC clusters running with Northflank microVM secure runtime, the init container is no longer used by default.

**Faster build startup.** Builds with volumes now start faster.

**Headless service performance.** The headless services now reduce network load on the service mesh under high spawn and churn rates.

**Ephemeral storage field restored on jobs.** The field had been inadvertently removed from the jobs resource form.

**Fixed: support for BYOC node pools to scale down and up from zero.**

**Fixed: release-variant immutability error after platform rollback.**

## Networking

**Egress IP and Load Balancer audit logs.** Audit log tabs are now available on both Load Balancer and Egress IP detail pages.

**Fixed: ports/network update not reflecting status change.** Service status was not being updated when load balancing config, domains, or network-only deployments were changed.

**Egress IP and Load Balancer** no longer expose internal error details in API responses or the UI.

## Observability & Metrics

**Probes and restart charts.** New charts show average probe latency and reason-for-restart breakdowns on service detail pages.

**Addon charts on backup/restore page.** Addon metrics are now available from the backup/restore view.

**Additional instance events.** Instance events lists now include Northflank resource creation timestamps and when the workload executes the start command. This is helpful to see how long an AI sandbox takes to start up.

**Fixed: deployment metrics not loading when switching from live-tailing to a fixed time range.**

**Fixed: job run metrics timeframe bugs.**

**Log queries: pod start/end fallback.** Log queries now fall back to pod start/end times when no explicit range is set.

## Permissions & RBAC

**New API token UI.** Team-scoped and organisation-scoped API tokens have new dedicated management UIs. The token detail page now shows permissions pulled from the associated role, with a direct link to that role.

**Revoked token deletion.** Revoked API tokens can now be explicitly deleted.

**Role data in audit logs.** Role changes are now correctly attributed and visible in the audit log.

## Developer Experience

**Uppercase letters in OpenTofu object names.** Resource and output names in OpenTofu nodes now accept uppercase characters.

**Secret file size limit increased to 3,500 KiB.**

**API request body limit increased to 10 MB.**

**Team-scoped endpoint for project secrets.**

**Tooltip on truncated resource names.** Services, jobs, and volumes now show a tooltip when names are truncated in table views.

**OpenTofu version fix.** Pinned away from version 4.55 which has a known bug reading existing state.

**VCS sync state in UI.** Repository selectors and other version control elements in the UI now display real-time status when syncing with external providers.

## Addons

**Latest patch-level versions.** Addon creation and upgrade flows now surface the latest available patch-level version per addon type.

**Redis: configurable disabled commands.** Disabled commands can now be configured per Redis addon instance.

## Fixes

- **Bitbucket repository listing** — fixed repository listings sometimes being incomplete
- **Release webhook triggers** — fixed multiple typos that had likely prevented triggers from ever firing
- **AWS S3 log sink** — increased retry count to prevent pauses on transient failures
- **Build cache invalidation** — Ceph clone-chain depth and auto-invalidate at a configurable limit for affected BYOC clusters
- **Build progress: unnamed stages** — multi-stage Dockerfiles with unnamed stages no longer show as having incomplete steps
- **Backups table** — now defaults to newest-first sort order
- **Backup schedule retention time** — custom values can now be entered, not just preset options
- **Shell copy/paste** — fixed a double-paste bug and cleaned up hints in the web terminal
- **Permissions group titles and icons** — several entries were missing icons or had inconsistent casing]]>
  </content:encoded>
</item><item>
  <title>Best GitHub Actions alternatives in 2026</title>
  <link>https://northflank.com/blog/github-actions-alternatives</link>
  <pubDate>2026-04-27T16:20:00.000Z</pubDate>
  <description>
    <![CDATA[Best GitHub Actions alternatives in 2026: Northflank, CircleCI, GitLab CI, Buildkite, Travis CI, and Harness compared by capability, hosting model, and use case.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/github_actions_alternatives_95739a4be5.png" alt="Best GitHub Actions alternatives in 2026" /><InfoBox className="BodyStyle">

## TL;DR: What are the best GitHub Actions alternatives in 2026?

GitHub Actions is a CI/CD platform built into GitHub that uses YAML-based workflow syntax to automate builds, tests, and deployments. It works well for teams operating entirely within the GitHub ecosystem, but teams with more complex infrastructure requirements, hosting needs, or cost constraints often evaluate alternatives.

1. [**Northflank**](https://northflank.com/): Best for teams that need CI/CD, managed deployments, managed databases, and preview environments from a single platform, with [BYOC support](https://northflank.com/features/bring-your-own-cloud) across multiple cloud providers.
2. **CircleCI**: Best for teams needing customisable pipelines with Docker, Kubernetes, and VM execution environments, and support for both cloud and self-hosted runners.
3. **GitLab CI**: Best for teams already using GitLab who want CI/CD integrated into their existing DevOps platform.
4. **Buildkite**: Best for teams that need infrastructure control through self-hosted or cloud-hosted agents with a managed control plane.
5. **Travis CI**: Best for teams wanting straightforward YAML-based pipeline configuration with cloud and self-hosted runner options.
6. **Harness**: Best for enterprises that need deployment pipelines with approval gates, feature flags, and compliance tooling across a broad DevOps platform.

The right alternative depends on your infrastructure requirements, whether you need integrated hosting and databases alongside CI/CD, your team's preferred execution environment, and your cost model.

</InfoBox>

## What is GitHub Actions and where does it fall short?

GitHub Actions is a CI/CD automation platform built into GitHub. Workflows are defined in YAML files stored in the repository, and are triggered by GitHub events such as pushes, pull requests, or scheduled runs. Builds run on GitHub-hosted runners or on self-hosted runners that teams manage themselves.

GitHub Actions works well for repositories hosted on GitHub that need build, test, and basic deployment automation. Teams typically evaluate alternatives when they encounter the following.

- **Concurrency limits:** The free tier limits concurrent jobs. Paid tiers provide more concurrency but at usage-based cost that scales with pipeline volume.
- **No integrated hosting or managed services:** GitHub Actions handles CI/CD only. Hosting applications, managing databases, or running services alongside pipelines requires separate tooling.
- **Runner management overhead:** Self-hosted runners give more control but require teams to provision, maintain, and scale their own runner infrastructure.
- **Usage-based pricing at scale:** Costs are based on compute minutes and storage. As pipeline volume grows, costs can become difficult to forecast.
- **GitHub ecosystem dependency:** Workflows, secrets, and integrations are tightly coupled to GitHub. Teams moving toward multi-cloud or hybrid infrastructure may find this constraining.

## Quick comparison of GitHub Actions alternatives

The table below compares GitHub Actions against six alternatives across key CI/CD capabilities to help your team identify the right fit.

| **Capability** | **GitHub Actions** | **Northflank** | **CircleCI** | **GitLab CI** | **Buildkite** | **Travis CI** | **Harness** |
| --- | --- | --- | --- | --- | --- | --- | --- |
| **Hosting model** | Cloud (GitHub-hosted or self-hosted runners) | Cloud and BYOC | Cloud and self-hosted | Cloud and self-hosted | Cloud-managed control plane with self-hosted or hosted agents | Cloud and self-hosted | Cloud and self-hosted |
| **Container support** | Via Docker actions | Docker and Kubernetes | Docker, Kubernetes, and VM | Docker, Kubernetes, and VM | Container-based agents | Docker support | Docker and Kubernetes |
| **Integrated deployments and hosting** | No | Yes | No | No | No | No | No |
| **Preview environments** | No (requires external tooling) | Yes | No | No | No | No | No |
| **Managed databases** | No | Yes | No | No | No | No | No |
| **Self-hosted runners / agents** | Yes (self-managed) | BYOC on your own infrastructure | Yes | Yes | Yes (self-hosted and hosted agents) | Yes | Yes ||


## What are the best GitHub Actions alternatives in 2026?

The following sections cover each alternative by what it provides, how it differs from GitHub Actions, and which teams it suits.

### 1. Northflank

[Northflank](https://northflank.com/) is a developer platform that provides CI/CD pipelines, managed deployments, managed databases, preview environments, and [Bring Your Own Cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) support from a single control plane. Unlike GitHub Actions, Northflank handles not only CI/CD but also application hosting, service management, and database provisioning in the same platform.

CI/CD pipelines are triggered from Git commits and pull requests across GitHub, GitLab, and Bitbucket. Preview environments spin up automatically on branch push or pull request, giving teams an isolated environment that mirrors production configuration for every change.

For teams that want to retain GitHub Actions for CI while using Northflank for deployments and hosting, Northflank supports integration with existing GitHub Actions workflows. See [how to use GitHub Actions with Northflank](https://northflank.com/docs/v1/application/infrastructure-as-code/use-github-actions-with-northflank) for configuration details.

 ![](https://assets.northflank.com/today1_843ac3c2a6.webp) 
 
 **Key capabilities:**

- [Built-in CI/CD pipelines](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) triggered from Git commits and pull requests, supporting GitHub, GitLab, and Bitbucket.
- [Preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) that spin up on branch push or pull request with configurable triggers and lifetimes.
- [Managed Kubernetes](https://northflank.com/product/app-platform) across EKS, GKE, AKS, and bare-metal clusters.
- [BYOC support](https://northflank.com/features/bring-your-own-cloud) across AWS, GCP, Azure, Oracle, and on-premises infrastructure.
- Managed databases including [PostgreSQL](https://northflank.com/dbaas/managed-postgresql), [MySQL](https://northflank.com/dbaas/managed-mysql), [MongoDB](https://northflank.com/dbaas/mongodb-on-northflank), and [Redis](https://northflank.com/dbaas/managed-redis).
- [GPU workload support](https://northflank.com/product/gpu-paas) for inference, model serving, and AI training jobs.
- [Secrets and config management](https://northflank.com/docs/v1/application/secure/manage-secret-groups), [RBAC](https://northflank.com/docs/v1/application/secure/use-role-based-access-control), and [audit logging](https://northflank.com/docs/v1/application/observe/audit-logs).

**Best for:** Teams that need CI/CD, managed deployments, preview environments, and managed databases from one platform, with support for multi-cloud and on-premises infrastructure.

<InfoBox className="BodyStyle">

Get started with a [free plan](https://app.northflank.com/signup), follow the [getting started guide](https://northflank.com/docs/v1/application/getting-started/introduction-to-northflank), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo) if you have specific infrastructure or compliance requirements. See the [pricing page](https://northflank.com/pricing) for full details on compute, database, and GPU workload costs.

</InfoBox>

### 2. CircleCI

CircleCI provides CI/CD pipelines with support for Docker, Kubernetes, and VM execution environments. Pipelines are defined in YAML and can run on CircleCI's cloud runners or on self-hosted runners. It supports GitHub, GitLab, and Bitbucket as source control integrations.

CircleCI provides caching, test parallelism, and resource class configuration per job. It also offers autoscaling for self-hosted runner fleets.

 ![](https://assets.northflank.com/today2_5574af1db5.webp) 

**Key capabilities:**

- YAML-based pipeline configuration with Docker, Kubernetes, and VM execution environments.
- Cloud-hosted runners and self-hosted runner support.
- Caching, test parallelism, and configurable resource classes per job.
- Integrations with GitHub, GitLab, Bitbucket, AWS, GCP, and Azure.
- Autoscaling for self-hosted runner fleets.

**Best for:** Teams that need configurable pipelines with multiple execution environment options and support for both cloud and self-hosted runners.

**Considerations:** CircleCI does not provide integrated application hosting, managed databases, or preview environments. These require separate tooling.

### 3. GitLab CI

GitLab CI is the CI/CD component built into the GitLab DevOps platform. Pipelines are defined in `.gitlab-ci.yml` files in the repository and run on GitLab-hosted runners or self-hosted runners. GitLab CI is available on both GitLab.com (cloud) and self-hosted GitLab installations.

GitLab CI integrates with GitLab's broader platform including source code management, issue tracking, security scanning, and container registry. Teams using GitLab as their source control platform can use GitLab CI without configuring a separate CI/CD tool.

 ![](https://assets.northflank.com/today3_e478a014ae.webp) 
 
**Key capabilities:**

- YAML-based pipeline configuration integrated into the GitLab UI.
- Support for Docker, Kubernetes, and cloud provider deployments.
- Integrated with GitLab's source control, container registry, and security scanning.
- Available on GitLab.com (cloud) and self-hosted GitLab installations.
- Configurable permissions and environment-level access controls.

**Best for:** Teams already using GitLab for source control who want CI/CD integrated into their existing platform without configuring a separate tool.

**Considerations:** GitLab CI is most effective for teams on the GitLab platform. Using it with repositories hosted elsewhere requires additional configuration.

### 4. Buildkite

Buildkite provides CI/CD pipelines through a hybrid model: a cloud-managed control plane handles pipeline orchestration and the UI, while builds run on agents that teams deploy on their own infrastructure or on Buildkite's hosted agents. Buildkite offers hosted Linux and Mac agents alongside the self-hosted agent option.

Pipelines are defined as code and support dynamic pipeline generation at runtime. Buildkite provides a plugin ecosystem for extending pipeline steps.

 ![](https://assets.northflank.com/today4_e4baf62a38.webp) 

**Key capabilities:**

- Cloud-managed control plane with self-hosted or Buildkite-hosted agents.
- Hosted Linux and Mac agents available.
- Dynamic pipeline generation at runtime.
- Plugin ecosystem for extending pipeline steps.
- Integrates with any cloud, VM, or on-premises environment.

**Best for:** Teams that need infrastructure control over their build environment while using a cloud-managed control plane for pipeline orchestration.

**Considerations:** Buildkite does not provide integrated application hosting, managed databases, or preview environments.

### 5. Travis CI

Travis CI provides CI/CD pipelines using YAML-based configuration. It supports cloud-hosted builds and self-hosted runners, and integrates with GitHub, Bitbucket, and GitLab. Builds run in isolated environments and support multiple language runtimes.

Travis CI offers both cloud and self-hosted (Travis CI Server) deployment options.

 ![](https://assets.northflank.com/today5_ffc66b03c2.webp) 

**Key capabilities:**

- YAML-based pipeline configuration.
- Cloud-hosted builds and self-hosted runner support via Travis CI Server.
- Integrates with GitHub, Bitbucket, and GitLab.
- Supports parallel build jobs.

**Best for:** Teams that need straightforward YAML-based pipeline configuration with cloud and self-hosted runner options.

**Considerations:** Travis CI does not provide integrated hosting, managed databases, or preview environments. Teams with more advanced infrastructure or scaling requirements should evaluate whether Travis CI covers their use case.

### 6. Harness

Harness is a DevOps platform that provides CI, CD, feature management, infrastructure as code management, cloud cost management, and security testing as separate modules. The CD module supports deployment pipelines with approval gates, canary and blue/green deployment strategies, rollback controls, and audit logging.

Harness supports deployment to cloud providers, Kubernetes clusters, and on-premises infrastructure. It integrates with GitHub, GitLab, Bitbucket, and Jenkins.

 ![](https://assets.northflank.com/today6_7801f2b02f.webp) 

**Key capabilities:**

- CI and CD pipeline modules with YAML-based configuration.
- Approval gates, deployment policies, and audit logging.
- Canary and blue/green deployment strategy support.
- Feature management and experimentation module.
- Infrastructure as code management and cloud cost management modules.
- Deployment to Kubernetes, cloud providers, and on-premises targets.

**Best for:** Enterprises that need deployment pipelines with approval gates, compliance controls, and a broad DevOps platform covering CI, CD, feature flags, and cost management.

**Considerations:** Harness covers a broad platform surface. Teams that only need CI/CD may find the platform more complex than required for their use case.

*See [top Harness alternatives](https://northflank.com/blog/top-harness-alternatives) if you are evaluating Harness specifically.*

## How to choose the right GitHub Actions alternative

The right GitHub Actions alternative depends on what your team needs beyond basic CI/CD automation.

For teams that need CI/CD combined with application hosting, managed databases, and preview environments in a single platform, Northflank covers all of these from one control plane. For teams that need configurable pipelines with multiple execution environments and strong self-hosted runner support, CircleCI and Buildkite both provide this. For teams already on GitLab, GitLab CI is the natural fit. For enterprises with compliance requirements and complex deployment governance, Harness provides approval gates and audit tooling.

Key factors to evaluate:

- **Execution environment:** Does the platform support your required runtime — Docker, Kubernetes, VM, or bare metal?
- **Hosting and services:** Do you need application hosting, managed databases, or preview environments alongside CI/CD, or are you comfortable connecting separate tools?
- **Runner model:** Do you need cloud-hosted runners, self-hosted runners, or a hybrid model?
- **Source control integration:** Does the platform integrate with your source control provider?
- **Pricing model:** Evaluate whether usage-based, per-seat, or flat-rate pricing suits your pipeline volume and team size.

## Frequently asked questions about GitHub Actions alternatives

### What is GitHub Actions?

GitHub Actions is a CI/CD automation platform built into GitHub. Workflows are defined in YAML files stored in the repository and triggered by GitHub events such as pushes, pull requests, or scheduled runs. Builds run on GitHub-hosted runners or on self-hosted runners.

### Why do teams look for GitHub Actions alternatives?

Teams typically look for alternatives when they hit concurrency limits on the free tier, need integrated application hosting or managed databases alongside CI/CD, want more control over their build infrastructure, find usage-based pricing difficult to forecast at scale, or need to operate across multiple source control providers.

### Can I use Northflank alongside GitHub Actions?

Yes. Northflank integrates with GitHub Actions, allowing teams to keep existing CI workflows in GitHub Actions while using Northflank for deployments, managed services, and hosting. See [how to use GitHub Actions with Northflank](https://northflank.com/docs/v1/application/infrastructure-as-code/use-github-actions-with-northflank) for configuration details.

### What is the difference between GitHub Actions and GitLab CI?

GitHub Actions is built into GitHub and uses GitHub events to trigger workflows. GitLab CI is built into the GitLab platform and uses GitLab events. Both use YAML-based pipeline configuration. GitLab CI is available on both GitLab.com and self-hosted GitLab installations. GitHub Actions is only available on GitHub.

### What is a self-hosted runner?

A self-hosted runner is a machine that you provision and manage yourself to run CI/CD pipeline jobs. Self-hosted runners give teams control over the runtime environment, hardware, networking, and security configuration. GitHub Actions, CircleCI, GitLab CI, Buildkite, Travis CI, and Harness all support self-hosted runners or agents.

### Does GitHub Actions support Kubernetes deployments?

GitHub Actions supports deploying to Kubernetes clusters through Actions in the marketplace and custom workflow steps. It does not provide native managed Kubernetes or a Kubernetes abstraction layer. Teams that need managed Kubernetes alongside their CI/CD pipeline typically use a platform like Northflank or pair GitHub Actions with a separate Kubernetes management tool.]]>
  </content:encoded>
</item><item>
  <title>Best VMware alternatives in 2026</title>
  <link>https://northflank.com/blog/best-vmware-alternatives-in-2026</link>
  <pubDate>2026-04-27T16:15:00.000Z</pubDate>
  <description>
    <![CDATA[Best VMware alternatives in 2026: Proxmox VE, Hyper-V, Nutanix AHV, and OpenStack compared, plus how Northflank helps teams move to container-based infrastructure.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/vmware_alternatives_14b295ab88.png" alt="Best VMware alternatives in 2026" /><InfoBox className="BodyStyle">

## TL;DR: What are the best VMware alternatives in 2026?

Since Broadcom's acquisition of VMware in late 2023, organisations have been reassessing their virtualisation infrastructure. Perpetual licences are gone, products are now sold in subscription bundles, and per-core pricing has raised costs for many deployments.

1. **Proxmox VE:** Best for Linux-savvy teams that need an open-source hypervisor with KVM and LXC support, without a licence fee.
2. **Microsoft Hyper-V:** Best for organisations already running Windows Server that need a built-in hypervisor integrated with the Microsoft ecosystem.
3. **Nutanix AHV:** Best for organisations looking to consolidate compute, storage, and networking under a single hyperconverged platform.
4. **OpenStack:** Best for large organisations with cloud operations teams that need open-source private cloud infrastructure at scale.

> If you are moving off VMs entirely rather than replacing the hypervisor, [**Northflank**](https://northflank.com/) provides a cloud-native platform with built-in CI/CD, managed Kubernetes, managed databases, and [BYOC support](https://northflank.com/features/bring-your-own-cloud), covering the operational layer that teams would otherwise need to build on top of Kubernetes themselves.
> 

The right path depends on how closely tied your workloads are to VMs, your team's operational capacity, and whether you want to migrate incrementally or move to a cloud-native model.

</InfoBox>

## What changed with VMware under Broadcom?

Broadcom completed its acquisition of VMware in November 2023. Since then, the licensing model has changed significantly. Organisations evaluating VMware alternatives are typically responding to one or more of the following changes.

- **Perpetual licences eliminated:** From January 2024, no new perpetual licences for any VMware product are available. All new purchases are subscription-based on one, three, or five-year terms.
- **Product catalogue consolidated:** VMware's catalogue of over 160 products has been reduced to a small number of subscription bundles, including VMware Cloud Foundation (VCF) and vSphere Foundation (VVF). Standalone components such as vSAN, NSX, and the Aria Suite are no longer available as individual purchases.
- **Per-core pricing:** Infrastructure products are now licensed per physical CPU core, replacing the previous per-socket model.
- **Essentials Plus discontinued:** The vSphere Essentials Plus kit was replaced by higher-tier subscription offerings in late 2024. Smaller deployments that do not require the full bundle feature set are now required to purchase components they may not use.
- **Forced bundle purchasing:** New purchases must include a full bundle. Teams that only need the hypervisor are required to purchase vSAN, NSX, and management tooling as part of the package.


## What are the best hypervisor replacements for VMware?

Teams leaving VMware generally have two paths: replacing the hypervisor with an alternative that preserves existing VM-based workflows, or moving off VMs entirely and adopting container-based infrastructure. If your workloads are tightly coupled to virtual machines and a full re-architecture is not currently feasible, the following platforms provide hypervisor alternatives

### Proxmox VE

Proxmox VE is an open-source server virtualisation platform built on KVM and LXC. It provides a web-based management interface, live migration, clustering, integrated backup, and high availability support. The software is available at no licence cost, with optional paid enterprise support subscriptions available from Proxmox.

Proxmox VE supports both full virtualisation via KVM and container-based virtualisation via LXC on the same platform. Storage options include local storage, NFS, iSCSI, Ceph, and ZFS. The cluster manager supports multi-node deployments with shared storage.

![CleanShot 2025-05-12 at 13.30.09@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_12_at_13_30_09_2x_f2ad667df5.png)

**Key capabilities:**

- KVM-based full virtualisation and LXC container support on the same host.
- Web-based management interface with live migration and HA clustering.
- Integrated backup and snapshot support.
- No licence fee; enterprise support subscriptions available from Proxmox.

**Considerations:** Proxmox VE requires hands-on system administration. Its ecosystem of integrations and third-party tooling is smaller than VMware's. Teams without existing Linux infrastructure experience will need to factor in operational onboarding time.

### Microsoft Hyper-V

Hyper-V is Microsoft's hypervisor technology built into Windows Server and Windows. It is a type-1 hypervisor that runs directly on hardware and supports a wide range of guest operating systems including Windows, Linux, and FreeBSD. It is included with Windows Server licences at no additional hypervisor cost.

Hyper-V in Windows Server supports live migration, high availability via Windows Server Failover Clustering, Hyper-V Replica for disaster recovery, and shielded virtual machines for sensitive workloads. It integrates with Azure Local for hybrid cloud scenarios and supports management via Windows Admin Center and System Center Virtual Machine Manager.

![CleanShot 2025-05-12 at 13.30.37@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_12_at_13_30_37_2x_6887291b49.png)

**Key capabilities:**

- Type-1 hypervisor included with Windows Server licences.
- Supports Windows, Linux, and FreeBSD guest operating systems.
- Live migration, HA clustering, and Hyper-V Replica for disaster recovery.
- Integration with Azure Local for hybrid cloud management.
- Shielded VMs with Secure Boot and TPM 2.0 support.

**Considerations:** Hyper-V's advanced enterprise features are primarily designed for Windows Server environments. Teams running primarily container-first workloads or needing tight integration with non-Microsoft tooling should evaluate whether Hyper-V meets their requirements.

### Nutanix AHV

Nutanix AHV is the hypervisor component of the Nutanix Cloud Infrastructure platform. It is built on KVM and managed through the Prism interface, which provides a unified control plane for compute, storage, and networking across Nutanix clusters. AHV is included with Nutanix licences.

AHV integrates with Nutanix's broader platform including AOS Storage, Flow Network Security, Nutanix Disaster Recovery, and the Nutanix Kubernetes Platform. Organisations adopting AHV are typically also adopting the broader Nutanix stack.

![CleanShot 2025-05-12 at 13.33.35@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_12_at_13_33_35_2x_2f09d322e2.png)

**Key capabilities:**

- KVM-based hypervisor managed through the Prism UI.
- Integrated with Nutanix AOS Storage, Flow networking, and disaster recovery products.
- Nutanix Kubernetes Platform available for container workloads.
- Included with Nutanix infrastructure licences.

**Considerations:** AHV is not available as a standalone product. Adopting AHV means adopting Nutanix's hardware and software ecosystem. Licensing is subscription-based and tied to Nutanix infrastructure.

### OpenStack

OpenStack is an open-source cloud computing platform that provides compute, storage, networking, and identity services through APIs and a dashboard. It is developed and maintained by the OpenInfra Foundation. The most recent release is OpenStack 2026.1 (Gazpacho).

OpenStack controls compute, storage, and networking resources across large infrastructure deployments. It is deployed in telco, public cloud, and private cloud environments. The OpenStack project reports over 40 million cores running on OpenStack infrastructure globally.

OpenStack does not include a managed service layer. Deploying and operating it requires a team with cloud infrastructure engineering experience, or a managed service provider such as Red Hat, Mirantis, or Canonical.

![CleanShot 2025-05-12 at 13.33.54@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_12_at_13_33_54_2x_6a54a76807.png)

**Key capabilities:**

- Open-source compute, storage, networking, and identity services via APIs and dashboard.
- Supports virtual machines, bare metal, and container workloads.
- Multi-tenancy, role-based access control, and project isolation.
- Active upstream development with bi-annual releases.

**Considerations:** OpenStack requires significant operational investment. Deployment, upgrades, and day-to-day operations need a team with dedicated cloud infrastructure experience. Managed distributions from Red Hat, Mirantis, or Canonical reduce this burden but add licence costs.


## Comparing hypervisor alternatives to VMware

The table below compares each hypervisor alternative to VMware vSphere across licensing model, best fit, and key considerations.

| Platform | Type | Licensing | Best for | Considerations |
| --- | --- | --- | --- | --- |
| **VMware vSphere (Broadcom)** | Enterprise hypervisor | Per-core subscription bundle | Existing VMware deployments | Perpetual licences no longer available; bundle purchasing required |
| **Proxmox VE** | Open-source hypervisor | Free; paid support available | Teams needing KVM and LXC without a licence fee | Requires Linux administration experience |
| **Microsoft Hyper-V** | Windows hypervisor | Included with Windows Server | Windows Server environments | Advanced features tied to Windows Server ecosystem |
| **Nutanix AHV** | Hyperconverged platform | Subscription (per node) | Organisations consolidating compute, storage, and networking | Requires adoption of the full Nutanix platform |
| **OpenStack** | Open-source private cloud | Free; paid distributions available | Large organisations with cloud infrastructure teams | High operational overhead; requires dedicated engineering capacity |


## What does moving off VMs entirely look like?

For teams that are not constrained to VM-based workflows, containers and Kubernetes provide a different operational model. Instead of managing virtual machines and guest operating systems, teams manage containerised workloads declaratively.

Containers run application processes without requiring a full guest OS per workload. Kubernetes provides orchestration across container workloads, covering scheduling, scaling, networking, and health management.

![image.png](https://assets.northflank.com/image_ea67da4539.png)

Containers are not a direct replacement for every VM-based workload. Applications that require full OS-level isolation, specific kernel versions, or persistent low-level system configuration may not be suitable for containerisation without application changes. Teams should assess workload compatibility before planning a migration off VMs.

For workloads that are compatible with containers, Kubernetes provides declarative workload management, horizontal autoscaling, rolling deployments and rollbacks, integration with CI/CD pipelines, and portability across cloud providers and on-premises infrastructure.

## How does Northflank fit into a VMware migration?

[Northflank](https://northflank.com/) is a developer platform built on Kubernetes that provides CI/CD pipelines, managed databases, preview environments, GPU workload support, and [Bring Your Own Cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) from a single control plane. It provides a managed abstraction layer over Kubernetes, removing the need to configure cluster components, write infrastructure manifests, or assemble observability tooling independently.

For teams migrating off VMware towards containerised workloads, BYOC support allows Northflank's orchestration layer to run on their own AWS, GCP, Azure, Oracle, or on-premises infrastructure. This is relevant for teams migrating from on-premises VMware who want to retain infrastructure control during the transition.

![northflank-home-page.png](https://assets.northflank.com/northflank_home_page_a457933045.png)

**Key capabilities:**

- [Built-in CI/CD pipelines](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) with Git-based deployment triggers supporting GitHub, GitLab, and Bitbucket.
- [Managed Kubernetes](https://northflank.com/product/app-platform) across EKS, GKE, AKS, and bare-metal clusters.
- [BYOC support](https://northflank.com/features/bring-your-own-cloud) across AWS, GCP, Azure, Oracle, and on-premises infrastructure.
- Managed databases including [PostgreSQL](https://northflank.com/dbaas/managed-postgresql), [MySQL](https://northflank.com/dbaas/managed-mysql), [MongoDB](https://northflank.com/dbaas/mongodb-on-northflank), and [Redis](https://northflank.com/dbaas/managed-redis).
- [GPU workload support](https://northflank.com/product/gpu-paas) for inference, model serving, and AI training jobs.
- [Secure sandboxes and microVMs](https://northflank.com/product/sandboxes) for running untrusted or AI-generated code with hardware-level isolation.
- [Secrets and config management](https://northflank.com/docs/v1/application/secure/manage-secret-groups), [RBAC](https://northflank.com/docs/v1/application/secure/use-role-based-access-control), and [audit logging](https://northflank.com/docs/v1/application/observe/audit-logs).

**Best for:** Teams moving off VMware and adopting container-based infrastructure who want managed Kubernetes and CI/CD without building platform tooling from scratch.

*See [how Clock uses Northflank to manage 30,000 deployments](https://northflank.com/blog/scaling-30-000-deployments-with-100-uptime-how-clock-uses-northflank-to-simplify-infrastructure) for a production example.*

<InfoBox className="BodyStyle">

Get started with a [free plan](https://app.northflank.com/signup), follow the [getting started guide](https://northflank.com/docs/v1/application/getting-started/introduction-to-northflank), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo) if you have specific infrastructure or compliance requirements. See the [pricing page](https://northflank.com/pricing) for full details on compute, database, and GPU workload costs.

</InfoBox>

## How to approach a VMware migration

Migrating off VMware requires planning across licensing, workload compatibility, tooling, and team readiness. The core steps are:

1. **Audit your current VMware environment.** Identify all vSphere hosts, virtual machines, storage configurations, and network dependencies. Note which workloads are candidates for containerisation and which require a VM-based replacement.
2. **Assess your licence position.** Determine your current subscription status and renewal dates. Understand which Broadcom bundles your existing workloads map to and what the cost difference is compared to alternatives.
3. **Choose a migration path.** For VM-based workloads, evaluate Proxmox VE, Hyper-V, Nutanix AHV, or OpenStack based on your team's capabilities and infrastructure requirements. For workloads suitable for containers, evaluate managed Kubernetes platforms such as Northflank.
4. **Pilot with non-critical workloads.** Run the target platform in parallel with VMware on a subset of workloads before committing to a full migration.
5. **Migrate data and configurations.** Use provider-specific migration tooling or manual export/import processes to move VM images, storage volumes, and network configurations.
6. **Validate and cut over.** Run tests on the target environment before decommissioning VMware infrastructure.

### Common migration challenges

- **Workload compatibility:** Some applications depend on specific VMware features such as vSAN storage policies or NSX microsegmentation. These need to be mapped to equivalent capabilities on the target platform.
- **Operational skill gaps:** Proxmox VE and OpenStack require Linux and infrastructure administration experience. Nutanix AHV reduces operational overhead but requires familiarity with the Prism platform.
- **Licence transition timing:** Broadcom subscription renewals are time-limited. Teams need to plan migration timelines against upcoming renewal dates to avoid committing to additional subscription terms on the outgoing platform.
- **Storage migration:** Moving VM disk images and storage configurations is typically the most time-consuming part of a hypervisor migration.

## Frequently asked questions about VMware alternatives

### What is the best open-source alternative to VMware?

Proxmox VE is a widely adopted open-source alternative to VMware vSphere. It is built on KVM and LXC, provides a web-based management interface, and includes HA clustering, live migration, and backup capabilities at no licence cost. Enterprise support subscriptions are available from Proxmox. OpenStack is an alternative for organisations that need a full private cloud platform rather than a standalone hypervisor.

### Can Proxmox replace VMware vSphere?

Proxmox VE provides core hypervisor and VM management capabilities, including live migration, clustering, and backup. It does not replicate every feature in the broader VMware stack, such as NSX-level network virtualisation or vSAN's storage policy management. For teams that used vSphere primarily as a hypervisor without heavy reliance on vSAN or NSX, Proxmox VE covers the core use case.

### Is Hyper-V a good replacement for VMware?

Hyper-V is a viable replacement for organisations running Windows Server workloads. It is included with Windows Server licences, supports live migration, HA clustering, and disaster recovery via Hyper-V Replica, and integrates with Azure Local for hybrid scenarios. Teams should evaluate Hyper-V against their specific guest OS and tooling requirements.

### What did Broadcom change about VMware licensing?

Broadcom eliminated perpetual licences from January 2024. All new VMware purchases are subscription-based on one, three, or five-year terms. The product catalogue was consolidated from over 160 products to a small number of bundles including VMware Cloud Foundation and vSphere Foundation. Standalone products including vSAN, NSX, and the Aria Suite are no longer available as individual purchases. Per-core pricing replaced per-socket pricing for infrastructure products.

### Do I need to replace VMs entirely when moving off VMware?

No. Proxmox VE, Hyper-V, Nutanix AHV, and OpenStack all provide VM-based infrastructure and allow teams to migrate workloads without re-architecting applications. Moving to containers is an option for teams whose workloads are compatible with containerisation, but it is not a requirement for leaving VMware.

### What is the latest version of OpenStack?

The most recent release is OpenStack 2026.1, codenamed Gazpacho. OpenStack follows a bi-annual release schedule coordinated by the OpenInfra Foundation.

## Choosing the right VMware alternative

The right path off VMware depends on your workloads, your team's operational capacity, and how much of your infrastructure you want to continue managing at the VM level.

For teams that need to preserve VM-based workflows, Proxmox VE provides a direct open-source replacement with no licence fee. Hyper-V is the appropriate choice for Windows Server environments. Nutanix AHV suits organisations looking to consolidate infrastructure under a single managed platform. OpenStack fits large organisations with dedicated cloud operations teams.

For teams moving to container-based infrastructure, Northflank provides a managed Kubernetes platform with built-in CI/CD, databases, and BYOC support.

<InfoBox className="BodyStyle">

Get started with a [free plan](https://app.northflank.com/signup), follow the [getting started guide](https://northflank.com/docs/v1/application/getting-started/introduction-to-northflank), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo) if you have specific infrastructure or compliance requirements. See the [pricing page](https://northflank.com/pricing) for full details on compute, database, and GPU workload costs.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>What is continuous deployment? </title>
  <link>https://northflank.com/blog/continuous-deployment</link>
  <pubDate>2026-04-27T16:15:00.000Z</pubDate>
  <description>
    <![CDATA[Continuous deployment is the automatic release of code that passes all pipeline quality gates to production. Learn how it works, strategies, and tooling.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/continuous_deployment_1f828c0c0c.png" alt="What is continuous deployment? " /><InfoBox className="BodyStyle">


## TL;DR: What is continuous deployment?

- Continuous deployment is the practice of automatically releasing every code change that passes all pipeline quality gates directly to production, without manual intervention.
- It differs from continuous delivery in one key way: continuous delivery keeps code in a deployable state but requires a manual step to release. Continuous deployment removes that step entirely.
- Continuous deployment requires mature automated testing, observability, and rollback tooling before it is safe to operate at production scale.
- Platforms like [Northflank](https://northflank.com/) provide the pipeline, preview environments, rollback controls, and secrets management that continuous deployment workflows depend on.

</InfoBox>

Continuous deployment is the engineering practice of automatically releasing code changes to production as soon as they pass all quality gates in the CI/CD pipeline. It is one of the most advanced automation practices in software delivery and sits at the end of the DevOps automation spectrum.

This article covers how continuous deployment works, how it differs from continuous integration and continuous delivery, what deployment strategies teams use, and what is required to operate it safely.

## What is continuous deployment?

Continuous deployment is a software release practice in which every code change that passes a predefined set of automated tests is deployed directly to production without requiring manual approval or intervention.

In a continuous deployment pipeline, the sequence runs as follows: a developer merges a change, the CI system runs builds and tests, and if all quality gates pass, the change is deployed to production automatically. No human needs to trigger the final step.

This is the key distinction between continuous deployment and continuous delivery. Continuous delivery automates the pipeline up to the point of release but requires a human to approve and trigger the final deployment. Continuous deployment removes that approval step, so validated changes reach users as soon as the pipeline completes.

## How does continuous deployment differ from continuous integration and continuous delivery?

These three practices are part of the same pipeline and are often abbreviated as CI/CD, which can cause confusion. Each covers a distinct phase of the software delivery process.

**Continuous integration (CI)** is the practice of automatically building and testing code every time a change is committed to a shared repository. The goal is to catch integration errors early by running automated tests on every commit.

**Continuous delivery (CD)** extends CI by ensuring the codebase is always in a deployable state. Every change that passes CI is packaged and staged for release, but a human still decides when and whether to push it to production.

**Continuous deployment** takes continuous delivery one step further. If the pipeline passes all automated checks, the change is deployed to production automatically. There is no manual gate between a passing build and a live release.

| Practice | What it automates | Manual step required |
| --- | --- | --- |
| Continuous integration | Build and test on every commit | Yes, to promote to staging |
| Continuous delivery | Build, test, and stage for release | Yes, to deploy to production |
| Continuous deployment | Build, test, stage, and deploy to production | No |

## How does a continuous deployment pipeline work?

A continuous deployment pipeline is a sequence of automated stages that a code change passes through before it reaches production. The exact stages vary by team and stack, but a typical pipeline includes the following steps.

1. **Code commit**: A developer merges a change into the main branch.
2. **Build**: The CI system compiles the code and builds a deployable artefact, typically a container image.
3. **Automated testing**: The pipeline runs unit tests, integration tests, and any environment-specific checks defined for the pipeline.
4. **Staging deployment**: The build is deployed to a staging or pre-production environment that mirrors production configuration.
5. **Post-deploy validation**: Smoke tests and health checks run against the staging environment to verify the build functions as expected.
6. **Production deployment**: If all checks pass, the change is deployed to production automatically.
7. **Observability**: Logging and metrics confirm the deployment completed successfully and the service is healthy.

Each stage acts as a quality gate. If any stage fails, the pipeline stops and the change does not progress.

## What deployment strategies does continuous deployment use?

How a change reaches production depends on the deployment strategy the team has configured. Different strategies balance risk, speed, and rollback capability differently.

- **Rolling deployment**: replaces instances of the previous version incrementally with the new version. At any point during the rollout, some instances run the old version and some run the new version. This reduces downtime but means both versions are live simultaneously during the rollout.

- **Blue/green deployment**: maintains two identical environments, blue (current) and green (new). The new version is deployed to the green environment, validated, and traffic is switched from blue to green. If an issue is found, traffic switches back to blue immediately.

- **Canary deployment**: routes a small percentage of traffic to the new version while the majority continues on the old version. The canary is monitored, and if it behaves as expected, traffic is shifted gradually until the full release is complete. If an issue is detected, traffic is shifted back.

- **Feature flags**: decouple deployment from release. Code is deployed to production but the feature remains disabled. The flag is enabled separately, allowing the team to control exposure independently of the deployment pipeline.

## What are the benefits of continuous deployment?

When a continuous deployment pipeline is operating correctly, teams see the following changes to their delivery process.

- **Shorter feedback loops**: Changes reach users as soon as they pass testing, which means real-world feedback on new behaviour is available within minutes of a merge rather than days or weeks.

- **Smaller change sets**: Frequent, automated deployments encourage small, incremental changes. Smaller changes are easier to test, easier to reason about, and easier to roll back if something goes wrong.

- **Reduced deployment risk**: Because each change is small and tested before it reaches production, the blast radius of any individual deployment is limited. Incidents caused by large release bundles are reduced.

- **No deployment bottlenecks**: Manual deployment steps are a bottleneck in delivery pipelines. Removing them allows engineering teams to merge and move on without waiting for a release window or approval.

## What does a team need before adopting continuous deployment?

Continuous deployment places significant requirements on the testing and observability infrastructure already in place. Teams that adopt it before these foundations are ready typically experience increased production incidents.

Before adopting continuous deployment, a team should have the following in place.

- **Comprehensive automated test coverage**: Unit tests, integration tests, and environment-specific tests must be automated and run in the pipeline. Manual testing cannot act as the gate for production deployments.

- **Staging environments that mirror production**: Tests run in staging only catch issues if staging accurately reflects production configuration, data shapes, and load patterns.

- **Automated rollback capability**: If a bad deploy reaches production, the team needs to be able to revert quickly. Rollback should be automated or at minimum a single-step operation, not a manual process.

- **Observability in production**: Logging, metrics, and alerting need to be in place before continuous deployment is live. Without observability, a bad deploy may not be detected until users report errors.

- **Configuration managed as code**: Environment-specific configuration, secrets, and API endpoints should be versioned and managed through the pipeline, not set manually per environment.

## When is continuous deployment not appropriate?

Continuous deployment is not the right model for every team or every service. There are scenarios where a manual gate on production deployments remains appropriate.

Teams in regulated industries, such as financial services, healthcare, or government, may have compliance requirements that mandate human approval before production changes. In these cases, continuous delivery with documented approval gates is the appropriate model.

Services with hard dependencies on external release coordination, such as hardware firmware, mobile app releases pending app store review, or versioned public APIs with breaking changes, may not be deployable on a fully automated basis.

Teams that have not yet built mature test coverage and observability infrastructure are likely to experience more production incidents with continuous deployment than without it. In those cases, continuous delivery provides the same pipeline automation benefits while retaining a manual gate until the foundations are ready.

## How does Northflank support continuous deployment workflows?

[Northflank](https://northflank.com/) provides the pipeline infrastructure, environment management, and observability tooling that continuous deployment workflows depend on from a single control plane.

![northflank-release-page.png](https://assets.northflank.com/northflank_release_page_0657620f5a.png)

**Key capabilities for continuous deployment:**

- [Built-in CI/CD pipelines](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) triggered from Git commits and pull requests, supporting GitHub, GitLab, and Bitbucket.
- [Preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) that spin up automatically on branch push or pull request, allowing changes to be validated against production-equivalent configuration before deployment.
- [Rollback controls](https://northflank.com/docs/v1/application/release/run-and-manage-releases#roll-back-a-release) that allow teams to revert a release pipeline to a previous state or restore a full environment configuration.
- [Git-based deployment triggers](https://northflank.com/docs/v1/application/build/build-code-from-a-git-repository) that tie every deployed build to a specific commit, keeping environments traceable and reproducible.
- [Secrets and config management](https://northflank.com/docs/v1/application/secure/manage-secret-groups) with shared secret groups that keep preview, staging, and production environments consistently configured.
- [Centralised logging](https://northflank.com/docs/v1/application/observe/view-logs) across builds, deployments, and running containers with real-time log tailing and search.

Northflank supports deployment across its managed cloud and via [Bring Your Own Cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) on AWS, GCP, Azure, Oracle, and on-premises infrastructure.

<InfoBox className="BodyStyle">

Get started with a [free plan](https://app.northflank.com/signup), follow the [getting started guide](https://northflank.com/docs/v1/application/getting-started/introduction-to-northflank), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo) if you have specific infrastructure or compliance requirements. See the [pricing page](https://northflank.com/pricing) for full details on compute, database, and GPU workload costs.

</InfoBox>

## What tools do teams use for continuous deployment?

Several platforms and tools are used to implement continuous deployment pipelines. The right choice depends on the team's infrastructure, Kubernetes usage, and existing toolchain.

| Tool | Type | Best for |
| --- | --- | --- |
| [**Northflank**](https://northflank.com/) | Unified deployment platform | Teams needing CI/CD, preview environments, managed databases, and BYOC from one platform |
| **Argo CD** | GitOps delivery controller | Kubernetes-native teams managing deployments via Git as the source of truth |
| **Spinnaker** | Multi-cloud delivery platform | Organisations needing multi-cloud deployment strategies and approval pipelines |
| **Harness** | CD-as-a-service platform | Teams needing policy controls, approval gates, and built-in verification |
| **GitHub Actions** | CI-first workflow automation | Teams already on GitHub who need flexible deployment workflows via YAML |

## Frequently asked questions about continuous deployment

### What is the difference between continuous delivery and continuous deployment?

Continuous delivery ensures code is always in a deployable state and automates the pipeline up to the point of release. A human still approves and triggers the final deployment to production. Continuous deployment removes that final manual step, so every change that passes automated testing is released to production without human intervention.

### What is the difference between a deployment and a release?

A deployment is the act of placing new code into a production environment. A release is the act of making that code available to users. In many continuous deployment setups these happen simultaneously, but deployment strategies such as feature flags allow teams to deploy code without releasing it to users until a later point.

### How does continuous deployment relate to GitOps?

GitOps is a practice in which Git is used as the single source of truth for infrastructure and application configuration. Continuous deployment and GitOps are complementary. A GitOps controller such as Argo CD watches a Git repository and applies changes to the target environment automatically when the repository state changes, which is one implementation of continuous deployment for Kubernetes workloads.

### When should a team use continuous deployment?

Continuous deployment is appropriate for teams that have mature automated test coverage, observability in production, and automated rollback capability. It works best for services that deploy frequently and where fast feedback from production is valuable. Teams that are still building test infrastructure, or that have compliance requirements mandating manual approvals, are better suited to continuous delivery.

### Is continuous deployment suitable for every service?

No. Services with compliance requirements, hard dependencies on external release coordination such as app store reviews or versioned public APIs, or insufficient test coverage are not good candidates for fully automated production deployments. Continuous delivery with a manual gate is the appropriate model for those services.

### What is a deployment pipeline?

A deployment pipeline is the sequence of automated stages a code change passes through from commit to production. Typical stages include build, automated testing, staging deployment, post-deploy validation, and production deployment. Each stage acts as a quality gate. If a stage fails, the pipeline stops and the change does not progress.

### What is a canary deployment?

A canary deployment is a deployment strategy in which a small percentage of production traffic is routed to a new version of a service while the majority continues on the previous version. The new version is monitored, and if it behaves as expected, traffic is shifted gradually until the full release is complete. If an issue is detected, traffic is shifted back to the previous version.]]>
  </content:encoded>
</item><item>
  <title>Kata Containers vs Docker</title>
  <link>https://northflank.com/blog/kata-containers-vs-docker</link>
  <pubDate>2026-04-27T16:15:00.000Z</pubDate>
  <description>
    <![CDATA[Kata Containers runs workloads in lightweight VMs with dedicated kernels. Docker uses shared kernel isolation. Learn how they compare on security, performance, and when to use each.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/kata_containers_vs_docker_b3a094eeb2.png" alt="Kata Containers vs Docker" />Kata Containers and Docker both run containerised workloads, but they make fundamentally different tradeoffs around isolation, security, and operational complexity. Docker is the standard for cloud-native application deployment. Kata Containers is what you reach for when Docker's shared kernel model is not an acceptable security tradeoff.

This article compares Kata Containers and Docker on architecture, isolation strength, startup speed, and use case fit, and covers how to run both on [Northflank](https://northflank.com/).

<InfoBox className="BodyStyle">

**What is Northflank?**

[Northflank](https://northflank.com/) is a full-stack cloud platform that runs both Docker containers and Kata Containers microVM-backed sandboxes in the same control plane. Deploy services, sandboxes, databases, and GPU workloads without managing the underlying infrastructure. [Get started (self-serve)](https://app.northflank.com/signup) or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30).

</InfoBox>

## TL;DR: Kata Containers vs Docker

|  | Docker | Kata Containers |
| --- | --- | --- |
| **Type** | Container runtime | Container runtime with VM-level isolation |
| **Isolation** | OS-level (namespaces, cgroups) | Hardware-level (KVM via VMM) |
| **Kernel** | Shared host kernel | Dedicated guest kernel per workload |
| **Startup time** | Milliseconds | ~150ms to ~300ms depending on VMM and configuration |
| **Memory overhead** | Minimal | Low (varies by VMM) |
| **Kubernetes integration** | Native | Native via CRI / RuntimeClass |
| **Security boundary** | Process isolation | Hardware isolation |
| **Multi-tenant untrusted code** | Not recommended | Designed for it |
| **Best for** | Trusted internal workloads, CI/CD, cloud-native apps | Untrusted workloads, multi-tenant platforms, AI sandboxes |

## What is Docker?

Docker is a container runtime that packages applications and their dependencies into OCI-compliant images and runs them as isolated processes on the host operating system. Isolation is achieved using Linux namespaces (process, network, filesystem) and cgroups (CPU and memory limits). The container shares the host kernel.

Docker is the dominant deployment standard in cloud-native infrastructure. Kubernetes orchestrates Docker-compatible containers at scale, and the OCI image format means a container built once runs on any compliant runtime.

**Strengths of Docker**

- Millisecond startup, no OS boot required
- Minimal memory overhead
- Very high workload density
- OCI standard, runs on any compliant runtime
- Massive ecosystem and tooling
- Native Kubernetes integration

**Limitations of Docker**

- Shares the host kernel (a kernel vulnerability affects all containers on the host)
- Not suitable for running untrusted code from external sources
- Weaker isolation for multi-tenant environments
- Container escapes are possible via kernel exploits

## What are Kata Containers?

Kata Containers is an open-source container runtime that runs workloads inside lightweight virtual machines rather than standard container processes. It is maintained under the OpenInfra Foundation and supports multiple VMM backends: Cloud Hypervisor (default), Firecracker, and QEMU. Each workload gets its own dedicated guest kernel enforced by hardware virtualisation via KVM.

Kata Containers is not itself a VMM. It is the orchestration framework that sits on top of a VMM and makes microVMs integrate natively with container tooling. From Docker or Kubernetes' perspective, a Kata-backed workload behaves like a standard container. The isolation model underneath is hardware-enforced rather than OS-level. See [What are Kata Containers?](https://northflank.com/blog/what-are-kata-containers) for a full technical breakdown.

**Strengths of Kata Containers**

- Hardware-level isolation via KVM per workload
- Dedicated guest kernel per container
- Native Kubernetes integration via CRI / RuntimeClass
- Choice of VMM backend (Cloud Hypervisor, Firecracker, QEMU)
- Works with existing OCI container images
- Designed for untrusted and multi-tenant workloads

**Limitations of Kata Containers**

- Higher startup overhead than standard containers (150ms to 300ms, depending on VMM and configuration)
- Requires nested virtualisation support on cloud hosts
- Not all Kubernetes features behave identically inside a Kata VM
- Higher operational complexity than Docker for simple trusted workloads
- Requires KVM support on the host

## What is the key architectural difference between Kata Containers and Docker?

The core difference is the **isolation boundary**. Docker containers share the host kernel. Every container on the same host issues system calls directly to the same Linux kernel. A kernel vulnerability exploited by one container can affect the host and everything else running on it.

Kata Containers gives each workload its own dedicated Linux kernel inside a hardware-enforced boundary via KVM. To escape a Kata-backed workload, an attacker must first compromise the guest kernel, then escape the KVM hypervisor layer enforced by CPU hardware (Intel VT-x or AMD-V). That is a significantly harder attack path than a standard container escape.

For trusted workloads where you control what code runs, Docker's isolation is sufficient and the right default. For untrusted workloads, AI-generated code, customer-submitted scripts, or any multi-tenant environment, Docker's shared kernel model is the attack surface. See [Containers vs virtual machines](https://northflank.com/blog/containers-vs-virtual-machines) and [What is KVM?](https://northflank.com/blog/what-is-kvm) for more context on why this boundary matters.

See how isolation models differ:

![Container isolation (3).png](https://assets.northflank.com/Container_isolation_3_fce9c02898.png)

## When should you use Docker vs Kata Containers?

The decision comes down to your threat model. If you control what code runs and trust your workloads, Docker is sufficient. If you are running code from external users, AI agents, or any source you do not control, Docker's shared kernel model is a risk that Kata Containers is specifically designed to address.

| Use case | Docker | Kata Containers |
| --- | --- | --- |
| Internal services and APIs | Yes | Overkill |
| CI/CD build environments | Yes | Yes, if builds run untrusted code |
| Microservices on Kubernetes | Yes | Overkill for trusted workloads |
| Multi-tenant untrusted code execution | No | Yes |
| AI agent and LLM-generated code | No | Yes |
| Serverless functions | No | Yes |
| Code interpreter platforms | No | Yes |
| Compliance requiring kernel isolation | No | Yes |
| Maximum workload density | Yes | No |

## Can Kata Containers and Docker work together?

Yes. Kata Containers is not a replacement for Docker. It complements the container ecosystem. In a Kubernetes cluster, you can run standard Docker-compatible containers for trusted internal workloads and Kata-backed VMs for sandboxes and untrusted code, all managed through the same orchestration layer via RuntimeClass.

OCI-compliant container images work with Kata Containers without modification. The same image you run with Docker can run inside a Kata VM with a dedicated kernel boundary, without rebuilding or repackaging.

## How to run Docker and Kata Containers on Northflank

Running Docker containers for trusted workloads and Kata Containers microVMs for sandboxes and untrusted code in the same platform requires maintaining two different infrastructure stacks, two orchestration models, and two sets of networking and secrets configuration without the right tooling.

> [Northflank](https://northflank.com/) runs both in the same control plane. You connect a repo or bring a container image, and Northflank handles Kubernetes scheduling, autoscaling, TLS, secrets injection, real-time logs and metrics, and preview environments per pull request.
> 

For workloads that need hardware-level isolation, Northflank's [microVM-backed sandbox execution](https://northflank.com/product/sandboxes) runs Kata Containers with Cloud Hypervisor as the primary VMM, with Firecracker and gVisor applied depending on workload requirements.

The platform has been in production since 2021 across startups, public companies, and government deployments. Sandboxes spin up in approximately 1 to 2 seconds, with compute pricing starting at $0.01667 per vCPU per hour and $0.00833 per GB of memory per hour. See the [pricing page](https://northflank.com/pricing) for full details.

[BYOC](https://northflank.com/product/bring-your-own-cloud) (Bring Your Own Cloud) is self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal, so workloads and data stay within your own infrastructure.

<InfoBox className="BodyStyle">

**Get started with Northflank sandboxes**

- [Sandboxes on Northflank: overview and concepts](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank): architecture overview and core sandbox concepts
- [Deploy sandboxes on Northflank: step-by-step deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-on-northflank): step-by-step deployment guide
- [Deploy sandboxes in your cloud: BYOC deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud): run sandboxes inside your own VPC
- [Create a sandbox with the SDK: programmatic sandbox creation](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank#create-sandboxes-with-the-sdk): programmatic sandbox creation via the Northflank JS client

[Get started (self-serve)](https://app.northflank.com/signup), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) if you have specific infrastructure or compliance requirements.

</InfoBox>

## Frequently asked questions about Kata Containers vs Docker

### Is Kata Containers faster than Docker?

No. Docker containers start in milliseconds with no OS boot required. Kata Containers workloads boot a guest kernel, adding 150 to 300ms depending on VMM and configuration. For most security-sensitive workloads, that overhead is acceptable. The tradeoff is isolation strength, not speed.

### Can Kata Containers run Docker images?

Yes. Kata Containers is OCI-compatible. Container images built for Docker run inside Kata VMs without modification. The runtime changes. The image format does not.

### Does Kata Containers replace Docker?

No. Docker remains the standard for cloud-native application deployment and trusted internal workloads. Kata Containers is purpose-built for workloads where Docker's shared kernel model is an unacceptable security tradeoff. Most production platforms that handle untrusted code run both.

### Does Kata Containers work with Kubernetes?

Yes. Kata Containers implements the Container Runtime Interface and integrates with Kubernetes via RuntimeClass. Standard container pods and Kata-backed pods can run on the same cluster simultaneously.

### What is the difference between Kata Containers and gVisor?

Kata Containers runs each workload in a lightweight VM with a dedicated guest kernel, providing hardware-enforced isolation via KVM. gVisor intercepts syscalls in user space through its Sentry component without booting a VM. Kata provides stronger isolation for adversarial workloads. gVisor has lower overhead and works on hosts where nested virtualisation is unavailable. See [What is gVisor?](https://northflank.com/blog/what-is-gvisor) for a full explanation.

## Related articles on Kata Containers, Docker, and sandboxes

- [What are Kata Containers?](https://northflank.com/blog/what-are-kata-containers): a full technical breakdown of Kata Containers' architecture, VMM backends, and Kubernetes integration
- [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): how the three leading isolation technologies compare on security, performance, and operational complexity
- [Firecracker vs Docker](https://northflank.com/blog/firecracker-vs-docker): how Firecracker microVM isolation compares to standard container isolation
- [What is a microVM?](https://northflank.com/blog/what-is-a-microvm): how microVMs work and which technologies implement them
- [What is gVisor?](https://northflank.com/blog/what-is-gvisor): how gVisor compares to Kata Containers and when to use each
- [What is KVM?](https://northflank.com/blog/what-is-kvm): the hardware virtualisation layer that Kata Containers builds on
- [Containers vs virtual machines](https://northflank.com/blog/containers-vs-virtual-machines): the broader comparison covering containers, VMs, and microVMs in context
- [How to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents): isolation architectures for AI agent execution environments]]>
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</item><item>
  <title>10 best DigitalOcean alternatives in 2026</title>
  <link>https://northflank.com/blog/best-digitalocean-alternatives-2026</link>
  <pubDate>2026-04-27T16:15:00.000Z</pubDate>
  <description>
    <![CDATA[Best DigitalOcean alternatives in 2026: Northflank, Linode, Vultr, AWS, GCP, Azure, Hetzner, Kamatera, Cloudways, and Render, compared by use case and tradeoffs.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/digitalocean_alternatives_2dfb29a2ff.png" alt="10 best DigitalOcean alternatives in 2026" /><InfoBox className="BodyStyle">

## TL;DR: What are the best DigitalOcean alternatives in 2026?

Teams consider alternatives to DigitalOcean when they need stronger CI/CD tooling, more advanced Kubernetes features, multi-cloud flexibility, or different pricing for their specific workload profile.

1. [**Northflank**](https://northflank.com/) (cloud-native platform, not VPS): Best for teams that need built-in CI/CD, managed Kubernetes, and [Bring Your Own Cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) support from a single control plane, without managing separate infrastructure tooling.
2. **Linode (Akamai Cloud)**: Best for teams running standard VPS workloads with private instance networking requirements.
3. **Vultr**: Best for global compute with bare metal and high-frequency instance options.
4. **AWS**: Best for enterprises needing a broad managed services catalogue and compliance certifications.
5. **Google Cloud Platform (GCP)**: Best for Kubernetes-native and AI/ML workloads at scale.
6. **Microsoft Azure**: Best for organisations running Windows Server or hybrid cloud deployments.
7. **Hetzner**: Best for teams running self-managed VPS workloads that require dedicated server options alongside cloud compute.
8. **Kamatera**: Best for teams that need custom cloud server configurations with independent resource allocation.
9. **Cloudways**: Best for managed hosting without direct server administration.
10. **Render**: Best for Git-based web app and API deployments with minimal infrastructure overhead.

The right alternative depends on your workload type, required managed services, geographic coverage, and pricing model.

</InfoBox>

DigitalOcean is a cloud platform offering virtual machines (Droplets), managed Kubernetes (DOKS), managed databases, object storage, and an App Platform for Git-based deployments.

This article covers the best DigitalOcean alternatives in 2026, including their key capabilities, pricing, and ideal use cases.

## Why look for a DigitalOcean alternative?

Teams typically look for a DigitalOcean alternative when they need more advanced CI/CD pipelines, enterprise-grade Kubernetes features, hybrid cloud support, or better pricing for their workload profile.

- **Limitations of DigitalOcean Kubernetes (DOKS):** DOKS provides a managed control plane with worker nodes billed at standard Droplet rates, but does not include native multi-cluster networking or advanced node pool configurations.
- **CI/CD on DigitalOcean:** The App Platform supports Git-based deployments but does not cover canary or blue/green strategies, and advanced pipelines typically require external tooling.
- **Logging and monitoring on DigitalOcean:** Basic resource monitoring and Managed OpenSearch are included, but distributed tracing and cross-cluster observability require third-party tools.
- **Scalability limits on DigitalOcean:** Autoscaling is available through DOKS node pools and load balancers, but native multi-cluster management and advanced traffic routing are not provided.
- **Bring Your Own Cloud (BYOC):** DigitalOcean supports VPC networking and VPN connections, but does not provide a fully integrated [BYOC](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) solution for teams that need tight on-premises and cloud integration.

## Quick comparison of DigitalOcean alternatives

The table below compares all 10 DigitalOcean alternatives across best fit, starting price, and key tradeoffs to help your team identify the right platform for your workload.

| **Alternative** | **How it compares to DigitalOcean** | **Best for** | **Starting price** | **Drawbacks** |
| --- | --- | --- | --- | --- |
| **Northflank** | Supports BYOC (DigitalOcean does not); cloud-native CI/CD and Kubernetes; single-pane developer experience | Teams needing CI/CD, Kubernetes, and multi-cloud deployments from one platform | Free tier; pay-as-you-go from $2.70/month | No traditional VPS hosting |
| **Linode (Akamai Cloud)** | Akamai CDN integration and advanced networking; free DDoS protection included | Teams running standard VPS workloads with private networking requirements | $5/month | Limited managed services compared to hyperscalers |
| **Vultr** | Multiple global regions; bare metal and high-frequency instance options | Teams needing global compute coverage with a range of instance types | From $2.50/month | Fewer advanced managed services |
| **AWS** | Broader managed services catalogue; more compliance certifications | Enterprises needing scalable infrastructure with compliance requirements | Free tier available, then pay-as-you-go | Higher complexity and less predictable pricing |
| **Google Cloud Platform (GCP)** | Advanced AI/ML tooling; mature managed Kubernetes (GKE) | Teams running Kubernetes-native or AI/ML workloads | Free tier available, then pay-as-you-go | Steeper learning curve |
| **Microsoft Azure** | Hybrid and enterprise cloud focus; Microsoft ecosystem integration | Organisations running Windows workloads or hybrid deployments | Free tier available, then pay-as-you-go | Can be expensive for smaller teams |
| **Hetzner** | Cloud VPS and dedicated servers; EU and US data centres | Teams running self-managed VPS with European or US data residency requirements | From €3.79/month | Limited managed service variety compared to DigitalOcean |
| **Kamatera** | Configurable CPU, RAM, and storage per instance | Teams needing custom cloud server configurations with independent resource allocation | $4/month | Fewer pre-built integrations |
| **Cloudways** | Managed hosting layer on top of DigitalOcean, Vultr, Linode, AWS, and GCE | Teams managing web applications that want managed server operations | From $14/month | Limited low-level infrastructure control |
| **Render** | Developer-focused PaaS; Git-based deployment | Developers deploying web apps, APIs, and background jobs | Free tier available | Limited infrastructure control |


## What are the best DigitalOcean alternatives in 2026?

The following sections break down each alternative by what it provides, how it compares to DigitalOcean, and when it is the right choice for your workload.

### 1. Northflank

[Northflank](https://northflank.com/) is a developer platform that provides CI/CD pipelines, managed Kubernetes, managed databases, and [Bring Your Own Cloud (BYOC)](https://northflank.com/product/bring-your-own-cloud) support from a single control plane. Northflank is designed around cloud-native workloads rather than traditional VPS hosting.

BYOC support allows teams to run Northflank's orchestration layer on their own cloud accounts across AWS, GCP, Azure, and other providers, including bare-metal and on-premises infrastructure, so workloads are not tied to a single cloud. CI/CD is built directly into the platform, with deployments triggered from Git push without requiring external pipeline configuration.

![northflank-home-page.png](https://assets.northflank.com/northflank_home_page_a457933045.png)

**Key capabilities:**

- [Built-in CI/CD pipelines](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) with Git-based build and deployment triggers, supporting GitHub, GitLab, and Bitbucket.
- [Managed Kubernetes](https://northflank.com/product/app-platform) with a managed abstraction layer that handles orchestration across EKS, GKE, AKS, and bare-metal clusters.
- [BYOC support](https://northflank.com/features/bring-your-own-cloud) across AWS, GCP, Azure, Oracle, and on-premises infrastructure.
- Managed databases including [PostgreSQL](https://northflank.com/dbaas/managed-postgresql), [MongoDB](https://northflank.com/dbaas/mongodb-on-northflank), [MySQL](https://northflank.com/dbaas/managed-mysql), and [Redis](https://northflank.com/dbaas/managed-redis), with automated backups and point-in-time recovery.
- [Preview and release environments](https://northflank.com/product/preview-environments) with pipeline-based promotion from development through to production.
- [GPU workload support](https://northflank.com/product/gpu-paas) for inference, model serving, and AI training jobs across cloud and imported clusters.
- [Secure sandboxes and microVMs](https://northflank.com/product/sandboxes) for running untrusted or AI-generated code with hardware-level isolation.
- [Secrets and config management](https://northflank.com/docs/v1/application/secure/manage-secret-groups), [role-based access control](https://northflank.com/docs/v1/application/secure/use-role-based-access-control), and [audit logging](https://northflank.com/docs/v1/application/observe/audit-logs) for production environments.

**Best for:** Teams deploying services, databases, and AI workloads that need CI/CD, managed Kubernetes, and multi-cloud or on-premises flexibility from a single platform.

<InfoBox className="BodyStyle">

Get started with a [free plan](https://app.northflank.com/signup), follow the [getting started guide](https://northflank.com/docs/v1/application/getting-started/introduction-to-northflank), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo) if you have specific infrastructure or compliance requirements. See the [pricing page](https://northflank.com/pricing) for full details on compute, database, and GPU workload costs.

</InfoBox>

### 2. Linode (Akamai Cloud)

Linode, now operating under the Akamai Cloud brand, provides VPS instances, managed Kubernetes (LKE), managed databases, and object storage. Pricing starts at $5/month for a 1 GB shared CPU Nanode instance.

 ![](https://assets.northflank.com/linode_fd5c28d269.png) 

**Key capabilities:**

- Akamai edge CDN integration for content delivery and DDoS protection.
- VLAN support for private instance networking.
- Managed Kubernetes (LKE) and managed databases.

**Best for:** Teams running standard VPS workloads that require private instance networking.

### 3. Vultr

Vultr provides cloud compute across multiple global data centre locations. Instance types include standard shared compute, high-frequency compute, bare metal, and cloud GPU.

 ![](https://assets.northflank.com/vultr_355dc4cdae.png) 

**Key capabilities:**

- Multiple global data centre locations.
- High-frequency compute instances.
- Bare metal servers for workloads requiring direct hardware access.

**Best for:** Teams that need broad geographic coverage and a range of compute instance types, including bare metal.

### 4. AWS (Amazon Web Services)

AWS provides a broad set of cloud services covering compute, storage, databases, AI/ML, networking, and security. Compared to DigitalOcean, AWS provides more configuration options, compliance certifications, and global infrastructure.

 ![](https://assets.northflank.com/aws_12ae29f814.png) 

**Key capabilities:**

- Compute, storage, database, and AI/ML services across a large catalogue.
- Enterprise security and compliance certifications across multiple frameworks.
- Global infrastructure across multiple regions.

**Best for:** Enterprises needing scalable infrastructure with compliance requirements and a broad range of managed services.


### 5. Google Cloud Platform (GCP)

Google Cloud Platform provides managed Kubernetes through Google Kubernetes Engine (GKE), which includes autopilot mode, automatic node repair, and multi-cluster management. It also provides AI/ML tooling including Vertex AI and access to tensor processing units (TPUs).

 ![](https://assets.northflank.com/gke_15f05bcdd5.png) 

**Key capabilities:**

- GKE with autopilot mode, automatic node repair, and multi-cluster support.
- Global networking infrastructure.
- AI/ML services including Vertex AI and TPUs.

**Best for:** Teams running Kubernetes-native applications or AI/ML workloads that require managed infrastructure at scale.

### 6. Microsoft Azure

Azure integrates with the Microsoft ecosystem, covering Windows Server, Active Directory, SQL Server, and Microsoft 365 workloads. Hybrid cloud capabilities are available through Azure Arc, which extends Azure management to on-premises and third-party cloud environments.

 ![](https://assets.northflank.com/azure_532fba275e.png) 

**Key capabilities:**

- Integration with Windows Server, Active Directory, and Microsoft 365.
- Hybrid cloud management through Azure Arc.
- Compliance tooling for regulated industries.

**Best for:** Organisations running Microsoft-dependent workloads or requiring hybrid cloud management with on-premises infrastructure.

### 7. Hetzner
Hetzner provides cloud VPS instances and dedicated servers. Additional services include object storage, load balancers, and managed servers.

 ![](https://assets.northflank.com/Hetzner_221fc4449f.png) 

**Key capabilities:**

- Cloud VPS instances and dedicated servers.
- Object storage and load balancer services.
- Managed server options available.

**Best for:** Teams running self-managed VPS workloads that require dedicated server options alongside cloud compute.

### 8. Kamatera

Kamatera provides cloud server instances where CPU, RAM, and storage are configured independently. It also offers load balancers, block storage, a cloud firewall, and a virtual private cloud product.

 ![](https://assets.northflank.com/Kamatera_4f86836a0a.png) 

**Key capabilities:**

- Configurable CPU, RAM, and storage per instance.
- Cloud firewall, load balancers, and block storage available as add-ons.
- Managed cloud services available as an optional layer.

**Best for:** Teams that need custom cloud server configurations with independent resource allocation.

### 9. Cloudways

Cloudways is a managed cloud hosting platform that deploys on top of infrastructure from DigitalOcean, Vultr, Linode, AWS, or Google Compute Engine. It handles server management, security patching, and caching configuration.

 ![](https://assets.northflank.com/cloudways_3aca712093.png)
 
**Key capabilities:**

- Managed hosting layer on top of DigitalOcean, Vultr, Linode, AWS, and Google Compute Engine.
- Handles server patching and caching setup.
- Supports WordPress, Magento, Laravel, WooCommerce, and PHP applications.

**Best for:** Teams managing web applications that want managed server operations without building DevOps tooling in-house.

### 10. Render

Render is a PaaS platform for deploying web apps, APIs, background workers, and static sites from Git. It provides built-in autoscaling, DDoS mitigation, and managed TLS certificates. Deployment is triggered from Git push, with no container or server configuration required.

 ![](https://assets.northflank.com/render_s_home_page_3d51451377.png) 

**Key capabilities:**

- Git-based deployments for web services, APIs, and static sites.
- Built-in autoscaling and managed TLS certificates.
- Free static site hosting with DDoS mitigation.

**Best for:** Developers deploying web apps and APIs who want platform-managed infrastructure and minimal operational overhead.

## How to choose the right DigitalOcean alternative

The right DigitalOcean alternative depends on four main factors: workload type, required managed services, geographic reach, and pricing model.

For teams running standard VPS workloads, Linode and Hetzner both provide self-managed compute with multiple region options. For teams building containerised applications with CI/CD pipelines, Northflank provides built-in pipelines and managed Kubernetes without requiring separate tooling.

For enterprise-scale workloads requiring compliance certifications, high availability SLAs, or a large catalogue of managed services, AWS, GCP, and Azure are the appropriate choices. For compute without pre-packaged fixed plans, Kamatera and Vultr both support custom instance configurations.

Key factors to evaluate:

- **Compute options:** Check instance types, dedicated versus shared vCPU availability, and whether bare metal is available.
- **Managed services:** Confirm whether the provider offers managed Kubernetes, databases, and object storage natively.
- **Pricing model:** Providers use different billing models; check each provider's pricing page for current rates.
- **Geographic coverage:** Match available regions to your latency and data residency requirements.
- **CI/CD support:** Determine whether the platform provides built-in pipelines or requires external tooling.

## How to migrate from DigitalOcean

Migrating cloud providers requires planning to avoid downtime or data loss. The core steps are:

1. **Audit your current setup.** List all services, databases, networking configuration, and third-party integrations currently running on DigitalOcean. See [how to send logs from Northflank to DigitalOcean Spaces](https://northflank.com/guides/send-logs-to-a-digitalocean-space-from-northflank) for an example of running Northflank alongside DigitalOcean infrastructure during a migration.
2. **Select a new provider.** Match your workload requirements against the alternatives covered in this article.
3. **Provision the new environment.** Set up compute, networking, databases, and security policies on the new provider before moving workloads.
4. **Migrate data.** Use database export/import, snapshot tools, or provider-specific migration utilities to move your data.
5. **Test in the new environment.** Run functional and load tests before cutting over production traffic.
6. **Update DNS and switch traffic.** Once validation passes, update DNS records to point to the new provider.

### Common migration challenges

- **Downtime risk:** Run workloads on both platforms in parallel during the migration window to reduce exposure.
- **Configuration differences:** Networking features like VPC peering, firewall rules, and load balancer configuration may differ between providers and need to be adapted.
- **Egress and bandwidth costs:** Check the outbound data transfer pricing on your new provider, as models vary significantly between providers.

## Frequently asked questions about DigitalOcean alternatives

### What is better than DigitalOcean?

The answer depends on your workload. Northflank provides built-in CI/CD and BYOC support that DigitalOcean does not offer. Linode provides VPS infrastructure that may suit smaller workloads. AWS, GCP, and Azure provide more managed services and compliance tooling for enterprise requirements. [Northflank's BYOC offering](https://northflank.com/features/bring-your-own-cloud) allows teams to run workloads across multiple cloud providers from a single control plane.

### Who competes with DigitalOcean?

The main competitors include Northflank, Linode (Akamai Cloud), Vultr, AWS, Google Cloud, Microsoft Azure, Hetzner, Kamatera, Cloudways, and Render. Each targets a different segment of the market.

### Is DigitalOcean better than AWS?

AWS provides more services, global infrastructure, and compliance certifications. DigitalOcean provides a simpler interface and more predictable pricing. For small to mid-sized projects, DigitalOcean is easier to manage. For enterprise workloads at scale, AWS provides more depth.

### How much does DigitalOcean cost in 2026?

DigitalOcean Droplets start at $4/month for a basic shared CPU instance. As of January 2026, Droplets are billed per second with a 60-second minimum charge. Managed Kubernetes has no control plane fee; you pay for worker node Droplets.

### What are free alternatives to DigitalOcean?

Northflank provides a [free tier](https://northflank.com/pricing) for developers to deploy and test projects. AWS and Google Cloud both provide free tier allocations with usage limits. Render provides free static site hosting on its Hobby plan.

### Is DigitalOcean good for web hosting?

DigitalOcean Droplets are unmanaged virtual machines, meaning teams are responsible for server administration, patching, and backups. For teams that want managed web hosting on DigitalOcean infrastructure, Cloudways provides a managed layer on top of DigitalOcean without requiring direct server access.

## Choosing the right DigitalOcean alternative

The best DigitalOcean alternative depends on what your team needs from cloud infrastructure.

For teams that need CI/CD, managed Kubernetes, and multi-cloud flexibility from one platform, Northflank provides these from a single control plane.

<InfoBox className="BodyStyle">

Get started with a [free plan](https://app.northflank.com/signup), follow the [getting started guide](https://northflank.com/docs/v1/application/getting-started/introduction-to-northflank), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo) if you have specific infrastructure or compliance requirements. See the [pricing page](https://northflank.com/pricing) for full details on compute, database, and GPU workload costs.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>How to deploy vibe-coded apps to production</title>
  <link>https://northflank.com/blog/how-to-deploy-vibe-coded-apps</link>
  <pubDate>2026-04-27T13:45:00.000Z</pubDate>
  <description>
    <![CDATA[How to deploy a vibe-coded app on Northflank: push to GitHub, connect your repository, and get a live HTTPS URL in under two minutes. No infrastructure knowledge required.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/How_to_deploy_vibe_coded_apps_to_production_1f668b2e2b.png" alt="How to deploy vibe-coded apps to production" />You have a vibe-coded app that runs on localhost. Maybe you built it with Claude, Cursor, Lovable, or Bolt. It works. The question is where it actually runs in production and how you make sure it stays live when others use it.

This article covers how to deploy vibe-coded apps on [Northflank](https://northflank.com/), using a simple link-in-bio page as a concrete example. The same workflow applies to any vibe-coded app, regardless of which AI tool generated the code.

<InfoBox className="BodyStyle">

### TL;DR: How to deploy vibe-coded apps to production

Vibe coding gets you to a working app fast. [Northflank](https://northflank.com/) gets it live just as fast.

- Build your app with Claude, Cursor, Lovable, or any AI tool and push the code to GitHub.
- Connect the repository to Northflank. Framework detection handles the build configuration automatically.
- Northflank deploys the app, provisions TLS, and gives you a live URL in under two minutes.
- When your app grows to need a database, secrets management, or preview environments, the same platform covers it without changing your workflow.

> **What is Northflank?**
[Northflank](https://northflank.com/) is a full-stack cloud platform that deploys vibe-coded apps with production-grade infrastructure underneath: managed databases, secrets management, TLS, CI/CD pipelines, preview environments per pull request, and BYOC into your own cloud. No infrastructure code. No DevOps background required.
[Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30).
> 
</InfoBox>

## What we’re deploying

To keep things concrete, we will use a simple link-in-bio page built with Claude. This is one of the most common vibe-coded projects: a single-page site with a profile photo, name, bio, and a list of links. No backend, no database, no environment variables. The same deployment steps apply to any static frontend regardless of what you built or which AI tool generated the code.

Here is the prompt used to generate it:

```
Build a simple link-in-bio page.
Clean minimal design, dark background, centered layout.
Show a profile photo, name, short bio, and a list of links with icons.
Use React and Vite. Keep everything in one file.
```

The generated app has this structure:

```
link-in-bio/
├── src/
│   └── App.jsx
├── index.html
├── package.json
└── vite.config.js
```

One component, no external dependencies beyond React and Vite. The app builds to a `dist` folder with `npm run build`. That is all Northflank needs to deploy it.

<InfoBox className="BodyStyle">

After generating the app, add a `start` script to `package.json`. Buildpacks require a running process, and Vite's build output is a static folder with no server by default. Adding this script gives Northflank a command to run after the build:

```bash
  "scripts": {
    "start": "serve -s dist -l 3000",
  },
```

</InfoBox>

## Step 1: Push the code to GitHub

Northflank deploys from Git. If your code is not already in a repository, push it now.

```bash
git init
git add .
git commit -m "initial commit"
git remote add origin https://github.com/<your-username>/link-in-bio
git push -u origin main
```

If you used Lovable or Bolt, export the project and push it to GitHub from there. Cursor and Claude Code output directly to your local machine, so the same commands apply.

## Step 2: Create a Northflank account and project

[Sign up for Northflank](https://app.northflank.com/signup). The free tier includes two services, one database, and two cron jobs with always-on compute.

Once you are in the dashboard, [link your git account](https://northflank.com/docs/v1/application/getting-started/link-your-git-account) and [create a new project](https://northflank.com/docs/v1/application/getting-started/create-a-project). A project is a container for all the resources that belong to your app: services, databases, secrets, and pipelines.

1. Click [**New project**](https://app.northflank.com/s/account/projects/new) from the dashboard
2. Give it a name (for example, `link-in-bio`)
3. Choose a deployment target (`Northflank Cloud`)
4. Select a region closest to your users
5. Click **Create project**

![image - 2026-04-27T151029.578.png](https://assets.northflank.com/image_2026_04_27_T151029_578_666de65aa1.png)

## Step 3: Deploy the app

1. Inside the project, click [Create service](https://app.northflank.com/s/project/create/service)
2. Select **Combined** service and enter a name, for example `link-in-bio`
3. Select your repository from the dropdown and choose the branch to deploy from
4. Under **Build options**, select **Buildpack** to let Northflank detect your framework and configure the build automatically
5. Under **Networking**, add a public port. Set this to the same port specified in your `start` script in `package.json`. For this example, that is port `3000`. Northflank will provision a public HTTPS URL on this port automatically.
6. Leave resources at the default values. You can enable autoscaling from the resources panel at any time if traffic grows
7. Click **Create service**

![image - 2026-04-27T151024.270.png](https://assets.northflank.com/image_2026_04_27_T151024_270_b27d1f4320.png)

Northflank builds the app and deploys it. TLS is provisioned automatically. Your app is live on a `*.code.run` URL in under two minutes.

## Step 4: Add a custom domain (optional)

To use your own domain, first verify it in your [Northflank account settings](https://app.northflank.com/s/account/domains/) by adding a TXT record to your DNS provider. Once verified, add a subdomain and point it to your service port using the CNAME record Northflank provides. Northflank provisions a TLS certificate automatically.

For the full walkthrough, see [Add and verify a domain](https://northflank.com/docs/v1/application/getting-started/add-a-and-verify-domain) in the Northflank docs.

## What you get

By the end of this guide, the app will have:

- A live HTTPS URL
- Automatic redeployment on every push to `main`
- Always-on compute on the free tier

When your app grows beyond a static site and needs a database, secrets management, background workers, or preview environments per pull request, Northflank covers all of it from the same control plane without changing how you deploy.

## FAQ: deploying vibe-coded apps

### Do I need a Dockerfile to deploy on Northflank?

No. Northflank detects the runtime from your project files (package.json, requirements.txt, and so on) and builds automatically. A Dockerfile is supported if you want more control, but it is not required for standard Node.js, Python, Ruby, or Go applications.

### Can I deploy a full-stack vibe-coded app the same way?

Yes. For apps with a backend and database, the workflow is the same: connect the repository, add a managed database addon, link a secret group with the connection string, and deploy. Northflank supports PostgreSQL, MySQL, MongoDB, Redis, MinIO, and RabbitMQ as managed addons.

### What if my app needs environment variables?

Add them to a Northflank secret group and link the secret group to your service. Northflank injects them at build and runtime. They never appear in your code or build logs.

### Can I deploy a monorepo with frontend and backend in the same repository?

Yes. Northflank lets you set a root directory per service, so you can deploy the frontend and backend as separate services from the same repository. Each service has its own build configuration, environment variables, and scaling settings.

### How do I keep my app running if I leave the free tier?

Northflank's free tier includes always-on compute for two services and one database. Upgrading to a paid plan adds more services, higher resource limits, and BYOC deployment into your own cloud account.

## Conclusion

Deploying a vibe-coded app does not have to be the part where the momentum dies. Northflank handles the infrastructure, managed databases, secrets injection, TLS, CI/CD, and preview environments so you can stay focused on the product.

<InfoBox className="BodyStyle">

[Sign up for free](https://app.northflank.com/signup) and deploy your first vibe-coded app. Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) if you want to walk through your specific setup with an engineer.

</InfoBox>

## Related articles

- [**Best deployment platforms for vibe coders in 2026**](https://northflank.com/blog/best-deployment-platforms-for-vibe-coders): A comparison of Northflank, Vercel, Render, Railway, and Fly.io on databases, secrets management, preview environments, and full-stack scope.
- [**Top managed database services in 2026**](https://northflank.com/blog/top-managed-database-services): Managed Postgres, MySQL, Redis, MongoDB, and more for applications that need a database alongside their deployment platform.
- [**How to run AI-generated code safely**](https://northflank.com/blog/run-ai-generated-code): Covers isolation models and execution environments for AI-generated code that needs more than standard container deployment.
- [**How to auto-create preview environments on every PR**](https://northflank.com/blog/how-to-auto-create-preview-environments-on-every-pr): Step-by-step guide to setting up automatic preview environments on Northflank so every pull request gets its own isolated deployment.]]>
  </content:encoded>
</item><item>
  <title>What are Kata Containers?</title>
  <link>https://northflank.com/blog/what-are-kata-containers</link>
  <pubDate>2026-04-24T15:00:00.000Z</pubDate>
  <description>
    <![CDATA[Kata Containers runs workloads in lightweight VMs with dedicated kernels, integrating natively with Kubernetes. Learn how it works and when to use it over Firecracker or gVisor.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/what_are_kata_containers_dac8326cd0.png" alt="What are Kata Containers?" />Kata Containers is an open-source container runtime that runs workloads inside lightweight virtual machines rather than standard containers, while integrating with the same container tooling engineers already use. From Kubernetes' perspective, a Kata-backed workload looks like a standard container. Under the hood, each workload gets its own dedicated kernel and hardware-enforced isolation boundary.

This article covers how Kata Containers works, what its components are, how it compares to standard containers and alternative isolation approaches, and when it is the right tool.

<InfoBox className="BodyStyle">

## TL;DR: What are Kata Containers?

- Kata Containers is an open-source container runtime that runs workloads inside lightweight VMs, providing hardware-level isolation while integrating natively with Docker and Kubernetes
- It is an orchestration framework, not itself a VMM. It supports Cloud Hypervisor, Firecracker, and QEMU as interchangeable backends
- Each workload gets its own dedicated guest kernel, enforced by KVM hardware virtualisation
- It is an OpenInfra Foundation project, combining the former Intel Clear Containers and runV projects

> [Northflank](https://northflank.com/) is a full-stack cloud platform that uses Kata Containers with Cloud Hypervisor as its primary approach for microVM isolation in production, alongside Firecracker and gVisor depending on workload requirements. In production since 2021 across startups, public companies, and government deployments. [Get started (self-serve)](https://app.northflank.com/signup) or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) for specific infrastructure or compliance requirements.
> 

</InfoBox>

## What are Kata Containers?

Kata Containers is an open-source project that builds lightweight virtual machines that integrate with the container ecosystem. It is maintained under the OpenInfra Foundation and supports multiple VMM backends, including Cloud Hypervisor, Firecracker, and QEMU.

The core idea is straightforward. Standard containers share the host kernel. Kata Containers gives each workload its own guest kernel inside a lightweight VM, enforced by hardware virtualisation via [KVM](https://northflank.com/blog/what-is-kvm#what-is-kvm). The workload is isolated at the hardware level, not just the OS level. From the perspective of Kubernetes or Docker, the workload looks and behaves like a standard container. The isolation model underneath is fundamentally different.

Kata Containers is not itself a VMM. It is the orchestration layer that sits on top of Firecracker, Cloud Hypervisor, or QEMU and makes microVMs work natively with container tooling. For a broader explanation of what a microVM is and how KVM underpins it, see [What is a microVM?](https://northflank.com/blog/what-is-a-microvm) and [What is KVM?](https://northflank.com/blog/what-is-kvm).

## Why do standard containers need stronger isolation for some workloads?

Standard containers use Linux namespaces and cgroups to isolate processes from each other. They share the host kernel. Every syscall a containerised workload makes goes directly to the same kernel that every other container on that host is using. A kernel vulnerability exploited by one workload can affect the host and everything else running on it.

For workloads you control and trust, that tradeoff is acceptable. For untrusted code, AI-generated outputs, customer-submitted scripts, or any multi-tenant environment where different users share infrastructure, the shared kernel is the attack surface. See [Containers vs virtual machines](https://northflank.com/blog/containers-vs-virtual-machines) for a full breakdown of the isolation tradeoffs.

See how isolation models compare across standard containers, Kata Containers, and gVisor:

![Container isolation (3).png](https://assets.northflank.com/Container_isolation_3_fce9c02898.png)

## How does Kata Containers work?

When Kubernetes schedules a pod using Kata Containers, instead of starting a container process directly on the host, the Kata runtime provisions a lightweight VM. That VM boots a minimal guest kernel and starts the workload inside it. Networking and storage are connected through virtualised interfaces.

The key components are:

- **The Kata runtime (`kata-runtime`):** The CRI-compatible runtime that Kubernetes talks to. It receives pod scheduling requests and manages the VM lifecycle.
- **The VMM backend:** Kata supports Cloud Hypervisor (default, best performance for cloud workloads), Firecracker (minimal device model, fast boot), and QEMU (broadest hardware compatibility). The VMM creates and manages the actual VM. See [What is AWS Firecracker?](https://northflank.com/blog/what-is-aws-firecracker) for a technical breakdown of Firecracker's architecture.
- **The guest kernel:** A minimal Linux kernel that boots inside the VM. Each workload gets its own. This is the fundamental property that distinguishes Kata from standard containers.
- **The Kata agent:** A process running inside the VM that communicates with the Kata runtime on the host. It manages workload execution inside the VM on behalf of the runtime.

Boot time is in the range of 150 to 300ms, depending on VMM and configuration, reflecting the guest kernel boot overhead on top of the VMM startup.

## Kata Containers vs standard containers vs microVMs

See how Kata Containers compares across isolation, performance, and operational complexity:

|  | Standard container | Kata Containers | MicroVM (direct) |
| --- | --- | --- | --- |
| **Isolation model** | OS-level (namespaces, cgroups) | Hardware-level (KVM via VMM) | Hardware-level (KVM via VMM) |
| **Kernel** | Shared host kernel | Dedicated guest kernel | Dedicated guest kernel |
| **Boot time** | Milliseconds | ~150ms to ~300ms depending on VMM | ~125ms to ~300ms depending on VMM |
| **Memory overhead** | Minimal | Low | Less than 5 to 10 MiB |
| **Kubernetes integration** | Native | Native via CRI / RuntimeClass | Via Kata Containers |
| **Orchestration included** | Yes (Kubernetes) | Yes | No |
| **Best for** | Trusted internal workloads | Untrusted or multi-tenant workloads on Kubernetes | Custom serverless platforms |

The distinction between Kata Containers and a raw microVM like Firecracker is mostly operational. Firecracker provides the isolation primitive. Kata Containers provides the orchestration layer that makes it work at scale in Kubernetes without building custom infrastructure. Most teams that want microVM isolation in Kubernetes use Kata rather than managing Firecracker or Cloud Hypervisor directly. See [Firecracker vs Docker](https://northflank.com/blog/firecracker-vs-docker) for context on why microVM isolation matters over standard containers.

## What VMM backends does Kata Containers support?

Kata Containers is designed to work with multiple VMM backends, giving teams flexibility depending on their infrastructure and requirements.

- **Cloud Hypervisor** is the default and primary backend for most production use cases. It is a Rust-based VMM maintained by the Linux Foundation, targeting modern cloud workloads with support for GPU passthrough and live migration while keeping a small, auditable codebase.
- **Firecracker** is the AWS-built VMM optimised for minimal overhead, with approximately 125ms boot time to userspace and less than 5 MiB of memory overhead per instance in benchmarks. It is a good fit for environments where boot speed and density matter most.
- **QEMU** offers the broadest hardware compatibility of the three. It carries more overhead than Firecracker or Cloud Hypervisor but is the right choice when hardware compatibility is the primary requirement.

The VMM can be selected per workload or configured as the cluster default via RuntimeClass in Kubernetes.

## How does Kata Containers integrate with Kubernetes?

Kubernetes schedules workloads through the Container Runtime Interface. By default, containerd or CRI-O handle that interface and run standard containers. Kata Containers implements the same CRI, so Kubernetes can schedule workloads into Kata VMs without any changes to the control plane.

You create a RuntimeClass resource pointing to the Kata runtime handler and reference it in your pod spec. Pods assigned that RuntimeClass are scheduled into Kata-backed VMs. Pods without it run as standard containers. Both can run on the same cluster simultaneously.

This makes Kata the practical path to hardware-level isolation for teams already running Kubernetes, without replacing their existing orchestration setup.

## What are Kata Containers' limitations?

Understanding where Kata Containers fits also means understanding where it does not.

- **Boot overhead:** Each workload boots a guest kernel, adding 150 to 300ms compared to millisecond container startup. For workloads that need to start instantly, that overhead matters.
- **Resource overhead:** Running a guest kernel and VMM per workload uses more memory than a standard container, though modern VMM designs keep this in single-digit MiB.
- **Not all Kubernetes features work identically:** Some features that rely on direct access to host namespaces or specific kernel capabilities behave differently inside a Kata VM. Testing your specific workload is important before moving to production.
- **Nested virtualisation requirements:** Running Kata on a cloud VM requires the cloud provider to support nested virtualisation on that instance type. Not all providers or instance types support this. gVisor is a practical alternative in those environments. See [What is gVisor?](https://northflank.com/blog/what-is-gvisor)

## When should you use Kata Containers?

Kata Containers is a good fit when:

- **You need microVM isolation in Kubernetes without building orchestration from scratch.** Kata handles VM lifecycle, networking, and CRI integration. You bring the Kubernetes cluster.
- **You are running untrusted or multi-tenant workloads.** AI-generated code execution, customer-submitted scripts, or any environment where different users share infrastructure benefit from hardware-enforced kernel boundaries.
- **You want to choose your VMM.** The ability to swap between Cloud Hypervisor, Firecracker, and QEMU based on workload requirements gives flexibility that raw VMM deployments do not.
- **You are already running Kubernetes.** The RuntimeClass integration means adding Kata isolation to an existing cluster is an incremental change, not a rearchitecture.

When Firecracker directly is the better choice: if you are building custom serverless infrastructure and have the expertise to manage VMM orchestration, image builds, networking, and lifecycle management yourself, Firecracker gives you lower-level control. See [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor) for a detailed comparison.

## How does Northflank use Kata Containers?

Northflank uses Kata Containers with Cloud Hypervisor as its primary approach for microVM isolation, with Firecracker and gVisor applied depending on workload requirements.

The platform has been in production since 2021 across startups, public companies, and government deployments. Sandboxes spin up in approximately 1 to 2 seconds, with compute pricing starting at $0.01667 per vCPU per hour and $0.00833 per GB of memory per hour. See the [pricing page](https://northflank.com/pricing) for full details.

Northflank supports both ephemeral and persistent sandbox environments on managed cloud or inside your own VPC, self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal via [bring your own cloud](https://northflank.com/product/bring-your-own-cloud).

<InfoBox className="BodyStyle">

**Get started with Northflank sandboxes**

- [Sandboxes on Northflank: overview and concepts](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank): architecture overview and core sandbox concepts
- [Deploy sandboxes on Northflank: step-by-step deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-on-northflank): step-by-step deployment guide
- [Deploy sandboxes in your cloud: BYOC deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud): run sandboxes inside your own VPC
- [Create a sandbox with the SDK: programmatic sandbox creation](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank#create-sandboxes-with-the-sdk): programmatic sandbox creation via the Northflank JS client

[Get started (self-serve)](https://app.northflank.com/signup), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) if you have specific infrastructure or compliance requirements.

</InfoBox>

## Frequently asked questions about Kata Containers

### Are Kata Containers the same as Docker containers?

No. Docker containers share the host kernel and use namespaces and cgroups for isolation. Kata Containers run each workload inside a lightweight VM with a dedicated guest kernel enforced by hardware virtualisation. From Docker or Kubernetes' perspective, the interface is the same. The isolation model underneath is fundamentally different.

### Is Kata Containers a VMM?

No. Kata Containers is an orchestration framework that sits on top of a VMM. It supports Cloud Hypervisor, Firecracker, and QEMU as backends. The VMM creates and manages the actual VM. Kata manages the integration with container tooling and handles the runtime lifecycle.

### Does Kata Containers work with Kubernetes?

Yes. Kata Containers implements the Container Runtime Interface, so it integrates natively with Kubernetes via RuntimeClass. You can run Kata-backed pods and standard container pods on the same cluster simultaneously.

### What is the difference between Kata Containers and Firecracker?

Firecracker is a VMM that creates microVMs. Kata Containers is an orchestration framework that can use Firecracker as one of its VMM backends. You can run Kata Containers with Firecracker, Cloud Hypervisor, or QEMU. Firecracker alone does not include the orchestration layer needed to integrate with Kubernetes.

### What is the difference between Kata Containers and gVisor?

Kata Containers runs each workload in a lightweight VM with a dedicated guest kernel, providing hardware-enforced isolation via KVM. gVisor intercepts syscalls in user space through its Sentry component without booting a VM. Kata provides stronger isolation for adversarial workloads. gVisor has lower overhead and works on hosts where nested virtualisation is unavailable. See [What is gVisor?](https://northflank.com/blog/what-is-gvisor) for a full explanation.

### Which VMM backend should I use with Kata Containers?

Cloud Hypervisor is the default and the right choice for most production use cases. Use Firecracker if boot speed and density are the primary requirements. Use QEMU if you need maximum hardware compatibility. The VMM can be configured per RuntimeClass in Kubernetes, so different workloads can use different backends on the same cluster.

## Related articles on Kata Containers, microVMs, and sandboxes

- [What is a microVM?](https://northflank.com/blog/what-is-a-microvm): how microVMs work, which technologies implement them, and where Kata Containers fits in the stack
- [What is KVM?](https://northflank.com/blog/what-is-kvm): the hardware virtualisation layer that Kata Containers builds on
- [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): a detailed comparison of the three leading isolation technologies
- [What is AWS Firecracker?](https://northflank.com/blog/what-is-aws-firecracker): a technical breakdown of Firecracker's architecture and how Kata uses it as a backend
- [What is gVisor?](https://northflank.com/blog/what-is-gvisor): how gVisor compares to Kata Containers and when to use each
- [Firecracker vs Docker](https://northflank.com/blog/firecracker-vs-docker): how microVM isolation compares to standard container isolation
- [Containers vs virtual machines](https://northflank.com/blog/containers-vs-virtual-machines): the broader comparison covering containers, VMs, and where Kata Containers fits
- [How to spin up a secure code sandbox and microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh): a step-by-step guide to deploying Kata-backed workloads on Northflank]]>
  </content:encoded>
</item><item>
  <title>Best deployment platforms for vibe coders in 2026</title>
  <link>https://northflank.com/blog/best-deployment-platforms-for-vibe-coders</link>
  <pubDate>2026-04-24T14:30:00.000Z</pubDate>
  <description>
    <![CDATA[Best deployment platforms for vibe coders in 2026: compare Northflank, Vercel, Render, Railway, and Fly.io on databases, secrets management, preview environments, and full-stack scope.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/vmware_tanzu_alternatives_cfb2cda114.png" alt="Best deployment platforms for vibe coders in 2026" />Vibe coding tools have solved the code generation problem. You describe what you want, and Lovable, Bolt, Cursor, or Claude Code produces working application code in minutes. The problem most vibe coders hit next is deployment: where does this thing actually run, how do you connect it to a real database, and how do you keep credentials out of the code?

Some tools bundle hosting. Most do not. And the ones that do lock you into their infrastructure, which creates problems the moment you need more control, better pricing, or compliance requirements that their platform cannot meet.

<InfoBox className="BodyStyle">

## TL;DR: What are the best deployment platforms for vibe coders in 2026?

Most vibe coding tools either bundle opinionated hosting or generate code you have to deploy yourself. The platforms below address the deployment gap: they take AI-generated code and provide the underlying infrastructure.

- [**Northflank**](https://northflank.com/) – Full-stack deployment platform with managed databases, secrets injection, [preview environments per pull request](https://northflank.com/product/preview-environments), CI/CD, [Sandboxes](https://northflank.com/product/sandboxes), [GPU workloads](https://northflank.com/product/gpu-paas), and [self-serve BYOC](https://northflank.com/product/bring-your-own-cloud). The strongest option for vibe coders who need production-grade infrastructure without writing infrastructure code.
- **Vercel** – Frontend-focused deployment optimized for Next.js and React. Excellent DX for static and serverless apps. Limited for full-stack workloads with persistent services.
- **Render** – Simple cloud platform with managed Postgres and Redis. Good for straightforward web apps. Less suited for complex multi-service architectures.
- **Railway** – Template-based deployment with managed databases and usage-based pricing. Fast time to first deploy.
- **Fly.io** – Container-based deployment with global edge networking. More control but more configuration is required than the others.

> Most vibe coders hit the deployment gap, not the code generation gap. [Northflank](https://northflank.com/) closes that gap: managed databases, secrets injection, preview environments, sandboxes, and GPU workloads in one platform, without an infrastructure learning curve.
> 
</InfoBox>

## What should you look for in a deployment platform as a vibe coder?

These are the dimensions that matter most when your project needs to move beyond a prototype.

- **Managed databases.** Your app needs somewhere to store data. A platform that provisions a managed Postgres, MySQL, or Redis instance and connects it to your app automatically removes the hardest part of full-stack deployment.
- **Secrets management.** API keys, database credentials, and environment variables should never live in your code. A deployment platform that manages secrets and injects them at runtime is a security baseline, not a premium feature.
- **Preview environments.** Every pull request should spin up an isolated copy of your app and tear it down on merge. This lets you test changes without touching production.
- **CI/CD from Git.** Push to a branch, and the platform builds and deploys automatically.
- **Sandboxes.** If your app executes AI-generated or user-submitted code, you need isolated execution environments with microVM isolation. Not every platform provides this.
- **GPU workloads.** If your app runs inference, fine-tuning, or any ML workload, the deployment platform should support GPUs in the same control plane as your services and databases.
- **Managed or BYOC.** Managed infrastructure is fine early. When compliance requirements emerge or costs need to be controlled, you need the option to run inside your own cloud account.
- **Full-stack scope.** Static sites and serverless functions are not enough for most apps. Background workers, scheduled jobs, long-running services, and managed databases should all be available in the same platform.

## What are the best deployment platforms for vibe coders?

### 1. Northflank

[Northflank](https://northflank.com/product/deployments) handles the full deployment stack for AI-generated code without requiring any infrastructure knowledge. Connect a Git repository, and Northflank detects the framework, builds the application, and deploys it with TLS, environment variables, and health checks configured automatically. Managed databases (PostgreSQL, MySQL, MongoDB, Redis, MinIO, RabbitMQ) provision in minutes and connect to the application via scoped credentials injected through secret groups. Credentials never appear in code or logs.

![northflank-full-homepage.png](https://assets.northflank.com/northflank_full_homepage_7e43a6b554.png)

Preview environments spin up per pull request with isolated database instances and tear down automatically on merge. Background workers, scheduled jobs, and build pipelines run in the same control plane as the main application, so a vibe coder does not need a separate CI/CD tool, database provider, or secrets manager to ship a complete application. BYOC is self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal for projects that grow into systems with compliance or data residency requirements.

**Key features:**

- **Managed databases:** PostgreSQL, MySQL, MongoDB, Redis, MinIO, RabbitMQ, and more. Scoped credentials are injected automatically.
- **Secrets management:** Secret groups inject environment variables at build and runtime. Never stored in code or exposed in logs.
- **Preview environments:** Isolated app and database instances per pull request, torn down on merge.
- **CI/CD:** Automatic builds on push. Build pipelines, release flows, and GitOps sync built in.
- **Full-stack scope:** Services, workers, cron jobs, databases, and GPU workloads in the same control plane.
- **Sandboxes:** Firecracker, Kata Containers, and gVisor microVM isolation for AI agent workloads and untrusted code execution.
- **GPU workloads:** H100, H200, B200, A100, L4, L40S, TPUs, and more, with all-inclusive pricing, running alongside your services in the same control plane.
- **Managed or BYOC:** Northflank's managed cloud or self-serve BYOC into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal.
- **SOC 2 Type 2 certified:** Covers managed cloud and BYOC deployments.

**Best for:** Vibe coders who need production-grade deployment with managed databases, secrets management, and preview environments without writing infrastructure code. Projects that grow beyond prototypes into real production systems.

**Pricing:** Free tier includes two services, one database, and two cron jobs. Paid compute from $0.01667/vCPU-hour and $0.00833/GB-hour. [See full pricing.](https://northflank.com/pricing)

<InfoBox className="BodyStyle">

[Get started on Northflank](https://app.northflank.com/signup) (self-serve, no demo required). Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) with an engineer to walk through your deployment requirements.

</InfoBox>

### 2. Vercel

Vercel is the standard deployment platform for Next.js and React frontends. Git integration handles CI/CD automatically, preview deployments spin up per pull request, and the edge network provides fast global delivery. The ceiling for vibe coders is backend scope: long-running services, background workers, and persistent databases require external providers. Managed Postgres was sunset in 2024 and is now handled through Marketplace integrations like Neon and Supabase.

**Best for:** Vibe coders building Next.js or React frontends and JAMstack applications where the backend is minimal or handled by external APIs.

**Pricing:** Free tier available. Pro from $20/month per user. Enterprise custom.

### 3. Render

Render is a straightforward cloud platform with managed PostgreSQL, Redis, background workers, and static sites. Most frameworks deploy from a Git repository with minimal setup. The constraints appear at scale: pricing charges separately per service and database instance, which adds up faster than usage-based alternatives for multi-service applications. Preview environments are available on Professional plans and above.

**Best for:** Vibe coders building standard web applications with a single service and database, where simplicity matters more than infrastructure flexibility.

**Pricing:** Services from $7/month. Managed Postgres from $7/month. Free tier with limited hours.

### 4. Railway

Railway provides template-based deployment with managed PostgreSQL, MySQL, Redis, and MongoDB alongside resource-based pricing. Most stacks deploy in minutes with minimal configuration. Pricing is transparent but can become unpredictable for applications with variable traffic or multiple services. Preview environments exist but are less mature than on Northflank or Vercel.

**Best for:** Vibe coders who want the fastest path from code to a deployed application with a database.

**Pricing:** Hobby from $5/month plus resource usage. Pro from $20/month.

### 5. Fly.io

Fly.io deploys containerized applications globally using Firecracker microVMs and provides more control over the execution environment than the other platforms here. Any OCI-compliant container image deploys without modification. The tradeoff is configuration complexity: Fly.io requires familiarity with containerization concepts, and Fly Postgres is a self-managed cluster rather than a fully managed service.

**Best for:** Vibe coders comfortable with containerization who need geographic distribution or more infrastructure control than managed platforms provide.

**Pricing:** Free allowances included. Pay-per-second usage-based billing.

## Which platform should you choose?

The decision comes down to how much backend complexity your application needs and how much infrastructure configuration you are willing to handle.

If your application is a Next.js or React frontend with minimal backend, Vercel is the right choice. If you need a service and a database with minimal configuration, Render or Railway get you there fast. If you need the full stack, managed databases, secrets management, preview environments, background workers, Sandboxes, GPUs, and the option to deploy into your own cloud account as your project grows, [Northflank](https://northflank.com/) covers it without requiring you to learn infrastructure to get there.

| Platform | Managed databases | Secrets management | Preview environments | Sandboxes | GPU workloads | BYOC | Full-stack scope |
| --- | --- | --- | --- | --- | --- | --- | --- |
| **Northflank** | Yes (7+ types) | Yes, built-in | Yes, with isolated DBs | Yes (Firecracker, Kata, gVisor) | Yes  | Yes, self-serve | Yes |
| **Vercel** | Via Marketplace (Neon, Supabase, others) | Environment variables | Yes | Yes | No | No | Frontend-focused |
| **Render** | Postgres, Redis | Environment variables | Yes | No | No | No | Yes |
| **Railway** | Postgres, MySQL, Redis, MongoDB | Environment variables | Yes | No | No | No | Yes |
| **Fly.io** | Self-managed Postgres cluster | Secrets via flyctl | Yes | Yes | No | No | Yes |

## FAQ: deployment platforms for vibe coders

### Do I need a deployment platform if my vibe coding tool includes hosting?

Built-in hosting from Lovable, Bolt, or Replit works for prototypes. The limitations appear when you need a real database with your own data, secrets management, environment separation, or pricing that does not lock you into the tool's infrastructure. A separate deployment platform gives you portability and production-grade controls regardless of which AI coding tool generated the code.

### How do I deploy code from Cursor or Claude Code?

Cursor and Claude Code generate code to your local machine or repository. Push to a Git repository and connect it to a deployment platform. Northflank, Vercel, Render, and Railway all connect directly to Git repositories and deploy automatically on push.

### What is the easiest way to add a database to a vibe-coded app?

Use a deployment platform that provisions managed database instances and injects credentials automatically. On Northflank you add a database addon, and the platform creates a scoped user and injects the connection string as an environment variable. No manual database setup or credential management required.

### How do I keep API keys and credentials out of my AI-generated code?

Store API keys and database passwords in your deployment platform's secrets store, not in your application code or repository. Northflank's secret groups inject environment variables at build and runtime. The credentials never appear in the codebase or build logs.

### Can I deploy vibe-coded apps to my own cloud account?

Yes, with Northflank BYOC. Connect your AWS, GCP, Azure, or other cloud account, and Northflank deploys and manages your applications inside your own infrastructure. This matters when projects move from prototypes to production systems with compliance or data residency requirements.

## Conclusion

Vibe coding has removed the code generation barrier. The deployment barrier is what remains for most builders. Built-in hosting handles prototypes but falls short when applications need real databases, secrets management, environment isolation, and production-grade infrastructure.

Northflank removes the deployment barrier without replacing it with an infrastructure learning curve. Managed databases, secrets injection, preview environments, CI/CD, and BYOC deployment in one platform, configured by default, without writing infrastructure code.

<InfoBox className="BodyStyle">

You can [get started for free on Northflank](https://app.northflank.com/signup) or [talk to the team](https://cal.com/team/northflank/northflank-demo?duration=30) to deploy your first vibe-coded app.

</InfoBox>]]>
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</item><item>
  <title>How to run untrusted code on Kubernetes safely</title>
  <link>https://northflank.com/blog/how-to-run-untrusted-code-on-kubernetes</link>
  <pubDate>2026-04-23T14:15:00.000Z</pubDate>
  <description>
    <![CDATA[How to run untrusted code on Kubernetes safely: why standard containers are not enough, how to configure gVisor and Kata Containers via RuntimeClass, and what to apply beyond isolation.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/run_untrusted_code_on_Kubernetes_236e0b4e30.png" alt="How to run untrusted code on Kubernetes safely" />Running untrusted code on Kubernetes is not safe by default. Standard containers share the host kernel, which means a kernel vulnerability in one container can affect the host and every other workload on the same node. For AI-generated code, user-submitted scripts, or any workload where you do not control what executes at runtime, the default Kubernetes container model introduces risk that you need to address explicitly.

This article covers why standard containers are insufficient for untrusted code, what isolation mechanisms Kubernetes provides, how to configure them, and how [Northflank](https://northflank.com/product/sandboxes) removes the operational overhead of running this stack in production.

<InfoBox className="BodyStyle">

## TL;DR: running untrusted code on Kubernetes

- Standard Kubernetes containers share the host kernel via Linux namespaces and cgroups. This is not sufficient for untrusted code execution.
- Kubernetes supports stronger isolation via RuntimeClass: gVisor intercepts syscalls in user space, Kata Containers runs each pod in its own microVM with a dedicated kernel.
- For genuinely untrusted code, Kata Containers with Firecracker or Cloud Hypervisor is the right default. gVisor is appropriate for moderate-trust workloads with lower overhead requirements.
- Configuring and operating microVM isolation on Kubernetes requires significant engineering work. [Northflank](https://northflank.com/product/sandboxes) provides it out of the box, with self-serve BYOC into your existing cluster.

> **What is Northflank?**
[Northflank](https://northflank.com/) runs untrusted code safely on Kubernetes at production scale. Kata Containers, Firecracker, and gVisor isolation applied per workload, managed orchestration, BYOC into your own cluster, and a full-stack control plane including databases and GPU workloads. No months of infrastructure setup required.
>[Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30).

</InfoBox>

## Why standard Kubernetes containers are not safe for untrusted code

Standard Kubernetes containers use Linux namespaces to isolate processes and cgroups to limit resource consumption. These provide workload separation for trusted applications but do not create a hard security boundary between workloads and the host kernel.

When a container runs, its processes issue system calls directly to the host kernel. Every container on the same node shares that kernel. A kernel vulnerability exploited inside a container can allow an attacker to escape the container and access the host, the underlying node, and potentially every other workload running on it. For code you wrote and trust, this risk is manageable with defence-in-depth controls. For code generated by an LLM, submitted by a user, or coming from any external source, this shared kernel model is not an acceptable security boundary.

The additional controls that standard containers provide, seccomp profiles, AppArmor, capability dropping, and read-only root filesystems, reduce the attack surface but do not eliminate the fundamental risk of kernel sharing. They are hardening measures, not isolation boundaries.

## Isolation options for untrusted code on Kubernetes

Kubernetes supports multiple container runtimes via the Container Runtime Interface (CRI). A RuntimeClass resource lets you specify which runtime a pod uses. By changing the runtimeClassName on a pod, you can run it with a different isolation model without changing anything else about your application.

| Runtime | Isolation model | Startup overhead | Best for |
| --- | --- | --- | --- |
| **runc (default)** | Shared host kernel (namespaces, cgroups) | Milliseconds | Trusted internal workloads |
| **gVisor (runsc)** | Syscall interception (user-space kernel) | Low | Moderate-trust workloads, lower overhead |
| **Kata Containers** | Hardware-level (dedicated guest kernel via KVM) | ~200ms | Untrusted code, multi-tenant platforms |
| **Kata + Firecracker** | Hardware-level (KVM, minimal device model) | ~125ms | Production AI sandboxes, high-density untrusted execution |

### gVisor

gVisor intercepts system calls in user space using a component called Sentry. Instead of syscalls reaching the host kernel directly, they are intercepted and reimplemented by gVisor's user-space kernel. This significantly reduces the host kernel attack surface without running a full VM per workload.

gVisor is appropriate for workloads where you want stronger isolation than standard containers but cannot accept the startup overhead of a full microVM. It is not as strong as Kata Containers for genuinely adversarial code: gVisor still shares some host resources and its Sentry process runs on the host. For multi-tenant AI agent workloads where agents execute arbitrary LLM-generated code, Kata Containers provides a stronger boundary.

To use gVisor on Kubernetes:

```yaml
apiVersion: node.k8s.io/v1
kind: RuntimeClass
metadata:
  name: gvisor
handler: runsc
---
apiVersion: v1
kind: Pod
metadata:
  name: untrusted-workload
spec:
  runtimeClassName: gvisor
  containers:
  - name: app
    image: python:3.11-slim
    command: ["python", "-c", "print('Sandboxed')"]
```

### Kata Containers

Kata Containers runs each pod inside a lightweight virtual machine with its own dedicated Linux kernel. From Kubernetes' perspective, it looks like a normal container. Under the hood, every pod runs in its own microVM with hardware-enforced isolation via KVM. A kernel compromise inside the workload stays inside that microVM and cannot reach the host kernel or adjacent workloads.

Kata Containers supports multiple VMM backends: QEMU (maximum hardware compatibility), Cloud Hypervisor (better performance), and Firecracker (minimal overhead, fastest startup). For production untrusted code execution at scale, Firecracker via Kata is the strongest and most efficient option.

To use Kata Containers on Kubernetes:

```yaml
apiVersion: node.k8s.io/v1
kind: RuntimeClass
metadata:
  name: kata
handler: kata-clh  # Cloud Hypervisor backend
---
apiVersion: v1
kind: Pod
metadata:
  name: untrusted-workload
spec:
  runtimeClassName: kata
  containers:
  - name: app
    image: python:3.11-slim
    command: ["python", "-c", "print('Isolated')"]
```

## What you also need beyond RuntimeClass

Changing the RuntimeClass handles the isolation boundary. It does not handle everything else you need to run untrusted code safely in production.

**Network controls:** Untrusted code should not make arbitrary outbound network requests. Apply Kubernetes NetworkPolicies with default-deny egress and whitelist only the endpoints the workload needs to reach. Without this, isolated code can still exfiltrate data or call external APIs.

**Resource limits:** Set CPU, memory, and ephemeral storage limits on every pod running untrusted code. Runaway code can consume unbounded resources and affect adjacent workloads even with microVM isolation. Kubernetes does not apply limits by default.

**Secrets isolation:** Do not mount service account tokens or cluster secrets into pods running untrusted code. Set `automountServiceAccountToken: false` and only inject the specific secrets the workload requires.

**Ephemeral execution:** For workloads where state should not persist between runs, use short-lived pods and enforce restart policies that prevent reuse. Persistent filesystems accumulate state across executions and can carry data from one run into the next.

**Observability:** Log everything: syscall patterns, network connections, resource spikes, and unexpected process spawning. You need to know what untrusted code did before, during, and after execution for security forensics and compliance.

## The operational cost of running this yourself

Installing Kata Containers on a production Kubernetes cluster requires node-level kernel configuration, KVM support on each node, containerd integration via containerd-shim-kata-v2, and RuntimeClass configuration. You also need to configure networking for microVMs (typically with CNI plugins that support VM-level networking), manage kernel images for guest VMs, handle security patching for both the host kernel and the guest kernel images, and monitor the health of the microVM layer.

Most teams spend two to four months building and validating this stack before running their first production workload. Ongoing operational burden adds further engineering overhead for patching, scaling, and incident response. That is engineering time not spent on the product.

## How Northflank runs untrusted code on Kubernetes

[Northflank](https://northflank.com/product/sandboxes) provides production-grade untrusted code execution on Kubernetes without the setup and maintenance overhead. Kata Containers with Cloud Hypervisor, Firecracker, and gVisor are all available, applied per workload based on your threat model. Every sandbox runs in its own microVM with a dedicated kernel. Network controls, resource limits, secrets management, and observability are built in.

For enterprises already on Kubernetes, Northflank BYOC deploys into your existing EKS, GKE, AKS, or bare-metal cluster self-serve. Northflank manages the isolation layer and orchestration on your cluster while your data never leaves your own VPC. You keep your Kubernetes investment and compliance posture while gaining production-grade microVM isolation without months of infrastructure work.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

[cto.new migrated their entire untrusted code execution infrastructure to Northflank](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) in two days and went from unworkable provisioning to thousands of daily sandbox deployments with linear, per-second billing. That is what production untrusted code execution looks like when you do not build the isolation layer yourself.

[Get started on Northflank](https://app.northflank.com/signup) (self-serve, no demo required). Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to walk through your isolation requirements.

## FAQ: running untrusted code on Kubernetes

### Are standard Kubernetes containers safe for untrusted code?

No. Standard containers share the host kernel. A kernel vulnerability exploited inside a container can allow an attacker to escape to the host. For untrusted code from external users, AI agents, or any source you do not control, you need gVisor or Kata Containers to add a kernel-level isolation boundary.

### What is the difference between gVisor and Kata Containers for untrusted code?

gVisor intercepts syscalls in user space and reimplements a subset of the Linux kernel, reducing direct interaction with the host kernel without running a full VM per workload. Kata Containers runs each pod in its own microVM with a dedicated guest kernel, enforcing hardware-level isolation via KVM. For genuinely adversarial untrusted code, Kata Containers provides a stronger boundary. gVisor is appropriate when the threat model does not require full hardware isolation and lower overhead matters more.

### How do I configure Kata Containers on my Kubernetes cluster?

You need KVM support on each node, the containerd-shim-kata-v2 binary installed, a RuntimeClass resource configured with the Kata handler, and containerd configured to route pods with that RuntimeClass to the Kata shim. You also need to manage guest kernel images and configure networking. Northflank handles all of this if you want to skip the setup.

### What network controls should I apply to untrusted code on Kubernetes?

Apply a default-deny egress NetworkPolicy and whitelist only the specific endpoints the workload needs. Set `automountServiceAccountToken: false` to prevent access to the Kubernetes API. Use read-only root filesystems where possible. These controls supplement the isolation boundary and prevent exfiltration even when microVM isolation is in place.

### Can I mix trusted and untrusted workloads on the same Kubernetes cluster?

Yes. RuntimeClass lets you run trusted workloads with the default runc runtime and untrusted workloads with Kata Containers or gVisor on the same cluster. Node taints and affinities can further restrict which nodes handle untrusted workloads if your security posture requires physical separation.

### Does Northflank support BYOC for untrusted code execution on my existing Kubernetes cluster?

Yes. Northflank BYOC deploys into your existing EKS, GKE, AKS, or bare-metal Kubernetes cluster self-serve. Northflank manages the sandbox infrastructure layer on your cluster including Kata Containers, Firecracker, and gVisor isolation. Your data never leaves your own VPC.

## Conclusion

Running untrusted code on Kubernetes safely requires going beyond the default container model. You need a runtime that enforces a kernel-level isolation boundary between the workload and the host, network controls that prevent exfiltration, resource limits that contain runaway code, and observability that tells you what happened. Configuring and operating that stack in production is a multi-month engineering effort.

[Northflank](https://northflank.com/) provides it out of the box. Production-grade microVM isolation on Kubernetes, self-serve BYOC into your existing cluster, full-stack scope including databases and GPU workloads, and no months of infrastructure work to get there. The teams running untrusted code on Northflank did not spend that time on isolation infrastructure. They shipped.

<InfoBox className="BodyStyle">

[Sign up for free](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to see how Northflank handles untrusted code execution on your Kubernetes infrastructure.

</InfoBox>

## Related articles

- [**Sandboxes on Kubernetes: isolation options and how to run them in production**](https://northflank.com/blog/sandboxes-on-kubernetes): Covers the broader landscape of running AI agent sandboxes on Kubernetes including the Agent Sandbox CRD project.
- [**Best platforms for untrusted code execution in 2026**](https://northflank.com/blog/best-platforms-for-untrusted-code-execution): How to choose a sandbox platform when isolation model determines security outcomes.
- [**Kata Containers vs Firecracker vs gVisor**](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): A comparison of isolation technologies covering security model, performance, and when to use each.
- [**What is sandbox infrastructure?**](https://northflank.com/blog/sandbox-infrastructure): The full stack required to run isolated workloads safely at scale, beyond the isolation layer itself.]]>
  </content:encoded>
</item><item>
  <title>Sandboxes on Kubernetes: isolation options and how to run them in production</title>
  <link>https://northflank.com/blog/sandboxes-on-kubernetes</link>
  <pubDate>2026-04-22T10:45:00.000Z</pubDate>
  <description>
    <![CDATA[Sandboxes on Kubernetes: why standard containers are not enough for AI agents, isolation options with Kata Containers and gVisor, and how to run them in production.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Sandboxes_on_Kubernetes_2410868413.png" alt="Sandboxes on Kubernetes: isolation options and how to run them in production" />Most enterprises already run Kubernetes. When they need to run AI agents that execute untrusted code, the question is not whether to use Kubernetes; it is how to add the isolation, lifecycle management, and security controls that standard Kubernetes primitives do not provide out of the box.

This article covers why running sandboxes on Kubernetes requires more than a Pod manifest, what options exist today, and how [Northflank](https://northflank.com/) provides production-grade sandbox infrastructure on top of Kubernetes without the operational overhead of building it yourself.

<InfoBox className="BodyStyle">

## TL;DR: AI agent sandboxes on Kubernetes

- Standard Kubernetes containers share the host kernel. For untrusted code execution, this is not sufficient. You need Kata Containers or gVisor applied via RuntimeClass.
- Raw Kubernetes primitives (Deployments, StatefulSets, Pods) do not map cleanly to AI agent workload patterns: stateful, singleton, idle-heavy, with lifecycle controls like pause and resume.
- The Kubernetes Agent Sandbox project (kubernetes-sigs/agent-sandbox) is a new CRD and controller that fills this gap with a declarative API for isolated, stateful, singleton workloads.
- [Northflank](https://northflank.com/product/sandboxes) provides production-grade sandbox infrastructure built on Kubernetes, with Kata Containers, Firecracker, and gVisor isolation, managed orchestration, and self-serve BYOC into your existing AWS, GCP, Azure, or on-premises Kubernetes clusters.

> **What is Northflank?**
[Northflank](https://northflank.com/) is a full-stack cloud platform that runs production-grade sandbox infrastructure on Kubernetes. If your enterprise already runs on Kubernetes and needs AI agent isolation without building the stack yourself, that is exactly what Northflank provides. Kata Containers, Firecracker, gVisor, managed orchestration, BYOC into your own cluster, and a full-stack control plane including databases and GPU workloads. [Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30).
> 
</InfoBox>

## Why standard Kubernetes containers are not enough for AI agent sandboxes

Standard Kubernetes containers share the host kernel. Every container on a node issues system calls to the same Linux kernel. A kernel vulnerability in one container can affect the host and every other container on the same node. For trusted internal workloads where you control what code runs, this is acceptable. For AI agent workloads where the agent generates and executes code at runtime, it is not.

AI agents also do not map cleanly to existing Kubernetes workload types. A Deployment manages replicated, stateless pods. A StatefulSet manages numbered, stable pods in a set. An AI agent runtime is typically a singleton: one isolated environment per user session or task, mostly idle, needing persistent state, a stable identity, and the ability to pause and resume without losing context. Approximating this with a StatefulSet of size 1 plus a headless Service plus a PersistentVolumeClaim works at a small scale but becomes an operational problem at hundreds or thousands of concurrent agents.

## Isolation options for sandboxes on Kubernetes

Kubernetes supports multiple container runtimes via the Container Runtime Interface (CRI). By configuring a RuntimeClass, you can run pods with different isolation backends without changing your application code or manifests.

| Runtime | Isolation model | How it works on Kubernetes | Best for |
| --- | --- | --- | --- |
| **Standard containers (runc)** | OS-level (namespaces, cgroups) | Default runtime, shared host kernel | Trusted internal workloads |
| **gVisor (runsc)** | Syscall interception (user-space kernel) | RuntimeClass `gvisor`, intercepts syscalls before they reach the host kernel | Moderate-trust workloads, lower overhead than microVMs |
| **Kata Containers** | Hardware-level (KVM hypervisor) | RuntimeClass `kata`, each pod runs in its own microVM with a dedicated kernel | Untrusted code, multi-tenant AI agents |
| **Firecracker via Kata** | Hardware-level (KVM hypervisor) | Kata with Firecracker VMM backend, faster startup and lower overhead than QEMU | Production AI sandboxes at scale |

For multi-tenant AI agent workloads where agents execute LLM-generated code, Kata Containers or Firecracker is the right default. gVisor is appropriate when full VM overhead is not justified, and the threat model does not require hardware kernel isolation.

## The Kubernetes Agent Sandbox project

The [Agent Sandbox project](https://github.com/kubernetes-sigs/agent-sandbox) (kubernetes-sigs/agent-sandbox) is an open-source Kubernetes controller and set of CRDs developed under SIG Apps. It introduces a declarative API specifically designed for the workload pattern that AI agents require: stateful, singleton, idle-heavy environments with stable identity and lifecycle controls.

The project introduces three core resources:

- **Sandbox CRD** – A single, stateful pod with a stable hostname and network identity, persistent storage that survives restarts, and lifecycle controls covering creation, scheduled deletion, pausing, and resuming.
- **SandboxTemplate** – Reusable templates for creating Sandboxes with predefined security contexts, resource limits, and runtime configurations. Defines guardrails as code.
- **SandboxWarmPool** – A pool of pre-warmed Sandbox pods. When a new sandbox is requested, the controller claims from the warm pool rather than creating one from scratch, eliminating cold start latency.

The project supports gVisor and Kata Containers as isolation backends, configured via runtimeClassName. It is designed to be backend-agnostic. As of April 2026, the project is in active development and is not yet production-ready for all workloads.

## What the Agent Sandbox project does not solve

The Agent Sandbox project provides a better abstraction layer on top of Kubernetes primitives. It does not replace the operational burden of running the stack underneath it. You still need to configure and maintain Kata Containers or gVisor on your cluster, manage the RuntimeClass configurations, operate the controller, handle networking policies, manage secrets injection, wire in observability, and deal with the operational complexity of running microVMs on Kubernetes at scale.

For platform teams that want to adopt the Agent Sandbox abstraction but do not have the capacity to build the isolation layer underneath it, the gap between the CRD and a production deployment remains significant.

## How Northflank runs sandboxes on Kubernetes

[Northflank](https://northflank.com/product/sandboxes) provides the full sandbox infrastructure stack on top of Kubernetes, including the isolation layer, orchestration, networking, secrets management, and observability that enterprises need to run AI agent sandboxes in production.

Northflank supports Kata Containers with Cloud Hypervisor, Firecracker, and gVisor per workload. Every sandbox runs in its own microVM with a dedicated kernel. The orchestration, bin-packing, autoscaling, and microVM lifecycle management are handled by the platform. You get the security model of the Agent Sandbox project without building and operating the underlying infrastructure stack yourself.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

For enterprises already on Kubernetes, Northflank BYOC deploys the platform into your existing EKS, GKE, AKS, or bare-metal Kubernetes cluster self-serve. Northflank manages the sandbox infrastructure layer on your cluster while your data never leaves your own VPC. This means you keep your existing Kubernetes investment, your compliance posture, and your cloud billing relationships while gaining production-grade sandbox isolation without the engineering overhead.

[cto.new migrated their entire sandbox infrastructure to Northflank](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) in two days and went from unworkable provisioning to thousands of daily deployments for untrusted code with linear, per-second billing. That is what production sandbox infrastructure on Kubernetes looks like when you do not build it yourself.

**Pricing:** $0.01667/vCPU-hour and $0.00833/GB-hour, billed per second. BYOC deployments bill against your own cloud account.

<InfoBox className="BodyStyle">

[Get started on Northflank](https://app.northflank.com/signup) (self-serve, no demo required). Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to walk through how Northflank fits your Kubernetes environment.

</InfoBox>

## FAQ: AI agent sandboxes on Kubernetes

### Why can't I just use a Kubernetes Pod for each AI agent sandbox?

A standard Pod uses container isolation, which shares the host kernel. For AI agents that execute untrusted or LLM-generated code, kernel sharing introduces security risk. You also need a stable identity, persistent storage, lifecycle controls like pause and resume, and management at scale, none of which a raw Pod provides cleanly.

### How do I add microVM isolation to my existing Kubernetes cluster?

Install Kata Containers on your cluster and create a RuntimeClass resource that references the Kata handler. Pods that specify that RuntimeClass will run inside a Kata microVM with a dedicated kernel. gVisor follows the same pattern using the runsc runtime. Both require node-level configuration and ongoing maintenance.

### Does Northflank work with my existing Kubernetes cluster?

Yes. Northflank BYOC deploys into your existing EKS, GKE, AKS, or bare-metal Kubernetes cluster, self-serve. Northflank manages the sandbox infrastructure layer on your cluster while your data stays in your own VPC.

### What isolation does Northflank use for AI agent sandboxes on Kubernetes?

Northflank supports Kata Containers with Cloud Hypervisor, Firecracker, and gVisor, applied per workload based on your threat model. Every sandbox runs in its own microVM with a dedicated kernel. You choose the isolation model. Northflank handles the configuration and operational complexity underneath.

### Can I run databases and APIs alongside my sandboxes on Northflank?

Yes. Northflank runs sandboxes alongside managed databases (PostgreSQL, MySQL, MongoDB, Redis), background workers, APIs, CI/CD pipelines, and GPU workloads in the same control plane. You do not need a separate infrastructure stack for each workload type.

## Conclusion

Kubernetes is the right foundation for running AI agent sandboxes at enterprise scale. It provides scheduling, networking, storage orchestration, and horizontal scalability. What it does not provide out of the box is the isolation model, lifecycle management, and operational tooling that AI agent workloads specifically require.

The Kubernetes Agent Sandbox project is moving in the right direction by formalizing the workload abstraction. But the operational gap between installing the CRD and running production sandbox infrastructure at scale is real. Northflank closes that gap. You get production-grade microVM isolation on top of Kubernetes, self-serve BYOC into your existing cluster, and a full-stack control plane, without spending months building and maintaining the infrastructure layer yourself.

<InfoBox className="BodyStyle">

[Sign up for free](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to see how Northflank runs AI agent sandboxes on your Kubernetes infrastructure.

</InfoBox>

## Related articles

- [**Agent Sandbox on Kubernetes: how it works and how to run it in production**](https://northflank.com/blog/agent-sandbox-on-kubernetes): A detailed look at the kubernetes-sigs/agent-sandbox project, its CRDs, isolation backends, and operational reality.
- [**How to sandbox AI agents: microVMs, gVisor, and isolation strategies**](https://northflank.com/blog/how-to-sandbox-ai-agents): How to choose the right isolation technology for AI agent workloads based on threat model and performance requirements.
- [**Kata Containers vs Firecracker vs gVisor**](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): A comparison of microVM and isolation technologies covering security model, performance, and when to use each on Kubernetes.
- [**What is sandbox infrastructure?**](https://northflank.com/blog/sandbox-infrastructure): The full stack required to run isolated workloads safely at scale, beyond the isolation layer itself.]]>
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</item><item>
  <title>KVM vs QEMU: key differences and how they work together</title>
  <link>https://northflank.com/blog/kvm-vs-qemu</link>
  <pubDate>2026-04-20T17:30:00.000Z</pubDate>
  <description>
    <![CDATA[KVM vs QEMU: key differences, how they work together, and how both underpin Firecracker and Kata Containers for production microVM isolation.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Firecracker_vs_Docker_1_6b903cec05.png" alt="KVM vs QEMU: key differences and how they work together" />[KVM](https://northflank.com/blog/what-is-kvm) and QEMU are two of the most widely used open-source virtualisation technologies on Linux. If you have searched for the difference between them, you have probably found conflicting explanations. Some articles treat them as competing alternatives. They are not.

KVM and QEMU serve different roles in the virtualisation stack and are almost always used together. Understanding what each one does, where one ends and the other begins, and how they relate to modern isolation technologies like Firecracker and Kata Containers is what this article covers.

## TL;DR: KVM vs QEMU

|  | KVM | QEMU | Northflank |
| --- | --- | --- | --- |
| **Type** | Linux kernel module (Type 1 hypervisor) | User-space emulator and virtualizer | Full-stack cloud platform |
| **Role** | Hardware-accelerated CPU virtualization | Device emulation, machine abstraction, VM management | Managed microVM orchestration on top of KVM |
| **Runs in** | Kernel space | User space | Managed cloud or your own infrastructure (BYOC) |
| **Hardware required** | Intel VT-x or AMD-V | Not required (slower without) | Handled by the platform |
| **Performance** | Near-native with hardware extensions | Slow alone, near-native with KVM | Production-grade, near-native |
| **Cross-architecture** | No (same architecture only) | Yes (x86, ARM, RISC-V, PowerPC, and more) | Linux x86/ARM workloads |
| **Used for** | Firecracker, Kata Containers, cloud VMs | Traditional VMs, embedded dev, cross-arch testing | AI sandboxes, untrusted code execution, multi-tenant platforms |
| **Setup required** | Kernel configuration | VMM integration and device configuration | None — self-serve in minutes |

<InfoBox className="BodyStyle">

**What is Northflank?**

[Northflank](https://northflank.com/) is a full-stack cloud platform that runs microVM-backed workloads using Firecracker and Kata Containers, both of which are built on KVM. If you need production-grade isolation for AI agents, untrusted code execution, or multi-tenant workloads without managing the underlying virtualisation stack yourself, Northflank handles it.

[Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30).

</InfoBox>

## What is KVM?

[KVM](https://northflank.com/blog/what-is-kvm), or Kernel-based Virtual Machine, is a Linux kernel module that turns the Linux operating system into a Type 1 hypervisor. It was merged into the Linux kernel in version 2.6.20 and is now part of mainline Linux. KVM uses hardware virtualisation extensions built into modern CPUs, specifically Intel VT-x and AMD-V, to allow virtual machines to run with near-native performance.

KVM does not run virtual machines by itself. It exposes the hardware virtualisation capabilities of the CPU to user-space programs. What it provides is a set of kernel interfaces that allow a VMM (Virtual Machine Monitor) like QEMU or Firecracker to use hardware-level CPU isolation for each VM. Without a user-space program on top, KVM does nothing visible.

### What KVM provides

- Kernel-level hardware virtualisation using Intel VT-x or AMD-V
- Near-native CPU performance for virtual machines
- Memory isolation between VMs is enforced by the hardware
- The foundation that QEMU, Firecracker, and Kata Containers build on

### What KVM does not provide

- Device emulation (no network, disk, or display)
- A user interface or management layer
- Cross-architecture support (KVM requires host and guest to share the same CPU architecture)

## What is QEMU?

QEMU, or Quick Emulator, is an open-source machine emulator and virtualiser. It emulates complete computer systems, including CPU, memory, disk, network, and other hardware devices entirely in software. This means QEMU can run a guest operating system designed for ARM on an x86 host, or emulate a RISC-V system on AMD hardware, without any modification to the guest.

When QEMU runs without KVM, all CPU instructions are translated in software using its internal Tiny Code Generator (TCG). This is extremely flexible but very slow. When QEMU runs with KVM, it offloads CPU virtualisation to the kernel module and uses hardware acceleration, reducing overhead to near-native levels. QEMU handles everything KVM cannot: device emulation, disk I/O, networking, display output, and VM lifecycle management.

### What QEMU provides

- Full system emulation, including CPU, memory, disk, and network
- Cross-architecture support for development and testing
- Device emulation via VirtIO paravirtualized drivers for near-native I/O performance
- Snapshotting, live migration, and state save/restore
- The user-space component that makes KVM usable in practice

### What QEMU does not provide on its own

- Near-native performance without KVM or another hardware accelerator
- Kernel-level security boundaries between guests

## How KVM and QEMU work together

When you run a virtual machine with QEMU and KVM enabled, QEMU provides the device emulation and machine abstraction, while KVM handles CPU and memory virtualisation using hardware extensions. The result is a fully functional virtual machine with near-native CPU performance and complete hardware device support.

The typical stack looks like this: physical host CPU with Intel VT-x or AMD-V, Linux kernel with the KVM module loaded, QEMU running in user space as the VMM, guest operating system running inside the VM. KVM enforces the hardware boundary between the guest and the host kernel. QEMU manages everything the guest sees as its hardware.

This combination is what most production hypervisors use under the hood. libvirt, Proxmox, and OpenStack all manage QEMU/KVM virtual machines at scale.

## How Firecracker relates to KVM and QEMU

Firecracker is a purpose-built VMM developed by AWS as an alternative to QEMU. Like QEMU, it runs in user space and uses KVM for hardware-accelerated CPU virtualisation. Unlike QEMU, Firecracker strips out all non-essential device emulation: no USB, no graphics, no BIOS, no ACPI tables. What remains is a minimal VMM that boots a microVM in approximately 125ms with less than 5 MiB of memory overhead.

The tradeoff is that Firecracker's minimal device model makes it less flexible than QEMU but significantly faster and more secure for specific workloads. QEMU supports hundreds of devices and dozens of CPU architectures. Firecracker supports Linux guests only and emulates four devices. For serverless functions, AI sandbox execution, and multi-tenant code execution where boot speed and isolation matter more than device flexibility, Firecracker is the right VMM. For development environments, full system emulation, and cross-architecture testing, QEMU is the right tool.

|  | QEMU | Firecracker |
| --- | --- | --- |
| **Uses KVM** | Yes (optional) | Yes (required) |
| **Device emulation** | Full (USB, graphics, BIOS, ACPI) | Minimal (4 devices) |
| **Cross-architecture** | Yes | No (Linux x86/ARM only) |
| **Startup time** | Seconds | ~125ms |
| **Memory overhead** | Hundreds of MB | Less than 5 MiB |
| **Best for** | Development, testing, full VMs | Serverless, sandboxes, multi-tenant isolation |

## How Kata Containers uses KVM and QEMU

Kata Containers is a container runtime that provides VM-level isolation through standard container APIs. Each container runs in its own microVM with a dedicated kernel. Kata Containers supports multiple VMM backends: QEMU (default for maximum hardware compatibility), Cloud Hypervisor (better performance), and Firecracker (minimal overhead, fastest startup).

When Kata Containers uses QEMU as its VMM, each container gets a full QEMU/KVM virtual machine as its execution environment. From Kubernetes' perspective, it looks like a normal container. Under the hood, it has its own kernel and hardware isolation. Northflank uses Kata Containers with Cloud Hypervisor as its default microVM backend, with Firecracker and gVisor also available per workload.

## How Northflank uses KVM-based isolation

Running Firecracker and Kata Containers at production scale requires kernel configuration, VMM integration, network setup, orchestration, and ongoing maintenance. Most teams that attempt to build this stack from scratch spend months before running their first workload in production.

[Northflank](https://northflank.com/product/sandboxes) provides production-ready microVM isolation built on top of KVM, via Kata Containers with Cloud Hypervisor, Firecracker, and gVisor, applied per workload based on your threat model. You choose the isolation model. Northflank handles the kernel configuration, VMM lifecycle, orchestration, networking, and observability. Sandboxes run alongside managed databases, background workers, APIs, and GPU workloads in the same control plane.

[cto.new migrated their entire sandbox infrastructure to Northflank](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) in two days and went from unworkable provisioning to thousands of daily deployments for untrusted code with linear, per-second billing. That is what production KVM-based isolation looks like when you do not build it yourself.

<InfoBox className="BodyStyle">

[Get started on Northflank](https://app.northflank.com/signup) (self-serve, no demo required). Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to walk through your isolation requirements.

</InfoBox>

## FAQ: KVM vs QEMU

### What does Northflank use: KVM, QEMU, or Firecracker?

Northflank uses Kata Containers with Cloud Hypervisor as its default microVM backend, with Firecracker also available. Both use KVM for hardware-accelerated isolation. gVisor (user-space kernel interception, no KVM required) is also available for workloads where full microVM overhead is not needed.

### Are KVM and QEMU the same thing?

No. KVM is a Linux kernel module that provides hardware-accelerated CPU virtualisation. QEMU is a user-space emulator that handles device emulation and VM management. They are used together: QEMU uses KVM to accelerate CPU virtualisation while handling everything else itself.

### Can QEMU run without KVM?

Yes. QEMU runs in full software emulation mode without KVM using its Tiny Code Generator (TCG). This supports cross-architecture emulation (running ARM on x86, for example) but is significantly slower than hardware-accelerated virtualisation.

### Can KVM run without QEMU?

KVM is a kernel module that exposes hardware virtualisation interfaces. It requires a user-space VMM to actually run VMs. QEMU is the most common VMM used with KVM, but Firecracker and Cloud Hypervisor are alternatives that also use KVM.

### What is the difference between Firecracker and QEMU?

Both are VMMs that use KVM for CPU virtualisation. QEMU is a full-featured emulator supporting many device types and CPU architectures. Firecracker is a minimal VMM that removes all non-essential devices for maximum startup speed and minimal attack surface. Firecracker boots microVMs in ~125ms with less than 5 MiB overhead. QEMU boots full VMs in seconds with much higher overhead.

### When should I use QEMU vs Firecracker for sandbox workloads?

Use Firecracker for production sandbox workloads where startup speed, density, and minimal attack surface matter: AI agent execution, serverless functions, and multi-tenant code execution. Use QEMU when you need full hardware emulation, cross-architecture support, or broad device compatibility: development environments, firmware testing, legacy OS support.

## Conclusion

KVM and QEMU are not competing technologies. KVM is the kernel module that provides hardware-accelerated CPU virtualisation. QEMU is the user-space emulator that builds a complete virtual machine on top of it. Together, they form the foundation of most Linux virtualisation, and both underpin modern microVM technologies like Firecracker and Kata Containers.

For production workloads that need microVM isolation, the question is not KVM vs QEMU but which VMM to run on top of KVM, and whether you want to build and maintain that stack yourself. Northflank provides production-ready KVM-based isolation via Kata Containers, Firecracker, and gVisor without the infrastructure overhead.

<InfoBox className="BodyStyle">

[Sign up for free](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to see how Northflank handles microVM isolation for your workloads.

</InfoBox>

## Related articles

- [**What is AWS Firecracker?**](https://northflank.com/blog/what-is-aws-firecracker): A deep dive into how Firecracker works, its architecture, and why AWS built it on top of KVM for Lambda and Fargate.
- [**Kata Containers vs Firecracker vs gVisor**](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): A comparison of microVM and isolation technologies covering security model, performance, and when to use each.
- [**Firecracker vs Docker: key differences and when to use each**](https://northflank.com/blog/firecracker-vs-docker): A direct comparison of Docker containers and Firecracker microVMs on isolation, security, and use case fit.
- [**Containers vs virtual machines: key differences and when to use each**](https://northflank.com/blog/containers-vs-virtual-machines): The broader comparison covering containers, VMs, and microVMs in context.]]>
  </content:encoded>
</item><item>
  <title>What is KVM?</title>
  <link>https://northflank.com/blog/what-is-kvm</link>
  <pubDate>2026-04-17T14:15:00.000Z</pubDate>
  <description>
    <![CDATA[KVM (Kernel-based Virtual Machine) is a Linux kernel module that enables hardware-enforced virtualisation. Learn how KVM works, how it powers microVMs and sandboxes, and how it differs from VMware and QEMU.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/what_is_kvm_94a3644034.png" alt="What is KVM?" />KVM, or Kernel-based Virtual Machine, is a virtualisation module built into the Linux kernel that lets a Linux host run multiple isolated virtual machines. It uses CPU hardware virtualisation extensions, Intel VT-x or AMD-V, to enforce isolation between VMs at the hardware level, making it the foundation that technologies like Firecracker, QEMU, and Cloud Hypervisor build on.

This article covers how KVM works, what it is and is not, how it relates to microVMs and container sandboxing, and where it fits in the broader virtualisation stack. If you are looking for KVM switches (Keyboard, Video, Mouse hardware), that is a different technology entirely.

<InfoBox className="BodyStyle">

## TL;DR: What is KVM?

- KVM (Kernel-based Virtual Machine) is a Linux kernel module that exposes CPU hardware virtualisation extensions to user-space processes, enabling a Linux host to run isolated virtual machines
- It has been part of the mainline Linux kernel since version 2.6.20, merged in 2007, and requires Intel VT-x or AMD-V hardware support on the host CPU
- KVM is the virtualisation layer that Firecracker, QEMU, Cloud Hypervisor, and gVisor's KVM mode all build on
- Understanding KVM matters if you are running microVMs, building sandboxes, or evaluating isolation technologies for untrusted workloads

> [Northflank](https://northflank.com/) is a full-stack cloud platform that runs microVM-backed sandboxes using KVM-based technologies including Kata Containers, Firecracker, and Cloud Hypervisor, alongside gVisor for syscall-interception isolation. In production since 2021 across startups, public companies, and government deployments. [Get started (self-serve)](https://app.northflank.com/signup) or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) for specific infrastructure or compliance requirements.
> 

</InfoBox>

## What is KVM?

KVM is a full virtualisation solution built into the Linux kernel. It exposes hardware virtualisation capabilities (Intel VT-x on Intel processors, AMD-V on AMD processors) to processes running in user space. Any software that needs to create and manage virtual machines on Linux uses KVM as the underlying mechanism.

When KVM is loaded, the Linux host effectively becomes a hypervisor. Each virtual machine runs as a regular Linux process, but with its own virtualised CPU, memory, network interface, and storage. The hardware enforces isolation between VMs, so one VM cannot access the memory or resources of another.

KVM consists of two kernel modules: `kvm.ko`, which provides the core virtualisation infrastructure, and a processor-specific module, either `kvm-intel.ko` or `kvm-amd.ko` depending on the host CPU. Both are included in the mainline Linux kernel.

## What is the difference between a Type 1 and Type 2 hypervisor?

Hypervisors are commonly categorised as Type 1 (bare-metal) or Type 2 (hosted).

A Type 2 hypervisor runs on top of an existing operating system as an application. VirtualBox and VMware Workstation are Type 2. They are easy to install but add an extra software layer between the VM and the hardware, which increases overhead.

A Type 1 hypervisor runs directly on hardware without a general-purpose OS underneath. VMware ESXi and Xen are Type 1. They have lower overhead and are the standard for production virtualisation.

KVM blurs this distinction. It runs inside the Linux kernel, which means the host OS and the hypervisor are the same thing. When KVM is loaded, Linux itself becomes a Type 1 hypervisor. This is why KVM is sometimes described as a Type 1.5 hypervisor: it has the performance characteristics of bare-metal virtualisation while still running on a general-purpose OS.

## How does KVM work?

KVM works by exposing CPU hardware virtualisation extensions as file descriptors that user-space programs can interact with. The primary interface is `/dev/kvm`, a character device that a VMM (Virtual Machine Monitor) opens to create and manage VMs.

Here is the sequence at a high level:

**1. The VMM opens `/dev/kvm`:** A user-space program like QEMU, Firecracker, or Cloud Hypervisor opens this device to access KVM.

**2. The VMM creates a VM:** An ioctl call to `/dev/kvm` creates a new VM file descriptor.

**3. Virtual CPUs are created:** The VMM creates one or more vCPUs for the VM, each represented as a file descriptor.

**4. Memory is mapped:** The VMM maps guest physical memory into the process's address space.

**5. The vCPU enters guest mode:** The VMM issues a run ioctl and the CPU switches from host mode to guest mode, executing the guest code directly on the hardware.

**6. VM exits:** When the guest needs something it cannot handle alone (a device access, a privileged instruction), the CPU exits back to host mode, and the VMM handles the request before re-entering guest mode.

The guest code runs directly on the CPU hardware during step 5, which is why KVM delivers near-native performance. The VM exit mechanism is how isolation is enforced: the guest cannot access host resources directly.

## KVM vs other virtualisation approaches

|  | KVM | VMware ESXi | VirtualBox | Xen |
| --- | --- | --- | --- | --- |
| **Type** | Type 1 (in-kernel) | Type 1 (bare-metal) | Type 2 (hosted) | Type 1 (bare-metal) |
| **License** | Open source (GPL) | Commercial | Open source / Commercial | Open source (GPL) |
| **Host OS required** | Linux | No | Yes | No (Dom0 Linux) |
| **Performance** | Near-native | Near-native | Higher overhead | Near-native |
| **MicroVM support** | Yes (via Firecracker, CLH) | Limited | No | Limited |
| **Primary use case** | Cloud, servers, microVMs | Enterprise virtualisation | Desktop development | Cloud, servers |

KVM's open-source licence and inclusion in the Linux kernel make it the dominant virtualisation layer in cloud infrastructure. Most major cloud providers run their virtualisation stack on top of KVM or KVM-derived technology.

## Why does KVM matter for microVMs and container sandboxing?

This is where KVM becomes directly relevant to modern container security and AI workload isolation.

Standard containers share the host kernel. If a workload exploits a kernel vulnerability, it can affect the host and every other container on it. MicroVMs solve this by giving each workload its own dedicated kernel, and KVM is the enforcement layer that makes that boundary hardware-enforced rather than software-enforced.

Every major microVM technology uses KVM:

- **Firecracker:** uses KVM to create microVMs with approximately 125ms boot time and less than 5 MiB of memory overhead per instance. AWS Lambda and Fargate run on Firecracker. See [What is AWS Firecracker?](https://northflank.com/blog/what-is-aws-firecracker)
- **Cloud Hypervisor:** uses KVM to run cloud-optimised VMs with support for GPU passthrough and live migration
- **QEMU:** uses KVM for accelerated virtualisation when hardware extensions are available
- **gVisor's KVM mode:** uses KVM to intercept syscalls with better performance than its Systrap mode, without booting a full guest OS per workload. See [What is gVisor?](https://northflank.com/blog/what-is-gvisor)
- **Kata Containers:** orchestrates Firecracker, Cloud Hypervisor, or QEMU on top of KVM to bring microVM isolation to Kubernetes workloads. See [What is a microVM?](https://northflank.com/blog/what-is-a-microvm)

Without KVM on the host, none of these technologies can run. KVM is the hardware isolation primitive everything else builds on.

## What are KVM's requirements?

KVM requires the following to run:

- **A Linux host:** KVM is a Linux kernel module. It does not run on Windows or macOS hosts natively.
- **Hardware virtualisation support:** The host CPU must support Intel VT-x or AMD-V. Most server and desktop CPUs manufactured in the past decade include these extensions, but they may need to be enabled in the BIOS/UEFI.
- **Kernel version 2.6.20 or later:** KVM has been in the mainline Linux kernel since 2007, so this is satisfied by any modern Linux distribution.
- **Loaded kernel modules:** The `kvm.ko` and processor-specific modules must be loaded. Most distributions load them automatically if the CPU supports virtualisation.

For cloud environments where you are running VMs inside VMs, for example, running Firecracker microVMs on a cloud instance, the host cloud provider must expose hardware virtualisation to the guest instance. This is called nested virtualisation and not all cloud providers or instance types support it.

## What are KVM's limitations?

- **Linux only:** KVM is a Linux kernel feature. Running it on non-Linux hosts requires additional layers that negate most of its advantages.
- **Requires hardware virtualisation:** Hosts without Intel VT-x or AMD-V cannot use KVM. In environments where nested virtualisation is unavailable, technologies like gVisor's Systrap mode provide an alternative isolation approach without requiring KVM.
- **User-space tooling required:** KVM itself is just a kernel module. A VMM like QEMU, Firecracker, or Cloud Hypervisor is needed to actually create and manage VMs. KVM alone does not give you a usable virtualisation environment.
- **Operational complexity at scale:** Managing many KVM-based VMs in production requires orchestration tooling. Most teams use Kata Containers to abstract this complexity in Kubernetes environments.

## How does Northflank use KVM?

Northflank's [sandbox infrastructure](https://northflank.com/product/sandboxes) uses Kata Containers with Cloud Hypervisor as the primary VMM, with Firecracker applied for workloads that benefit from its minimal device model. gVisor is applied where syscall-interception isolation is sufficient or where nested virtualisation is unavailable.

The platform has been in production since 2021 across startups, public companies, and government deployments. Sandboxes spin up in approximately 1 to 2 seconds, with compute pricing starting at $0.01667 per vCPU per hour and $0.00833 per GB of memory per hour. See the [pricing page](https://northflank.com/pricing) for full details.

Northflank supports both ephemeral and persistent sandbox environments on managed cloud or inside your own VPC, self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal via [bring your own cloud](https://northflank.com/product/bring-your-own-cloud).

<InfoBox className="BodyStyle">

**Get started with Northflank sandboxes**

- [Sandboxes on Northflank: overview and concepts](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank): architecture overview and core sandbox concepts
- [Deploy sandboxes on Northflank: step-by-step deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-on-northflank): step-by-step deployment guide
- [Deploy sandboxes in your cloud: BYOC deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud): run sandboxes inside your own VPC
- [Create a sandbox with the SDK: programmatic sandbox creation](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank#create-sandboxes-with-the-sdk): programmatic sandbox creation via the Northflank JS client

[Get started (self-serve)](https://app.northflank.com/signup), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) if you have specific infrastructure or compliance requirements.

</InfoBox>

## Frequently asked questions about KVM

### What does KVM stand for?

KVM stands for Kernel-based Virtual Machine. It is a virtualisation module built into the Linux kernel that uses CPU hardware extensions to run isolated virtual machines on a Linux host.

### Is KVM a Type 1 or Type 2 hypervisor?

KVM runs inside the Linux kernel, so when it is loaded, the Linux host effectively becomes a bare-metal hypervisor. It is commonly described as Type 1 because it has the performance characteristics of bare-metal virtualisation, though technically it runs within a general-purpose OS.

### What is the difference between KVM and QEMU?

KVM is the kernel module that provides hardware-accelerated virtualisation. QEMU is a user-space emulator and VMM that uses KVM to run VMs. QEMU handles device emulation, VM lifecycle, and user interaction. KVM handles the hardware isolation. The two are complementary: QEMU without KVM runs in software emulation mode, which is significantly slower.

### What is the difference between KVM and a microVM?

KVM is the virtualisation layer. A microVM is a lightweight virtual machine that runs on top of KVM via a minimal VMM like Firecracker or Cloud Hypervisor. KVM enforces the hardware isolation boundary. The microVM is the workload running inside that boundary with a dedicated guest kernel and minimal device model.

### Does KVM work without hardware virtualisation extensions?

No. KVM requires Intel VT-x or AMD-V CPU extensions. Without them, the kernel modules will not load. QEMU can run without KVM in software emulation mode, but this is orders of magnitude slower and not suitable for production workloads.

### What is the difference between KVM and VMware?

KVM is an open-source Linux kernel module. VMware ESXi is a commercially licensed bare-metal hypervisor that runs independently of any general-purpose OS. Both provide hardware-level VM isolation, but KVM is free, included in Linux, and is the foundation of most open-source virtualisation and cloud infrastructure. VMware is common in enterprise environments with existing VMware tooling and support contracts.

### What is a KVM switch?

A KVM switch is a hardware device that lets you control multiple computers from a single keyboard, monitor, and mouse. It is an entirely different technology from Kernel-based Virtual Machine. This article covers KVM, the hypervisor. KVM switches are used in data centre operations and multi-machine desktop setups and are unrelated to virtualisation.

## Related articles on KVM, microVMs, and sandboxes

- [What is a microVM?](https://northflank.com/blog/what-is-a-microvm): how microVMs use KVM for hardware-enforced isolation and which technologies implement them
- [What is AWS Firecracker?](https://northflank.com/blog/what-is-aws-firecracker): a technical breakdown of Firecracker's architecture and how it uses KVM to create microVMs
- [What is gVisor?](https://northflank.com/blog/what-is-gvisor): how gVisor uses KVM in its KVM execution mode for syscall interception without booting a full VM
- [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): how the three leading KVM-based and syscall-interception isolation technologies compare
- [Firecracker vs gVisor](https://northflank.com/blog/firecracker-vs-gvisor): a focused comparison of hardware-level and syscall-level isolation approaches
- [Firecracker vs Docker](https://northflank.com/blog/firecracker-vs-docker): how microVM isolation compares to standard container isolation and when each is the right choice
- [Containers vs virtual machines](https://northflank.com/blog/containers-vs-virtual-machines): the broader comparison covering containers, VMs, and where KVM-based virtualisation fits in the stack
- [How to spin up a secure code sandbox and microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh): a step-by-step guide to deploying KVM-backed microVM workloads on Northflank]]>
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  <title>What is sandbox infrastructure? A guide for AI and engineering teams</title>
  <link>https://northflank.com/blog/what-is-sandbox-infrastructure</link>
  <pubDate>2026-04-16T14:15:00.000Z</pubDate>
  <description>
    <![CDATA[What is sandbox infrastructure? A guide covering isolation layers, orchestration, networking, lifecycle management, core use cases, and top platforms for AI and engineering teams.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/sandbox_infra_8f7ff532f0.png" alt="What is sandbox infrastructure? A guide for AI and engineering teams" /><InfoBox className="BodyStyle">

## TL;DR: What is sandbox infrastructure?

- **Sandbox infrastructure** is the full stack required to run isolated workloads safely at scale: isolation technology, orchestration, networking, secrets management, observability, and lifecycle management.
- The isolation layer (containers, [gVisor](https://northflank.com/blog/what-is-gvisor), or [microVMs](https://northflank.com/blog/what-is-a-microvm) like Firecracker) determines the security boundary. For untrusted code, microVM isolation with a dedicated kernel per workload is the minimum bar.
- Building sandbox infrastructure from scratch requires months of engineering work. Platforms like [Northflank](https://northflank.com/product/sandboxes) provide it out of the box.
- The key dimensions to evaluate are isolation model, session lifecycle, concurrency limits, BYOC support, and whether the sandbox runs alongside the rest of your stack.

> [Northflank](https://northflank.com/) is a full-stack cloud platform with production-ready sandbox infrastructure built in. Kata Containers, Firecracker, and gVisor isolation, orchestration, networking, secrets management, and observability, available in minutes without building or maintaining the stack yourself. [Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30).
> 
</InfoBox>

AI agents write code. Users submit scripts. LLM-powered tools generate and execute logic at runtime. Every one of these workloads needs somewhere safe to run. That is what sandbox infrastructure is: the systems, isolation layers, orchestration, networking, and lifecycle management that let you execute untrusted or unpredictable code without putting your production systems at risk.

This article explains what sandbox infrastructure is, what it consists of, why teams need it, and how to run it in production without building it yourself.

## What is sandbox infrastructure?

Sandbox infrastructure is the complete set of systems required to run isolated workloads safely in production. It is not just a container runtime or a single tool. It is a stack: an isolation technology to enforce security boundaries, an orchestrator to schedule and manage workloads, networking controls to restrict what code can access, secrets management to prevent credential leakage, observability to monitor what code actually does, and lifecycle management to provision and tear down environments efficiently.

The term is often used loosely to mean just the sandbox itself, but the sandbox is only the execution boundary. Everything around it is what makes it production-ready.

## What does sandbox infrastructure consist of?

### Isolation technology

The isolation layer determines the security boundary between untrusted code and everything else. Three models exist in practice:

| Technology | Isolation model | Kernel | Startup | Best for |
| --- | --- | --- | --- | --- |
| **Containers (Docker)** | OS-level (namespaces, cgroups) | Shared host kernel | Milliseconds | Trusted internal workloads |
| **gVisor** | Syscall interception (user-space kernel) | Intercepted, not shared | Fast | Moderate-trust workloads |
| **MicroVMs (Firecracker, Kata)** | Hardware-level (KVM hypervisor) | Dedicated guest kernel | ~125ms | Untrusted code, multi-tenant platforms |

For genuinely untrusted code, containers are not sufficient. A kernel vulnerability inside a container can affect the host and every other tenant. MicroVMs give each workload its own dedicated kernel, enforcing a hardware boundary. For most AI agent code execution and multi-tenant platforms, Firecracker or Kata Containers is the right baseline.

### Orchestration

Orchestration handles provisioning, scheduling, bin-packing, autoscaling, and lifecycle management across a fleet of sandboxes. Without it, you are spinning up individual environments by hand. With it, you can handle thousands of concurrent workloads, pre-warm pools for low-latency cold starts, and scale horizontally as demand grows. Kubernetes is the most common orchestration layer, but running microVMs on Kubernetes requires additional integration (typically via Kata Containers or firecracker-containerd).

### Networking controls

Untrusted code should not make arbitrary outbound network requests. Production sandbox infrastructure includes default-deny egress policies, per-sandbox firewall rules, and the ability to whitelist specific endpoints. Without network controls, a sandboxed workload can exfiltrate data, call external APIs, or participate in attacks even if the kernel is isolated.

### Secrets management

Sandbox workloads often need access to credentials, API keys, and connection strings. These must be injected securely without being baked into images or exposed in environment variables accessible to the workload. Production sandbox infrastructure provides scoped secret injection that gives each workload exactly what it needs and nothing more.

### Observability

Logs, metrics, and audit trails from sandbox executions matter when something goes wrong or when you need to prove to a compliance auditor what happened. Production sandbox infrastructure captures real-time execution logs, resource consumption metrics, and network activity per workload.

### Lifecycle management

Ephemeral sandboxes that are destroyed after each run prevent state accumulation between executions. Persistent sandboxes that retain filesystem state across runs support agent workflows that maintain context over time. Production sandbox infrastructure handles both models and manages teardown, cleanup, and resource reclamation efficiently.

## Why is sandbox infrastructure hard to build?

Building sandbox infrastructure from scratch means solving each layer independently. Choosing and configuring an isolation technology. Integrating microVMs with Kubernetes. Configuring networking policies. Setting up secrets injection. Wiring in observability. Managing persistent and ephemeral lifecycle patterns. Testing everything under concurrent load.

Most teams that attempt this often take months before running their first workload in production, with ongoing maintenance afterward. Engineering time spent on sandbox infrastructure is engineering time not spent on the product. For most teams, the economics of building and maintaining this stack do not make sense when purpose-built platforms exist.

## What should you look for in sandbox infrastructure?

- **Isolation model:** Containers, gVisor, and microVMs provide different security boundaries. For untrusted code, microVM isolation with a dedicated kernel is the minimum.
- **Session lifecycle:** Can you run both ephemeral sandboxes destroyed after each run and persistent sandboxes that retain state? Some platforms support only one model.
- **Concurrency and scaling:** What is the maximum concurrent sandbox count? Can it autoscale to meet burst demand without pre-provisioning?
- **BYOC deployment:** Can sandboxes run inside your own cloud account or on-premises? For regulated industries and enterprises, execution that never leaves your own VPC is a hard requirement.
- **Full-stack scope:** Does the sandbox run alongside databases, APIs, and background workers in the same control plane, or does it require a separate infrastructure stack?
- **Cold start latency:** How fast can new sandboxes be provisioned? For interactive agent workflows, cold start time directly affects the user experience.

## Core use cases for sandbox infrastructure

**AI coding agents:** Coding agents like Claude Code and Cursor generate and execute code in real time. Each execution needs an isolated environment with filesystem access, a terminal, and network controls. Sandbox infrastructure handles this at scale without exposing the host system to agent-generated code.

**Code interpreters:** LLM-powered code interpreter tools let users run Python, JavaScript, and shell commands through a chat interface. Every user submission is untrusted code. Sandbox infrastructure gives each execution its own isolated environment and tears it down after the run.

**Autonomous tool use:** Agents that call external APIs, write files, and run subprocesses need a controlled environment where those actions are scoped and auditable. Sandbox infrastructure provides the execution boundary and the observability layer around it.

**Reinforcement learning pipelines:** RL training involves agents running iterative code experiments across many parallel environments. Sandbox infrastructure handles the concurrent provisioning, isolation between runs, and resource cleanup that RL pipelines require.

## Top AI sandbox providers and tools in 2026

- [**Northflank**](https://northflank.com/) – The only platform here that covers the full stack: microVM isolation (Kata Containers, Firecracker, gVisor), unlimited sessions, self-serve BYOC into your own cloud or on-premises, managed databases, and GPU workloads in the same control plane. No session caps. No separate infrastructure required.
- **E2B** – Developer-friendly sandbox API built specifically for AI agents. Firecracker microVM isolation, clean Python and TypeScript SDKs, 150ms boot times. Sessions capped at 24 hours on Pro.
- **Daytona** – Stateful sandbox infrastructure for AI agents with sub-90ms cold starts. Docker isolation by default with optional Kata Containers. Good for coding agents that need fast environment provisioning.
- **Modal** – Serverless Python-first platform with gVisor isolation and deep GPU support. Scales to 20,000 concurrent containers. No BYOC option.

Running sandbox infrastructure in production without building it yourself requires a platform that has already solved each layer. [Northflank](https://northflank.com/product/sandboxes) provides production-ready sandbox infrastructure that teams use to run everything from AI coding agents to multi-tenant code interpreters.

Northflank supports Kata Containers with Cloud Hypervisor, Firecracker, and gVisor, applied per workload based on your threat model. Every sandbox runs in its own microVM. Orchestration, bin-packing, autoscaling, and microVM lifecycle management are handled by the platform. Sandboxes run alongside managed databases, background workers, APIs, and GPU workloads in the same control plane, so you do not need a separate infrastructure stack for execution.

Sessions run indefinitely with no platform-imposed limits, supporting both ephemeral workloads destroyed after each run and persistent environments that retain state across agent sessions. BYOC is self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal. Your data never leaves your own VPC.

[**cto.new migrated their entire sandbox infrastructure to Northflank**](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) in two days after EC2 metal instances made scaling costs unpredictable, going from unworkable provisioning to thousands of daily deployments with linear, per-second billing. That is what production-ready sandbox infrastructure looks like when you do not build it yourself.

**Pricing:** $0.01667/vCPU-hour and $0.00833/GB-hour, billed per second. BYOC deployments bill against your own cloud account.

> [Get started on Northflank](https://app.northflank.com/signup) (self-serve, no demo required). Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to walk through your sandbox requirements.
> 

## FAQ: sandbox infrastructure

### What is the difference between a sandbox and sandbox infrastructure?

A sandbox is an isolated execution environment for a single workload. Sandbox infrastructure is the full stack required to run sandboxes at scale in production: isolation technology, orchestration, networking, secrets management, observability, and lifecycle management. The sandbox is one component of the infrastructure.

### What isolation technology should I use for untrusted code?

For genuinely untrusted code, microVM isolation with a dedicated guest kernel per workload is the right default. Firecracker and Kata Containers both provide hardware-level isolation via KVM. gVisor provides a middle ground through syscall interception. Standard containers share the host kernel and are not sufficient for multi-tenant untrusted code execution.

### Can sandbox infrastructure run inside my own cloud account?

Yes, if the platform supports BYOC deployment. Northflank supports self-serve BYOC into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, and bare-metal. The orchestration and microVM lifecycle management run on your own infrastructure. Your data never leaves your VPC.

### What is the difference between ephemeral and persistent sandbox environments?

Ephemeral sandboxes are destroyed after each run with no state carried over. They prevent state accumulation between executions and are well-suited for short, discrete code execution tasks. Persistent sandboxes retain filesystem state across runs, supporting agent workflows that maintain context across multiple sessions. Northflank supports both models in the same control plane.

### Do I need a separate database and API layer alongside my sandbox infrastructure?

Most AI agent workloads need more than just a sandbox. They need databases for agent memory, APIs for tool calls, background workers for async tasks, and sometimes GPU workloads for inference. Platforms like Northflank run all of these in the same control plane as your sandboxes, eliminating the need to stitch together a separate infrastructure stack.

## Conclusion

Sandbox infrastructure is not a single tool. It is a stack: isolation, orchestration, networking, secrets, observability, and lifecycle management working together to let you execute untrusted or unpredictable code safely at scale. Building that stack yourself takes months and requires ongoing maintenance. For most teams, the right answer is a platform that has already been built.

[Northflank](https://northflank.com/product/sandboxes) provides production-ready sandbox infrastructure with microVM isolation, full-stack scope, and BYOC deployment, all without requiring you to build or maintain the stack yourself. If you are running AI agents, multi-tenant code execution, or any workload where you do not fully trust what runs, that is the infrastructure you need.

<InfoBox className="BodyStyle">

[Sign up for free](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to see how Northflank handles sandbox infrastructure for your stack.

</InfoBox>

## Related articles

- [**Top AI sandbox platforms for code execution in 2026**](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution): A ranked comparison of AI sandbox platforms covering isolation, session limits, pricing, and platform completeness.
- [**Containers vs virtual machines: key differences and when to use each**](https://northflank.com/blog/containers-vs-virtual-machines): The broader comparison covering containers, VMs, and microVMs in context.
- [**Firecracker vs Docker: key differences and when to use each**](https://northflank.com/blog/firecracker-vs-docker): A direct comparison of Docker containers and Firecracker microVMs on isolation, security, and use case fit.]]>
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  <title>What is gVisor?</title>
  <link>https://northflank.com/blog/what-is-gvisor</link>
  <pubDate>2026-04-16T13:30:00.000Z</pubDate>
  <description>
    <![CDATA[gVisor is a user-space application kernel written in Go that intercepts system calls to sandbox containers. Learn how it works, how it compares to microVMs, and when to use it.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/what_is_gvisor_402804ed70.png" alt="What is gVisor?" />gVisor is an open-source application kernel developed by Google that sandboxes containers by intercepting system calls in user space. It sits between your containerised workload and the host kernel, handling syscalls through its own sandboxed process rather than passing them directly to the host.

This article covers how gVisor works, what its components do, where it fits against containers and microVMs, its limitations, and when it is the right isolation tool.

<InfoBox className="BodyStyle">

## TL;DR: What is gVisor?

- gVisor is an open-source application kernel written in Go that sandboxes containers by intercepting system calls in user space, developed by Google
- It is not a VM and not a syscall filter. It takes a distinct third approach: a per-sandbox user-space kernel that handles syscalls on behalf of your workload
- Its core components are the Sentry, which intercepts and handles syscalls, and the Gofer, which handles filesystem access
- It integrates with Docker, containerd, and Kubernetes via its OCI runtime called `runsc`
- Platforms like [Northflank](https://northflank.com/) apply gVisor for workloads where syscall-interception isolation is sufficient, alongside microVM technologies for workloads requiring hardware-enforced boundaries

</InfoBox>

## What is gVisor?

gVisor is an application kernel that implements a Linux-compatible interface in user space. When a containerised workload makes a system call, gVisor's Sentry intercepts it and handles it without passing it to the host kernel. The host kernel's attack surface is reduced because the workload never talks to it directly.

It is not a VM in the conventional sense and not a syscall filter like seccomp-bpf, which still passes syscalls to the host kernel and relies on that kernel being unexploitable within the permitted set. gVisor takes a third approach: a per-sandbox application kernel that re-implements Linux system interfaces in Go, a memory-safe language.

The Sentry acts as both guest OS and VMM in KVM mode, leveraging virtualisation extensions for address space isolation and performance, with no virtualised hardware layer. In Systrap mode, seccomp intercepts syscalls and hands control to gVisor without requiring hardware virtualisation support.

gVisor ships an OCI-compatible runtime called `runsc` that integrates with Docker, containerd, and Kubernetes, so existing container workflows work with minimal changes.

## Why does container isolation need improving?

Standard containers share the host kernel. Every syscall a container makes goes directly to the same Linux kernel shared by every other container on that host. A kernel vulnerability exploited by one container can affect the host and everything else running on it.

For trusted internal workloads, that tradeoff is acceptable. For workloads you do not control (AI-generated code, customer-submitted scripts, third-party dependencies), the shared kernel is the attack surface. See [What is a microVM?](https://northflank.com/blog/what-is-a-microvm) for a full breakdown of why containers fall short for some workloads, and the approaches that address it.

## How does gVisor work?

gVisor inserts a user-space layer between your container and the host kernel. That layer has two main components.

### The Sentry

The Sentry is gVisor's application kernel. It is a user-space process, written in Go, that intercepts system calls made by the sandboxed workload and handles them internally. The workload thinks it is talking to a Linux kernel. It is actually talking to the Sentry.

Because the Sentry is written in Go, a memory-safe language, a large class of memory corruption vulnerabilities common in C-based kernels does not apply. The Sentry itself is also constrained by seccomp filters that limit which host kernel syscalls it can make, so even if the Sentry were compromised, its ability to interact with the host is limited.

### The Gofer

The Gofer is a separate process that handles filesystem access on behalf of the Sentry. Rather than letting the Sentry access the host filesystem directly, file operations go through the Gofer, which acts as a proxy. This is another layer of containment: the Sentry and the sandboxed workload are further isolated from host filesystem resources.

### Execution platforms: Systrap and KVM

gVisor supports two execution platforms that determine how syscall interception works.

- **Systrap:** uses seccomp to intercept syscalls and redirect them to the Sentry. It works on any Linux host without hardware virtualisation support, making it the more portable option. It has good performance for most workloads.
- **KVM:** uses virtualisation hardware to intercept syscalls, with the Sentry running as a guest. It is faster than Systrap on bare-metal hosts where KVM is available, but does not require a full guest OS. Despite using KVM, this is not a microVM; there is no dedicated guest kernel per workload.

See how gVisor's isolation model compares to standard containers and microVMs:

![Container isolation (3).png](https://assets.northflank.com/Container_isolation_3_fce9c02898.png)

## gVisor vs container vs microVM

|  | Container | gVisor | MicroVM |
| --- | --- | --- | --- |
| **Isolation model** | OS-level (namespaces, cgroups) | Syscall interception (user-space kernel) | Hardware-level (KVM) |
| **Kernel** | Shared host kernel | User-space Sentry per sandbox | Dedicated guest kernel per workload |
| **Boot time** | Milliseconds | Milliseconds | ~125ms to ~300ms depending on VMM |
| **Memory overhead** | Minimal | Low | Less than 5 to 10 MiB |
| **I/O overhead** | None | Syscall tax on I/O-heavy workloads | Near-native |
| **Hardware virtualisation required** | No | No (Systrap) / Optional (KVM mode) | Yes (KVM) |
| **Kubernetes integration** | Native | Via RuntimeClass (runsc) | Via Kata Containers / RuntimeClass |
| **Best for** | Trusted internal workloads | Enhanced container security, no nested virt | Untrusted or multi-tenant workloads |

gVisor sits between standard containers and microVMs on the isolation-overhead curve. It provides meaningfully stronger isolation than a standard container without the resource cost of a full microVM. For a broader comparison, see [Containers vs virtual machines](https://northflank.com/blog/containers-vs-virtual-machines) and [What is a microVM?](https://northflank.com/blog/what-is-a-microvm).

## What are gVisor's limitations?

gVisor is the right tool in the right context. It also has real constraints worth understanding before you deploy it.

- **Syscall compatibility:** The Sentry re-implements Linux system interfaces but does not cover every syscall. Some applications that depend on less common or recently added syscalls may not work correctly under gVisor. Testing your specific workload matters.
- **I/O overhead:** Syscall interception adds latency on I/O-heavy workloads. Benchmarks suggest this can be in the range of 10 to 30% slower than native containers depending on workload type. CPU-bound workloads are less affected.
- **Weaker isolation than microVMs:** Even in KVM mode, gVisor does not provide a dedicated guest kernel or virtualised hardware layer per workload. The sandbox retains a process model. For actively adversarial multi-tenant workloads, microVMs provide a broader hardware-enforced boundary.
- **Linux only:** gVisor runs Linux workloads. Windows containers are not supported.
- **Not a replacement for defence-in-depth:** gVisor is designed to complement other security controls. It uses seccomp and Linux primitives as additional containment layers around the Sentry itself, not as its primary isolation mechanism

## When should you use gVisor?

gVisor is a good fit when:

- **Nested virtualisation is unavailable**: If your host does not support KVM, you cannot run microVMs. gVisor's Systrap mode runs on any Linux host and provides meaningfully stronger isolation than standard containers.
- **You want enhanced container security without VM overhead**: For workloads where container isolation is insufficient but you do not need the full resource cost and operational complexity of microVMs, gVisor is a practical middle ground.
- **Your workloads are not I/O-bound**: The syscall overhead matters most on high-throughput filesystem or network workloads. CPU-bound workloads see much less impact.
- **You need defence-in-depth**: gVisor layers well with other security controls and is used in production environments as one part of a broader isolation strategy.

When microVMs are the better choice, gVisor is not the right tool. If your threat model involves actively adversarial workloads, multi-tenant environments where customers could be malicious, or untrusted code execution at scale, hardware-enforced isolation is the stronger guarantee. See [Firecracker vs gVisor](https://northflank.com/blog/firecracker-vs-gvisor) and [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor) for a detailed comparison.

## How does Northflank use gVisor?

Northflank applies gVisor for workloads where syscall-interception isolation is sufficient or where nested virtualisation is unavailable on the underlying host. It runs alongside Kata Containers with Cloud Hypervisor and Firecracker, with the isolation technology applied based on workload requirements. The platform has been in production since 2021 across startups, public companies, and government deployments.

Sandboxes spin up in approximately 1 to 2 seconds, with compute pricing starting at $0.01667 per vCPU per hour and $0.00833 per GB of memory per hour. See the [pricing page](https://northflank.com/pricing) for full details.

Northflank supports both ephemeral and persistent sandbox environments on managed cloud or inside your own VPC, self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal via [bring your own cloud](https://northflank.com/product/bring-your-own-cloud).

<InfoBox className="BodyStyle">

**Get started with Northflank sandboxes**

- [Sandboxes on Northflank: overview and concepts](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank): architecture overview and core sandbox concepts
- [Deploy sandboxes on Northflank: step-by-step deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-on-northflank): step-by-step deployment guide
- [Deploy sandboxes in your cloud: BYOC deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud): run sandboxes inside your own VPC
- [Create a sandbox with the SDK: programmatic sandbox creation](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank#create-sandboxes-with-the-sdk): programmatic sandbox creation via the Northflank JS client

[Get started (self-serve)](https://app.northflank.com/signup), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) if you have specific infrastructure or compliance requirements.

</InfoBox>

## Frequently asked questions about gVisor

### Is gVisor a virtual machine?

No. gVisor is an application kernel that runs in user space. While its KVM platform leverages virtualisation hardware for address space isolation and performance, it does not boot a dedicated guest kernel or emulate a full hardware layer per workload. The sandbox retains a process model in both execution modes, with the Sentry handling syscalls on behalf of the workload rather than a separate OS image running inside a VM boundary.

### Is gVisor the same as a container?

No. Standard containers share the host kernel and use Linux namespaces and cgroups for isolation. A gVisor sandbox intercepts syscalls through the Sentry before they reach the host kernel, providing a stronger isolation boundary than a standard container without requiring hardware virtualisation.

### What is the Sentry in gVisor?

The Sentry is gVisor's user-space application kernel. It is a Go process that intercepts system calls made by sandboxed workloads and handles them internally, without passing them to the host kernel. It is the core component that provides gVisor's isolation.

### Does gVisor work with Kubernetes?

Yes. gVisor ships an OCI runtime called `runsc`. In Kubernetes, you create a RuntimeClass pointing to `runsc` and reference it in your pod spec. Pods assigned that RuntimeClass run with gVisor's syscall interception rather than directly against the host kernel.

### What is the difference between gVisor and Firecracker?

Firecracker creates microVMs with hardware-enforced isolation via KVM. Each workload gets its own dedicated Linux kernel inside a hardware boundary. gVisor intercepts syscalls in user space through the Sentry without booting a VM. Firecracker provides stronger isolation for actively adversarial workloads. gVisor has lower overhead and simpler integration for workloads where syscall-interception isolation is sufficient. See [Firecracker vs gVisor](https://northflank.com/blog/firecracker-vs-gvisor) for a full comparison.

### What is the difference between gVisor's Systrap and KVM modes?

Systrap uses seccomp to intercept syscalls and redirect them to the Sentry. It runs on any Linux host and is the more portable option. KVM mode uses virtualisation hardware to intercept syscalls, with the Sentry running as a guest, and is faster on bare-metal hosts where KVM is available. Neither mode boots a full guest OS per workload.

## Related articles on gVisor, microVMs, and sandboxes

- [What is a microVM?](https://northflank.com/blog/what-is-a-microvm): how microVMs compare to containers and gVisor, and which technologies implement them
- [Firecracker vs gVisor](https://northflank.com/blog/firecracker-vs-gvisor): a detailed comparison of hardware-level and syscall-level isolation approaches
- [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): how the three leading isolation technologies compare on security, performance, and operational complexity
- [What is AWS Firecracker?](https://northflank.com/blog/what-is-aws-firecracker): a full technical breakdown of Firecracker's architecture and how it differs from gVisor
- [Firecracker vs Docker](https://northflank.com/blog/firecracker-vs-docker): how microVM isolation compares to standard container isolation and when each is the right choice
- [Containers vs virtual machines](https://northflank.com/blog/containers-vs-virtual-machines): the broader comparison covering containers, VMs, and where gVisor fits on the isolation curve
- [How to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents): isolation architectures for AI agent execution environments
- [Secure runtime for codegen tools](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale): how production platforms run AI-generated code safely]]>
  </content:encoded>
</item><item>
  <title>What is a microVM?</title>
  <link>https://northflank.com/blog/what-is-a-microvm</link>
  <pubDate>2026-04-15T15:00:00.000Z</pubDate>
  <description>
    <![CDATA[A microVM is a lightweight virtual machine that provides hardware-level isolation with container-like startup speed. Learn how microVMs work, how they compare to containers and VMs, and which technologies power them.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/what_is_a_microvm_5c36a5cf6c.png" alt="What is a microVM?" />A microVM is a lightweight virtual machine designed to run isolated workloads with minimal overhead. Unlike standard containers, each microVM runs its own Linux kernel enforced by hardware virtualisation, giving you a strong isolation boundary per workload without the resource cost of a full VM.

This article covers how microVMs work, how they compare to containers and traditional VMs, which technologies implement them, and when you need them.

<InfoBox className="BodyStyle">

## TL;DR: What is a microVM?

- A microVM is a lightweight virtual machine that gives each workload its own dedicated kernel and hardware-level isolation, with a fraction of the memory overhead of a traditional VM
- They boot in milliseconds to low hundreds of milliseconds and are purpose-built for running untrusted, multi-tenant, or security-sensitive workloads
- Technologies that implement microVMs include Firecracker, Cloud Hypervisor, and QEMU microVM. Kata Containers is the orchestration layer that runs them on Kubernetes
- Platforms like Northflank use microVMs to run AI sandboxes, untrusted code execution, and multi-tenant workloads in production, anywhere standard containers are too weak a boundary. See [how to spin up a secure code sandbox & microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh).

</InfoBox>

## What is a microVM?

A microVM is a lightweight virtual machine that provides hardware-enforced isolation per workload. Each microVM runs its own Linux kernel inside a KVM-enforced hardware boundary, completely separate from the host and from every other microVM on the same host.

Containers share the host kernel. MicroVMs do not. Each microVM gets its own kernel, its own memory space, and its own virtualised devices, with a stripped-down device model that keeps memory overhead in single-digit MiB and boot times in the low hundreds of milliseconds rather than the seconds or minutes a traditional VM requires.

MicroVMs sit between containers and traditional VMs on the isolation-vs-overhead tradeoff curve. For a broader comparison, see [Containers vs virtual machines](https://northflank.com/blog/containers-vs-virtual-machines).

## Why do standard containers fall short for some workloads?

Standard containers share the host kernel. Every container on a host issues system calls directly to the same Linux kernel. If one container exploits a kernel vulnerability, it can affect the host and every other container running on it.

See how isolation models differ across standard containers, microVMs, and syscall interception:

![Container isolation (3).png](https://assets.northflank.com/Container_isolation_3_8e6266d1c5.png)

For workloads you control and trust, internal APIs, CI/CD pipelines, your own application code, that tradeoff is acceptable.

The problem arises when you run code you do not control: AI-generated code, customer-submitted scripts, or any [multi-tenant environment](https://northflank.com/blog/kubernetes-multi-tenancy) where different users execute workloads on shared infrastructure.

In those cases, the shared kernel is the attack surface, and containers do not give you a strong enough boundary. That is the problem microVMs solve.

## How does a microVM work?

MicroVMs use three components working together: KVM, a Virtual Machine Monitor (VMM), and a minimal guest kernel.

- **KVM (Kernel-based Virtual Machine):** A Linux kernel module that exposes hardware virtualisation capabilities (Intel VT-x, AMD-V) to user-space processes. It is the foundation every microVM technology builds on.
- **The VMM (Virtual Machine Monitor):** Sits in user space and uses KVM to create and manage individual microVMs. It defines which virtual devices each microVM gets, allocates vCPUs and memory, and controls the lifecycle. Minimalism is deliberate: fewer emulated devices means a smaller attack surface. Firecracker implements only five virtio devices. QEMU supports a significantly larger device model by comparison.
- **The guest kernel:** Boots inside the microVM as a standard Linux kernel, stripped down but complete. From the workload's perspective, it is running on real hardware. From the host's perspective, it is a KVM-enforced hardware boundary.

With Firecracker the full startup takes approximately 125ms. With Kata Containers adding orchestration on top, somewhere in the 150 to 300ms range depending on VMM and configuration. 

## MicroVM vs container vs traditional VM

See how the three models compare across isolation, performance, and resource overhead.

|  | Container | MicroVM | Traditional VM |
| --- | --- | --- | --- |
| **Isolation model** | OS-level (namespaces, cgroups) | Hardware-level (KVM) | Hardware-level (hypervisor) |
| **Kernel** | Shared host kernel | Dedicated guest kernel | Dedicated guest kernel |
| **Boot time** | Milliseconds | ~125ms to ~300ms depending on VMM and configuration | Seconds to minutes |
| **Memory overhead** | Minimal | Less than 5 to 10 MiB | Hundreds of MiB |
| **Attack surface** | Medium (shared kernel) | Small (minimal device model) | Large (full hardware emulation) |
| **Kubernetes integration** | Native | Via Kata Containers / RuntimeClass | Not standard |
| **Best for** | Trusted internal workloads | Untrusted or multi-tenant workloads | Full OS isolation, legacy workloads |

Containers remain the right default for trusted workloads. MicroVMs are the right choice when the shared-kernel model is an unacceptable security tradeoff. Traditional VMs are rarely the right fit for high-density cloud workloads given their overhead.

## What technologies implement microVMs?

Several open-source technologies implement the microVM model, sharing the same underlying principle of a minimal VMM using KVM for hardware isolation, but differing in design goals and operational complexity.

### Firecracker

Firecracker is an open-source VMM built by AWS in Rust, with approximately 125ms to initiate user-space code and less than 5 MiB of memory overhead per instance. It supports up to 150 microVMs per second per host in benchmarks and powers AWS Lambda and Fargate. Firecracker does not include orchestration, so teams running it in Kubernetes typically do so through Kata Containers rather than directly. See [What is AWS Firecracker?](https://northflank.com/blog/what-is-aws-firecracker) for a full technical breakdown.

### Cloud Hypervisor

Cloud Hypervisor is an open-source Rust-based VMM maintained by the Linux Foundation. It targets modern cloud workloads and supports GPU passthrough and live migration while keeping a small, auditable codebase. It is Northflank's primary VMM for microVM-backed workloads.

### QEMU microVM

QEMU supports a microVM machine type with broader hardware compatibility than Firecracker or Cloud Hypervisor, but carries more overhead and a larger attack surface. It is the right choice when hardware compatibility matters more than minimum footprint.

### Kata Containers

Kata Containers is an orchestration framework that makes microVMs work natively with Kubernetes via the CRI. It is not itself a VMM; it sits on top of Firecracker, Cloud Hypervisor, or QEMU. From Kubernetes' perspective, a Kata-backed workload looks like a standard container. Under the hood, it runs in a microVM with a dedicated kernel. See [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor) for a detailed comparison.

### gVisor

gVisor is not a microVM. It is a user-space kernel written in Go that intercepts system calls between a container and the host kernel, reducing the host kernel's attack surface without booting a VM. It has lower overhead than microVMs but weaker isolation; no hardware-enforced boundary. For environments where nested virtualisation is unavailable or syscall-interception isolation is sufficient, it is a practical alternative to microVMs.

## What are the main use cases for microVMs?

MicroVMs are used wherever the shared-kernel model of containers is an unacceptable security tradeoff. The most common production use cases are:

- **AI code sandboxes and agent execution:** When an AI agent generates and runs code, that code is untrusted by definition. MicroVMs give each execution its own kernel boundary. See [How to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents) and [Best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents).
- **Multi-tenant SaaS platforms:** When different customers run workloads on shared infrastructure, container-level isolation is not sufficient if any workload could be adversarial. See [Kubernetes multi-tenancy](https://northflank.com/blog/kubernetes-multi-tenancy) and [Multi-tenant cloud deployment](https://northflank.com/blog/multi-tenant-cloud-deployment).
- **Serverless and function-as-a-service:** Fast boot times, minimal overhead, and strong per-invocation isolation are the exact requirements of FaaS platforms. AWS Lambda is the most prominent example.
- **CI/CD build isolation:** Build jobs execute arbitrary code from repositories. MicroVMs give each job a clean, isolated kernel environment that cannot affect other jobs on the same host.
- **Secure LLM inference and codegen tooling:** Platforms running AI-generated code or model outputs need isolation beyond containers. See [Secure runtime for codegen tools](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale) and [Remote code execution sandbox](https://northflank.com/blog/remote-code-execution-sandbox).

## How does Northflank run microVMs?

[Northflank](https://northflank.com/) runs microVM-backed workloads using Kata Containers with Cloud Hypervisor as the primary VMM, with Firecracker and gVisor applied depending on workload requirements. The platform has been in production since 2021 across startups, public companies, and government deployments. Sandboxes spin up in approximately 1 to 2 seconds, with compute pricing starting at $0.01667 per vCPU per hour and $0.00833 per GB of memory per hour. See the [pricing page](https://northflank.com/pricing) for full details.

Northflank supports both ephemeral and persistent sandbox environments on managed cloud or inside your own VPC, self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal via [bring your own cloud](https://northflank.com/product/bring-your-own-cloud).

<InfoBox className="BodyStyle">

**Get started with Northflank sandboxes**

- [Sandboxes on Northflank: overview and concepts](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank): architecture overview and core sandbox concepts
- [Deploy sandboxes on Northflank: step-by-step deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-on-northflank): step-by-step deployment guide
- [Deploy sandboxes in your cloud: BYOC deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud): run microVMs inside your own VPC
- [Create a sandbox with the SDK: programmatic sandbox creation](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank#create-sandboxes-with-the-sdk): programmatic sandbox creation via the Northflank JS client
- See [how to spin up a secure code sandbox & microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh).

[Get started (self-serve)](https://app.northflank.com/signup), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) if you have specific infrastructure or compliance requirements.

</InfoBox>

## Frequently asked questions about microVMs

### What is the difference between a microVM and a container?

Containers share the host kernel and use Linux namespaces and cgroups for isolation. A microVM gives each workload its own dedicated Linux kernel enforced by hardware virtualisation. The microVM isolation boundary is the KVM hypervisor layer, which is significantly harder to escape than the container boundary.

### What is the difference between a microVM and a VM?

Both run a dedicated guest kernel. Traditional VMs emulate full hardware stacks, including graphics, USB, and BIOS, taking seconds or minutes to boot with hundreds of MiB of memory overhead. MicroVMs strip the device model to the minimum needed for cloud workloads, booting in milliseconds to low hundreds of milliseconds with less than 5 to 10 MiB of overhead per instance.

### Does a microVM have its own kernel?

Yes. Each microVM boots its own Linux kernel inside a KVM-enforced hardware boundary. This is the fundamental property that distinguishes a microVM from a container.

### What is KVM and how does it relate to microVMs?

KVM (Kernel-based Virtual Machine) is a Linux kernel module that exposes CPU hardware virtualisation extensions (Intel VT-x, AMD-V) to user-space processes. Firecracker, Cloud Hypervisor, and QEMU all use KVM as the underlying virtualisation layer, meaning a host without KVM support cannot run microVMs.

### Which microVM technology should I use?

Use Kata Containers with Cloud Hypervisor or Firecracker for production-ready microVM isolation on Kubernetes without building custom orchestration. Use Firecracker directly if you are building a custom serverless platform with the infrastructure expertise to manage it. Use gVisor if nested virtualisation is unavailable or syscall-interception isolation is sufficient. See [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor) for a detailed comparison.

### What is gVisor and is it a microVM?

No. gVisor intercepts system calls between a container and the host kernel in user space. It does not use hardware virtualisation and does not boot a dedicated guest kernel. It reduces attack surface but does not provide the same hardware-enforced isolation boundary a microVM does.

## Related articles on microVMs, containers, and sandboxes

- [What is AWS Firecracker?](https://northflank.com/blog/what-is-aws-firecracker): a full technical breakdown of Firecracker's architecture, device model, and jailer security model
- [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): how the three leading isolation technologies compare on security, performance, and operational complexity
- [Containers vs virtual machines](https://northflank.com/blog/containers-vs-virtual-machines): the broader comparison covering containers, VMs, and microVMs in context
- [How to spin up a secure code sandbox and microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh): a step-by-step guide to deploying microVM-backed workloads on Northflank
- [How to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents): a practical guide to isolation architectures for AI agent execution environments
- [Firecracker vs gVisor](https://northflank.com/blog/firecracker-vs-gvisor): a focused comparison of the two most common isolation approaches]]>
  </content:encoded>
</item><item>
  <title>Firecracker vs Docker: key differences and when to use each</title>
  <link>https://northflank.com/blog/firecracker-vs-docker</link>
  <pubDate>2026-04-15T13:00:00.000Z</pubDate>
  <description>
    <![CDATA[Firecracker vs Docker: key differences in isolation model, startup time, and security boundaries, and when to use each for trusted workloads, untrusted code, and multi-tenant platforms.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Firecracker_vs_Docker_2754b7d9ef.png" alt="Firecracker vs Docker: key differences and when to use each" />Docker containers and Firecracker VMs are both ways to run isolated workloads on shared hardware. But they solve different problems, make different security tradeoffs, and are designed for different threat models. Understanding the difference matters more than ever as teams run AI-generated code, multi-tenant platforms, and untrusted workloads in production.

In this article, we compare Firecracker vs Docker on architecture, isolation strength, startup speed, and use case fit, and show you how to run both on [Northflank](https://northflank.com/).

## TL;DR: Firecracker vs Docker

|  | Docker | Firecracker |
| --- | --- | --- |
| **Type** | Container runtime | MicroVM monitor (VMM) |
| **Isolation** | OS-level (namespaces, cgroups) | Hardware-level (KVM hypervisor) |
| **Kernel** | Shared host kernel | Dedicated guest kernel per microVM |
| **Startup time** | Milliseconds | ~125ms |
| **Memory overhead** | Tens of MB | Less than 5 MiB per microVM |
| **Density** | Very high | High (up to 150 microVMs/sec per host) |
| **Security boundary** | Process isolation | Hardware isolation |
| **Multi-tenant untrusted code** | Not recommended | Designed for it |
| **Best for** | Internal workloads, CI/CD, cloud-native apps | Sandboxes, serverless, untrusted code execution |

<InfoBox className="BodyStyle">

**What is Northflank?**

[Northflank](https://northflank.com/) is a full-stack cloud platform that runs both Docker containers and Firecracker microVMs in the same control plane. If your workloads need Firecracker-level isolation but you do not want to build the orchestration layer yourself, that is exactly what Northflank handles. Deploy services, sandboxes, databases, and GPU workloads without managing the underlying infrastructure.

[Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30).

</InfoBox>

## What is Docker?

Docker is a container runtime that packages applications and their dependencies into OCI-compliant images and runs them as isolated processes on the host operating system. Isolation is achieved using Linux namespaces (process, network, filesystem) and cgroups (CPU and memory limits). The container shares the host kernel.

Docker is the dominant deployment standard in cloud-native infrastructure. Kubernetes orchestrates Docker-compatible containers at scale, and the OCI image format means a container built once runs on any compliant runtime. Docker is fast, lightweight, and has a massive ecosystem.

### Pros of Docker

- Millisecond startup, no OS boot required
- Minimal memory overhead
- Very high workload density
- OCI standard; runs on any compliant runtime
- Massive ecosystem and tooling
- Native Kubernetes integration

### Cons of Docker

- Shares the host kernel; a kernel vulnerability affects all containers on the host
- Not suitable for running untrusted code from external sources
- Weaker isolation for multi-tenant environments
- Container escapes are possible via kernel exploits

## What is Firecracker?

Firecracker is an open-source virtual machine monitor (VMM) built by AWS and released in 2018. It creates and manages lightweight virtual machines called microVMs using Linux KVM. Each microVM runs its own dedicated Linux kernel, completely isolated from the host and from other microVMs at the hardware level. Firecracker powers AWS Lambda and AWS Fargate, handling trillions of function executions monthly.

Firecracker's design philosophy is minimalism. It strips out all non-essential hardware emulation: no graphics, no USB, no BIOS, no ACPI. What remains is a tight, fast, secure VMM that boots a microVM in approximately 125ms with less than 5 MiB of memory overhead per instance. It supports creating up to 150 microVMs per second on a single host.

### Pros of Firecracker

- Hardware-level isolation via KVM
- Dedicated guest kernel per workload
- ~125ms startup, less than 5 MiB memory per microVM
- Up to 150 microVMs per second per host
- Built for multi-tenant untrusted workloads
- Powers AWS Lambda and Fargate at scale

### Cons of Firecracker

- Slightly higher overhead than containers
- Linux guests only, no Windows support
- Steeper operational complexity than Docker
- Not a drop-in replacement for Docker
- Smaller ecosystem than Docker

## What is the key architectural difference?

The core difference is the isolation boundary. Docker containers share the host kernel. Every container on the same host issues system calls to the same Linux kernel. A kernel vulnerability is a vulnerability in every container on that host.

Firecracker VMs each run their own Linux kernel inside a KVM-enforced hardware boundary. To escape a Firecracker VM, an attacker must first compromise the guest kernel, then escape the KVM hypervisor, which is enforced by CPU hardware (Intel VT-x or AMD-V) and has been hardened across 15+ years of production use. That is a significantly harder attack path than a container escape.

For single-tenant workloads where you control what code runs, Docker's isolation is sufficient. For multi-tenant workloads where different customers or users execute arbitrary code on shared infrastructure, Docker's shared kernel model introduces a risk that Firecracker is specifically designed to eliminate.

## When should you use Docker vs Firecracker?

The decision comes down to your threat model. If you control what code runs and trust the workloads, Docker is sufficient and the right default. If you are running code from external users, AI agents, or any source you do not control, Docker's shared kernel model introduces risk that Firecracker is specifically designed to eliminate.

| Use case | Docker | Firecracker |
| --- | --- | --- |
| Internal services and APIs | Yes | Overkill |
| CI/CD build environments | Yes | Overkill |
| Microservices on Kubernetes | Yes | No |
| Multi-tenant untrusted code execution | No | Yes |
| AI agent and LLM-generated code | No | Yes |
| Serverless functions | No | Yes |
| Code interpreter platforms | No | Yes |
| Compliance requiring kernel isolation | No | Yes |
| Maximum workload density | Yes | No |

## Can Firecracker and Docker work together?

Yes. Firecracker is not a replacement for Docker. It is a complement to the container ecosystem. Projects like Kata Containers integrate Firecracker as a backend for Kubernetes, providing microVM isolation through standard container APIs. From Kubernetes' perspective, the workload looks like a container. Under the hood, it runs in a Firecracker microVM with a dedicated kernel.

This means you can run standard Docker containers for trusted internal workloads and Firecracker-backed microVMs for sandboxes and untrusted code, all managed through the same orchestration layer. Northflank supports exactly this model.

## How to run Docker and Firecracker on Northflank

Running Docker containers for trusted workloads and Firecracker microVMs for sandboxes and untrusted code sounds straightforward until you have to maintain both. Two separate infrastructure stacks, two orchestration models, two sets of networking and secrets configuration, and two things to debug when something breaks. Most teams that go down this path spend months on the plumbing before they ship anything.

[Northflank](https://northflank.com/product/deployments) runs both in the same control plane. You connect a repo or bring a container image, and Northflank handles Kubernetes scheduling, autoscaling, TLS, secrets injection, real-time logs and metrics, and preview environments per pull request. No cluster setup. No YAML maintenance.

For workloads that need Firecracker-level isolation, Northflank's [microVM-backed sandbox execution](https://northflank.com/product/sandboxes) runs Kata Containers with Cloud Hypervisor, Firecracker, and gVisor per workload. You choose the isolation model based on the threat model, not based on what your infrastructure can support. [cto.new migrated their entire sandbox infrastructure to Northflank](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) in two days and went from unworkable provisioning to thousands of daily deployments for untrusted code with linear, per-second billing. Standard containers and microVM sandboxes run alongside managed databases, background jobs, and GPU workloads in the same place.

[BYOC](https://northflank.com/product/bring-your-own-cloud) is self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal. Northflank manages the platform on your infrastructure while your data never leaves your own VPC. For teams with compliance requirements, that is the difference between passing a security review and not.

## FAQ: Firecracker vs Docker

### Is Firecracker VM faster than Docker?

No. Docker containers start in milliseconds and have lower overhead. Firecracker microVMs start in approximately 125ms and consume slightly more resources due to the virtualisation layer. For most workloads, the difference is negligible. The tradeoff is isolation strength, not speed.

### Does Northflank support both Docker and Firecracker?

Yes. Northflank runs standard Docker-compatible containers for general workloads and Firecracker microVM-backed sandboxes for workloads requiring stronger isolation. Both run in the same control plane alongside databases, background jobs, and GPU workloads.

### Can Firecracker run Docker images?

Not directly. Firecracker is a VMM, not a container runtime. It boots a Linux kernel inside a microVM. Projects like Kata Containers and firecracker-containerd bridge the gap, allowing you to run OCI-compatible container images on Firecracker-backed microVMs via standard container APIs.

### Does Firecracker replace Docker for production workloads?

No. Docker remains the standard for cloud-native application deployment. Firecracker is purpose-built for workloads where the shared-kernel model of containers is not an acceptable security tradeoff, specifically multi-tenant and untrusted code execution. Most production platforms use both.

## Conclusion

Docker and Firecracker are not competing technologies. Docker is the standard for cloud-native application deployment. Firecracker is what you reach for when Docker's shared kernel model is not an acceptable security tradeoff, specifically multi-tenant workloads, AI-generated code execution, and serverless platforms where different customers run arbitrary code on shared infrastructure. Most production platforms that handle untrusted code run both.

The hard part is not choosing between them. It is running both in production without stitching together two separate infrastructure stacks. Northflank solves that. You get Docker container orchestration and Firecracker microVM sandboxes in the same control plane, with managed databases, GPU workloads, CI/CD pipelines, and BYOC deployment into your own cloud. The teams running untrusted code on [Northflank](https://northflank.com/) did not spend months building isolation infrastructure. They shipped.

<InfoBox className="BodyStyle">

[Sign up for free](https://app.northflank.com/signup) and deploy your first workload in minutes. Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to walk through how Northflank handles both Docker and Firecracker workloads in your stack.

</InfoBox>

## Related articles

- [**What is AWS Firecracker?**](https://northflank.com/blog/what-is-aws-firecracker): A deep dive into how Firecracker works, its architecture, and why AWS built it for Lambda and Fargate.
- [**Kata Containers vs Firecracker vs gVisor**](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): A comparison of microVM and isolation technologies covering security model, performance, and when to use each.
- [**Containers vs virtual machines: key differences and when to use each**](https://northflank.com/blog/containers-vs-virtual-machines): The broader comparison covering containers, VMs, and microVMs in context.
- [**Best platforms for untrusted code execution in 2026**](https://northflank.com/blog/best-platforms-for-untrusted-code-execution): How to choose a sandbox platform when isolation model determines security outcomes.]]>
  </content:encoded>
</item><item>
  <title>LangSmith Sandboxes alternatives for secure AI code execution</title>
  <link>https://northflank.com/blog/langsmith-sandboxes-alternatives</link>
  <pubDate>2026-04-14T16:15:00.000Z</pubDate>
  <description>
    <![CDATA[LangSmith Sandboxes launched in March 2026 and are currently in private preview. This guide covers sandbox alternatives for secure AI agent code execution, with pricing comparisons.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/langsmith_sandboxes_alternatives_3d5d496fc8.png" alt="LangSmith Sandboxes alternatives for secure AI code execution" /><InfoBox className="BodyStyle">

## TL;DR: LangSmith Sandboxes alternatives for secure AI agent code execution

- LangSmith Sandboxes launched in March 2026 and are currently in private preview, with waitlist-only access and APIs subject to change.
- Sandbox Alternatives available today include Northflank, E2B, Modal, [Fly.io](http://fly.io/) Sprites, and CodeSandbox
- Key factors to evaluate include isolation model (microVM vs container), BYOC (Bring Your Own Cloud) support, GPU availability, session limits, and pricing at scale.
- Northflank supports both ephemeral and persistent environments, self-serve BYOC into AWS, GCP, Azure, Oracle, CoreWeave, Civo, and bare-metal, and on-demand GPU allocation with per-second billing.

</InfoBox>

LangSmith Sandboxes entered private preview in March 2026 as LangChain's managed sandbox offering for running untrusted agent-generated code. This guide covers what LangSmith Sandboxes provide, what to look for in a sandbox alternative, and a comparison of five production-ready sandbox platforms available today.

## What are LangSmith Sandboxes?

LangSmith Sandboxes are isolated execution environments built into the LangSmith platform for running agent-generated code without exposing host infrastructure.

Each sandbox runs in a hardware-virtualized microVM with kernel-level isolation between sandboxes. LangSmith Sandboxes integrate with the existing LangSmith SDK, so teams already using the Python or JavaScript client for tracing or deployment can create sandboxes without adding a new dependency.

The product includes sandbox templates (reusable image and resource configurations), warm pools for pre-provisioned sandboxes, an auth proxy for injecting credentials without hardcoding secrets, persistent state across sessions, and long-running session support via WebSockets. LangSmith Sandboxes also include native integration with LangChain's Deep Agents framework and the Open SWE project.

LangSmith Sandboxes are in private preview, with APIs and features subject to change. Access requires signing up for the waitlist.

## What should you look for in a sandbox alternative?

Sandbox platforms differ in meaningful ways across isolation model, deployment flexibility, and pricing. These are the dimensions worth evaluating before committing.

- **Isolation model**: Platforms use different isolation approaches: Firecracker microVMs, gVisor (syscall interception), Kata Containers with Cloud Hypervisor, or combinations. For running untrusted or AI-generated code, hardware-level microVM isolation is generally the more defensible choice. See our comparison of [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor) for a deeper breakdown.
- **Ephemeral vs persistent sessions**: Some platforms cap session length. If your agents run for hours or days, verify the session limit before committing. See our guide to [ephemeral sandbox environments](https://northflank.com/blog/ephemeral-sandbox-environments) and [persistent sandboxes](https://northflank.com/blog/persistent-sandboxes).
- **BYOC (Bring Your Own Cloud) and deployment flexibility**: If your organization has data residency requirements, existing cloud spend commitments, or compliance constraints, check whether the platform supports bring your own cloud and how self-serve that process is. Most platforms in this space require contacting sales for BYOC access. See our guide to [BYOC AI sandbox platforms](https://northflank.com/blog/best-byoc-sandbox-platforms).
- **GPU support**: Most managed sandbox platforms in this space do not offer GPU compute. If your agents run inference or any GPU-bound workload, this is a hard requirement to check before evaluating further.
- **Pricing model:** Per-second billing is standard across most platforms. The meaningful differences are unit prices, what is included in the base cost, and whether BYOC is available to reduce costs at scale.
- **Production readiness**: Preview products and early-access platforms carry deployment risk. For production workloads, verify how long a platform has been running at scale and what compliance certifications it holds.

## Which sandbox platforms are the best alternatives for secure AI code execution?

The following sandbox platforms range from focused code execution to full-stack AI infrastructure. Each section covers isolation model, key capabilities, BYOC support, GPU availability, and session limits.

### 1. Northflank

[Northflank](https://northflank.com/) provides a full infrastructure platform for AI workloads: microVM [sandboxes](https://northflank.com/product/sandboxes), databases, APIs, workers, GPU workloads, CI/CD pipelines, and observability, running either in Northflank's managed cloud or inside your own VPC.

Sandboxes on Northflank use Kata Containers with Cloud Hypervisor, Firecracker, or gVisor, depending on workload and isolation requirements. Both ephemeral and persistent sessions are supported with no imposed time limits. Northflank accepts any OCI-compliant container image from any registry. Sandbox creation takes approximately 1–2 seconds.

A key differentiator compared to most platforms in this space is self-serve [bring your own cloud](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud) (Deploy sandboxes in your cloud). Northflank supports deployment into AWS, GCP, Azure, Oracle, CoreWeave, Civo, and bare-metal without requiring a sales process. For teams in regulated industries, BYOC support is frequently a hard requirement in security reviews.

Northflank supports on-demand GPU allocation across L4, A100 (40GB and 80GB), H100, H200, and [more](https://northflank.com/gpu) with per-second billing. GPU pricing is all-inclusive: CPU and RAM are not billed separately on top of GPU time.

The platform has been running in production since 2021 across startups, public companies, and government deployments. It is SOC 2 Type II certified and includes horizontal autoscaling, bin-packing for density at scale, and multi-tenant architecture.

**What Northflank supports:**

- MicroVM isolation with Kata Containers, Firecracker, and gVisor
- Both ephemeral and persistent environments with no session time limits
- Self-serve BYOC into AWS, GCP, Azure, Oracle, CoreWeave, Civo, bare-metal, and on-premises
- On-demand GPUs (L4, A100, H100, H200) with per-second billing, CPU and RAM included
- Databases (PostgreSQL, MySQL, Redis, MongoDB) deployable alongside sandboxes
- API, CLI, SSH, and UI access
- Built-in CI/CD, secrets management, observability, and RBAC
- SOC 2 Type II certified

**Pricing:** CPU at $0.01667/vCPU-hour, memory at $0.00833/GB-hour. H100 at $2.74/hour all-inclusive. See the [Northflank pricing page](https://northflank.com/pricing) for full details.

<InfoBox className="BodyStyle">

**Get started with Northflank sandboxes**

- [Sandboxes on Northflank documentation](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank): overview and concepts
- [Deploy sandboxes on Northflank](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-on-northflank): step-by-step deployment guide
- [Deploy sandboxes in your cloud](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud): BYOC deployment guide
- [Create sandbox with SDK](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank#create-sandboxes-with-the-sdk): programmatic sandbox creation

[Get started](https://app.northflank.com/signup) directly (self-serve), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo) for specific infrastructure or compliance requirements.

</InfoBox>

### 2. E2B

E2B is a managed sandbox platform focused on AI agent code execution. It runs Firecracker microVMs and provides Python and TypeScript SDKs. Sandboxes run continuously for up to 24 hours on the Pro plan or 1 hour on the Hobby plan. For longer workloads, E2B supports pause and resume, where pausing resets the runtime window while preserving the sandbox state.

BYOC is available but not self-serve: access is limited to AWS and GCP, and teams need to contact sales to get started. GPU compute is not available on E2B. For a detailed comparison, see our [E2B vs Modal](https://northflank.com/blog/e2b-vs-modal) and [self-hostable alternatives to E2B](https://northflank.com/blog/self-hostable-alternatives-to-e2b-for-ai-agents) articles.

**Session limits:** 1 hour (Hobby), 24 hours (Pro).
**BYOC:** Available, AWS and GCP only, contact sales.
**GPU:** Not available.
**Pricing:** $0.0504/vCPU-hr, $0.0162/GiB-hr. Storage included (10GB Hobby, 20GB Pro). Hobby plan is free with a one-time $100 credit. Pro at $150/month.

### 3. Modal

Modal is a serverless compute platform with a dedicated sandbox interface for running arbitrary code. Sandboxes on Modal are created at runtime via the API: you define the container image, resources, and commands to execute dynamically. Modal uses gVisor for sandbox isolation.

Sandbox timeout defaults to 5 minutes and can be configured up to 24 hours. For workloads exceeding 24 hours, Modal recommends using filesystem snapshots to preserve state and restore with a subsequent sandbox. Modal supports GPU compute across L4, A100, H100, H200, and B200, with GPU pricing separate from CPU and memory. There is no BYOC option. See our [E2B vs Modal](https://northflank.com/blog/e2b-vs-modal) comparison for more context.

**Session limits:** Default 5 minutes, configurable up to 24 hours.
**BYOC:** Not available.
**GPU:** Available (L4, A100, H100, H200, B200), billed separately from CPU and memory.
**Pricing:** $0.1419/physical core-hr (equivalent to 2 vCPU), $0.0242/GiB-hr memory. H100 at $3.95/hr. Starter plan includes $30/month free credits.

### 4. Fly.io Sprites

Sprites is Fly.io's sandbox product for running arbitrary code in persistent, hardware-isolated environments. Each Sprite is a Firecracker microVM with a persistent ext4 filesystem backed by NVMe storage. When a Sprite goes idle, compute charges stop, the filesystem is backed up to durable object storage, and it restores on the next request.

Every Sprite gets a unique URL for HTTP access. Sprites support up to 8 CPUs and 16GB RAM per instance. The platform provides CLI, REST API, and JavaScript and Go SDKs. There is no BYOC option and no GPU support.

**Session limits:** None.
**BYOC:** Not available.
**GPU:** Not available.
**Pricing:** Per second, based on actual cgroup CPU usage. $0.07/CPU-hr, $0.04375/GB-hr, $0.00068/GB-hr NVMe storage. No charge when idle. See our [top Fly.io Sprites alternatives](https://northflank.com/blog/top-fly-io-sprites-alternatives-for-secure-ai-code-execution-and-sandboxed-environments) for broader context.

### 5. CodeSandbox

CodeSandbox is a browser and VM sandbox platform, now part of Together AI. The CodeSandbox SDK supports programmatic creation and management of VM sandboxes. VM sandboxes run on microVMs with snapshot and fork capabilities and have no imposed session time limits.

The Scale plan supports up to 250 concurrent VM sandboxes and 16 vCPUs with 32 GiB RAM. Enterprise scales up to 64 vCPUs and 128 GiB RAM. BYOC is available on Enterprise as a dedicated cluster, requiring contact with sales. GPU compute is not available. See our [CodeSandbox alternatives](https://northflank.com/blog/codesandbox-alternatives) article for more context.

**Session limits:** Unlimited.
**BYOC:** Enterprise only, custom dedicated cluster, contact sales.
**GPU:** Not available.
**Pricing:** Credit-based at $0.015/credit ($0.075/core-hr equivalent). Build plan free with 40 hours/month. Scale from $170/month with 160 included VM hours and on-demand credits at $0.15/hour.

## Sandbox pricing comparison: LangSmith Sandboxes alternatives

*Pricing as of April 2026. Verify current rates on each platform's pricing page before making cost decisions.*

### PaaS pricing

The following table shows pricing for PaaS deployments, where you are using the platform's own infrastructure.

| Platform | CPU | Memory | Storage | GPU | Billing model |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | $0.01667/vCPU-hr | $0.00833/GB-hr | $0.15/GB-month | L4: $0.80/hr, A100 40GB: $1.42/hr, A100 80GB: $1.76/hr, H100: $2.74/hr, H200: $3.14/hr | Per second |
| **E2B** | $0.0504/vCPU-hr | $0.0162/GiB-hr | 10–20GB included free | Not available | Per second |
| **Modal Sandboxes** | $0.1419/physical core-hr (2 vCPU) | $0.0242/GiB-hr | — | L4: $0.80/hr, A100 40GB: $2.10/hr, A100 80GB: $2.50/hr, H100: $3.95/hr, H200: $4.54/hr | Per second |
| **Fly.io Sprites** | $0.07/CPU-hr | $0.04375/GB-hr | $0.00068/GB-hr (hot NVMe) | Not available | Per second, actual cgroup usage |
| **CodeSandbox** | $0.075/core-hr (credit-based: $0.015/credit) | Bundled with VM tier | Included | Not available | Credit-based |

### BYOC (Bring Your Own Cloud) pricing

The following table shows BYOC pricing, where you deploy sandboxes inside your own cloud account, and the platform provides the control plane.

| Platform | BYOC available | Clouds supported | Access model | Pricing model |
| --- | --- | --- | --- | --- |
| **Northflank** | Yes, fully self-serve | AWS, GCP, Azure, Oracle, CoreWeave, any neoclouds, Civo, bare-metal, on-premises | Self-serve, enterprise contracts available | Your existing cloud bill + CPU $0.01389/vCPU/hr and Memory $0.00139/GB/hr |
| **E2B** | Yes, limited and not self-serve | AWS and GCP only | Contact sales | Starts at $50/sandbox/month on top of your existing cloud bill |
| **Modal** | No | Managed only | — | — |
| **Fly.io Sprites** | No | Managed only | — | — |
| **CodeSandbox** | Enterprise only | Custom dedicated cluster | Contact sales | Custom |

### Cost comparison at scale

*Based on 200 sandboxes, plan: nf-compute-100-4, infra node: m7i.2xlarge.*

| Model | Provider | Cloud cost | Sandbox vendor cost | Total |
| --- | --- | --- | --- | --- |
| **PaaS** | Northflank | — | $7,200.00 | $7,200.00 |
| **PaaS** | E2B | — | $16,819.20 | $16,819.20 |
| **PaaS** | Modal | — | $24,491.50 | $24,491.50 |
| **PaaS** | Fly Sprites | — | $35,770.00 | $35,770.00 |
| **BYOC (0.2 request modifier)** | Northflank | $1,500.00 | $560.00 | $2,060.00 |
| **BYOC** | E2B | $1,500.00 | $10,000.00 | $11,500.00 |

Northflank's BYOC pricing includes a default overcommit via the request modifier. A request modifier of 0.2 means each sandbox requests 20% of its plan's resources as a guaranteed minimum but can burst to the full plan limit when capacity is available. This allows more sandboxes per node: for example, 40 instead of 8 at a 0.2 modifier, reducing both cloud infrastructure costs and the Northflank management fee at scale. For more, see our guides on [best BYOC sandbox platforms](https://northflank.com/blog/best-byoc-sandbox-platforms) and [top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes).

## FAQ: LangSmith Sandboxes and alternatives

### Are LangSmith Sandboxes available yet?

LangSmith Sandboxes launched in private preview in March 2026. Access requires signing up for the waitlist. APIs and features may change before general availability.

### Which LangSmith Sandboxes alternatives support GPU workloads?

Of the platforms covered in this article, Northflank and Modal both support GPU compute. Northflank supports L4, A100 (40GB and 80GB), H100, H200, and [more](https://northflank.com/gpu) with per-second all-inclusive billing. Modal supports L4, A100, H100, H200, and B200 with GPU billed separately from CPU and memory.

### Which LangSmith Sandboxes alternatives support BYOC?

Northflank supports self-serve BYOC into AWS, GCP, Azure, Oracle, CoreWeave, Civo, bare-metal, and on-premises. E2B offers BYOC for AWS and GCP, but access requires contacting sales. CodeSandbox offers BYOC on Enterprise plans via a dedicated cluster, also requiring contact with sales. Modal and Fly.io Sprites are managed-only.]]>
  </content:encoded>
</item><item>
  <title>Containers vs virtual machines: key differences and when to use each (2026)</title>
  <link>https://northflank.com/blog/containers-vs-virtual-machines</link>
  <pubDate>2026-04-14T11:15:00.000Z</pubDate>
  <description>
    <![CDATA[Containers vs virtual machines: key differences in isolation, startup time, memory overhead, and when to use each, including microVMs for workloads that need hardware-level isolation.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Containers_vs_virtual_machines_40ff897e9a.png" alt="Containers vs virtual machines: key differences and when to use each (2026)" />Containers and virtual machines are the two dominant approaches to running isolated workloads on shared hardware. Both package an application and its dependencies into a portable, isolated environment. But they achieve that isolation in fundamentally different ways, and the difference determines startup time, resource usage, security boundaries, and the right deployment pattern for your workloads.

If you are choosing between the two, or trying to understand how they fit together, this guide covers the architecture, tradeoffs, and practical guidance on when to use each.

## TL;DR: containers vs virtual machines

|  | Containers | Virtual machines |
| --- | --- | --- |
| **Isolation** | OS-level (namespaces, cgroups) | Hardware-level (hypervisor) |
| **Kernel** | Shared host kernel | Dedicated guest kernel per VM |
| **Startup time** | Milliseconds | Seconds to minutes |
| **Memory overhead** | Tens of MB | Hundreds of MB to several GB |
| **Density** | 100+ per host typical | 10–15 per host typical |
| **Security boundary** | Process isolation | Hardware isolation |
| **Best for** | Cloud-native apps, microservices, CI/CD | Legacy workloads, strong isolation, multi-OS |

<InfoBox className="BodyStyle">

**What is Northflank?**

[Northflank](https://northflank.com/) is a full-stack cloud platform that takes the complexity out of running containerized workloads at scale. You get Kubernetes-powered container orchestration, microVM isolation (Kata Containers, Firecracker, gVisor) for workloads that need it, managed databases, GPU support, CI/CD pipelines, and BYOC deployment into your own cloud, all in one place. No YAML. No cluster management. No stitching tools together.

[Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30).

</InfoBox>

## What is a container?

A container is a lightweight, portable executable package that includes an application and all its dependencies: libraries, binaries, and configuration. Containers run as isolated processes on the host operating system using Linux kernel features: namespaces isolate the process view, cgroups enforce resource limits, and a layered copy-on-write filesystem shares base image layers across containers.

Because containers share the host kernel, they do not boot a full operating system. A container starts in milliseconds. Multiple containers from the same image share base layers in memory, so running three containers from a 40MB image does not consume 120MB. On a server with 128GB RAM, a typical deployment runs 100 to 200 containers versus 10 to 15 VMs.

### Pros of containers

- Millisecond startup, no OS boot required
- Significantly lower memory and storage overhead than VMs
- Much higher density on the same hardware
- Portable across environments via the OCI image standard
- Native unit of deployment on Kubernetes
- Fast iteration and CI/CD integration

### Cons of containers

- Share the host kernel, so a kernel vulnerability can affect all containers on the same host
- Not suitable for workloads that require a different OS from the host
- Weaker isolation for multi-tenant environments running untrusted code

## What is a virtual machine?

A virtual machine is a software-based emulation of a complete physical computer. A hypervisor runs on the physical host and allocates CPU, memory, and storage to each VM. Each VM runs its own complete operating system with its own kernel, entirely isolated from the host and from other VMs on the same machine.

Because each VM boots a full kernel and OS, startup takes seconds to minutes and memory overhead starts at several hundred megabytes even for a minimal install. The tradeoff is stronger isolation. A vulnerability exploited inside a VM stays inside that VM. To reach the host or adjacent VMs, an attacker must also escape the hypervisor, which is a significantly harder attack path.

### Pros of virtual machines

- Hardware-level isolation enforced by the hypervisor
- Each VM runs its own kernel, so a compromise stays contained
- Supports any OS regardless of the host OS
- Required for many compliance frameworks (HIPAA, PCI-DSS, FedRAMP)
- Full control over the guest OS kernel and configuration

### Cons of virtual machines

- Slow startup due to full OS boot
- High memory and storage overhead per workload
- Much lower density than containers on the same hardware
- More complex provisioning and lifecycle management

## What are microVMs?

MicroVMs like Firecracker and Kata Containers combine VM-level hardware isolation with container-level startup speed. They boot a minimal guest kernel per workload in under 200ms with less than 5MB of memory overhead, providing hardware-level kernel separation without the startup cost of a traditional VM.

MicroVMs matter when the threat model requires kernel isolation, but container startup speed is still important. Running untrusted code, AI-generated scripts, or multi-tenant workloads where one tenant's code must not affect another tenant's kernel are the primary use cases. [Northflank](https://northflank.com/product/sandboxes) supports Kata Containers with Cloud Hypervisor, Firecracker, and gVisor per workload, so you can apply the right isolation model based on what the workload actually requires.

## When to use containers vs virtual machines?

| Use case | Containers | Virtual machines |
| --- | --- | --- |
| Microservices and cloud-native apps | Yes | No |
| CI/CD pipeline environments | Yes | No |
| Horizontal scaling and frequent deploys | Yes | No |
| Multi-tenant untrusted code execution | No (use microVMs) | Yes |
| Legacy applications requiring specific OS | No | Yes |
| Windows workloads on Linux hosts | No | Yes |
| Compliance frameworks (HIPAA, PCI-DSS, FedRAMP) | No (unless microVMs) | Yes |
| Kernel customization required | No | Yes |
| Maximum workload density | Yes | No |

## How containers and VMs work together

Containers and VMs are not mutually exclusive. Running containers inside VMs is the standard pattern on AWS, GCP, and Azure. [IDC research shows](https://www.vmware.com/docs/vmw-idc-containers-vms-modern-it-infrastructure-white-paper) that approximately 85% of containers will continue to run inside virtual machines (VMs) through 2028 to maintain security. The VM provides hardware isolation and cloud network isolation. Kubernetes runs inside those VMs and orchestrates containers on top. You get hardware-level security for multi-tenancy and compliance, with container-level density and deployment speed for your applications.

The typical production stack looks like this: physical host, hypervisor, VM with a Linux OS, container runtime (containerd or CRI-O), Kubernetes, and your application containers. Each layer adds isolation and manageability.

## How to run containers on Northflank

Building the stack described above, VMs, Kubernetes, container runtime, networking, secrets, and observability, takes months of engineering time and requires ongoing maintenance. [Northflank](https://northflank.com/product/deployments) gives you all of it out of the box.

You connect a repo or bring a container image, and Northflank handles the rest: scheduling on Kubernetes, autoscaling, TLS, secrets injection, real-time logs and metrics, and preview environments per pull request. No cluster setup. No YAML maintenance. Teams that previously spent weeks provisioning infrastructure get to production in minutes.

For workloads that need stronger isolation than standard containers, Northflank's [microVM-backed sandbox execution](https://northflank.com/product/sandboxes) runs Kata Containers with Cloud Hypervisor, Firecracker, and gVisor per workload. This is the same isolation layer used by companies like [cto.new to run thousands of daily sandbox deployments for untrusted code](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes). You can run standard containers and microVM sandboxes in the same control plane, alongside databases, background jobs, and GPU workloads, without splitting your infrastructure across tools.

[BYOC](https://northflank.com/product/bring-your-own-cloud) is self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal. Northflank manages the platform layer on your infrastructure, while your data never leaves your own VPC. For teams with compliance requirements, that distinction matters.

## FAQ: containers vs virtual machines

### Are containers less secure than virtual machines?

Containers provide weaker isolation than VMs by default. Because containers share the host kernel, a kernel vulnerability can potentially affect all containers on the same host. VMs isolate at the hypervisor level, so a compromise in one VM does not directly affect others. For multi-tenant workloads running untrusted code, microVMs like Firecracker and Kata Containers provide hardware isolation at near-container startup speeds.

### Do containers replace virtual machines?

No. Containers and VMs solve different problems and commonly run together in production. The VM provides hardware isolation and cloud networking. The container provides application packaging and orchestration. Most teams use both, and most cloud providers run Kubernetes inside VMs.

### How much faster do containers start compared to VMs?

Containers start in milliseconds because they do not boot a kernel. A typical container is ready in under one second. A traditional VM takes seconds to minutes to boot a full OS. MicroVMs like Firecracker start in approximately 125ms, providing a middle path for workloads that need isolation but cannot accept slow startup.

### What is the difference between Docker and a virtual machine?

Docker is a container runtime that packages applications and dependencies into OCI-compliant images and runs them as isolated processes sharing the host kernel. A VM runs a complete guest operating system on virtualized hardware managed by a hypervisor. Docker containers are lighter and faster but provide weaker isolation. A VM behaves like an independent machine with its own kernel.

### When should I use Kata Containers or Firecracker instead of standard containers?

Use microVMs when your threat model requires kernel isolation between workloads: running untrusted or AI-generated code, operating a multi-tenant platform where customers execute arbitrary code, or meeting compliance requirements that prohibit shared-kernel execution. Standard containers are sufficient for trusted internal workloads where you control what code runs.

### Can I run containers and microVMs on Northflank?

Yes. Northflank supports standard containers for general workloads and microVM-backed sandboxes (Kata Containers, Firecracker, gVisor) for workloads that require stronger isolation. Both run in the same control plane alongside databases, background jobs, and GPU workloads.

## Conclusion

Containers provide fast, lightweight, portable application packaging for cloud-native workloads. Virtual machines provide hardware-level isolation and full OS flexibility for workloads that require it. MicroVMs like Firecracker and Kata Containers bridge both, providing hardware isolation at container speed for multi-tenant and security-sensitive workloads. Most production deployments use all three, and managing that stack yourself is a significant engineering investment.

[Northflank](https://northflank.com) takes that investment off your plate. You get Kubernetes-powered container orchestration, microVM sandbox isolation, managed databases, GPU workloads, CI/CD pipelines, and BYOC deployment into your own cloud, all from a single control plane. The teams that build on [Northflank](https://northflank.com) ship faster because they are not maintaining infrastructure.

<InfoBox className="BodyStyle">

[Sign up for free](https://app.northflank.com/signup) and deploy your first containerized workload in minutes. Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) if you want to walk through how Northflank fits your stack

</InfoBox>

## Related articles

- [**How to spin up a secure code sandbox and microVM in seconds with Northflank**](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh): A practical guide to running Firecracker, gVisor, and Kata Containers on Northflank for workloads that need hardware-level isolation.
- [**Kata Containers vs Firecracker vs gVisor**](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): A comparison of microVM and isolation technologies covering security model, performance, and when to use each.
- [**Best platforms for untrusted code execution in 2026**](https://northflank.com/blog/best-platforms-for-untrusted-code-execution): How to choose a sandbox platform when isolation model determines security outcomes.
- [**Top internal developer portals in 2026**](https://northflank.com/blog/top-internal-developer-portals): How platform teams use Northflank to give developers self-service container deployment without managing Kubernetes directly.]]>
  </content:encoded>
</item><item>
  <title>Top HopX.ai alternatives for AI sandbox and agent infrastructure in 2026</title>
  <link>https://northflank.com/blog/hopx-ai-alternatives</link>
  <pubDate>2026-04-13T15:45:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the top HopX.ai alternatives for AI sandbox and agent infrastructure in 2026, including Northflank, E2B, Modal, Fly.io Sprites, and CodeSandbox. Covers isolation models, BYOC, GPU support, session limits, and pricing.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/hopx_ai_alternatives_99986186b3.png" alt="Top HopX.ai alternatives for AI sandbox and agent infrastructure in 2026" /><InfoBox className="BodyStyle">

## TL;DR: Top HopX.ai alternatives in 2026

HopX.ai is a managed sandbox platform built by Bunnyshell. It runs Firecracker microVMs and targets AI agents, code execution, CI/CD isolation, and MCP server hosting. You may be evaluating alternatives for reasons such as GPU support, a self-serve bring your own cloud (BYOC) path, full-stack infrastructure beyond code execution, or pricing at scale. Here are the top options at a glance:

- **Northflank:** Full-stack AI infrastructure platform with microVM [sandboxes](https://northflank.com/product/sandboxes) (Kata Containers and Firecracker) and gVisor, bring your own cloud (BYOC) into AWS, GCP, Azure, Oracle, CoreWeave, Civo, and bare-metal, GPU support, both persistent and ephemeral sessions, databases, and CI/CD pipelines. In production since 2021.
- **E2B:** Managed sandbox platform with Firecracker microVM isolation, Python and TypeScript SDKs, and per-second billing. Session limit of 24 hours on the Pro plan.
- **Modal:** Serverless compute platform with a dedicated sandbox interface for running arbitrary code in dynamically defined containers. No BYOC option.
- **Fly.io Sprites:** Stateful, Firecracker-based sandboxes with persistent ext4 filesystems and checkpoint/restore. No BYOC.
- **CodeSandbox:** Browser and VM sandbox platform, now part of Together AI. Credit-based billing with SDK access for programmatic sandbox creation.

</InfoBox>

## What should you look for in a HopX.ai alternative?

Not every sandbox platform makes the same architectural tradeoffs. Before evaluating alternatives to HopX.ai, it helps to know which dimensions are relevant to your workload.

- **Isolation model:** Platforms use different approaches: Firecracker microVMs, gVisor (syscall interception), Kata Containers with Cloud Hypervisor, standard containers, or combinations. The right choice depends on your threat model. For running untrusted or AI-generated code, hardware-level isolation (microVMs) is generally the more defensible option. See our comparison of [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor) for a deeper breakdown.
- **BYOC and deployment flexibility:** If your organization has data residency requirements, compliance constraints, or existing cloud spend commitments, verify whether the platform supports bring your own cloud and how self-serve that process is. See our guide to [BYOC AI sandbox platforms](https://northflank.com/blog/best-byoc-sandbox-platforms).
- **GPU support:** Most sandbox platforms in this space do not offer GPU compute. If your agents run inference, fine-tuning, or any GPU-bound workload, this is a hard requirement to check early.
- **Ephemeral vs persistent sessions:** Some platforms cap session length (for instance, E2B limits Hobby to 1 hour, Pro to 24 hours). If your workload runs for hours or days, verify the session limit before committing. See our breakdown of [ephemeral sandbox environments](https://northflank.com/blog/ephemeral-sandbox-environments) and [persistent sandboxes](https://northflank.com/blog/persistent-sandboxes) for more context.
- **Full-stack vs point solution:** A code execution endpoint is different from a platform that also runs databases, background workers, pipelines, and observability alongside sandboxes. Know which you need.
- **Pricing model and billing granularity:** Per-second billing is common across most platforms. The real differences are in unit prices, what is included in the base price, and whether BYOC is available to reduce costs at scale. We cover this in detail in the pricing section below.

## What are HopX.ai alternatives at a glance? (Comparison)

The table below compares the top alternatives to HopX.ai across isolation model, bring your own cloud support, GPU availability, session limits, billing, and primary use case.

| Platform | Isolation | BYOC | GPU | Session limit | Billing | Best for |
| --- | --- | --- | --- | --- | --- | --- |
| **Northflank** | Kata Containers, Firecracker, gVisor | Yes (self-serve) | Yes | None | Per second | Full-stack AI infra, compliance, BYOC |
| **E2B** | Firecracker microVMs | Limited (not self-serve) | No | 1hr (Hobby), 24hr (Pro) | Per second | AI agent prototypes, coding agents |
| **Modal** | gVisor | No | Yes | 24hr max (5min default, configurable via timeout parameter) | Per second | Python/ML workloads, batch inference |
| **Fly.io Sprites** | Firecracker microVMs | No | No | None | Per second (cgroup usage) | Stateful persistent environments |
| **CodeSandbox** | microVM | No | No | Unlimited | Credit-based ($0.015/credit) | Web tooling, snapshot/fork workflows |

## Compute pricing comparison for sandbox providers

*Pricing as of April 2026. Verify current rates on each platform's pricing page before making cost decisions.*

### PaaS pricing

The following table shows pricing for PaaS deployments, where you are using the platform's own infrastructure.

| Platform | CPU | Memory | Storage | GPU | Billing model |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | $0.01667/vCPU-hr | $0.00833/GB-hr | $0.15/GB-month | L4: $0.80/hr, A100 40GB: $1.42/hr, A100 80GB: $1.76/hr, H100: $2.74/hr, H200: $3.14/hr | Per second |
| **E2B** | $0.0504/vCPU-hr | $0.0162/GiB-hr | 10–20GB included free | Not available | Per second |
| **Modal Sandboxes** | $0.1419/physical core-hr (2 vCPU) | $0.0242/GiB-hr | — | L4: $0.80/hr, A100 40GB: $2.10/hr, A100 80GB: $2.50/hr, H100: $3.95/hr, H200: $4.54/hr | Per second |
| **Fly.io Sprites** | $0.07/CPU-hr | $0.04375/GB-hr | $0.00068/GB-hr (hot NVMe) | Not available | Per second, actual cgroup usage |
| **CodeSandbox** | $0.075/core-hr (credit-based: $0.015/credit) | Bundled with VM tier | Included | Not available | Credit-based |

### Bring your own cloud pricing

The following table shows BYOC pricing, where you deploy sandboxes inside your own cloud account, and the platform provides the control plane.

| Platform | BYOC available | Clouds supported | Access model | Pricing model |
| --- | --- | --- | --- | --- |
| **Northflank** | Yes, fully self-serve | AWS, GCP, Azure, Oracle, CoreWeave, any neoclouds, Civo, bare-metal, on-premises | Self-serve, enterprise contracts available | Your existing cloud bill, CPU $0.01389/vCPU/hour and Memory $0.00139/GB/hour |
| **E2B** | Yes, limited and not self-serve | AWS and GCP only | Contact sales | Starts at $50/sandbox/month on top of your existing cloud bill |
| **Modal** | No | Managed only | — | — |
| **Fly.io Sprites** | No | Managed only | — | — |
| **CodeSandbox** | Enterprise only | Custom dedicated cluster | Enterprise plan, contact sales | Custom |

## What are the top alternatives to HopX.ai?

The platforms below cover a range of use cases, from focused code execution to full-stack AI infrastructure. Each section describes what the platform provides and where it draws the line.

### 1. Northflank

[Northflank](https://northflank.com/) provides a full infrastructure platform for AI workloads, not just a code execution runtime. While HopX covers sandboxed execution, Northflank covers the full stack around it: microVM sandboxes, databases, APIs, workers, GPU workloads, CI/CD pipelines, and observability, running either in Northflank's managed cloud or inside your own VPC.

[Sandboxes on Northflank](https://northflank.com/product/sandboxes) support Kata Containers with Cloud Hypervisor, Firecracker, and gVisor depending on your isolation requirements. Sessions can run ephemerally or persist indefinitely with no forced time limits. Northflank accepts any OCI-compliant container image from any registry without modification.

The most significant differentiator compared to most platforms in this space is self-serve [bring your own cloud](https://northflank.com/product/bring-your-own-cloud). You can deploy into AWS, GCP, Azure, Oracle, CoreWeave, Civo, or bare-metal without going through a sales process. For teams in regulated industries, this distinction often determines whether a platform passes a security review at all.

Northflank also supports on-demand GPU allocation (L4, A100, H100, H200) with per-second billing. GPU pricing is all-inclusive: CPU and RAM are not billed separately on top of GPU time. Sandbox creation takes approximately 1–2 seconds.

Northflank has been running millions of microVMs monthly since 2021 across startups, public companies, and government deployments. It includes horizontal autoscaling and bin-packing for density at scale. For deeper context, see our guide to [multi-tenant cloud deployment](https://northflank.com/blog/multi-tenant-cloud-deployment) and [agent sandboxes on Kubernetes](https://northflank.com/blog/agent-sandbox-on-kubernetes).

**What Northflank supports:**

- MicroVM isolation with Kata Containers, Firecracker, and gVisor
- Both ephemeral and persistent environments, no session time limits
- Self-serve bring your own cloud into AWS, GCP, Azure, Oracle, CoreWeave, Civo, bare-metal
- On-demand GPUs (L4, A100, H100, H200) with per-second billing, CPU and RAM included
- Databases (PostgreSQL, MySQL, Redis, MongoDB) deployable alongside sandboxes
- API, CLI, SSH, and UI access
- Built-in CI/CD, secrets management, observability, and RBAC
- SOC 2 Type II compliant

**Pricing:** CPU at $0.01667/vCPU-hour, memory at $0.00833/GB-hour. H100 at $2.74/hour all-inclusive. See the [Northflank pricing page](https://northflank.com/pricing) for full details and the cost calculator.

### Northflank vs alternatives: cost comparison at scale

*Based on 200 sandboxes, plan: nf-compute-100-4, infra node: m7i.2xlarge.*

| Model | Provider | Cloud cost | Sandbox vendor cost | Total |
| --- | --- | --- | --- | --- |
| **PaaS** | Northflank | — | $7,200.00 | $7,200.00 |
| **PaaS** | E2B | — | $16,819.20 | $16,819.20 |
| **PaaS** | Modal | — | $24,491.50 | $24,491.50 |
| **PaaS** | Fly Sprites | — | $35,770.00 | $35,770.00 |
| **PaaS** | Runloop | — | $30,484.80 | $30,484.80 |
| **BYOC (0.2 request modifier)** | Northflank | $1,500.00 | $560.00 | $2,060.00 |
| **BYOC** | E2B | $1,500.00 | $10,000.00 | $11,500.00 |

Northflank's BYOC pricing includes a default overcommit via the request modifier. A request modifier of 0.2 means each sandbox requests 20% of its plan's resources as a guaranteed minimum, but can burst up to the full plan limit when capacity is available. This allows more sandboxes per node: for example, 40 instead of 8 at a 0.2 request modifier, which reduces both cloud infrastructure costs and the Northflank management fee at scale. For more, see our guide on [best BYOC sandbox platforms](https://northflank.com/blog/best-byoc-sandbox-platforms) and [top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes).

<InfoBox className="BodyStyle">

**Get started with Northflank sandboxes**

- [Sandboxes on Northflank documentation](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank): overview and concepts
- [Deploy sandboxes on Northflank](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-on-northflank): step-by-step deployment guide
- [Deploy sandboxes in your cloud](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud): BYOC deployment guide
- [Create sandbox with SDK](https://northflank.com/docs/v1/application/sandboxes/create-sandbox-with-sdk): programmatic sandbox creation

[Get started](https://app.northflank.com/signup) directly (self-serve), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo) for specific infrastructure or compliance requirements.

</InfoBox>

**Best for:** Teams that need full infrastructure control, compliance-sensitive workloads, GPU support, long-running stateful agents, or anyone building a production AI platform who needs more than a code execution endpoint.

### 2. E2B

E2B is a managed sandbox platform focused on AI agent code execution. It runs Firecracker microVMs and provides Python and TypeScript SDKs. It integrates with LangChain, OpenAI, and Anthropic tooling.

**What E2B supports:**

- Firecracker microVM isolation
- Python, JavaScript, and TypeScript SDKs
- Snapshots, AutoResume, and Git integration
- SSH and interactive terminal access
- Persistent volumes and MCP gateway
- Session limits: 1 hour (Hobby), 24 hours (Pro)

**Pricing:** Free Hobby tier with $100 one-time credit. Pro at $150/month. Usage billed at $0.0504/vCPU-hr and $0.0162/GiB-hr. Storage included (10GB on Hobby, 20GB on Pro). For a detailed comparison, see our [E2B vs Modal](https://northflank.com/blog/e2b-vs-modal) and [self-hostable alternatives to E2B](https://northflank.com/blog/self-hostable-alternatives-to-e2b-for-ai-agents) articles.

**Best for:** Teams building AI coding agents or code interpreter workflows on managed infrastructure who do not need sessions longer than 24 hours.

### 3. Modal

Modal is a serverless compute platform with a dedicated sandbox interface for running arbitrary code in dynamically defined containers. Sandboxes on Modal are created at runtime via the API: you specify the container image, resources, and commands to execute.

Modal uses gVisor for sandbox isolation. The platform has no BYOC option. GPU billing is separate from CPU and RAM.

**What Modal supports:**

- gVisor isolation
- API for defining and running sandboxes at runtime
- Snapshots (beta)
- Volumes, cloud bucket mounts, and distributed queues
- GPU support (L4, A100, H100, H200, B200)
- Web endpoints, cron jobs, and job queues alongside sandboxes

**Pricing:** Per second. Sandbox CPU at $0.1419/physical core-hr (equivalent to 2 vCPU). Memory at $0.0242/GiB-hr. GPU billed separately: H100 at $3.95/hr, A100 80GB at $2.50/hr. Starter plan includes $30/month free credits. Team plan at $250/month with $100/month free credits. See our [E2B vs Modal](https://northflank.com/blog/e2b-vs-modal) comparison for more context.

**Best for:** Python-centric ML teams running batch jobs, model inference, and data pipelines who want sandboxing integrated with a broader serverless compute workflow.

### 4. Fly.io Sprites

Sprites is Fly.io's sandbox product for running arbitrary code in persistent, hardware-isolated environments. Each Sprite is a Firecracker microVM with a persistent ext4 filesystem backed by NVMe storage. When a Sprite goes idle, compute is released and the filesystem is backed up to durable object storage, then restored on the next request.

Sprites support checkpoint/restore in approximately 300ms. Every Sprite gets a unique URL for HTTP access. Sprites has no BYOC option and no GPU support.

**What Sprites supports:**

- Firecracker microVM isolation
- Persistent ext4 filesystem (100GB default, auto-grows)
- Checkpoint/restore (~300ms)
- Unique per-Sprite HTTP URLs
- CLI, REST API, JavaScript and Go SDKs
- Up to 8 CPUs and 16GB RAM per Sprite

**Pricing:** Per second, based on actual cgroup CPU usage. CPU at $0.07/CPU-hr, memory at $0.04375/GB-hr, storage at $0.00068/GB-hr (NVMe). $30 trial credits on signup.

**Best for:** Teams that need persistent stateful environments for long-running agents, or are already on Fly.io infrastructure. For more, see our [top Fly.io Sprites alternatives](https://northflank.com/blog/top-fly-io-sprites-alternatives-for-secure-ai-code-execution-and-sandboxed-environments) article.

### 5. CodeSandbox

CodeSandbox is a browser and VM sandbox platform, now part of Together AI. The CodeSandbox SDK supports programmatic creation and management of VM sandboxes. VM sandboxes run on microVMs with snapshot and fork capabilities.

**What CodeSandbox supports:**

- microVM isolation
- SDK for programmatic sandbox creation and management
- Snapshot and fork support
- Browser-based sandbox editor
- Unlimited session length
- SOC 2 Type II compliance
- Up to 64 vCPU and 128 GiB RAM on Enterprise

**Pricing:** Free Build plan with 40 hours/month of VM credits. Scale plan from $170/month with 160 included VM hours and on-demand credits at $0.015/credit ($0.075/core-hr equivalent). Enterprise pricing is custom.

**Best for:** Teams already using CodeSandbox for development workflows, web-focused coding agents, or use cases where snapshot and fork are central to the product. See our [CodeSandbox alternatives](https://northflank.com/blog/codesandbox-alternatives) article for more context.

## Which HopX.ai alternative should you choose?

| If you need... | Platform to consider |
| --- | --- |
| Full-stack AI infrastructure with databases, GPUs, CI/CD, and observability under one control plane | Northflank |
| Self-serve BYOC into AWS, GCP, Azure, Oracle, CoreWeave, Civo, or bare-metal | Northflank |
| On-demand GPU support with per-second billing | Northflank or Modal |
| A direct managed swap with Firecracker isolation and clean SDKs, sessions up to 24 hours | E2B |
| Python-first serverless compute with sandboxing alongside batch jobs and ML inference | Modal |
| Persistent stateful environments with checkpoint/restore and per-cgroup billing | Fly.io Sprites |
| Snapshot and fork semantics, or an existing CodeSandbox workflow | CodeSandbox |

For a deeper look at how these platforms compare in specific scenarios, see our guides on [best code execution sandboxes for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents), [how to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents), and [best platforms for high-concurrency sandbox environments](https://northflank.com/blog/best-platforms-for-high-concurrency-sandbox-environments).

## Frequently asked questions about HopX.ai alternatives

### What is HopX.ai used for?

HopX.ai provides isolated cloud sandbox environments for running untrusted code, AI agent workloads, CI/CD test isolation, data processing jobs, desktop automation, and MCP server hosting. It runs Firecracker microVMs and is built by Bunnyshell.

### What is the best HopX.ai alternative for enterprise workloads?

The relevant factors for enterprise workloads are BYOC availability, compliance certifications, session duration, GPU support, and the ability to run full infrastructure in a private VPC. Northflank is SOC 2 Type II certified, supports self-serve BYOC into major cloud providers and bare-metal, imposes no session limits, and includes GPU support with per-second billing. See our guide on [best enterprise AI sandbox platforms](https://northflank.com/blog/best-enterprise-ai-sandbox-platforms) for more detail.]]>
  </content:encoded>
</item><item>
  <title>6 best Railway alternatives in 2026: Pricing, flexibility &amp; BYOC</title>
  <link>https://northflank.com/blog/railway-alternatives</link>
  <pubDate>2026-04-13T12:49:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for Railway alternatives that don’t rely on one-time credits, offer better control, and support BYOC? Here are 6 platforms like Northflank, Heroku, Render, DigitalOcean App Platform and Fly.io that let you deploy apps your way in 2026.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/railway_alternatives_69952eb113.png" alt="6 best Railway alternatives in 2026: Pricing, flexibility &amp; BYOC" />> *"I just want my app to stay online without constantly worrying about credits or limitations." ~ someone on [Reddit](https://www.reddit.com/r/programminghelp/comments/1hi11b3/looking_for_help_with_railwayapp_alternatives/), sharing a common experience with Railway's pricing model and in search of Railway alternatives.*

If you're reading this, it means you've run into some limits with Railway. It could be because your app stopped running once your free credit ran out. Or you're looking for a platform that supports [Bring Your Own Cloud (BYOC)](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) self-serve. Or you need better visibility into what's happening during deploys.

Those concerns go beyond pricing. As of mid-2026, Railway has had a recurring pattern of outages and degraded performance, including a December 2025 incident that paused builds across all plan tiers in their EU West region. If uptime matters to your project, that's worth factoring in from the start. Northflank has historically maintained 99.99% uptime, contractually guaranteed under enterprise SLAs.

Now that you're looking for the best Railway alternatives, we'll help you figure that out, along with some tips on making the most suitable choice for your project needs.

<div>
	<center>
		<a href="https://app.northflank.com/signup">
<Button variant={["large", "gradient"]}>Find the right platform for your next project</Button>
		</a>
	</center>
</div>



<InfoBox className='BodyStyle'>

### Quick look: top Railway alternatives in 2026

In a hurry? Here's a quick breakdown of some of the best Railway alternatives for 2026:

1. [**Northflank**](https://northflank.com/) – BYOC support, static IPs, clean observability and No forced shutdown.

2. [**Fly.io**](https://fly.io/) – Run full-stack apps globally with static IPs and region-aware deploys.

3. [**Coolify**](https://coolify.io/) – Self-hosted, Docker-native, and great for devs who prefer full control.

4. [**DigitalOcean App Platform**](https://www.digitalocean.com/products/app-platform) – Simple deploys and good observability, best for teams already using DO.

5. [**Render**](https://render.com/) – Familiar interface, free tier with uptime limits, but no BYOC.

6. [**Heroku**](https://heroku.com/) – Still stable, CLI-driven, and works well for legacy Postgres-backed apps.

</InfoBox>


We’ll walk you through 6 Railway alternatives that give you more flexibility, control, and long-term reliability.

## What to look for in Railway alternatives

If Railway’s limitations have slowed you down, let’s look at a few things to prioritize when choosing a more suitable alternative:

### 1. No forced shutdown due to credits

On Railway, apps stop running once your $5 free trial credit runs out. If you’re building something meant to stay live, even during testing, you’ll want a platform that supports continuous uptime, even at lower tiers.

### 2. Bring Your Own Cloud (BYOC) support or multicloud flexibility

Some platforms let you deploy to your own cloud account (AWS, GCP, Azure). If you’re working in a regulated industry or want full infrastructure control, [Bring Your Own Cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) (BYOC) support is a major win.

 ![](https://assets.northflank.com/byoc_2_f6d305f5cf.png) 

### 3. Detailed logs and deployment observability

If something breaks, you need answers fast. Look for platforms that offer clear logs, build info, and deploy history out of the box, not just vague error messages.

### 4. Customizing build environments isn't always straightforward

Railway uses [Nixpacks](https://docs.railway.app/builds/nixpacks) to detect and build your app automatically, which works well for standard setups. But if your project has specific build steps, dependencies, or non-default behavior, you’ll hit limits fast. The main workaround is to use a [custom Dockerfile](https://docs.railway.app/builds/dockerfile), which gives you more control but also adds more to manage.

Platforms like [Northflank](https://northflank.com/features/build) or [Coolify](https://www.coolify.io/) let you customize builds more easily or support Docker-first workflows out of the box. So if you're looking for more flexibility without giving up visibility, this is something to consider.

### 5. Basic secret management, but not much beyond that

Railway supports environment variables out of the box, which is fine for getting started. But if you’re managing sensitive credentials at scale, or need things like audit trails, versioning, or role-based access, the built-in setup can start to feel limited.

The [official docs](https://docs.railway.com/guides/variables#using-doppler-for-secrets-management) even suggest integrating with third-party tools like [Doppler](https://www.doppler.com/), which provide more advanced secret management. With Doppler, you get features like Git-style versioning, sync across environments, and clearer control over who sees what.

If secret handling is part of your team’s workflow, this could be a deciding factor.

### 6. Persistent storage support is still limited

Railway now supports [volumes](https://docs.railway.com/reference/volumes), which let you persist data across deploys. That’s a welcome improvement, but the feature is still new and may not be flexible enough if your app relies heavily on persistent storage or shared volumes.

Some alternatives like [Northflank](https://northflank.com/docs/v1/application/production-workloads/persistent-storage-in-production) or [Render](https://render.com/docs/disks) offer more mature options for mounting volumes, managing data across services, or scaling stateful apps. So if storage is a critical part of your setup, it’s worth double-checking how each platform handles it.

### 7. Managing background workers and persistent services

If your app needs to run background jobs or queue workers, Railway doesn’t make it easy. There’s no built-in support for background workers, the suggested workaround (from a [public feedback](https://station.railway.com/feedback/background-workers-support-1ab6b861) thread) is to spin up a second service manually inside your project. It works, but it’s not ideal, especially for production apps or event-driven systems.

Platforms like [Render](https://render.com/) and [Northflank](https://northflank.com/) handle this better. They let you define background workers or cron jobs as first-class services, so you don’t have to hack things together just to get long-running jobs working reliably.

### 8. Flexible pricing for small and growing apps

Paying based on one-time credits makes sense for testing, but it’s not practical long-term. Alternatives that let you scale gradually or price based on usage give you more room to grow without surprises.

### 9. Reliable support

Some platforms reserve their best support for paying users, and even then, it varies. Prioritize tools with consistent communication, product updates, and a clear way to raise issues.

### 10. Freedom from vendor lock-in

If your platform doesn’t let you export workloads or migrate easily, you’re stuck. BYOC-friendly platforms or those with open standards make it easier to leave without re-architecting everything. 

## Quick comparison table of 6 Railway alternatives

If you're in a hurry, this chart gives you a quick side-by-side look at how top Railway alternatives compare on BYOC support, deploy visibility, pricing flexibility, and staying online without credit-based shutdown

| **Platform** | **BYOC Support** | **Deployment + Worker Support** | **Pricing Flexibility** | **Credit-Free Uptime** |
| --- | --- | --- | --- | --- |
| Northflank | Yes | Full logs, built-in workers | Usage-based or free | Yes |
| Fly.io | No | CLI-focused, supports workers | Pay-as-you-go | Yes |
| Coolify | Yes | Self-managed logs & workers | VPS = cost control | Yes |
| DigitalOcean App Platform | No | Logs + limited worker support | Affordable tiers | Yes (on paid plans) |
| Render | No | Deploy logs + native workers | More transparent | Yes |
| Heroku | No | Mature logs + worker dynos | Paid only | No (free tier shuts down) |

## 6 best Railway alternatives in 2026

Now that you’ve seen the quick comparison, let’s break down each option in more detail, including how they handle pricing, deployments, and infrastructure control.

### 1. Northflank – For uptime, better observability, and full control

 ![](https://assets.northflank.com/northflank_s_home_page_07aca80fa1.png) 

[Northflank](https://northflank.com/) gives you detailed deployment logs, clear metrics, and the ability to run apps in your own cloud using [BYOC (Bring Your Own Cloud)](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment). You can also manage secrets, scale services, and run background jobs, all with one clean UI. Northflank also supports [persistent storage volumes](https://northflank.com/docs/v1/application/databases-and-persistence/add-a-volume) and [custom Docker builds](https://northflank.com/docs/v1/application/build/build-with-a-dockerfile) out of the box, giving you full flexibility for [stateful services](https://northflank.com/docs/v1/application/databases-and-persistence/stateful-workloads-on-northflank) and non-standard [build](https://northflank.com/features/build) processes.

If your app stopped running after your free credit ran out on Railway, this solves that too. There’s no sleep mode, no timeouts, and no unexpected shutdowns when usage credits run out.

Compared to Railway, you get more control, more visibility, and production-ready features without needing workarounds.

> *Go with this if you want your app to stay live without upgrade pressure, and you care about full control over logs, secrets, background jobs, persistent storage, and where your services run, even in your own cloud.*
> 

### 2. Fly.io – For globally distributed apps and usage-based billing

 ![](https://assets.northflank.com/fly_io_87f030b697.png) 

If you’ve outgrown Railway’s free tier or need more control over where your app runs, [Fly.io](http://fly.io/) might be a better fit. It lets you deploy apps to multiple regions, closer to your users, and assigns static IPv4/IPv6 addresses by default. There’s no credit-based shutdown; billing is based on actual usage, so your app won’t suddenly stop running when a one-time credit runs out.

Fly uses a CLI-first workflow, which gives you more control over deployment and scaling, but comes with a slightly steeper learning curve compared to Railway’s beginner-friendly UI. It also supports persistent volumes and scheduled jobs, though secret management is basic and more advanced workflows may require third-party integrations or manual setup.

> *Choose this if your priority is low latency, global reach, or usage-based scaling, and you’re okay with managing builds and secrets with more manual setup.*
> 

### 3. Coolify – For full control and self-hosting, with no pricing surprises

 ![](https://assets.northflank.com/coolify_f726938e5e.png) 

If you’re running into limits with Railway’s pricing model or want to avoid being tied to a hosted provider, [Coolify](https://www.coolify.io/) gives you full control, but you'll need to bring your own infrastructure. It’s an open-source platform you install on a VPS or your own machine. That means no credit caps, no forced upgrades, and no surprise shutdowns; you decide how it runs.

Coolify supports Docker out of the box, gives you full visibility into logs and builds, and supports BYOC-style workflows across multiple cloud providers. You get flexibility and ownership, but you also take on more setup and maintenance. You also get native support for persistent volumes, cron jobs, and build customization. Secret management is basic unless extended via your hosting setup.

> *Choose this if you’re comfortable with self-hosting and want full access to your environment, including build customization, BYOC workflows, background jobs, and long-term flexibility without usage-based limits.*
> 

### 4. DigitalOcean App Platform – For teams that want stability and better pricing clarity

 ![](https://assets.northflank.com/Digitalocean_app_platform_s_home_page_a7b876bb7f.png)

Railway’s $5 trial credit can run out quickly, especially if your app needs to run 24/7. [DigitalOcean App Platform](https://www.digitalocean.com/products/app-platform) gives you consistent monthly pricing from the start, so you don’t have to deal with usage caps or credits. You can deploy from GitHub and autoscale services and access detailed logs and metrics through a production-ready dashboard.

It doesn’t support BYOC, but you’re building on infrastructure trusted by many teams, with built-in tools for databases, static sites, and monitoring. Persistent storage is available for databases and apps with attached volumes. Custom Docker builds are supported via the container-based workflow. Secret management and cron jobs are also built in.

> *Go with this if you want clearer pricing, better uptime than credit-based platforms, and a managed experience with logs, metrics, and persistent storage on paid tiers.*
> 

See this [full breakdown of DigitalOcean alternatives](https://northflank.com/blog/best-digitalocean-alternatives-2025)

### 5. Render – For a familiar experience, with fewer limitations

 ![](https://assets.northflank.com/render_s_home_page_05c1d65908.png)

If you like Railway’s clean UI and simple Git-based deploys, [Render](https://render.com/) gives you that, but with fewer constraints around pricing and uptime. There’s no credit-based shutdown on the free tier, and you’ll get better visibility into deploy logs, background workers, and service status.

Render gives you native support for background workers, cron jobs, persistent volumes, and environment secrets. It’s also more flexible than Railway for customizing build processes. It’s still a PaaS like Railway, so you won’t get BYOC or full infrastructure control, but it’s more stable for apps that need to stay online longer.

> *Choose this if you want a PaaS similar to Railway, but with native support for background workers, persistent volumes, and better deploy visibility, without credit shutdowns.*
> 

See how it compares in this [Render alternatives article](https://northflank.com/blog/render-alternatives)

### 6. Heroku – For reliability and a CLI-first experience

 ![](https://assets.northflank.com/heroku_092e1c7f09.png) 

If your Railway project stopped running after the $5 credit ran out, [Heroku](https://www.heroku.com/) offers a more predictable setup, but only if you’re okay paying from the start. There’s no free tier anymore, but what you get is a stable platform with a mature ecosystem, one-click add-ons, and a developer-friendly CLI. It's great for teams that are used to Git-based deploys and want something that just works for production apps.

Heroku supports background workers through separate dynos, persistent storage via add-ons, secret management through config vars, and flexible builds with buildpacks or Docker.

> Choose this if you prefer a CLI-first experience, and you're fine with paying from day one for a mature, stable platform, even if it lacks BYOC or advanced observability by default.
> 

Learn more in this [Heroku alternatives article](https://northflank.com/blog/top-heroku-alternatives)

## Tips for choosing the best Railway alternative

It’s easy to get caught up comparing dashboards or free tier limits, but the bigger question is how the platform fits your project needs over time. A few things to keep in mind when deciding:

- **Will your app need to stay online 24/7?**
    
    If your app stops running the moment you hit a usage limit, that’s a problem. Go for a platform that supports continuous uptime, even on the lowest tier.
    
- **Do you want to run in your own cloud?**
    
    If you're thinking about long-term flexibility or working in regulated environments, choose a platform that supports BYOC or multi-cloud setups like [Northflank](https://app.northflank.com/signup).
    
- **Do you care about deployment visibility?**
    
    Detailed logs, build info, and clear error output will save you hours. Look for platforms that show what’s happening, not just if something went wrong.
    
- **How predictable is the pricing?**
    
    Platforms that rely on one-time credits or unclear resource caps can create surprise costs. Find one with transparent billing and fair pricing as your app grows.
    
- **Is it easy to leave?**
    
    Some platforms make it hard to migrate later. If you care about ownership and future-proofing, avoid anything that locks you in.
    

If your setup is a bit more involved, here are a few more things to check:

- **Will you need background jobs or scheduled workers?**
    
    Some platforms treat these as first-class services. Others (like Railway) require manual workarounds that might not scale well.
    
- **Do you need persistent volume storage?**
    
    If your app handles uploads or databases with local file storage, confirm that the platform has reliable, production-ready volume support.
    
- **Will you need to customize builds?**
    
    Platforms that rely entirely on automatic build detection (e.g., via Nixpacks) might not work for projects with custom steps. Docker support or advanced build configs can save time later.
    
- **How do you manage secrets?**
    
    If you're working across environments or teams, basic env vars might not be enough. Some platforms integrate with tools like Doppler to make secret management more scalable and secure.
    

## Common questions about Railway and alternatives

If you’re unsure which direction to go after hitting limits with Railway, this section clears up some of the most searched questions around switching, BYOC support, and which tools are more suitable for production.

### What is better than Railway?

That depends on your needs. If you want full control, no sleep mode, and the ability to run in your own cloud, platforms like [Northflank](https://app.northflank.com/) or [Coolify](https://www.coolify.io/) stand out. Northflank, for example, keeps your app online without usage credit shutdowns and lets you deploy in your own cloud with [BYOC](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment). If you're focused on global distribution, [Fly.io](https://fly.io/) gives you more flexibility. And if you're looking for more stability or clearer billing, [**D**igitalOcean App Platform](https://www.digitalocean.com/products/app-platform) or [Render](https://render.com/) might be a better fit.

### How does Railway compare to Render or Heroku?

All three tools aim to simplify app deployment, but they differ in structure:

- Railway has a beginner-friendly UI and fast setup, but relies on credits for uptime and has some visibility limitations.
- Render is similar but doesn’t use credit-based billing and gives you more visibility into builds and logs.
- Heroku is CLI-first and stable, but entirely paid now and doesn’t support modern features like BYOC.

### Is Railway good for production apps?

It depends on your scale. For personal projects or MVPs, Railway can work well. But for long-running apps, or if you need things like static IPs, BYOC, or deeper observability, you’ll likely run into limitations. In that case, a platform like Northflank or DigitalOcean App Platform might be a better fit.

If you also need features like background workers, persistent storage, or more control over your build pipeline, these can be difficult to manage on Railway without custom workarounds or third-party integrations.

### Does Railway support BYOC?

Railway doesn't support Bring Your Own Cloud (BYOC) self-serve. It is limited to Enterprises. If that's important to you, Northflank is a better alternative.

## Keep your app running with more control and fewer limitations

Railway makes it easy to get started, but if you've dealt with credit shutdowns, limited visibility, or lack of BYOC support, those issues can slow you down as your app grows.

You don’t have to stick with those limitations.

Platforms like [Northflank](https://app.northflank.com/signup), [Fly.io](https://fly.io/), and [Coolify](https://www.coolify.io/) give you more flexibility, letting you run apps in your own cloud, avoid forced downtime, and get the deployment visibility needed for production.

If you also need better handling of background jobs, more control over how your app is built and deployed, or persistent storage that doesn’t feel like an afterthought, switching to something like Northflank can save you time and unblock your roadmap.

If you're ready to move to a platform that grows with your needs and gives you more control, start here:

<div>
  <center>
    <a href="https://app.northflank.com/signup">
      <Button variant={["large", "gradient"]}>Deploy without limitations →</Button>
    </a>
  </center>
</div>]]>
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</item><item>
  <title>Best agent cloud platforms in 2026</title>
  <link>https://northflank.com/blog/best-agent-cloud-platforms</link>
  <pubDate>2026-04-10T15:30:00.000Z</pubDate>
  <description>
    <![CDATA[Agent cloud platforms give AI agents the infrastructure to run: sandboxes, persistent compute, GPUs, and BYOC. Compare the leading options in 2026.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/best_agent_cloud_platforms_53b559d853.png" alt="Best agent cloud platforms in 2026" /><InfoBox className="BodyStyle">

## TL;DR: Best agent cloud platforms in 2026

- Agent cloud refers to the full infrastructure stack AI agents run on: isolated sandboxes for code execution, persistent compute for stateful workloads, background workers, storage, and optionally GPU inference.
- Most purpose-built sandbox tools cover isolated code execution only. Production agents typically need more than that.
- Key evaluation dimensions: isolation model, ephemeral vs persistent environments, GPU availability, BYOC (Bring Your Own Cloud) support, and pricing model.
- [Northflank](https://northflank.com/product/sandboxes) covers the full stack: microVM-based sandboxes (Kata Containers, Firecracker) and gVisor, both ephemeral and persistent environments with no forced time limits, on-demand GPUs, and self-serve BYOC across AWS, GCP, Azure, Oracle, Civo, CoreWeave, on-premises, and bare-metal. In production since 2021.

</InfoBox>

In infrastructure terms, "agent cloud" refers to the compute and orchestration layer that AI agents execute on. This article covers that meaning: what agents need to run in the cloud, and which platforms cover that scope today.

You are building something that runs agents, executes code those agents write, maintains state across sessions, and likely needs to do all of that inside your own cloud or at a cost that scales with your workload. The question is which platforms can handle that scope, and where each one draws the line.

## What is an agent cloud?

An agent cloud is the infrastructure layer that AI agents run on. It covers the environments where agents execute code, the compute that keeps long-running agents alive, the storage that preserves state across sessions, and the orchestration that ties all of it together.

The term covers a wide range. Some platforms in this category provide only sandboxed code execution. Others provide the full stack: agents, background workers, APIs, databases, and GPU inference under one control plane. Understanding where a platform sits on that spectrum matters before you commit to it.

For a foundational definition of what sandboxes are within this stack, see [what is an AI sandbox](https://northflank.com/blog/what-is-an-ai-sandbox).

## What does an agent need to run in the cloud?

Before comparing platforms, it helps to be clear about what the infrastructure layer needs to provide. Most production agents need more than isolated code execution.

- **Sandboxes and isolated code execution:** Agents write and execute code that may be LLM-generated, user-submitted, or untrusted. It needs to run in an environment isolated from your host system and other tenants. The isolation model matters: container-level isolation shares the host kernel, while microVMs (Firecracker, Kata Containers) and kernel-sandboxing tools like gVisor give each workload stronger isolation than standard containers. See [best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents), [how to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents).
- **Ephemeral vs. persistent environments:** Stateless sandboxes work for short-lived tasks. Agents with memory, session history, or accumulated state need environments that persist between runs. Some platforms impose hard session limits that break long-horizon workflows. See [ephemeral execution environments for AI agents](https://northflank.com/blog/ephemeral-execution-environments-ai-agents), [persistent sandboxes](https://northflank.com/blog/persistent-sandboxes).
- **Background workers and async jobs:** Agents spawn async tasks and scheduled jobs. A sandbox handles isolated execution of a single workload; a full runtime handles the lifecycle of workers and background processes alongside that. See [code execution environment for autonomous agents](https://northflank.com/blog/code-execution-environment-for-autonomous-agents), [top AI agent runtime tools](https://northflank.com/blog/top-ai-agent-runtime-tools).
- **GPU compute:** Inference and compute-heavy tool use require GPUs. On-demand availability without quota requests or reserved capacity is a meaningful practical distinction between platforms.
- **BYOC and deployment model:** For enterprise deployments with data residency requirements or teams that need execution inside their own VPC, self-serve BYOC is a hard requirement. Related: [self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes), [top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes).

## What should you look for when evaluating agent cloud platforms?

These are the dimensions that tend to be decisive when choosing infrastructure for agent workloads.

| Criteria | Why it matters |
| --- | --- |
| Isolation model | MicroVM vs container-level security for untrusted code |
| Ephemeral and persistent | Whether the platform supports both stateless and stateful workloads |
| Session limits | Maximum sandbox duration; relevant for long-horizon agent tasks |
| GPU availability | Required for inference and training workloads |
| BYOC support | Running execution inside your own VPC for compliance or data residency |
| Pricing model | Per-second billing, PaaS vs BYOC cost structure |
| SDK and API access | Integration surface for agent frameworks |

## Agent cloud platform comparison at a glance

The table below covers isolation model, environment support, GPU and BYOC availability, compute pricing, and billing model across all platforms in this comparison. Pricing as of April 2026. Verify current rates on each platform's pricing page before making cost decisions.

| Platform | Isolation model | Ephemeral | Persistent | GPU | BYOC (Bring Your Own Cloud) | CPU pricing | Memory pricing | Billing |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Northflank | MicroVM (Kata, Firecracker) + gVisor | Yes | Yes | Yes, L4: $0.80/hr, A100 40GB: $1.42/hr, A100 80GB: $1.76/hr, H100: $2.74/hr, H200: $3.14/hr, and [more](https://northflank.com/gpu) | Yes, self-serve | $0.01667/vCPU-hr | $0.00833/GB-hr | Per second |
| E2B | MicroVM (Firecracker) | Yes | Yes | Do not provide GPU compute | Limited (enterprise only, AWS & GCP only), requires contacting sales | $0.0504/vCPU-hr | $0.0162/GiB-hr | Per second |
| Modal | gVisor | Yes | No | Yes, L4: $0.80/hr, A100 40GB: $2.10/hr, A100 80GB: $2.50/hr, H100: $3.95/hr, H200: $4.54/hr | No | $0.1419/core-hr (2 vCPU) | $0.0242/GiB-hr | Per second |
| Fly.io Sprites | MicroVM (Firecracker) | Yes | Yes | Do not provide GPU compute | No | $0.07/CPU-hr | $0.04375/GB-hr | Per second, no idle |
| Runloop | MicroVM + container (two-layer) | Yes | Yes | Do not provide GPU compute | Enterprise, requires contacting sales | $0.108/CPU-hr | $0.0252/GB-hr | Per second |
| Vercel Sandbox | MicroVM (Firecracker) | Yes | Beta | Do not provide GPU compute | No | $0.128/vCPU-hr | $0.0212/GB-hr | Active CPU only |
| Cloudflare Sandbox | Container | Yes | No | Do not provide GPU compute | No | $0.072/vCPU-hr | $0.009/GiB-hr | Active CPU |

## Which are the best agent cloud platforms in 2026?

The platforms below range from full-stack production runtimes to purpose-built sandbox tools. Each has a distinct scope and trade-off profile worth understanding before you commit.

### 1. Northflank

[Northflank](https://northflank.com/) is a production infrastructure platform that covers the complete stack an AI product needs: agents, APIs, background workers, databases, cron jobs, and isolated sandbox execution in one control plane. CPU and GPU workloads are both supported.

[Sandboxes on Northflank](https://northflank.com/product/sandboxes) use microVM-based isolation with Kata Containers and Firecracker, and gVisor, applied per workload depending on security and performance requirements. Environment creation takes 1-2 seconds end-to-end, accounting for the full orchestration cycle.

- MicroVM isolation (Kata Containers, Firecracker) and gVisor applied per workload type
- Both ephemeral and persistent environments with no forced time limits
- On-demand GPUs (L4, A100 40GB/80GB, H100, H200, and [more](https://northflank.com/gpu)) without quota requests or reservation
- Self-serve BYOC (Bring Your Own Cloud) across AWS, GCP, Azure, Oracle, CoreWeave, on-premises, and bare-metal
- API, CLI, and SSH access
- In production since 2021 across startups, public companies, and government deployments

### Cost at scale comparison: 200 sandboxes

The table below shows total monthly cost across providers at 200 sandboxes, using equivalent compute specs.

*Based on 200 sandboxes, plan nf-compute-100-4, infra node m7i.2xlarge. Pricing as of April 2026.*

| Model | Provider | Cloud cost | Vendor cost | Total |
| --- | --- | --- | --- | --- |
| PaaS | Northflank | - | $7,200.00 | $7,200.00 |
| PaaS | E2B | - | $16,819.20 | $16,819.20 |
| PaaS | Modal | - | $24,491.50 | $24,491.50 |
| PaaS | Fly Sprites | - | $35,770.00 | $35,770.00 |
| PaaS | Runloop | - | $30,484.80 | $30,484.80 |
| PaaS | Vercel Sandbox | - | $31,068.80 | $31,068.80 |
| BYOC (0.2 request modifier) | Northflank | $1,500.00 | $560.00 | $2,060.00 |
| BYOC | E2B | $1,500.00 | $10,000.00 | $11,500.00 |

The BYOC row for Northflank uses a request modifier of 0.2. Each sandbox requests 20% of its plan's resources as a guaranteed minimum and can burst to the full plan limit when capacity is available on the node. This allows more sandboxes to run on the same hardware, reducing both cloud provider costs and the Northflank management fee. The modifier is configurable.

<InfoBox className="BodyStyle">

To get started, see the [sandboxes on Northflank](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank) and [deploy sandboxes on Northflank](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-on-northflank) documentation, or follow the guide to [deploy sandboxes in your cloud](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud) for BYOC deployments. To integrate via code, see [create sandbox with SDK](https://northflank.com/docs/v1/application/sandboxes/create-sandbox-with-sdk).

Teams can [get started directly](https://app.northflank.com/signup) (self-serve) or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo) for specific infrastructure or compliance requirements.

</InfoBox>

### 2. E2B

E2B is a purpose-built sandbox tool for AI agents and LLM applications. It uses Firecracker microVMs and provides Python and TypeScript SDKs.

- Hobby tier: free, $100 usage credit, sessions up to 1 hour, up to 20 concurrent sandboxes
- Pro tier: $150/month plus usage, sessions up to 24 hours, up to 100 concurrent sandboxes
- No GPU compute
- BYOC is available but limited to enterprise customers on AWS and GCP; requires contacting sales

E2B covers the sandbox layer. If your agents need persistent workers, databases, background jobs, or GPU inference alongside code execution, you will need to run those on separate infrastructure.

### 3. Modal

Modal is a serverless Python-first platform that runs sandboxes in isolated gVisor environments.

- Supports scaling to 50,000 or more concurrent sessions.
- Sandbox pricing uses a separate, higher compute tier than standard Modal workloads
- GPU support across L4, A10, A100, H100, H200, and B200; GPU rates on the sandbox tier match standard Modal GPU pricing
- No BYOC; managed infrastructure only

For more comparison, see [E2B vs Modal](https://northflank.com/blog/e2b-vs-modal)

### 4. Fly.io Sprites

Sprites is a sandbox product from Fly.io built on Firecracker VMs.

- Each Sprite has a persistent filesystem (ext4) with checkpoint and restore support
- Up to 8 vCPUs and 16GB RAM per Sprite
- Per-second billing with no idle charge
- No GPU support
- No BYOC

Related: [E2B vs Sprites](https://northflank.com/blog/e2b-vs-sprites-dev).

### 5. Runloop

Runloop focuses on sandbox environments (called Devboxes) for AI agent workflows, with evaluation tooling alongside.

- Basic plan: free with $50 in trial credits
- Pro plan: $250/month plus usage
- VPC deployment available on enterprise plans

### 6. Vercel Sandbox

Vercel Sandbox runs sandboxes in Firecracker microVMs on Vercel's managed infrastructure.

- Node.js and Python runtimes available
- Maximum session duration: 5 hours on Pro and Enterprise, 45 minutes on Hobby
- Persistent sandboxes with auto-save and resume available in beta
- No GPU support
- No BYOC; managed infrastructure only, single region

### 7. Cloudflare Sandbox

Cloudflare Sandbox is a beta product built on Cloudflare's Containers infrastructure, available on the Workers Paid plan.

- Provides a Linux environment with support for commands, file management, background processes, and exposing services from Workers applications
- Container-based isolation
- No GPU support
- No BYOC

## Why Northflank covers more of the agent cloud stack

Most platforms in this list handle one layer: sandboxed code execution. Northflank covers the full runtime: sandboxes, persistent services, background workers, databases, and GPU workloads under one control plane, with self-serve BYOC for teams that need execution inside their own VPC.

For production AI products, the operational surface is wider than isolated code execution. Agents need persistent memory, spawn async tasks, call APIs, and sometimes need GPU access for inference. Running each of those on separate platforms adds coordination overhead and more failure surfaces.

<InfoBox className="BodyStyle">

If you are evaluating Northflank for agent infrastructure, see the [sandboxes on Northflank](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank) and [deploy sandboxes in your cloud](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud) documentation, or follow the hands-on guide to [spinning up a secure sandbox and microVM](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh).

You can [get started directly](https://app.northflank.com/signup) (self-serve) or [book a call](https://cal.com/team/northflank/northflank-demo) with the team.

</InfoBox>

## FAQ: Agent cloud platforms

### What do AI agents need to run in the cloud?

At minimum, agents need isolated execution environments so untrusted code does not affect host systems or other tenants. Production agents typically also need persistent compute to maintain state across sessions, background workers for async tasks, storage for memory and outputs, and sometimes GPU access for inference. See [code execution environment for autonomous agents](https://northflank.com/blog/code-execution-environment-for-autonomous-agents) for a more detailed breakdown.

### What is the difference between a sandbox and an agent cloud platform?

A sandbox provides an isolated environment for executing code. An agent cloud platform covers a broader scope: sandboxes for execution, plus persistent compute, storage, workers, databases, and orchestration. Most purpose-built sandbox tools in this list focus on the execution layer only.

### Which agent cloud platforms support BYOC (Bring Your Own Cloud)?

Of the platforms compared here, Northflank supports self-serve BYOC across AWS, GCP, Azure, Oracle, Civo, CoreWeave, on-premises, and bare-metal. E2B and Runloop both offer BYOC but with limitations and require contacting sales. Modal, Fly.io Sprites, Vercel Sandbox, and Cloudflare Sandbox are managed-only. See [self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes) and [top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes).

### What is the difference between ephemeral and persistent sandbox environments?

Ephemeral sandboxes spin up for a task and terminate when it completes, leaving no state behind. Persistent sandboxes retain filesystem state, memory, and installed packages across runs. Agents with session history or accumulated context need persistent environments. See [ephemeral execution environments for AI agents](https://northflank.com/blog/ephemeral-execution-environments-ai-agents) and [persistent sandboxes](https://northflank.com/blog/persistent-sandboxes).]]>
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</item><item>
  <title>Modal vs Vercel Sandbox: comparing AI sandbox environments in 2026</title>
  <link>https://northflank.com/blog/modal-vs-vercel-sandbox</link>
  <pubDate>2026-04-09T15:30:00.000Z</pubDate>
  <description>
    <![CDATA[Modal Sandboxes and Vercel Sandbox differ on isolation model, GPU support, session limits, regions, and pricing. Here is a technical breakdown of both platforms for teams evaluating AI sandbox infrastructure.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/modal_vs_vercel_sandbox_eaf98b8659.png" alt="Modal vs Vercel Sandbox: comparing AI sandbox environments in 2026" /><InfoBox className="BodyStyle">

## TL;DR: Modal vs Vercel Sandbox

- Modal Sandboxes use gVisor for isolation, while Vercel Sandbox uses Firecracker microVMs. Both are designed to run untrusted or AI-generated code in isolated environments, but they differ on isolation model, GPU support, session limits, regions, and pricing structure.
- Modal supports GPU workloads, multi-region deployments, and sessions of up to 24 hours. Sandboxes are primarily Python-first, with JavaScript and Go SDKs available. There is no bring-your-own-cloud (BYOC) option.
- Vercel Sandbox supports Node.js and Python runtimes, sessions of up to 5 hours on Pro, and is currently limited to the iad1 (US East) region. There is also no BYOC option.
- Platforms like Northflank cover a wider surface area: self-serve bring-your-own-cloud (BYOC) across multiple clouds, a broader isolation stack using Kata Containers, Firecracker, and gVisor, no platform-imposed session time limits, and full workload orchestration alongside sandboxes.

</InfoBox>

If you are evaluating Modal and Vercel Sandbox for AI agent workloads, the differences in isolation model, GPU availability, session limits, and region coverage are worth working through before committing to either platform.

This article breaks down both platforms on the dimensions that tend to drive infrastructure decisions at scale.

## What is Modal Sandboxes?

Modal is a serverless compute platform that includes sandboxes as a first-class product. Modal Sandboxes are dynamically defined containers for executing untrusted or agent-generated code, created and managed programmatically via the Modal SDK. Each sandbox runs inside gVisor, a container runtime developed by Google that intercepts system calls to provide strong isolation without requiring a full virtual machine.

Modal Sandboxes are Python-first, though JavaScript and Go SDKs are also available. Sandboxes support custom container images defined at runtime, GPU workloads, filesystem snapshots for state persistence, tunnels for direct connectivity, and fine-grained networking controls. The platform targets AI agent workflows, reinforcement learning environments, code interpreters, and any workload that requires running code you did not write.

## What is Vercel Sandbox?

Vercel Sandbox is a compute primitive designed to run untrusted or user-generated code in isolated, ephemeral Linux VMs. It uses Firecracker microVMs for isolation and runs on Amazon Linux 2023 with Node.js (node24, node22) and Python (python3.13) runtimes available by default.

Vercel Sandbox is built to sit inside the Vercel ecosystem. Authentication uses Vercel OIDC tokens by default, which are generated automatically for Vercel-hosted projects. The SDK supports TypeScript and Python. Persistent sandboxes are available as a beta feature. The platform is currently limited to the iad1 (US East) region.

## A quick comparison of Modal Sandboxes, Vercel Sandbox, and Northflank

The table below compares Modal Sandboxes and Vercel Sandbox across isolation, session limits, BYOC, GPU support, and pricing, with Northflank included as an option for teams whose requirements extend beyond what either platform covers.

| Feature | Modal Sandboxes | Vercel Sandbox | Northflank |
| --- | --- | --- | --- |
| Isolation model | gVisor | Firecracker microVM | Kata Containers, Firecracker, gVisor |
| Session limit | 5 min default, up to 24hr | 45min (Hobby), 5hr (Pro/Enterprise) | No forced time limit |
| Max concurrency | 50,000+ (platform) | 10 (Hobby), 2,000 (Pro/Enterprise) | Horizontal autoscaling |
| GPU support | Yes (L4, A10, A100, H100, H200, B200 and more) | No | Yes (L4, A100, H100, H200 and [more](https://northflank.com/gpu)) |
| Bring your own cloud (BYOC) | No | No | Self-serve, AWS/GCP/Azure/Oracle/CoreWeave/bare-metal |
| Regions | US, EU, AP, UK, CA, SA, ME, MX, AF (with cost multiplier) | iad1 only | US West, US Central, US East, EU West, Asia East + 600 BYOC regions |
| SDK languages | Python (primary), JavaScript, Go | TypeScript, Python | API, CLI, SSH, UI, GitOps |
| Persistent sandboxes | Via filesystem and memory snapshots | Beta (auto-save) | Yes (ephemeral and persistent) |
| Open source | No | No | No |
| CPU pricing | $0.1419/physical core-hr (2 vCPU equivalent) | $0.128/vCPU-hr (active CPU) | $0.01667/vCPU-hr |
| Memory pricing | $0.0242/GiB-hr | $0.0212/GB-hr (provisioned) | $0.00833/GB-hr |
| Billing model | Per second | Active CPU only | Per second |

Modal and Northflank both bill per second of a running sandbox. Vercel Sandbox bills on active CPU time only (time spent waiting on I/O such as network requests, database calls, and model API responses does not count toward CPU billing), though memory is billed as provisioned regardless.

### Cost comparison at scale (Modal vs Vercel Sandbox vs Northflank)

To make the per-unit pricing difference concrete, here is what 200 sandboxes costs across providers under the same conditions.

*Based on 200 sandboxes, plan: nf-compute-100-4, infra node: m7i.2xlarge. Pricing as of April 2026.*

| Model | Provider | Cloud | Sandbox vendor | Total |
| --- | --- | --- | --- | --- |
| PaaS | Northflank | — | $7,200.00 | $7,200.00 |
| PaaS | Modal | — | $24,491.50 | $24,491.50 |
| PaaS | Vercel Sandbox | — | $31,068.80 | $31,068.80 |
| BYOC (0.2 request modifier)* | Northflank | $1,500.00 | $560.00 | $2,060.00 |

*Through Northflank's BYOC plans, there is a default overcommit (request modifier) that allows you to run more sandboxes on the same hardware. A request modifier of 0.2 means each sandbox requests 20% of its plan's resources as a guaranteed minimum but can burst to the full plan limit if capacity is available. Instead of fitting 8 sandboxes per node, you could fit 40, reducing both infrastructure cost and the Northflank management fee.

> Verify current rates on each platform's pricing page before making cost decisions.
> 

## How do Modal Sandboxes and Vercel Sandbox compare?

Both platforms provide isolated environments for running untrusted code, but they differ in isolation approach, runtime flexibility, session management, and what sits around the sandbox itself.

### Sandbox isolation model

Modal Sandboxes and Vercel Sandbox take different approaches to isolation. Modal uses gVisor, a container runtime by Google that intercepts Linux system calls in user space rather than passing them directly to the host kernel. This provides strong isolation without requiring a full VM. Vercel Sandbox uses Firecracker microVMs, which give each sandbox its own kernel, limiting the impact of container escape vulnerabilities to that individual workload rather than the host or neighbouring tenants.

Northflank supports Firecracker alongside Kata Containers and gVisor, applied per workload depending on isolation requirements. See these guides on [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor) and [Firecracker vs gVisor](https://northflank.com/blog/firecracker-vs-gvisor) for a technical breakdown of the trade-offs between these approaches.

### Session limits and concurrency

Modal Sandboxes have a default timeout of 5 minutes, configurable up to 24 hours via the `timeout` parameter. For workloads that require state beyond 24 hours, Modal's filesystem snapshots can be used to preserve state and restore it in a subsequent sandbox. Idle timeouts are also supported (a sandbox can be configured to terminate automatically after a period of inactivity).

Vercel Sandbox caps sessions at 45 minutes on Hobby and 5 hours on Pro and Enterprise plans. Concurrency is 10 on Hobby and up to 2,000 on Pro and Enterprise. Persistent sandboxes (which auto-save state on stop and resume where they left off) are available in beta.

Session length is worth factoring in early if your workload involves long-running agents, multi-step pipelines, or background tasks. Northflank sandboxes have no platform-imposed session time limit. For more on how session lifecycle affects agent architecture, see [ephemeral execution environments for AI agents](https://northflank.com/blog/ephemeral-execution-environments-ai-agents).

### Supported runtimes and languages

Modal Sandboxes support custom container images defined at runtime, which means any language or runtime that runs in a container is supported. Images can be built dynamically from code, making Modal flexible for Python-heavy workflows, Node.js, and less common stacks. The primary SDK is Python, with JavaScript and Go available.

Vercel Sandbox ships with a fixed set of runtimes: node24, node22, and python3.13, running on Amazon Linux 2023. Additional packages can be installed at runtime, but the base OS and available runtimes are more constrained than Modal's custom image system.

### Bring-your-own-cloud (BYOC) support

BYOC (deploying sandbox infrastructure inside your own cloud account or VPC) is relevant for teams with data residency requirements, security policies, or existing cloud spend they want to use.

Neither Modal nor Vercel Sandbox offers a BYOC deployment option. Both platforms run on managed infrastructure only.

Northflank supports [bring-your-own-cloud](https://northflank.com/product/bring-your-own-cloud) (BYOC) on a self-serve basis across AWS, GCP, Azure, Oracle, CoreWeave, Civo, bare-metal, and on-premises. See the [deploy sandboxes in your cloud](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud) documentation for setup details.

### GPU support

Modal supports GPU workloads including L4, A10, A100 (40GB and 80GB), L40S, H100, H200, and B200. Region selection applies a cost multiplier on top of base GPU pricing.

Vercel Sandbox does not provide GPU compute. If your workload requires GPU inference, training, or compute-intensive agent tasks alongside sandboxed code execution, you would need to provision GPU infrastructure separately.

Northflank supports on-demand [GPUs](https://northflank.com/gpu) without quota requests: NVIDIA L4 at $0.80/hr, A100 40GB at $1.42/hr, A100 80GB at $1.76/hr, H100 at $2.74/hr, and H200 at $3.14/hr. GPU workloads run on the same platform as sandboxes, APIs, workers, and databases.

### Regions and availability

Modal supports region selection across US, EU, AP, UK, Canada, South America, Middle East, Mexico, and Africa. Region selection adds a cost multiplier: 1.25x for US/EU/UK/AP regions, and 2.5x for CA/SA/ME/MX/AF regions. All Function inputs and outputs route through Modal's control plane in us-east-1 regardless of the selected region.

Vercel Sandbox currently runs in iad1 (US East) only. This is a meaningful constraint if your users or your agent infrastructure are based in Europe or Asia. Latency for sandbox interactions from outside the US will reflect that single-region deployment.

Northflank's [managed cloud](https://northflank.com/cloud/northflank) covers US West, US Central, US East, EU West, and Asia East. BYOC extends this to 600 BYOC regions via supported cloud providers and bare-metal deployments.

### Developer experience and SDKs

Modal is Python-first, with JavaScript and Go SDKs also available. Sandboxes are defined and managed entirely in code, with no UI-based management. Modal also provides fine-grained networking controls (network access can be fully blocked or restricted via CIDR allowlist) and tunnels for direct sandbox connectivity.

Vercel Sandbox provides TypeScript and Python SDKs alongside a CLI. Authentication integrates with Vercel's OIDC token system, which is generated automatically for Vercel-hosted projects. For external environments, access tokens are available as an alternative.

Northflank provides API, CLI, SSH, and UI access, with GitOps support for infrastructure-as-code workflows. The [create sandbox with SDK](https://northflank.com/docs/v1/application/sandboxes/create-sandbox-with-sdk) documentation covers programmatic sandbox provisioning and lifecycle management.

## When does Modal Sandboxes fit your requirements?

Modal supports GPU workloads alongside sandboxed code execution, custom container images defined at runtime, sessions of up to 24 hours, and multi-region deployments. Networking controls allow outbound access to be fully blocked or restricted via CIDR allowlist.

See also: [e2b vs modal](https://northflank.com/blog/e2b-vs-modal), [e2b vs modal vs fly.io sprites](https://northflank.com/blog/e2b-vs-modal-vs-fly-io-sprites), and [daytona vs modal](https://northflank.com/blog/daytona-vs-modal) for additional comparisons in this space.

## When does Vercel Sandbox fit your requirements?

Vercel Sandbox is a fit for teams already on the Vercel platform whose workloads run within the supported runtimes (node24, node22, python3.13) and the 5-hour session limit. Authentication via Vercel OIDC tokens works automatically for Vercel-hosted projects. The active CPU billing model means idle I/O time is not billed.

The single-region constraint (iad1) and the absence of bring-your-own-cloud (BYOC) are the trade-offs to factor in. See [top Vercel Sandbox alternatives](https://northflank.com/blog/top-vercel-sandbox-alternatives-for-secure-ai-code-execution-and-sandbox-environments) for a broader comparison.

## What does Northflank offer beyond Modal Sandboxes and Vercel Sandbox?

Northflank covers [sandbox execution](https://northflank.com/product/sandboxes) as part of a broader workload platform that also runs APIs, background workers, databases, GPU inference, and CI/CD pipelines.

Key differences from Modal Sandboxes and Vercel Sandbox:

- **Isolation stack:** Northflank supports Kata Containers, Firecracker, and gVisor applied per workload. Modal uses gVisor only for sandboxes. Vercel Sandbox uses Firecracker. See [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor) for a technical breakdown.
- **Bring-your-own-cloud (BYOC):** Self-serve across AWS, GCP, Azure, Oracle, Civo, CoreWeave, and bare-metal. Neither Modal nor Vercel Sandbox offers a BYOC option. See [self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes) and [top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes) for more on deployment models.
- **Session limits:** Northflank sandboxes have no platform-imposed session time limit. Sandboxes can be ephemeral or persistent.
- **GPU support:** On-demand GPUs including L4, A100, H100, and H200, and [more](https://northflank.com/gpu), running on the same platform as sandboxes.

<InfoBox className="BodyStyle">

To get started, see the [sandboxes on Northflank](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank) and [deploy sandboxes on Northflank cloud](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-on-northflank) documentation, or follow the [hands-on guide to spinning up a secure sandbox and microVM](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh). For a broader look at agent isolation patterns, see [how to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents).

Teams can [get started directly](https://app.northflank.com/signup) (self-serve) or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) for teams with specific infrastructure or compliance requirements.

</InfoBox>

## Frequently asked questions about Modal vs Vercel Sandbox

### What is the difference between Modal Sandboxes and Vercel Sandbox?

Modal Sandboxes use gVisor for isolation and support GPU workloads, custom container images, multi-region deployments, and sessions of up to 24 hours. Vercel Sandbox uses Firecracker microVMs, supports Node.js and Python runtimes, caps sessions at 5 hours on Pro, and is currently available in the iad1 region only. Northflank supports a broader isolation stack (Kata Containers, Firecracker, and gVisor), self-serve BYOC, and no platform-imposed session time limit.

### What isolation model does Modal Sandboxes use?

Modal Sandboxes use gVisor, a container runtime by Google that intercepts Linux system calls in user space. This provides strong isolation without requiring a full VM per sandbox.

### Does Vercel Sandbox support GPU workloads?

Vercel Sandbox does not provide GPU compute. However, Northflank supports on-demand GPU workloads including L4, A100, H100, and H200 on the same platform as sandboxes.

### Do Modal Sandboxes or Vercel Sandbox support bring-your-own-cloud (BYOC)?

Neither Modal nor Vercel Sandbox offers a BYOC deployment option. Both run on managed infrastructure only.

### How does Modal Sandbox pricing work?

Modal Sandboxes are billed per second at $0.00003942/core/sec (1 physical core = 2 vCPU equivalent) for CPU and $0.00000672/GiB/sec for memory. Region selection adds a cost multiplier of 1.25x for US/EU/UK/AP regions and 2.5x for other regions. GPU workloads use Modal's standard GPU pricing.

### Which sandbox platform supports the longest session times?

Modal Sandboxes support sessions of up to 24 hours via the `timeout` parameter. Vercel Sandbox supports sessions of up to 5 hours on Pro and Enterprise plans. Northflank sandboxes have no platform-imposed session time limit.]]>
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  <title>Top internal developer portals in 2026</title>
  <link>https://northflank.com/blog/top-internal-developer-portals</link>
  <pubDate>2026-04-09T14:15:00.000Z</pubDate>
  <description>
    <![CDATA[Top internal developer portals in 2026: compare Northflank, Backstage, Port, Cortex, and Humanitec on execution layer, setup time, BYOC support, and what most teams actually need.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/idp_f93a57797f.png" alt="Top internal developer portals in 2026" /><InfoBox className="BodyStyle">

## TL;DR: What are the top internal developer portals in 2026?

An internal developer portal gives developers a single place to discover services, access workflows, and interact with infrastructure. Most teams searching for a portal actually need a platform: the thing that provisions and runs workloads, not just a UI on top of them.

- [**Northflank**](https://northflank.com/) – A full internal developer platform with a built-in portal. Deploys and runs services, databases, jobs, pipelines, and GPU workloads from the same control plane. Self-serve BYOC into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, and bare-metal.
- **Backstage** – Open-source portal framework from Spotify. Service catalog, documentation, and software templates. Requires dedicated engineers to build and maintain. Portal only, no execution layer.
- **Port** – No-code commercial portal with customizable catalogs and self-service actions. Portal only, no execution layer.
- **Cortex** – Commercial portal focused on service ownership and scorecards. Portal only, no execution layer.
- **Humanitec** – Platform orchestrator that wraps existing Terraform and CI/CD tooling. Requires a separate portal and existing infrastructure toolchain.

> Most portal tools give you visibility into workloads you already run elsewhere. Northflank gives you the portal and the platform together, so developers can discover, deploy, and operate their infrastructure without switching tools.
> 
</InfoBox>

## What is an internal developer portal?

An internal developer portal is a centralized interface where developers discover services, documentation, APIs, and workflows inside their organization. Common features include a service catalog, software templates, self-service actions, and integrations with CI/CD and cloud providers.

The term is frequently confused with an internal developer platform. The portal is the UI. The platform is the engine that provisions infrastructure, runs deployments, manages secrets, and orchestrates the software delivery lifecycle. A portal without a platform underneath cannot provision, deploy, or operate anything on its own.

## What’s the difference between an internal developer portal and a platform?

Most teams searching for a portal discover they need more than visibility. They need self-service: a developer should be able to spin up a new environment, deploy a service, or connect a database without filing a ticket or waiting on an ops team. A portal alone cannot do that. The platform underneath is what makes self-service possible.

The two are not mutually exclusive. Many organizations run a portal like Backstage or Port as the interface to a separate platform layer. That architecture works, but requires integrating and maintaining multiple tools. Platforms like Northflank include both layers in one: the portal surfaces what is running, and the platform actually runs it.

## What should you look for in an internal developer platform or portal?

These are the dimensions that matter when evaluating tools for developer self-service.

- **Execution layer:** Does the tool actually run workloads, or does it only display them? A portal-only tool requires a separate platform for provisioning and deployment.
- **Self-service scope:** Can a developer provision a new environment, deploy a service, connect a database, and configure secrets without involving an ops team? The best platforms provide this end-to-end.
- **Time to production:** How long does it take to go from signup to a running workload? Setup time ranges from minutes for full platforms to months for Backstage.
- **Maintenance burden:** Open-source portals like Backstage require dedicated engineering time to deploy, customize, and maintain. Commercial tools reduce that overhead but add licensing costs.
- **BYOC and deployment model:** Can the platform run inside your own cloud account? This matters for compliance, data residency, and teams with existing cloud commitments.
- **Developer experience:** Do developers adopt the tool voluntarily, or is it enforced? The best platforms reduce friction to the point where the paved path is the path of least resistance.
- **Workload coverage:** Does the platform handle all workload types your team needs: services, databases, background jobs, scheduled tasks, GPU workloads, and CI/CD pipelines?

## What are the top internal developer portals in 2026?

### 1. Northflank

[Northflank](https://northflank.com/product/idp) is a full internal developer platform with a built-in portal interface. Developers interact with a UI, API, CLI, or GitOps workflow to deploy services, spin up databases, configure pipelines, run background jobs, and access GPU workloads without managing Kubernetes YAML or writing infrastructure code. The platform handles the operational complexity underneath: scheduling, autoscaling, networking, secrets management, TLS, and observability. Platform teams configure guardrails, access controls, and deployment templates once. Developers self-serve from those templates without needing to understand the infrastructure layer.

![image-74.png](https://assets.northflank.com/image_74_a50d717270.png)

What separates Northflank from portal-only tools is that it runs workloads rather than just cataloging them. A developer does not need a separate CI/CD system, a separate secrets manager, a separate database provider, or a separate orchestrator to make Northflank useful. Everything is in the same control plane, including preview environments that spin up per pull request and tear down on merge. BYOC is available self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, and bare-metal, meaning the platform runs inside your own infrastructure without requiring a lengthy enterprise sales process.

**Key features:**

- **Full execution layer:** Deploys and runs services, databases, workers, scheduled jobs, and GPU workloads. No separate platform required.
- **Built-in portal:** UI, API, CLI, and GitOps access. Developers interact with infrastructure through a clean interface without managing Kubernetes directly.
- **Preview environments:** Isolated environments per pull request with databases, secrets, and networking. Torn down automatically on merge.
- **Pipeline and CI/CD:** Build pipelines, release flows, and GitOps sync built in. No external CI/CD integration required to deploy.
- **Managed databases:** PostgreSQL, MySQL, MongoDB, Redis, MinIO, and RabbitMQ as managed addons in the same control plane.
- **Secrets management:** Secret groups inject environment variables and connection strings directly into services and jobs.
- **BYOC:** Self-serve deployment into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal.
- **SOC 2 Type 2 certified:** Covers managed cloud and BYOC deployments.
- **Access:** UI, API, CLI, and GitOps.

**Best for:** Engineering teams that need a full [internal developer platform](https://northflank.com/product/idp) with a built-in portal, self-serve infrastructure provisioning, and [BYOC deployment](https://northflank.com/product/bring-your-own-cloud) without building or maintaining the toolchain themselves.

**Pricing:** Free tier includes two services, one database, and two cron jobs. Paid compute from $0.01667/vCPU-hour and $0.00833/GB-hour. [See full pricing.](https://northflank.com/pricing)

<InfoBox className="BodyStyle">

[Get started on Northflank](https://app.northflank.com/signup) (self-serve, no demo required). Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) with an engineer to walk through your platform requirements.

</InfoBox>

### 2. Backstage

Backstage is an open-source portal framework from Spotify, now a CNCF project. It provides a service catalog, TechDocs documentation system, and software templates for scaffolding new services. A plugin architecture covers Kubernetes status, CI/CD, cloud cost dashboards, and more. It is a portal, not a platform. It catalogs workloads that run elsewhere but cannot provision or deploy them. Most organizations dedicate one to three engineers to building and maintaining it. External adoption rates average around 10%, largely due to the complexity of customization and ongoing maintenance.

**Best for:** Large organizations with a dedicated platform engineering team that wants maximum customization and is prepared to invest in building and maintaining the portal long-term.

**Pricing:** Open-source and free. Engineering time to deploy and maintain is the primary cost. 

### 3. Port

Port is a commercial no-code portal with a customizable blueprint-based data model. Teams define entity types, relationships, and self-service actions through a UI without writing code. It integrates with GitHub, GitLab, Jira, PagerDuty, and Kubernetes. Most teams reach a working portal in days rather than months. It is a portal layer, not a platform. Self-service actions trigger webhooks in external systems, so actual execution depends on your underlying toolchain.

**Best for:** Teams with an existing infrastructure stack that need a customizable portal layer faster than Backstage allows, without writing code to do it.

**Pricing:** Free tier available. Basic plan from $30/user/month. Enterprise custom.

### 4. Cortex

Cortex is a commercial portal focused on service ownership and standards enforcement. It provides a service catalog with ownership tracking, scorecards that benchmark services against maturity criteria, and automated workflows that surface gaps. It integrates with GitHub, PagerDuty, and Datadog. It is a governance layer, not a platform. It does not provision infrastructure or run deployments. It is purpose-built for organizations with 50 or more engineers where service ownership has become ambiguous and quality standards need systematic enforcement.

**Best for:** Engineering organizations with large microservice estates that need systematic service ownership tracking and standards enforcement across teams.

**Pricing:** Contact Cortex for pricing.

### 5. Humanitec

Humanitec is a platform orchestrator. It sits between your developer interface and your infrastructure, wrapping existing Terraform and OpenTofu modules with a governance and deployment automation layer. Platform teams define resource modules and rules once. The orchestrator applies them per environment when a developer or agent requests a deployment.

It does not replace your infrastructure toolchain. You still need CI/CD, cloud accounts, Terraform modules, and a separate portal like Backstage or Port. It is well suited for large enterprises with a mature toolchain that needs standardization applied on top. For teams without that foundation, assembling it alongside Humanitec requires significant engineering investment.

**Best for:** Large enterprises with existing CI/CD and IaC toolchains that need a governance and deployment automation layer to standardize how developers and agents interact with infrastructure.

**Pricing:** Tiered plans for small, growing, and large teams. Contact Humanitec for pricing. Self-hosted option available for regulated environments.

## Which tool should you choose?

The decision splits on whether you need a portal, an orchestrator, or a full platform.

If your infrastructure is already running and you need a better interface for discovery, documentation, and self-service on top of it, Backstage, Port, and Cortex address that. Backstage, if you have the engineering capacity for full customization. Port if you want no-code flexibility faster. Cortex if service ownership and standards enforcement are the primary need. If you have a mature toolchain and need a governance layer applied on top of it, Humanitec covers that.

If you need to provision and run workloads as well as surface them, a portal or orchestrator alone is not enough. Northflank covers the portal, the platform, and the execution layer in one, so developers can deploy and operate infrastructure without assembling or maintaining a separate toolchain.

| Tool | Type | Execution layer | BYOC | Setup time |
| --- | --- | --- | --- | --- |
| **Northflank** | Platform + portal | Yes | Yes, self-serve | Minutes |
| **Backstage** | Portal only | No | No | Weeks to months |
| **Port** | Portal only | No | No | Days |
| **Cortex** | Portal only | No | No | Weeks to months |
| **Humanitec** | Orchestrator | Wraps existing IaC | Self-hosted available | Days to weeks |

## FAQ: internal developer portals

### Do I need a portal or a platform?

If your deployment and infrastructure stack is already working and you need better visibility and self-service on top of it, a portal like Backstage or Port addresses that. If you need to provision infrastructure, deploy workloads, and manage the full software delivery lifecycle without building a toolchain yourself, a platform like Northflank is the more appropriate starting point.

### Can I use Northflank as an internal developer portal?

Yes. Northflank provides a UI, API, CLI, and GitOps interface that platform teams configure and developers use to provision environments, deploy services, connect databases, and manage pipelines. It includes a service catalog, template-based self-service, preview environments, and observability. Unlike portal-only tools, it also runs the workloads rather than just displaying them.

## Conclusion

Most teams searching for an internal developer portal discover they need more than a catalog and a UI. They need self-service: the ability for developers to provision environments, deploy services, and operate infrastructure without waiting on an ops team. Portal tools like Backstage, Port, and Cortex provide the interface layer but require a separate platform underneath to actually execute anything. Humanitec sits one layer deeper as an orchestrator, standardizing how requests map to infrastructure, but still requires a portal and an existing toolchain to function.

Northflank provides all three layers together. Developers get a clean interface for self-service. Platform teams get guardrails, BYOC deployment, and a full infrastructure stack without building or maintaining the toolchain themselves.

<InfoBox className="BodyStyle">

You can [get started for free on Northflank](https://app.northflank.com/signup) or [talk to the team](https://cal.com/team/northflank/northflank-demo?duration=30) to walk through your platform requirements.

</InfoBox>

## Related articles

- [**Top internal developer platform tools in 2026**](https://northflank.com/blog/top-six-internal-developer-platforms): A comparison of IDP tools including Northflank, Backstage, Humanitec, and others across execution model, setup time, and workload coverage.
- [**How to build an internal developer platform**](https://northflank.com/blog/how-to-build-an-internal-developer-platform): Covers the components of a production IDP, what takes the most time to build, and when to use a platform versus building your own.]]>
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  <title>E2B vs Vercel Sandbox: comparing AI sandbox environments in 2026</title>
  <link>https://northflank.com/blog/e2b-vs-vercel-sandbox</link>
  <pubDate>2026-04-08T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[E2B and Vercel Sandbox both use Firecracker microVMs for AI code execution but differ on session limits, BYOC support, GPU availability, regions, and pricing. Here is a technical breakdown.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/e2b_vs_vercel_sandbox_07590fd9fc.png" alt="E2B vs Vercel Sandbox: comparing AI sandbox environments in 2026" /><InfoBox className="BodyStyle">

## TL;DR: E2B vs Vercel Sandbox

- Both E2B and Vercel Sandbox use Firecracker microVMs for isolated code execution, but they differ on session limits, regions, GPU support, bring-your-own-cloud (BYOC) availability, and pricing structure.
- E2B is LLM-agnostic and open-source, with up to 24-hour sessions on the Pro plan, broader language support, and an MCP gateway. BYOC is available for enterprise customers only and requires contacting sales.
- Vercel Sandbox is tightly integrated with the Vercel platform, supports TypeScript and Python SDKs, but is currently limited to the iad1 (US East) region and caps sessions at 5 hours on Pro. There is no BYOC option.
- Platforms like Northflank cover a wider surface area: self-serve bring-your-own-cloud (BYOC) across multiple clouds, GPU support, no forced session time limits, and a broader isolation stack using Kata Containers, Firecracker, and gVisor.

</InfoBox>

If you are evaluating E2B and Vercel Sandbox for AI agent workloads, the differences in session length, runtime flexibility, region availability, and BYOC support matter more than they might initially appear.

This article breaks down both platforms on the dimensions that tend to drive infrastructure decisions at scale.

## What is E2B?

E2B is an open-source sandbox platform built for running AI-generated code in isolated environments. Each sandbox runs inside a Firecracker microVM, giving workloads VM-level isolation with low overhead. E2B is LLM-agnostic and works with OpenAI, Anthropic, Mistral, Llama, and others via Python and JavaScript/TypeScript SDKs.

The platform targets a broad set of AI use cases: coding agents, data analysis, reinforcement learning, computer use agents, and vibe coding workflows. E2B also provides an MCP gateway, letting agents interact with external services from inside the sandbox. Custom sandbox templates let you pre-install packages and configure environments for specific workloads.

## What is Vercel Sandbox?

Vercel Sandbox is a compute primitive designed to run untrusted or user-generated code in isolated, ephemeral Linux VMs. Like E2B, it uses Firecracker microVMs for isolation. Sandboxes run on Amazon Linux 2023 with Node.js (node24, node22) and Python (python3.13) runtimes available by default.

Vercel Sandbox is built to sit inside the Vercel ecosystem. Authentication uses Vercel OIDC tokens by default, which are generated automatically for Vercel-hosted projects. The SDK supports TypeScript and Python. Persistent sandboxes are available as a beta feature. The platform is currently limited to the iad1 (US East) region.

## A quick comparison of E2B, Vercel Sandbox, and Northflank

The table below compares E2B and Vercel Sandbox across isolation, session limits, BYOC, GPU support, and pricing, with Northflank included as an option for teams whose requirements extend beyond what either platform covers.

| Feature | E2B | Vercel Sandbox | Northflank |
| --- | --- | --- | --- |
| Isolation model | Firecracker microVM | Firecracker microVM | Kata Containers, Firecracker, gVisor |
| Session limit | 1hr (Hobby), 24hr (Pro) | 45min (Hobby), 5hr (Pro/Enterprise) | No forced time limit |
| Max concurrency | 20 (Hobby), up to 1,100 (Pro + add-on) | 10 (Hobby), 2,000 (Pro/Enterprise) | Horizontal autoscaling |
| GPU support | No | No | Yes (L4, A100, H100, H200 and [more](https://northflank.com/gpu)) |
| Bring your own cloud (BYOC) | Enterprise only, AWS + GCP, contact sales | No | Self-serve, AWS/GCP/Azure/Oracle/CoreWeave/bare-metal |
| Regions | Multiple | iad1 only | US West, US Central, US East, EU West, Asia East + 600 BYOC regions |
| SDK languages | Python, JavaScript/TypeScript | TypeScript, Python | API, CLI, SSH, UI, GitOps |
| Persistent sandboxes | Yes (pause/resume, snapshots) | Beta | Yes (ephemeral and persistent) |
| Open source | Yes | No | No |
| CPU pricing (PaaS) | $0.0504/vCPU-hr | $0.128/vCPU-hr (active CPU) | $0.01667/vCPU-hr |
| Memory pricing (PaaS) | $0.0162/GiB-hr | $0.0212/GB-hr (provisioned) | $0.00833/GB-hr |
| Billing model | Per second | Active CPU only | Per second |

E2B and Northflank both bill per second of a running sandbox. Vercel Sandbox bills on active CPU time only (time spent waiting on I/O such as network requests, database calls, and model API responses does not count toward CPU billing), though memory is billed as provisioned regardless.

### Cost comparison at scale (E2B vs Vercel Sandbox vs Northflank)

To make the per-unit pricing difference concrete, here is what 200 sandboxes costs across providers under the same conditions.

*Based on 200 sandboxes, plan: nf-compute-100-4, infra node: m7i.2xlarge. Pricing as of April 2026.*

| Model | Provider | Cloud | Sandbox vendor | Total |
| --- | --- | --- | --- | --- |
| PaaS | Northflank | — | $7,200.00 | $7,200.00 |
| PaaS | E2B | — | $16,819.20 | $16,819.20 |
| PaaS | Vercel Sandbox | — | $31,068.80 | $31,068.80 |
| BYOC (0.2 request modifier)* | Northflank | $1,500.00 | $560.00 | $2,060.00 |
| BYOC | E2B | $1,500.00 | $10,000.00 | $11,500.00 |

*Through Northflank's BYOC plans, there is a default overcommit (request modifier) that allows you to run more sandboxes on the same hardware. A request modifier of 0.2 means each sandbox requests 20% of its plan's resources as a guaranteed minimum but can burst to the full plan limit if capacity is available. Instead of fitting 8 sandboxes per node, you could fit 40, reducing both infrastructure cost and the Northflank management fee.

> Verify current rates on each platform's pricing page before making cost decisions.
> 

## How do E2B and Vercel Sandbox compare? (Detailed comparison)

Both platforms share the same underlying isolation primitive (Firecracker microVMs) but diverge quickly on configuration, runtime support, and what sits around the sandbox itself.

### Sandbox isolation model

Both E2B and Vercel Sandbox run workloads inside Firecracker microVMs. Firecracker is an open-source VMM developed by AWS that boots lightweight VMs with a minimal device model, reducing attack surface compared to traditional VMs. Each sandbox gets its own kernel, which limits the impact of container escape vulnerabilities to that individual workload rather than the host or neighbouring tenants.

Northflank supports Firecracker alongside Kata Containers and gVisor, applied per workload depending on isolation requirements. See these guides on [what is AWS Firecracker](https://northflank.com/blog/what-is-aws-firecracker) and [Firecracker vs gVisor](https://northflank.com/blog/firecracker-vs-gvisor) for how Firecracker compares to other isolation primitives in detail.

### Session limits and concurrency

E2B supports sandbox sessions of up to 1 hour on the Hobby plan and up to 24 hours on the Pro plan. Concurrency runs from 20 on Hobby up to 1,100 on Pro with an add-on purchase. Sandboxes can pause and resume, with state preserved indefinitely via snapshots.

Vercel Sandbox caps sessions at 45 minutes on Hobby and 5 hours on Pro and Enterprise plans. Concurrency is 10 on Hobby and up to 2,000 on Pro and Enterprise. Persistent sandboxes (which auto-save state on stop and resume where they left off) are available in beta.

Session length is worth factoring in early if your workload involves long-running agents, multi-step pipelines, or background tasks. Northflank sandboxes have no platform-imposed session time limit. For more on how session lifecycle affects agent architecture, see [ephemeral execution environments for AI agents](https://northflank.com/blog/ephemeral-execution-environments-ai-agents).

### Supported runtimes and languages

E2B supports any language that runs on Linux. You can build custom sandbox templates to pre-install specific packages, runtimes, or system libraries. This makes it flexible for Python-heavy data workflows, JavaScript runtimes, or more unusual stacks like Ruby or C++.

Vercel Sandbox ships with a fixed set of runtimes: node24, node22, and python3.13, running on Amazon Linux 2023. You can install additional packages at runtime, but the base OS and available runtimes are more constrained than E2B's template system.

### Bring-your-own-cloud (BYOC) support

BYOC (deploying sandbox infrastructure inside your own cloud account or VPC) is relevant for teams with data residency requirements, security policies, or existing cloud spend they want to use.

E2B offers BYOC for enterprise customers on AWS and GCP only. It is not self-serve and requires contacting the E2B team to onboard. Pricing is not publicly disclosed.

Vercel Sandbox has no BYOC option. Sandboxes run on Vercel's managed infrastructure in the iad1 region only.

Northflank supports bring-your-own-cloud (BYOC) on a self-serve basis across AWS, GCP, Azure, Oracle, CoreWeave, Civo, bare-metal, and on-premises. Pricing is listed on the pricing page: CPU at $0.01389/vCPU-hr and memory at $0.00139/GB-hr, on top of your existing cloud bill.

Northflank also supports a request modifier (overcommit) on BYOC plans. A modifier of 0.2 means each sandbox requests 20% of its plan's resources as a guaranteed minimum but can burst to the full plan limit if capacity is available on the node. See the [deploy sandboxes in your cloud](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud) documentation for setup details.

### GPU support

Neither E2B nor Vercel Sandbox provides GPU compute. If your AI workloads require inference, training, or compute-intensive agent tasks alongside sandboxed code execution, you would need to manage GPU infrastructure separately.

Northflank supports on-demand GPUs without quota requests: NVIDIA L4 at $0.80/hr, A100 40GB at $1.42/hr, A100 80GB at $1.76/hr, H100 at $2.74/hr, and H200 at $3.14/hr. GPU workloads run on the same platform as sandboxes, APIs, workers, and databases.

### Regions and availability

E2B operates across multiple regions. Vercel Sandbox currently runs in iad1 (US East) only. This is a meaningful constraint if your users or your agent infrastructure are based in Europe or Asia. Latency for sandbox interactions from outside the US will reflect that single-region deployment.

Northflank's managed cloud covers US West, US Central, US East, EU West, and Asia East. BYOC extends this to 600 BYOC regions via supported cloud providers and bare-metal deployments.

### Developer experience and SDKs

E2B provides Python and JavaScript/TypeScript SDKs, a CLI, SSH access, and an MCP gateway for connecting sandboxes to external services. The codebase is open-source, which is relevant for teams that want to audit, fork, or contribute to the platform.

Vercel Sandbox provides TypeScript and Python SDKs alongside a CLI. Authentication integrates with Vercel's OIDC token system, which is generated automatically for Vercel-hosted projects. For external environments, access tokens are available as an alternative.

Northflank provides API, CLI, SSH, and UI access, with GitOps support for infrastructure-as-code workflows. The [create sandbox with SDK](https://northflank.com/docs/v1/application/sandboxes/create-sandbox-with-sdk) documentation covers programmatic sandbox provisioning and lifecycle management.

## When does E2B fit your requirements?

E2B supports LLM-agnostic, open-source workflows and sessions of up to 24 hours on the Pro plan. If your stack goes beyond Node.js and Python, the custom template system lets you pre-install packages and configure the base environment for your specific runtime. The MCP gateway supports agent workflows that need to interact with external services from inside the sandbox.

See also: [best alternatives to E2B for running untrusted code](https://northflank.com/blog/best-alternatives-to-e2b-dev-for-running-untrusted-code-in-secure-sandboxes) and the [Daytona vs E2B comparison](https://northflank.com/blog/daytona-vs-e2b-ai-code-execution-sandboxes).

## When does Vercel Sandbox fit your requirements?

Vercel Sandbox is a fit for teams already on the Vercel platform whose workloads run within the supported runtimes (node24, node22, python3.13) and the 5-hour session limit. Authentication via Vercel OIDC tokens works automatically for Vercel-hosted projects. The active CPU billing model means idle I/O time is not billed.

The single-region constraint (iad1) and the absence of bring-your-own-cloud (BYOC) are the trade-offs to factor in. See [top Vercel Sandbox alternatives](https://northflank.com/blog/top-vercel-sandbox-alternatives-for-secure-ai-code-execution-and-sandbox-environments) for a broader comparison.

## What does Northflank offer beyond E2B and Vercel Sandbox?

Northflank covers [sandbox execution](https://northflank.com/product/sandboxes) as part of a broader workload platform that also runs APIs, background workers, databases, GPU inference, and CI/CD pipelines.

Key differences from E2B and Vercel Sandbox:

- **Isolation stack:** Northflank supports Kata Containers, Firecracker, and gVisor applied per workload. See [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor) for a technical breakdown of the trade-offs.
- **Bring-your-own-cloud (BYOC):** Self-serve across AWS, GCP, Azure, Oracle, Civo, CoreWeave, and bare-metal. See [self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes) and [top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes) for more on deployment models.
- **Session limits:** Northflank sandboxes have no platform-imposed session time limit. Sandboxes can be ephemeral or persistent.
- **GPU support:** On-demand GPUs, including L4, A100, H100, and H200, and [more](https://northflank.com/gpu), running on the same platform as sandboxes.

<InfoBox className="BodyStyle">

To get started, see the [sandboxes on Northflank](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank) and [deploy sandboxes on Northflank cloud](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-on-northflank) documentation, or follow the [hands-on guide to spinning up a secure sandbox and microVM](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh). For a broader look at agent isolation patterns, see [how to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents).

Teams can [get started directly](https://app.northflank.com/signup) (self-serve) or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) for teams with specific infrastructure or compliance requirements.

</InfoBox>

## Frequently asked questions about E2B vs Vercel Sandbox

### What is the difference between E2B and Vercel Sandbox?

Both use Firecracker microVMs for sandbox isolation. E2B is LLM-agnostic and open-source, with broader language support via custom templates and sessions of up to 24 hours on the Pro plan. Vercel Sandbox supports Node.js and Python runtimes, sessions of up to 5 hours on Pro, and is currently available in the iad1 region only. Northflank supports a broader isolation stack (Kata Containers, Firecracker, and gVisor), self-serve BYOC, GPU workloads, and no platform-imposed session time limit.

### Does Vercel Sandbox support bring-your-own-cloud (BYOC)?

Vercel Sandbox runs on Vercel's managed infrastructure in the iad1 region only and does not currently offer a BYOC deployment option.

### Does E2B support GPU workloads?

No. E2B does not provide GPU compute. However, Northflank supports on-demand GPU workloads including L4, A100, H100, and H200 on the same platform as sandboxes.

### How does Vercel Sandbox pricing work?

Vercel Sandbox bills on active CPU time (time spent on I/O does not count), provisioned memory, network egress, sandbox creations, and snapshot storage. The Pro plan includes a $20/month credit, after which usage is billed at list rates: $0.128/vCPU-hr for active CPU and $0.0212/GB-hr for provisioned memory.

### Which sandbox platform supports the longest session times?

E2B supports sessions of up to 24 hours on the Pro plan. Vercel Sandbox supports sessions of up to 5 hours on Pro and Enterprise plans. Northflank sandboxes have no platform-imposed session time limit.

### Can you run sandboxes in your own cloud with E2B?

E2B offers bring-your-own-cloud (BYOC) for enterprise customers on AWS and GCP only. It is not self-serve and requires contacting the E2B team.]]>
  </content:encoded>
</item><item>
  <title>Top managed database services in 2026</title>
  <link>https://northflank.com/blog/top-managed-database-services</link>
  <pubDate>2026-04-08T14:45:00.000Z</pubDate>
  <description>
    <![CDATA[Top managed database services in 2026: compare Northflank, Supabase, PlanetScale, Neon, and CockroachDB on database coverage, BYOC support, pricing, and full-stack integration.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/managed_database_b72c2e6505.png" alt="Top managed database services in 2026" /><InfoBox className="BodyStyle">

## TL;DR: What are the top managed database services in 2026?

Managed database services provision, operate, and maintain your database infrastructure so your engineering team does not have to. The right platform depends on which database engines your stack uses, whether you need to run inside your own cloud account, and how much you need beyond the database itself.

- **Northflank** – Managed PostgreSQL, MySQL, MongoDB, Redis, MinIO, Memcached, and RabbitMQ. Run on Northflank's managed cloud or [self-serve BYOC](https://northflank.com/product/bring-your-own-cloud) into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, and bare-metal. Deploy databases alongside your applications, workers, and CI/CD in the same control plane.
- **Supabase** – Managed PostgreSQL bundled with authentication, real-time subscriptions, and auto-generated APIs. Best for MVPs and SaaS teams that want a full backend layer out of the box.
- **PlanetScale** – Managed MySQL and PostgreSQL with schema branching and non-blocking migrations via Vitess. Best for teams at extreme MySQL scale or teams that want Git-like schema workflows.
- **Neon** – Serverless PostgreSQL with scale-to-zero and database branching. Best for variable workloads and branch-per-PR development workflows.
- **CockroachDB** – Distributed SQL database with PostgreSQL wire compatibility, multi-region survivability, and horizontal scaling. Best for globally distributed applications that require strong consistency across regions.

> Most teams running a single database engine start with Supabase, Neon, or PlanetScale depending on their workflow. Teams that need multiple database types, BYOC deployment, or databases running alongside their application stack evaluate Northflank. Teams building globally distributed applications with strict consistency requirements evaluate CockroachDB.
> 
</InfoBox>

## Why managed database services matter

Running a database yourself means handling provisioning, version upgrades, backups, failover, connection pooling, and monitoring. That is significant engineering overhead, especially as your team scales and multiple environments need to stay in sync. Managed database services take that operational surface off your plate entirely.

The tradeoff with most providers is scope. Most managed database services cover one or two database types on their own infrastructure with no path to BYOC. That works until your stack grows, compliance surfaces as a requirement, or you need Redis alongside Postgres without adding a second provider.

## What should you look for in a managed database service?

These are the dimensions that matter when evaluating managed database platforms for production workloads.

- **Database coverage:** Does the platform support the full set of engines your stack uses? Postgres and MySQL are table stakes. Redis for caching, MongoDB for documents, RabbitMQ for messaging, and MinIO for object storage are common additions that most providers do not cover.
- **Deployment model:** Can you deploy inside your own cloud account, or are you locked into the provider's infrastructure? BYOC matters when compliance, data residency, or cost optimization are requirements.
- **Backups and restore:** Automated backups, point-in-time recovery, and import from external sources are non-negotiable for production workloads.
- **Scaling:** Can you scale vertically and horizontally without downtime, from the UI, API, and CLI?
- **Observability:** Real-time logs, performance metrics, and connection monitoring should be built in.
- **Stack integration:** If your database needs to run alongside services, workers, and pipelines, a platform that handles the full stack in one control plane reduces operational complexity significantly.
- **Pricing model:** Usage-based, per-second billing with no hidden fees scales more predictably than flat monthly tiers with opaque jumps.

## What are the top managed database services?

### 1. Northflank

[Northflank](https://northflank.com/) is a full-stack cloud platform with managed databases as a native first-class feature. You can deploy PostgreSQL, MySQL, MongoDB, Redis, MinIO, Memcached, and RabbitMQ as managed addons alongside your applications, background workers, CI/CD pipelines, and GPU workloads from the same control plane. Databases provision in minutes via UI, API, or CLI. Connection details are injected directly into connected services via secret groups, with no manual environment variable management required.

![northflank-full-homepage.png](https://assets.northflank.com/northflank_full_homepage_7e43a6b554.png)

Every addon includes automated backups with restore and fork support, horizontal and vertical scaling, real-time logs and metrics, TLS, and pause/restart for development databases. BYOC is available self-serve into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, and bare-metal, meaning your databases run inside your own infrastructure with your data never leaving your VPC. Postgres ships with pgvector for AI similarity search, connection poolers for high-connection workloads, and automated failover. Redis supports Sentinel for high availability and configurable eviction policies. Preview environments spin up isolated database instances per pull request and tear them down on merge.

**Key features:**

- **Database catalog:** PostgreSQL (pgvector, connection poolers, automated failover), MySQL, MongoDB, Redis (Sentinel, HA), MinIO (S3-compatible), Memcached, and RabbitMQ.
- **Automated backups:** Create, import, restore, and fork across all addon types. Import from URL, file upload, or connection string.
- **Scaling:** Horizontal and vertical scaling from the UI, API, or CLI. Adjustable at any time without downtime.
- **Secret group injection:** Connection details are injected directly into services and jobs.
- **Preview environments:** Isolated database instances per pull request with automatic teardown on merge.
- **Managed cloud or BYOC:** Run on Northflank's managed cloud or self-serve BYOC into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal.
- **SOC 2 Type 2 certified:** Covers managed cloud and BYOC deployments.
- **Access:** UI, API, CLI, and GitOps.

**Best for:** Teams that need multiple database types on a single platform, SaaS and AI teams running databases alongside services and workers, and enterprises with compliance or data residency requirements that need databases running inside their own infrastructure.

**Pricing:** PostgreSQL from $2.70/month. MySQL from $3.91/month. Redis and RabbitMQ from $2.21/month. All billed usage-based per second with no hidden fees.

<InfoBox className="BodyStyle">

[Get started on Northflank](https://app.northflank.com/signup) (self-serve, no demo required). Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) with an engineer to walk through your database requirements.

</InfoBox>

### 2. Supabase

Supabase is an open-source Firebase alternative built on PostgreSQL. It bundles a managed Postgres database with authentication, real-time subscriptions, auto-generated REST and GraphQL APIs, and edge functions in a single platform. For MVPs and early-stage SaaS products where the built-in auth and API layer reduce setup time, it is a practical starting point. Free tier projects provision instantly with 500MB database storage and 50,000 monthly active users for auth.

Supabase is PostgreSQL-only with no Redis, MongoDB, or messaging layer. The managed offering runs on Supabase's AWS infrastructure with no BYOC option. Free tier projects pause after one week of inactivity. HIPAA compliance requires the Team plan at $599/month plus an add-on.

**Best for:** MVPs, early-stage SaaS, and teams that want auth, real-time, and APIs bundled with Postgres out of the box.

**Pricing:** Free with 500MB database. Pro at $25/month. Team at $599/month. Enterprise custom.

### 3. PlanetScale

PlanetScale is a managed MySQL and PostgreSQL platform built on Vitess, the sharding layer that powers YouTube's database infrastructure. Its core differentiator is the developer workflow: schema branching that lets teams create, review, and merge schema changes like pull requests, with non-blocking migrations that avoid table locks in production. PlanetScale supports BYOC on AWS and GCP for enterprise customers.

MySQL on PlanetScale does not enforce foreign key constraints at the database level due to Vitess's sharding architecture. PlanetScale removed its free tier in 2024. It is the strongest option for teams at extreme MySQL scale or teams that need safe, non-blocking schema migrations as a production primitive.

**Best for:** Teams at extreme MySQL scale, teams that need non-blocking schema migrations, and Postgres teams that value the schema branching workflow.

**Pricing:** From $5/month. Enterprise custom. BYOC is available on AWS and GCP for enterprise plans.

### 4. Neon

Neon offers serverless PostgreSQL with separated storage and compute, enabling scale-to-zero for idle databases and database branching in development workflows. Compute resumes in around 150ms when a connection arrives. The branching feature creates full copy-on-write copies of a database in milliseconds, which integrates cleanly into branch-per-PR preview environment workflows. Neon was acquired by Databricks in 2025.

Neon is PostgreSQL-only with no Redis, MongoDB, or other database types, and managed-only with no BYOC option. Cold starts affect latency-sensitive applications that need consistent response times. For production teams that need multiple databases in one platform or BYOC deployment, the alternatives are more appropriate.

**Best for:** Serverless-first applications with variable traffic, development workflows that need branch-per-PR databases, and teams minimizing database costs for staging and preview environments.

**Pricing:** Free plan with 100 projects and 100 compute unit hours per project. Launch from $15/month. Scale from $701/month.

### 5. CockroachDB

CockroachDB is a distributed SQL database with PostgreSQL wire compatibility designed for globally distributed applications that require strong consistency across regions. It replicates data automatically across nodes, survives node and datacenter failures without manual intervention, and provides ACID transactions across distributed writes. The managed cloud offering runs on AWS, GCP, and Azure with three tiers: Basic, Standard, and Advanced.

CockroachDB is PostgreSQL-compatible but not a drop-in replacement. Some PostgreSQL features behave differently due to the distributed architecture, and teams migrating from standard Postgres should validate compatibility before committing. Multi-region deployments increase cost significantly through additional compute, storage replication, and cross-region data transfer charges. It is managed-only with no BYOC option on standard plans. For teams that need distributed SQL with global survivability and are comfortable with the operational and cost tradeoffs, CockroachDB is the category leader.

**Best for:** Globally distributed applications that require strong transactional consistency across regions, fintech, and mission-critical workloads that need zero-downtime datacenter failure recovery.

**Pricing:** Basic free tier available. Standard and Advanced plans are usage-based on compute, storage, and transfer. Enterprise custom.

## Which platform should you choose?

If you need more than one database type, or databases running alongside your application services in one place, Northflank is the only option here that covers the full stack with self-serve BYOC. Supabase, PlanetScale, Neon, and CockroachDB are each purpose-built for a specific database engine and use case.

If managed infrastructure is acceptable and you need just PostgreSQL, the choice comes down to what you need beyond the database: Supabase for auth and APIs bundled in, PlanetScale for MySQL scale and schema branching, Neon for serverless economics and branch-per-PR workflows, and CockroachDB for globally distributed SQL with multi-region survivability.

| Platform | Databases | BYOC | Free tier | Entry pricing | Billing model |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | Postgres, MySQL, MongoDB, Redis, MinIO, Memcached, RabbitMQ | Yes, self-serve | Yes, 1 free database | Postgres from $2.70/month | Per second, usage-based |
| **Supabase** | PostgreSQL | No | Yes (pauses after 1 week) | Pro from $25/month | Tiered + usage overages |
| **PlanetScale** | MySQL, PostgreSQL | Enterprise (AWS & GCP) | No | From $5/month | Tiered + row-based overages |
| **Neon** | PostgreSQL | No | Yes (100 projects, 100 CU hours each) | Launch from $15/month | Serverless compute units |
| **CockroachDB** | Distributed SQL (PostgreSQL-compatible) | No (standard plans) | Yes (Basic tier) | Standard usage-based | Compute, storage, and transfer |

## FAQ: managed database services

### What is the difference between a managed database and self-hosting?

With a managed database, the provider handles provisioning, patching, backups, failover, and scaling. With a self-hosted database, your team owns the full operational stack. Managed databases cost more per unit of compute but reduce engineering overhead significantly, especially as your environment count grows.

### Can I run databases inside my own cloud account on Northflank?

Yes. Northflank BYOC lets you deploy managed databases inside your own AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal infrastructure, self-serve. The managed experience is identical to Northflank's managed cloud, but your data never leaves your own infrastructure.

### Does Northflank support high availability for databases?

Yes. PostgreSQL and Redis support primary and read replicas with automated failover. Redis Sentinel is available for automated master discovery and failover. Replica counts and compute plans are adjustable from the resources page at any time without downtime.

### Which platform is best for preview environments with isolated databases?

Northflank and Neon both support branch-per-PR database environments. Northflank spins up isolated addon instances per preview environment across all supported database types. Neon creates lightweight copy-on-write Postgres branches in milliseconds. For teams using multiple database types, Northflank covers the full stack. For Postgres-only teams, Neon's branching is purpose-built for this workflow.

## Conclusion

Most managed database decisions come down to one question: do you need a single database engine optimized for a specific workflow, or do you need the full stack?

Supabase, PlanetScale, Neon, and CockroachDB each address a specific use case. Supabase bundles Postgres with auth and APIs. PlanetScale covers MySQL scale and schema branching. Neon provides serverless Postgres economics. CockroachDB handles globally distributed SQL with multi-region survivability.

Northflank is the right call when you need more than one database type, when databases need to run alongside application services in a single platform, or when compliance requires execution inside your own infrastructure.

<InfoBox className="BodyStyle">

You can [get started for free on Northflank](https://app.northflank.com/signup) or [talk to the team](https://cal.com/team/northflank/northflank-demo?duration=30) to walk through your database requirements.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Best European-based PaaS providers in 2026</title>
  <link>https://northflank.com/blog/best-european-paas-providers</link>
  <pubDate>2026-04-07T16:45:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the best European-based PaaS providers in 2026, including Northflank, Clever Cloud, Scalingo, and Sliplane. EU regions, self-serve BYOC, pricing, and GDPR compliance compared.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/best_european_paas_providers_0e147e5048.png" alt="Best European-based PaaS providers in 2026" />European-based PaaS providers are a distinct evaluation category for engineering teams that need their application infrastructure to sit under EU or UK data protection frameworks.

This article covers the main providers in this space and where they differ on data residency, region coverage, pricing, and Bring Your Own Cloud (BYOC) support.

<InfoBox className="BodyStyle">

## TL;DR: Top European-based PaaS providers at a glance

- European-based PaaS providers are platforms with a European HQ, EU-based infrastructure, or both, subject to EU or UK data protection frameworks
- Key evaluation criteria: legal jurisdiction, EU region availability, BYOC support, GDPR compliance, and engineering support coverage
- Northflank is a Kubernetes-based PaaS for deploying services, databases, jobs, GPU and CPU workloads, sandboxes, and CI/CD pipelines, either on [Northflank's managed cloud](https://northflank.com/features/managed-cloud) across [multiple Europe West regions](https://northflank.com/cloud/northflank/regions) or self-serve inside your own cloud account, bare-metal, or on-premises infrastructure via [BYOC](https://northflank.com/product/bring-your-own-cloud) (the only provider in this list with self-serve BYOC)
- Other providers covered: Clever Cloud (France), Scalingo (France), Sliplane (Germany)

</InfoBox>

## What is a European-based PaaS provider?

A European-based PaaS provider is a Platform-as-a-Service (PaaS) that is incorporated under EU or UK law, operates EU-region infrastructure, and is subject to EU or UK data protection frameworks.

The category matters because legal incorporation determines which regulatory frameworks a provider operates under, and by extension, what rights customers have over their data and what obligations the provider carries.

A provider's physical data center locations are one factor, but the legal entity that controls and processes that data is another, and both are relevant when evaluating infrastructure for regulated environments.

## Why do engineering teams choose European PaaS providers?

There are several practical reasons engineering and infrastructure teams prioritise European-based providers.

### Legal jurisdiction and data sovereignty

Data protection regulations in some jurisdictions include provisions that can require companies incorporated there to disclose customer data to government entities, regardless of where that data is physically stored. For European companies in regulated sectors, including health, finance, and the public sector, working with a provider incorporated under EU or UK law means the provider operates under the same regulatory framework as the customer. This alignment can be a hard contractual or compliance requirement rather than a preference.

### EU region coverage and latency

Deploying to a region geographically close to European end users reduces round-trip latency and keeps data traffic within EU network infrastructure. Shorter distances between application servers and end users can affect performance for latency-sensitive workloads. Region availability also determines whether specific data residency requirements, such as keeping data within a particular country or jurisdiction, can be met at the infrastructure level.

### Engineering support in European time zones

For teams operating in CET or GMT, having a vendor with a European engineering and support team matters during incidents and onboarding. Response time during an outage is affected by where the vendor's team is located. This becomes more relevant as teams scale and move toward enterprise agreements with defined SLAs.

### Bring Your Own Cloud (BYOC) and EU data residency

Running workloads on a provider's shared infrastructure, even in an EU region, means data sits on multi-tenant infrastructure that the provider controls. With [Bring Your Own Cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) (BYOC), customer workloads, databases, and storage run inside the customer's own cloud account. Data does not transit the platform vendor's infrastructure.

This is a meaningful distinction for teams with strict data sovereignty, compliance audit, or sector-specific requirements. The control plane manages deployments; the runtime operates inside the customer's VPC. The practical result is that the platform vendor does not have access to workload data at rest.

See also: [What's the best PaaS that can run in my own cloud account?](https://northflank.com/blog/best-paas-that-runs-in-my-own-cloud-account-bypc-self-hosted-paas)

## Which European PaaS providers should you consider in 2026?

The providers below were evaluated on European HQ or legal incorporation, EU region availability, BYOC support, pricing transparency, and PaaS capabilities, including application deployment, managed databases, and CI/CD.

| Provider | HQ | Own EU regions | BYOC (Bring Your Own Cloud) | Certifications | Starting price |
| --- | --- | --- | --- | --- | --- |
| Northflank | London, UK | Europe West, Europe West (Frankfurt), Europe West (Netherlands), Europe West (Zurich) | Yes (self-serve) | SOC 2 Type 2 | Free tier, pay-as-you-go ($2.70/mo for the smallest paid plan) |
| Clever Cloud | Nantes, France | France (+ EU partners) | On-premises option (enterprise) | ISO 27001:2022, HDS | Consumption-based |
| Scalingo | Strasbourg, France | France (2 regions) | No | ISO 27001, HDS, SecNumCloud (IaaS) | Free trial; consumption-based |
| Sliplane | Germany | Germany, Finland | No | GDPR | €9/mo (2 vCPU, 2 GB) |

### 1. Northflank

Northflank is a Kubernetes-based PaaS registered in London. You can deploy services, databases, jobs, GPU workloads, and sandboxes either on [Northflank's managed cloud](https://northflank.com/features/managed-cloud) across multiple Europe West regions or inside your own infrastructure via [BYOC](https://northflank.com/product/bring-your-own-cloud), where your workloads never leave your own cloud account.

The platform covers the full application lifecycle, from CI/CD and preview environments through to production scaling, observability, and GPU inference. Northflank has an engineering team in Europe available to support customers, and Enterprise plans include 24/7 support and a custom SLA.

- **EU managed regions:** Deploy to [Europe West](https://northflank.com/cloud/northflank/regions/europe-west), [Europe West (Frankfurt)](https://northflank.com/cloud/northflank/regions/europe-west-frankfurt), [Europe West (Netherlands)](https://northflank.com/cloud/northflank/regions/europe-west-netherlands), and [Europe West (Zurich)](https://northflank.com/cloud/northflank/regions/europe-west-zurich), with NVIDIA L4 GPU availability in Europe West. See also [other EMEA provider regions](https://northflank.com/cloud/northflank/regions/europe-west#other-provider-regions-in-emea).
- **BYOC (Bring Your Own Cloud):** The only provider in this list with self-serve BYOC. Connect [AWS](https://northflank.com/cloud/aws/regions), [GCP](https://northflank.com/cloud/gcp#google-cloud-platform-regions-available-on-northflank), [Azure](https://northflank.com/cloud/azure#azure-regions-available-on-northflank), [OCI](https://northflank.com/cloud/oci#oracle-regions-available-on-northflank), [Civo](https://northflank.com/cloud/civo#civo-regions-available-on-northflank), or [CoreWeave](https://northflank.com/cloud/coreweave) accounts, or bring bare-metal and on-premises Kubernetes clusters, and deploy workloads directly into your own infrastructure. Available on the pay-as-you-go plan with no sales conversation required.
- **Deployments:** CI/CD with per-branch pipelines, [Dockerfile and buildpack support](https://northflank.com/product/deployments), GitOps, IaC templates, and rollback.
- **Preview environments:** [Ephemeral full-stack clones](https://northflank.com/product/preview-environments) per pull request or branch.
- **Sandboxes:** [Isolated microVM environments](https://northflank.com/product/sandboxes) for running untrusted code at scale, including multi-tenant workloads and AI agent execution.
- **GPU workloads:** Run [inference, training, and notebooks](https://northflank.com/product/gpu-paas) across NVIDIA H100, H200, A100, L4, and other GPU types on managed cloud and BYOC.
- **Customer VPC deployments:** [Deploy your software into customer VPCs](https://northflank.com/product/customer-vpc-deployments) for software vendors selling to enterprises that require self-hosting in their own cloud environment.
- **Internal developer platform:** [Self-service infrastructure](https://northflank.com/product/idp) for large engineering organisations, with RBAC, team management, and templated environments.
- **Kubernetes orchestration:** Manage workloads across [EKS, GKE, AKS, and other Kubernetes distributions](https://northflank.com/product/app-platform).
- **Databases:** Managed PostgreSQL, MongoDB, Redis, and MySQL with backups, high availability, and point-in-time recovery.
- **Security and compliance:** SOC 2 Type 2 certified. See [northflank.com/security](https://northflank.com/security).
- **Pricing:** See [northflank.com/pricing](https://northflank.com/pricing). Free Sandbox tier (2 services, 1 database, 2 cron jobs, always-on). Pay-as-you-go from $2.70/month (0.1 shared vCPU, 256 MB RAM). Most popular plan: $24/month (1 dedicated vCPU, 2 GB RAM). Billed per second. NVIDIA L4 GPU from $0.80/hr. Network egress $0.06/GB. Enterprise plans include 24/7 support and a custom SLA.

<InfoBox className="BodyStyle">

Teams can [get started directly (self-serve)](https://app.northflank.com/signup) or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) for teams with specific infrastructure or compliance requirements.

</InfoBox>

Related reading: [Best PaaS providers](https://northflank.com/blog/best-paas-providers) · [Global PaaS with multi-region deployments](https://northflank.com/blog/global-paas-with-multi-region-deployments)

### 2. Clever Cloud

Clever Cloud is a French PaaS based in Nantes, incorporated under French and EU law. It deploys applications from Git with automatic language detection, hosting infrastructure in France and with EU-based partners. An on-premises option is available for enterprise customers through their sales team.

- **Runtimes:** Node.js, Python, PHP, Java, Ruby, Rust, Go, Scala, Elixir, Haskell, .NET Core, Docker, and static applications
- **Databases:** PostgreSQL, MySQL, MongoDB, Redis
- **Deployment:** Git-based, with auto-scaling within a customer-defined resource ceiling, and automatic monitoring
- **Observability:** Metrics, logs, and alerts included. Grafana dashboards integrated into the console.
- **IAM:** User access management via Keycloak, with role-based permissions
- **Certifications:** ISO/IEC 27001:2022, Health Data Hosting (HDS), GDPR-compliant. SecNumCloud zone available through partner Cloud Temple.
- **Pricing:** Consumption-based, billed per second within a customer-defined resource ceiling.

### 3. Scalingo

Scalingo is a French PaaS based in Strasbourg, incorporated under French and EU law. It offers a Heroku-compatible push-to-deploy workflow across two regions in France: osc-fr1 and snc-fr1. No BYOC option is available.

- **Runtimes:** Ruby on Rails, Node.js, PHP, Python, Java, Go, Elixir, Scala, and others
- **Databases:** PostgreSQL, MySQL, MongoDB, Redis, Elasticsearch, OpenSearch, InfluxDB
- **Deployment:** Git-based deploys, CLI and API access, autoscaling, zero-downtime rolling deploys, rollbacks
- **Additional workloads:** Workers and cron jobs supported
- **Networking:** Private networks for secure communication between applications
- **AI:** pgvector on PostgreSQL and KNN on OpenSearch for vector search and LLM applications
- **Pricing:** App runtimes from €7.20/container/month. Billed by the minute. 30-day free trial.

### 4. Sliplane

Sliplane is a German container hosting platform, subject to German and EU law. It uses a flat per-server billing model: you provision a server and deploy unlimited Docker containers on it via GitHub push-to-deploy. Databases run as containers on the provisioned server, with no separately managed database services, no GPU support, and no BYOC.

- **EU server locations:** Germany and Finland
- **Includes:** Free SSL, free subdomains, secrets management, health checks, daily volume backups, log monitoring, 1 TB bandwidth per server
- **Pricing (ex-VAT):** Base 2 vCPU shared / 2 GB RAM / 40 GB disk at €9/month. Medium 3 vCPU / 4 GB / 80 GB at €24/month. Large 4 vCPU / 8 GB / 160 GB at €44/month. X-Large 8 vCPU / 16 GB / 240 GB at €76/month. Additional bandwidth at €2/TB.

## Other European cloud providers

Two names come up frequently in searches for European cloud options but operate at the infrastructure layer rather than as developer PaaS platforms.

- **Scaleway** (Paris, France) is a European cloud provider offering IaaS, managed databases, Kubernetes, and serverless services across EU regions in France, the Netherlands, and Poland. Teams looking for raw compute or managed Kubernetes in EU regions may evaluate it at the infrastructure layer.
- **OVHcloud** (Roubaix, France) is among Europe's largest cloud infrastructure providers, offering bare metal, VPS, public cloud compute, Managed Kubernetes, and managed databases. Teams running OVHcloud infrastructure may use its Managed Kubernetes service as a deployment target, though this requires Kubernetes operational expertise rather than a PaaS workflow.

## Frequently asked questions about European PaaS providers

### What are the best European-based PaaS providers?

The main European-based PaaS providers for application deployment include Northflank (UK), Clever Cloud (France), and Scalingo (France). Sliplane (Germany) covers container hosting for smaller workloads. Each differs on region coverage, BYOC support, certifications, and pricing model.

### What is the difference between a European PaaS and a European cloud provider?

A PaaS abstracts the infrastructure layer so teams deploy applications rather than configuring servers. A cloud provider exposes raw compute, storage, and networking that teams configure themselves. Scaleway and OVHcloud are examples of European cloud providers. Northflank, Clever Cloud, and Scalingo are European PaaS providers. Some providers offer elements of both.

### Which European PaaS providers support Bring Your Own Cloud (BYOC)?

Among the providers covered in this article, Northflank is the only one that offers self-serve BYOC into existing cloud accounts, including AWS, GCP, Azure, OCI, Civo, CoreWeave, and on-premises. Clever Cloud offers an enterprise on-premises option that installs on customer hardware through their sales team. Scalingo and Sliplane run on their own managed infrastructure.

### Do European PaaS providers comply with GDPR?

All providers in this article are incorporated under EU or UK law and subject to GDPR. In practice, compliance also depends on how each application is configured and what data it processes. Northflank holds SOC 2 Type 2. Clever Cloud holds ISO 27001:2022 and HDS. Scalingo holds ISO 27001 and HDS, with a SecNumCloud-qualified IaaS layer.

### What is the largest European cloud provider?

OVHcloud (Roubaix, France) is among the largest European-headquartered cloud infrastructure providers by infrastructure scale. This is a different category from PaaS. For application deployment, Northflank covers multiple Europe West regions on its managed cloud and supports self-serve BYOC across EU regions on AWS, GCP, Azure, OCI, Civo, and CoreWeave. Clever Cloud and Scalingo are French PaaS providers covered in this article

### Is there a European alternative to Heroku?

Northflank is a European alternative to Heroku that covers application deployments, managed databases, preview environments, GPU workloads, sandboxes, and self-serve BYOC across multiple Europe West regions and any EU region available on AWS, GCP, Azure, OCI, Civo, and CoreWeave, as well as bare-metal and on-premises Kubernetes clusters.]]>
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  <title>What is a customer deployment platform? A guide for developers and SaaS vendors</title>
  <link>https://northflank.com/blog/customer-deployment-platform</link>
  <pubDate>2026-04-06T15:30:00.000Z</pubDate>
  <description>
    <![CDATA[A customer deployment platform helps teams deploy software in their own cloud or their customers' environments. Learn what it covers, who uses it, and what to look for.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/customer_deployment_platform_23d613e8d4.png" alt="What is a customer deployment platform? A guide for developers and SaaS vendors" />*A customer deployment platform is a system for deploying and managing software in a customer-controlled environment, whether that means a developer's own cloud account or a software vendor's customer's infrastructure. The term is used in at least two distinct contexts: teams that want to run workloads in their own cloud rather than a shared platform, and software vendors that need to deploy and operate their product inside their customers' cloud environments.*

The term "customer deployment platform" does not have a single agreed definition. Depending on who is using it, it can refer to a PaaS that runs inside a team's own cloud account, or a platform a software vendor uses to deploy and manage their product inside their customers' environments. Both are valid uses of the term, and both are covered in this article.

This guide covers what a customer deployment platform involves across both use cases, who uses them, how they differ from general deployment platforms, and what to look for when evaluating one.

<InfoBox className="BodyStyle">

## Key takeaways: what you need to know about customer deployment platforms

- "Customer deployment platform" covers at least two distinct use cases: a platform teams use to deploy their own workloads in their own cloud account, and a platform vendors use to deploy their software inside their customers' cloud environments.
- For developers and engineers, a customer deployment platform provides a consistent deployment layer on top of their own cloud infrastructure, giving them a managed developer experience while keeping workloads, data, and costs within their own cloud account.
- For SaaS vendors, a customer deployment platform handles the operational complexity of deploying and managing software across many customer environments, including on-premises, BYOC (Bring Your Own Cloud), and air-gapped infrastructure.
- The key distinction from a general deployment platform is where workloads run. General platforms host workloads in the provider's shared infrastructure. Customer deployment platforms run workloads in the customer's own cloud account or on-premises environment.
- [Northflank](https://northflank.com/product/bring-your-own-cloud) supports both use cases: developers and teams can deploy in their own AWS, GCP, Azure, or on-premises infrastructure via BYOC, and SaaS vendors can deploy and manage their software inside their customers' cloud environments via Northflank's [customer VPC deployment](https://northflank.com/product/customer-vpc-deployments) offering. Northflank is [SOC 2 Type II certified](https://northflank.com/security).

</InfoBox>

Engineering teams looking to run workloads in their own cloud rather than a shared PaaS, and SaaS vendors needing to deploy their product inside their customers' environments, share the same underlying question: how do you get software running in a specific cloud account and keep it operating reliably over time?

## What is a customer deployment platform?

A customer deployment platform is a system that provides the deployment, orchestration, and operational layer for software running in a customer-controlled environment. What distinguishes it from a general deployment platform is that the infrastructure belongs to the customer, not the platform provider.

The term covers at least two architecturally distinct use cases:

### Deploying in your own cloud

In this context, the customer is the team or organisation using the platform. An engineering team wants to run their workloads in their own AWS, GCP, Azure, or on-premises environment rather than in a provider's shared infrastructure.

A customer deployment platform in this sense is a managed layer that sits on top of the team's own cloud account and handles deployment orchestration, Kubernetes lifecycle management, CI/CD pipelines, and developer tooling. The team brings the cloud; the platform manages the workload and deployment layer on top of it.

### Deploying in your customer's cloud

In this context, the customer is the vendor's paying customer. A SaaS vendor or ISV (independent software vendor) needs to deploy and manage their product inside their customers' cloud accounts or on-premises environments.

Enterprise customers in regulated industries often require software to run within their own cloud boundary rather than in a vendor-hosted SaaS environment. In this sense, a customer deployment platform provides the control plane the vendor uses to provision, deploy, update, and monitor their software across many customer environments through a single interface.

*Both use cases share the same underlying principle: workloads and, in most configurations, data run in the customer's environment rather than the platform provider's.*

## Who uses a customer deployment platform?

The use cases above map to three distinct audiences, each with different requirements.

### Developers and engineering teams

Teams that want to run workloads in their own cloud account rather than a shared PaaS. 

Common reasons include data residency requirements, cost optimisation through existing cloud commitments and reserved instance pricing, security and compliance policies that restrict where workloads can run, and avoiding dependence on a single provider's shared infrastructure.

These teams want the developer experience of a managed PaaS but need the workloads to stay within their own cloud boundary.

### SaaS vendors and ISVs

Software vendors whose enterprise customers require software to run within their own cloud account rather than in vendor-hosted SaaS.

This is common in financial services, healthcare, government, and other regulated industries where data sovereignty, compliance frameworks, or internal security policies restrict the use of third-party hosted software.

Supporting this at scale, across many customers on different cloud providers, becomes difficult to manage without a platform that handles provisioning, CI/CD, observability, and configuration management across customer environments.

### Enterprise platform teams

Internal DevOps or platform engineering teams building tooling for their own engineering organisation.

They need workloads to run within their company's own cloud boundary for security, compliance, or cost reasons, but want a consistent developer experience across teams rather than raw Kubernetes or bespoke IaC.

## How does a customer deployment platform differ from a general deployment platform?

The table below outlines the key differences:

|  | General deployment platform | Customer deployment platform |
| --- | --- | --- |
| Where workloads run | Provider's shared infrastructure | Customer's own cloud account or on-premises |
| Where data lives | Provider's environment | Customer's environment |
| Infrastructure management | Fully managed by provider | Customer brings infrastructure, platform manages workloads |
| Data residency and compliance | Depends on provider's certifications | Customer controls their own boundary |
| Cost model | Provider charges for compute and storage | Customer uses their own cloud account and existing commitments |

The key tradeoff is control versus simplicity. General deployment platforms are simpler to get started with since the provider handles all infrastructure. Customer deployment platforms give teams and vendors more control over where workloads run and where data lives, at the cost of managing the underlying cloud infrastructure themselves or bringing it to the platform.

## What should a customer deployment platform provide?

The specific capabilities vary between platforms. The following are among the things worth evaluating when assessing a customer deployment platform:

### Consistent deployment experience across clouds

When deploying across AWS, GCP, Azure, or on-premises, the platform should provide a consistent developer experience regardless of the underlying infrastructure. Teams should not need to learn separate workflows per cloud provider, and vendors deploying across many customer environments should be able to manage all of them from a single interface.

### CI/CD and release pipelines

Automated build, test, and deployment pipelines that work within the customer's cloud environment. Support for progressive rollouts, canary deployments, and rollback on failure. For SaaS vendors, this also means the ability to target specific customer environments for updates without a manual process per customer.

### Kubernetes orchestration

Many modern customer deployment platforms build on Kubernetes as the runtime layer. The platform should handle cluster provisioning and lifecycle management, so teams do not have to manage Kubernetes cluster operations directly. Support for managed Kubernetes services such as EKS on AWS, GKE on GCP, and AKS on Azure is worth verifying for multi-cloud use cases.

### Observability and monitoring

Metrics, logs, and health signals collected within the customer's environment. For SaaS vendors managing many customer deployments, observability needs to work across all environments without the vendor accessing application-level customer data. This typically requires separation at the logging and metrics layer.

### Multi-tenancy and isolation

For SaaS vendors supporting multiple customer environments, the platform needs to maintain isolation between customer deployments. This typically involves dedicated infrastructure per customer, namespace separation, encrypted secrets, and network policies. Hard isolation is designed to prevent one customer's workload from accessing another's.

### Security and compliance controls

Audit trails, RBAC, mTLS service mesh, and encryption at rest and in transit. For enterprise teams and vendors operating in regulated industries, these are often baseline requirements rather than optional features.

## How do Northflank's BYOC and customer VPC deployment products work?

Northflank addresses both use cases described in this article through two related but distinct products.

### BYOC: a PaaS in your own cloud

Northflank's [BYOC product](https://northflank.com/product/bring-your-own-cloud) provides a managed platform layer on top of the customer's own AWS, GCP, Azure, Oracle, or on-premises infrastructure. The team or organisation brings their cloud account or existing Kubernetes cluster; Northflank manages the platform layer, including deployment pipelines, services, databases, and workloads on top of it.

![northflank-byoc-paas.png](https://assets.northflank.com/northflank_byoc_paas_bf7454d6a8.png)

The developer experience is consistent across all environments. Northflank's UI, CLI, and API work the same way regardless of the underlying cloud, and features like [preview environments](https://northflank.com/product/preview-environments), [stateful workloads](https://northflank.com/docs/v1/application/databases-and-persistence/stateful-workloads-on-northflank), and [release pipelines](https://northflank.com/docs/v1/application/release) are available across all clouds. Teams can also run across multiple cloud providers simultaneously from a single interface.

This suits engineering teams that want a managed PaaS experience while keeping workloads within their own cloud boundary, as well as enterprise teams that need to consolidate infrastructure management across multiple cloud accounts.

### Customer VPC deployment: deploying in your customers' cloud

Northflank's [customer VPC deployment product](https://northflank.com/product/customer-vpc-deployments) provides a control plane for SaaS vendors deploying and managing their application directly inside their customers' cloud environments. Vendors define their application once and Northflank handles provisioning, CI/CD, monitoring, and update delivery across their customers' AWS, GCP, Azure, Oracle, and on-premises environments from a single interface.

![customer-vpc-deployment.png](https://assets.northflank.com/customer_vpc_deployment_77fc1ad30d.png)

Each customer deployment runs on dedicated infrastructure with namespace-level separation, mTLS, encrypted secrets, and network policies applied by default. New customer environments can be provisioned in minutes, reducing what would otherwise take weeks of manual setup. Northflank's control plane connects to customer environments through secure cross-account links without credential sharing.

<InfoBox className="BodyStyle">

Vendors can [get started directly](https://app.northflank.com/signup) (self-serve) or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo) for teams with specific infrastructure or compliance requirements.

</InfoBox>

See the following guides for more on deploying software in customer environments, implementation approaches on AWS, and the software distribution layer:

- [SaaS deployment in customer environments](https://northflank.com/blog/saas-deployment-in-customer-environment): why enterprise customers require software to run in their own environments and the key deployment models
- [Deploying SaaS in a customer VPC](https://northflank.com/blog/deploy-saas-in-customer-vpc): a technical walkthrough of implementation approaches on AWS, including AWS PrivateLink, in-VPC deployment, and VPC Lattice
- [Software distribution platforms](https://northflank.com/blog/software-distribution-platform): the software distribution layer, including licensing, packaging, delivery, and distribution models

## Frequently asked questions about customer deployment platforms

### What is the difference between a customer deployment platform and a PaaS?

A traditional PaaS runs workloads on the provider's shared infrastructure. A customer deployment platform runs workloads in the customer's own cloud account or on-premises environment. The key difference is where the compute, storage, and data reside. Some platforms, like Northflank, offer both options: a [managed cloud](https://northflank.com/features/managed-cloud) where the provider hosts everything, and a [BYOC mode](https://northflank.com/features/bring-your-own-cloud) where the customer brings their own infrastructure.

### What does BYOC mean in the context of a deployment platform?

BYOC stands for Bring Your Own Cloud. In the context of a deployment platform, it means the customer provides their own cloud account or Kubernetes cluster, and the platform deploys and manages workloads within it. The customer retains control over the underlying infrastructure while the platform handles the operational layer on top.

### Can a customer deployment platform support air-gapped environments?

Some customer deployment platforms support air-gapped deployments, where the customer environment has no internet connectivity. This typically requires offline distribution methods such as downloadable installation bundles and local registry setup. Not every platform supports air-gapped environments, so this is worth verifying if your customers operate in restricted network environments.

### What is the difference between deploying in your own cloud and deploying in your customer's cloud?

Deploying in your own cloud means running your team's or organisation's workloads in your own AWS, GCP, Azure, or on-premises account. Deploying in your customer's cloud means a software vendor running their product inside their customer's cloud account. Both involve customer-controlled infrastructure, but the relationship is different: in the first case the team is both the customer and the operator, in the second the vendor operates software on behalf of their customer inside that customer's environment.

### What cloud providers does a customer deployment platform typically support?

This varies by platform. Common cloud providers supported include AWS, GCP, and Azure. Some platforms, like Northflank, also support Oracle Cloud, Civo, CoreWeave, bare-metal, and on-premises Kubernetes distributions. Multi-cloud support is worth verifying if your customers are distributed across different providers or if you need to support on-premises deployments.]]>
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</item><item>
  <title>What is a software distribution platform? A guide for SaaS vendors</title>
  <link>https://northflank.com/blog/software-distribution-platform</link>
  <pubDate>2026-04-03T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[A software distribution platform helps software vendors deliver, deploy, and manage their software in customer environments. Learn what it involves and what to look for.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/software_distribution_platform_df817534ea.png" alt="What is a software distribution platform? A guide for SaaS vendors" />*A software distribution platform is a system that enables software vendors to deliver, deploy, license, and manage their software in customer-controlled environments, including on-premises infrastructure, customer cloud accounts, and air-gapped networks. It handles the operational complexity of getting software from a vendor's systems into a customer's environment and managing it across customer deployments over time.*

The term "software distribution platform" is used to describe at least two distinct categories of tools that serve different purposes. Understanding which category applies to your use case is the first step to evaluating whether you need one and what to look for.

This guide covers what vendor-to-customer software distribution platforms do, why software vendors use them, the key capabilities they provide, and how the distribution model differs depending on how much operational control the vendor wants to maintain in the customer's environment.

<InfoBox className="BodyStyle">

## Key takeaways: what you need to know about software distribution platforms

- The term covers at least two distinct categories: platforms that help IT administrators deploy software internally to employees, and platforms that help software vendors distribute their software to paying customers who run it in their own infrastructure. This article focuses on the second.
- Vendor-to-customer software distribution platforms handle some or all of the following: packaging, delivery, licensing, deployment, updates, and monitoring across customer environments including on-premises, BYOC (Bring Your Own Cloud), air-gapped networks, and customer cloud accounts.
- Enterprise customers increasingly require software to run in their own infrastructure rather than in vendor-hosted SaaS. A software distribution platform is one way vendors reduce the need to build bespoke distribution infrastructure per customer.
- Distribution models vary. Some platforms package software for customers to pull and install themselves. Others actively deploy and manage software inside the customer's environment from a central control plane. These are different approaches and suit different vendor requirements.

> [Northflank](https://northflank.com/product/customer-vpc-deployments) provides a control plane for deploying and managing your application inside your customers' cloud environments across AWS, GCP, Azure, Oracle, and on-premises infrastructure from a single interface, so vendors do not have to build that distribution infrastructure themselves. Vendors define their application once and Northflank handles provisioning, CI/CD, monitoring, and update delivery across their customers' environments. Northflank is SOC 2 Type II certified.
> 

</InfoBox>

## What is a software distribution platform?

A software distribution platform, at a general level, is a system that helps move software from where it is built to where it needs to run, and manages it there over time. In the context of this article, that means getting software from a vendor's systems into a customer's infrastructure and keeping it running, updated, and licensed correctly.

The term covers at least two distinct categories of tools:

### IT admin distribution platforms

These platforms help IT departments distribute software internally to employees' computers and devices within an organisation. They focus on deploying software to employee workstations, managing patches across corporate devices, and maintaining software inventory on internal networks.

This is not the focus of this article. If you are a software vendor distributing your product to paying customers, this category of tool is not designed for your use case.

### Vendor-to-customer distribution platforms

These platforms help software vendors, including ISVs (independent software vendors) and SaaS companies with self-hosted offerings, deliver and manage their software in customer-controlled environments. The customer might be running the vendor's software on their own cloud account, in their on-premises data centre, or in an air-gapped network.

This is the category this article covers.

| Category | Who uses it | What it does |
| --- | --- | --- |
| IT admin distribution | IT departments | Deploys software to employee devices within an organisation |
| Vendor-to-customer distribution | Software vendors, ISVs | Delivers and manages commercial software in customer-controlled environments |

## Why do software vendors use distribution platforms?

SaaS is the default deployment model for most software vendors. The vendor hosts everything, the customer accesses it via a browser or API, and the vendor controls the full operational stack. For many customers, this works well.

Enterprise customers in regulated industries often cannot use vendor-hosted SaaS for certain workloads. Factors that drive this include:

- Data residency regulations that require software to run within specific geographic or organisational boundaries
- Internal security policies that prohibit sending certain data to third-party cloud environments
- Compliance frameworks in industries such as financial services, healthcare, government, and defence that lead organisations to require software runs in customer-controlled infrastructure.
- Air-gapped network requirements for organisations that operate with no internet connectivity

To sell to these customers, vendors need to support some form of self-hosted or customer-environment deployment, whether on-premises, BYOC (Bring Your Own Cloud), or air-gapped. 

Supporting one or two of these deployments manually is feasible. Across many customers, each with different infrastructure, configurations, and update schedules, it becomes a significant operational challenge. This is the problem vendor-to-customer software distribution platforms are designed to address.

For more background on why enterprise customers require this, see our [guide to SaaS deployment in customer environments](https://northflank.com/blog/saas-deployment-in-customer-environment).

## What does a vendor-to-customer software distribution platform do?

The specific capabilities vary between platforms. Most vendor-to-customer distribution platforms address some combination of the following:

### Artifact management and versioning

Storing and managing software artifacts, such as container images, Helm charts, and installation bundles. Controlling which customers have access to which versions. Supporting version tagging, immutable artifact addressing, and customer-specific release channels so that different customers receive different versions of the software.

### License management

Enforcing commercial terms across distributed customer installations. This typically includes controlling feature access based on subscription tier, setting license expiration dates, managing seat limits or usage quotas, and revoking access when a subscription ends. License enforcement mechanisms vary between platforms.

### Deployment and installation

Getting software installed and running in the customer's environment. How this works depends on the distribution model the vendor uses and the platform they are on. Some platforms package software into installer bundles that the customer pulls and installs themselves. Others provide agents or control planes that handle deployment directly in the customer's environment.

### Update delivery

Delivering new software versions to customer environments. This typically involves some mechanism for staged rollouts, targeting specific customers or release channels, rolling back on failure, and respecting customer maintenance windows. The degree of automation and control varies between platforms.

### Observability and monitoring

Collecting operational telemetry from customer environments, such as metrics, logs, deployment status, and version adoption data. This is technically challenging in customer-controlled environments because the vendor may have agreed not to access the customer's data directly. Distribution platforms often collect operational signals separately from application-level data, though the specific access model varies by platform and customer agreement.

### Customer portal

Providing customers with a self-service interface for managing their installation: accessing software versions, viewing documentation, downloading installation scripts or artifacts, and checking deployment health. The depth of functionality in customer portals varies between platforms.

### Air-gap support

Supporting distribution to customers on air-gapped networks with no internet connectivity. This involves offline distribution methods such as downloadable bundles containing all dependencies, local registry setup, and installation processes that do not require outbound internet access during deployment.

## What deployment environments does a software distribution platform need to support?

The range of environments vendors need to support varies depending on their target customers. Common deployment targets include:

- Customer cloud accounts on AWS, GCP, Azure, and other providers (BYOC)
- On-premises data centres and private cloud infrastructure
- Air-gapped networks with no internet connectivity
- Kubernetes clusters using managed services such as EKS, GKE, or AKS, or self-managed distributions
- VM-based and bare-metal environments

Not every platform supports all of these environments equally. Some are focused primarily on Kubernetes-based deployments. Others support a wider range of targets, including VMs and bare metal. Vendors should verify which deployment targets a platform supports before adopting it, particularly if their customers operate in air-gapped or non-Kubernetes environments.

## How does a software distribution platform differ from related tools?

The table below outlines how vendor-to-customer distribution platforms relate to tools that are often used alongside them:

| Tool | What it does | What it does not cover |
| --- | --- | --- |
| Container registry | Stores and serves container images | Does not handle licensing, deployment orchestration, or customer portals |
| CI/CD platform | Builds, tests, and delivers software, typically to the vendor's own environments | Does not manage distribution to customer-controlled environments |
| Deployment tool (Helm, Terraform) | Automates installation and configuration | Does not manage licensing, multi-customer update delivery, or observability |
| Marketplace platform | Handles billing, storefronts, and reseller channels | Does not handle technical delivery to customer infrastructure |

Distribution platforms typically sit on top of or integrate with these tools. A CI/CD pipeline produces the artifacts. A container registry stores them. A deployment tool handles installation. A distribution platform provides the layer that manages this across many customer environments, enforces licensing, and maintains visibility into what is deployed where.

## What distribution model suits your use case?

Vendor-to-customer distribution platforms do not all work the same way. There are broadly two approaches, and they suit different vendor requirements.

- **Pull-based distribution**: the vendor packages software into an installer or bundle that the customer pulls from a registry or portal and installs in their own environment. The vendor provides tools that make installation repeatable and manageable, but the customer drives the installation process. This suits environments where the vendor does not have or require access to customer infrastructure.
- **Managed deployment**: the vendor actively deploys and manages software inside the customer's environment from a centralised control plane. The customer provides the infrastructure (their cloud account or on-premises cluster), but the vendor operates the software within it, handling updates, monitoring, and configuration centrally. This is closer to a managed service model delivered inside the customer's environment.

The right model depends on what the customer requires and how much operational responsibility the vendor wants to take on.

<InfoBox className="BodyStyle">

[Northflank](https://northflank.com/product/customer-vpc-deployments) provides a control plane for the managed deployment model: vendors define their application once, and Northflank handles provisioning, CI/CD, monitoring, and update delivery across their customers' cloud environments, including AWS, GCP, Azure, Oracle, and on-premises infrastructure, from a single interface.

Each customer deployment runs on dedicated infrastructure with namespace-level separation, mTLS, encrypted secrets, and network policies applied by default. Vendors can [get started directly](https://app.northflank.com/signup) (self-serve) or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo) for teams with specific infrastructure or compliance requirements.

</InfoBox>

For a technical walkthrough of how managed deployment into customer VPCs works, including AWS PrivateLink, in-VPC deployment, and VPC Lattice, see our [guide to deploying SaaS in a customer VPC](https://northflank.com/blog/deploy-saas-in-customer-vpc).

## Frequently asked questions about software distribution platforms

### How do I know which software distribution model suits my use case?

It depends on what your customers' environments look like and how much operational responsibility you want to take on. If your customers need to install and manage software themselves in environments you cannot access, a pull-based model is likely more appropriate. If your customers require software to run in their own cloud account but want the vendor to handle ongoing operations, a managed deployment model may be a better fit. Some vendors support both depending on customer requirements.

### What is the difference between a software distribution platform and a container registry?

A container registry stores and serves container images. A vendor-to-customer distribution platform may use a registry as one component but typically adds capabilities such as licensing enforcement, deployment orchestration, update management, and customer self-service on top. A registry alone does not handle the broader lifecycle of software distribution to customer environments.

### What is the difference between a software distribution platform and a CI/CD platform?

A CI/CD platform builds, tests, and delivers software, typically to the vendor's own environments or pipelines. A distribution platform manages delivery to customer-controlled environments: handling licensing per customer, managing update delivery across many installations, collecting telemetry, and providing customer self-service. They address different stages of the software delivery lifecycle and are often used together.

### Do I need a software distribution platform if I only offer SaaS?

Not necessarily. If all your customers use vendor-hosted SaaS and none require software to run in their own infrastructure, a dedicated distribution platform may not be necessary. The use case for vendor-to-customer distribution platforms arises when customers require on-premises, BYOC, or air-gapped deployment options.

### What deployment environments should a software distribution platform support?

This depends on your customers' requirements. Common environments include customer cloud accounts on major providers (AWS, GCP, Azure), on-premises data centres, air-gapped networks, Kubernetes clusters (managed and self-managed), and VM or bare-metal infrastructure. Vendors should verify which environments a platform supports before adopting it, particularly for non-Kubernetes or air-gapped use cases.]]>
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  <title>How to deploy SaaS in a customer VPC: implementation approaches and tradeoffs</title>
  <link>https://northflank.com/blog/deploy-saas-in-customer-vpc</link>
  <pubDate>2026-04-02T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[Deploy SaaS in a customer VPC using AWS PrivateLink, in-VPC/BYOC deployment, or VPC Lattice. Compare the approaches, tradeoffs, and what it takes to operate them at scale.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/deploy_saas_in_customer_vpc_1_807960fac6.png" alt="How to deploy SaaS in a customer VPC: implementation approaches and tradeoffs" />*Deploying SaaS in a customer VPC involves running vendor-operated software or exposing vendor services privately within a customer's own cloud environment. On AWS, common approaches include AWS PrivateLink, in-VPC or BYOC (Bring Your Own Cloud) deployment, and AWS VPC Lattice. Each suits different requirements around data isolation, networking, and operational complexity. The right approach depends on the customer's compliance framework, security policy, and infrastructure requirements.*

For SaaS vendors, supporting customer VPC deployments is increasingly a requirement during enterprise procurement. The implementation, however, is where much of the complexity lies. "Deploy in our VPC" can mean different things to different customers, and the approach that satisfies one customer's requirements may not satisfy another's.

This guide covers what deploying SaaS in a customer VPC involves, the main implementation approaches on AWS, how they differ architecturally, what it takes to operate them at scale, and how vendors approach automating the infrastructure.

For background on deployment models, compliance drivers, and why enterprise customers require this, see our [overview of SaaS deployment in customer environments](https://northflank.com/blog/saas-deployment-in-customer-environment).

<InfoBox className="BodyStyle">

## Key takeaways: what engineers need to know before deploying SaaS in a customer VPC

- "Deploy SaaS in a customer VPC" covers at least two different things: exposing a vendor-hosted service privately into the customer's network, or running vendor-operated components directly inside the customer's cloud account. These satisfy different requirements.
- AWS PrivateLink exposes a vendor-hosted service privately to a customer VPC over the AWS network. The vendor's application stays in the vendor's account, and data is processed there.
- In-VPC or BYOC (Bring Your Own Cloud) deployment runs vendor-operated components directly inside the customer's cloud account, so data processing happens within the customer's environment during normal operation.
- AWS VPC Lattice is a fully managed application networking service for connecting services and resources across accounts and VPCs. Like PrivateLink, the vendor's application runs in the vendor's account.
- Operating in-VPC deployments across multiple customer VPCs at scale requires significant engineering investment. Each environment needs, among other things, its own provisioning, CI/CD, observability, configuration management, and cross-account connectivity infrastructure.
- Platforms like [Northflank](https://northflank.com/product/customer-vpc-deployments) provide a control plane for deploying and managing your application inside your customers' cloud environments across AWS, GCP, Azure, Oracle, and on-premises infrastructure from a single interface, so vendors do not have to build that infrastructure themselves. Define your application once and deploy it across your customers' environments, with hard isolation, encrypted secrets, mTLS, audit trails, and customer-specific configurations per deployment. Northflank is [SOC 2 Type II certified](https://northflank.com/security).

</InfoBox>

When a customer says "we need this deployed in our VPC," the first thing worth clarifying is what they mean. Some customers need their data to stay within their own cloud boundary. Others need private network connectivity to a vendor service without traversing the public internet. These are different requirements, and they map to different implementation approaches.

## What does it mean to deploy SaaS in a customer VPC?

The phrase is used to describe at least two architecturally distinct models that are frequently conflated. Understanding which model a customer is asking for is the prerequisite to choosing the right implementation approach.

### Service exposure into a customer VPC

The vendor's application continues to run in the vendor's account. A private network path is established so the customer's VPC can reach the vendor's service without going over the public internet. The customer's traffic stays within the cloud provider's network, but data processing still happens in the vendor's environment.

### In-VPC deployment

The vendor deploys and operates application components, such as containers, databases, or services, directly inside the customer's cloud account. Data processing happens within the customer's environment during normal operation. The vendor manages these components remotely through a control plane in their own account.

The distinction between these two models is worth establishing early because they address different requirements. If a customer's compliance requirement is that data must not leave their cloud environment, service exposure approaches like PrivateLink may not be sufficient, depending on how the application processes data. If the requirement is private network connectivity without public internet exposure, PrivateLink is often simpler to implement and operate.

## What are some common approaches to deploying SaaS in a customer VPC?

The approaches below are among those most commonly used on AWS. Other patterns exist, including VPC peering, Transit Gateway architectures, and custom cross-account networking configurations, each with their own tradeoffs. This guide focuses on three that are purpose-built for SaaS use cases:

### AWS PrivateLink

AWS PrivateLink is a highly available, scalable technology that enables private connectivity between VPCs and services without requiring an internet gateway, NAT device, public IP address, or VPN connection. For SaaS use cases, it allows a vendor to expose their service to a customer's VPC over the AWS network.

On the vendor side, the application typically runs behind a Network Load Balancer (NLB) in the vendor's VPC. The vendor creates a VPC endpoint service and associates it with the NLB, then grants specific customer AWS accounts permission to connect.

On the customer side, an interface VPC endpoint is created in the customer's VPC, provisioning Elastic Network Interfaces (ENIs) with private IP addresses drawn from the customer's subnet. Traffic flows through these ENIs over the AWS network. The vendor can require manual approval for each connection request, and private DNS can be configured for consistent hostname access within the customer's VPC.

**Suited for**: customers that require private network connectivity to a vendor service without data traversing the public internet, where the requirement is network-level isolation rather than compute or data isolation.

**Tradeoffs**:

- The vendor's application runs in the vendor's account. Data is processed in the vendor's environment, not the customer's. This does not satisfy requirements that mandate compute or data stays within the customer's cloud boundary.
- PrivateLink is AWS-specific and does not extend natively to GCP or Azure customer environments.
- When exposing a service to multiple tenants through a shared NLB, tenant routing and isolation require careful design. A pooled NLB across tenants introduces cross-tenancy considerations that need to be addressed at the application or network policy layer.
- Interface endpoints are accessible from within the customer's VPC and from connected networks such as Site-to-Site VPN or Direct Connect, but not directly from the public internet without additional tooling.

### In-VPC deployment (BYOC)

In-VPC deployment, also referred to as BYOC (Bring Your Own Cloud), means the vendor deploys and operates application components directly inside the customer's cloud account. The vendor's control plane, which handles CI/CD, configuration management, monitoring, and update delivery, stays in the vendor's account and connects to the customer's environment through a secure cross-account mechanism.

On AWS this typically uses IAM cross-account roles with least-privilege permissions. The equivalent on GCP is cross-project service accounts; on Azure it is managed identities. The vendor's control plane uses these mechanisms to provision resources, push deployments, and read operational telemetry without storing credentials in the customer's environment. Data processed by the application does not leave the customer's environment during normal operation.

**Suited for**: customers whose compliance framework or security policy requires that application compute and data remain within their own cloud boundary. Common for HIPAA, FedRAMP, and air-gapped use cases where service exposure approaches do not satisfy the isolation requirement.

**Tradeoffs**:

- The vendor must provision and manage infrastructure in each customer's cloud account separately. This becomes difficult to scale without automation.
- Each customer environment is a separate operational surface: provisioning, updates, observability, and incident response each need to be managed across account boundaries.
- The customer takes on some responsibility for the underlying cloud infrastructure, including network configuration, IAM, and the managed Kubernetes service if one is used.
- This approach can work across AWS, GCP, Azure, and on-premises Kubernetes environments, which suits customers on different cloud providers.

### AWS VPC Lattice

Amazon VPC Lattice is a fully managed application networking service that connects services and resources across multiple VPCs and accounts within a service network. For SaaS use cases, vendors create a VPC Lattice service in their account and share it with customer accounts using AWS Resource Access Manager (RAM). The customer associates the shared service with their VPC Lattice service network and applies auth policies to control which of their services can communicate with it.

VPC Lattice auth policies use IAM resource policies, allowing fine-grained control over which principals in the customer's account can invoke the vendor's service, down to specific paths or actions. VPC Lattice also supports on-premises access via a VPC endpoint powered by AWS PrivateLink, for customers connecting over Direct Connect or VPN.

**Suited for**: multi-account microservices architectures where the SaaS product is one service among several in the customer's environment, and fine-grained IAM-based service-to-service access control is a requirement.

**Tradeoffs**:

- Like PrivateLink, the vendor's application runs in the vendor's account. The customer's data is sent to the vendor's environment for processing.
- VPC Lattice is AWS-specific.
- Transitive sharing is not supported through AWS RAM. The vendor must share the service directly with each customer account. A customer account cannot re-share the vendor's service with another account.
- Auth policies are not supported on resource configurations within a service network, only on services. This is worth checking against your specific architecture requirements.

## How do these SaaS deployment approaches compare?

The table below summarises some of the key differences across the three SaaS deployment approaches covered in this guide:

| Approach | Where the vendor's app runs | Data processed in customer environment | Cloud support | Operational complexity for vendor |
| --- | --- | --- | --- | --- |
| AWS PrivateLink | Vendor account | No | AWS only | Low |
| In-VPC / BYOC | Customer account | Yes | Multi-cloud | High |
| AWS VPC Lattice | Vendor account | No | AWS only | Medium |

PrivateLink and VPC Lattice address private network connectivity to vendor-hosted services. In-VPC deployment addresses compute and data isolation within the customer's environment. These address different requirements, and one approach cannot generally substitute for another when the customer's requirement is specific about where data is processed.

## What does it take to operate in-VPC deployments across multiple customer environments?

If in-VPC deployment is the right approach, the operational challenge scales with the number of customer environments. Supporting one is a project. Supporting ten or more without automation is a significant engineering commitment.

Some of the key things that need to be built and operated for each customer environment include:

- **Environment provisioning**: in most modern implementations, each customer environment uses a Kubernetes cluster on the customer's cloud provider (EKS on AWS, GKE on GCP, AKS on Azure), VPC configuration, networking setup, and cloud provider roles for cross-account access. Done manually, this can take several weeks per customer, depending on the environment's complexity and the customer's internal security review process.
- **Cross-account access management**: the vendor's control plane needs a persistent, secure connection to each customer environment using least-privilege cloud provider mechanisms, maintained across all customer environments without storing credentials.
- **CI/CD across multiple environments**: the delivery pipeline needs to support targeting specific customer environments, progressive rollouts, and rollback on failure. Some customers require a change approval step before any update is applied to their environment.
- **Configuration management**: each customer environment may have variations such as custom domains, feature flag states, or regional configurations. A configuration layer that applies per-customer overrides without forking the application codebase is needed.
- **Observability without data access**: operational telemetry, such as CPU usage, memory, and error rates, needs to be collected from each customer environment without reading application-level data. This typically involves separation at the logging and metrics layer.
- **Onboarding**: provisioning a new customer environment, completing security configuration, networking setup, and testing can take several weeks without automation. The timeline varies depending on the customer's environment complexity and internal approval processes.

## How do vendors automate in-VPC deployments at scale?

A common approach uses Infrastructure as Code tooling, such as Terraform, Pulumi, or AWS CDK, to define the customer environment template once and provision it repeatably across customer accounts. Kubernetes provides a consistent runtime layer across AWS EKS, GCP GKE, and Azure AKS. GitOps pipelines, using tools such as Argo CD or Flux, handle deployment delivery to multiple target environments from a single source of truth.

Building this system involves significant upfront engineering investment, including designing and maintaining a control plane, building the multi-environment CI/CD pipeline, handling cross-account connectivity, and building the configuration management layer, among other things. For many SaaS vendors, this infrastructure sits outside their core product.

![customer-vpc-deployment.png](https://assets.northflank.com/customer_vpc_deployment_77fc1ad30d.png)

[Northflank](https://northflank.com/product/customer-vpc-deployments) provides a control plane for deploying and managing your application inside your customers' cloud environments across AWS, GCP, Azure, Oracle, and on-premises infrastructure from a single interface, so vendors do not have to build that infrastructure themselves.

Vendors define their application once using Northflank's templates, and Northflank handles provisioning, CI/CD, monitoring, and update delivery across customer environments. Each customer deployment runs on dedicated infrastructure with namespace-level separation, mTLS, encrypted secrets, and network policies applied by default.

<InfoBox className="BodyStyle">

Vendors can [get started directly](https://app.northflank.com/signup) (self-serve) or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo) for teams with specific infrastructure or compliance requirements.

</InfoBox>

For more on how the control plane and data plane separate across account boundaries, Northflank's [BYOC guide](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) covers the underlying architecture in detail.

## Frequently asked questions about deploying SaaS in a customer VPC

### Is AWS PrivateLink the same as deploying an application in a customer VPC?

No. With AWS PrivateLink, the vendor's application continues to run in the vendor's account. PrivateLink establishes a private network path so the customer's VPC can reach the vendor's service without going over the public internet. The customer's data is sent to the vendor's environment for processing. In-VPC deployment runs the vendor's application components inside the customer's own cloud account so that data processing happens within the customer's environment during normal operation.

### What is the difference between AWS PrivateLink and in-VPC deployment for SaaS?

PrivateLink provides private network connectivity to a vendor-hosted service. In-VPC deployment provisions vendor-operated application components inside the customer's cloud account. The key difference is where data is processed. PrivateLink keeps the application in the vendor's environment. In-VPC deployment keeps the application and data within the customer's environment during normal operation. The right choice depends on whether the customer's requirement is network-level isolation or compute and data isolation.

### Does in-VPC deployment work across AWS, GCP, and Azure?

It can, provided the vendor's application is containerised. Kubernetes is the most common runtime layer for this, with AWS EKS, GCP GKE, and Azure AKS providing managed Kubernetes in the customer's environment. Cross-account access mechanisms differ per cloud provider: IAM roles on AWS, service accounts on GCP, and managed identities on Azure.

### How does a vendor's control plane connect to a customer VPC without sharing credentials?

On AWS, the vendor's control plane assumes an IAM role in the customer's account using cross-account role assumption. The customer creates the role and grants the vendor's account permission to assume it, with permissions scoped to what the control plane needs. On GCP, cross-project service accounts work similarly. On Azure, managed identities are used. In these cases, no long-lived credentials need to be stored or shared.

### What is AWS VPC Lattice and how does it differ from PrivateLink for SaaS?

Amazon VPC Lattice is a fully managed application networking service that connects services and resources across multiple VPCs and accounts within a service network. Unlike PrivateLink, which exposes service endpoints over the AWS network, VPC Lattice supports more complex multi-service architectures with fine-grained IAM-based auth policies at the service and service network level. Both keep the vendor's application in the vendor's account. VPC Lattice is well suited to multi-account microservices architectures where service-to-service access control across accounts is a primary requirement.]]>
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</item><item>
  <title>SaaS deployment in customer environments: a guide for SaaS vendors</title>
  <link>https://northflank.com/blog/saas-deployment-in-customer-environment</link>
  <pubDate>2026-04-01T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[SaaS deployment in a customer environment runs your software inside a customer's VPC. Learn the patterns, operational challenges, and how to automate it.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/saas_deployment_in_customer_environment_ca11cf93d3.png" alt="SaaS deployment in customer environments: a guide for SaaS vendors" />*SaaS deployment in a customer environment means running your software inside a customer's own cloud account (AWS, GCP, or Azure, for example), rather than in your infrastructure. In most implementations, the vendor manages the application and updates centrally through a control plane. Depending on the deployment pattern, some or all of the customer's workloads and data remain inside their own cloud account.*

Enterprise customers in regulated industries often require software to run within their own cloud environment rather than in a vendor's shared infrastructure. For SaaS vendors moving upmarket, this is one of the more frequent requirements that comes up during enterprise procurement.

This guide covers what SaaS deployment in a customer environment involves, why enterprises require it, the three main SaaS deployment patterns for customer-hosted infrastructure, the operational challenges of supporting it at scale, and how vendors approach automating it.

<InfoBox className="BodyStyle">

## Key takeaways: what you need to know about SaaS deployment in customer environments

- Enterprise customers in regulated industries such as healthcare, financial services, and government often require software to run inside their own cloud account due to factors such as data residency regulations, internal security policies, and compliance frameworks.
- SaaS deployment in a customer environment typically separates the vendor's control plane, which manages deployments and updates, from the application plane, which runs fully or partially inside the customer's VPC depending on the deployment pattern.
- Supporting one or two customer VPC deployments manually is feasible. Supporting ten or more without automation requires significant dedicated engineering investment.
- Platforms like [Northflank](https://northflank.com/product/customer-vpc-deployments) provide a control plane for deploying and managing applications across customer cloud environments such as AWS, GCP, Azure, and Oracle, as well as on-premises infrastructure, from a single interface. Northflank is [SOC 2 Type II certified](https://northflank.com/security). Each customer deployment includes built-in namespace isolation, mTLS, encrypted secrets, and audit trails across customer environments. Vendors do not have to build that control plane infrastructure themselves.

</InfoBox>

When an enterprise customer requires deployment inside their own cloud, it is typically driven by compliance frameworks, internal security policies, or data residency requirements. 

Understanding what drives that requirement, and what it takes to support it, is what the rest of this guide covers.

## Why do enterprise customers require software to run inside their own VPC?

Many enterprises operate under regulatory frameworks or internal policies that prohibit sending certain data to a third-party cloud environment, regardless of the vendor's certifications.

The specific drivers vary by industry:

- **Healthcare**: the Health Insurance Portability and Accountability Act (HIPAA) requires covered entities to implement appropriate safeguards for protected health information and to establish Business Associate Agreements with any third party that handles it. Many healthcare organisations satisfy this by keeping PHI within their own cloud environment.
- **Financial services**: firms regulated under the Payment Card Industry Data Security Standard (PCI-DSS) or under financial regulatory frameworks such as those set by the Financial Conduct Authority (FCA) often have strict internal policies about where data can be processed and stored.
- **Government and public sector**: the Federal Risk and Authorization Management Program (FedRAMP) requires software to run within approved infrastructure boundaries. Air-gapped deployments are required for certain defence and intelligence use cases.
- **Data residency regulations**: the General Data Protection Regulation (GDPR) restricts transfers of personal data outside the EU unless adequate protections are in place. Many organisations address this by keeping data within EU-based infrastructure.
- **Internal security posture**: some enterprises, regardless of regulatory requirements, have a blanket policy against granting third-party vendors access to data in their environment.

In many cases, these requirements apply regardless of a vendor's security certifications or audit history.

## What does SaaS deployment in a customer environment mean?

The term covers several different deployment models, which are often used loosely and interchangeably. The table below defines them precisely.

| Deployment model | Where the app runs | Who manages it | Where data lives |
| --- | --- | --- | --- |
| Multi-tenant SaaS | Vendor infrastructure (shared) | Vendor | Vendor cloud |
| Single-tenant SaaS | Vendor infrastructure (dedicated) | Vendor | Vendor cloud |
| Customer VPC / BYOC (Bring Your Own Cloud) | Customer cloud account | Vendor manages application, customer manages infrastructure | Customer cloud |
| On-premises | Customer data centre or private cloud | Customer, or vendor under a managed service agreement | Customer infra |

### How the SaaS deployment model works in practice

The terms customer VPC deployment and BYOC (Bring Your Own Cloud) are often used interchangeably, though implementations vary between vendors. In most cases, the vendor deploys and operates software inside the customer's own cloud account. The key architectural concept is the separation of the control plane from the application plane.

The control plane typically stays in the vendor's account. It handles deployment orchestration, CI/CD pipelines, configuration management, monitoring, and update delivery. Depending on the pattern, some or all of your services, databases, and compute resources operate inside the customer's VPC as the application plane. In some configurations, data may cross the boundary between the vendor's account and the customer's environment, for example when application logic in the vendor's account accesses a remote data store in the customer's VPC.

The two planes connect through a secure cross-account link, using cloud provider mechanisms such as IAM roles on AWS, service accounts on GCP, or managed identities on Azure, all scoped to least-privilege access. The vendor can push deployments and read health signals without accessing the customer's application-level data directly.

## What are the common SaaS deployment patterns for customer-hosted infrastructure?

Not every customer deployment requires moving your entire application stack into the customer's environment. There are three distinct patterns, each with different tradeoffs.

### Distributed data store

The control plane and application logic run in the vendor's account. Only the database or data storage layer is provisioned inside the customer's VPC. The application accesses the remote data store through a cross-account role or equivalent mechanism on the customer's cloud provider.

This is the least operationally complex pattern. It works when the customer's compliance requirement applies to data at rest rather than to compute or processing. The vendor retains control over the application layer, but the customer takes on some responsibility for the availability and backup of their remote data store.

### Distributed application plane

The control plane runs in the vendor's account. Certain application services, typically those that process sensitive data or need to run close to the customer's data, are deployed into the customer's VPC. The rest of the application continues to run centrally.

This pattern works when only specific services handle regulated data. The complexity increases because you now have application services running across two separate environments that need to communicate reliably across account boundaries. This also introduces network latency between services running in different environments, which may affect performance depending on how frequently those services communicate.

### Full remote application plane

The entire application plane runs inside the customer's VPC. The vendor retains only the control plane in their own account. All compute, storage, services, and networking exist within the customer's environment.

This pattern is typical for customers in heavily regulated industries or for air-gapped deployments where no outbound connectivity to the vendor's infrastructure is permitted. It is also the most operationally demanding pattern for the vendor to support.

## What compliance requirements drive demand for customer VPC deployments?

The table below maps common regulatory frameworks to the deployment requirements they create.

| Regulation / framework | Industry | What it requires |
| --- | --- | --- |
| HIPAA | Healthcare (US) | Covered entities must implement appropriate safeguards for PHI and establish BAAs with third parties that handle it |
| FedRAMP | Government (US) | Software must run on FedRAMP-authorised infrastructure |
| GDPR / EU data residency | Any (EU data subjects) | Transfers of personal data outside the EU require adequate protections such as Standard Contractual Clauses or an adequacy decision |
| PCI-DSS | Financial services | Cardholder data must be stored and processed within a PCI-DSS compliant environment with strict access controls |

Some enterprises impose deployment requirements beyond what their regulatory framework technically mandates. A financial services firm might require customer VPC deployment not because PCI-DSS demands it, but because their information security team has a blanket policy against third-party vendors touching infrastructure near customer data. Internal policies are often harder to work around than the regulations themselves.

## What does it take to support customer VPC deployments at scale?

Supporting one customer VPC deployment is a project. Supporting ten is a system. Supporting fifty without a dedicated platform is a significant ongoing engineering commitment.

Here is what you need to build and operate for each customer environment:

- **Environment provisioning**: in most modern implementations, each new customer needs a Kubernetes cluster (EKS, GKE, or AKS), VPC configuration, networking setup, and cloud provider roles for cross-account access. Done manually, this can take several weeks per customer depending on the complexity of the environment.
- **Secure cross-account connectivity**: your control plane needs a persistent, secure connection to each customer environment using least-privilege cloud provider mechanisms, managed across every customer without credential sharing.
- **CI/CD across multiple environments**: your pipeline needs to support multi-environment targeting, progressive rollouts to specific customers, and rollback on failure. Some customers require a change approval step before any update is applied.
- **Configuration management**: each customer will have variations such as custom domains, feature flag states, or regional configurations. You need a layer that applies per-customer overrides without forking your codebase.
- **Observability without data access**: your pipeline needs to collect operational telemetry such as CPU usage, memory, and error rates without reading application-level data. This requires separation at the logging and metrics layer.
- **Onboarding time**: without automation, getting a new customer environment provisioned, reviewed, and tested typically takes two to six weeks.

## How do SaaS vendors automate deployments across customer cloud environments?

A common approach uses Infrastructure as Code tooling, such as Terraform, Pulumi, or AWS CDK, to define the customer environment template once and provision it repeatably. Kubernetes provides the runtime layer that works consistently across AWS EKS, GCP GKE, and Azure AKS. GitOps pipelines handle deployment delivery to multiple target environments from a single source of truth.

The challenge is that building this system requires upfront engineering investment. You need to design and maintain the control plane, build the multi-environment CI/CD pipeline, handle the cross-account connectivity layer, and build the configuration management system. For most SaaS vendors, this infrastructure work sits outside their core product.

![customer-vpc-deployment.png](https://assets.northflank.com/customer_vpc_deployment_77fc1ad30d.png)

<InfoBox className="BodyStyle">

[Northflank](https://northflank.com/product/customer-vpc-deployments) provides a control plane for deploying and managing applications across customer cloud environments such as AWS, GCP, Azure, and Oracle, as well as on-premises infrastructure, from a single interface. Vendors can [get started directly](https://app.northflank.com/signup) (self-serve) or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo) for enterprise teams with specific infrastructure or compliance requirements.

Vendors define their application once using Northflank's templates, and Northflank handles provisioning, CI/CD, monitoring, and update delivery across customer environments. Each customer deployment runs on dedicated infrastructure with namespace-level separation, mTLS, encrypted secrets, and network policies applied by default.

</InfoBox>

For more on how the control plane and data plane separate across account boundaries, Northflank's [BYOC guide](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) covers the underlying architecture in detail, including how cross-account connections are managed without credential sharing.

## What should you evaluate before offering customer VPC deployments?

Before committing the engineering investment, these are the questions worth working through:

- How many enterprise customers in the last 12 months required customer-hosted deployment as a condition of procurement?
- Which compliance frameworks do your target customers operate under?
- How many customer environments do you expect to support in the next 12 to 24 months?
- Are your target customers concentrated on one cloud provider or distributed across AWS, GCP, Azure, or other infrastructure?
- Do you have the engineering capacity to build and maintain a custom control plane?

The answers determine whether the engineering investment in building in-house is proportionate or whether an existing platform covers your requirements.

## Frequently asked questions about SaaS deployment in customer environments

### What is the difference between BYOC (Bring Your Own Cloud) and customer VPC deployment?

The terms are used interchangeably in most contexts. BYOC (Bring Your Own Cloud) describes the same deployment model from the customer's perspective. The customer provides their cloud account, and the vendor deploys into it. Customer VPC deployment describes the same model from the vendor's perspective. Both refer to software running inside the customer's cloud account, managed by the vendor through a separate control plane.

### How does a vendor manage updates across multiple customer cloud environments?

Updates are delivered from the vendor's control plane to each customer's application plane through the secure cross-account connection. This requires a CI/CD pipeline that supports multi-environment targeting, progressive rollouts, and rollback on failure. Some customers will also require a change approval step before updates are applied to their environment.

### Is customer VPC deployment the same as on-premises deployment?

No. Customer VPC deployment runs inside a customer's cloud account on infrastructure managed by a cloud provider such as AWS, GCP, or Azure. On-premises deployment runs on hardware within the customer's own data centre or private infrastructure. The operational challenges differ: on-premises environments often involve air-gapped networks, no managed Kubernetes services, and more restricted connectivity.

### How long does it take to onboard a new customer into their own VPC?

Without automation, onboarding a new customer can take several weeks, accounting for infrastructure provisioning, security configuration, networking setup, and testing. The timeline varies depending on the complexity of the customer's environment and their internal approval processes. With a platform that automates provisioning and uses pre-defined application templates, this can be reduced significantly for customers on standard cloud providers.]]>
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</item><item>
  <title>App Runner is in maintenance mode. 9 top AWS App Runner alternatives in 2026</title>
  <link>https://northflank.com/blog/aws-app-runner-alternatives</link>
  <pubDate>2026-04-01T13:18:00.000Z</pubDate>
  <description>
    <![CDATA[ Looking for AWS App Runner alternatives? Compare Northflank, AWS Fargate, Google Cloud Run, and more to find the best platform for your containerized applications.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/9_best_AWS_App_Runner_alternatives_808d780346.png" alt="App Runner is in maintenance mode. 9 top AWS App Runner alternatives in 2026" />> App Runner is in maintenance mode. Is AWS App Runner the right choice for deploying containerized applications, or are there better alternatives?
> 

If you’ve used [AWS App Runner](https://aws.amazon.com/apprunner/), you know it takes care of scaling, networking, and load balancing without much effort. That works well if you just need to deploy a web app or API, but it starts to feel limiting when you need more control, lower costs, or flexibility outside AWS.

App Runner is built to simplify deployments, not to handle background jobs, event-driven workloads, or complex networking needs.

Some platforms provide more customization, better pricing, or multi-cloud support. These alternatives give you options based on how much control you need over your workload.

You don’t have much time? Here’s a quick list of AWS App Runner alternatives:

1. [Northflank](https://northflank.com/) → A fully managed alternative with built-in CI/CD and multi-cloud support.
2. [AWS Fargate](https://aws.amazon.com/fargate/) → A serverless AWS-native alternative with more networking control.
3. [Google Cloud Run](https://cloud.google.com/run) → A fully managed serverless platform for Google Cloud users.
4. [Azure Container Apps](https://azure.microsoft.com/en-us/products/container-apps) → A flexible serverless option integrated with Azure Functions.
5. [DigitalOcean App Platform](https://www.digitalocean.com/products/app-platform) → A simple, Heroku-like alternative for quick deployments.
6. [Railway](https://railway.com/) → A developer-friendly PaaS with automatic scaling and Git-based workflows.
7. [Render](https://render.com/) →  A flexible PaaS with built-in security features.
8. [Google Kubernetes Engine (GKE)](https://www.google.com/aclk?sa=l&ai=DChsSEwik59Psn4SMAxXpgFAGHUj_Hx0YACICCAEQABoCZGc&co=1&ase=2&gclid=Cj0KCQjw4cS-BhDGARIsABg4_J1BBklQm9oEhSUF5TCbWIOCNFwYRHhK9XOaCu_eSRn829ag6gdSroIaAn2YEALw_wcB&sig=AOD64_1BQQ7K3_5DnIEZZeTvW_lTEgL9vg&q&nis=4&adurl&ved=2ahUKEwjJlM3sn4SMAxW0UEEAHTmILcwQ0Qx6BAgMEAE) → A managed Kubernetes service with full customization.
9. [OpenShift](https://www.redhat.com/en/technologies/cloud-computing/openshift) → An enterprise-grade Kubernetes alternative with hybrid cloud support.

Each of these platforms takes a different approach to containerized applications. Let’s look at what makes them stand out.

## Why look for AWS APP Runner alternatives?

You might start looking for an alternative in a couple of common scenarios. App Runner is in maintenance mode. It could be because costs are adding up, you need more customization, or you want a platform that isn’t tied to AWS.

For instance, a developer on [Reddit](https://www.reddit.com/r/aws/comments/1gndj2l/app_runner_underated/) shared that they had to stop using AWS App Runner after running into unexpected limitations. They needed to integrate with RDS but found the setup unnecessarily complex. Debugging builds was frustrating due to the lack of detailed logs, and App Runner wasn’t built for background processing, which made it a poor fit for their workload.

 ![Screenshot from Reddit](https://assets.northflank.com/reddit_screenshot_for_aws_app_runner_alternative_576a29702d.png) 

If you’re in a similar position, these are some of the most common reasons to switch:

### Limited customization

Scaling, networking, and deployment settings are mostly automated, which means less control over configurations like load balancing, private networking, and scaling policies.

### Pricing concerns

AWS App Runner charges based on vCPU and memory usage ([AWS Pricing](https://www.google.com/aclk?sa=l&ai=DChsSEwjp3MbHuYSMAxVwHa0GHeo-FkcYACICCAEQABoCcHY&co=1&ase=2&gclid=Cj0KCQjw4cS-BhDGARIsABg4_J3bx8vnohCRvpQ5soTTHl84p06oggZeHEJK8dVW-V_cTp7929G0PK0aAmiwEALw_wcB&ei=bHHRZ9HrAtX0i-gPuoeA4Ak&sig=AOD64_1yPRVozhSQEcL1L2Ui6MxoaSoA7Q&q&sqi=2&nis=4&adurl&ved=2ahUKEwjR0bvHuYSMAxVV-gIHHboDAJwQ0Qx6BAgNEAE)), which can get expensive for high-traffic applications.  For example, 1 vCPU and 2GB RAM on App Runner costs $56/month, while the same on Northflank costs $24/month. Some platforms provide more predictable pricing.

### Tied to the AWS ecosystem
AWS App Runner is designed for AWS-native deployments. If you need multi-cloud flexibility, a platform like Northflank lets you deploy across [AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), and [Azure](https://northflank.com/cloud/azure) (multi-cloud deployment guide).

If you prefer to stay within AWS but need more control, Northflank BYOC (Bring Your Own Cloud) gives you an AWS App Runner experience inside your AWS account while offering more flexibility, lower cost, and the ability to run in other clouds. This means you get the benefits of a managed container platform while keeping full control over your infrastructure.

### Workload limitations

Optimized for web applications and APIs but doesn’t support background jobs, batch processing, or event-driven workloads. If your application needs these, a Kubernetes-based solution or another [managed service](https://northflank.com/features/managed-cloud) might be a better fit. 

### Limited CI/CD and workflow customization

AWS App Runner automates builds and deployments but limits customization. It doesn’t support custom build steps, advanced deployment strategies, or flexible rollback controls. If you need more control over CI/CD workflows, platforms like Northflank allow custom pipelines, rollback policies, and Git-based automation ([CI/CD on Northflank](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank)).

### Compute limits

AWS App Runner has predefined compute configurations, with a maximum of 4 vCPUs and 12 GB RAM. This makes it less suitable for compute-heavy workloads, such as machine learning inference, high-traffic APIs, and large databases. You might need a more flexible alternative if your application requires more processing power or memory.

If you’ve run into any of these challenges, an alternative might work better for your use case. Let’s look at the best AWS App Runner alternatives and how they compare.

## Quick comparison table: AWS App Runner vs alternatives

| Alternative | Fully managed | Customization & control | Multi-cloud support | Ideal for |
| --- | --- | --- | --- | --- |
| Northflank | Yes | More flexible (custom networking, scaling, CI/CD pipelines) | Yes (AWS, GCP, Azure) | Teams needing multi-cloud, CI/CD, scheduled workloads, and Kubernetes support |
| AWS Fargate | Yes | More control (networking, scaling, IAM policies) | No (AWS-only) | AWS users needing serverless containers with more configuration |
| Google Cloud Run | Yes | Configurable scaling, networking, and rollbacks | No (GCP-only) | Developers needing serverless container hosting in GCP |
| Azure Container Apps | Yes | Integrated with Azure Functions & Kubernetes | No (Azure-only) | Teams deploying on Microsoft Azure’s ecosystem |
| DigitalOcean App Platform | Yes | Less control (simplified, Heroku-like setup) | No (DigitalOcean-only) | Developers looking for a simple, managed PaaS |
| Railway | Yes | Less control (minimal setup, automated scaling) | No (Railway-only) | Indie developers and startups needing quick deployments  |
| Render | Yes | More flexible than Railway (custom scaling, networking) | No (Render-only) | Teams needing a flexible, managed PaaS |
| Google Kubernetes Engine (GKE) | No (requires setup) | Full Kubernetes control | Yes (multi-cloud capable with Anthos) | Teams needing full Kubernetes orchestration on GCP |
| OpenShift | No (self-managed option available) | Enterprise-grade Kubernetes with security features | Yes (supports hybrid & multi-cloud) | Enterprises needing Kubernetes with advanced security and governance |

## 9 best AWS App Runner alternatives

We know you need a scalable and cost-friendly way to run your containerized applications. AWS App Runner simplifies deployments, but as we mentioned earlier, it limits control over networking, scaling, and configurations. Let’s go over the best alternatives in detail and see what makes them stand out.

### 1. Northflank (fully managed alternative with multi-cloud support)

You need a fully managed platform that scales your applications, integrates with CI/CD, and works across multiple cloud providers. Northflank handles deployments while giving you custom networking, storage, and scaling options.

 ![Northflank](https://assets.northflank.com/northflank_s_home_page_66c1ef025b.png) 

**Why choose Northflank**

- Automatically [scales](https://northflank.com/docs/v1/application/scale/autoscale-deployments) your containers and [microservices](https://northflank.com/docs/v1/application/run/run-containers-and-micro-services)
- Built-in [CI/CD](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) workflows with GitHub, GitLab, and Bitbucket (See this [guide on integrating GitLab and Bitbucket](https://northflank.com/blog/integrating-with-gitlab-and-bitbucket))
- [Multi-cloud support](https://northflank.com/docs/v1/application/cloud-providers/use-other-cloud-providers-with-northflank) across AWS, GCP, and Azure (See [AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp) and [Azure](https://northflank.com/cloud/azure) on Northflank)
- Supports both stateless and [stateful](https://northflank.com/docs/v1/application/databases-and-persistence/stateful-workloads-on-northflank) workloads, including [persistent storage](https://northflank.com/docs/v1/application/production-workloads/persistent-storage-in-production) and scheduled jobs.
- Lower pricing compared to AWS App Runner.

**What to keep in mind**

- Works best for teams that need flexibility across multiple cloud providers
- Not tied to AWS-specific services, so some AWS-native integrations require setup

**Works well for**: Teams that need multi-cloud deployments, built-in CI/CD, and Kubernetes support 

*See how [Weights company uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*


### 2. AWS Fargate (serverless container option within AWS)

You want to stay within AWS but need more configuration options than AWS App Runner. [AWS Fargate](https://aws.amazon.com/fargate/) runs containers without managing servers, giving you better networking, security, and workload flexibility.

 ![AWS Fargate](https://assets.northflank.com/AWS_fargate_c333aff4e3.png) 


**Why choose AWS Fargate**

- Works with [ECS](https://northflank.com/blog/aws-ecs-elastic-container-service-deep-dive-and-alternatives) and [EKS](https://aws.amazon.com/eks/), allowing you to orchestrate your containers
- More control over networking, IAM policies, and security groups
- Supports background processing and batch jobs, which App Runner does not

**What to keep in mind** 

- Requires manual setup for VPC networking, task definitions, and scaling policies
- No automatic HTTPS provisioning, unlike AWS App Runner

**Works well for**: AWS users needing more customization, security controls, and workload flexibility

 

### 3. Google Cloud Run (fully managed alternative for GCP)

If you work with Google Cloud, [Cloud Run](https://www.google.com/aclk?sa=l&ai=DChsSEwi7ldP0-oSMAxWij1AGHbsrOBsYACICCAEQARoCZGc&co=1&ase=2&gclid=Cj0KCQjw4cS-BhDGARIsABg4_J0epwWiRJEzw2YTYUkCDIoPOsE165971zJSxxDNgPSjLcV1_Pg6nfgaAi20EALw_wcB&sig=AOD64_0xpyk88vgsjooYWAlBS-_lR2_-Vw&q&nis=4&adurl&ved=2ahUKEwjGrcz0-oSMAxUoWEEAHQG2PV4Q0Qx6BAgKEAE) provides a fully managed, serverless platform that scales applications automatically.

 ![Google Cloud Run](https://assets.northflank.com/cloudrun_home_page_a1ce4d09f3.png) 

**Why choose Google Cloud Run**

- Supports Knative, making it easier to move workloads across environments
- Pay-as-you-go pricing that can help reduce costs for low-traffic applications
- More control over networking and rollbacks than AWS App Runner

**What to keep in mind**

- Limited to Google Cloud, so it is not an option for multi-cloud setups
- Some networking and security features require additional setup

**Works well for**: Developers who need a serverless container solution within Google Cloud

*See the [Best Google Cloud Run alternatives in 2026](https://northflank.com/blog/best-google-cloud-run-alternatives-in-2025)*

### 4. Azure Container Apps (flexible serverless option within Azure)

You work with Azure and need a serverless container platform that integrates with Azure Functions and Kubernetes. [Azure Container Apps](https://azure.microsoft.com/en-us/products/container-apps) allows you to run containerized applications without managing servers while providing event-driven scaling and built-in networking features.

 ![azure container apps](https://assets.northflank.com/azure_container_apps_ef61416f2a.png) 

**Why choose Azure Container Apps**

- Event-driven scaling and support for HTTP-based applications
- Kubernetes integration without requiring full cluster management
- Built-in security and networking tools for Azure-based applications

What to keep in mind

- Limited to Azure’s ecosystem
- Scaling options require manual configuration

**Works well for**: Teams that need serverless containerized workloads within Azure

### 5. DigitalOcean App Platform (simple and managed hosting)

You want a Heroku-like experience with automatic scaling and a simple interface. [DigitalOcean App Platform](https://www.digitalocean.com/products/app-platform) makes container and static site deployments easier.

 ![Digitalocean app platform](https://assets.northflank.com/Digitalocean_app_platform_s_home_page_b7f6009848.png) 

**Why choose DigitalOcean App Platform**

- Simple UI to deploy web services, databases, and static sites
- Lower cost for small applications
- Built-in auto-scaling

**What to keep in mind**

- Less control over networking and scaling compared to AWS App Runner
- Limited integrations outside the DigitalOcean ecosystem

**Works well for**: Developers who want a simplified hosting experience with minimal setup

*See the [10 best DigitalOcean alternatives in 2026 for developers and teams](https://northflank.com/blog/best-digitalocean-alternatives-2025)*

### 6. Railway (developer-first platform with easy automation)

You need a simple way to deploy and scale applications without handling infrastructure. [Railway](https://railway.com/) provides an automated deployment process with minimal setup.

 ![railway](https://assets.northflank.com/railway_c4a6bd38bc.png) 

**Why choose Railway**

- Serverless deployment with automated scaling
- Supports containers, databases, and serverless functions
- Git-based CI/CD workflow for seamless deployments

**What to keep in mind**

- Less control over networking and scaling policies
- Limited to Railway’s platform

**Works well for**: Developers looking for a fast and easy way to deploy applications

Read this [case study to see how Catalog found an ideal alternative to traditional PaaS offerings like Railway.](https://northflank.com/blog/case-study-how-catalog-built-a-scalable-streaming-music-platform-with-northflank-cloud-platform-idp) 

### 7. Render (a flexible alternative with security features)

You need a fully managed hosting platform that provides private networking, security tools, and flexible configurations. [Render](https://render.com/) gives you more control over deployments than Railway.

 ![Render](https://assets.northflank.com/render_s_home_page_3d51451377.png) 

**Why choose Render**

- More networking and scaling flexibility compared to Railway
- Automatic HTTPS, private networking, and DDoS protection
- Supports complex applications with database integrations

**What to keep in mind**

- Not multi-cloud, limited to Render’s platform
- More setup required for advanced applications

**Works well for**: Teams that need a flexible, managed PaaS with more control over networking and security

### 8. Google Kubernetes Engine (GKE) (managed Kubernetes service on GCP)

You need full Kubernetes control to manage containerized workloads at scale. [GKE](https://www.google.com/aclk?sa=l&ai=DChsSEwiau6S-g4WMAxUrjlAGHQ6kHbUYACICCAEQABoCZGc&co=1&ase=2&gclid=Cj0KCQjw4cS-BhDGARIsABg4_J1r_PFDVPsiHoCNKTGkmrZ8-GUksqPzbHFjwdcckmRahApGfBticKEaArpREALw_wcB&sig=AOD64_2xuWgE4HCO4Cg6jIPuIbUEebIeUA&q&nis=4&adurl&ved=2ahUKEwimqp6-g4WMAxUbUEEAHfjYNScQ0Qx6BAgFEAE) provides a managed Kubernetes service with custom networking and security options.

 ![Google Kubernetes Engine](https://assets.northflank.com/gke_15f05bcdd5.png) 

**Why choose Google Kubernetes Engine**

- Supports Kubernetes-native workloads
- More flexibility for networking and scaling
- Can be deployed across multiple cloud providers with Anthos

**What to keep in mind**

- Requires Kubernetes knowledge to configure properly
- Not fully serverless, requires cluster management

**Works well for**: Teams that need full Kubernetes orchestration within Google Cloud

See how you can [easily create or import Google Kubernetes Engine (GKE) clusters with Northflank BYOC in your own cloud account](https://northflank.com/cloud/gcp).

### 9. OpenShift (enterprise Kubernetes for hybrid cloud)

You need an enterprise-grade Kubernetes solution with security and automation features. [OpenShift](https://www.redhat.com/en/technologies/cloud-computing/openshift) supports hybrid and multi-cloud environments.

 ![Openshift](https://assets.northflank.com/openshift_c7d1bed4e5.png) 

**Why choose OpenShift**

- Supports hybrid and multi-cloud deployments
- Built-in security and compliance features
- Full control over Kubernetes workloads

**What to keep in mind**

- Requires Kubernetes expertise
- More complex setup than AWS App Runner

**Works well for**: Enterprises needing Kubernetes with security and governance features


## Find the AWS App Runner alternative that works for you

Now we've gotten to a point where we've gone over 9 top AWS App Runner alternatives, but at the end of the day, the best choice depends on what you need from your container platform. If multi-cloud flexibility, built-in CI/CD, and Kubernetes support matter to you, Northflank lets you deploy across AWS, GCP, and Azure with custom networking and scaling.

If you want to stay within AWS but need more control, AWS Fargate gives you better networking, security, and workload management.

If your priority is low-cost automation, Google Cloud Run, Railway, and Render provide fully managed container hosting with automatic scaling.

> 💡
How to decide
> - Do you need multi-cloud deployments? → Northflank supports AWS, GCP, and Azure
> - Do you want to stay within AWS? → AWS Fargate gives more control
> - Is cost your biggest concern? → Google Cloud Run, Railway, or Render are budget-friendly
> - Do you need Kubernetes-based orchestration? → Google Kubernetes Engine or OpenShift

You don't have to settle if AWS App Runner doesn’t fit your needs. You can [get started with Northflank for free](https://app.northflank.com/signup) and try a fully managed container platform built for flexibility and scalability.

## Frequently asked questions (FAQs)

If you're still deciding on an AWS App Runner alternative, these answers might help.

**Is AWS App Runner cheaper than AWS Fargate?**

App Runner charges per instance, while Fargate bills per CPU/memory used. App Runner can be cheaper for variable workloads, but Fargate offers more control. Northflank provides predictable, usage-based [pricing](https://northflank.com/docs/v1/application/billing/pricing-on-northflank) with multi-cloud support.

**What is the difference between AWS App Runner and AWS Elastic Beanstalk?**

App Runner is built for containerized applications with automatic scaling. Elastic Beanstalk supports EC2-based applications and automates infrastructure setup.

**Does AWS App Runner scale to zero?**

Yes, but cold starts may affect response times.

**Is AWS App Runner serverless?**

Yes, it is fully managed and handles deployments, networking, and scaling.

**What is the difference between ECS and AWS Fargate?**

ECS lets you run containers on EC2 or Fargate. Fargate removes infrastructure management, running containers without provisioning servers.]]>
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</item><item>
  <title>Top OpenSandbox alternatives for managed AI sandbox infrastructure in 2026</title>
  <link>https://northflank.com/blog/opensandbox-alternatives</link>
  <pubDate>2026-03-31T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[OpenSandbox alternatives in 2026 compared: Northflank, E2B, Modal, Fly.io Sprites, and Vercel Sandbox, covering managed hosting, BYOC, isolation, persistence, GPU support, and pricing.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/opensandbox_alternatives_fa17767427.png" alt="Top OpenSandbox alternatives for managed AI sandbox infrastructure in 2026" /><InfoBox className="BodyStyle">

## TL;DR: Top OpenSandbox alternatives in 2026

OpenSandbox alternatives cover the range from fully managed sandbox platforms to Bring Your Own Cloud deployments, each filling the gap that OpenSandbox's self-hosted model leaves for teams that need managed infrastructure, compliance coverage, or GPU support.

- OpenSandbox is open-source and free to use, but requires you to provision, operate, and scale the Docker or Kubernetes infrastructure yourself. Teams that need a managed service, SOC 2 compliance, or BYOC (Bring Your Own Cloud) with managed orchestration need to look elsewhere.
- The key evaluation criteria when comparing alternatives are whether managed hosting is available, which isolation model the platform uses, BYOC support, persistence, GPU access, and compliance certifications.
- [Northflank](https://northflank.com/product/sandboxes) provides production-grade sandbox infrastructure backed by Firecracker, Kata Containers, and gVisor, with both ephemeral and persistent environments and no forced time limits, self-serve BYOC across AWS, GCP, Azure, Oracle, CoreWeave, Civo, bare-metal, and on-premises infrastructure, SOC 2 Type 2 compliance, on-demand GPU support, and a full workload runtime for APIs, workers, databases, and jobs alongside sandboxes. Northflank has been running this class of workload in production since 2021 across startups, public companies, and government deployments.

</InfoBox>

OpenSandbox is a capable open-source platform for teams willing to manage their own infrastructure, but not every team has the engineering capacity or compliance posture to run it in production.

This article compares the top OpenSandbox alternatives across managed hosting, isolation model, BYOC support, persistence, GPU access, compliance, and pricing.

## What is OpenSandbox and why look for alternatives?

[OpenSandbox](https://northflank.com/blog/alibaba-opensandbox-architecture-use-cases) is an open-source sandbox platform released by Alibaba under the Apache 2.0 license in March 2026. It provides a unified API for running untrusted code in isolated environments, with Docker runtime for local development and Kubernetes runtime for production scale. It supports gVisor, Kata Containers, and Firecracker microVM as secure container runtimes, and offers multi-language SDKs for Python, Java/Kotlin, JavaScript/TypeScript, and C#/.NET.

The tradeoff is that OpenSandbox is entirely self-hosted. You provision the infrastructure, run the server, manage Kubernetes at scale, and handle Day 2 operations yourself. There is no managed hosting option, no built-in compliance certifications, and no BYOC model in the sense of a vendor managing orchestration while workloads run in your own cloud. For teams at the prototype stage or with strong infrastructure capacity, this is fine. For teams that need production reliability without the operational overhead, an alternative makes more sense.

## What should you evaluate when comparing OpenSandbox alternatives?

Before choosing a platform, work through these questions:

- **Managed vs self-hosted:** Do you want the vendor to handle orchestration, scaling, and Day 2 operations, or are you prepared to run the infrastructure yourself?
- **Isolation model:** Containers, gVisor, and microVMs (Firecracker, Kata Containers) offer meaningfully different security guarantees for untrusted code.
- **BYOC (Bring Your Own Cloud) availability:** If workloads cannot leave your own infrastructure, check whether BYOC is available self-serve or only through an enterprise sales process.
- **Ephemeral vs persistent environments:** Does the platform support persistent state across sessions, or is execution stateless by default?
- **GPU support:** Not all platforms include GPU access within the same control plane as sandbox execution.
- **Compliance:** SOC 2, HIPAA, and GDPR coverage varies. Verify what each provider is certified for.
- **Pricing model:** OpenSandbox costs only your infrastructure. Managed alternatives charge for compute, and pricing structures vary significantly across platforms.

## What are the top OpenSandbox alternatives?

Each platform below fills a different gap in what OpenSandbox provides.

### 1. Northflank

[Northflank](https://northflank.com/product/sandboxes) provides production-grade sandbox infrastructure backed by Firecracker, Kata Containers, and gVisor, with orchestration, multi-tenant isolation, autoscaling, and bin-packing handled at the infrastructure level. It is the only platform in this list that covers sandboxed code execution alongside production deployments, databases, and GPU workloads in one control plane.

**Key capabilities:**

- Firecracker, Kata Containers, and gVisor applied depending on the workload
- Both ephemeral and persistent environments with no forced time limits
- End-to-end sandbox creation at 1-2 seconds, covering the full stack
- Self-serve [BYOC](https://northflank.com/product/bring-your-own-cloud) across AWS EKS, GKE, AKS, Oracle Kubernetes, CoreWeave, Civo, bare-metal, and on-premises distributions including OpenShift and RKE2, or run on [Northflank's managed cloud](https://northflank.com/features/managed-cloud)
- On-demand GPU access (NVIDIA H100, A100, L4, and [others](https://northflank.com/gpu)) with no quota requests
- Full workload runtime: APIs, workers, databases, and background jobs alongside sandboxes in the same control plane
- API, CLI, and SSH access
- Multi-tenant architecture
- [SOC 2 Type 2 certified](https://northflank.com/security), in production since 2021 across startups, public companies, and government deployments

Northflank is the right choice when you need the managed infrastructure that OpenSandbox does not provide, require compliance coverage, or need GPU workloads and databases running alongside sandbox execution without managing separate systems.

<InfoBox className="BodyStyle">

**Next steps:**

- [Get started with Northflank](https://app.northflank.com/signup) (self-serve)
- [Northflank Sandboxes](https://northflank.com/product/sandboxes)
- [Hands-on guide: spin up a secure sandbox and microVM in seconds](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
- [Book a demo with a Northflank engineer](https://cal.com/team/northflank/northflank-demo)

</InfoBox>

### 2. E2B

E2B provides isolated sandbox environments for AI agents and code execution, with Python and JavaScript SDKs.

**Key capabilities:**

- Isolated Linux VMs created on demand via API
- Pause and resume with full state preserved (filesystem and memory)
- Paused sandboxes retained indefinitely with no automatic deletion
- Continuous runtime limit of 24 hours (Pro) or 1 hour (Base) per session, reset on pause and resume
- AutoResume for automatic sandbox resumption on network reconnection
- Snapshots for saving and restoring sandbox state
- SSH access, interactive terminal, proxy tunneling, and custom domain support
- Git integration and cloud storage bucket connectivity
- MCP gateway
- BYOC available on Enterprise for AWS and GCP only (requires contacting sales)

### 3. Modal

Modal is a serverless compute platform with a sandbox interface for executing untrusted or dynamically defined code.

**Key capabilities:**

- gVisor-based sandbox isolation
- Sandbox environments defined and spawned at runtime with custom container images
- Sandbox timeouts configurable up to 24 hours, with Filesystem Snapshots for longer workflows
- GPU access configurable per sandbox
- Tunnels for direct external connections and granular egress network policies
- Filesystem snapshots for state preservation and restoration
- Python SDK (primary), JavaScript and Go SDKs
- No BYOC deployment option

### 4. Fly.io Sprites

Sprites are persistent, hardware-isolated Linux environments built on Fly.io's infrastructure.

**Key capabilities:**

- Firecracker microVM isolation per Sprite
- Persistent ext4 filesystem backed by NVMe hot storage during execution and durable object storage at rest
- Sprites create in approximately 1-2 seconds
- Automatic idle behaviour: compute charges stop when idle, filesystem is preserved
- Checkpoints with copy-on-write (approximately 300ms, non-disruptive to the running environment)
- Unique HTTPS URL per Sprite for exposing services or APIs
- No BYOC

### 5. Vercel Sandbox

Vercel Sandbox provides on-demand, isolated microVM environments for running untrusted code, tightly integrated with Vercel's deployment infrastructure.

**Key capabilities:**

- Firecracker microVM isolation
- Node.js 22 and Python 3.13 runtimes on Amazon Linux 2023
- Session limits: 5 minutes default, up to 45 minutes on Hobby, up to 5 hours on Pro and Enterprise
- Snapshotting for saving and restoring sandbox state (snapshots expire after 30 days by default)
- Up to 8 vCPUs and 2GB RAM per vCPU
- Active CPU billing only (billed when code is actively running, not during I/O wait)
- TypeScript and Python SDKs, CLI
- Currently available in the `iad1` region only
- No BYOC

## How do OpenSandbox alternatives compare on pricing?

Pricing varies significantly across these platforms. The table below is sourced from each platform's official pricing pages.

| Platform | Free tier | Paid starting point | CPU pricing | Memory pricing | BYOC |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | Yes (sandbox tier) | Pay-as-you-go | $0.01667/vCPU-hr | $0.00833/GB-hr | Self-serve |
| **E2B** | Yes ($100 one-time credit, 1-hr sessions) | $150/month (Pro) | $0.000014/vCPU-s | Included in CPU price | Enterprise only (AWS, GCP) |
| **Modal** | Yes ($30/month compute credits) | $250/month (Team) | $0.00003942/core-s | $0.00000672/GiB-s | No BYOC |
| **Fly.io Sprites** | Yes ($30 trial credits) | Pay-as-you-go | $0.07/CPU-hr | $0.04375/GB-hr | No BYOC |
| **Vercel Sandbox** | Yes (Hobby, 4 vCPU max, 45-min max) | Pro (charged against $20/month credit) | $0.128/active CPU-hr | $0.0212/GB-hr | No BYOC |
| **OpenSandbox** | Free (open source, self-host) | Free (self-host) | Your infrastructure costs | Your infrastructure costs | Self-managed |

For Northflank GPU pricing (H100, A100, L4, and others), see the full [Northflank pricing page](https://northflank.com/pricing).

## Which OpenSandbox alternative fits your situation?

The right platform depends on your primary requirement.

| If you need... | Consider... |
| --- | --- |
| Managed infrastructure with no self-hosting overhead | Northflank, E2B, Modal, Fly.io Sprites, or Vercel Sandbox |
| Self-serve BYOC with managed orchestration | Northflank |
| Both ephemeral and persistent environments with no forced time limits | Northflank |
| Full workload runtime alongside sandboxes (databases, APIs, workers, GPU) | Northflank |
| SOC 2 Type 2 compliance with BYOC deployment | Northflank |
| MicroVM isolation with pause, resume, and SDK-first integration | E2B |
| gVisor-based isolation with runtime-defined environments and GPU access | Modal |
| Persistent Linux environments with automatic idle behaviour and checkpointing | Fly.io Sprites |
| Short-lived Firecracker microVM execution within the Vercel ecosystem | Vercel Sandbox |
| Free, self-hosted sandbox infrastructure with Kubernetes support | OpenSandbox |

## FAQ: Common questions about OpenSandbox alternatives

The questions below cover what engineering teams most commonly ask when comparing OpenSandbox to managed sandbox alternatives.

### What is OpenSandbox?

OpenSandbox is an open-source sandbox platform released by Alibaba under the Apache 2.0 license. It provides a unified API for running untrusted code in isolated containers, with Docker runtime for local development and Kubernetes runtime for production scale. It supports gVisor, Kata Containers, and Firecracker microVM as secure runtimes, and offers multi-language SDKs. It is entirely self-hosted with no managed hosting option.

### Why would you use a managed sandbox instead of OpenSandbox?

OpenSandbox requires you to provision and operate the underlying infrastructure yourself. Teams that do not have the capacity to run production Kubernetes at scale, need SOC 2 or HIPAA compliance coverage, require a vendor SLA, or want GPU workloads and databases alongside sandboxes without building and maintaining that stack will find managed alternatives more practical.

### Which OpenSandbox alternative supports self-serve BYOC (Bring Your Own Cloud)?

Northflank supports BYOC self-serve across AWS EKS, GKE, AKS, Oracle Kubernetes, CoreWeave, Civo, bare-metal, and on-premises infrastructure, including OpenShift and RKE2. E2B BYOC is available on Enterprise for AWS and GCP only and requires contacting their team. Modal, Fly.io Sprites, and Vercel Sandbox run on the vendor's infrastructure only.

### Which OpenSandbox alternatives support persistent environments?

Northflank supports both ephemeral and persistent environments with no forced time limits. Fly.io Sprites maintain a persistent ext4 filesystem across sessions with automatic idle behaviour. E2B supports persistent state via pause and resume, with continuous runtime limits per session that reset on pause. Modal supports snapshot-based state preservation with sandbox timeouts configurable up to 24 hours. Vercel Sandbox supports snapshotting with sessions up to 5 hours on Pro and Enterprise.

### Which OpenSandbox alternatives support GPU workloads?

Northflank supports on-demand GPU workloads (NVIDIA H100, A100, L4, and [others](https://northflank.com/gpu)) within the same platform as sandbox execution, with no quota requests required. Modal also provides GPU access configurable per sandbox.

## Related articles on OpenSandbox alternatives

The articles below go deeper on sandbox infrastructure, isolation technologies, and deployment models relevant to this comparison.

- [Alibaba OpenSandbox architecture and use cases](https://northflank.com/blog/alibaba-opensandbox-architecture-use-cases): A detailed breakdown of how OpenSandbox works, its architecture, and where it fits in the AI sandbox landscape.
- [Self-hostable alternatives to E2B for AI agents](https://northflank.com/blog/self-hostable-alternatives-to-e2b-for-ai-agents): Covers options for teams that need AI code execution infrastructure within their own cloud.
- [Top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes): A comparison of sandbox providers that support deployment inside your own cloud infrastructure.
- [Self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes): Covers the three deployment models for running sandbox infrastructure in your own infrastructure.
- [Best sandbox runners](https://northflank.com/blog/best-sandbox-runners): A broader comparison of sandbox runners covering isolation models, persistence, and platform scope.
- [Top AI sandbox platforms for code execution](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution): A full ranked comparison of AI sandbox platforms with pricing, isolation, and session lifecycle breakdowns.]]>
  </content:encoded>
</item><item>
  <title>What is a persistent sandbox environment? [2026 guide]</title>
  <link>https://northflank.com/blog/persistent-sandbox-environment</link>
  <pubDate>2026-03-30T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[Persistent sandbox environments retain state across sessions. Learn how persistence models work, the tradeoffs, and how to choose the right platform in 2026.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/persistent_sandbox_environment_df3c3507c4.png" alt="What is a persistent sandbox environment? [2026 guide]" /><InfoBox className="BodyStyle">

## TL;DR: What is a persistent sandbox environment? Key considerations

- A persistent sandbox environment is an isolated execution context that retains its state across sessions, so files, installed packages, and working directories survive when the session ends and are available when it resumes.
- Unlike ephemeral sandboxes that destroy everything on teardown, persistent environments are designed for workloads that need continuity: multi-step agents, long-running pipelines, and stateful development tools.
- The key considerations are what your platform actually persists (filesystem only, memory, or full process state), how it handles lifecycle and cleanup, and whether the isolation model is strong enough for long-lived untrusted workloads.

> [Northflank](https://northflank.com/product/sandboxes) supports both persistent and ephemeral sandbox environments with microVM and advanced runtime isolation (Firecracker, gVisor, Kata Containers), persistent volumes from 4GB to 64TB, S3-compatible object storage, no forced session limits, and bring-your-own-cloud deployment across AWS, GCP, Azure, Oracle Cloud, CoreWeave, Civo, bare-metal, and on-premises.
> 

</InfoBox>

A persistent sandbox environment solves a problem that ephemeral execution can't: continuity. Ephemeral sandboxes are the right default for stateless, one-shot workloads. But the moment your agent needs to pick up where it left off, or your pipeline needs to accumulate state across multiple runs, ephemeral execution works against you.

This article covers how persistent sandbox environments work technically, the different persistence models available, the operational and security challenges they introduce, and what to prioritize when choosing a platform.

## What is a persistent sandbox environment?

A persistent sandbox environment is an isolated execution context where state survives between sessions. When the connection closes and reopens, or when your agent makes a new call, the files you wrote, the packages you installed, and the working directory you built up are still there.

The isolation model is the same as any sandbox: code running inside can't affect the host system, access other tenants' data, or reach resources outside its defined scope. What changes is the lifecycle. Instead of being discarded after each run, the environment persists until you explicitly terminate it.

You'll reach for persistent sandbox environments in these situations:

- **Multi-step AI agents** that clone repos, install dependencies, run tests, and iterate across multiple tool calls
- **Long-running data pipelines** that process files continuously and accumulate output over time
- **Agent-powered development tools** where the environment needs to feel like a continuous workspace to the user
- **Stateful background workers** that maintain process state between invocations

## Persistent vs ephemeral sandbox environments

The distinction comes down to what survives when an execution ends.

|  | Ephemeral | Persistent |
| --- | --- | --- |
| State after run | Destroyed | Retained |
| Filesystem | Wiped | Survives |
| Installed packages | Gone | Still there |
| Running processes | Terminated | Platform-dependent |
| Security cleanup | Automatic | Requires lifecycle management |
| Best for | Stateless, one-shot execution | Multi-step, stateful workloads |

Well-designed platforms like [Northflank](https://northflank.com/product/sandboxes) let you configure this per workload, with ephemeral and persistent modes running on the same control plane.

## How does a persistent sandbox environment work?

Persistence is implemented differently depending on the platform and the workload's requirements. There are three main approaches, each with different tradeoffs.

### Filesystem persistence via volumes

The most common model. A persistent volume is attached to the sandbox at creation time. The filesystem, including installed packages, written files, and working directories, is backed by that volume and survives when the execution ends. The next session mounts the same volume and picks up exactly where the previous one left off.

This handles the majority of agent use cases. What it doesn't handle is in-memory process state. If your agent had a running server or in-memory cache, that's gone when the session ends. Storage costs also grow continuously without active lifecycle management.

### Snapshot-based persistence

Some platforms capture filesystem state at a point in time and restore from it on the next call. The environment doesn't stay running between sessions; it's recreated from the snapshot each time. This is useful for workloads that need a consistent starting point. The tradeoff is restore latency and the operational overhead of managing which snapshots to keep.

### Full pause and resume

The entire execution environment, including memory, running processes, and filesystem, is checkpointed and resumed from exactly that state. When the session ends, the environment is paused rather than destroyed. This is the closest to how a developer's local machine works and the most resource-intensive model.

<InfoBox className="BodyStyle">

**Run persistent sandboxes in your own infrastructure**

If you need persistent sandbox environments for AI agents or multi-tenant workloads, [Northflank](https://northflank.com/product/sandboxes) provides persistent volumes from 4GB to 64TB, S3-compatible object storage, stateful databases alongside sandboxes, no forced session limits, and BYOC (Bring Your Own Cloud) deployment inside your own VPC.

[Get started with Northflank](https://app.northflank.com/signup) or [schedule a demo](https://cal.com/team/northflank/northflank-demo).

**Related resources:**

- [How to spin up a secure code sandbox and microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
- [What are persistent sandboxes?](https://northflank.com/blog/persistent-sandboxes)
- [Best persistent sandbox platforms for AI agents](https://northflank.com/blog/best-persistent-sandbox-platforms)

</InfoBox>

## What are the operational challenges of persistent sandbox environments?

Persistent sandboxes introduce challenges that ephemeral environments don't have. Plan for these before you commit to an implementation.

- **Storage cost accumulation:** Persistent volumes grow over time. Without active cleanup policies, storage costs compound. You need per-sandbox quotas and automated lifecycle rules.
- **Stale state and environment drift:** Long-lived environments accumulate installed packages and configuration changes that diverge from your base image over time. What works in a fresh environment may behave differently in one that's been running for weeks.
- **Security implications of surviving state:** Sensitive data written during a session, credentials, intermediate outputs, user data, stays in the volume until you explicitly remove it. This is a compliance and security surface you have to manage deliberately.
- **Zombie environments:** Persistent sandboxes that are never terminated accumulate silently. Without lifecycle policies enforcing termination, idle environments consume storage and compute indefinitely.
- **Stronger isolation requirements:** Long-lived environments running untrusted code need stronger isolation than short-lived ones. Container-level isolation is not sufficient for multi-tenant persistent workloads. You need microVM-level isolation: Firecracker, Kata Containers, or gVisor.

## How Northflank implements persistent sandbox environments

[Northflank](https://northflank.com/product/sandboxes) provides secure sandboxes for running untrusted code at scale with microVM and advanced runtime isolation (Kata Containers, Firecracker, gVisor), supporting both ephemeral and persistent environments in [managed cloud](https://northflank.com/features/managed-cloud) or [your own VPC](https://northflank.com/features/bring-your-own-cloud).

> Northflank has been running secure sandboxes in production since 2021 across startups, public companies, and government deployments. If you need GPUs, workers, APIs, or databases running alongside your sandboxes, they run in the same platform.
> 

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

Here's what Northflank provides out of the box:

### Persistence and storage

Persistent environments are stateful services backed by persistent volumes. Filesystem state survives between executions. Volumes start at 4GB and scale to 64TB with multi-read-write support. You can attach S3-compatible object storage for artifacts and deploy stateful databases, including PostgreSQL, Redis, MySQL, and MongoDB, alongside your sandboxes in the same control plane. There are no forced session time limits.

### Isolation and runtime

Northflank supports Firecracker, gVisor, and Kata Containers, applied per workload based on your security requirements. End-to-end sandbox creation runs at 1-2 seconds, covering the full stack.

### Ephemeral and persistent in one platform

You get both modes as first-class options. Ephemeral sandboxes for stateless short-lived execution and persistent environments for stateful workloads, configured per workload and managed from the same control plane. You don't need separate vendors as your requirements grow.

### Bring your own cloud

Most enterprise teams deploying persistent sandboxed workloads can't accept their code or data leaving their own infrastructure. Northflank supports [bring-your-own-cloud](https://northflank.com/product/bring-your-own-cloud) deployment inside your own VPC on AWS, GCP, Azure, Oracle Cloud, CoreWeave, Civo, bare-metal, and on-premises, available self-serve.

### Full workload runtime

You can run APIs, background workers, databases, and AI agent infrastructure alongside your sandbox pool on the same platform. [GPU workloads](https://northflank.com/product/gpu-paas) are supported with on-demand provisioning and no quota requests.

### Access and pricing

Sandboxes are accessible via API, CLI, and SSH. CPU at $0.01667/vCPU-hour, memory at $0.00833/GB-hour. Full pricing including GPUs is on the [Northflank pricing page](https://northflank.com/pricing).

## What should you prioritize when choosing a persistent sandbox environment?

Work through these questions before committing to a platform:

- **What needs to persist?** If your agent only needs filesystem state between sessions, volume-backed persistence is sufficient. If you need running processes and in-memory state to survive, you need full pause-resume support.
- **How long do environments run?** Long-running workloads need platforms without session time limits. Short-session workloads with occasional persistence can tolerate snapshot-based approaches.
- **What's your isolation requirement?** For multi-tenant persistent environments running untrusted code, microVM-level isolation is a hard requirement. Container-level isolation leaves you exposed as environment lifespan grows.
- **Where does the code run?** If you have data residency or compliance requirements, BYOC deployment is a hard requirement.
- **What does your full stack look like?** If your agents need databases, workers, and APIs alongside sandboxes, a platform that handles all of this in one control plane reduces operational overhead significantly.

## Frequently asked questions about persistent sandbox environments

### What is the difference between a persistent sandbox and an ephemeral sandbox?

An ephemeral sandbox is destroyed after each execution with nothing carrying over between runs. A persistent sandbox retains its state, so files, installed packages, and working directories survive when the session ends. Ephemeral sandboxes are simpler operationally; persistent ones are necessary for stateful workloads.

### What happens to a persistent sandbox when it times out?

It depends on the platform. Some impose session limits and destroy the environment when reached, requiring you to implement checkpointing to preserve state. Others, like Northflank, impose no forced time limits and let environments run until you explicitly terminate them.

### How is a persistent sandbox different from a VM?

A VM is a general-purpose compute resource. A persistent sandbox is an isolated execution environment with enforced security boundaries, resource limits, and a managed lifecycle specifically designed for running untrusted or AI-generated code safely. The persistence mechanism is similar, but the isolation model and operational constraints are different.

### What are the security risks of persistent sandbox environments?

The main risks are state accumulation and environment drift. Sensitive data written to persistent volumes stays there until explicitly removed, and long-lived environments have a larger attack surface than ephemeral ones. Mitigations include microVM-level isolation, storage quotas, automated lifecycle policies, and deliberate cleanup after each session.

### Can you use both ephemeral and persistent sandboxes on the same platform?

Yes, on platforms that support both modes. Northflank supports ephemeral and persistent environments as first-class options configured per workload, both managed from the same control plane.

## Related articles on persistent sandbox environments and secure execution

- [What are persistent sandboxes?](https://northflank.com/blog/persistent-sandboxes)
- [Best persistent sandbox platforms for AI agents](https://northflank.com/blog/best-persistent-sandbox-platforms)
- [Ephemeral sandbox environments](https://northflank.com/blog/ephemeral-sandbox-environments)
- [What is a sandbox environment?](https://northflank.com/blog/what-is-a-sandbox-environment)
- [How to sandbox AI agents in 2026: microVMs, gVisor, and isolation strategies](https://northflank.com/blog/how-to-sandbox-ai-agents)
- [Best platforms for long-running sandbox environments](https://northflank.com/blog/best-platforms-for-long-running-sandbox-environments)]]>
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</item><item>
  <title>How to get free AWS credits for your startup in 2026</title>
  <link>https://northflank.com/blog/how-to-get-free-aws-credits-for-your-startup</link>
  <pubDate>2026-03-30T06:15:00.000Z</pubDate>
  <description>
    <![CDATA[Unlock up to $100K in free AWS credits for your startup with this 2025 guide—eligibility tips, top programs like Stripe Atlas &amp; YC, and how Northflank helps you stretch credits and ship faster.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/aws_credit_041f4d971c.png" alt="How to get free AWS credits for your startup in 2026" /><InfoBox className="BodyStyle">

## TL;DR: How to get free AWS credits for your startup in 2026

Most founders only know about AWS Activate. There are at least five legitimate routes to free AWS credits, and several of them can be stacked.

1. **[AWS Activate Founders](https://aws.amazon.com/activate/)** – $1,000 in credits. Best for bootstrapped startups with fewer than 10 employees and under $1M in revenue. No accelerator or VC affiliation required.
2. **[YC Startup School](https://www.startupschool.org/)** – $2,500 in credits. Best for any early-stage founder. Apply with just an idea.
3. **[Stripe Atlas](https://stripe.com/atlas)** – $5,000 in credits. Best for founders incorporating a US entity. Credits are included automatically with no separate application.
4. **[FounderPass](https://www.founderpass.com/)** – Up to $5,000 in credits. Best for founders who will also use the other tools in the bundle. Requires a $99/year membership.
5. **VC or accelerator partner** – $10,000 to $100,000+. Best for startups in recognized programs. Credits are issued via your program's AWS Activate partner referral link.

Credits from different partner channels can be stacked. Combining YC Startup School, Stripe Atlas, and FounderPass can get you up to $12,500 before you talk to a single VC.

Once you have credits, [Northflank](https://northflank.com/) deploys to your own AWS account using those credits directly. CI/CD, managed databases, preview environments, and GPU workloads without touching the AWS console. Your Activate credits apply as normal with no markup.

</InfoBox>

If you are building a startup, AWS credits can cover months of cloud infrastructure costs while you focus on the product. Amazon offers generous credit programs through AWS Activate and a network of partner programs that most founders do not know about. This guide covers every legitimate route to get free AWS credits in 2026, how to stack them, and how to make sure you do not burn through them before you ship.

## What are AWS credits?

AWS credits are prepaid funds applied to your AWS account balance that cover usage across most AWS services. They work like a gift card: usage is deducted from your credit balance before any charges hit your payment method. Credits typically last 12 months from the date of issuance.
 
Credits cover most core AWS services including EC2, Lambda, S3, RDS, and DynamoDB. They do not cover AWS Marketplace purchases, premium support plans, or third-party SaaS products sold through AWS.

## Why does AWS give out free credits?

AWS issues credits to startups through Activate because the economics make sense for Amazon. A startup that builds on AWS while pre-revenue is likely to stay on AWS when it starts generating revenue and paying full price. Credits are a customer acquisition cost, not charity.
 
That is worth understanding as a founder. The credits are real and genuinely useful. The expectation is that you will stay on AWS infrastructure long-term. If you want to keep your options open across cloud providers, platforms like Northflank let you use your AWS credits while keeping your workloads portable across GCP, Azure, and other providers.

<InfoBox className="BodyStyle">

**What is Northflank?**

Northflank is a full-stack cloud platform that deploys to your own AWS account using your credits. You get managed infrastructure, CI/CD, databases, secrets management, preview environments, and GPU workloads without touching the AWS console.

[Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30).

</InfoBox>

## How to get AWS credits: primary routes

### 1. AWS Activate Founders
 
[AWS Activate Founders](https://aws.amazon.com/activate/) is the direct route for bootstrapped startups with no VC or accelerator affiliation.
 
* Credits: $1,000 in AWS credits plus $350 in developer support credits
* Eligibility: fewer than 10 employees, less than $1M in annual revenue or funding, no affiliation with a VC or accelerator, no previous AWS Activate credits
This is the baseline for solo founders and pre-seed teams. Apply directly at aws.amazon.com/activate.
 
 ### 2. AWS Activate Portfolio
 
If you are affiliated with a recognized [AWS Activate partner](https://aws.amazon.com/activate/portfolio-detail/) such as a VC, accelerator, or incubator, you qualify for significantly larger credit packages.
 
* Credits: $5,000 to $100,000+ depending on the partner and stage
* Eligibility: must apply through a partner with an AWS Activate referral link
If you are part of any well-known startup program, ask your program director whether they are an AWS Activate partner. Most major accelerators and VC firms have a referral link they can share.

## Alternative routes to AWS credits

### 1. YC Startup School
 
[YC Startup School](https://www.startupschool.org/) is open to any founder and includes $2,500 in AWS credits as part of the program perks. The application process is straightforward and does not require a working product.
 
### 2. Stripe Atlas
 
Incorporating through [Stripe Atlas](https://stripe.com/atlas) includes $5,000 in AWS credits via AWS Activate automatically. No separate application is required. If you are planning to incorporate a US entity, this is one of the most efficient ways to access credits without any additional effort.
 
### 3. FounderPass
 
[FounderPass](https://www.founderpass.com/) is a paid membership ($99/year) that bundles discounts and perks across developer tools including up to $5,000 in AWS credits. Worth considering if you are also using the other tools in the bundle.
 
### 4. Accelerator and VC programs
 
Most major accelerator and VC programs hold AWS Activate partner status and can issue credits ranging from $10,000 to $100,000+. If you are part of Y Combinator, Techstars, Antler, On Deck, or similar programs, contact your program director or internal startup support team for a direct Activate referral link.

## Can you stack AWS credits?

Yes, in some cases. Credits from different Activate partners can stack if they come from separate partner channels. For example, combining YC Startup School, Stripe Atlas, and FounderPass gives you up to $12,500 in credits from three separate sources.
 
| Source | Credits |
|---|---|
| YC Startup School | $2,500 |
| Stripe Atlas | $5,000 |
| FounderPass | Up to $5,000 |
| **Total** | **Up to $12,500** |
 
AWS will flag attempts to apply the same program tier twice from the same company. Stacking works across different partner channels, not within the same one.

## How to use AWS credits without wasting them
 
Getting credits is the easy part. Most startups burn through them faster than expected because of idle resources, over-provisioned instances, and infrastructure that keeps running when it should not.
 
1. **Use serverless where traffic is unpredictable.** Lambda, DynamoDB, and S3 scale to zero and charge only for actual usage. For workloads with variable traffic, serverless is significantly cheaper than always-on EC2.
 
2. **Use Spot Instances for GPU and batch workloads.** For ML training, rendering, and batch jobs, Spot Instances cost 70 to 90 percent less than on-demand EC2. Use them for workloads that can tolerate interruptions.
 
3. **Set AWS Budgets alerts before you launch anything.** Configure billing alerts at 50%, 75%, and 90% of your credit balance. You need to know when you are approaching the limit before you hit it, not after.
 
4. **Shut down non-production environments after hours.** Dev and staging environments running 24/7 consume credits continuously. Automate shutdown schedules for environments that only need to run during working hours.

## How Northflank helps you make AWS credits go further
 
Using AWS credits without a managed infrastructure layer means dealing with IAM roles, VPCs, security groups, instance types, and a billing dashboard that is difficult to read. For most early-stage teams without dedicated DevOps, AWS becomes a side project instead of a deployment platform.
 
[Northflank](https://northflank.com/product/bring-your-own-cloud) connects to your AWS account and deploys your applications, databases, and GPU workloads using your credits, without requiring you to interact with the AWS console directly. CI/CD, secrets management, logs, preview environments, and autoscaling are built in. Idle resources shut down automatically. You get real-time visibility into what is running and what it costs.
 
Because Northflank deploys to your own AWS account, your Activate credits apply directly. There is no markup and no vendor lock-in. If you later want to run workloads on GCP or Azure, Northflank supports that from the same control plane without re-architecting your infrastructure.
 
[Get started with Northflank](https://app.northflank.com/signup) and deploy to your AWS account in minutes.

## Conclusion
 
AWS credits are available to most founders through multiple legitimate routes, and stacking programs from different partner channels can get you well beyond the $1,000 Founders tier baseline. The credits are genuinely useful for covering infrastructure costs early.
 
Getting the credits is straightforward. Making them last requires keeping infrastructure lean, shutting down idle resources, and not spending engineering time managing AWS directly. Northflank handles the infrastructure layer so your team can focus on shipping, and your credits go toward actual product usage rather than DevOps overhead.
 
[Get started with Northflank](https://app.northflank.com/signup) and connect your AWS account in minutes. Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) if you want to walk through the BYOC setup.

## FAQ: AWS credits for startups
 
### How long do AWS credits last?
 
Most AWS Activate credits last 12 months from the date of issuance. Credits from partner programs may have different expiry terms. Check the credit details in your AWS Billing console for the exact expiry date.
 
### Can I use AWS credits on any AWS service?
 
Credits apply to most core AWS services including EC2, Lambda, S3, RDS, DynamoDB, and EKS. They do not cover AWS Marketplace purchases, premium support plans, or third-party SaaS products sold through AWS.
 
### Can I stack credits from multiple sources?
 
Yes, if they come from different AWS Activate partner channels. Applying through YC Startup School, Stripe Atlas, and FounderPass can give you up to $12,500 in stacked credits. Attempting to apply the same program tier twice from the same company will be flagged.
 
### Can I reapply for AWS credits after they expire?
 
No. AWS Activate credits are typically one-time per company. Once you have used them or they have expired, you cannot reapply for the same program tier.
 
### Can I transfer AWS credits to a different AWS account?
 
No. Credits are locked to the account to which they were issued and cannot be transferred.
 
### Do AWS credits work with Northflank BYOC?
 
Yes. When you connect your AWS account to Northflank via BYOC, all usage is billed directly to your AWS account. Your Activate credits apply as normal. Northflank charges separately for the platform layer only, not for the underlying AWS compute.
 
### What happens if I run out of AWS credits before 12 months?
 
Usage continues and AWS charges your payment method at standard rates. Set billing alerts at 50%, 75%, and 90% of your credit balance so you have time to optimize before credits run out.]]>
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</item><item>
  <title>Best sandbox runners for AI agents and code execution in 2026</title>
  <link>https://northflank.com/blog/best-sandbox-runners</link>
  <pubDate>2026-03-27T16:45:00.000Z</pubDate>
  <description>
    <![CDATA[Best sandbox runners in 2026 compared: Northflank, E2B, Modal, Fly.io Sprites, Vercel Sandbox, Cloudflare Sandbox, and CodeSandbox, covering isolation, BYOC, persistence, and GPU support.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/best_sandbox_runners_d175d8b839.png" alt="Best sandbox runners for AI agents and code execution in 2026" /><InfoBox className="BodyStyle">

## TL;DR: Best sandbox runners in 2026

Sandbox runners are isolated execution environments for running code safely, whether from AI agents, user submissions, or untrusted scripts. The right one depends on what isolation model you need, whether your workload requires persistent state, and how much infrastructure you want the platform to handle.

- Sandbox runners range from container-based environments to microVM-backed platforms with hardware-level isolation. The isolation model determines how safely you can run untrusted code at scale.
- For teams building AI products, the most important evaluation criteria are isolation strength, ephemeral vs persistent support, BYOC (Bring Your Own Cloud) availability, and whether the platform covers the full workload runtime alongside sandbox execution.
- [Northflank](https://northflank.com/product/sandboxes) provides production-grade sandbox infrastructure backed by Firecracker, Kata Containers, and gVisor, with both ephemeral and persistent environments and no forced time limits, self-serve BYOC across AWS, GCP, Azure, Oracle, CoreWeave, Civo, bare-metal, and on-premises infrastructure, SOC 2 Type 2 compliance, on-demand GPU support, and a full workload runtime for APIs, workers, databases, and jobs alongside sandboxes. Northflank has been running this class of workload in production since 2021 across startups, public companies, and government deployments.

</InfoBox>

Sandbox runners cover a wider range of tools than most comparisons acknowledge, from browser-based execution environments to production-grade microVM platforms running at scale.

This article compares the best sandbox runners in 2026 across isolation model, persistence, BYOC (Bring Your Own Cloud) support, GPU access, and platform scope, so you can match the right one to your use case.

## What is a sandbox runner?

A sandbox runner is an isolated execution environment where code runs without affecting your host system, other tenants, or production infrastructure. The isolation boundary determines the security model: standard Linux containers share the host kernel and rely on namespace separation, while microVMs (Firecracker, Kata Containers) give each workload a dedicated kernel, and gVisor intercepts system calls in user space to reduce the kernel attack surface.

Sandbox runners are used for AI agent code execution, user-submitted scripts, code interpretation, and any workload where you cannot trust the code being run. See [what is a sandbox environment?](https://northflank.com/blog/what-is-a-sandbox-environment) and [what is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox) for deeper breakdowns.

## What should you look for in a sandbox runner?

The evaluation criteria depend on your workload, but these questions are worth working through before committing to a platform:

- **What isolation model does it use?** Containers, gVisor, and microVMs offer meaningfully different security guarantees. For untrusted code at scale, microVM-level isolation provides a dedicated kernel per workload. See [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor) for a technical breakdown.
- **Does it support both ephemeral and persistent environments?** Ephemeral environments handle stateless, short-lived execution. Persistent environments let agents maintain state across sessions without rebuilding from scratch. Many platforms support only one model.
- **Can it deploy inside your own infrastructure?** If workloads cannot leave your network for compliance or data residency reasons, check whether BYOC (Bring Your Own Cloud) is available self-serve or only on an enterprise plan requiring a sales process.
- **Does it cover the full workload runtime?** A sandbox API is not the same as a full platform. If you need databases, background workers, GPU jobs, and production services alongside sandbox execution, check whether the platform covers all of this or only code execution.
- **Does it support GPU workloads?** Not all sandbox runners include GPU access. Confirm this is available within the same platform if your workloads require it.
- **What compliance certifications does it hold?** SOC 2, HIPAA, and GDPR coverage varies across platforms.

## What are the best sandbox runners in 2026?

Each platform below takes a different approach to sandbox execution. Here is what they provide and where they fit.

### 1. Northflank

[Northflank](https://northflank.com/product/sandboxes) provides production-grade sandbox infrastructure backed by Firecracker, Kata Containers, and gVisor, with orchestration, multi-tenant isolation, autoscaling, and bin-packing handled at the infrastructure level. It is the only platform in this list that covers sandboxed code execution alongside production deployments, databases, and GPU workloads in one control plane.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

**Key capabilities:**

- Firecracker, Kata Containers, and gVisor applied depending on the workload
- Both ephemeral and persistent environments with no forced time limits
- End-to-end sandbox creation at 1-2 seconds, covering the full stack
- Self-serve [BYOC](https://northflank.com/product/bring-your-own-cloud) (Bring Your Own Cloud) across AWS EKS, GKE, AKS, Oracle Kubernetes, CoreWeave, Civo, bare-metal, and on-premises distributions including OpenShift and RKE2, or run on [Northflank's managed cloud](https://northflank.com/features/managed-cloud)
- On-demand GPU access (NVIDIA H100, A100, L4, and [others](https://northflank.com/gpu)) with no quota requests
- Full workload runtime: APIs, workers, databases, and background jobs run alongside sandboxes in the same control plane
- API, CLI, and SSH access
- Multi-tenant architecture
- [SOC 2 Type 2 certified](https://northflank.com/security), in production since 2021 across startups, public companies, and government deployments
- CPU at $0.01667/vCPU-hour, memory at $0.00833/GB-hour. See full GPU and compute [pricing](https://northflank.com/pricing)

#### Cost comparison at scale
To make the pricing difference concrete, here is what 200 sandboxes costs across providers under the same conditions.

_Based on 200 sandboxes, plan: nf-compute-100-4, infra node: m7i.2xlarge_

| Model | Provider | Cloud | Sandbox vendor | Total |
| --- | --- | --- | --- | --- |
| PaaS | Northflank | — | $7,200.00 | $7,200.00 |
| PaaS | E2B | — | $16,819.20 | $16,819.20 |
| PaaS | Modal | — | $24,491.50 | $24,491.50 |
| PaaS | Fly Sprites | — | $35,770.00 | $35,770.00 |
| PaaS | Vercel Sandbox | — | $31,068.80 | $31,068.80 |
| BYOC (0.2 overcommit)* | Northflank | $1,500.00 | $560.00 | $2,060.00 |
| BYOC | E2B | $1,500.00 | $10,000.00 | $11,500.00 |

*Through Northflank's plans on BYOC, there's a default overcommit which allows a customer to spawn more services and sandboxes on the same amount of compute. A request modifier of 0.2 means each sandbox only requests 20% of its plan's resources as a guaranteed minimum, but can burst up to the full plan limit if there's available capacity on the node. So instead of fitting 8 sandboxes per node, you could fit 40 on the same hardware, reducing both infrastructure cost and the Northflank management fee.

<InfoBox className="BodyStyle">

Northflank is the right choice when you need isolation guarantees beyond containers, want to avoid managing separate infrastructure for execution and production, or require workloads to stay within your own cloud under compliance constraints.

**Next steps:**

- [Get started with Northflank](https://app.northflank.com/signup)
- [Northflank Sandboxes](https://northflank.com/product/sandboxes)
- [Hands-on guide: spin up a secure sandbox and microVM in seconds](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
- [Book a demo with a Northflank engineer](https://cal.com/team/northflank/northflank-demo)

</InfoBox>

### 2. E2B

E2B provides isolated sandbox environments for AI agents and code execution, with Python and JavaScript SDKs.

**Key capabilities:**

- Isolated Linux VMs created on demand via API
- Pause and resume with full state preserved (filesystem and memory)
- Paused sandboxes retained indefinitely with no automatic deletion
- Continuous runtime limit of 24 hours (Pro) or 1 hour (Base) per session, reset on pause and resume
- AutoResume for automatic sandbox resumption on network reconnection
- Snapshots for saving and restoring sandbox state
- SSH access, interactive terminal, proxy tunneling, and custom domain support
- Git integration and cloud storage bucket connectivity
- MCP gateway
- BYOC available on Enterprise for AWS and GCP only (requires contacting sales)

### 3. Modal

Modal is a serverless compute platform with a sandbox interface for executing untrusted or dynamically defined code.

**Key capabilities:**

- gVisor-based sandbox isolation
- Sandbox environments defined and spawned at runtime with custom container images
- Sandbox timeouts configurable up to 24 hours, with Filesystem Snapshots for longer workflows
- GPU access configurable per sandbox
- Tunnels for direct external connections and granular egress network policies
- Filesystem snapshots for state preservation and restoration
- Python SDK (primary), JavaScript and Go SDKs
- No BYOC deployment option

### 4. Fly.io Sprites

Sprites are persistent, hardware-isolated Linux environments built on Fly.io's infrastructure.

**Key capabilities:**

- Firecracker microVM isolation per Sprite
- Persistent ext4 filesystem backed by NVMe hot storage during execution and durable object storage at rest
- Sprites create in approximately 1-2 seconds
- Automatic idle behaviour: compute charges stop when idle, filesystem is preserved
- Warm and cold states: warm Sprites resume quickly from hibernation
- Checkpoints with copy-on-write (approximately 300ms, non-disruptive to the running environment)
- Unique HTTPS URL per Sprite for exposing services or APIs
- Up to 8 vCPUs and 16GB RAM per Sprite
- CLI, JavaScript, and Go SDKs
- No BYOC

### 5. Vercel Sandbox

Vercel Sandbox provides on-demand, isolated microVM environments for running untrusted code, tightly integrated with Vercel's deployment infrastructure.

**Key capabilities:**

- Firecracker microVM isolation
- Node.js 22 and Python 3.13 runtimes on Amazon Linux 2023
- Session limits: 5 minutes default, up to 45 minutes on Hobby, up to 5 hours on Pro and Enterprise
- Snapshotting for saving and restoring sandbox state
- Up to 8 vCPUs and 2GB RAM per vCPU
- Active CPU billing only (billed when code is actively running)
- TypeScript and Python SDKs, CLI
- Runs on Vercel's infrastructure only, no BYOC

### 6. Cloudflare Sandbox

Cloudflare Sandbox provides isolated Linux container environments for running untrusted code, built on Cloudflare Containers and Durable Objects. It is currently in Beta and available on the Workers Paid plan.

**Key capabilities:**

- Isolated Linux containers (not microVMs), each with a full Ubuntu environment
- State is maintained while the container is active; state resets after inactivity (10 minutes by default, configurable)
- Python and Node.js code interpreter with persistent execution contexts while active
- Docker-in-Docker support
- Preview URLs via automatic subdomain routing
- WebSocket support for real-time streaming
- Browser terminal access
- S3-compatible object storage mounting (R2, S3, GCS) for persistence across sessions
- TypeScript SDK
- Integrates with Cloudflare Workers, R2, KV, and Workers AI
- No BYOC

### 7. CodeSandbox

CodeSandbox, now part of Together AI, provides microVM-based sandbox environments for AI agents, code interpretation, and developer workflows.

**Key capabilities:**

- MicroVM infrastructure with snapshot and restore
- VM restore within 2 seconds
- Sandbox state persistence across sessions via snapshots
- Customisable hibernation periods
- CodeSandbox SDK for programmatic sandbox management
- Supports AI agents, development environments, code interpretation, and CI/CD
- No BYOC

## Which sandbox runner fits your use case?

The right platform depends on your primary requirement. Use the table below to narrow down your options.

| If you need... | Consider... |
| --- | --- |
| MicroVM isolation (Firecracker, Kata Containers, or gVisor) with self-serve BYOC (Bring Your Own Cloud) | Northflank |
| Both ephemeral and persistent environments with no forced time limits | Northflank |
| Full workload runtime alongside sandboxes (databases, APIs, workers, GPU) | Northflank |
| On-demand GPU support within the same platform as sandboxes | Northflank |
| SOC 2 Type 2 compliance with self-serve BYOC deployment | Northflank |
| API-driven sandbox execution with pause, resume, and AutoResume | E2B |
| gVisor-based isolation with runtime-defined environments and GPU access | Modal |
| Persistent Linux environments with automatic idle behaviour and checkpointing | Fly.io Sprites |
| Short-lived Firecracker microVM execution within the Vercel ecosystem | Vercel Sandbox |
| Container-based sandboxes integrated with Cloudflare's developer platform | Cloudflare Sandbox |
| MicroVM sandboxes with snapshot and restore for AI agents and code playgrounds | CodeSandbox |

### How do sandbox runners compare on pricing?
Pricing as of April 2026. Billing models differ across platforms (some bill based on active CPU usage only, others bill for the entire duration the sandbox is running). Verify current rates on each platform's pricing page before making cost decisions.

| Platform | CPU | Memory | Storage | GPU | Billing model |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | $0.01667/vCPU-hr | $0.00833/GB-hr | $0.15/GB-month | L4: $0.80/hr, A100 40GB: $1.42/hr, A100 80GB: $1.76/hr, H100: $2.74/hr, H200: $3.14/hr | Per second |
| **Fly.io Sprites** | $0.07/CPU-hr | $0.04375/GB-hr | $0.00068/GB-hr (hot NVMe) | Do not provide GPU compute | Per second, actual cgroup usage. No charge when idle |
| **Cloudflare Sandbox** | $0.072/vCPU-hr | $0.009/GiB-hr | $0.000252/GB-hr | Do not provide GPU compute | Active CPU; memory and disk provisioned. Requires $5/month Workers Paid plan |
| **CodeSandbox** | $0.075/core-hr (credit-based: $0.015/credit) | Bundled with VM tier | Included | Do not provide GPU compute | Credit-based ($0.015/credit) |
| **E2B** | $0.0504/vCPU-hr | $0.0162/GiB-hr | 10–20GB included free | Do not provide GPU compute | Per second |
| **Modal Sandboxes** | $0.1419/physical core-hr (2 vCPU) | $0.0242/GiB-hr | — | L4: $0.80/hr, A100 40GB: $2.10/hr, A100 80GB: $2.50/hr, H100: $3.95/hr, H200: $4.54/hr | Per second |
| **Vercel Sandbox** | $0.128/vCPU-hr | $0.0212/GB-hr | $0.023/GB-month (snapshots) | Do not provide GPU compute | Active CPU only |

### BYOC support across sandbox runners
The table below shows how each platform handles BYOC deployment, which clouds are supported, and whether it requires a sales process.
| Platform | BYOC available | Clouds supported | Access model | Pricing model |
| --- | --- | --- | --- | --- |
| **Northflank** | Yes, fully self-serve | AWS, GCP, Azure, Oracle, CoreWeave, any neoclouds, Civo, bare-metal, on-premises | Self-serve, enterprise contracts available for larger commits (with bulk discounts) | Your existing cloud bill, CPU $0.01389/vCPU-hr and Memory $0.00139/GB-hr* |
| **E2B** | Yes, limited and not self-serve | AWS and GCP only | Not publicly disclosed, need to contact sales | Starts at $50/sandbox/month, on top of your existing cloud bill |
| **Modal** | No | Managed only | — | — |
| **Fly.io Sprites** | No | Managed only | — | — |
| **Vercel Sandbox** | No | Managed only (iad1 region only) | — | — |
| **Cloudflare Sandbox** | No | Managed only | — | — |
| **CodeSandbox** | Enterprise only | Custom dedicated cluster | Enterprise plan, contact sales | Custom |

## FAQ: Common questions about sandbox runners

The questions below cover what engineering teams most commonly ask when evaluating sandbox runners.

### What is the difference between a sandbox runner and a development environment?

A sandbox runner is designed to execute arbitrary or untrusted code safely, typically for AI agents, user-submitted scripts, or code interpretation. A development environment is designed for developers to write, run, and iterate on code they own. Some platforms serve both purposes, but the security requirements and isolation models differ significantly between the two use cases.

### Which sandbox runners support self-serve BYOC (Bring Your Own Cloud)?

Northflank supports BYOC self-serve across AWS EKS, GKE, AKS, Oracle Kubernetes, CoreWeave, Civo, bare-metal, and on-premises infrastructure, including OpenShift and RKE2. E2B BYOC is available on Enterprise for AWS and GCP only and requires contacting their team. Modal, Vercel Sandbox, Cloudflare Sandbox, and Fly.io Sprites run on the vendor's infrastructure only.

### Which sandbox runners support persistent environments?

Northflank supports both ephemeral and persistent environments with no forced time limits. Fly.io Sprites maintain a persistent ext4 filesystem across sessions with automatic idle behaviour. E2B supports persistent state via pause and resume, with continuous runtime limits per session that reset on pause. CodeSandbox supports persistence via snapshots with VM restore within 2 seconds. Modal supports snapshot-based state preservation. Cloudflare Sandbox state resets after inactivity unless S3-compatible object storage is mounted. Vercel Sandbox sessions run up to 5 hours on Pro and Enterprise with snapshotting available.

### Which sandbox runners support GPU workloads?

Northflank supports on-demand GPU workloads (NVIDIA H100, A100, L4, and others) within the same platform as sandbox execution. Modal also provides GPU access configurable per sandbox.

## Related articles on sandbox runners

The articles below go deeper on isolation technologies, deployment models, and sandbox infrastructure covered in this guide.

- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox): A detailed explainer on AI sandbox infrastructure, isolation models, and use cases.
- [Top AI sandbox platforms for code execution](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution): A full ranked comparison of AI sandbox platforms with pricing, isolation, and session lifecycle breakdowns.
- [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): A technical comparison of the isolation technologies used across the sandbox runners in this guide.
- [Top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes): A comparison of sandbox providers that support deployment inside your own cloud infrastructure.
- [Self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes): Covers the three deployment models for running sandbox infrastructure in your own infrastructure.
- [Ephemeral sandbox environments](https://northflank.com/blog/ephemeral-sandbox-environments): Explains the tradeoffs between ephemeral and persistent sandbox models and when each fits the workload.]]>
  </content:encoded>
</item><item>
  <title>Top Runloop alternatives for AI agent sandbox infrastructure in 2026</title>
  <link>https://northflank.com/blog/runloop-alternatives</link>
  <pubDate>2026-03-26T15:45:00.000Z</pubDate>
  <description>
    <![CDATA[Runloop alternatives in 2026: compare Northflank, E2B, Modal, Fly.io Sprites, and Vercel Sandbox across isolation, BYOC, persistence, and GPU support.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/runloop_alternatives_7b88490078.png" alt="Top Runloop alternatives for AI agent sandbox infrastructure in 2026" /><InfoBox className="BodyStyle">

## TL;DR: Top Runloop alternatives in 2026

Runloop provides Devbox environments for AI coding agents with two layers of isolation (VM and container), Snapshots for suspend and resume, Blueprints for reusable templates, and built-in benchmarks and evals.

If you are evaluating Runloop alternatives for reasons such as needing Bring Your Own Cloud (BYOC) support, persistent environments with no forced runtime limits, a full workload runtime beyond sandbox execution, or on-demand GPU support, here is a breakdown of the top options.

- **Northflank:** fullstack workload platform that allows you to run sandboxes alongside APIs, workers, databases, and GPU workloads in one place, with microVM isolation, self-serve BYOC, both ephemeral and persistent environments with no forced time limits, and SOC 2 Type 2 compliance
- **E2B:** API-driven sandbox platform with Python and JavaScript SDKs, pause and resume with full state preservation, and AutoResume
- **Modal:** serverless compute platform with gVisor-based sandbox isolation, dynamically defined environments at runtime, and GPU support
- **Fly.io Sprites:** persistent, hardware-isolated Linux environments using Firecracker microVMs with automatic idle behaviour and checkpoint and restore
- **Vercel Sandbox:** Firecracker microVM-based sandbox for running untrusted code, integrated with Vercel's deployment infrastructure

> **Worth noting**: [Northflank](https://northflank.com/product/sandboxes) provides production-grade sandbox infrastructure backed by Firecracker, Kata Containers, and gVisor, with both ephemeral and persistent environments and no forced time limits, self-serve BYOC across AWS, GCP, Azure, Oracle, CoreWeave, Civo, bare-metal, and on-premises infrastructure, SOC 2 Type 2 compliance, on-demand GPU support, and a full workload runtime for APIs, workers, databases, and jobs alongside sandboxes. Northflank has been running this class of workload in production since 2021 across startups, public companies, and government deployments.
> 

</InfoBox>

Runloop alternatives span a range of approaches, from sandbox-only platforms to full workload runtimes. The right choice depends on what you need beyond the sandbox itself.

This article compares the top alternatives across isolation model, persistence, BYOC support, GPU access, and platform scope.

## What should you look for when evaluating Runloop alternatives?

Before comparing platforms, clarify your requirements across these dimensions.

- **Isolation model:** What layer does the platform isolate at? Container namespacing, gVisor syscall interception, and microVMs (Firecracker, Kata Containers) offer meaningfully different security guarantees for untrusted code.
- **Ephemeral vs persistent environments:** Some platforms are designed for short-lived execution sessions. Others support persistent state that survives across sessions without manual snapshot logic. Clarify whether your workload needs state to persist and for how long.
- **BYOC availability:** If your workloads cannot leave your own infrastructure, check whether BYOC is available self-serve or only on an enterprise plan requiring a sales process.
- **Platform scope:** A sandbox API is not the same as a full workload platform. If you need databases, background workers, GPU inference, and production services alongside sandbox execution, look for platforms that cover the full stack.
- **Compliance:** SOC 2, HIPAA, and GDPR coverage varies across platforms. Verify what each provider is certified for before evaluating.
- **GPU support:** Not all sandbox platforms include GPU access. If your agent workloads require GPU compute, confirm it is available within the same platform.

## Which are the top Runloop alternatives?

Each platform below takes a different approach to sandbox infrastructure. Here is what they provide and where they fit.

### 1. Northflank

[Northflank](https://northflank.com/product/sandboxes) provides production-grade sandbox infrastructure backed by Firecracker, Kata Containers, and gVisor, with orchestration, multi-tenant isolation, autoscaling, and bin-packing handled at the infrastructure level. It is the only platform in this list that covers sandboxed code execution alongside production deployments, databases, and GPU workloads in one control plane.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

**Key capabilities:**

- MicroVM isolation (Firecracker, Kata Containers, and gVisor) applied depending on the workload
- Both ephemeral and persistent environments with no forced time limits
- End-to-end sandbox creation at 1-2 seconds, covering the full stack
- Self-serve [BYOC](https://northflank.com/product/bring-your-own-cloud) across AWS EKS, GKE, AKS, Oracle Kubernetes, CoreWeave, Civo, bare-metal, and on-premises distributions, including OpenShift and RKE2, or run on [Northflank's managed cloud](https://northflank.com/features/managed-cloud)
- On-demand GPU access (NVIDIA H100, A100, L4, and others) with no quota requests
- Full workload runtime: APIs, workers, databases, and background jobs run alongside sandboxes in the same control plane
- API, CLI, and SSH access
- Multi-tenant architecture
- [SOC 2 Type 2 certified](https://northflank.com/security), in production since 2021 across startups, public companies, and government deployments
- CPU at $0.01667/vCPU-hour, memory at $0.00833/GB-hour. See full GPU and compute [pricing](https://northflank.com/pricing)

Northflank is the right choice when you need isolation guarantees beyond containers, want to avoid managing separate infrastructure for execution and production, or require workloads to stay within your own cloud under compliance constraints.

#### Cost comparison at scale
To make the pricing difference concrete, here is what 200 sandboxes costs across providers under the same conditions.

_Based on 200 sandboxes, plan: nf-compute-100-4, infra node: m7i.2xlarge_

| Model | Provider | Cloud | Sandbox vendor | Total |
| --- | --- | --- | --- | --- |
| PaaS | Northflank | — | $7,200.00 | $7,200.00 |
| PaaS | E2B | — | $16,819.20 | $16,819.20 |
| PaaS | Modal | — | $24,491.50 | $24,491.50 |
| PaaS | Fly Sprites | — | $35,770.00 | $35,770.00 |
| PaaS | Vercel Sandbox | — | $31,068.80 | $31,068.80 |
| BYOC (0.2 overcommit)* | Northflank | $1,500.00 | $560.00 | $2,060.00 |
| BYOC | E2B | $1,500.00 | $10,000.00 | $11,500.00 |

*Through Northflank's plans on BYOC, there's a default overcommit which allows a customer to spawn more services and sandboxes on the same amount of compute. A request modifier of 0.2 means each sandbox only requests 20% of its plan's resources as a guaranteed minimum, but can burst up to the full plan limit if there's available capacity on the node. So instead of fitting 8 sandboxes per node, you could fit 40 on the same hardware, reducing both infrastructure cost and the Northflank management fee.

<InfoBox className="BodyStyle">

**Next steps:**

- [Get started with Northflank](https://app.northflank.com/signup)
- [Northflank Sandboxes](https://northflank.com/product/sandboxes)
- [Hands-on guide: spin up a secure sandbox and microVM in seconds](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
- [Book a demo with a Northflank engineer](https://cal.com/team/northflank/northflank-demo)

</InfoBox>

### 2. E2B

E2B provides isolated sandbox environments for AI agents and code execution, with Python and JavaScript SDKs.

**Key capabilities:**

- Isolated Linux VMs created on demand via API
- Pause and resume with full state preserved (filesystem and memory)
- Paused sandboxes are retained indefinitely with no automatic deletion
- Continuous runtime limit of 24 hours (Pro) or 1 hour (Base) per session, reset on pause and resume
- AutoResume for automatic sandbox resumption on network reconnection
- Snapshots for saving and restoring sandbox state
- SSH access, interactive terminal, proxy tunneling, and custom domain support
- Git integration and cloud storage bucket connectivity
- MCP gateway
- BYOC available on Enterprise for AWS and GCP only (requires contacting sales)

### 3. Modal

Modal is a serverless compute platform with a sandbox interface for executing untrusted or dynamically defined code.

**Key capabilities:**

- gVisor-based sandbox isolation
- Sandbox environments defined and spawned at runtime with custom container images
- Sandbox timeouts configurable up to 24 hours, with Filesystem Snapshots for longer workflows
- GPU access configurable per sandbox
- Tunnels for direct external connections and granular egress network policies
- Filesystem snapshots for state preservation and restoration
- Python SDK (primary), JavaScript and Go SDKs

### 4. Fly.io Sprites

Sprites are persistent, hardware-isolated Linux environments built on Fly.io's infrastructure.

**Key capabilities:**

- Firecracker microVM isolation per Sprite
- Persistent ext4 filesystem backed by NVMe hot storage during execution and durable object storage at rest
- Automatic idle behaviour: compute charges stop when idle, filesystem is preserved
- Warm and cold states: warm Sprites resume quickly from hibernation
- Checkpoints with copy-on-write (approximately 300ms, non-disruptive to the running environment)
- Unique HTTPS URL per Sprite for exposing services or APIs
- CLI, JavaScript, and Go SDKs
- No BYOC

### 5. Vercel Sandbox

Vercel Sandbox provides on-demand, isolated microVM environments for running untrusted code, tightly integrated with Vercel's deployment infrastructure.

**Key capabilities:**

- Firecracker microVM isolation
- Node.js 22 and Python 3.13 runtimes, running on Amazon Linux 2023
- Session limits: 5 minutes default, up to 45 minutes on Hobby, up to 5 hours on Pro and Enterprise
- Up to 8 vCPUs and 2GB RAM per vCPU
- Snapshotting for saving and restoring sandbox state
- Active CPU billing only (billed when code is actively running)
- TypeScript and Python SDKs, CLI
- Runs on Vercel's infrastructure only, no BYOC

## Which Runloop alternative fits your situation?

The right platform depends on your primary requirement. Use the table below to narrow down your options.

| If you need... | Consider... |
| --- | --- |
| MicroVM isolation (Firecracker, Kata Containers, or gVisor) with self-serve BYOC | Northflank |
| Both ephemeral and persistent environments with no forced time limits | Northflank |
| Full workload runtime alongside sandboxes (databases, APIs, workers, GPU) | Northflank |
| On-demand GPU support within the same platform as sandboxes | Northflank |
| SOC 2 Type 2 compliance with BYOC deployment | Northflank |
| MicroVM isolation with pause and resume, SDK-first integration | E2B |
| gVisor-based isolation with runtime-defined environments and GPU access | Modal |
| Persistent Linux environments with automatic idle behaviour and checkpointing | Fly.io Sprites |
| Short-lived Firecracker microVM execution within the Vercel ecosystem | Vercel Sandbox |

## FAQ: Common questions about Runloop alternatives

The questions below cover what engineering teams most commonly ask when comparing Runloop alternatives.

### What does Runloop provide?

Runloop provides Devbox environments for AI coding agents, with two layers of isolation (VM and container), Blueprints for reusable templates, Snapshots for suspend and resume, Repo Connect for automatic environment inference from git repositories, and built-in benchmark and eval tooling. VPC deployment is available on the Enterprise plan.

### What isolation model does Runloop use?

Runloop uses two layers of isolation: a VM layer and a container layer. The specific VM technology is described on their site as a micro-VM.

### Which Runloop alternative supports self-serve BYOC?

Northflank supports BYOC self-serve across AWS EKS, GKE, AKS, Oracle Kubernetes, CoreWeave, Civo, bare-metal, and on-premises infrastructure. E2B BYOC is available on Enterprise for AWS and GCP only, and requires contacting their team. Modal and Vercel Sandbox do not offer BYOC. Fly.io Sprites run on Fly.io's infrastructure only.

### Which Runloop alternative supports persistent environments with no forced time limits?

Northflank supports both ephemeral and persistent environments with no forced time limits. Fly.io Sprites are persistent with automatic idle behaviour. E2B supports persistent state via pause and resume, with continuous runtime limits of 24 hours (Pro) or 1 hour (Base) per session, reset on pause. Modal sandbox timeouts are configurable up to 24 hours. Vercel Sandbox sessions run up to 5 hours on Pro and Enterprise.

### Which Runloop alternatives support GPU workloads?

Northflank supports on-demand GPU workloads (NVIDIA H100, A100, L4, and others) within the same platform as sandboxes. Modal also provides GPU access configurable per sandbox.

## Related articles on Runloop alternatives

The articles below go deeper on sandbox infrastructure, isolation technologies, and deployment models relevant to this comparison.

- [Top AI sandbox platforms for code execution](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution): A full ranked comparison of AI sandbox platforms with pricing, isolation, and session lifecycle breakdowns.
- [Best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents): Covers how to evaluate sandbox platforms for AI agent workflows, including isolation, latency, and SDK requirements.
- [Top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes): A comparison of sandbox providers that support deployment inside your own cloud infrastructure.
- [Self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes): Covers the three deployment models for running sandbox infrastructure in your own infrastructure.
- [Top Fly.io Sprites alternatives for secure AI code execution](https://northflank.com/blog/top-fly-io-sprites-alternatives-for-secure-ai-code-execution-and-sandboxed-environments): A direct comparison for teams evaluating Sprites against other persistent, isolated environment options.
- [Ephemeral sandbox environments](https://northflank.com/blog/ephemeral-sandbox-environments): Explains the tradeoffs between ephemeral and persistent sandbox models and when each fits the workload.]]>
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  <title>Best enterprise AI sandbox platforms in 2026</title>
  <link>https://northflank.com/blog/best-enterprise-ai-sandbox-platforms</link>
  <pubDate>2026-03-26T13:30:00.000Z</pubDate>
  <description>
    <![CDATA[Best enterprise AI sandbox platforms in 2026: compare Northflank, E2B, Blaxel, Modal, and Fly.io Sprites on SOC 2, HIPAA, BYOC, RBAC, audit logging, and data residency.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/spectro_cloud_alternatives_5_9533777c3c.png" alt="Best enterprise AI sandbox platforms in 2026" /><InfoBox className="BodyStyle">

## TL;DR: What are the best enterprise AI sandbox platforms in 2026?

Enterprise teams evaluating AI sandbox platforms face requirements that go beyond isolation and cold start times. Data residency, compliance certifications, RBAC, audit logging, and the ability to run inside your own infrastructure all determine whether a platform can pass a security review. These are the platforms built to meet those requirements.

- **Northflank** – SOC 2 Type 2 certified. [Self-serve BYOC](https://northflank.com/product/bring-your-own-cloud) into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, and bare-metal. Full-stack platform with [microVM isolation](https://northflank.com/product/sandboxes), RBAC, audit logs, SSO, and unlimited sessions.
- **E2B** – SOC 2 Type 2 certified. Enterprise tier includes BYOC on AWS & GCP, on-premises deployment with Firecracker microVM isolation.
- **Modal** – SOC 2 compliant. Python-first serverless platform with gVisor isolation and enterprise plans that include Okta SSO, audit logs, and HIPAA. Managed-only, no BYOC.
- **Fly.io Sprites** – SOC 2 compliant. Persistent Firecracker microVMs with idle billing. Better suited for teams where managed infrastructure is acceptable.
</InfoBox>

## What enterprise teams actually need from a sandbox platform

Most sandbox decisions are driven by developer experience and pricing. Enterprise decisions are driven by different questions: where does my data go, who can see it, can I prove it to an auditor, and will this pass our security review? A platform that routes execution through third-party infrastructure introduces a third-party data processor into your compliance chain, triggering GDPR data processing agreements, HIPAA Business Associate Agreements, and auditor scrutiny. 

Beyond data residency, enterprise platforms need multi-tenant isolation at scale, granular access controls, audit trails, and SSO. The platforms that clear procurement are the ones that treat compliance as a first-class requirement rather than an afterthought.

## What should you look for in an enterprise AI sandbox platform?

These are the dimensions that separate enterprise-ready platforms from developer tools that have not yet been through a security review.

- **Compliance certifications.** SOC 2 Type 2 is the baseline that enterprise customers expect. HIPAA matters for healthcare. Verify certifications cover the deployment model you plan to use, not just the vendor's managed cloud.
- **Data residency controls.** Can you restrict execution to specific geographic regions? Enterprise customers in regulated industries often require data to stay within a country or regional boundaries.
- **BYOC and deployment model.** Managed-only platforms send your code to the vendor's infrastructure. Enterprise teams with strict data sovereignty requirements need execution inside their own VPC, on-premises, or bare-metal.
- **RBAC and access controls.** Granular role-based access controls, team-level permissions, and API token scoping determine whether your security and compliance teams can enforce least-privilege access.
- **Audit logging.** SOC 2 Type 2 audits require demonstrable audit trails. Verify what the platform logs, how long logs are retained, and whether they can be exported to your SIEM.
- **SSO integration.** Enterprise teams expect SAML or OIDC-based SSO for centralized identity management. Platforms that rely on username and password only will not pass procurement.
- **Multi-tenant isolation.** For SaaS companies deploying AI sandbox infrastructure for their own customers, each customer's workloads must be isolated at the kernel level from every other customer's.

## What are the best enterprise AI sandbox platforms?

### 1. Northflank

[Northflank](https://northflank.com/product/sandboxes) is a full-stack cloud platform with enterprise features built in from day one, not bolted on for sales. SOC 2 Type 2 certification covers the platform across managed cloud and BYOC deployments. [BYOC is available self-serve](https://northflank.com/product/bring-your-own-cloud) into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, and bare-metal, with no enterprise sales process required. Your data never leaves your infrastructure.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

For regulated industries and government deployments, Northflank handles air-gapped and on-premises deployments where execution must happen entirely within your physical perimeter. The platform has been in production since 2019 across startups, public companies, and government deployments. Multi-tenant isolation uses Kata Containers with Cloud Hypervisor, Firecracker, and gVisor per workload, ensuring different customers or teams cannot share kernel state or filesystem access.

**Key features:**

- **SOC 2 Type 2 certified:** Covers managed cloud and BYOC deployments. Trust center available [here](https://trust.northflank.com/).
- **Self-serve BYOC:** Deploy into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal. No enterprise sales process required.
- **RBAC:** Role-based access controls at organisation, team, and project level. API roles with scoped permissions. MFA enforcement.
- **SSO:** SAML and OIDC-based SSO with automatic role assignment based on identity provider groups.
- **Audit logging:** Full audit trail across all platform actions. Exportable for SIEM integration.
- **Multi-tenant isolation:** Kata Containers, Firecracker, and gVisor applied per workload. Every sandbox runs in its own microVM.
- **Full-stack scope:** Databases, persistent volumes, background jobs, and GPU workloads alongside sandboxes in the same control plane.
- **Air-gap and on-premises support:** Execution inside your own data center with no public cloud dependency.
- **Access:** UI, API, CLI, and GitOps

[cto.new migrated their entire sandbox infrastructure to Northflank](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) in two days after EC2 metal instances made scaling costs unpredictable, going from unworkable provisioning to thousands of daily deployments with linear, per-second billing.

**Best for:** Enterprise teams in regulated industries, SaaS companies deploying multi-tenant sandbox infrastructure for customers, and platform engineering teams that need compliance, BYOC, and a full infrastructure stack without going through enterprise sales.

**Pricing:** $0.01667/vCPU-hour, $0.00833/GB-hour, H100 GPU at $2.74/hour all-inclusive. BYOC deployments bill against your own cloud account.

<InfoBox className="BodyStyle">

[Get started on Northflank](https://app.northflank.com/signup) (self-serve, no demo required). Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) with an engineer if you want to walk through your enterprise requirements.

</InfoBox>

### 2. E2B

E2B is SOC 2 Type 2 certified and offers an enterprise tier that includes BYOC on AWS & GCP, on-premises deployment, and HIPAA compliance with Business Associate Agreements. Sandboxes use Firecracker microVM isolation with boot times under 200ms, and the Python and TypeScript SDKs integrate cleanly with LangChain, OpenAI, and Anthropic tooling.

The enterprise constraints are worth understanding. BYOC is limited to AWS & GCP, and on-premises deployment requires the customer to operate the full runtime stack, including the control plane. That is closer to self-hosting than managed BYOC and puts a significant operational burden on your team. HIPAA and enterprise features require a sales conversation rather than self-serve access.

**Best for:** Enterprise teams on AWS & GCP that need Firecracker microVM isolation, HIPAA compliance, and SDK-first integration into AI agent workflows.

**Pricing:** Enterprise custom pricing. Managed tiers: Hobby free with $100 credit, Pro at $150/month with 100 concurrent sandboxes and 24-hour sessions.

### 3. Modal

Modal is SOC 2 compliant and offers HIPAA-compatible deployment on its Enterprise plan alongside Okta SSO, audit logs, and embedded ML engineering services. It scales to 20,000 concurrent containers with sub-second cold starts and gVisor isolation, making it the strongest managed option for high-volume Python and GPU workloads.

Modal is managed-only with no BYOC option, and environments are defined through Modal's Python SDK rather than arbitrary container images. For regulated enterprises that need execution inside their own infrastructure, Modal does not qualify. For Python-first ML enterprises where managed infrastructure is acceptable, and GPU workloads are the priority, Modal's enterprise tier is well-suited.

**Best for:** Enterprise Python and ML teams running GPU-intensive AI workloads at scale where managed infrastructure is acceptable and BYOC is not required.

**Pricing:** Enterprise custom pricing. Team plan at $250/month. Sandbox CPU at $0.1419/core/hr.

### 4. Fly.io Sprites

Sprites are persistent Firecracker microVMs with 100GB NVMe storage and idle billing that stops when environments are not in use. Fly.io holds SOC 2 Type 2 attestation and is HIPAA-ready with pre-signed BAAs available, which means Sprites can be deployed in regulated environments. The platform also supports GDPR compliance through a pre-signed DPA.

The enterprise caveat is that Sprites are early-stage and the platform is primarily built for developer workflows rather than large enterprise deployments. There is no BYOC option and no on-premises path. For enterprises where managed infrastructure is acceptable and HIPAA or SOC 2 is the requirement, Sprites is a more viable option than the article originally suggested.

**Best for**: Developer teams and regulated teams where managed infrastructure is acceptable, Firecracker isolation is required, and persistent warm environments with idle billing fit the workload pattern.

**Pricing**: $0.07/CPU-hour and $0.04375/GB-hour, no charge when idle.

## Which platform should you choose for enterprise AI sandboxes?

If your enterprise requires execution inside your own infrastructure, Northflank is the only option here with self-serve BYOC across multiple cloud providers and on-premises, with managed orchestration on your hardware. E2B offers BYOC on AWS & GCP through enterprise engagement, but your team operates the full runtime stack. If managed infrastructure is acceptable, Modal fits GPU-heavy Python workloads. Fly.io Sprites works where managed Firecracker isolation and HIPAA coverage are sufficient.

| Platform | SOC 2 Type 2 | BYOC | Deployment |
| --- | --- | --- | --- |
| **Northflank** | Yes | Yes, self-serve | Managed or BYOC |
| **E2B** | Yes | Yes (AWS & GCP only), enterprise | Managed or customer-operated |
| **Modal** | Yes | No | Managed only |
| **Fly.io Sprites** | Yes | No | Managed only |

### How do enterprise AI sandbox platforms compare on pricing?

Pricing as of April 2026. Billing models differ across platforms (some bill based on active CPU usage only, others bill for the entire duration the sandbox is running). Verify current rates on each platform's pricing page before making cost decisions.

| Platform | CPU | Memory | Storage | GPU | Billing model |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | $0.01667/vCPU-hr | $0.00833/GB-hr | $0.15/GB-month | L4: $0.80/hr, A100 40GB: $1.42/hr, A100 80GB: $1.76/hr, H100: $2.74/hr, H200: $3.14/hr | Per second |
| **E2B** | $0.0504/vCPU-hr | $0.0162/GiB-hr | 10–20GB included free | Do not provide GPU compute | Per second |
| **Fly.io Sprites** | $0.07/CPU-hr | $0.04375/GB-hr | $0.00068/GB-hr (hot NVMe) | Do not provide GPU compute | Per second, actual cgroup usage. No charge when idle |
| **Modal Sandboxes** | $0.1419/physical core-hr (2 vCPU) | $0.0242/GiB-hr | — | L4: $0.80/hr, A100 40GB: $2.10/hr, A100 80GB: $2.50/hr, H100: $3.95/hr, H200: $4.54/hr | Per second |

### BYOC support across enterprise AI sandbox platforms

The table below shows how each platform handles BYOC deployment, which clouds are supported, and whether it requires a sales process.

| Platform | BYOC available | Clouds supported | Access model | Pricing model |
| --- | --- | --- | --- | --- |
| **Northflank** | Yes, fully self-serve | AWS, GCP, Azure, Oracle, CoreWeave, any neoclouds, Civo, bare-metal, on-premises | Self-serve, enterprise contracts available for larger commits (with bulk discounts) | Your existing cloud bill, CPU $0.01389/vCPU-hr and Memory $0.00139/GB-hr |
| **E2B** | Yes, limited and not self-serve | AWS and GCP only | Not publicly disclosed, need to contact sales | Starts at $50/sandbox/month, on top of your existing cloud bill |
| **Modal** | No | Managed only | — | — |
| **Fly.io Sprites** | No | Managed only | — | — |


## FAQ: enterprise AI sandbox platforms

### What compliance certifications should I require from a sandbox platform?

SOC 2 Type 2 is the baseline for B2B enterprise deployments. HIPAA with a Business Associate Agreement is required for healthcare data. ISO 27001 is increasingly expected by European enterprise customers. Verify that certifications cover the specific deployment model you plan to use, since some vendors hold certifications for managed cloud but not for BYOC or on-premises deployments.

### Does a managed sandbox platform count as a third-party data processor?

Yes. If your sandbox workloads process personal data and execution runs on the vendor's infrastructure, the vendor is a third-party data processor under GDPR. This requires a Data Processing Agreement and can complicate compliance audits. Teams with strict data residency requirements need execution inside their own infrastructure via BYOC or on-premises deployment.

### Which platforms support SSO for enterprise identity management?

Northflank supports SAML and OIDC-based SSO with automatic role assignment from identity provider groups. Modal's Enterprise plan includes Okta SSO. E2B Enterprise includes SSO.

### What is the difference between SOC 2 Type 1 and Type 2?

SOC 2 Type 1 verifies that controls are designed correctly at a point in time. SOC 2 Type 2 verifies that controls operate effectively over an extended period, typically six to twelve months. Enterprise procurement teams require Type 2 because it demonstrates sustained compliance rather than a point-in-time snapshot.

### Can I run enterprise AI sandbox workloads in an air-gapped environment?

Only Northflank explicitly supports air-gapped on-premises deployments where execution has no dependency on any public cloud or internet connectivity. E2B's self-hosted model can be configured for restricted network environments, but requires your team to operate the full runtime stack. All other platforms on this list require internet connectivity to function.

### What should I ask vendors during enterprise procurement?

Ask for their SOC 2 Type 2 report and trust center link. Ask whether certifications cover BYOC and on-premises deployments specifically. Ask what data leaves their infrastructure during normal operation. Ask how audit logs are structured and whether they can be exported to your SIEM. Ask about their incident response process and breach notification timelines. Ask whether their BYOC deployment model satisfies your data residency requirements.

## Conclusion

Enterprise AI sandbox procurement is a security and compliance decision as much as a technical one. The isolation model matters. The certification depth matters. Whether your data leaves your own infrastructure matters most of all.

Northflank is the strongest option for enterprise teams that need self-serve BYOC, managed orchestration inside their own infrastructure, and a compliance posture that covers both managed cloud and on-premises deployments. E2B covers HIPAA and BYOC on AWS & GCP for enterprises comfortable managing the runtime stack themselves. Modal and Fly.io fits enterprises where managed infrastructure is fine.

<InfoBox className="BodyStyle">

You can [get started for free on Northflank](https://app.northflank.com/signup) or [talk to the team](https://cal.com/team/northflank/northflank-demo?duration=30) to walk through your enterprise requirements.

</InfoBox>

## Related articles: enterprise sandbox infrastructure

If you want to go deeper on the topics covered in this guide, these articles are a good next step.

- [**Best BYOC sandbox platforms in 2026**](https://northflank.com/blog/best-byoc-sandbox-platforms): Covers the platforms that support running sandbox execution inside your own cloud account, with a focus on deployment model and operational responsibility.
- [**Best on-premises AI sandbox platforms in 2026**](https://northflank.com/blog/best-on-premises-ai-sandbox-platforms): Covers platforms that support execution on hardware you own, with specific attention to air-gapped deployments and who manages orchestration on your hardware.
- [**Self-hosted AI sandboxes: guide to secure code execution**](https://northflank.com/blog/self-hosted-ai-sandboxes): Covers how DIY, self-hosted, and BYOC sandbox approaches differ operationally, and what full self-hosting actually involves.
- [**Best platforms for untrusted code execution in 2026**](https://northflank.com/blog/best-platforms-for-untrusted-code-execution): Covers isolation model selection, multi-tenant design, and network controls for teams running code they do not control.]]>
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</item><item>
  <title>Best persistent sandbox platforms for AI agents (2026)</title>
  <link>https://northflank.com/blog/best-persistent-sandbox-platforms</link>
  <pubDate>2026-03-25T17:15:00.000Z</pubDate>
  <description>
    <![CDATA[A comparison of the best persistent sandbox platforms for AI agents in 2026, covering how persistence works on each, GPU support, BYOC deployment, and which platform fits your use case.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/best_persistent_sandbox_platforms_679317c0ff.png" alt="Best persistent sandbox platforms for AI agents (2026)" />*A comparison of the best persistent sandbox platforms for AI agents in 2026, covering how persistence works on each platform, GPU support, BYOC, and which use cases each fits.*

Persistent sandboxes have moved from a niche requirement to something a lot of production AI agent teams are actively evaluating. The reasons are practical: agents that build up a working environment incrementally, maintain a filesystem between executions, or run long-horizon tasks need somewhere to keep state. Ephemeral-only platforms force you to engineer around that.

The platforms covered here approach persistence differently. Some keep filesystem state via volumes. Some offer full pause/resume including memory. Some are purpose-built for sandboxing; others are full workload runtimes. This article covers what each platform actually does so you can evaluate them against your specific requirements.

If you're new to the concept, [what are persistent sandboxes](https://northflank.com/blog/persistent-sandboxes) covers the fundamentals before you get into platform comparisons.

<InfoBox className="BodyStyle">

## TL;DR: Key takeaways on the best persistent sandbox platforms

- Persistence models vary significantly across platforms: filesystem-only, full memory/process pause-resume, and snapshot-based approaches all exist and have different tradeoffs.
- The right platform depends on what your agent actually needs to persist, how long it needs to run, and where your code needs to execute.
- No single platform is best for every use case.
- [**Northflank**](https://northflank.com/) is the only platform here that supports both persistent and ephemeral environments, full workload orchestration (agents, APIs, workers, databases), bring your own cloud (BYOC) deployment across your own cloud accounts, on-premises, and bare metal infrastructure, GPU access, and SOC 2 Type 2 compliance, all self-serve. It has been in production since 2021 across startups, public companies, and government deployments.

</InfoBox>

## What are the key things to evaluate before choosing a persistent sandbox platform?

Not all persistent sandboxes work the same way under the hood, and the differences matter when you're building production agent workflows.

- **What actually persists?** Some platforms persist only the filesystem. Others persist filesystem and memory including running processes. Others use snapshots to create new environments from a saved state. These are meaningfully different capabilities.
- **Is there a session time cap?** Some platforms impose a continuous runtime limit before you have to pause. Others run indefinitely until you terminate the environment.
- **What's the isolation model?** MicroVM-based isolation (Firecracker, Kata Containers) and user-space kernel sandboxing (gVisor) offer different tradeoffs between security, performance, and compatibility.
- **Do you need GPU access?** Not all sandbox platforms support GPU workloads. If your agents run inference or training, this narrows the list.
- **Can you deploy in your own infrastructure?** If your organisation has data residency or compliance requirements, bring your own cloud (BYOC) support is a hard requirement.
- **Is it just sandboxes, or a full platform?** Some platforms only run code execution. Others let you run agents, APIs, databases, and workers alongside sandboxes without stitching together separate services.

## What are the top persistent sandbox platforms for AI agents in 2026?

The platforms below cover the main options for teams evaluating persistent sandbox infrastructure today. Each takes a different approach to persistence, isolation, and deployment.

### 1. Northflank

[Northflank](https://northflank.com/) is a full workload runtime that supports both persistent and ephemeral sandbox environments as first-class options, built for teams running production AI platforms.

**How persistence works:** Persistent environments are stateful services backed by persistent volumes. Filesystem state survives between executions. Volumes start at 4GB and scale to 64TB with multi-read-write support. You can attach S3-compatible object storage and deploy stateful databases (Redis, Postgres, MySQL, MongoDB) alongside sandboxes. No forced session time limit.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

**Key Northflank offerings**:

- **Environments:** Both persistent and ephemeral, configured per workload
- **Isolation:** MicroVM-based, including Kata Containers, Firecracker, and gVisor, depending on workload
- **BYOC:** Self-serve deployment across AWS, GCP, Azure, Oracle, Civo, CoreWeave, on-premises, and bare metal
- **GPU:** On-demand, self-service provisioning, no quota requests
- **Access:** API, CLI, and SSH
- **Compliance:** SOC 2 Type 2 certified ([security](https://northflank.com/security))
- **Spin-up:** 1–2 seconds end-to-end
- **Full workload runtime:** Run agents, APIs, workers, databases, and background jobs alongside sandboxes
- **Production track record:** In use since 2021 across startups, public companies, and government deployments
- **Pricing:** CPU at $0.01667/vCPU/hour, memory at $0.00833/GB/hour ([full GPU and compute pricing](https://northflank.com/pricing))

<InfoBox className="BodyStyle">

**Running AI agents in production?**

Northflank sandboxes support persistent and ephemeral environments, MicroVM isolation, GPU workloads, and BYOC (Bring Your Own Cloud) deployment in one platform.

- [Sandbox environments overview](https://northflank.com/product/sandboxes): how persistent and ephemeral environments work on Northflank
- [Get started](https://app.northflank.com/signup): self-serve setup
- [Pricing](https://northflank.com/pricing): CPU, memory, and GPU pricing
- [Talk to an engineer](https://cal.com/team/northflank/northflank-demo): for specific infrastructure or compliance requirements

</InfoBox>

### 2. Fly Sprites

Fly Sprites are persistent Linux microVMs built by Fly.io that hibernate when idle and wake on demand.

- **Filesystem:** Persists across hibernation (files, installed packages, git repos, and databases on disk all survive)
- **Memory:** RAM does not persist (running processes stop and in-memory data is lost when a Sprite idles)
- **Network config:** Open ports and URL settings persist across hibernation
- **Storage:** 100GB per Sprite
- **Wake time:** 100–500ms warm, 1–2 seconds cold
- **Checkpoints:** Save filesystem state only (processes stopped during creation, in-memory state not included)
- **Services:** Defined processes that auto-restart on wake, used to keep servers running across hibernation cycles
- **Isolation:** Firecracker microVMs
- **GPU:** Not supported on Sprites
- **BYOC:** Not available (runs on Fly.io's managed infrastructure)

### 3. e2b

e2b is a platform built specifically for running AI-generated code in secure sandboxes, with a pause/resume model that saves both filesystem and memory state.

- **Filesystem:** Persists on pause/resume
- **Memory:** Persists on pause/resume (running processes, loaded variables, and data are all saved)
- **Pause performance:** Approximately 4 seconds per 1 GiB of RAM to pause; approximately 1 second to resume
- **Paused sandbox retention:** Kept indefinitely, no automatic deletion
- **Continuous runtime cap:** 24 hours on Pro tier, 1 hour on Base tier (resets after each pause/resume cycle)
- **Snapshots:** Available as a separate feature. Saves filesystem and memory state and can be used to create new sandboxes from a saved state.
- **Isolation:** Firecracker microVMs
- **GPU:** Not supported in sandboxes
- **BYOC:** AWS and GCP only, enterprise customers only

### 4. Modal

Modal is a serverless infrastructure platform covering inference, training, batch compute, and sandboxes, with a snapshot-based persistence model across three maturity tiers.

- **Filesystem Snapshots:** Stable (copies filesystem at a point in time, stored as an Image, used to create new sandboxes, persist indefinitely). Restoring creates a new sandbox, not a resume of the original.
- **Directory Snapshots:** Beta (snapshots a specific directory within a running sandbox, expires after 30 days)
- **Memory Snapshots:** Alpha, not recommended for production (copies full sandbox state including memory, expires after 7 days, snapshotting terminates the sandbox, sandboxes with memory snapshots enabled cannot run with GPUs)
- **Volumes:** Network filesystems (Modal Volumes) can be mounted into sandboxes for persistent storage
- **Isolation:** gVisor
- **GPU:** Supported across the platform, but not available when memory snapshots are enabled
- **BYOC:** Not available
- **SDK model:** Python-first (environments are defined through Modal's Python library)

## How do persistent sandbox platforms compare on pricing?

Pricing as of April 2026. Billing models differ across platforms (some bill based on active CPU usage only, others bill for the entire duration the sandbox is running). Verify current rates on each platform's pricing page before making cost decisions.

| Platform | CPU | Memory | Storage | GPU | Billing model |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | $0.01667/vCPU-hr | $0.00833/GB-hr | $0.15/GB-month | L4: $0.80/hr, A100 40GB: $1.42/hr, A100 80GB: $1.76/hr, H100: $2.74/hr, H200: $3.14/hr | Per second |
| **E2B** | $0.0504/vCPU-hr | $0.0162/GiB-hr | 10–20GB included free | Do not provide GPU compute | Per second |
| **Fly.io Sprites** | $0.07/CPU-hr | $0.04375/GB-hr | $0.00068/GB-hr (hot NVMe) | Do not provide GPU compute | Per second, actual cgroup usage. No charge when idle |
| **Modal Sandboxes** | $0.1419/physical core-hr (2 vCPU) | $0.0242/GiB-hr | — | L4: $0.80/hr, A100 40GB: $2.10/hr, A100 80GB: $2.50/hr, H100: $3.95/hr, H200: $4.54/hr | Per second |

## BYOC support across persistent sandbox platforms

The table below shows how each platform handles BYOC deployment, which clouds are supported, and whether it requires a sales process.

| Platform | BYOC available | Clouds supported | Access model | Pricing model |
| --- | --- | --- | --- | --- |
| **Northflank** | Yes, fully self-serve | AWS, GCP, Azure, Oracle, CoreWeave, any neoclouds, Civo, bare-metal, on-premises | Self-serve, enterprise contracts available for larger commits (with bulk discounts) | Your existing cloud bill, CPU $0.01389/vCPU-hr and Memory $0.00139/GB-hr |
| **E2B** | Yes, limited and not self-serve | AWS and GCP only | Not publicly disclosed, need to contact sales | Starts at $50/sandbox/month, on top of your existing cloud bill |
| **Modal** | No | Managed only | — | — |
| **Fly.io Sprites** | No | Managed only | — | — |

## Which persistent sandbox platform fits your situation?

Here's a quick reference for matching your use case to the right platform.

| If your situation is... | Consider... |
| --- | --- |
| You need both persistent and ephemeral environments on one platform | Northflank |
| You need to deploy sandboxes inside your own cloud account or VPC | Northflank |
| You need GPU support in your sandbox workloads | Northflank |
| You need agents, APIs, workers, and databases running alongside sandboxes | Northflank |
| You need filesystem state to survive between executions with no session cap | Northflank or Fly Sprites |
| You need full memory and process state to survive a pause/resume | e2b |
| You need to save a filesystem snapshot and spin up new sandboxes from it | Modal (Filesystem Snapshots) |
| You want a persistent Linux environment that hibernates when idle and wakes on demand | Fly Sprites |
| Your workloads are Python-first and primarily ML/inference | Modal |

## FAQ: best persistent sandbox platforms

### Which persistent sandbox platform is best for enterprise teams?

For teams with data residency or compliance requirements, Northflank (with SOC 2 Type 2 compliance) is the only platform here that supports BYOC deployment self-serve across multiple cloud providers and on-premises infrastructure.

### Do any of these platforms support GPU workloads in persistent sandboxes?

Northflank supports GPU workloads in both persistent and ephemeral environments. Modal supports GPUs but sandboxes with memory snapshot support enabled cannot run with GPUs. Fly Sprites are CPU-only. e2b does not support GPU sandboxes.

### Can I use both persistent and ephemeral sandboxes on the same platform?

Yes on Northflank, both are supported as first-class options and you configure per workload. On the other platforms, the persistence model is more fixed to the platform's primary design.

### How long do persistent sandboxes last on each platform?

Northflank persistent environments run until you terminate them with no forced time limits. Fly Sprites idle automatically but the filesystem persists indefinitely. e2b paused sandboxes are kept indefinitely, though continuous runtime before a pause is capped. Modal Filesystem Snapshots persist indefinitely; Directory Snapshots expire after 30 days; Memory Snapshots expire after 7 days.

## Related articles on persistent sandbox platforms and sandbox infrastructure

- [What are persistent sandboxes?](https://northflank.com/blog/persistent-sandboxes): The foundational explainer on what persistent sandboxes are, how they differ from ephemeral environments, and when you need them.
- [Ephemeral sandbox environments](https://northflank.com/blog/ephemeral-sandbox-environments): Covers how ephemeral sandboxes work and when they're the right choice over persistent ones.
- [E2B vs Modal vs Fly.io Sprites](https://northflank.com/blog/e2b-vs-modal-vs-fly-io-sprites): A deeper comparison of three of the platforms covered in this article, with more detail on isolation models and use case fit.
- [E2B vs Sprites dev](https://northflank.com/blog/e2b-vs-sprites-dev): A head-to-head comparison of e2b and Fly Sprites, covering persistence approach, isolation, and use case differences.
- [Self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes): Covers the self-hosted and BYOC route for teams with compliance or data residency requirements.
- [Top AI sandbox platforms for code execution](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution): A broader overview of sandbox platforms for code execution beyond the persistence-focused platforms covered here.
- [Best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents): A platform comparison for agent builders evaluating code execution sandbox options.]]>
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  <title>Agent Sandbox on Kubernetes: how it works and how to run it in production</title>
  <link>https://northflank.com/blog/agent-sandbox-on-kubernetes</link>
  <pubDate>2026-03-24T17:15:00.000Z</pubDate>
  <description>
    <![CDATA[A guide to Agent Sandbox on Kubernetes, covering how the SIG project works, the isolation models it supports, what raw Kubernetes primitives do not provide, and how to run agent sandboxes in production.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/agent_sandbox_on_kubernetes_356a71d42d.png" alt="Agent Sandbox on Kubernetes: how it works and how to run it in production" /><InfoBox className="BodyStyle">

## TL;DR: Key takeaways on agent sandbox on Kubernetes

Agent sandbox on Kubernetes refers to a specific open-source project under Kubernetes SIG Apps (kubernetes-sigs/agent-sandbox) that provides a declarative, CRD-based API for running isolated, stateful AI agent workloads on Kubernetes.

- Agent sandbox fills a gap that raw Kubernetes primitives do not cover natively: managing long-running, stateful, singleton workloads with stable identity, lifecycle controls (pause, resume, scheduled deletion), and strong isolation for untrusted code execution.
- The project supports gVisor and Kata Containers as isolation backends, both of which provide stronger isolation than standard container namespacing.

> [Northflank](https://northflank.com/product/sandboxes) provides production-grade sandbox infrastructure backed by Firecracker, Kata Containers, and gVisor, with both ephemeral and persistent environments, self-serve BYOC across AWS, GCP, Azure, Oracle, CoreWeave, Civo, bare-metal, and on-premises infrastructure, SOC 2 Type 2 compliance, GPU support, and a full workload runtime for APIs, workers, databases, and jobs alongside sandboxes. Northflank has been running this class of workload in production since 2021.
> 

</InfoBox>

The agent sandbox project on Kubernetes formalises infrastructure patterns that platform engineers running AI workloads have been assembling manually.

This article covers how the project works, what gap it fills over raw Kubernetes primitives, and what the operational reality looks like when running agent sandboxes in production.

## What is agent sandbox on Kubernetes?

Agent sandbox is an open-source Kubernetes controller and set of CRDs developed under Kubernetes SIG Apps, hosted at kubernetes-sigs/agent-sandbox.

It provides a declarative, standardised API for managing workloads that require the characteristics of a long-running, stateful, singleton container with a stable identity, much like a lightweight, single-container VM experience built on Kubernetes primitives.

As AI applications shift from short-lived inference requests to long-running autonomous agents that maintain context, execute code, and interact with tools, mapping those workloads onto existing Kubernetes primitives requires workarounds that the Sandbox CRD is designed to replace.

## What problem does agent sandbox solve on Kubernetes?

Kubernetes excels at two workload models: stateless, replicated applications managed by Deployments, and stable, numbered sets of stateful pods managed by StatefulSets. Agent workloads fit neither model cleanly.

An AI agent runtime is typically a singleton: one isolated environment per user session or task, not a replicated pool. It needs persistent storage that survives restarts, a stable hostname and network identity, and lifecycle controls that let it be paused when idle and resumed without losing state. It also executes code that may be untrusted, which requires isolation beyond standard container namespacing.

Before the agent sandbox project, the closest approximation using raw Kubernetes primitives required combining a StatefulSet of size 1, a headless Service, and a PersistentVolumeClaim. This approach lacks specialised lifecycle management like hibernation. Instead of one resource modelling the workload, you are assembling three or more, with no built-in support for pause, resume, warm pools, or scheduled deletion.

Agent sandbox adds a consumption layer on top of Kubernetes primitives designed specifically for agent workload patterns.

*See [Kubernetes multi-tenancy](https://northflank.com/blog/kubernetes-multi-tenancy) for more on how Kubernetes handles workload isolation at scale.*

## How does agent sandbox work?

Agent sandbox follows the standard Kubernetes controller pattern. You create a Sandbox custom resource, and the controller manages the underlying runtime resources. The core CRD and its extensions are:

- **Sandbox:** The core resource. It provides a declarative API for managing a single, stateful pod with stable identity and persistent storage, including a stable hostname and network identity, persistent storage that survives restarts, and lifecycle management covering creation, scheduled deletion, pausing, and resuming.
- **SandboxTemplate:** Defines reusable templates for creating Sandboxes, making it easier to manage large numbers of similar Sandbox configurations without duplicating definitions.
- **SandboxClaim:** Allows users or higher-level frameworks to request execution environments from a template, abstracting away the provisioning details. LangChain, ADK, and similar frameworks can request a sandbox via SandboxClaim without managing the underlying Sandbox configuration directly.
- **SandboxWarmPool:** Manages a pool of pre-warmed Sandbox pods that can be quickly allocated, reducing the time it takes to get a new sandbox running. The warm pool pattern is particularly relevant for the Kata Containers remote hypervisor, where cold start latency is higher due to external VM creation. Pre-warming trades idle compute cost for reduced provisioning latency.

A minimal Sandbox looks like this (from the official agent sandbox documentation):

```yaml
apiVersion: agents.x-k8s.io/v1alpha1
kind: Sandbox
metadata:
  name: my-sandbox
spec:
  podTemplate:
    spec:
      containers:
      - name: my-container
        image: <IMAGE>
```

Once created, the sandbox is accessible via its stable hostname `my-sandbox`. The controller handles pod creation, storage binding, and lifecycle management from there.

## What isolation models does agent sandbox support?

Agent sandbox supports gVisor and Kata Containers as runtime isolation backends. Both are configured via Kubernetes `runtimeClassName`, making the project backend-agnostic by design:

- **gVisor:** Intercepts system calls in user space via its `runsc` runtime, reducing the kernel attack surface without requiring a full VM per workload. It provides kernel and network isolation suitable for multi-tenant, untrusted code execution.
- **Kata Containers:** Runs each pod inside a lightweight virtual machine, giving each workload a dedicated kernel. This provides stronger isolation at the cost of higher startup latency, which the warm pool pattern is designed to offset.

Standard container namespacing shares the host kernel across all containers on a node. A kernel-level vulnerability in any workload can affect the host and other tenants. Both gVisor and Kata Containers address this by interposing between the workload and the host kernel, limiting the impact of a kernel-level exploit to that workload.

*For a detailed technical comparison of the isolation technologies the project supports, see [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor).*

## What does agent sandbox not handle on its own?

Agent sandbox provides the isolation primitive and the declarative API, but not the surrounding production infrastructure your platform needs.

You install it onto an existing Kubernetes cluster, which means provisioning and managing the underlying cluster infrastructure is outside its scope. The project includes configuration options for API QPS and worker counts for teams running it at scale.

For teams that need sandbox infrastructure without that operational overhead, the options are either a managed sandbox provider or a Bring Your Own Cloud (BYOC) platform that handles orchestration while deploying into your own infrastructure.

*See [self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes) for a breakdown of deployment models and tradeoffs.*

## What does Northflank provide for running agent sandboxes in production?

[Northflank](https://northflank.com/product/sandboxes) provides production-grade sandbox infrastructure backed by Firecracker, Kata Containers, and gVisor, with orchestration, multi-tenant isolation, autoscaling, and bin-packing handled at the infrastructure level.

Sandboxes run on [Northflank's managed cloud](https://northflank.com/features/managed-cloud) or inside your own infrastructure via the [Bring Your Own Cloud (BYOC)](https://northflank.com/product/bring-your-own-cloud) deployment model, across AWS EKS, GKE, AKS, Oracle Kubernetes, CoreWeave, Civo, bare-metal, and on-premises distributions including OpenShift and RKE2. For teams deploying inside a customer VPC, see [customer VPC deployments](https://northflank.com/product/customer-vpc-deployments). BYOC is available self-serve.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

Key capabilities include:

- **Isolation:** Firecracker, Kata Containers, and gVisor applied depending on the workload. End-to-end sandbox creation runs at 1-2 seconds, covering the full stack.
- **Ephemeral and persistent environments:** Both modes supported with no forced time limits. Persistent volumes, S3-compatible object storage, and stateful databases run alongside sandboxes in the same control plane.
- **Full workload runtime:** APIs, workers, GPU workloads, and databases run in the same platform as sandboxes, so teams do not need a separate system as requirements grow beyond code execution.
- **GPU support:** NVIDIA H100, A100, L4, and others on demand.
- **Compliance:** [SOC 2 Type 2 certified](https://northflank.com/security), with BYOC deployment for data residency and regulated industries.

Northflank has been running this class of workload in production since 2021 across startups, public companies, government deployments, and regulated industries. [cto.new](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) runs thousands of daily code executions on Northflank's sandbox infrastructure and scaled to 30,000+ users without infrastructure changes.

CPU is priced at $0.01667/vCPU-hour and memory at $0.00833/GB-hour. See the full GPU and compute [pricing](https://northflank.com/pricing).

<InfoBox className="BodyStyle">

For a hands-on walkthrough of spinning up a secure sandbox and microVM on Northflank, see this [step-by-step guide](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh).

[Get started on Northflank](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) with the engineering team to discuss your requirements.

</InfoBox>

## FAQ: Common questions about agent sandbox on Kubernetes

The questions below cover what engineering teams most commonly ask when evaluating agent sandbox on Kubernetes.

### What is agent sandbox on Kubernetes?

Agent sandbox on Kubernetes is an open-source Kubernetes controller and set of CRDs developed under SIG Apps (kubernetes-sigs/agent-sandbox). It provides a declarative API for managing isolated, stateful, singleton workloads on Kubernetes, with built-in support for lifecycle management, stable identity, persistent storage, and isolation runtimes like gVisor and Kata Containers.

### What Kubernetes resources does agent sandbox replace?

Agent sandbox replaces the manual combination of a StatefulSet of size 1, a headless Service, and a PersistentVolumeClaim that engineers currently use to approximate singleton stateful workloads. The Sandbox CRD models this pattern as a single resource with specialised lifecycle controls that the raw primitives do not provide.

### What isolation technology does agent sandbox use?

The project supports gVisor and Kata Containers as isolation backends, configured via `runtimeClassName`. Both provide stronger isolation than standard container namespacing by interposing between the workload and the host kernel. The project is designed to be backend-agnostic.

### What is the warm pool pattern in agent sandbox?

The SandboxWarmPool CRD manages a pool of pre-warmed Sandbox pods. When a new sandbox is requested, the controller claims a pod from the warm pool rather than creating one from scratch, reducing startup latency. This is particularly useful for Kata Containers workloads where VM creation adds cold start overhead.

### What is the difference between agent sandbox and a sandbox infrastructure provider?

Agent sandbox is a Kubernetes controller you install and operate on your own cluster. It provides the isolation primitive and API but not the surrounding production infrastructure. A sandbox infrastructure provider handles infrastructure operations, autoscaling, multi-tenancy, and platform-level concerns. Platforms like Northflank run the same isolation technologies (Kata Containers, gVisor, and Firecracker) on either a managed cloud or inside your own infrastructure via the Bring Your Own Cloud (BYOC) deployment model, so you get the isolation model without the operational overhead of running the controller yourself.

### Can agent sandbox run on any Kubernetes cluster?

Yes. The project is Kubernetes-native and installs via `kubectl apply`. It runs on any conformant Kubernetes cluster. The GKE documentation covers a specific GKE implementation with managed gVisor and Pod Snapshots, but the core project itself is not GKE-specific.

## Related articles on agent sandbox on Kubernetes

The articles below go deeper on specific aspects of the infrastructure covered in this guide.

- [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): A technical comparison of the isolation technologies the agent sandbox project supports as backends.
- [Kubernetes multi-tenancy](https://northflank.com/blog/kubernetes-multi-tenancy): Covers how Kubernetes handles workload isolation across tenants and where agent sandbox fits in that model.
- [Self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes): Covers the three deployment models for running sandbox infrastructure in your own infrastructure and how to evaluate them.
- [How to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents): A practical guide to sandboxing agents, covering architecture patterns and isolation requirements.
- [Top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes): A comparison of sandbox providers that support deployment inside your own cloud infrastructure.
- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox): A detailed explainer on AI sandbox infrastructure and how isolation models differ.]]>
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</item><item>
  <title>Sandbox providers: types, categories, and top platforms in 2026</title>
  <link>https://northflank.com/blog/sandbox-providers</link>
  <pubDate>2026-03-23T17:30:00.000Z</pubDate>
  <description>
    <![CDATA[A guide to the main types of sandbox providers in 2026, covering AI code execution, network security, and developer environments, with a comparison of top platforms and what to evaluate before choosing one.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/sandbox_providers_2c1933099c.png" alt="Sandbox providers: types, categories, and top platforms in 2026" /><InfoBox className="BodyStyle">

## TL;DR: Key takeaways on sandbox providers

Sandbox providers cover several distinct categories, and the right one depends entirely on what you are building and what your infrastructure requirements are.

- Sandbox providers range from managed cloud services and self-hosted runtimes for AI code execution to browser-based developer tools and malware analysis environments. Knowing which category you need narrows the field immediately.
- For AI code execution, the critical evaluation criteria are isolation model, session lifecycle (ephemeral vs persistent), BYOC (Bring Your Own Cloud) support, compliance coverage, and whether the platform covers your full workload runtime or just code execution.

> [Northflank](https://northflank.com/product/sandboxes) provides secure sandbox infrastructure backed by microVM isolation (Kata Containers, Firecracker and gVisor, applied per workload), support for both ephemeral and persistent environments with no forced time limits, self-serve BYOC across AWS, GCP, Azure, Oracle, CoreWeave, and bare-metal, SOC 2 Type 2 compliance, GPU support, and a full workload runtime for APIs, workers, databases, and jobs alongside sandboxes.
> 

</InfoBox>

Sandbox providers span several distinct categories. Depending on your use case, you could be looking for isolated runtimes for AI-generated code, malware analysis environments, or browser-based developer tools.

This guide maps the main categories, explains what distinguishes each one, and goes deep on AI code execution sandbox providers, the category most relevant to engineering teams building AI products, multi-tenant platforms, and agent infrastructure.

## What is a sandbox provider?

A sandbox provider is a vendor or platform that delivers isolated execution environments, either as managed infrastructure or a self-hosted runtime. The core function of any sandbox is containment: workloads inside the sandbox cannot affect what is outside it.

The isolation technology determines how strong that containment is. At one end, standard Linux containers share the host kernel and rely on namespace separation. 

At the other end, microVMs (such as Firecracker and those managed by Kata Containers) give each workload a dedicated kernel, which limits the impact of a kernel-level exploit to that workload.

The type of provider you need depends on which threat model you are protecting against and what your architecture looks like.

*For a detailed breakdown of isolation models and lifecycle patterns, see [what is a sandbox environment?](https://northflank.com/blog/what-is-a-sandbox-environment)*

## What are the main types of sandbox providers?

The main categories of sandbox providers differ in isolation model, use case, and the type of team evaluating them:

### AI code execution sandbox providers

These providers deliver isolated runtimes for executing code generated by LLMs and AI agents. The defining characteristics are fast environment creation, strong workload isolation, and APIs or SDKs that fit into agent orchestration flows.

Use cases include:

- AI coding assistants running generated code
- Multi-tenant SaaS platforms where each customer executes custom logic
- [Reinforcement learning pipelines](https://northflank.com/blog/reinforcement-learning-agents-in-secure-sandboxes) running parallel code evaluations
- Any product where untrusted code executes on shared infrastructure

*See [what is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox) for a deeper look at this category.*

### Network and security sandbox providers

These providers deliver isolated environments for detonating and analyzing potentially malicious files, URLs, and executables. The defining characteristics are deep inspection capabilities, behavioral analysis, and integration with threat intelligence workflows. Use cases include malware detonation, URL and file reputation scoring, threat detection, and SOC automation pipelines.

### Developer environment sandbox providers

These providers deliver browser-based IDEs, pull request preview environments, and cloud development containers. The use cases are frontend prototyping, collaborative development, and ephemeral environments tied to a code review workflow. For alternatives in this space, see [CodeSandbox alternatives](https://northflank.com/blog/codesandbox-alternatives).

## What should you look for when evaluating an AI code execution sandbox provider?

When you are choosing between sandbox providers for AI workloads, there are a few questions worth working through before you commit to a platform:

- **What isolation model does the provider use?** Containers, gVisor, and microVMs offer meaningfully different security guarantees. For truly untrusted code, microVM-level isolation is the current standard. See [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor) for a technical breakdown.
- **Does it support both ephemeral and persistent environments?** Ephemeral sandboxes handle stateless, short-lived execution. Persistent environments are needed when an agent or user session must survive across multiple interactions or days. Many providers support only one or the other.
- **What does "cold start" actually measure?** Check whether the figure covers only the microVM start step or the full environment readiness, including network attachment, filesystem mount, and process initialization.
- **Can the provider deploy inside your own infrastructure?** Regulated industries and enterprise AI teams often have requirements that prevent execution workloads from leaving their own infrastructure. The BYOC (Bring Your Own Cloud) deployment model keeps the execution plane inside your VPC. See [self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes) and [top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes) for more on deployment models.
- **Is the provider compliant?** If you are building for regulated industries or selling into enterprises, compliance certifications like SOC 2 Type 2 are often a hard requirement at the procurement stage. Check what the provider is certified for and whether it covers your specific requirements.
- **What is the platform scope?** Sandbox-only products require you to manage separate infrastructure for databases, APIs, GPU workloads, and background jobs. If sandboxes are core to your product architecture, you will likely need more than just isolated code execution as your requirements grow.

## What are the main AI code execution sandbox providers?

The platforms below are the most commonly evaluated options for AI agent infrastructure and production code execution.

- **Northflank:** A workload platform with microVM-based sandbox infrastructure (Firecracker, Kata Containers, and gVisor, applied per workload), support for both ephemeral and persistent environments with no forced time limits, self-serve BYOC (Bring Your Own Cloud) across AWS, GCP, Azure, Oracle, CoreWeave, and bare-metal, SOC 2 Type 2 compliance, and GPU support alongside APIs, workers, and databases in the same control plane.
- **E2B:** An API-driven sandbox platform for AI agent developers, with Python and JavaScript SDKs. Supports sandbox persistence through snapshots and AutoResume.
- **Modal:** A serverless compute platform with gVisor-based sandbox isolation and Python, JavaScript, and Go SDKs. Sandbox timeouts are configurable up to 24 hours, with snapshot-based state preservation for longer workflows.
- **Vercel Sandbox:** A Firecracker microVM-based sandbox product for running untrusted code. Supports Node.js and Python runtimes, snapshotting, and a TypeScript SDK.
- **Together Code Sandbox:** A sandbox product built on CodeSandbox SDK infrastructure, using Firecracker VMs with memory snapshot and restore support.

### How do sandbox providers compare on pricing?
_Pricing as of April 2026. Billing models differ across platforms (some bill based on active CPU usage only, others bill for the entire duration the sandbox is running). Verify current rates on each platform's pricing page before making cost decisions._

| Platform | CPU | Memory | Storage | GPU | Billing model |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | $0.01667/vCPU-hr | $0.00833/GB-hr | $0.15/GB-month | L4: $0.80/hr, A100 40GB: $1.42/hr, A100 80GB: $1.76/hr, H100: $2.74/hr, H200: $3.14/hr | Per second |
| **E2B** | $0.0504/vCPU-hr | $0.0162/GiB-hr | 10–20GB included free | Do not provide GPU compute | Per second |
| **Modal Sandboxes** | $0.1419/physical core-hr (2 vCPU) | $0.0242/GiB-hr | — | L4: $0.80/hr, A100 40GB: $2.10/hr, A100 80GB: $2.50/hr, H100: $3.95/hr, H200: $4.54/hr | Per second |
| **Vercel Sandbox** | $0.128/vCPU-hr | $0.0212/GB-hr | $0.023/GB-month (snapshots) | Do not provide GPU compute | Active CPU only |

*For a full ranked breakdown with pricing, isolation details, and session lifecycle comparisons, see [top AI sandbox platforms for code execution](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution).*

### BYOC support across sandbox providers
The table below shows how each platform handles BYOC deployment, which clouds are supported, and whether it requires a sales process.
| Platform | BYOC available | Clouds supported | Access model | Pricing model |
| --- | --- | --- | --- | --- |
| **Northflank** | Yes, fully self-serve | AWS, GCP, Azure, Oracle, CoreWeave, any neoclouds, Civo, bare-metal, on-premises | Self-serve, enterprise contracts available for larger commits (with bulk discounts) | Your existing cloud bill, CPU $0.01389/vCPU-hr and Memory $0.00139/GB-hr* |
| **E2B** | Yes, limited and not self-serve | AWS and GCP only | Not publicly disclosed, need to contact sales | Starts at $50/sandbox/month, on top of your existing cloud bill |
| **Modal** | No | Managed only | — | — |
| **Vercel Sandbox** | No | Managed only (iad1 region only) | — | — |

## What Northflank provides as a sandbox provider

[Northflank](https://northflank.com/product/sandboxes) is a workload platform that includes secure sandbox infrastructure as a first-class product. Sandboxes run on Northflank's managed cloud or inside your own VPC, with BYOC (Bring Your Own Cloud) available self-serve across AWS, GCP, Azure, Oracle Cloud, CoreWeave, Civo, bare-metal, and on-premises infrastructure.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

Key capabilities include:

- **Isolation:** Firecracker, Kata Containers, and gVisor applied per workload at the infrastructure level, with orchestration, multi-tenant isolation, autoscaling, and bin-packing handled by the platform. End-to-end sandbox creation runs at 1-2 seconds, covering the full stack.
- **Ephemeral and persistent environments:** Run sandboxes ephemerally for stateless jobs or make them persistent with no forced time limits. Persistent volumes, S3-compatible object storage, and stateful databases (Postgres, Redis, MySQL, MongoDB) run alongside sandboxes in the same control plane.
- **Full workload runtime:** APIs, workers, GPU workloads, and databases run in the same platform as sandboxes, so teams do not need to manage separate vendors as requirements grow.
- **GPU support:** NVIDIA H100, A100, L4, and others. H100 is priced at $2.74/hour. See full GPU and compute [pricing](https://northflank.com/pricing).
- **Compliance:** [SOC 2 Type 2 certified](https://northflank.com/security), with BYOC deployment for data residency and regulated industries.

> Northflank has been running microVM workloads in production since 2021 across startups, public companies, government deployments, and regulated industries. cto.new runs [thousands of daily code executions](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) on Northflank's sandbox infrastructure and scaled to 30,000+ users without infrastructure changes.
> 

CPU is priced at $0.01667/vCPU-hour and memory at $0.00833/GB-hour. See the full GPU and compute [pricing](https://northflank.com/pricing).

### Cost comparison at scale
To make the pricing difference concrete, here is what 200 sandboxes costs across providers under the same conditions.

*Based on 200 sandboxes, plan: nf-compute-100-4, infra node: m7i.2xlarge*

| Model | Provider | Cloud | Sandbox vendor | Total |
| --- | --- | --- | --- | --- |
| PaaS | Northflank | — | $7,200.00 | $7,200.00 |
| PaaS | E2B | — | $16,819.20 | $16,819.20 |
| PaaS | Modal | — | $24,491.50 | $24,491.50 |
| PaaS | Vercel Sandbox | — | $31,068.80 | $31,068.80 |
| BYOC (0.2 overcommit)* | Northflank | $1,500.00 | $560.00 | $2,060.00 |
| BYOC | E2B | $1,500.00 | $10,000.00 | $11,500.00 |

*Through Northflank's plans on BYOC, there's a default overcommit, which allows a customer to spawn more services and sandboxes on the same amount of compute. A request modifier of 0.2 means each sandbox only requests 20% of its plan's resources as a guaranteed minimum, but can burst up to the full plan limit if there's available capacity on the node. So instead of fitting 8 sandboxes per node, you could fit 40 on the same hardware, reducing both infrastructure cost and the Northflank management fee.

<InfoBox className="BodyStyle">

For a hands-on walkthrough of spinning up a secure sandbox and microVM on Northflank, see this [step-by-step guide](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh). [Get started on Northflank](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) with the engineering team to discuss your requirements.

</InfoBox>

## FAQ: Common questions about sandbox providers

The questions below cover what engineering teams most commonly ask when evaluating sandbox providers.

### What are the main types of sandbox providers?

The main categories are AI code execution sandbox providers, network and security sandbox providers, and developer environment sandbox providers. Each serves a different use case and a different set of technical requirements.

### What is the difference between a managed sandbox provider and a BYOC sandbox provider?

A managed sandbox provider runs all execution infrastructure in the vendor's cloud. A BYOC (Bring Your Own Cloud) sandbox provider keeps the execution plane inside your own cloud account or VPC while the vendor manages orchestration. BYOC is relevant when workloads must meet data residency requirements, access private services, or stay within a regulated network boundary. Platforms like Northflank support both deployment models, with BYOC available self-serve across multiple cloud providers and on-premises infrastructure.

### What are the best sandbox providers for AI code execution?

The most commonly evaluated platforms are Northflank, E2B, Modal, Vercel Sandbox, and Together Code Sandbox. The right choice depends on your isolation requirements, session lifecycle needs, deployment model, and whether you need the sandbox to sit alongside broader workload infrastructure.

### What kind of company is a sandbox provider?

In the software infrastructure category, sandbox providers are typically developer infrastructure companies offering compute platforms, serverless runtimes, or application platforms. They range from narrow, sandbox-specific products to full workload platforms where sandboxes are one capability among many.

### Do sandbox providers support GPU workloads?

Some do. Northflank, for instance, supports NVIDIA GPU workloads (H100, A100, L4, and others) within the same platform as sandbox execution. Not all sandbox-focused providers include GPU support, so verify this against your requirements before evaluating.

### How does compliance affect sandbox provider selection?

If you are building for regulated industries or selling into enterprises, compliance certifications are often a hard requirement at the procurement stage. This can include SOC 2 Type 2, ISO 27001, HIPAA, or GDPR depending on your industry and region. For instance, Northflank is [SOC 2 Type 2 certified](https://northflank.com/security). Verify compliance status and which certifications apply to your specific requirements directly with any provider you are evaluating.

## Related articles on sandbox providers

The articles below go deeper on specific aspects of sandbox infrastructure covered in this guide.

- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox): A detailed explainer on what AI sandboxes are, why they are needed, and how isolation models differ.
- [Top AI sandbox platforms for code execution](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution): A full ranked comparison of AI sandbox platforms with pricing, isolation, and session lifecycle breakdowns.
- [How to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents): A practical guide to sandboxing agents, covering architecture patterns and isolation requirements.
- [Top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes): A comparison of sandbox providers that support deployment inside your own cloud infrastructure.
- [Self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes): Covers the three deployment models for self-hosted sandbox infrastructure and how to evaluate them.
- [Ephemeral sandbox environments](https://northflank.com/blog/ephemeral-sandbox-environments): Explains ephemeral execution patterns and when they are and are not the right fit.
- [Persistent sandboxes](https://northflank.com/blog/persistent-sandboxes): Covers when and how to use persistent sandbox environments for stateful workloads.]]>
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</item><item>
  <title>Best platforms for untrusted code execution in 2026</title>
  <link>https://northflank.com/blog/best-platforms-for-untrusted-code-execution</link>
  <pubDate>2026-03-23T10:00:00.000Z</pubDate>
  <description>
    <![CDATA[Best platforms for untrusted code execution in 2026: compare Northflank, E2B, Modal, and Fly.io Sprites on isolation model, multi-tenant security, network controls, and pricing.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/spectro_cloud_alternatives_4_1644e3adb8.png" alt="Best platforms for untrusted code execution in 2026" /><InfoBox className="BodyStyle">

## TL;DR: What are the best platforms for untrusted code execution in 2026?

Running untrusted code means executing logic you did not write, did not review, and cannot fully predict. AI-generated code, user-submitted scripts, and LLM tool calls all fall into this category. The platform you use determines whether a bad run stays contained or becomes a security incident.

- **Northflank** – The broadest isolation lineup available: Kata Containers with Cloud Hypervisor, Firecracker, and gVisor applied per workload. Full-stack platform with BYOC, unlimited sessions, databases, and GPUs alongside sandboxes.
- **E2B** – Purpose-built for AI agent code execution with Firecracker microVM isolation and clean Python and TypeScript SDKs. Strong isolation, 24-hour session cap on Pro.
- **Modal** – gVisor isolation with massive autoscaling to 20,000 concurrent containers. Python-first, no BYOC, the right call for ML-heavy workloads.
- **Fly.io Sprites** – Persistent Firecracker microVMs with 100GB NVMe storage and idle billing. Built for long-running agent environments, not high-volume ephemeral execution.

</InfoBox>

## Why isolation is the central question for untrusted code execution

Not all code is equally risky to run. The code your engineers wrote is reviewed and trusted. Code a user submits, or an AI agent generates at runtime, is none of those things. It could access files it should not, make unauthorised network requests, consume unbounded resources, or exploit a kernel vulnerability to escape the execution environment entirely.

Standard containers share the host kernel, meaning a vulnerability inside one can affect the host and every other tenant. MicroVMs like Firecracker and Kata Containers give each workload its own dedicated kernel. gVisor intercepts system calls in user space without the full VM overhead. The platform you choose here is a security decision as much as an infrastructure one.

## What should you look for in a platform for untrusted code execution?

These are the dimensions that matter most when running code you do not control.

- **Isolation model.** Containers, gVisor, and microVMs provide different levels of protection. For genuinely untrusted code, microVM isolation with a dedicated kernel per workload is the right default. Container isolation is insufficient when the threat model includes kernel exploits.
- **Multi-tenant design.** If multiple users or agents are running code on shared infrastructure, tenant isolation must be enforced by default. Verify that workloads from different tenants cannot share resources, kernel state, or filesystem access.
- **Network controls.** Untrusted code should not be able to make arbitrary outbound network requests. Look for platforms that support default-deny egress policies, outbound firewall rules, and the ability to whitelist specific endpoints.
- **Resource limits.** Runaway code can consume CPU, memory, and disk. Per-sandbox resource caps prevent a single bad run from affecting other workloads or running up an unexpected bill.
- **Lifecycle controls.** Ephemeral environments that are destroyed after each run prevent state accumulation between executions. Persistent environments introduce the risk of one run contaminating the next.
- **Observability.** Logs, metrics, and audit trails matter when something goes wrong. You need to know what the code did, what resources it accessed, and what network requests it made.

## What are the best platforms for untrusted code execution?

### 1. Northflank

Northflank is a full-stack cloud platform with native support for untrusted code execution, accessible via UI, API, CLI, and GitOps. You define your sandbox environment once, specifying isolation model, storage, secrets, and lifecycle rules, then provision it from a CLI command, an API call, a Git trigger, or directly from an agent pipeline.

What separates Northflank for untrusted code specifically is the isolation choice: Kata Containers with Cloud Hypervisor, Firecracker, or gVisor, applied per workload based on your threat model. No other sandbox platform offers this breadth. Northflank's engineering team contributes to Kata Containers, QEMU, and Cloud Hypervisor upstream, so the isolation layer is actively maintained rather than bolted on.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

Beyond isolation, Northflank is the only option here where sandboxes run alongside databases, APIs, background workers, and GPU workloads in the same control plane. Sessions run indefinitely with no platform-imposed cutoff. Any OCI-compliant image works without modification. BYOC deployment keeps execution inside your own AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal infrastructure, self-serve, no enterprise sales required.

[cto.new migrated their entire sandbox infrastructure to Northflank](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) in two days after EC2 metal instances made scaling costs unpredictable, going from unworkable provisioning to thousands of daily deployments with linear, per-second billing.

**Key features:**

- **Isolation options:** Kata Containers with Cloud Hypervisor, Firecracker, and gVisor applied per workload. Every sandbox runs in its own microVM with true multi-tenant isolation.
- **Any OCI image:** Accepts any container from Docker Hub, GitHub Container Registry, or private registries without modification. No SDK-defined image constraints.
- **No session limits:** Sandboxes run for seconds or weeks with no platform-imposed cutoff. Ephemeral and persistent environments in the same control plane.
- **Network controls:** Granular egress policies and per-sandbox resource limits to constrain what untrusted code can access and consume.
- **Full-stack scope:** Run databases, persistent volumes, background jobs, and GPU workloads alongside your sandboxes.
- **Managed or BYOC:** Run on Northflank’s managed cloud or self-serve deployment into your own AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal.
- **SOC 2 Type 2 certified:** Relevant for regulated industries running untrusted code at scale.

**Best for:** Production multi-tenant platforms running untrusted or AI-generated code, teams that need isolation flexibility across Kata Containers, Firecracker, and gVisor, and anyone who needs a full infrastructure stack alongside their sandboxes.

**Pricing:** $0.01667/vCPU-hour, $0.00833/GB-hour, H100 GPU at $2.74/hour all-inclusive. BYOC deployments bill against your own cloud account.

<InfoBox className="BodyStyle">

[Get started on Northflank](https://app.northflank.com/signup) (self-serve, no demo required). Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) with an engineer to walk through your isolation requirements.

</InfoBox>

### 2. E2B

E2B is purpose-built for AI agent code execution, and Firecracker microVM isolation is the default. Each sandbox runs in a dedicated lightweight VM with its own kernel, providing hardware-level separation between untrusted code and the host. Boot times sit around 150ms and the Python and TypeScript SDKs integrate cleanly with LangChain, OpenAI, and Anthropic tooling.

The 24-hour session cap on Pro and one hour on Base limits E2B for longer-running workloads, but for the majority of untrusted code execution patterns, where each run is short and self-contained, E2B covers the isolation requirement well. BYOC is available but limited to AWS enterprise customers only.

**Best for:** AI coding agents, Code Interpreter-style tools, and teams that need fast Firecracker microVM isolation with a clean SDK and do not need sessions beyond 24 hours.

**Pricing:** Hobby free with $100 one-time credit and 20 concurrent sandboxes. Pro at $150/month with 100 concurrent sandboxes and 24-hour sessions.

### 3. Modal

Modal uses gVisor for sandbox isolation. gVisor intercepts system calls in user space, reducing direct interaction with the host kernel without the full overhead of running a separate VM per workload. It is not as strong as Firecracker or Kata Containers for untrusted code, but it is significantly stronger than standard container isolation and is sufficient for many production workloads.

Where Modal earns its place in this list is scale. It handles 20,000 concurrent containers with sub-second cold starts, and companies like Lovable and Quora run millions of executions through it. Environments are defined dynamically through Modal's Python SDK at runtime, which suits agent workloads that need to assemble execution environments programmatically. No BYOC option.

**Best for:** Python-first teams running high-volume untrusted code execution alongside ML inference or data pipelines, where gVisor-level isolation is sufficient for the threat model.

**Pricing:** Starter is free with $30/month in credits and 100 concurrent containers. Team at $250/month with 1,000 containers. Sandbox CPU at $0.1419/core/hr.

### 4. Fly.io Sprites

Sprites runs on Firecracker microVMs with 100GB persistent NVMe storage per sandbox and checkpoint/restore in around 300ms. The Firecracker isolation provides the same hardware-level kernel separation as E2B, which makes it a legitimate option for untrusted code where strong isolation is required. The idle billing model stops charging when environments are not in use, preserving state indefinitely.

Sprites is better suited to persistent, long-running agent environments than to high-volume ephemeral untrusted code execution. Sandbox creation takes one to twelve seconds, which is too slow for use cases that need to spin up many sandboxes quickly. There is no BYOC option, and the platform is early-stage compared to the others here.

**Best for:** Untrusted code workloads that need strong Firecracker isolation and persistent state between sessions, particularly for individual developers or teams already on Fly.io.

**Pricing:** $0.07/CPU-hour and $0.04375/GB-hour, no charge when idle.

## Which platform should you choose for untrusted code execution?

The isolation model is the deciding factor. If you are running code from external users, AI agents, or any source you do not fully trust, microVM isolation with a dedicated kernel per workload is the right default. Containers are not sufficient.

Northflank gives you the most flexibility with Kata Containers, Firecracker, and gVisor selectable per workload, alongside the full infrastructure stack. E2B gives you Firecracker by default with the cleanest SDK experience. Fly.io Sprites gives you Firecracker with persistent state. Modal gives you gVisor with exceptional scale.

| Platform | Isolation | Default for untrusted code | BYOC | Session limit |
| --- | --- | --- | --- | --- |
| **Northflank** | Kata Containers, Firecracker, gVisor | Strong (microVM) | Yes, self-serve | Unlimited |
| **E2B** | Firecracker | Strong (microVM) | AWS and GCP only, enterprise | 24 hours |
| **Modal** | gVisor | Moderate (user-space kernel) | No | None |
| **Fly.io Sprites** | Firecracker | Strong (microVM) | No | None |

### How do platforms for untrusted code execution compare on pricing?

Pricing as of April 2026. Billing models differ across platforms (some bill based on active CPU usage only, others bill for the entire duration the sandbox is running). Verify current rates on each platform's pricing page before making cost decisions.

| Platform | CPU | Memory | Storage | GPU | Billing model |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | $0.01667/vCPU-hr | $0.00833/GB-hr | $0.15/GB-month | L4: $0.80/hr, A100 40GB: $1.42/hr, A100 80GB: $1.76/hr, H100: $2.74/hr, H200: $3.14/hr | Per second |
| **E2B** | $0.0504/vCPU-hr | $0.0162/GiB-hr | 10–20GB included free | Do not provide GPU compute | Per second |
| **Fly.io Sprites** | $0.07/CPU-hr | $0.04375/GB-hr | $0.00068/GB-hr (hot NVMe) | Do not provide GPU compute | Per second, actual cgroup usage. No charge when idle |
| **Modal Sandboxes** | $0.1419/physical core-hr (2 vCPU) | $0.0242/GiB-hr | — | L4: $0.80/hr, A100 40GB: $2.10/hr, A100 80GB: $2.50/hr, H100: $3.95/hr, H200: $4.54/hr | Per second |

### BYOC support across untrusted code execution platforms

The table below shows how each platform handles BYOC deployment, which clouds are supported, and whether it requires a sales process.

| Platform | BYOC available | Clouds supported | Access model | Pricing model |
| --- | --- | --- | --- | --- |
| **Northflank** | Yes, fully self-serve | AWS, GCP, Azure, Oracle, CoreWeave, any neoclouds, Civo, bare-metal, on-premises | Self-serve, enterprise contracts available for larger commits (with bulk discounts) | Your existing cloud bill, CPU $0.01389/vCPU-hr and Memory $0.00139/GB-hr |
| **E2B** | Yes, limited and not self-serve | AWS and GCP only | Not publicly disclosed, need to contact sales | Starts at $50/sandbox/month, on top of your existing cloud bill |
| **Modal** | No | Managed only | — | — |
| **Fly.io Sprites** | No | Managed only | — | — |

## FAQ: untrusted code execution platforms

### What makes code untrusted?

Code is untrusted when you did not write it and cannot fully predict or audit what it will do at runtime. This includes user-submitted scripts, AI-generated code, LLM tool calls, and code from third-party plugins or integrations. Running untrusted code requires isolation strong enough to contain misbehaviour, whether intentional or accidental.

### Why are containers not enough for untrusted code execution?

Containers share the host kernel using Linux namespaces and cgroups. A kernel vulnerability inside a container can allow an attacker to escape to the host and affect other tenants. MicroVMs give each workload a dedicated kernel, so a compromise inside the sandbox cannot reach the host kernel. For code you do not fully trust, that hardware boundary is the difference between a contained incident and a serious breach.

### What is the difference between Firecracker and gVisor for untrusted code?

Firecracker runs each workload inside a lightweight VM with its own kernel, providing hardware-level isolation. gVisor intercepts system calls in user space and reimplements a subset of Linux kernel behaviour, reducing direct interaction with the host kernel without the overhead of a full VM. Firecracker provides stronger isolation; gVisor provides a lighter-weight middle ground between containers and full microVMs.

### Can I run untrusted code in a multi-tenant system safely?

Yes, but only with the right isolation model. For multi-tenant untrusted code execution, you need microVM isolation so that one tenant's workload cannot affect another's kernel state or filesystem. You also need per-sandbox resource limits, network egress controls, and ephemeral environments that are destroyed after each run. Northflank, E2B, and Fly.io Sprites all provide microVM isolation by default for multi-tenant workloads.

### What network controls should I apply to untrusted code?

At a minimum, apply a default-deny egress policy so sandboxed code cannot make arbitrary outbound requests. Whitelist only the specific endpoints the code needs to reach. Disable inbound connections unless required. Some platforms, like Northflank and Modal, expose granular network controls directly. Others require you to configure networking at the infrastructure level.

### How do I prevent untrusted code from consuming unbounded resources?

Apply per-sandbox CPU, memory, and disk limits. Most sandbox platforms expose these controls at the API or configuration level. Set a maximum execution time to prevent infinite loops from running indefinitely. Northflank supports autoscaling with configurable resource thresholds and per-workload cost tracking.

## Conclusion

Untrusted code execution is one of the few infrastructure decisions where getting the security model wrong has immediate and serious consequences. The isolation model determines whether a bad run stays contained inside a sandbox or escapes to affect your host, your other tenants, or your production systems. For production multi-tenant platforms running genuinely untrusted code, microVM isolation is the minimum bar. Containers are not. 

Northflank is the strongest option for teams that need isolation flexibility, a full infrastructure stack, and no concurrency caps. E2B is the right call for teams that want Firecracker out of the box with a clean SDK. Fly.io Sprites suits long-running agent environments where persistent state matters. Modal covers gVisor at a scale few platforms can match.

<InfoBox className="BodyStyle">

You can [get started for free on Northflank](https://app.northflank.com/signup) or [talk to the team](https://cal.com/team/northflank/northflank-demo?duration=30) to walk through your untrusted code execution requirements.

</InfoBox>

## Related articles: isolation and secure code execution

If you want to go deeper on the topics covered in this guide, these articles are a good next step.

- [**How to sandbox AI agents: microVMs, gVisor, and isolation strategies**](https://northflank.com/blog/how-to-sandbox-ai-agents): Technical deep-dive into isolation technologies and how to choose between Firecracker, Kata Containers, and gVisor based on your threat model.
- [**Self-hosted AI sandboxes: guide to secure code execution**](https://northflank.com/blog/self-hosted-ai-sandboxes): Covers deployment models for teams that need execution inside their own infrastructure.
- [**Remote code execution sandbox: secure isolation at scale**](https://northflank.com/blog/remote-code-execution-sandbox): Architecture guide covering isolation models, security controls, and what production-grade untrusted code execution actually requires.]]>
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</item><item>
  <title>What is Alibaba OpenSandbox? Architecture, use cases, and how it works</title>
  <link>https://northflank.com/blog/alibaba-opensandbox-architecture-use-cases</link>
  <pubDate>2026-03-20T17:15:00.000Z</pubDate>
  <description>
    <![CDATA[Alibaba OpenSandbox is an open-source AI agent sandbox. Learn its architecture, use cases, and what teams need to run sandbox infrastructure at scale.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/alibaba_opensandbox_architecture_use_cases_5a0a6da40a.png" alt="What is Alibaba OpenSandbox? Architecture, use cases, and how it works" /><InfoBox className="BodyStyle">

## TL;DR: Key takeaways on Alibaba OpenSandbox and sandbox infrastructure

- Alibaba OpenSandbox is an open-source sandbox platform for AI applications, released under the Apache 2.0 license. It provides multi-language SDKs, standardised sandbox APIs, and Docker and Kubernetes runtimes.
- Its architecture is built on four layers: SDKs, Specs, Runtime, and Sandbox Instances. A Go-based execution daemon (execd) is injected into each container at runtime and handles code execution, file operations, and command execution.
- It supports four sandbox scenarios: Coding Agents, GUI Agents, Code Execution, and RL Training, with integrations for Claude Code, Gemini CLI, OpenAI Codex, LangGraph, Google ADK, Playwright, and Chrome.
- OpenSandbox handles the execution protocol layer. However, running sandbox workloads in production also requires lifecycle orchestration, multi-tenancy, scaling, and persistent storage, which are areas sandbox infrastructure platforms cover.

> [Northflank Sandboxes](https://northflank.com/product/sandboxes) runs untrusted code at scale using microVM isolation (Kata Containers, Firecracker, and gVisor), on Northflank's managed cloud or inside your own VPC. Both ephemeral and persistent environments are supported, with sandbox creation taking around 1–2 seconds. BYOC (Bring Your Own Cloud) deployment is available self-serve across AWS, GCP, Azure, Oracle Cloud, Civo, CoreWeave, and on-premises infrastructure. Northflank has been in production since 2021 across startups, public companies, and government deployments.
> 

</InfoBox>

Alibaba OpenSandbox is an open-source sandbox platform released in March 2026 under the Apache 2.0 license. It sits at the execution layer of the AI agent stack, providing a standardised API for running AI-generated code, browser automation, GUI interactions, and reinforcement learning workloads inside isolated environments.

This article covers what OpenSandbox is, how its architecture works, what it supports, and what production-grade sandbox infrastructure involves.

## What is Alibaba OpenSandbox?

Alibaba OpenSandbox is a general-purpose sandbox platform for AI applications. It provides multi-language SDKs, sandbox lifecycle and execution APIs, and Docker and Kubernetes runtimes.

It is built on the same internal infrastructure Alibaba uses for large-scale AI workloads and is designed for four primary scenarios:

- **Coding Agents**: environments for agents that write, test, and debug code
- **GUI Agents**: environments with full VNC desktop support for agents that interact with graphical interfaces
- **Code Execution**: runtimes for script and compute execution
- **RL Training**: isolated environments for [reinforcement learning workloads](https://northflank.com/blog/reinforcement-learning-agents-in-secure-sandboxes)

## How does OpenSandbox's architecture work?

OpenSandbox is built on a four-layer modular stack where client logic is decoupled from execution environments:

- **SDKs Layer**: Client libraries for managing sandbox lifecycle and executing code. Exposes four components: Sandbox for provisioning and teardown, Filesystem for file operations, Commands for shell execution, and CodeInterpreter for stateful multi-language code execution
- **Specs Layer**: Two OpenAPI specifications define the contract between SDKs and runtimes: the Sandbox Lifecycle Spec (sandbox creation, pause, resume, deletion, TTL management) and the Sandbox Execution Spec (code execution, command execution, file operations, and metrics).
- **Runtime Layer**: A FastAPI-based server manages sandbox orchestration. Supports Docker for local and single-node use, and Kubernetes for distributed deployments. The Kubernetes runtime includes BatchSandbox for sandbox pooling and batch creation, and supports Kata Containers and gVisor as secure container runtimes.
- **Sandbox Instances Layer**: Each sandbox runs a container with an injected Go-based execution daemon called execd. It is injected at creation time without modifying the base image. execd starts a Jupyter Server inside the container and handles stateful code execution, with output streamed via Server-Sent Events (SSE).

## What sandbox environments and integrations does OpenSandbox support?

OpenSandbox supports four environment types, each targeting a different agent workload.

| Environment type | What it supports |
| --- | --- |
| Coding Agents | Code writing, testing, and debugging inside isolated environments |
| GUI Agents | Full VNC desktop environments for graphical interface interaction |
| Code Execution | Script and compute execution across multiple languages |
| RL Training | Isolated environments for reinforcement learning workloads |

It also includes integrations across AI frameworks and developer tools. The table below covers what is currently supported.

| Category | Integrations |
| --- | --- |
| Coding agent CLIs | Claude Code, Gemini CLI, OpenAI Codex, Kimi CLI |
| Orchestration frameworks | LangGraph, Google ADK |
| Browser automation | Chrome (headless), Playwright |
| Desktop environments | VNC, VS Code Server |

## What runtimes and SDKs does OpenSandbox provide?

OpenSandbox supports two runtimes for local and production use, and provides SDKs across multiple languages.

### Runtimes

OpenSandbox supports two runtimes:

- **Docker**: for local development and single-node use. Supports host networking and bridge mode with HTTP routing.
- **Kubernetes**: for distributed deployments. Includes the BatchSandbox runtime with sandbox pooling and batch creation, and is compatible with the Kubernetes SIG agent-sandbox project. Supports Kata Containers and gVisor as secure container runtimes.

### SDKs

OpenSandbox currently provides SDKs in the following languages.

| Language | Status |
| --- | --- |
| Python | Available |
| JavaScript / TypeScript | Available |
| Java / Kotlin | Available |
| C# / .NET | Available |
| Go | Roadmap |

## What does production-grade sandbox infrastructure require?

OpenSandbox defines how sandboxes are created, how code runs inside them, and how output is streamed back. Running that at production scale involves additional considerations beyond the protocol layer itself.

These are the operational areas teams need to account for:

- **Lifecycle orchestration**: provisioning, monitoring, pausing, resuming, and tearing down sandboxes reliably across concurrent sessions
- **Multi-tenancy**: enforcing isolation between tenants at the infrastructure level, not just the application level
- **Scaling**: handling demand spikes, pre-warmed pool sizing, and bin-packing workloads across nodes
- **Cold start latency**: end-to-end sandbox creation involves image pulling, execd injection, container start, and runtime initialisation. This is longer than VMM boot time alone
- **Persistent storage**: stateful agent sessions need volumes or databases that survive restarts; this is on OpenSandbox's roadmap but not yet available
- **Observability**: monitoring sandbox health and resource consumption across concurrent environments
- **BYOC (Bring Your Own Cloud) deployment**: teams with data sovereignty or compliance requirements need execution inside their own cloud account or VPC

Teams building on OpenSandbox take on responsibility for these layers.

*For a deeper look at how self-hosted and managed sandbox approaches compare, see [Self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes).*

## How does Northflank deliver production-grade sandbox infrastructure?

Northflank provides sandbox infrastructure for running untrusted code at scale. It handles microVM orchestration, multi-tenancy, scaling, and lifecycle management. Workloads run on Northflank's managed cloud or inside your own VPC.

<InfoBox className="BodyStyle">

[**Northflank Sandboxes**](https://northflank.com/product/sandboxes) runs every workload in its own microVM using Kata Containers, Firecracker, or gVisor depending on workload requirements. BYOC (Bring Your Own Cloud) deployment is self-serve across AWS, GCP, Azure, Oracle Cloud, Civo, CoreWeave, and on-premises infrastructure. Both ephemeral and persistent environments are supported. Sandbox creation takes around 1–2 seconds end-to-end. GPU support is available on-demand without quota requests. Northflank has been in production since 2021.

</InfoBox>

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

Here is how OpenSandbox and Northflank compare across the main infrastructure dimensions:

|  | OpenSandbox | Northflank |
| --- | --- | --- |
| Deployment model | Self-managed (Docker or Kubernetes) | Managed cloud or BYOC (Bring Your Own Cloud) self-serve |
| Isolation | Container-level, with Kata / gVisor on Kubernetes | MicroVM per workload (Kata, Firecracker, gVisor) |
| Persistent environments | Roadmap | Available |
| BYOC (Bring Your Own Cloud) | Self-managed | Self-serve across AWS, GCP, Azure, Oracle Cloud, Civo, CoreWeave, and on-premises |
| Scaling and orchestration | Self-managed | Platform-managed |
| GPU support | Runtime-dependent | Available on-demand |
| Pricing | Open source (infrastructure costs apply) | CPU $0.01667/vCPU/hour, Memory $0.00833/GB/hour (Full GPU and compute [pricing](https://northflank.com/pricing)) |
| License | Apache 2.0 | Commercial |

*For a broader comparison of sandbox platforms, see [Best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents), [Top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes), and [Self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes).*

## FAQ: Alibaba OpenSandbox and sandbox infrastructure

### What problem does OpenSandbox solve?

OpenSandbox provides a standardised execution layer for AI agent workloads, giving developers a single API for running code, managing files, and executing commands inside isolated environments across Docker and Kubernetes runtimes.

### Is Alibaba OpenSandbox open source?

Yes, OpenSandbox is released under the Apache 2.0 license. The source code, including SDKs, server, specs, and examples, is available on GitHub at github.com/alibaba/OpenSandbox.

### What runtimes does OpenSandbox support?

OpenSandbox supports Docker for local and single-node deployments and Kubernetes for distributed deployments. The Kubernetes runtime supports Kata Containers and gVisor as secure container runtimes. Teams running sandbox workloads in production can use [Northflank Sandboxes](https://northflank.com/product/sandboxes), which provides Kata Containers, Firecracker, and gVisor isolation with a managed control plane.

### What SDK languages does OpenSandbox support?

OpenSandbox currently provides SDKs for Python, JavaScript/TypeScript, Java/Kotlin, and C#/.NET. A Go SDK is listed on the project roadmap.

### Who is OpenSandbox designed for?

OpenSandbox is designed for platform engineers and AI agent developers who want an open-source, self-managed execution layer they can run on Docker locally or on Kubernetes in production.

### How does OpenSandbox relate to Kubernetes?

OpenSandbox's Kubernetes runtime supports distributed sandbox deployments. It includes a BatchSandbox implementation for high-throughput batch creation and is compatible with the Kubernetes SIG agent-sandbox project. It also supports Kata Containers and gVisor as secure container runtimes. For background on multi-tenancy considerations on Kubernetes, see [Kubernetes multi-tenancy](https://northflank.com/blog/kubernetes-multi-tenancy).

## More on AI sandbox infrastructure and secure code execution

The articles below cover isolation models, sandbox platforms, and infrastructure options for running untrusted code at scale:

- [How to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents): Covers isolation strategies for AI agent workloads, including microVMs, gVisor, and how to match an isolation model to your threat profile.
- [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): A detailed comparison of the three major isolation technologies used in production sandbox infrastructure, including how they handle kernel boundaries and which workloads each fits.
- [Self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes): Breaks down the difference between DIY, self-hosted, and BYOC sandbox approaches and what changes operationally with each.
- [Best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents): Compares leading sandbox platforms across isolation models, lifecycle design, session limits, and operational responsibility.
- [Top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes): Compares sandbox platforms that support bring-your-own-cloud deployment across isolation, lifecycle design, and operational overhead.
- [Self-hostable alternatives to E2B for AI agents](https://northflank.com/blog/self-hostable-alternatives-to-e2b-for-ai-agents): Covers the top self-hostable options for AI agent code execution, including how they compare on isolation strength and deployment complexity.
- [Kubernetes multi-tenancy](https://northflank.com/blog/kubernetes-multi-tenancy): Explains how multi-tenancy works on Kubernetes and the isolation considerations relevant to running sandbox workloads across tenants.
- [Ephemeral sandbox environments](https://northflank.com/blog/ephemeral-sandbox-environments): Covers how ephemeral sandbox environments work, when to use them, and how they differ from persistent execution environments.]]>
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</item><item>
  <title>How to run AI-generated code safely</title>
  <link>https://northflank.com/blog/run-ai-generated-code</link>
  <pubDate>2026-03-19T17:15:00.000Z</pubDate>
  <description>
    <![CDATA[Learn how to run AI-generated code safely using sandboxes, microVM isolation, and the right execution pattern for your use case.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/run_ai_generated_code_449f4d31f8.png" alt="How to run AI-generated code safely" /><InfoBox className="BodyStyle">

## TL;DR: Key takeaways on running AI-generated code safely

Running AI-generated code safely requires an isolated execution environment that enforces boundaries around the filesystem, process space, network, and kernel. Standard Docker containers share the host kernel and are not sufficient for untrusted code.

- The right isolation model depends on your use case: hardened containers work for internal trusted code, gVisor adds syscall-level protection for moderate-risk workloads, and microVMs (Firecracker, Kata Containers) provide hardware-level isolation for truly untrusted or multi-tenant execution.
- Each execution pattern (one-shot coding assistant output, multi-step agent tool calls, user-submitted prompts in a product, CI pipelines) carries a different threat profile and maps to different infrastructure requirements.
- Building and operating sandbox infrastructure yourself is a significant engineering commitment. Hosted sandbox platforms handle the infrastructure layer so you can focus on your product.

> [Northflank](https://northflank.com/product/sandboxes) provides microVM-backed sandboxes using Kata Containers, Cloud Hypervisor, Firecracker, and gVisor, with any OCI container image support, unlimited session duration, and both ephemeral and persistent execution modes. BYOC (Bring Your Own Cloud) deployment is available self-serve across AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, and on-premises or bare-metal infrastructure. It has been in production since 2021 across early-stage startups, public companies, and government deployments.
> 

</InfoBox>

Running AI-generated code safely is an infrastructure requirement for any AI product, developer tool, or autonomous agent system that executes LLM-generated code.

If your product or pipeline executes code produced by an LLM, you are operating an untrusted code execution surface, and the infrastructure decisions you make around it have direct security consequences.

This article covers the threat model for running AI-generated code, the four key enforcement boundaries you need, how common execution patterns map to infrastructure requirements, how to choose an isolation model, and how to evaluate sandbox platforms.

## Why does AI-generated code need a dedicated runtime?

When a language model generates code, that code should be treated as untrusted unless it has been reviewed before execution. It cannot be fully predicted, and it may be influenced by prompt injection attacks that are invisible to the system running it.

Running it directly on your application servers, or inside a shared container environment, exposes your infrastructure to:

- **Filesystem access:** Code can read environment variables, API keys, and configuration files it was never intended to reach.
- **Network exfiltration:** Without outbound controls, generated code can send data to external endpoints.
- **Resource exhaustion:** Uncontrolled CPU and memory consumption can degrade or take down adjacent workloads.
- **Privilege escalation:** A kernel vulnerability in a shared-kernel environment can allow a compromised workload to break out and access the host.

This differs from running your own application code, where the code is authored by engineers you trust, reviewed before deployment, and scoped to known behaviour. AI-generated code has none of those properties. It is produced at runtime, in response to user input or agent decisions, and it executes without the review cycle that applies to engineer-authored code.

*For a broader look at what makes AI sandboxes a distinct category from traditional container isolation, see [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox)*

## What are the four key enforcement boundaries for running AI-generated code safely?

Before choosing an isolation technology for running AI-generated code, you need to understand what you are enforcing. A production-grade execution environment enforces boundaries across four key dimensions:

| Boundary | What it prevents |
| --- | --- |
| Filesystem isolation | Access to host files, secrets, and credentials |
| Process isolation | Interference with other workloads and host processes |
| Network isolation | Unauthorised outbound connections and data exfiltration |
| Kernel isolation | Privilege escalation and host access via kernel exploits |

Standard Docker containers provide filesystem and process isolation through Linux namespaces and cgroups by default. They do not address kernel isolation because they share the host kernel. If a container workload exploits a kernel vulnerability, that vulnerability exposes the host, and from the host, adjacent workloads on the same node are reachable.

For AI-generated code from unknown or untrusted sources, kernel isolation is not optional.

*See this guide on [remote code execution sandboxes](https://northflank.com/blog/remote-code-execution-sandbox) for a deeper look at how isolation models map to each of these boundaries.*

## How does your AI-generated code execution pattern affect your infrastructure requirements?

Not every AI-generated code execution use case carries the same risk profile. Here is how common execution patterns differ in what they require:

- **Coding assistant output (one-shot):** A user asks an LLM to generate a script. It runs once and returns output. The main risk is the code itself being malicious or accidentally destructive. Requires: strong isolation, fast startup, and ephemeral teardown.
- **Agent tool calls (multi-step, stateful):** An autonomous agent runs multiple steps in sequence, each shaped by previous results. Sessions can last minutes to hours. Requires: isolation that persists across steps within a session, scoped outbound networking for tool calls, and clean teardown between user sessions.
- **User-submitted prompts in a multi-tenant product:** Multiple users' AI-generated code runs on shared infrastructure simultaneously. A bug or exploit from one user must not reach another. Requires: per-tenant isolation at the kernel level, not just the application level.
- **CI pipelines running LLM-generated tests:** Code generated by an AI coding assistant runs inside your CI pipeline. The risk is lower than fully untrusted user code, but prompt injection via repository content is a documented attack vector. Requires: process and network isolation at minimum.

*For a detailed look at autonomous agent execution environments, see these guides on [Code execution environment for autonomous agents](https://northflank.com/blog/code-execution-environment-for-autonomous-agents) and [Ephemeral execution environments for AI agents](https://northflank.com/blog/ephemeral-execution-environments-ai-agents).*

## How do you choose an isolation model for running AI-generated code?

Three isolation approaches are in common use for running AI-generated code. Each provides a different kernel boundary and comes with different operational tradeoffs. Start by asking which of the following fits your use case:

### Are hardened containers enough?

Containers isolate workloads using Linux namespaces and cgroups while sharing the host kernel. A hardened configuration adds seccomp profiles to restrict syscall surface, drops unnecessary Linux capabilities, enforces read-only root filesystems, and applies cgroup resource limits.

This is acceptable for internal workloads where you control and trust the code being executed. It is not sufficient for AI-generated code from external users or LLM agents operating on untrusted input.

### When is gVisor the right choice?

gVisor intercepts syscalls through a user-space process called the Sentry, which handles them without passing them directly to the host kernel. Container workloads interact with the Sentry rather than directly with the host kernel, which reduces the kernel attack surface.

This fits workloads where full microVM isolation is not justified, but standard container isolation is insufficient. There are tradeoffs: some I/O overhead, limited compatibility with certain kernel features, and an additional attack surface from the interception layer itself.

### When do you need microVMs?

MicroVMs provide each workload with a dedicated guest kernel running inside a lightweight virtual machine. A compromise of the guest kernel does not directly expose the host kernel; an attacker must also escape the hypervisor boundary.

Firecracker is designed for fast boot times and is used in production at scale for serverless and multi-tenant workloads. Kata Containers runs OCI-compliant containers inside microVMs and integrates natively with Kubernetes, making it a common choice for Kubernetes-based sandbox infrastructure.

For AI-generated code in multi-tenant or user-facing products, microVM isolation is the standard approach.

The table below summarises how each isolation model compares across kernel boundary and use case:

| Isolation model | Kernel boundary | Best suited for |
| --- | --- | --- |
| Hardened containers | Shared | Internal, trusted code |
| gVisor | Intercepted (user-space) | Moderate-risk, compute-heavy workloads |
| Firecracker microVM | Dedicated guest kernel | Serverless, untrusted, multi-tenant |
| Kata Containers | Dedicated guest kernel via Kubernetes | Production Kubernetes with VM-grade isolation |

*For a detailed comparison of these technologies, see [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor) and [How to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents).*

## Should you build your own sandbox runtime or use a hosted one?

Building microVM-based sandbox infrastructure yourself requires maintaining a VMM (Firecracker, Cloud Hypervisor, or QEMU), integrating it with your container runtime and orchestrator, managing pre-warmed pool sizing and drain logic, handling cold start latency, and operating all of it reliably at scale. It is a significant ongoing engineering commitment, not a one-time setup.

Hosted sandbox platforms handle the infrastructure layer. You define the container image, submit your workload, and the platform handles isolation, scheduling, scaling, and teardown.

When evaluating a hosted sandbox platform, the key criteria are isolation technology, session duration limits, BYOC (Bring Your Own Cloud) availability, container image flexibility, and platform scope beyond just sandbox execution.

<InfoBox className="BodyStyle">

[Northflank](https://northflank.com/product/sandboxes) provides hosted microVM-backed sandbox infrastructure using Kata Containers, Firecracker, and gVisor, with both ephemeral and persistent execution modes.

Sandbox creation takes around 1-2 seconds. Any OCI container image works without modification. BYOC deployment is available self-serve across AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, and on-premises or bare-metal infrastructure. Northflank has been running sandbox workloads in production since 2021 across startups, public companies, and government deployments.

> *See this guide on [how to spin up a secure code sandbox and microVM with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh).*
> 

</InfoBox>

## How does Northflank handle AI-generated code execution in production?

[Northflank](https://northflank.com/product/sandboxes) is a cloud platform that provides microVM-backed sandbox infrastructure and full workload orchestration, including APIs, workers, databases, GPU workloads, and CI/CD.

Deploy on Northflank's [managed cloud](https://northflank.com/features/managed-cloud) or inside your own VPC via self-serve BYOC ([Bring Your Own Cloud](https://northflank.com/product/bring-your-own-cloud)). Northflank supports multi-tenant architectures for running untrusted code at scale and has operated sandbox infrastructure in production since 2021 across startups, public companies, and government deployments.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

Here is what Northflank provides for running AI-generated code in production:

- **Multiple isolation technologies:** Northflank runs workloads using Kata Containers with Cloud Hypervisor, Firecracker, and gVisor depending on workload requirements.
- **Any OCI image:** Sandboxes accept any container from Docker Hub, GitHub Container Registry, or private registries without modification. No proprietary SDK or custom image format is required.
- **No forced time limits:** Sandboxes run for seconds or weeks depending on your use case, with no imposed session duration limits.
- **BYOC, self-serve:** BYOC deployment is available self-serve across major cloud providers and on-premises or bare-metal infrastructure. Northflank handles orchestration while workloads run inside your own cloud account or VPC.
- **Full workload runtime:** Databases, backend APIs, GPU workloads, and CI/CD run on the same platform alongside sandbox execution.

As an example of Northflank in production, [cto.new](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) uses Northflank to run secure multi-tenant sandboxes, handling thousands of daily container deployments across a free AI coding platform serving over 30,000 developers."

## FAQ: Running AI-generated code safely

### What is the safest way to run AI-generated code?

Run it inside a microVM-based sandbox using Firecracker or Kata Containers as the isolation foundation. Each execution gets a dedicated guest kernel, isolating it from the host and from other workloads. Layer scoped network policies and hard resource limits on top of that isolation.

### Can you run AI-generated code in a Docker container?

You can, but standard containers share the host kernel. For internal trusted code this is often acceptable with a hardened configuration. For untrusted AI-generated code or multi-tenant platforms, container isolation alone carries meaningful kernel-level risk.

### What is the difference between gVisor and a microVM for AI code execution?

gVisor intercepts syscalls in user space, reducing but not eliminating kernel exposure. MicroVMs give each workload a dedicated guest kernel via hardware virtualisation. MicroVMs provide stronger isolation; gVisor has lower overhead on compute-heavy workloads where I/O is not the bottleneck.

### How do you handle network access when running AI-generated code?

Apply default-deny outbound policies and explicitly allow only the endpoints the workload needs. For agent workloads making tool calls, scope outbound access to known API endpoints. Unrestricted egress is an exfiltration risk.

### What does bring-your-own-cloud mean for sandbox infrastructure?

BYOC means sandbox execution runs inside your own cloud account (AWS, GCP, Azure, etc.) rather than on shared third-party infrastructure. The platform handles orchestration while your data and workloads stay inside your VPC, which is important for compliance-sensitive or data-sensitive applications.

### Is ephemeral execution always the right pattern for AI-generated code?

Not always. One-shot executions suit ephemeral sandboxes well. Agent workflows that maintain state across many steps within a session need a persistent execution environment for the duration of that session, while still using ephemeral teardown between sessions.

## Related articles on running AI-generated code and sandbox infrastructure

The articles below cover specific aspects of sandbox infrastructure for AI-generated code execution in more depth.

- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox): Covers the definition, isolation technologies, and common use cases for AI sandboxes, including code interpreters and multi-tenant SaaS platforms.
- [How to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents): A practical guide to isolation strategies for AI agents, including microVM, gVisor, and hardened container configurations.
- [Remote code execution sandbox](https://northflank.com/blog/remote-code-execution-sandbox): Explains the full threat model for remote code execution and how isolation models compare in production.
- [Secure runtime for codegen tools](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale): Covers how to build and operate a secure runtime specifically for code generation tools at scale.
- [Best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents): Platform comparison across isolation strength, session limits, BYOC support, and pricing.
- [Code execution environment for autonomous agents](https://northflank.com/blog/code-execution-environment-for-autonomous-agents): Details the specific infrastructure requirements for multi-step stateful agent execution environments.
- [Ephemeral execution environments for AI agents](https://northflank.com/blog/ephemeral-execution-environments-ai-agents): Covers ephemeral vs persistent execution patterns and how they apply to agent workloads.
- [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): Side-by-side comparison of the three main isolation technologies with tradeoffs for each use case.]]>
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</item><item>
  <title>Best on-premises AI sandbox platforms in 2026</title>
  <link>https://northflank.com/blog/best-on-premises-ai-sandbox-platforms</link>
  <pubDate>2026-03-19T13:15:00.000Z</pubDate>
  <description>
    <![CDATA[Best on-premises AI sandbox platforms in 2026: compare Northflank, E2B, and Daytona on deployment model, isolation, orchestration responsibility, and what it takes to run sandboxes on your own hardware.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/on_prem_sandbox_platforms_af1642dbca.png" alt="Best on-premises AI sandbox platforms in 2026" /><InfoBox className="BodyStyle">

## TL;DR: What are the best on-premises AI sandbox platforms in 2026?

Running AI sandbox workloads on-premises means execution happens on hardware you own, inside your own data center, with no dependency on any public cloud provider. Most sandbox platforms do not support this at all. These are the ones that do.

- **Northflank** – The only platform on this list with production-ready, self-serve on-premises deployment. Runs sandbox execution on your own bare-metal or data center hardware with full microVM isolation, managed orchestration, unlimited sessions, databases, GPUs, and CI/CD.
- **E2B** – On-premises available as part of enterprise self-hosting. You operate the full runtime stack, including the control plane. Firecracker microVM isolation and clean Python and TypeScript SDKs.
- **Daytona** – On-premises deployment supported via Kubernetes. You manage the full infrastructure layer; Daytona provides the control plane remotely. Docker-based isolation by default. Currently experimental.
</InfoBox>

## Why on-premises matters for AI sandbox infrastructure

Most sandbox platforms are cloud-only. That is fine for the majority of use cases, but a specific set of requirements makes on-premises non-negotiable. 

Regulated industries like financial services, healthcare, and government operate under compliance frameworks that prohibit sensitive data from leaving controlled infrastructure entirely, including public cloud accounts. Air-gapped environments by definition cannot reach external APIs. Teams with existing data center investments or specific hardware requirements need execution to happen where their systems already live. 

The gap between on-premises and cloud deployment is meaningful. Cloud platforms still depend on a third-party provider's infrastructure and physical security. On-premises means your own hardware, your own network, your own physical perimeter. Among sandbox platforms, only a handful support this at all, and among those, the depth of support and the operational model differ significantly.

## What should you look for in an on-premises AI sandbox platform?

Not all on-premises implementations are equal. These are the dimensions that matter when execution must run on your own hardware.

- **Air-gap compatibility.** Can the platform operate without outbound internet access? Some platforms phone home for licensing, telemetry, or updates in ways that break air-gapped deployments. Verify what network access the control plane requires before committing.
- **Bare-metal support.** On-premises often means bare-metal servers rather than VMs. Not all platforms are designed to run on bare-metal without a hypervisor layer underneath. Check whether the platform supports direct bare-metal deployment or requires an intermediate virtualisation layer.
- **Orchestration model.** Does the vendor manage orchestration on your hardware, or do you? The difference between a platform that deploys and operates inside your data center versus one that hands you Helm charts and walks away is significant in terms of engineering overhead.
- **Isolation on your hardware.** microVM isolation on bare-metal behaves differently from microVM isolation on cloud VMs. Confirm the isolation technology works correctly on your specific hardware configuration, particularly for Kata Containers and Firecracker which have hardware dependencies.
- **Compliance certifications.** SOC 2 Type 2, HIPAA, FedRAMP, and other certifications matter but only if they cover on-premises deployments specifically. Some certifications apply only to managed cloud offerings.
- **Upgrade and maintenance model.** On-premises infrastructure requires patching, upgrades, and operational maintenance. Understand whether the vendor handles this remotely or whether your team is responsible for the full Day 2 operational surface.

## What are the best on-premises AI sandbox platforms?

Most sandbox platforms simply do not support on-premises deployment. The three below are the options that exist today, and they differ significantly on who operates what once your hardware is in the picture.

### 1. Northflank

Northflank is a full-stack platform with production-ready on-premises and bare-metal deployment available self-serve. You connect your own data center or bare-metal infrastructure, and Northflank manages orchestration, scheduling, autoscaling, and microVM provisioning on your hardware while your data never leaves your physical perimeter. No enterprise sales process required.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

What makes Northflank different from the other options here is that the vendor manages the operational layer on your hardware. Your team owns the servers. Northflank operates the platform. Sandboxes run alongside databases, APIs, background workers, and GPU workloads in the same control plane. Isolation uses Kata Containers with Cloud Hypervisor, Firecracker, and gVisor applied per workload. Sessions run indefinitely with no platform-imposed time limits. Any OCI-compliant image from any registry works without modification.

**Key features:**

- **Self-serve on-premises deployment:** Connect bare-metal or data center hardware without going through enterprise sales. Available to any team on the platform.
- **Managed orchestration on your hardware:** Northflank handles scheduling, autoscaling, bin-packing, and microVM lifecycle management. You own the hardware; Northflank operates it.
- **Isolation options:** Kata Containers with Cloud Hypervisor, Firecracker, and gVisor applied per workload. Every sandbox runs in its own microVM with true multi-tenant isolation.
- **No session limits:** Sandboxes run for seconds or weeks with no platform-imposed cutoff. Ephemeral and persistent environments supported in the same control plane.
- **Full-stack scope:** Run databases (Postgres, MySQL, MongoDB, Redis), persistent volumes, S3-compatible storage, background jobs, and GPU workloads alongside your sandboxes, all on your hardware.
- **GitOps-compatible:** Sandbox environment templates version-controlled and synced bidirectionally with a Git repository.
- **SOC 2 Type 2 certified:** Relevant for regulated industries and government deployments requiring compliance coverage on-premises.

> [cto.new migrated their entire sandbox infrastructure to Northflank](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) in two days after EC2 metal instances made scaling costs unpredictable, going from unworkable provisioning to thousands of daily deployments with linear, per-second billing.
> 

**Best for:** Teams with air-gapped or on-premises requirements, regulated industries where data cannot leave physical infrastructure, and platform engineering teams that need managed orchestration on their own hardware without a lengthy enterprise sales process.

**Pricing:** $0.01667/vCPU-hour, $0.00833/GB-hour, H100 GPU at $2.74/hour all-inclusive. On-premises deployments bill against your own infrastructure costs.

<InfoBox className="BodyStyle">

[Get started on Northflank](https://app.northflank.com/signup) (self-serve, no demo required). Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) with an engineer if you want to walk through your on-premises architecture first.

**Understand how Northflank deploys on-premises and manages sandboxes on your hardware:**

- How Northflank sandboxes are provisioned and used for secure code execution: https://northflank.com/product/sandboxes
- How bring your own cloud deployments allow workloads to run inside your own infrastructure: https://northflank.com/product/bring-your-own-cloud
- How sandbox workloads can be deployed directly into customer VPC and on-premises environments: https://northflank.com/product/customer-vpc-deployments
- How microVM sandbox environments are created using Firecracker, gVisor, and Kata Containers: https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh
</InfoBox>

### 2. E2B

E2B offers on-premises deployment as part of its enterprise self-hosting option. On-premises is available for enterprise customers and requires operating the full runtime stack on your own infrastructure. Sandboxes use Firecracker microVM isolation with boot times under 200ms.

The key distinction from Northflank is operational responsibility. In E2B's on-premises model, your team operates the full runtime stack, including the control plane, not just the compute layer. E2B manages neither the control plane nor the execution plane once deployed on your hardware. That is a significant engineering commitment, and it is not self-serve.

**Best for:** Enterprise teams who need on-premises Firecracker microVM sandboxes and have the engineering capacity to operate the full runtime stack themselves.

**Pricing:** Enterprise custom pricing for on-premises deployments. Managed tiers: Hobby free with $100 credit and 20 concurrent sandboxes. Pro at $150/month with 100 concurrent sandboxes and 24-hour sessions.

### 3. Daytona

Daytona supports on-premises deployment via Kubernetes, including bare-metal Kubernetes clusters. You deploy Daytona onto your Kubernetes infrastructure, and Daytona uses Kubernetes to run the nodes while its own orchestrator runs the sandboxes on top. You create custom regions and runners in your environment, and Daytona connects them to its control plane via a provisioned token.

Your team owns and operates the full infrastructure layer, including the Kubernetes cluster, compute nodes, scaling, and networking. Daytona provides the control plane remotely but does not manage orchestration on your hardware. Isolation defaults to Docker containers, which is weaker than microVM isolation for genuinely untrusted code. On-premises deployment is currently experimental and requires contacting Daytona support to request access.

**Best for:** Teams with existing Kubernetes infrastructure on-premises and the engineering capacity to operate it alongside Daytona's control plane.

**Pricing:** Usage-based with $200 free credits. Contact Daytona for on-premises pricing.

>**Note:** On-premises deployment is currently experimental and requires contacting Daytona support to request access.
>

## Which platform should you choose for on-premises sandboxes?

The core question is who operates the platform once it is running on your hardware. Northflank is the only option here where the vendor manages orchestration on your hardware. You own the infrastructure; Northflank operates the platform on it. E2B and Daytona both require you to operate the infrastructure layer yourself, with the vendor providing only the control plane remotely. If your requirement is on-premises execution without taking on full operational responsibility, Northflank is the only option that covers it.

| Platform | On-prem support | Access model | Isolation | Control plane | Infrastructure responsibility |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | Yes | Self-serve | Kata Containers, Firecracker, gVisor | Managed by Northflank | Hardware only |
| **E2B** | Yes, enterprise | Enterprise only | Firecracker | Customer operates | Full stack including control plane |
| **Daytona** | Experimental | Request via support | Docker (default) | Daytona (remote) | Full Kubernetes infrastructure |

## FAQ: on-premises AI sandbox platforms

### What does on-premises mean for AI sandbox platforms?

On-premises means sandbox execution runs on hardware you own and operate, inside your own data center, with no dependency on a public cloud provider. The vendor may still provide the control plane remotely, but the compute and execution happen on your physical infrastructure.

### Can on-premises sandbox platforms work in air-gapped environments?

It depends on the platform. Northflank supports air-gapped deployments for regulated industries and government deployments. Verify with any vendor whether their control plane requires outbound internet access before deploying in an air-gapped environment, as some platforms phone home for licensing or updates in ways that break air-gapped setups.

### Why do most sandbox platforms not support on-premises deployment?

On-premises is significantly harder to support than cloud offerings. The vendor cannot control the hardware environment, networking configuration, or infrastructure reliability. Most sandbox platforms prioritize managed cloud and add on-premises only for large enterprise customers, if at all.

### Does Northflank manage my on-premises infrastructure?

Northflank manages orchestration, scheduling, autoscaling, and microVM lifecycle on your infrastructure. You own and maintain the hardware. Northflank operates the platform layer on top of it, which means your team is not responsible for managing sandbox orchestration directly.

### What isolation technology works on bare-metal with Northflank?

Northflank supports Kata Containers with Cloud Hypervisor, Firecracker, and gVisor on bare-metal. The specific isolation technology available depends on your hardware configuration. For bare-metal deployments with specific hardware requirements, the Northflank engineering team can advise on the right isolation approach during onboarding.

### What is the difference between E2B on-premises and E2B self-hosting?

They are the same thing. E2B's on-premises option is its self-hosted deployment model. Your team operates the full runtime stack, including the control plane. This is available to enterprise customers only and is not self-serve.

## Conclusion

On-premises AI sandbox infrastructure is a small category for good reason. It requires the vendor to support deployment onto hardware they do not control, in environments with networking configurations they cannot predict, at a level of operational complexity that most sandbox-focused platforms are not built for. 

Northflank is the only platform here with production-ready, self-serve on-premises deployment where the vendor manages orchestration on your hardware. E2B is an option for enterprise teams willing to operate the full runtime stack themselves. Daytona is available experimentally for teams with strong Kubernetes expertise. If your requirement is on-premises execution with managed orchestration rather than full self-hosting, Northflank is the only option that covers it.

<InfoBox className="BodyStyle">

You can [get started for free on Northflank](https://app.northflank.com/signup) or [talk to the team](https://cal.com/team/northflank/northflank-demo?duration=30) to walk through your on-premises requirements.

</InfoBox>

## Related articles: on-premises and self-hosted sandbox infrastructure

If you want to go deeper on the topics covered in this guide, these articles are a good next step.

- [**Best BYOC sandbox platforms in 2026**](https://northflank.com/blog/best-byoc-sandbox-platforms): Covers the full BYOC landscape, including cloud deployment options alongside on-premises, with a detailed comparison of Northflank, E2B, and Daytona.
- [**Self-hosted AI sandboxes: guide to secure code execution**](https://northflank.com/blog/self-hosted-ai-sandboxes): Covers how DIY, self-hosted, and BYOC sandbox approaches differ operationally, and what full self-hosting actually involves.
- [**How to sandbox AI agents: microVMs, gVisor, and isolation strategies**](https://northflank.com/blog/how-to-sandbox-ai-agents): Technical deep-dive into isolation technologies and how to choose between them for on-premises workloads.]]>
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  <title>Top tools for ephemeral environments in 2026</title>
  <link>https://northflank.com/blog/tools-for-ephemeral-environments</link>
  <pubDate>2026-03-18T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[Top tools for ephemeral environments in 2026: Northflank, Bunnyshell, Okteto, and Uffizzi compared across preview environments, sandbox execution, and on-demand provisioning.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/tools_for_ephemeral_environments_9d22203552.png" alt="Top tools for ephemeral environments in 2026" /><InfoBox className="BodyStyle">

## TL;DR: Top tools for ephemeral environments at a glance

Ephemeral environment tools differ in stack scope, isolation model, and how environments are triggered. The right one depends on what your workloads need. See the top tools for ephemeral environments below:

- **Northflank:** Provides full-stack ephemeral preview environments and sandboxed execution environments. Supports Git PR, API, CLI, UI, and GitOps triggers. Includes managed databases, background jobs, secrets, microVM isolation, and bring-your-own-cloud (BYOC) support across AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, on-premises, and bare-metal infrastructure.
- **Bunnyshell:** Ephemeral environments per PR defined via Docker Compose, Helm, Kubernetes manifests, or Terraform.
- **Okteto:** Kubernetes-native preview environments triggered via GitHub Actions or GitLab CI/CD.
- **Uffizzi:** Open-source ephemeral environments using virtual clusters and Docker Compose.

> **Worth noting**: Northflank provides full-stack ephemeral preview environments and sandboxed execution environments on the same platform, with support for microVM-based isolation (Firecracker, gVisor, Kata Containers), BYOC (Bring Your Own Cloud) across AWS, GCP, Azure, Civo, CoreWeave, Oracle Cloud, and on-premises infrastructure, and environment creation in roughly 1-2 seconds.
> 

</InfoBox>

Tools for ephemeral environments are not all built for the same problem. Some focus on preview environments triggered by pull requests, others on on-demand provisioning via API, and others on isolated execution for AI agent workloads or untrusted code.

This guide covers the top tools for ephemeral environments in 2026, what each one provides, and how to match them to your use case.

## What are tools for ephemeral environments?

Ephemeral environment tools are platforms and runtimes that create short-lived, isolated environments on demand and destroy them when their purpose is served.

Unlike persistent staging environments, ephemeral environments carry no long-term state and are tied to a specific event: a pull request, an API call, a CI pipeline step, or an agent task.

They range from managed platforms that provision full application stacks per PR, to open-source Kubernetes-native tools that create virtual clusters per branch, to sandbox runtimes that isolate AI-generated or untrusted code at the kernel level.

Choosing between them starts with understanding what your environments need to include and how they need to be triggered.

## What should you look for in ephemeral environment tools?

Before evaluating specific tools, map your requirements across these five dimensions:

- **Trigger model:** Git pull request triggers are the most common, but some teams need environments triggered from a CI pipeline step, an API call, a CLI command, or an internal tool. Not all platforms support all trigger modes. If you are building an internal developer platform or running AI agent pipelines, API-driven provisioning is a hard requirement.
- **Stack scope:** Most tools provision containers. Far fewer provision managed databases, background jobs, and encrypted secrets alongside your services. If your integration tests need a live database with seeded data, or your preview environments need secrets injected at runtime, you need full-stack scope.
- **Isolation model:** For standard preview environments where your own engineers author the code, container-level isolation is typically sufficient. For AI agent workloads or untrusted code execution, you need stronger isolation. Containers share a host kernel, which means a kernel vulnerability can break isolation entirely. microVM-based runtimes (Firecracker, gVisor, Kata Containers) give each environment its own kernel boundary.
- **Hosting model:** Managed platforms handle the infrastructure for you. BYOC (Bring Your Own Cloud) lets you run environments inside your own cloud account or VPC, which matters if you have data residency requirements or need environments to access private infrastructure. Self-hosted options give you full control at the cost of operational overhead.
- **Lifecycle control:** Ephemeral environments that are not cleaned up automatically lead to environment sprawl and cost overruns. Look for teardown policies tied to PR close or merge, idle shutdown after a configurable period, and duration-based expiry.

## Top tools for ephemeral environments compared

The four tools below differ in trigger model, stack scope, isolation depth, and hosting model. Here is what each one provides.

### 1. Northflank

[Northflank](https://northflank.com/product/preview-environments) is a full-stack deployment platform with native support for ephemeral preview environments and sandboxed code execution. You define a preview environment template specifying your services, managed databases, background jobs, secrets, and lifecycle rules, then trigger it however fits your workflow.

![northflank-previews.png](https://assets.northflank.com/northflank_previews_89a97262d2.png)

**What Northflank provides:**

- **Trigger model:** Git pull request triggers, branch pushes, manual UI actions, CLI commands, and direct REST API calls. The API covers environment creation, listing, pausing, resuming, and deletion. Arguments can be passed at run time to parameterize each environment.
- **Stack scope:** Each preview environment can include services, managed database addons (PostgreSQL, MySQL, MongoDB, Redis), scheduled jobs, and encrypted secret groups. Your preview environments can run real integration tests, not just frontend or API smoke tests.
- **Sandbox isolation:** Northflank supports microVM-based sandboxes using Firecracker, gVisor, and Kata Containers. Both ephemeral and persistent sandbox modes are available, covering teams running AI agent pipelines or sandboxed code execution where container-level isolation is not sufficient.
- **Hosting model:** Northflank runs on its own managed infrastructure or on your own cloud account via BYOC ([Bring Your Own Cloud](https://northflank.com/product/bring-your-own-cloud)). BYOC supports AWS, GCP, Azure, Civo, CoreWeave, Oracle Cloud, on-premises, and bare-metal infrastructure, available self-serve.
- **Lifecycle control:** Configure duration-based teardown, idle shutdown policies, and active hours restrictions per environment template. Once policies are set, environments clean up without manual intervention.

<InfoBox className="BodyStyle">

**Go deeper on Northflank:**

- [How to auto-create preview environments on every PR](https://northflank.com/blog/how-to-auto-create-preview-environments-on-every-pr): Step-by-step walkthrough for setting up automated preview environments using Git pull request triggers.
- [Ephemeral sandbox environments](https://northflank.com/blog/ephemeral-sandbox-environments): Covers isolation models for sandboxed workloads, including container-based and microVM options.
- [Ephemeral execution environments for AI agents](https://northflank.com/blog/ephemeral-execution-environments-ai-agents): Covers ephemeral execution environments for AI agent pipelines and untrusted code workloads.
- [Set up a preview environment](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment): Full setup documentation for preview environments on Northflank.
- [Create and manage previews](https://northflank.com/docs/v1/application/release/create-and-manage-previews): Documentation for creating and managing preview environment lifecycles on Northflank.

[Get started on Northflank](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo).

</InfoBox>

### 2. Bunnyshell

Bunnyshell provides ephemeral environments per pull request using YAML-based environment definitions. Environments are defined using Docker Compose, Helm, Kubernetes manifests, or Terraform components.

**What Bunnyshell provides:**

- **Trigger model:** GitHub, GitLab, and Bitbucket as Git providers. Environments trigger on pull request open and tear down on merge or close. A public API and SDK are available for programmatic lifecycle operations.
- **Stack scope:** Environments cover multi-service setups. Database support depends on how you define your environment components: container databases, cloud-managed databases, and SaaS databases are supported via component definitions.
- **Lifecycle control:** Lifecycle workflows cover deploy, destroy, start, and stop.
- **Configuration:** Variable groups and interpolation handle environment-specific configuration. Data seeding is supported for container databases, cloud-managed databases, and SaaS databases.

### 3. Okteto

Okteto provides Kubernetes-native preview environments triggered via GitHub Actions or GitLab CI/CD.

**What Okteto provides:**

- **Trigger model:** GitHub Actions and GitLab CI/CD. Environments trigger on pull request open and clean up on close or merge via the CI/CD workflow.
- **Stack scope:** Deploys application services into Kubernetes namespaces.
- **Scope options:** Environments can be scoped globally (visible to all team members) or personally (visible only to the creator and those explicitly shared with).
- **Lifecycle control:** Automatic cleanup on PR close or merge. Garbage collection with configurable sleep and delete periods is available at the admin level.

### 4. Uffizzi

Uffizzi is an open-source platform for ephemeral environments built around virtual clusters and Docker Compose definitions. Each environment gets its own lightweight virtual Kubernetes cluster, providing cluster-level isolation per environment.

**What Uffizzi provides:**

- **Trigger model:** GitHub Actions and GitLab CI for triggering environments from CI pipelines. Environments can also be triggered and managed via CLI or dashboard.
- **Stack scope:** Supports Docker Compose, Helm, Kustomize, and Kubernetes manifests.
- **Lifecycle control:** Automatic teardown on PR close or merge, or via a configurable TTL.
- **IDP integration:** Provides a Backstage plugin for teams building internal developer platforms on the Backstage framework.

## How do ephemeral environment tools compare?

The table below maps common ephemeral environment use cases to the tools that support them.

| If you need... | Consider... |
| --- | --- |
| Full-stack preview environments per PR, including managed databases, background jobs, and secrets | Northflank |
| API-driven or programmatic environment provisioning outside the Git PR lifecycle | Northflank (full REST API, CLI, UI, GitOps), Bunnyshell (public API and SDK) |
| microVM-based sandbox isolation for AI agent workloads or untrusted code execution | Northflank |
| BYOC support to run environments inside your own cloud account or VPC | Northflank (self-serve: AWS, GCP, Azure, Civo, CoreWeave, Oracle Cloud, on-premises, bare metal), Okteto (managed: AWS and GCP) |
| Kubernetes-native preview environments triggered via GitHub Actions or GitLab CI/CD | Okteto (GitHub Actions, GitLab CI/CD), Uffizzi (GitHub Actions, GitLab CI) |
| Both ephemeral and persistent sandbox environment modes on the same platform | Northflank |

## FAQ: tools for ephemeral environments

### What is the difference between a preview environment and a sandbox environment?

A preview environment is a full-stack deployment of your application triggered by a Git event like a pull request. It mirrors your production stack for testing and review purposes. A sandbox environment is an isolated runtime focused on execution safety, used to run untrusted or AI-generated code where stronger isolation at the kernel level is required.

### Can ephemeral environments include databases?

Some tools support managed database provisioning as part of the environment. Northflank provisions managed databases (PostgreSQL, MySQL, MongoDB, Redis) alongside services, jobs, and secrets in a single environment template. Bunnyshell supports databases via component definitions, including container databases, cloud-managed databases, and SaaS databases.

### How are ephemeral environments triggered?

The most common trigger is a Git pull request: an environment spins up when the PR opens and tears down when it closes or merges. Some platforms also support API calls, CLI commands, CI pipeline steps, and UI actions as triggers. Northflank supports all of these.

### What is the difference between a managed and a self-hosted ephemeral environment tool?

A managed tool handles the underlying infrastructure for you. You define your environment and the platform runs it on shared or dedicated cloud infrastructure. A self-hosted tool runs on infrastructure you own and manage. BYOC sits between the two: a managed control plane runs on the provider's infrastructure, but your workloads run inside your own cloud account or VPC.

### Which ephemeral environment tools support bring-your-own-cloud?

Northflank supports BYOC across AWS, GCP, Azure, Civo, CoreWeave, Oracle Cloud, on-premises, and bare-metal infrastructure, available self-serve. Okteto supports BYOC and self-hosted deployment. Uffizzi can be self-hosted on your own Kubernetes cluster. Bunnyshell deploys environments to external Kubernetes clusters you provide.

## Related articles: tools and platforms for ephemeral environments

If you want to go deeper on specific use cases and comparisons covered in this guide, these articles are a good next step.

- [Ephemeral sandbox environments](https://northflank.com/blog/ephemeral-sandbox-environments): Covers isolation models for ephemeral sandboxes in depth, including container-based, microVM, and full VM options, and when each is appropriate.
- [Ephemeral execution environments for AI agents](https://northflank.com/blog/ephemeral-execution-environments-ai-agents): Covers why AI agent workloads require ephemeral execution environments and how to implement them in production, including isolation model selection and lifecycle management.
- [Best platforms for on-demand preview environments](https://northflank.com/blog/best-platforms-for-on-demand-preview-environments): Compares platforms that support API-driven, programmatic environment provisioning outside the standard PR lifecycle.
- [Northflank preview environments](https://northflank.com/product/preview-environments): Covers Northflank's full-stack preview environment capabilities including trigger modes, database provisioning, and lifecycle controls.
- [How to auto-create preview environments on every PR](https://northflank.com/blog/how-to-auto-create-preview-environments-on-every-pr): Step-by-step guide to configuring automated preview environments on Northflank using Git pull request triggers.
- [Kubernetes preview environments comparison](https://northflank.com/blog/kubernetes-preview-environments-comparison): Compares Kubernetes-native preview environment platforms across cluster architecture, isolation strategy, and workload support.
- [Preview environment platforms](https://northflank.com/blog/preview-environment-platforms): Broader comparison of ten preview environment platforms across GitOps-driven infrastructure, frontend pipelines, and full-stack automated previews.
- [Code execution environments for autonomous agents](https://northflank.com/blog/code-execution-environment-for-autonomous-agents): Covers infrastructure requirements for running autonomous agent workloads, including isolation, lifecycle, and execution environment design.]]>
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</item><item>
  <title>Best BYOC sandbox platforms in 2026</title>
  <link>https://northflank.com/blog/best-byoc-sandbox-platforms</link>
  <pubDate>2026-03-18T15:30:00.000Z</pubDate>
  <description>
    <![CDATA[Best BYOC sandbox platforms in 2026: compare Northflank, E2B, and Daytona on deployment breadth, access model, isolation, orchestration, and what it takes to run sandboxes inside your own VPC.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/byoc_sandbox_platforms_f89e6a0e70.png" alt="Best BYOC sandbox platforms in 2026" /><InfoBox className="BodyStyle">

## TL;DR: What are the best BYOC sandbox platforms in 2026?

Most sandbox platforms run your code in their infrastructure. That works fine until your workloads need to access private APIs, stay inside a regulated network boundary, or comply with data residency requirements. At that point, bring-your-own-cloud becomes the requirement. These are the platforms worth evaluating when execution must run inside your own VPC.

- **Northflank** – The only platform on this list with production-ready, self-serve BYOC across AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, and bare-metal. Full microVM isolation, unlimited sessions, databases, GPUs, and CI/CD, all running inside your own infrastructure.
- **E2B** – BYOC available for AWS only, enterprise customers only. Firecracker microVM isolation and clean Python and TypeScript SDKs.
- **Daytona** – Customer-managed compute option available for cloud and on-premises. You operate the infrastructure layer; Daytona provides the control plane. Docker-based isolation by default (Experimental).

</InfoBox>

## Why BYOC matters for sandbox infrastructure

Most teams start with a managed sandbox and hit the BYOC requirement later. The trigger is usually one of a few things: your agent needs to access an internal API that cannot be exposed to a third-party network, your security team flags that customer data is leaving the VPC, or a compliance audit surfaces that code execution is happening outside your infrastructure boundary.
When that happens, a managed sandbox stops being an option. Most sandbox tools are managed-only by design, and among the handful that do support running execution inside customer infrastructure, the depth of support varies considerably. Some limit it to one cloud provider. Some require enterprise sales. Some make you operate the compute layer yourself.

## What should you look for in a BYOC sandbox platform?

Not all BYOC implementations are equal. These are the dimensions that matter when execution must run inside your own infrastructure.

- **Deployment breadth.** Does BYOC cover only one cloud provider or multiple? Single-cloud BYOC locks you into one vendor's infrastructure. Broader support across AWS, GCP, Azure, and on-premises gives you flexibility as your infrastructure evolves.
- **Access model.** Is BYOC gated behind enterprise sales, or can you set it up self-serve? Platforms that require a sales conversation to unlock BYOC add friction and delay, especially for teams that need to move quickly.
- **Operational responsibility.** Who manages what in BYOC mode? Some platforms hand you the full infrastructure layer to operate yourself. Others, like Northflank, handle orchestration, autoscaling, and microVM provisioning inside your infrastructure while you retain ownership of the compute. That distinction affects how much engineering time BYOC actually costs your team.
- **Isolation model.** Container-based isolation is weaker than microVM isolation regardless of where it runs. In BYOC mode you want the same isolation guarantees you would expect from a managed deployment: dedicated kernels per workload, no shared host kernel between tenants.
- **Full-stack scope.** A sandbox that runs inside your VPC but cannot run alongside your databases, APIs, or GPU workloads means you still need additional vendors. Platforms that handle the full stack in BYOC mode reduce operational surface area as your requirements grow.
- **Compliance coverage.** SOC 2, HIPAA, FedRAMP, and data residency requirements all influence which platforms are viable. Verify certifications and whether BYOC deployment satisfies your specific compliance framework before committing.

## What are the best BYOC sandbox platforms?

### 1. Northflank

Northflank is a full-stack cloud platform with production-ready BYOC support across the broadest range of infrastructure targets available from any sandbox platform. You deploy into your own AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal environment, and Northflank manages orchestration, scheduling, autoscaling, and microVM provisioning while your data never leaves your VPC. BYOC is available self-serve with no enterprise sales process required.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_b7ccb3b65d.png)

What sets Northflank apart from every other option here is that BYOC is not a narrow feature. It is the same platform, running inside your infrastructure. Sandboxes run alongside databases, APIs, background workers, and GPU workloads in the same control plane. Isolation uses Kata Containers with Cloud Hypervisor, Firecracker, and gVisor applied per workload. Sessions run indefinitely with no platform-imposed time limits. Any OCI-compliant image from any registry works without modification.

**Key features:**

- **Self-serve BYOC:** Deploy into AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, or bare-metal. No enterprise sales required. Available to any team on the platform.
- **Full orchestration inside your VPC:** Northflank handles scheduling, autoscaling, bin-packing, and microVM lifecycle management. You own the compute; Northflank operates it.
- **Isolation options:** Kata Containers with Cloud Hypervisor, Firecracker, and gVisor applied per workload. Every sandbox runs in its own microVM with true multi-tenant isolation.
- **No session limits:** Sandboxes run for seconds or weeks with no platform-imposed cutoff. Ephemeral and persistent environments supported in the same control plane.
- **Full-stack scope:** Run databases (Postgres, MySQL, MongoDB, Redis), persistent volumes, S3-compatible storage, background jobs, and GPU workloads alongside your sandboxes.
- **GitOps-compatible:** Sandbox environment templates version-controlled and synced bidirectionally with a Git repository.
- **SOC 2 Type 2 certified:** Relevant for regulated industries and government deployments.

[cto.new migrated their entire sandbox infrastructure to Northflank](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) in two days after EC2 metal instances made scaling costs unpredictable, going from unworkable provisioning to thousands of daily deployments with linear, per-second billing.

**Best for:** Teams with compliance, data residency, or network boundary requirements. Enterprise teams building multi-tenant agent infrastructure inside their own VPC. Platform engineering teams that need BYOC without going through enterprise sales.

**Pricing:** $0.01667/vCPU-hour, $0.00833/GB-hour, H100 GPU at $2.74/hour all-inclusive. BYOC deployments bill against your own cloud account.

<InfoBox className="BodyStyle">

[Get started on Northflank](https://app.northflank.com/signup) (self-serve, no demo required). Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) with an engineer if you want to walk through your architecture first.

**Bonus**: Understand how Northflank sandboxes run inside your infrastructure and how BYOC deployments work:

- How Northflank sandboxes are provisioned and used for secure code execution - https://northflank.com/product/sandboxes
- How bring your own cloud deployments allow workloads to run inside your cloud accounts - https://northflank.com/product/bring-your-own-cloud
- How sandbox workloads can be deployed directly into customer VPC environments - https://northflank.com/product/customer-vpc-deployments
- How microVM sandbox environments are created using Firecracker, gVisor, and Kata Containers - https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh

</InfoBox>

### 2. E2B

E2B offers a BYOC deployment option that runs sandboxes inside your own AWS VPC, with E2B managing the control plane. It is the only other platform on this list with any BYOC capability. Sandboxes use Firecracker microVM isolation with boot times under 200ms, and the Python and TypeScript SDKs integrate cleanly with LangChain, OpenAI, and Anthropic tooling.

The constraints are significant. BYOC is limited to AWS only and is available exclusively to enterprise customers. In BYOC mode, the customer manages the VPC, AWS account, and compute nodes, including orchestrators and edge controllers. E2B manages the control plane. That operational responsibility sits with your team, not the vendor.

**Best for:** Enterprise teams on AWS that need sandbox execution inside their VPC and are comfortable managing compute nodes themselves.

**Pricing:** Enterprise custom pricing for BYOC. Managed tiers: Hobby free with $100 credit and 20 concurrent sandboxes. Pro at $150/month with 100 concurrent sandboxes and 24-hour sessions.

### 3. Daytona

Daytona supports customer-managed compute where sandboxes run on your own cloud or on-premises infrastructure, with Daytona providing the control plane. You create custom regions and runners in your environment, and Daytona connects them to its control plane via a provisioned token.

The tradeoff is operational responsibility. You own and operate the full infrastructure layer, including compute nodes, scaling, and networking. Daytona does not manage orchestration inside your environment the way Northflank does. Isolation defaults to Docker containers in all deployment modes, which is weaker than microVM isolation for genuinely untrusted code.

**Best for:** Teams that want sandbox execution inside their own infrastructure and have the engineering capacity to operate the compute layer themselves.

**Pricing:** Usage-based with $200 free credits.

<InfoBox className="BodyStyle">

**Note:** Customer-managed compute is currently experimental and requires contacting Daytona support to request access.

</InfoBox>

## How do BYOC sandbox platforms compare on pricing?
_Pricing as of April 2026. Billing models differ across platforms (some bill based on active CPU usage only, others bill for the entire duration the sandbox is running). Verify current rates on each platform's pricing page before making cost decisions._

| Platform | CPU | Memory | Storage | GPU | Billing model |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | $0.01667/vCPU-hr | $0.00833/GB-hr | $0.15/GB-month | L4: $0.80/hr, A100 40GB: $1.42/hr, A100 80GB: $1.76/hr, H100: $2.74/hr, H200: $3.14/hr | Per second |
| **E2B** | $0.0504/vCPU-hr | $0.0162/GiB-hr | 10–20GB included free | Do not provide GPU compute | Per second |
| **Daytona** | $0.0504/vCPU-hr | $0.0162/GiB-hr | $0.000108/GiB-hr (5GB free) | Do not provide GPU compute | Per second |

## Which platform should you choose for BYOC sandboxes?
The table below shows how each platform handles BYOC deployment, which clouds are supported, and whether it requires a sales process.
| Platform | BYOC available | Clouds supported | Access model | Pricing model |
| --- | --- | --- | --- | --- |
| **Northflank** | Yes, fully self-serve | AWS, GCP, Azure, Oracle, CoreWeave, any neoclouds, Civo, bare-metal, on-premises | Self-serve, enterprise contracts available for larger commits (with bulk discounts) | Your existing cloud bill, CPU $0.01389/vCPU-hr and Memory $0.00139/GB-hr |
| **E2B** | Yes, limited and not self-serve | AWS and GCP only | Not publicly disclosed, need to contact sales | Starts at $50/sandbox/month, on top of your existing cloud bill |
| **Daytona** | Yes, limited and not self-serve | Not publicly disclosed | You operate the infrastructure layer; Daytona provides the control plane | Not publicly disclosed |

## FAQ: BYOC sandbox platforms

### What does BYOC mean for sandbox platforms?

BYOC means the execution plane runs inside infrastructure you control, such as your own cloud account or VPC, while the platform handles orchestration, lifecycle management, and APIs. Your code runs on your compute, not the vendor's.

### How is BYOC different from self-hosting?

Self-hosting means you operate the full runtime stack yourself, including the control plane. BYOC separates responsibilities: execution runs in your infrastructure while the vendor manages orchestration. Northflank extends this to on-premises and air-gapped environments, which is relevant for regulated industries and government deployments.

### Why do most sandbox platforms not offer BYOC?

Building a BYOC execution model is significantly more complex than a single managed cloud offering. It requires the vendor to support multiple cloud providers, manage orchestration across customer-controlled infrastructure, and handle networking configurations they do not control. Most sandbox platforms prioritize a simpler managed offering and add BYOC only for enterprise customers, if at all.

### Which clouds does Northflank BYOC support?

AWS, GCP, Azure, Oracle Cloud, CoreWeave, Civo, on-premises, and bare-metal. It is available self-serve to any team on the platform, with no enterprise sales process required.

### Does E2B BYOC support more than AWS?

No. E2B's BYOC option is limited to AWS and is only available to enterprise customers. In BYOC mode, the customer manages the VPC, compute nodes, and AWS account. E2B manages the control plane.

### Can I run databases alongside sandboxes in BYOC mode on Northflank?

Yes. Northflank runs databases including Postgres, MySQL, MongoDB, and Redis in the same control plane as your sandboxes, whether on managed infrastructure or inside your own VPC via BYOC.

## Conclusion

BYOC is not a feature most teams need on day one. It becomes the requirement when your agent workloads interact with private systems, when compliance surfaces as a constraint, or when the economics of managed sandboxing break down at scale. The earlier you understand which platforms actually support it, the less painful that transition becomes.

Northflank is the only platform here with production-ready, self-serve BYOC that covers multiple cloud providers, handles orchestration inside your infrastructure, and runs the full stack alongside your sandboxes. E2B is the only other option, limited to AWS enterprise customers. Daytona is an option if you can operate the infrastructure yourself.

<InfoBox className="BodyStyle">

You can [get started for free on Northflank](https://app.northflank.com/signup) or [talk to the team](https://cal.com/team/northflank/northflank-demo?duration=30) to walk through your BYOC requirements.

</InfoBox>

## Related articles: BYOC and sandbox infrastructure

If you want to go deeper on the topics covered in this guide, these articles are a good next step.

- [**Self-hosted AI sandboxes: guide to secure code execution**](https://northflank.com/blog/self-hosted-ai-sandboxes): Covers how self-hosted and BYOC sandbox approaches differ operationally, and when each makes sense for regulated or compliance-sensitive workloads.
- [**How to sandbox AI agents: microVMs, gVisor, and isolation strategies**](https://northflank.com/blog/how-to-sandbox-ai-agents): Technical deep-dive into isolation technologies including Firecracker, Kata Containers, and gVisor, and how to choose between them based on your threat model.]]>
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  <title>Running reinforcement learning (RL) agents in secure sandboxes</title>
  <link>https://northflank.com/blog/reinforcement-learning-agents-in-secure-sandboxes</link>
  <pubDate>2026-03-17T17:30:00.000Z</pubDate>
  <description>
    <![CDATA[Reinforcement learning agents need isolated, secure, stateful sandboxes at scale. Learn what RL environment infrastructure needs to handle in production.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/reinforcement_learning_agents_in_secure_sandboxes_92894d2fb8.png" alt="Running reinforcement learning (RL) agents in secure sandboxes" />*Running reinforcement learning (RL) agents in secure sandboxes means isolating each training episode in its own containerised environment, so agent actions affect only that episode's state and cannot interfere with other concurrent rollouts.*

*At production scale, this requires infrastructure that can manage large numbers of these environments in parallel, spin them up and reset them quickly between episodes, and maintain hard isolation boundaries while keeping latency overhead to a minimum.*

If you are building or evaluating infrastructure for reinforcement learning (RL) training, this article covers what your sandbox environments need to handle and how to think about each requirement at production scale.

<InfoBox className="BodyStyle">

## TL;DR: Key takeaways on running reinforcement learning (RL) agents in secure sandboxes

- Production-scale reinforcement learning (RL) training typically runs large numbers of isolated, stateful sandbox environments concurrently. Each environment holds per-episode state, resets between rollouts, and must not affect other concurrently running environments.
- Running RL agents in secure sandboxes at scale involves several infrastructure considerations: container spin-up and reset speed, support for ephemeral and persistent environment modes, orchestration at high concurrency, lightweight isolation between rollouts, and data residency controls for proprietary training data.
- Common infrastructure pitfalls include state leakage between rollouts, reset latency accumulating into throughput loss, resource contention when inference and environment execution are co-located on the same machine, non-deterministic environments, and orchestration overhead that becomes harder to predict and diagnose as rollout counts increase.

> Platforms like Northflank are built for this workload. Northflank supports 100,000+ concurrent sandbox environments, environment creation in around 1-2 seconds, both ephemeral and persistent filesystem state per environment, and microVM-based isolation using Kata, Firecracker, and gVisor depending on workload. It also offers self-serve BYOC (Bring Your Own Cloud) deployment that is production-ready, on-demand GPUs without quota requests, and API, CLI, and SSH access.
> 

</InfoBox>

## What is a reinforcement learning (RL) agent?

A reinforcement learning (RL) agent is a system that learns to make decisions by interacting with an environment and receiving a reward signal based on the outcomes of its actions.

The agent observes the current state of the environment, takes an action, receives a reward, and observes the resulting new state. It repeats this loop across many episodes, with the policy being updated periodically based on the collected experience.

Each episode produces a rollout, a complete sequence of agent actions and environment responses from start to termination. In production RL training, many rollouts are collected concurrently to generate enough data to update the agent's policy at each training step.

This is where sandboxes come in. An RL sandbox is an isolated execution environment that maintains its own state for the duration of a rollout and can be reset to a clean baseline between rollouts without affecting other concurrently running environments.

## Why do reinforcement learning (RL) agents need isolated sandbox environments?

In stateful reinforcement learning (RL) environments, the agent's actions directly modify the environment's state.

Depending on the task, the agent might write files, execute code, modify a database, call external tools, or navigate a simulated interface. That state needs to persist within an episode so the agent experiences the consequences of its actions across multiple steps.

At the same time, that state needs to be isolated from other concurrent rollouts and reset cleanly at the start of each new episode.

Without isolation, a single failed rollout can corrupt the state of other concurrently running environments. Without clean resets, training data becomes harder to reproduce reliably. Without fast spin-up and reset, environment overhead accumulates across episode boundaries and reduces the rate at which training data is collected.

Three properties production RL sandbox environments should provide:

- **Isolation:** Each rollout runs in its own environment with no shared filesystem, process namespace, or network state between concurrent episodes.
- **Stateful resets:** The environment holds per-episode state during a rollout, then resets to a known baseline at episode end.
- **Reproducibility:** Given the same initial state and the same sequence of actions, an environment should produce the same observations and rewards. This is important for debugging, for comparing policy versions, and for training stability.

<InfoBox className="BodyStyle">

**Northflank for reinforcement learning (RL) sandbox infrastructure**

Northflank provides container orchestration for high-concurrency workloads. It supports 100,000+ concurrent sandbox environments, environment creation in around 1-2 seconds, and both persistent and ephemeral filesystem state per environment.

Isolation is microVM-based, using Kata, Firecracker, and gVisor depending on the workload. [Bring Your Own Cloud (BYOC)](https://northflank.com/product/bring-your-own-cloud) deployment is self-serve and production-ready, with support for deploying inside your own cloud or VPC. On-demand GPUs are available without quota requests, and access is via API, CLI, or SSH.

Northflank has been in production since 2021 across startups, public companies, and government deployments. [See how Northflank handles sandbox infrastructure](https://northflank.com/product/sandboxes).

</InfoBox>

## What are the infrastructure requirements for running reinforcement learning (RL) agents at scale?

The main infrastructure considerations when running RL agents at scale are container lifecycle speed, stateful reset management, CPU and GPU resource separation, high-concurrency orchestration, isolation model selection, and data residency controls.

### Fast environment spin-up and reset

Full environment creation includes container scheduling, image pulling, runtime initialisation, and application startup. Environment creation time is a factor in training throughput, particularly in high-frequency training loops where environments are created and reset repeatedly across many rollouts.

Reset latency is a separate concern. Even if your container starts quickly, a slow reset routine adds overhead to every episode boundary and compounds into a measurable reduction in training data collection at high rollout counts.

### Stateful and ephemeral environment modes

**Ephemeral environments** are created fresh for each rollout and discarded after the episode ends. They provide a strong isolation guarantee since no state persists between rollouts. See [Ephemeral sandbox environments](https://northflank.com/blog/ephemeral-sandbox-environments) for how this works in practice.

**Persistent environments** hold state across multiple steps within an episode. Some implementations snapshot that state at checkpoints, allowing the environment to fork from a known point rather than rebuilding from scratch. This suits long-horizon tasks where the agent builds up filesystem state or database records over many actions.

### CPU and GPU resource separation

Environment execution is CPU-bound in most cases. Policy inference is GPU-bound and latency-sensitive. Running both on the same machine can create resource contention that degrades inference latency and environment throughput. A common pattern at production scale is to separate inference nodes from environment nodes and connect them via an async API so each scales independently. See [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor) for more on isolation technologies used in multi-tenant sandbox clusters.

### Parallelism at the orchestration layer

Running thousands of environments introduces orchestration challenges beyond what container runtime alone handles. You need a control plane that can manage that many concurrent containers without becoming the bottleneck itself. Kubernetes-based orchestration is a common approach at this scale. See [Kubernetes multi-tenancy](https://northflank.com/blog/kubernetes-multi-tenancy) for how this is typically structured.

### Isolation without heavyweight virtualisation overhead

For production RL training on untrusted or unpredictable code, hard isolation between rollouts is a common requirement. Full VMs take too long to boot for high-reset-frequency workloads. Process-level namespacing alone is not sufficient. The practical options are microVMs (Firecracker, Kata Containers) or syscall sandboxing (gVisor), each with different trade-offs around boot time, memory overhead, and compatibility. See [Firecracker vs gVisor](https://northflank.com/blog/firecracker-vs-gvisor) for a detailed comparison.

### BYOC for data residency

Teams building RL pipelines on proprietary data often need training environments to run inside their own cloud or on-premise infrastructure. Rollout trajectories, reward signals, and environment states can contain sensitive information that should not leave the company's network boundary. See [Top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes) and [Self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes) for how different platforms approach this.

## Running reinforcement learning (RL) agents on Northflank

The infrastructure requirements covered in this article are what Northflank is built to handle: fast environment creation, clean stateful resets, hard isolation between rollouts, support for ephemeral and persistent modes, high-concurrency orchestration, and BYOC deployment.

[Northflank](https://northflank.com/product/sandboxes) supports 100,000+ concurrent sandbox environments, environment creation in around 1-2 seconds, microVM-based isolation using Kata, Firecracker, and gVisor depending on workload, and self-serve [Bring Your Own Cloud (BYOC)](https://northflank.com/product/bring-your-own-cloud) deployment that is production-ready. On-demand GPUs are available without quota requests. Access is via API, CLI, or SSH. Northflank has been in production since 2021 across startups, public companies, and government deployments.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

<InfoBox className="BodyStyle">

To see how this works in practice, the guide below walks through spinning up a secure sandbox with Firecracker, gVisor, and Kata on Northflank:

[How to spin up a secure code sandbox and microVM with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)

For an overview of Northflank's sandbox infrastructure, see [Northflank sandboxes](https://northflank.com/product/sandboxes)

To get started or speak with the team about your infrastructure requirements: [Get started](https://app.northflank.com/signup) or [Book a demo](https://cal.com/team/northflank/northflank-demo)

</InfoBox>

## FAQ: Running reinforcement learning (RL) agents in secure sandboxes

### What is a reinforcement learning (RL) agent?

An RL agent is a system that learns by interacting with an environment. It observes state, takes actions, receives rewards, and updates its policy to maximise cumulative reward over time. RL agents are used in robotics, game AI, LLM post-training pipelines, and agentic AI systems that learn through environment interaction.

### What is the difference between a reinforcement learning (RL) agent and an LLM agent?

An LLM agent uses a large language model to reason and take actions, typically through prompting, tool calls, and multi-turn reasoning. A reinforcement learning (RL) agent learns a policy through trial-and-error interaction with an environment. The two are often combined in modern LLM training pipelines: approaches like RLHF use human preference signals, while GRPO and PPO-based post-training use verifiable environment feedback to improve decision-making.

### Does agentic AI use reinforcement learning?

Many agentic AI systems use reinforcement learning (RL) during training. RLHF is used to align LLMs with human preferences. More recent approaches like GRPO and PPO-based post-training fine-tune LLMs using verifiable reward signals from environment interaction. The inference-time behaviour of these agents may not look like classical RL, but the training pipeline often involves RL environment infrastructure, particularly for approaches that use verifiable environment feedback.

### How many environments do you need for reinforcement learning (RL) training?

It depends on the task and training algorithm. Production-scale reinforcement learning (RL) training commonly runs hundreds to tens of thousands of parallel environments per training step. Larger experiments or those using async RL architectures can require 100,000 or more concurrent environments. The number of parallel environments directly affects how quickly the model collects training data and how efficiently the inference cluster is utilised.

### What isolation model works for reinforcement learning (RL) sandbox environments?

MicroVMs (Firecracker, Kata Containers) and syscall sandboxing (gVisor) are the main practical options. Each has different trade-offs around boot time, memory overhead, and compatibility. The right option depends on your task profile, reset frequency, and the nature of the code running inside environments. Northflank, for example, uses Kata, Firecracker, and gVisor, applying the appropriate isolation model based on the workload.

### What is a rollout in reinforcement learning (RL)?

A rollout is a complete sequence of interactions between a reinforcement learning (RL) agent and its environment for a single training episode. It consists of state observations, agent actions, environment transitions, and reward signals from episode start to termination. RL training typically collects multiple rollouts to generate sufficient data for each policy update step.

## Related articles on reinforcement learning (RL) agents and sandboxes

- [What is an AI sandbox](https://northflank.com/blog/what-is-an-ai-sandbox): A foundational overview of what sandbox environments are, how they provide isolation, and where they are used across AI workloads.
- [Ephemeral sandbox environments](https://northflank.com/blog/ephemeral-sandbox-environments): How ephemeral execution environments work, when to use them, and how they differ from persistent sandboxes.
- [How to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents): A practical guide to sandboxing AI agents, covering isolation methods, filesystem controls, and network restrictions.
- [Kubernetes multi-tenancy](https://northflank.com/blog/kubernetes-multi-tenancy): How Kubernetes handles multi-tenant workloads and what that means for running large numbers of isolated environments on shared infrastructure.
- [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor): A detailed comparison of the three main container isolation technologies used in secure sandbox environments.
- [Firecracker vs gVisor](https://northflank.com/blog/firecracker-vs-gvisor): A focused comparison of Firecracker microVMs and gVisor syscall sandboxing for workloads that need fast, lightweight isolation.
- [What is AWS Firecracker](https://northflank.com/blog/what-is-aws-firecracker): How Firecracker works, what it provides over standard containers, and where it fits in a sandbox infrastructure stack.
- [Top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes): A look at sandbox platforms that support bring-your-own-cloud deployment for teams with data residency requirements.
- [Self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes): Options for teams that need to run sandbox infrastructure entirely within their own infrastructure.]]>
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</item><item>
  <title>Best platforms for high concurrency sandbox environments in 2026</title>
  <link>https://northflank.com/blog/best-platforms-for-high-concurrency-sandbox-environments</link>
  <pubDate>2026-03-17T15:00:00.000Z</pubDate>
  <description>
    <![CDATA[Best platforms for high concurrency sandboxes in 2026: compare Northflank, Modal, E2B, CodeSandbox, and Fly.io Sprites on autoscaling, cold starts, isolation, BYOC support, and pricing.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/high_concurrency_sandbox_environments_77a6c14ba7.png" alt="Best platforms for high concurrency sandbox environments in 2026" /><InfoBox className="BodyStyle">

## TL;DR: What are the best platforms for high concurrency sandboxes in 2026?

Running one or two sandboxes is a solved problem. Running thousands simultaneously, each isolated, each provisioned in milliseconds, each billing only for what it uses, is where most platforms hit a ceiling. These are the platforms built to handle concurrency at scale.

- **Northflank** – The only platform on this list that combines horizontal autoscaling, intelligent bin-packing, and [production-grade microVM isolation (Kata Containers, Firecracker, and gVisor)](https://northflank.com/product/sandboxes) in one control plane. Processes millions of isolated workloads monthly. Runs sandboxes alongside databases, GPUs, and APIs with [BYOC into AWS, GCP, Azure, or bare-metal](https://northflank.com/product/bring-your-own-cloud), all without a concurrency cap.
- **Modal** – Scales to 20,000 concurrent containers with sub-second cold starts. The strongest managed option for Python-first teams running high-volume parallel workloads.
- **E2B** – Up to 100 concurrent sandboxes on Pro with Firecracker microVM isolation and clean Python and TypeScript SDKs. Custom concurrency available on Enterprise.
- **CodeSandbox** – Fork-based parallelism lets you spawn multiple agents from the same base environment state without setup overhead.
- **Fly.io Sprites** – Persistent microVM sandboxes that idle automatically and wake fast. Better suited for moderate concurrency with long-running sessions than raw throughput at scale.

</InfoBox>

## Why concurrency is the hard problem in sandbox infrastructure

Spinning up a single isolated sandbox is straightforward. The hard part is what happens at scale: thousands of agents running in parallel, each needing its own isolated environment, each provisioned in under a second, each tearing down cleanly without leaving orphaned processes or inflating your bill.

Most platforms handle low concurrency fine during development and start showing cracks when you move to production. Rate limits kick in. Provisioning queues back up. Cold starts that were acceptable at ten sandboxes become a bottleneck at ten thousand. Bin-packing efficiency starts to matter because idle compute at scale gets expensive fast.

The platforms worth evaluating for high concurrency have three things in common: sub-second provisioning, autoscaling that does not require manual intervention, and pricing that scales linearly with actual usage rather than jumping with each tier upgrade.

## What are the best platforms for high-concurrency sandboxes?

Most sandbox platforms that handle concurrency well are either sandbox-only tools or full infrastructure platforms. That distinction matters when your agent pipeline needs more than just parallel execution.

### 1. Northflank

[Northflank](https://northflank.com/product/sandboxes) is a full-stack cloud platform with native support for high-concurrency sandbox environments, accessible via UI, API, CLI, and GitOps. You define your sandbox environment once, specifying isolation model, storage, secrets, and lifecycle rules, then scale it horizontally without touching the configuration.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

What separates Northflank at scale is the combination of intelligent bin-packing, horizontal autoscaling, and microVM isolation applied per workload. Most platforms that expose concurrency controls provision containers only. Northflank orchestrates the full stack: sandboxes alongside databases, background workers, GPU workloads, and APIs, all autoscaling together in one control plane. Northflank has been processing millions of isolated workloads monthly since 2021 across startups, public companies, and government deployments.

**Key features:**

- **Horizontal autoscaling:** Set minimum and maximum sandbox counts. Autoscaling handles demand spikes based on CPU, memory, and RPS thresholds without manual intervention.
- **Intelligent bin-packing:** Maximizes workload density across available compute without breaking isolation boundaries between tenants.
- **Sub-second cold starts:** Boot a microVM in under a second. Isolated environments are provisioned instantly for parallel agent tasks and batch jobs.
- **Isolation options:** Kata Containers with Cloud Hypervisor, Firecracker, and gVisor applied per workload. Every sandbox runs in its own microVM with true multi-tenant isolation.
- **Any OCI image:** Accepts any container from Docker Hub, GitHub Container Registry, or private registries without modification. No SDK-defined image constraints.
- **Managed or BYOC:** Deploy on Northflank's managed infrastructure or inside your own cloud. BYOC supports AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, and bare-metal, self-serve with no enterprise sales required.
- **SOC 2 Type 2 certified:** Relevant for teams running multi-tenant workloads with compliance requirements.

[cto.new migrated their entire sandbox infrastructure to Northflank](https://northflank.com/product/sandboxes) in two days after EC2 metal instances made scaling costs unpredictable, going from unworkable provisioning to thousands of daily deployments with linear, per-second billing.

**Best for:** Teams running thousands of concurrent sandboxes in production. Platform engineering teams building multi-tenant agent infrastructure. Enterprise teams that need BYOC, compliance controls, and autoscaling without operational overhead.

**Pricing:** $0.01667/vCPU-hour, $0.00833/GB-hour, H100 GPU at $2.74/hour all-inclusive. BYOC deployments bill against your own cloud account.

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[Get started on Northflank](https://app.northflank.com/signup) (self-serve, no demo required). Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) with an engineer if you want to walk through your architecture first.

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### 2. Modal

Modal is the strongest managed option for raw concurrency. It scales to 20,000 concurrent containers with sub-second cold starts and is built specifically for high-volume parallel execution. Companies like Lovable and Quora run millions of executions through it. The Team plan supports up to 1,000 concurrent containers, and enterprise plans go further.

Sandboxes use gVisor isolation, and environments are defined dynamically through Modal's Python SDK rather than pre-built images, which makes it easy to parameterize each sandbox at runtime. The tradeoff is the Python-first model and no BYOC option.

**Best for:** Python-heavy teams running high-volume parallel workloads, ML evaluation pipelines, and batch jobs where raw concurrency is the primary requirement.

**Pricing:** Starter is free with $30/month in compute credits and up to 100 concurrent containers. Team at $250/month with up to 1,000 containers. CPU from $0.1419/core /hr.

### 3. E2B

E2B supports up to 100 concurrent sandboxes on Pro and custom limits on Enterprise, built around Firecracker microVM isolation with boot times under 200ms. The Hobby plan caps at 20 concurrent sandboxes, which rules it out for most production workloads. BYOC is limited to AWS enterprise customers only.

**Best for:** Teams building AI coding agents, model evaluation pipelines, and Code Interpreter-style tools that need clean SDK integration and Firecracker isolation at scale.

**Pricing:** Hobby free with $100 one-time credit, 20 concurrent sandboxes. Pro at $150/month with 100 concurrent sandboxes and 24-hour sessions. Enterprise for custom concurrency limits.

### 4. CodeSandbox

CodeSandbox handles concurrency through its fork and snapshot model. You create a base environment once, snapshot it, then branch as many parallel instances as you need from that snapshot in under two seconds. That makes it efficient for running many agent iterations against the same starting state without redundant setup overhead per sandbox.

Backed by Together AI, it accepts Dev Container images and standard environment formats. There is no hard-published concurrency limit, and the fork model means spinning up parallel instances is fast. No BYOC option, and it skews toward web-focused use cases.

**Best for:** Parallel agent runs from shared state, A/B testing agent workflows, and web-focused coding tools where fork-based concurrency fits the use case.

**Pricing:** Community Build plan is free with 10 concurrent VM sandboxes. Scale plan from $170/month with up to 250 concurrent VMs. Enterprise is custom. VM credits are priced at $0.015/hour.

### 5. Fly.io Sprites

Sprites are persistent Linux microVMs that idle automatically and resume in around 300ms. They are not optimized for raw concurrent throughput the way Modal or Northflank are, but their idle billing model means you can keep a large pool of warm environments ready without paying for always-on compute. That pattern suits moderate concurrency with unpredictable usage, where you want environments ready but cannot justify keeping them all running.

Sandbox creation takes one to twelve seconds; there is no BYOC, and the platform is early-stage. For teams whose concurrency needs are moderate and whose primary requirement is warm persistent environments rather than thousands of simultaneous cold starts, Sprites is worth considering.

**Best for:** Moderate concurrency with persistent warm environments, teams already on Fly.io, and use cases where idle billing matters more than peak throughput.

**Pricing:** $0.07/CPU-hour and $0.04375/GB-hour, no charge when idle.

## Which platform should you choose for high-concurrency sandboxes?

If raw concurrent throughput is the requirement, Modal and Northflank are the two options built for it. Modal reaches 20,000 concurrent containers but is Python-only with no BYOC. Northflank handles the same scale with stronger isolation options, any OCI image, and BYOC deployment into your own infrastructure.

For teams whose concurrency needs are real but not extreme, E2B covers most production use cases on its enterprise tier. CodeSandbox is strong for specific patterns: fork-based parallelism and high-frequency provisioning, respectively. Fly.io Sprites is better suited to warm pool concurrency than peak throughput.

| Platform | Concurrent sandboxes | Cold start | BYOC | Isolation |
| --- | --- | --- | --- | --- |
| **Northflank** | Scales to millions monthly, autoscaling built-in | Sub-second | Yes (AWS, GCP, Azure, bare-metal) | Kata Containers, Firecracker, gVisor |
| **Modal** | Up to 20,000 (managed) | Sub-second | No | gVisor |
| **E2B** | 20 (Hobby), 100 (Pro), custom Enterprise | Under 200ms | AWS and GCP only, enterprise | Firecracker |
| **CodeSandbox** | 10 (Build), 250 (Scale), custom Enterprise | Under 2 seconds (from snapshot) | No | microVM |
| **Fly.io Sprites** | Moderate, idle pool model | 1 to 12 seconds | No | Firecracker |

### How do high-concurrency sandbox platforms compare on pricing?
Pricing as of April 2026. Billing models differ across platforms (some bill based on active CPU usage only, others bill for the entire duration the sandbox is running). Verify current rates on each platform's pricing page before making cost decisions.

| Platform | CPU | Memory | Storage | GPU | Billing model |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | $0.01667/vCPU-hr | $0.00833/GB-hr | $0.15/GB-month | L4: $0.80/hr, A100 40GB: $1.42/hr, A100 80GB: $1.76/hr, H100: $2.74/hr, H200: $3.14/hr | Per second |
| **E2B** | $0.0504/vCPU-hr | $0.0162/GiB-hr | 10–20GB included free | Do not provide GPU compute | Per second |
| **Fly.io Sprites** | $0.07/CPU-hr | $0.04375/GB-hr | $0.00068/GB-hr (hot NVMe) | Do not provide GPU compute | Per second, actual cgroup usage. No charge when idle |
| **CodeSandbox** | $0.075/core-hr (credit-based: $0.015/credit) | Bundled with VM tier | Included | Do not provide GPU compute | Credit-based ($0.015/credit) |
| **Modal Sandboxes** | $0.1419/physical core-hr (2 vCPU) | $0.0242/GiB-hr | — | L4: $0.80/hr, A100 40GB: $2.10/hr, A100 80GB: $2.50/hr, H100: $3.95/hr, H200: $4.54/hr | Per second |

### BYOC support across high-concurrency sandbox platforms
The table below shows more detail on how each platform handles BYOC deployment, which clouds are supported, and whether it requires a sales process.
| Platform | BYOC available | Clouds supported | Access model | Pricing model |
| --- | --- | --- | --- | --- |
| **Northflank** | Yes, fully self-serve | AWS, GCP, Azure, Oracle, CoreWeave, any neoclouds, Civo, bare-metal, on-premises | Self-serve, enterprise contracts available for larger commits (with bulk discounts) | Your existing cloud bill, CPU $0.01389/vCPU-hr and Memory $0.00139/GB-hr |
| **E2B** | Yes, limited and not self-serve | AWS and GCP only | Not publicly disclosed, need to contact sales | Starts at $50/sandbox/month, on top of your existing cloud bill |
| **Modal** | No | Managed only | — | — |
| **Fly.io Sprites** | No | Managed only | — | — |
| **CodeSandbox** | Enterprise only | Custom dedicated cluster | Enterprise plan, contact sales | Custom |

## FAQ: high concurrency sandbox environments

### What limits sandbox concurrency on most platforms?

Most platforms impose concurrency limits at the plan level. E2B caps Hobby at 20 concurrent sandboxes and raises that on Pro and Enterprise. Modal caps the Team plan at 1,000 containers. Platforms like Northflank that handle autoscaling at the infrastructure level do not impose the same kind of hard plan-level caps because bin-packing and scheduling are handled by the platform itself.

### What is bin-packing, and why does it matter for concurrency?

Bin-packing is the process of scheduling workloads onto available compute as efficiently as possible without over-provisioning. At high concurrency, poor bin-packing means you pay for idle nodes while sandboxes queue for resources. Northflank's autoscaler handles bin-packing automatically, which keeps costs linear with actual usage rather than jumping with each new node.

### Does isolation quality change at high concurrency?

It should not, but it does on some platforms. Shared-kernel container isolation under high load can create noisy-neighbor problems where one tenant's workload affects another's performance. MicroVM isolation with dedicated kernels per workload prevents this because each sandbox has its own isolated kernel, regardless of how many are running simultaneously.

### Which platform handles concurrent ML evaluation workloads best?

Northflank and Modal are the strongest options here. Modal is Python-first and optimized for ML, scales to 20,000 containers, and has deep GPU support. Northflank handles the same scale with more isolation flexibility, any OCI image, and BYOC for teams that need evaluation workloads running inside their own infrastructure.

### Can I run concurrent sandboxes in my own cloud?

Yes, with Northflank. BYOC deployment is available self-serve across AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, and bare-metal. E2B also offers BYOC, but only on AWS and only for enterprise customers. Every other platform on this list is managed-only.

### How does cold start speed affect high concurrency workloads?

At low concurrency, a 200ms cold start is negligible. With thousands of concurrent provisioning requests, it becomes a queue management problem. Platforms that provision sequentially will back up. Platforms with parallel provisioning pipelines and pre-warmed capacity handle burst concurrency without queuing. Northflank's sub-second microVM boot is designed with this in mind.

## Conclusion

High concurrency is where sandbox infrastructure gets genuinely hard. The easy path is picking a platform that works at ten sandboxes and hoping it holds at ten thousand. It usually does not.

Northflank is the strongest option for teams that need concurrent microVM isolation at scale, autoscaling without operational overhead, and the flexibility to run inside their own infrastructure. Modal is the right call for Python-first teams that need raw throughput and do not need BYOC. The other platforms here each handle concurrency well within their constraints.

<InfoBox className="BodyStyle">

You can [get started for free on Northflank](https://app.northflank.com/signup) or [talk to the team](https://cal.com/team/northflank/northflank-demo?duration=30) to walk through your concurrency requirements.

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  <title>What are ephemeral environments? How they work and when to use them</title>
  <link>https://northflank.com/blog/what-are-ephemeral-environments</link>
  <pubDate>2026-03-16T17:30:00.000Z</pubDate>
  <description>
    <![CDATA[Ephemeral environments are short-lived, on-demand deployments destroyed after use. Learn the types, how they work, and implementation challenges.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/what_are_ephemeral_environments_49619c9a64.png" alt="What are ephemeral environments? How they work and when to use them" />Ephemeral environments are short-lived, isolated deployments that are created on demand, used for a specific purpose, and destroyed once that purpose is complete.

This article covers what ephemeral environments are, how they work, the different types used in production, the key challenges teams run into when implementing them at scale, and how platforms like Northflank support them in production.

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## TL;DR: Key takeaways on ephemeral environments

- Ephemeral environments are temporary, isolated deployments tied to a specific task (a pull request, a test run, or an AI agent session) and torn down once that task is complete.
- The main types used in production are preview environments, continuous integration (CI) test environments, sandbox environments, and AI execution environments.
- The ephemeral pattern is not limited to developer workflows. Sandbox environments and AI execution environments follow the same ephemeral pattern (created on demand, destroyed after use) but require stronger isolation models because the workloads or code running inside cannot be fully trusted.
- Lifecycle automation (trigger, create, run, teardown) is what makes ephemeral environments practical at scale. The more of the lifecycle you leave unautomated, the more time your team spends managing environments instead of shipping code.

> Platforms like [Northflank](https://northflank.com/product/preview-environments) support full-stack preview environments triggered by Git pull requests, including databases, microservices, and background jobs, with configurable automated provisioning and teardown, alongside microVM-isolated execution environments for sandboxed and agent workloads.
> 

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## What are ephemeral environments?

An ephemeral environment is a short-lived, isolated deployment created on demand for a specific task and destroyed when that task is complete. In DevOps, "ephemeral" describes infrastructure with a lifecycle tied to a task rather than a calendar. The environment exists for as long as it is needed, then it is gone.

This contrasts with the traditional model of maintaining a small number of long-lived shared environments (dev, QA, staging) that persist indefinitely and are shared across the team. Those environments accumulate stale state, diverge from production over time, and create bottlenecks when multiple engineers need to test simultaneously.

## What are the common types of ephemeral environments used in production?

The types of ephemeral environments in active use today reflect different triggers, audiences, and isolation requirements. Understanding the differences helps you pick the right model for your use case.

### Preview environments

A preview environment is a deployment of your application tied to a pull request or feature branch. It gives developers, QA, and product stakeholders a way to review changes in an isolated environment before code is merged. The environment is typically torn down when the PR is closed or merged.

For a deeper look at implementation challenges, see [the what and why of ephemeral preview environments on Kubernetes](https://northflank.com/blog/the-what-and-why-of-ephemeral-preview-environments-on-kubernetes-sandbox-testing).

### CI/CD test environments

These environments are created to run automated tests (integration tests, end-to-end tests, or smoke tests) against an isolated environment. They are typically torn down once testing is complete.

### Sandbox environments

Sandbox environments are used when the workload running inside cannot be fully trusted: third-party integrations, user-submitted code, or anything requiring a hard security boundary between the workload and your host infrastructure. The right isolation model depends on your threat model and workload requirements.

For a full breakdown of isolation models, see the [ephemeral sandbox environments guide](https://northflank.com/blog/ephemeral-sandbox-environments).

### AI execution environments

AI agents generate and execute code at runtime without a human reviewing each run. The code is produced by a model dynamically, which changes the threat model compared to standard developer workflows. Each agent session or task gets an isolated runtime created per session and scoped to that session.

For a detailed look at the implementation challenges, see [ephemeral execution environments for AI agents](https://northflank.com/blog/ephemeral-execution-environments-ai-agents).

### How do the common ephemeral environment types compare?

The table below maps each type to its common triggers and primary user.

| Type | Common triggers | Primary user |
| --- | --- | --- |
| Preview environment | Pull request opened, branch push | Developers, QA, product |
| CI/CD test environment | Pipeline run, commit, scheduled run | CI systems, engineering teams |
| Sandbox environment | API call, developer request, pipeline step | Engineering teams running security-sensitive or multi-tenant workloads |
| AI execution environment | Agent task, webhook, queue event | Engineering teams running AI agent workloads |

## How does an ephemeral environment lifecycle work?

The lifecycle runs through four stages. The specific details vary by type, but the pattern holds across the environment types described above:

1. **Trigger:** an event initiates environment creation. This could be a PR being opened, a pipeline starting, an agent calling a tool, or a developer requesting a sandbox.
2. **Create:** the environment is provisioned. Containers or VMs start, networking is configured, and services are deployed.
3. **Run:** the environment serves its purpose. A team member reviews a feature, tests execute, or an agent runs code.
4. **Teardown:** the environment is destroyed and resources are released. If teardown requires manual intervention, environments may accumulate and consume resources beyond their intended lifetime.

Lifecycle automation is what separates a working ephemeral environment strategy from a theoretical one. Without automating the full lifecycle, managing environments becomes an increasing operational burden.

For a practical implementation walkthrough for preview environments, see [how to auto-create preview environments on every PR](https://northflank.com/blog/how-to-auto-create-preview-environments-on-every-pr).

## How do ephemeral environments work on Kubernetes?

Kubernetes is a common substrate for running ephemeral environments at scale. Each environment typically maps to one or more namespaces, with services and deployments provisioned per environment within those namespaces.

Teams running preview environments across parallel feature branches can generate many concurrent environments quickly. Cluster resource limits, namespace sprawl, and reliable teardown when environments are no longer needed all become operational concerns that compound as team size grows.

If you are evaluating how different approaches handle this, the [Kubernetes preview environments comparison](https://northflank.com/blog/kubernetes-preview-environments-comparison) covers the main options in detail.

## What are the main challenges with ephemeral environments?

Ephemeral environments introduce operational complexity that grows with team size and environment volume. The following are common challenges teams encounter.

### State management

Ephemeral environments are stateless by design, but the workflows that use them often are not. The state each environment needs (credentials, dependencies, data) may have to be reproduced on every creation, which can require upfront investment in automation.

### Environment fidelity

An ephemeral environment is only useful if it behaves like production. The more services your application has, the harder this becomes. Microservices architectures can require many services per environment depending on the application. Each service needs correct configuration, service discovery, and data to behave like production. Partial environments that skip dependent services can produce misleading test results.

### Cost control

Environments that outlive their intended purpose consume resources unnecessarily. Without teardown automation, tracking and cleaning up environments becomes a manual operational burden.

### Creation speed vs. isolation depth

Container-based environments start faster but share a kernel with the host. MicroVM-based environments provide a kernel boundary but add startup latency relative to containers. The right model depends on your threat model, not just your latency target.

For a broader look at how platforms handle these trade-offs, see [best platforms for on-demand preview environments](https://northflank.com/blog/best-platforms-for-on-demand-preview-environments).

## How does Northflank handle ephemeral environments?

Northflank provides infrastructure for ephemeral environments across both development workflows and sandbox or agent execution workloads.

![northflank-previews.png](https://assets.northflank.com/northflank_previews_89a97262d2.png)

For development workflows, [Northflank preview environments](https://northflank.com/product/preview-environments) spin up full-stack environments on pull requests. Each environment includes:

- databases, microservices, and background jobs as configured in your pipeline
- Git-triggered creation managed through Northflank's UI or IaC templates
- configurable automated teardown based on policies you set
- snapshots and caching to reduce build times for teams running high volumes of concurrent environments

For sandbox and agent execution workloads, Northflank supports microVM-based isolation using Firecracker, gVisor, and Kata Containers. Environments can be created via the Northflank API, CLI, or UI, with lifecycle management tied to external triggers. Northflank runs across AWS, GCP, Azure, on-premises, and bare-metal infrastructure through [bring-your-own-cloud](https://northflank.com/product/bring-your-own-cloud) support.

Northflank is used across a range of organisations, from early-stage startups to public companies and government deployments.

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**Get started:**

- [Preview environments on Northflank](https://northflank.com/product/preview-environments): product overview of how Northflank handles full-stack preview environment creation, teardown, and lifecycle management.
- [Set up a preview environment](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment): step-by-step documentation for configuring preview environments on Northflank.
- [Try Northflank](https://app.northflank.com/signup): create a free account and spin up your first environment.
- [Book a demo](https://cal.com/team/northflank/northflank-demo): talk to the team about your infrastructure requirements.

</InfoBox>

## FAQ: Ephemeral environments

### What are ephemeral environments?

Ephemeral environments are temporary, isolated deployments created on demand for a specific task and destroyed once that task is complete. Each environment is created fresh and carries no state from previous runs.

### What does ephemeral mean in tech?

In software infrastructure, ephemeral describes a resource with a short, task-scoped lifecycle. An ephemeral environment exists only as long as it is needed, then is torn down and its resources are released.

### How are ephemeral environments different from staging?

Staging is a long-lived, shared environment maintained continuously. Ephemeral environments are short-lived and per-task. Multiple ephemeral environments can run in parallel, each tied to a specific branch, test run, or request. Staging persists; ephemeral environments do not.

### When do ephemeral environments need stronger isolation?

When the code running inside is not authored by your own engineers (user-submitted code, third-party integrations, or AI-generated code), container-based isolation may not be sufficient. MicroVM-based runtimes like Firecracker or gVisor provide a kernel boundary that containers do not.

## Related ephemeral environments articles

The articles below go deeper on specific aspects of ephemeral environments covered in this guide:

- [The what and why of ephemeral preview environments on Kubernetes](https://northflank.com/blog/the-what-and-why-of-ephemeral-preview-environments-on-kubernetes-sandbox-testing): a detailed look at how preview environments work in practice, the challenges of matching production for full-stack applications, and Kubernetes-specific implementation considerations.
- [Ephemeral sandbox environments guide](https://northflank.com/blog/ephemeral-sandbox-environments): covers isolation models in depth, including containers, microVMs, and full VMs, with guidance on when each applies and the trade-offs at each level.
- [Ephemeral execution environments for AI agents](https://northflank.com/blog/ephemeral-execution-environments-ai-agents): covers why AI agent workloads benefit from ephemeral isolated runtimes, how to implement them, and the operational challenges at scale.
- [How to auto-create preview environments on every PR](https://northflank.com/blog/how-to-auto-create-preview-environments-on-every-pr): a step-by-step implementation guide for automating preview environment creation from pull requests.
- [Kubernetes preview environments comparison](https://northflank.com/blog/kubernetes-preview-environments-comparison): compares the main approaches to running preview environments on Kubernetes clusters.
- [Best platforms for on-demand preview environments](https://northflank.com/blog/best-platforms-for-on-demand-preview-environments): a platform comparison for teams evaluating tooling options for preview environment management.
- [Preview environment platforms](https://northflank.com/blog/preview-environment-platforms): a broader look at the platform landscape for teams getting started with preview environment tooling.
- [Code execution environments for autonomous agents](https://northflank.com/blog/code-execution-environment-for-autonomous-agents): covers the infrastructure requirements for running agent-generated code safely in isolated runtimes.]]>
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  <title>Best platforms for long-running sandbox environments in 2026</title>
  <link>https://northflank.com/blog/best-platforms-for-long-running-sandbox-environments</link>
  <pubDate>2026-03-16T15:45:00.000Z</pubDate>
  <description>
    <![CDATA[Best platforms for long-running sandbox environments in 2026: compare Northflank, E2B, CodeSandbox, Modal, and Fly.io Sprites on session limits, state persistence, BYOC support, and pricing.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/long_running_sandbox_environments_506ea7b259.png" alt="Best platforms for long-running sandbox environments in 2026" /><InfoBox className="BodyStyle">

## TL;DR: What are the best platforms for long-running sandboxes in 2026?

Most sandbox platforms are built around short-lived execution. They work well when each run is isolated and stateless, but fall apart the moment your agent needs to maintain a working environment across sessions, build up state over time, or run a task that outlasts an arbitrary platform timeout. These are the platforms worth evaluating when persistence is the requirement.

- **Northflank** – Full-stack AI infrastructure platform with managed cloud and [BYOC deployment](https://northflank.com/product/bring-your-own-cloud) into AWS, GCP, Azure, or bare-metal. [Production-grade microVM sandboxes](https://northflank.com/product/sandboxes) with Kata Containers, Firecracker, and gVisor isolation, unlimited sessions, databases, GPUs, CI/CD, and observability all in one place.
- **E2B** – Up to 24 hours on Pro with session persistence and snapshot support. Best for agents that need structured execution windows.
- **CodeSandbox** – Snapshot and fork environments with VM restore in under two seconds. State persists across sessions without rebuilding.
- **Modal** – Unlimited session duration with gVisor isolation and snapshot primitives for saving and restoring sandbox state.
- **Fly.io Sprites** – Persistent Linux VMs with 100GB NVMe storage that survive between sessions and idles automatically when not in use.

</InfoBox>

## Why session length matters for sandbox environments

Most early sandbox decisions are made under prototype conditions, where every run is short, stateless, and independent. That works fine for quick code execution or one-shot agent tasks. It stops working the moment your agent needs to hold state.

A coding agent refactoring a large codebase across multiple interactions cannot start from scratch each time. An AI assistant maintaining memory of a user's project needs an environment that survives beyond a single session. A data pipeline agent processing files for hours cannot hit a platform timeout mid-run. These are not edge cases. They are the default shape of production agent workflows.

Short session limits force workarounds: checkpointing state to external storage, rebuilding environments on every run, and re-downloading datasets. Each one adds complexity and latency. Choosing a platform built for persistence from the start avoids the problem entirely.

## What are the best platforms for long-running sandboxes?

Session length is only part of the equation. The platforms below differ significantly in how they handle state, what survives between runs, and how much infrastructure sits around the sandbox itself. Here is how they compare.

### 1. Northflank - Full-stack AI infrastructure with unlimited sessions

[Northflank](https://northflank.com/) is a full-stack AI cloud platform with native support for long-running and persistent sandbox environments, accessible via UI, API, CLI, and GitOps. You define your sandbox environment once, specifying isolation model, storage, attached databases, secrets, and lifecycle rules, then provision it however fits your workflow: from a CLI command, an API call in a CI step, a Git trigger, or directly from an agent pipeline.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

What sets Northflank apart for long-running use cases is the combination of no forced session limits and full-stack scope. Most sandbox platforms provision containers only. Northflank provisions databases, persistent volumes, S3-compatible object storage, background jobs, and encrypted secrets alongside your sandboxes, all from a single template on every trigger.

**Key features:**

- **No session limits:** Sandboxes run for seconds or weeks with no platform-imposed cutoff. Ephemeral and persistent environments are supported in the same control plane.
- **Persistent storage:** Attach volumes from 4GB to 64TB with multi-read-write support. Mount S3-compatible object storage for artifacts. Deploy managed databases alongside sandboxes for agent memory and execution history.
- **Isolation options:** Kata Containers with Cloud Hypervisor, Firecracker, and gVisor, applied per workload. Northflank's engineering team actively contributes to Kata Containers, QEMU, and Cloud Hypervisor upstream.
- **API-first provisioning:** Trigger, list, pause, resume, and delete sandbox environments programmatically from any CI system, script, or orchestration layer.
- **Managed or BYOC:** Deploy on Northflank's managed infrastructure or run sandboxes inside your own cloud. BYOC supports AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, and bare-metal, self-serve with no enterprise sales process required.
- **GitOps-compatible:** Sandbox environment templates can be version-controlled and synced bidirectionally with a Git repository.
- **SOC 2 Type 2 certified:** Relevant for teams with compliance requirements or regulated infrastructure.

[cto.new migrated their entire sandbox infrastructure to Northflank](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) in two days after EC2 metal instances made scaling costs unpredictable, going from unworkable provisioning to thousands of daily deployments with linear, per-second billing.

**Best for:** Production agents that maintain state across days or weeks. Platform engineering teams building agent infrastructure. Enterprise teams that need BYOC, compliance controls, and persistent storage at scale.

**Pricing:** $0.01667/vCPU-hour, $0.00833/GB-hour, H100 GPU at $2.74/hour all-inclusive. BYOC deployments bill against your own cloud account.

<InfoBox className="BodyStyle">

[Get started on Northflank](https://app.northflank.com/signup) (self-serve, no demo required). Or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) with an engineer if you want to walk through your architecture first.

</InfoBox>

### 2. E2B

E2B supports session persistence and snapshots, letting you pause a sandbox and resume it later from the same state. On the Pro plan, sessions run for up to 24 hours, which covers the majority of agent workflows that do not need multi-day continuity. The Python and TypeScript SDKs handle the full lifecycle, including creation, execution, filesystem access, and teardown, and integrate cleanly with LangChain, OpenAI, and Anthropic tooling.

The 24-hour cap is the real constraint here. Workflows that span multiple days require either an upgrade to enterprise or engineering around the limit with external state management. BYOC is available but limited to AWS enterprise customers only.

**Best for:** Agents with structured execution windows under 24 hours who want clean SDK integration and reliable persistence within a session.

**Pricing:** Free tier with $100 one-time credit. Pro at $150/month with 24-hour sessions and configurable CPU and RAM.

### 3. CodeSandbox

CodeSandbox persists environment state across sessions and supports snapshot and fork workflows that are genuinely useful for long-running agent work. You can save a sandbox at any point, branch from it, and restore in under two seconds, which makes it practical for agents that need to experiment across multiple paths from the same base state.

Backed by Together AI, it accepts Dev Container images and standard environment formats. There is no BYOC option, and the platform skews toward web-focused use cases, but for teams building iterative agent workflows where resuming from a known state is more important than raw session length, it holds up well.

**Best for:** Iterative agent workflows, parallel runs from shared state, and web-focused coding tools where snapshot and restore matter more than unlimited session length.

**Pricing:** Community plan is free. Production at $0.0446/vCPU-hour plus $0.0149/GB-RAM-hour.

### 4. Modal

Modal supports unlimited session duration with no platform-imposed cap. Sandboxes use gVisor isolation and sit inside a broader ML infrastructure stack that scales to 20,000 concurrent containers with sub-second cold starts. The platform also provides snapshot primitives for saving and restoring sandbox state, which is useful for long-running workflows that need checkpoints.

The tradeoff is the SDK model. Environments are defined through Modal's Python library rather than arbitrary container images, which limits flexibility for teams not already working Python-first. There is no BYOC option.

**Best for:** Python-heavy agents running long ML inference, training, or data processing jobs that need persistence without a session cap.

**Pricing:** Usage-based per second. CPU from around $0.047/vCPU-hour. GPU billed separately from CPU and RAM.

### 5. Fly.io Sprites

Sprites are persistent Linux VMs with 100GB NVMe storage that survive indefinitely between sessions. They checkpoint and restore in around 300ms and idle automatically when not in use, so you pay nothing when the environment is sitting dormant. That billing model is well-suited for long-running agent environments that have unpredictable usage patterns, where always-on compute would be wasteful but cold-start rebuild time is unacceptable.

Fly.io CEO Kurt Mackey put it plainly: ephemeral sandboxes are obsolete for agents that need a real working environment. Sprites are built around that idea. The tradeoff is that sandbox creation takes one to twelve seconds, there is no BYOC, and the platform is still early-stage relative to the others here.

**Best for:** Agents that need a persistent warm environment between irregular sessions, and teams already on Fly.io who want session persistence without always-on costs.

**Pricing:** $0.07/CPU-hour and $0.04375/GB-hour of memory, no charge when idle.

## Which platform should you choose for long-running sandboxes?

The right choice depends on how long your sessions actually need to run and what surrounds the sandbox.

For sessions measured in days or weeks, Northflank is the only option with no cap. Northflank adds microVM isolation, persistent volumes, databases, and BYOC on top of that.

For sessions under 24 hours, E2B covers most production use cases cleanly. CodeSandbox and Fly.io Sprites work well when persistence between irregular sessions matters more than raw duration. Modal fits if your long-running workloads are Python and ML first.

| Platform | Session limit | Persistence model | BYOC | Isolation |
| --- | --- | --- | --- | --- |
| **Northflank** | Unlimited | Volumes, databases, S3 | Yes (AWS, GCP, Azure, bare-metal) | Kata Containers, Firecracker, gVisor |
| **E2B** | 24 hours | Session snapshots | AWS and GCP only, enterprise | Firecracker |
| **CodeSandbox** | None | Snapshots, fork and restore | No | microVM |
| **Modal** | None | Snapshot primitives | No | gVisor |
| **Fly.io Sprites** | None | 100GB NVMe, survives idle | No | Firecracker |

### How do long-running sandbox platforms compare on pricing?
Pricing as of April 2026. Billing models differ across platforms (some bill based on active CPU usage only, others bill for the entire duration the sandbox is running). Verify current rates on each platform's pricing page before making cost decisions.

| Platform | CPU | Memory | Storage | GPU | Billing model |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | $0.01667/vCPU-hr | $0.00833/GB-hr | $0.15/GB-month | L4: $0.80/hr, A100 40GB: $1.42/hr, A100 80GB: $1.76/hr, H100: $2.74/hr, H200: $3.14/hr | Per second |
| **E2B** | $0.0504/vCPU-hr | $0.0162/GiB-hr | 10–20GB included free | Do not provide GPU compute | Per second |
| **Fly.io Sprites** | $0.07/CPU-hr | $0.04375/GB-hr | $0.00068/GB-hr (hot NVMe) | Do not provide GPU compute | Per second, actual cgroup usage. No charge when idle |
| **CodeSandbox** | $0.075/core-hr (credit-based: $0.015/credit) | Bundled with VM tier | Included | Do not provide GPU compute | Credit-based ($0.015/credit) |
| **Modal Sandboxes** | $0.1419/physical core-hr (2 vCPU) | $0.0242/GiB-hr | — | L4: $0.80/hr, A100 40GB: $2.10/hr, A100 80GB: $2.50/hr, H100: $3.95/hr, H200: $4.54/hr | Per second |

### BYOC support across long-running sandbox platforms

The table below shows more detail on how each platform handles BYOC deployment, which clouds are supported, and whether it requires a sales process.

| Platform | BYOC available | Clouds supported | Access model | Pricing model |
| --- | --- | --- | --- | --- |
| **Northflank** | Yes, fully self-serve | AWS, GCP, Azure, Oracle, CoreWeave, any neoclouds, Civo, bare-metal, on-premises | Self-serve, enterprise contracts available for larger commits (with bulk discounts) | Your existing cloud bill, CPU $0.01389/vCPU-hr and Memory $0.00139/GB-hr |
| **E2B** | Yes, limited and not self-serve | AWS and GCP only | Not publicly disclosed, need to contact sales | Starts at $50/sandbox/month, on top of your existing cloud bill |
| **Modal** | No | Managed only | — | — |
| **Fly.io Sprites** | No | Managed only | — | — |
| **CodeSandbox** | Enterprise only | Custom dedicated cluster | Enterprise plan, contact sales | Custom |

## FAQ: long-running sandbox environments

### Why do some sandbox platforms impose session limits?

Session limits are usually a cost control mechanism. Running live containers indefinitely is expensive, and platforms with managed infrastructure pass that constraint to users. Platforms like Northflank and Fly.io Sprites solve this with idle-based billing or per-second pricing rather than hard cutoffs.

### What is the difference between session persistence and state persistence?

Session persistence means the container stays alive and active. State persistence means the filesystem and environment survive even when the container shuts down or idles. Fly.io Sprites persist the state even when the environment is not running. E2B and Northflank support both, depending on how you configure your environment.

### Can I attach a database to a long-running sandbox?

On most sandbox-only platforms, no. Northflank is the exception. You can deploy Postgres, Redis, MySQL, or MongoDB in the same control plane as your sandbox and connect them directly. For other platforms, you would need an external database service.

### Which platform is best for agents that work across multiple days?

Northflank supports unlimited session duration. Northflank adds stronger isolation, persistent volumes, and BYOC.

### Does Fly.io Sprites charge when a sandbox is idle?

No. Sprites automatically idle when not in use, and billing stops. The 100GB NVMe filesystem persists through idle periods, so the environment is exactly as the agent left it when it wakes up.

### What happens to the sandbox state when a session times out on E2B?

On E2B, state within a session persists across executions while the session is active. When the session times out, all in-memory state and ephemeral filesystem data are lost. Snapshots let you save and restore specific states before a timeout, but this requires intentional checkpointing in your agent workflow.

## Conclusion

Ephemeral sandboxes made sense when agents were simple, and tasks were short. Production agents in 2026 hold state, build up environments over time, and run tasks that span hours or days. The platform you pick needs to match that reality.

Northflank is the strongest option for teams that need unlimited sessions, real persistence with volumes and databases, and the flexibility to run inside their own infrastructure. The other platforms here each cover a slice of the problem well. Northflank is the one that covers it end to end.

<InfoBox className="BodyStyle">

You can [get started for free on Northflank](https://app.northflank.com/signup) or [talk to the team](https://cal.com/team/northflank/northflank-demo?duration=30) to see how persistent sandbox infrastructure fits your stack.

</InfoBox>]]>
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</item><item>
  <title>Best platforms for on-demand preview environments in 2026</title>
  <link>https://northflank.com/blog/best-platforms-for-on-demand-preview-environments</link>
  <pubDate>2026-03-13T16:30:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the best platforms for on-demand preview environments in 2026: Northflank, Okteto, Uffizzi, Bunnyshell, and Codefresh. Full-stack, API-driven, lifecycle-controlled.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/best_platforms_for_on_demand_preview_environments_e11b6f70f1.png" alt="Best platforms for on-demand preview environments in 2026" /><InfoBox className="BodyStyle">

## TL;DR: Best platforms for on-demand preview environments at a glance

Most teams trigger preview environments from a Git pull request. But a growing category of use cases (internal developer platforms, CI orchestration, AI agent pipelines, and programmatic testing workflows) requires spinning up isolated environments on demand, programmatically, outside the PR lifecycle.

This article compares the best platforms for on-demand preview environments in 2026: how they handle API-driven provisioning, full-stack scope, lifecycle control, and cost at scale.

**Platforms covered:**

1. **Northflank:** Full-stack on-demand preview environments via API, CLI, UI, GitOps, or Git triggers. Supports databases, jobs, and secrets; managed or BYOC (Bring Your Own Cloud).
2. **Okteto:** Kubernetes developer environments with per-branch previews triggered through GitHub Actions or GitLab CI.
3. **Uffizzi:** Open-source platform for ephemeral environments with virtual cluster support and CI integration.
4. **Bunnyshell:** Template-driven ephemeral environments for multi-service setups.
5. **Codefresh:** CI/CD platform with preview environment capabilities built around its pipeline and GitOps systems.

</InfoBox>

## What are on-demand preview environments?

A preview environment is an isolated, ephemeral copy of your application stack provisioned for a specific event and destroyed when that event resolves. Most developers are familiar with PR-triggered previews: a pull request opens, an environment spins up, the PR merges, and the environment tears down.

On-demand preview environments take that further. Instead of waiting for a Git event, you provision environments programmatically (via API call, CLI command, CI step, or workflow trigger) and control their lifecycle independently of any Git event.

This is relevant for a distinct set of use cases:

- **Internal developer platforms (IDPs):** Platform teams building self-service environment provisioning for product engineers
- **AI agent pipelines:** Agents that need isolated execution environments created and destroyed between runs
- **Load and integration testing:** CI systems that spin up environments to test against before merging
- **Stakeholder reviews:** Marketing or product teams requesting environments for specific builds without needing a PR

The distinction from standard PR previews is important. PR-triggered environments are tied to a Git event and managed by the platform's Git integration. On-demand environments can be triggered by anything that can make an API call: a CI script, a Slack bot, an internal tool, or an AI orchestration layer.

## Why is on-demand environment provisioning hard to get right?

The infrastructure problem here is non-trivial. Spinning up a single container per branch is straightforward. Spinning up a reproducible, production-like environment with databases, seeded data, background workers, secrets, and isolated networking (on demand, at scale, with automatic teardown) requires a platform with considerably more depth.

Common failure modes teams run into:

- Environments that provision a frontend or API but leave out the database, making them useless for real integration testing
- No secret injection, so credentials must be hardcoded or manually configured per environment
- No lifecycle control beyond manual deletion, leading to environment sprawl and growing cloud costs
- Environments that take 10 or more minutes to provision, blocking the workflow that triggered them
- No proper API surface, meaning environments can only be created from a UI or a Git event

## What are the best platforms for on-demand preview environments in 2026?

The platforms below differ in their triggering model, stack scope, infrastructure ownership model, and level of API control. The right one depends on what your environments need to include and how your team triggers them.

### 1. Northflank

[Northflank](https://northflank.com/product/preview-environments) is a full-stack deployment platform with native support for on-demand preview environments via API, CLI, UI, GitOps, and Git triggers. You define a preview environment template (specifying the services, databases, jobs, secrets, and lifecycle rules) and trigger it however fits your workflow: by opening a PR, running a CLI command, calling the API from a CI step, or invoking it from an AI agent pipeline.

<InfoBox className="BodyStyle">

What sets Northflank apart for on-demand use cases is the combination of a complete REST API for programmatic provisioning and full-stack scope. Most platforms that expose an API provision containers only. Northflank provisions managed databases (PostgreSQL, MySQL, MongoDB, Redis), background jobs, and encrypted secrets alongside your services, all from a single template, on every trigger.

</InfoBox>

![northflank-previews.png](https://assets.northflank.com/northflank_previews_89a97262d2.png)

**Key features:**

- **Multiple trigger modes:** Environments can be triggered by Git pull requests, branch pushes, manual UI actions, CLI commands, or direct API calls. You are not locked into the PR lifecycle.
- **Full-stack scope:** Each preview environment can include services, managed database addons (PostgreSQL, MySQL, MongoDB, Redis), scheduled jobs, and encrypted secret groups. Containers alone are not the limit.
- **API-first provisioning:** The [Run preview blueprint API](https://northflank.com/docs/v1/api/project/preview-blueprints/run-preview-blueprint) lets you trigger a full environment from any CI system, script, or orchestration layer. Arguments can be passed at run time to parameterize each environment.
- **Lifecycle controls:** Configure a duration after which the environment tears down, and an idle shutdown policy per blueprint. Once configured, environments clean up without manual intervention. Active hours (restricting automatic environment creation to specific days and times) are available for BYOC projects.
- **Managed or bring your own cloud (BYOC):** Deploy on [Northflank's managed infrastructure](https://northflank.com/features/managed-cloud) or run preview environments inside your own infrastructure via [BYOC](https://northflank.com/product/bring-your-own-cloud). BYOC supports AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-premises, and bare-metal (self-serve).
- **GitOps-compatible:** Preview environment templates can be version-controlled and managed via GitOps.
- **SOC 2 Type 2 certified:** Relevant for teams with compliance requirements or regulated infrastructure. See [Northflank's security page](https://northflank.com/security) for details.

**Best for:** Startups and growing engineering teams who want self-serve, full-stack preview environments without infrastructure overhead. Platform engineering teams building IDPs. Enterprise teams that need BYOC, compliance controls, and per-environment lifecycle automation at scale.

<InfoBox className="BodyStyle">

**Why Northflank fits on-demand workflows:**

Northflank supports multiple entry points: UI, CLI, API, and GitOps. The API covers run, list, pause, resume, and delete. Lifecycle controls include duration policies, idle shutdown, and active hours on BYOC projects.

If you want to see how the full setup works in practice, the [guide on how to auto-create preview environments on every PR](https://northflank.com/blog/how-to-auto-create-preview-environments-on-every-pr) walks through the template configuration step by step.

[Get started on Northflank](https://app.northflank.com/signup) (self serve - no demo required). Or [book a demo with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) if you want to walk through your architecture first.

</InfoBox>

### 2. Okteto

Okteto provides Kubernetes-based developer environments with per-branch preview deployments triggered through GitHub Actions or GitLab CI/CD pipelines.

**Key features:**

- PR-triggered preview environments via GitHub Actions and GitLab CI/CD
- Namespace isolation per preview environment on Kubernetes
- Global and personal scope options: previews can be visible to the full team or restricted to the creator
- Garbage collection with configurable sleep and delete periods, set by admins in the Admin Dashboard

**Best for:** Teams on Kubernetes who need per-branch CI-triggered previews and want namespace-isolated environments managed through GitHub Actions or GitLab CI.

### 3. Uffizzi

Uffizzi is an open-source platform for ephemeral environments built around virtual clusters and Docker Compose definitions.

**Key features:**

- Ephemeral virtual clusters per environment, each with its own lightweight Kubernetes cluster
- Docker Compose-based environment definitions for multi-service setups, with support for existing Helm charts, Kustomize configurations, and Kubernetes manifests
- CI pipeline integration via GitHub Actions and GitLab CI; environments can be triggered from pipeline steps as well as PR events
- Automatic cleanup on PR close or merge, or via configurable TTL

**Best for:** Teams that need multi-service ephemeral environments using Docker Compose or virtual clusters, and platform teams who want a self-hostable or open-source option.

### 4. Bunnyshell

Bunnyshell provides template-driven ephemeral environments deployable to external Kubernetes clusters.

**Key features:**

- YAML-based environment definitions that specify all services, databases, and dependencies together; supports Docker Compose, Helm, Kubernetes manifests, and Terraform components
- Git integration with GitHub, GitLab, and Azure DevOps for PR-triggered environments with teardown on merge
- API and SDK available for programmatic environment lifecycle operations, including start, stop, deploy, and destroy

**Best for:** Engineering and QA teams that need multi-service environments across their own Kubernetes cluster, and teams that want API-driven lifecycle control alongside Git triggers.

### 5. Codefresh

Codefresh provides CI/CD with environment capabilities built around its pipeline and GitOps systems.

**Key features:**

- On-demand environment launch from Docker images within the Codefresh pipeline UI, generating a shareable URL for demos and quick feature reviews
- Dynamic preview environments triggered by pull requests, using Helm and Kubernetes, with per-PR namespace isolation
- GitOps-native environment promotion through ArgoCD ApplicationSets, with Codefresh's hosted GitOps runtime as an option for teams who do not want to self-manage ArgoCD
- Codefresh API for pipeline and environment operations

**Best for:** Teams already in the Codefresh ecosystem who want preview environments without adopting a separate platform; not a standalone preview environment solution.

## How do you choose the right platform for on-demand preview environments?

The right platform depends on where your trigger lives, how much of your stack needs to spin up, and who owns the infrastructure. These four factors separate the platforms most clearly.

| Factor | What to consider | Platform |
| --- | --- | --- |
| **API-driven provisioning** | Can you trigger an environment from any CI step, script, or external system outside a Git event? | **Northflank:** REST API covers run, pause, resume, and delete; environments include databases, jobs, and secrets. |
|  |  | **Bunnyshell:** API and SDK for lifecycle operations. |
|  |  | **Uffizzi:** REST API and CLI. |
|  |  | **Okteto / Codefresh:** Primarily Git and CI-triggered. |
| **Full-stack scope** | Does the environment include databases and jobs alongside containers? | **Northflank:** Services, databases, jobs, and secrets. |
|  |  | **Bunnyshell:** Multi-service with database containers. |
|  |  | **Uffizzi:** Multi-service; database containers supported, no managed cloud databases. |
|  |  | **Okteto / Codefresh:** Container-focused. |
| **Infrastructure model** | Managed SaaS, BYOC, or self-hosted? | **Northflank:** Managed or BYOC (AWS, GCP, Azure, Oracle, CoreWeave, Civo, on-prem, bare-metal). |
|  |  | **Okteto:** Managed SaaS, BYOC, or self-hosted. |
|  |  | **Uffizzi:** Managed SaaS, self-hosted, or open-source. |
|  |  | **Bunnyshell:** Managed or BYOC. |
|  |  | **Codefresh:** SaaS or on-premises. |
| **Lifecycle controls** | Can environments shut down on a schedule, idle timeout, or TTL? | **Northflank:** Duration and idle shutdown per template; active hours to restrict environment creation to specific days and times (BYOC projects). |
|  |  | **Bunnyshell:** Start/stop schedules; auto-destroy on PR close or merge when configured. |
|  |  | **Uffizzi:** Auto-cleanup on PR close or set TTL. |
|  |  | **Okteto:** Admin-configured garbage collection. |

## FAQ: best platforms for on-demand preview environments in 2026

### What is the difference between a PR preview environment and an on-demand environment?

A PR preview environment is triggered by opening a pull request and tied to that PR's lifecycle. An on-demand preview environment is triggered programmatically via API, CLI, or CI step, outside the Git event lifecycle. This matters when environments need to be created by automation, AI agents, or internal tools that don't map to a PR.

### Which platforms support API-driven environment provisioning?

Northflank, Bunnyshell, and Uffizzi all expose REST APIs for managing environments programmatically. Northflank's [preview blueprint API](https://northflank.com/docs/v1/api/project/preview-blueprints/run-preview-blueprint) covers the full lifecycle: run, list, pause, resume, and delete. Okteto is primarily triggered through GitHub Actions or GitLab CI.

### Do on-demand preview environments support databases?

Support varies. Northflank provisions managed databases (PostgreSQL, MySQL, MongoDB, Redis) as part of a preview environment template. Bunnyshell supports database containers via its YAML environment definition. Uffizzi supports database containers via Docker Compose but does not provision managed cloud databases. Okteto and Codefresh are container-focused and require external services for databases.

### How do you prevent environment cost from growing out of control at scale?

The key controls are automatic teardown on PR close or idle timeout, duration policies, and scheduled active hours. Northflank exposes duration and idle shutdown controls per template; active hours, which restrict automatic environment creation to specific days and times, are available on BYOC projects. See [how Northflank handles preview environment lifecycle](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) for the full configuration reference.

### What is the best platform for on-demand preview environments on Kubernetes?

For managed Kubernetes with BYOC flexibility and full-stack scope including databases and jobs, Northflank is the most complete option. Uffizzi supports open-source virtual cluster isolation per environment. Okteto is designed for per-branch previews triggered through CI pipelines. See the [Kubernetes preview environments comparison](https://northflank.com/blog/kubernetes-preview-environments-comparison) for a deeper breakdown.

### Is Northflank self-serve?

Yes. You can [sign up and start deploying](https://app.northflank.com/signup) without a sales conversation. BYOC is also available self-serve across all supported clouds and on-premises infrastructure.

## Related articles on best platforms for on-demand preview environments

The articles below cover adjacent angles, from foundational concepts to platform-specific comparisons and setup guides.

- [10 best preview environment platforms (frontend, backend & GitOps)](https://northflank.com/blog/preview-environment-platforms): Broader comparison of 10 platforms across frontend, backend, and GitOps use cases, including Vercel, Render, and Netlify alongside full-stack options.
- [How to auto-create preview environments on every PR](https://northflank.com/blog/how-to-auto-create-preview-environments-on-every-pr): Step-by-step guide to configuring Northflank preview blueprints with Git triggers, naming conventions, and lifecycle rules.
- [Top 5 Kubernetes preview environments comparison](https://northflank.com/blog/kubernetes-preview-environments-comparison): Infrastructure-focused comparison covering cluster architecture, isolation strategy, and workload support for Kubernetes-native teams.
- [The what and why of ephemeral preview environments on Kubernetes](https://northflank.com/blog/the-what-and-why-of-ephemeral-preview-environments-on-kubernetes-sandbox-testing): Foundational guide to how ephemeral preview environments work on Kubernetes and why they improve delivery workflows.
- [Ephemeral execution environments for AI agents](https://northflank.com/blog/ephemeral-execution-environments-ai-agents): Covers on-demand environment use cases for AI agent pipelines that require isolated execution environments per run.
- [How to set up a preview environment on Northflank](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment): Full configuration reference for preview blueprints, Git triggers, secrets, active hours, and lifecycle controls.
- [Northflank preview environments](https://northflank.com/product/preview-environments): Overview of Northflank's preview environment capabilities including full-stack scope, managed infrastructure, and BYOC options.]]>
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</item><item>
  <title>How to auto-create preview environments on every PR</title>
  <link>https://northflank.com/blog/how-to-auto-create-preview-environments-on-every-pr</link>
  <pubDate>2026-03-12T17:15:00.000Z</pubDate>
  <description>
    <![CDATA[Learn how to automatically create preview environments for every pull request on Northflank. This guide covers what preview environments are, how they work, and how to set them up.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/how_to_auto_create_preview_environments_on_every_pr_9bb7e38d6c.png" alt="How to auto-create preview environments on every PR" /><InfoBox className="BodyStyle">

## TL;DR: What are preview environments and how do they work?

A **preview environment** is an ephemeral, on-demand environment provisioned when a specific Git event occurs, like opening a pull request (PR) or pushing code to a feature branch. It is tied to that event: when the PR is merged or closed, the environment is destroyed.

Key things to know about preview environments:

- They can be created automatically via [Git triggers](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment#add-a-git-trigger), removing the need for manual setup
- Each one is typically isolated to a single branch or feature
- Depending on the platform, they can include all the services your app needs: databases, queues, jobs, and microservices
- They can be configured to tear down automatically once the PR is resolved, keeping costs low
- Stakeholders can get a live, shareable URL to review the feature in a real environment

> On [Northflank](https://northflank.com/product/preview-environments), preview environments are full-stack environments built into your pipeline. They are automatically provisioned on every pull request or new commit to a branch, and you get full control over the environment lifecycle, from creation to termination. You can [set Git triggers](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment#add-a-git-trigger), [configure naming conventions](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment#choose-a-naming-convention), and [define active time windows](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment#set-preview-environment-duration-and-creation-times) for how long each environment stays running.
> 

</InfoBox>

If your team is sharing a single staging environment to test every feature before merging, you are likely dealing with deployment queues, conflicting changes, and slow feedback cycles. Preview environments solve this.

Every code change gets its own clean, production-like space to be tested and reviewed, without blocking other developers or polluting a shared environment.

In this guide, you will learn what preview environments are, how they work, how they compare to traditional environments, and how to set up preview environments for pull requests on Northflank, step by step.

## What is a preview environment?

A preview environment is a short-lived, isolated environment created on demand to test the code changes in a specific branch before they are merged into the main branch.

Unlike a shared staging environment where all developers are deploying their features at the same time, a preview environment is scoped to a single branch.

This means a developer can validate their changes in isolation, without interference from other developers' code.

## How do preview environments differ from traditional environments?

To understand the value of preview environments, it helps to look at what goes wrong with the alternative.

In a traditional long-lived staging environment, multiple teams are deploying different features at the same time. That shared environment becomes a bottleneck. When a bug appears, it is often unclear whether the cause is your change, someone else's deployment, or the environment's accumulated state, which tends to diverge from production over time.

Teams end up waiting in a queue to deploy and test. The feedback loop slows down. And because the environment has been running for weeks or months, its configuration may no longer reflect production, making it unreliable as a testing baseline.

Preview environments are designed to address exactly this. Each one is:

- **Isolated:** only the changes from a single branch are deployed, so there is no cross-contamination from other developers
- **Automated:** provisioned as soon as a PR is opened or a commit is pushed
- **Fresh:** created as a clean copy each time, so you are always testing against a consistent baseline
- **Ephemeral:** destroyed once the PR is resolved, so resources are not running longer than necessary

Here is a direct comparison:

| Characteristic | Traditional environments | Preview environments |
| --- | --- | --- |
| Lifespan | Persistent, long-lived | Ephemeral, short-lived |
| Cost | Higher; resources run continuously | Lower potential cost; resources are not running permanently |
| Sharing | Shared across teams and features | Isolated per branch or PR |
| Accessibility | Typically limited to the engineering team | Can provide a shareable URL accessible to any stakeholder |
| State consistency | Can diverge from production over time | Designed to be a fresh copy each time |
| Feedback cycle | Slower due to shared resource bottlenecks | Faster, with immediate access for review |

## What is the typical workflow for creating a preview environment?

The most common trigger for a preview environment is opening a pull request. Here is what the workflow looks like end-to-end:

![preview-env-worklow.png](https://assets.northflank.com/preview_env_worklow_81dd265edd.png)

1. A developer finishes work on a feature branch and opens a PR
2. The PR triggers an automated pipeline that spins up a new preview environment
3. The feature's code changes are deployed to that environment
4. Stakeholders, QA engineers, product managers, and designers get a live URL to review the feature
5. Feedback is gathered from different perspectives, in a real environment, not a local build
6. When the PR is merged or closed, the preview environment is automatically destroyed

This workflow removes the coordination overhead of shared staging, gives reviewers a real environment to test against, and shortens the time between writing code and getting feedback.

## How to set up preview environments for pull requests on Northflank

Northflank supports [full-stack preview environments](https://northflank.com/product/preview-environments) that are built directly into your deployment pipeline. You can include databases, message queues, background jobs, and microservices in a preview, not just the frontend or backend service. A full breakdown of what you can include is covered after the setup steps.

You also have the option to use [Northflank's managed infrastructure](https://northflank.com/features/managed-cloud) or [bring your own cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) if you want environments deployed into your own cloud account.

![preview-env-flow.jpg](https://assets.northflank.com/preview_env_flow_4da30a0d05.jpg)

Preview environments on Northflank are defined using blueprints, which makes them reproducible and easy to manage at scale. The blueprint specifies the Git trigger, naming convention, included services, and lifecycle rules.

Here is how to get set up:

### Step 1: Create a Northflank account and project

[Sign up for a Northflank account](https://app.northflank.com/signup) if you have not already. Before creating resources, [link your Git account](https://northflank.com/docs/v1/application/getting-started/link-your-git-account) so Northflank can watch your repositories for pull request triggers.

Then create a new [project](https://northflank.com/docs/v1/application/getting-started/create-a-project) and add the resources your application needs, such as [services](https://northflank.com/docs/v1/application/getting-started/build-and-deploy-your-code#create-a-combined-service) or [addons](https://northflank.com/docs/v1/application/databases-and-persistence/stateful-workloads-on-northflank) like a database.

![create-nf-project.png](https://assets.northflank.com/create_nf_project_a17887ed76.png)

### Step 2: Navigate to Environments

Inside your project, click the **Environments** tab in the top navigation. This takes you to the Environments board, where you can manage your preview environments and workflows.

![create-environments.png](https://assets.northflank.com/create_environments_3f7f6ee423.png)

### Step 3: Create a preview blueprint

From the Environments board, click **Create preview blueprint** in the top right corner. This opens the blueprint editor, where you define the rules for how preview environments should be automatically created.

![create-preview-blueprint.png](https://assets.northflank.com/create_preview_blueprint_a1d24bd5c4.png)

### Step 4: Configure the preview blueprint

In the **Set up a preview blueprint** modal, start by giving your blueprint a name under **Basic information** (for example, `previews`), then set the **Naming convention** to **Pull request ID** so each environment is named after its PR number (e.g., `pr-1234`).

![set-up-preview-blueprint-naming.png](https://assets.northflank.com/set_up_preview_blueprint_naming_b358b897e0.png)

Next, under **Repository**, click **+ Add trigger**, set the **Kind** to **Git pull request** (marked as recommended), select your repository, and leave the branch trigger rule as `*` so it fires on pull requests from any branch.

![set-up-preview-blueprint-repository.png](https://assets.northflank.com/set_up_preview_blueprint_repository_4a69d9b70c.png)

Once that is done, click **Continue**.

### Step 5: Customize the blueprint

Once you click **Continue**, you land in the visual blueprint editor. This is where you build out the workflow that runs every time a PR triggers your blueprint.

From the panel on the right, you can drag nodes onto the canvas to define what happens when a preview environment is created.

Most blueprints start with a **Build on trigger** node, which builds your code when the PR is opened, followed by a deployment service node that runs the built image.

From there, you can add more nodes depending on what your application needs, such as a database addon, a subdomain for public access, or a secret group to manage environment variables.

> For a full reference on available nodes and configuration options, see the [preview environments documentation](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment).
> 

The screenshot below shows an example of a fully configured blueprint with a build service, a MongoDB addon, and a secret group already set up:

![preview-blueprint-editor.png](https://assets.northflank.com/preview_blueprint_editor_ab915e7718.png)

Northflank also supports [snapshot and caching mechanisms](https://northflank.com/product/preview-environments) to speed up environment creation on subsequent PRs.

### Step 6: Save and activate

Click **Save preview blueprint**. Your blueprint will now appear under the **Preview environments** column in the Environments board.

From this point on, every new PR against the configured repository will automatically trigger the creation of a preview environment.

You can also trigger the creation of a preview environment manually by selecting **Run** next to your blueprint, which is useful for testing your setup before you open a real PR.

Here is what a project looks like with preview environments actively running across multiple open PRs:

![workflow-preview-environments.png](https://assets.northflank.com/workflow_preview_environments_9117976c11.png)

## What can you include in a Northflank preview environment?

Because Northflank preview environments are full-stack, you are not limited to just deploying your application code. A single preview can include:

- **Services** for your frontend, backend, or API
- **Database addons** like PostgreSQL, MySQL, or MongoDB, either fresh instances or seeded with test data
- **Caches and message queues** like Redis or RabbitMQ
- **Background jobs** and cron tasks
- **Other microservices** your application depends on

This means your preview environment reflects how your application actually runs in production, not a stripped-down version of it. Reviewers are testing the real thing.

For a full reference on configuring preview blueprints, see the [Northflank preview environments documentation](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) and the [API reference for running and listing previews](https://northflank.com/docs/v1/api/project/pipelines/run-preview-environment).

## How do preview environments speed up software delivery?

Preview environments compress the feedback loop in a few concrete ways.

First, they make code reviewable by non-engineers. When a designer, product manager, or QA engineer can click a URL and see the feature running in a real environment, they do not have to wait for a staging deployment or ask an engineer to set something up locally. They can test immediately.

Second, they catch integration bugs earlier. Because each preview environment mirrors production with all its services and dependencies, issues that only surface in a real environment, like a broken API integration or an edge case in the database layer, are found before the code is merged, not after.

Third, they remove the queueing bottleneck of shared staging. Multiple PRs can have their own environments running in parallel, so no team is waiting on another.

For more on the mechanics behind this approach, see [The what and why of ephemeral preview environments](https://northflank.com/blog/the-what-and-why-of-ephemeral-preview-environments-on-kubernetes-sandbox-testing) and [a comparison of preview environment platforms](https://northflank.com/blog/preview-environment-platforms).

## How do costs stay manageable with preview environments?

The concern that comes up most often about preview environments is cost: if you are spinning up a full stack for every PR, does that get expensive quickly?

The answer is that Northflank gives you several mechanisms to keep costs in check:

- **Automatic teardown:** environments are destroyed when the PR is merged or closed, so they never run longer than necessary
- **Active time windows:** you can configure a schedule for when the environment should be running, for example, only during business hours
- **TTL (time-to-live):** you can set a maximum duration for any preview environment, after which it is shut down regardless of PR status
- **Idle shutdown policies:** Northflank can detect when an environment is inactive and shut it down automatically

These controls mean you are not paying for idle environments over the weekend or for PRs that were opened and abandoned.

## FAQ: Preview environments on Northflank

### FAQ: What triggers a preview environment to be created?

On Northflank, the most common trigger is opening a pull request. You configure this in the preview blueprint by setting the trigger kind to **Git pull request** and specifying the repository. You can also trigger preview environment creation manually from the Environments board or via the [Northflank API](https://northflank.com/docs/v1/api/project/pipelines/run-preview-environment).

### FAQ: Can a preview environment include a database?

Yes. Northflank preview environments are full-stack, so you can include database addons (PostgreSQL, MySQL, MongoDB, and others), message queues, background jobs, and other services. Each preview environment gets its own isolated instances of these services.

### FAQ: How is a preview environment different from a staging environment?

A staging environment is persistent and shared across your team, meaning everyone deploys to the same place. A preview environment is ephemeral and isolated to a single PR or branch, so each developer gets their own clean space to test. With staging, you do final validation before a release; with preview environments, you validate every change before it is even merged. See [Northflank's preview environments overview](https://northflank.com/product/preview-environments) for a full feature breakdown.

### FAQ: How do Northflank preview environments compare to other platforms?

Northflank sits in a category of platforms that support automated PR preview environments with full-stack capabilities. For detailed comparisons, see [Northflank vs Render for preview environments](https://northflank.com/blog/alternatives-to-render-preview-environments), [Northflank vs Railway for preview environments](https://northflank.com/blog/alternatives-railway-preview-environments), and [Kubernetes preview environment platform comparisons](https://northflank.com/blog/kubernetes-preview-environments-comparison).

### FAQ: Can I use preview environments with my own cloud account?

Yes. Northflank supports a [bring-your-own-cloud](https://northflank.com/product/bring-your-own-cloud) (BYOC) model, which means preview environments can be deployed into your own AWS, GCP, or Azure account rather than [Northflank's managed infrastructure](https://northflank.com/features/managed-cloud).

## Start previewing every pull request automatically

Preview environments close the gap between writing code and getting real feedback on it. Every PR gets its own isolated, full-stack environment, spun up automatically, available to the whole team, and torn down when it is no longer needed.

<InfoBox className="BodyStyle">

If you want to set up preview environments for pull requests today, [Northflank's blueprint system](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) gives you the automation and control to do it without writing custom scripts or managing infrastructure by hand. [Create an account](https://app.northflank.com/signup), and you can have your first preview environment running on the next PR your team opens.

</InfoBox>]]>
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</item><item>
  <title>What are persistent sandboxes? (and why AI agents need them)</title>
  <link>https://northflank.com/blog/persistent-sandboxes</link>
  <pubDate>2026-03-11T17:45:00.000Z</pubDate>
  <description>
    <![CDATA[Persistent sandboxes retain filesystem state across executions, giving AI agents a continuous workspace. Learn how they work, when to use them, and what to look for in a platform.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/persistent_sandboxes_fc118f0caa.png" alt="What are persistent sandboxes? (and why AI agents need them)" />> *Persistent sandboxes are isolated execution environments that retain their state across sessions, giving AI agents and developers a continuous workspace that picks up exactly where it left off.*
> 

If you've ever built an AI agent that needs to pick up where it left off, with the same files and same installed packages, you've already felt the problem that persistent sandboxes solve.

Most sandbox environments are ephemeral by design. They spin up, run some code, and disappear. That works for a lot of use cases. But the moment your agent needs to resume a task, maintain a working directory across sessions, or keep a long-running service alive between calls, ephemeral execution starts fighting against you.

This article breaks down what persistent sandboxes are, why they've become important for AI agent infrastructure, and what you should be evaluating when you're choosing a platform that supports them.

<InfoBox className="BodyStyle">

## TL;DR: Key takeaways on persistent sandboxes

- A persistent sandbox is an isolated execution environment that retains its filesystem state across executions, giving agents a persistent working directory and installed environment to return to.
- Ephemeral sandboxes are destroyed after each run; persistent ones survive.
- Persistent sandboxes are most relevant for AI agents that need to resume tasks, accumulate state, or run long-horizon workflows.
- The tradeoff: persistent sandboxes require more infrastructure thinking around storage, security, and lifecycle management.

> [**Northflank**](https://northflank.com/) supports both persistent and ephemeral sandbox environments. It offers MicroVM-based isolation (Kata Containers, Firecracker, and gVisor depending on workload), 1–2s end-to-end environment spin-up, on-demand GPUs, [bring your own cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) deployment across your own cloud accounts, on-premises, and bare metal infrastructure, API/CLI/SSH access, and SOC 2 Type 2 compliance. It has been in production since 2021 across startups, public companies, and government deployments.
> 

</InfoBox>

## What is a persistent sandbox?

A persistent sandbox is an isolated execution environment that keeps its state between sessions. When you close the connection and come back later, or when your agent makes a new call, everything is still there: the files you wrote and the packages you installed.

The word "sandbox" here is doing its usual job: this is an environment with enforced isolation, meaning code running inside it can't affect the host system or other tenants. The word "persistent" describes what happens to that environment over time. It isn't discarded when the session ends.

Compare this to an [ephemeral sandbox](https://northflank.com/blog/ephemeral-sandbox-environments), which is created fresh for each execution and discarded when the run ends. Ephemeral environments are great for untrusted one-shot code execution. Persistent environments are what you reach for when continuity of state is important.

*If you want a deeper look at how the two compare in practice, [ephemeral execution environments for AI agents](https://northflank.com/blog/ephemeral-execution-environments-ai-agents) covers the ephemeral side in more detail.*

## What's the difference between persistent and ephemeral sandboxes?

The distinction comes down to what survives when an execution ends. Here's a quick breakdown:

|  | Ephemeral sandbox | Persistent sandbox |
| --- | --- | --- |
| **State after run** | Destroyed | Retained |
| **Filesystem** | Wiped | Survives across executions |
| **Installed packages** | Gone | Still there |
| **Running processes** | Terminated | Platform-dependent |
| **Best for** | Stateless, one-shot execution | Multi-step, stateful workloads |
| **Security cleanup** | Automatic | Requires lifecycle management |

With an **ephemeral sandbox**, each run starts from a clean slate. Nothing carries over from the previous session: no files, no installed dependencies, no process state. This is useful for security-sensitive workloads where you want guaranteed cleanup, and for stateless tasks where you don't need continuity.

With a **persistent sandbox**, the environment survives between runs. Your agent can write a file during session one, disconnect, and find that file intact during session two. This is much closer to how a developer's local machine works, which is part of why it maps well to agent workflows.

In practice, the choice between persistent and ephemeral isn't always binary. Well-designed platforms let you choose per-workload: spin up short-lived execution pools for stateless tasks, and maintain long-running stateful services for the workflows that need them.

## Why do AI agents need persistent sandboxes?

The shift toward persistent sandboxes is largely being driven by how AI agents are being built and deployed today.

Early sandbox use cases were straightforward: run user-submitted code safely, return the output, tear it down. The sandbox was a one-shot execution container. But AI agents, especially those built to complete multi-step tasks autonomously, don't work like that.

For instance, take an agent that's been asked to build a feature in a codebase. It needs to clone a repo, install dependencies, run tests, iterate on a fix, and re-run tests. If each tool call spins up a fresh sandbox, the agent has to reinstall everything from scratch every time. The iteration loop gets expensive and slow.

Let's say you also have an agent running a background data pipeline, processing files as they arrive, maintaining a working directory, and accumulating output. That's not a stateless task. It needs an environment that behaves like a running service, not a function invocation.

Persistent sandboxes are also key for:

- **Multi-step coding agents** that need to build up a working environment incrementally
- **Long-horizon research agents** that read, write, and revise documents over extended periods
- **Agent-powered development tools** where the environment needs to feel continuous to the user
- **Stateful tool execution** where the agent uses shell, filesystem, and process state as part of its reasoning loop

<InfoBox className="BodyStyle">

**Running AI agents in production?**

Northflank is a full workload runtime built for exactly this. You can run agents, APIs, workers, databases, and background jobs on a single platform, with persistent and ephemeral sandbox environments as first-class options.

- [Sandbox environments overview](https://northflank.com/product/sandboxes) - see how persistent and ephemeral environments work on Northflank
- [Get started](https://app.northflank.com/signup) - self-serve setup
- [Pricing](https://northflank.com/pricing) - CPU, memory, and GPU pricing
- [How to deploy ClawdBot on Northflank](https://northflank.com/blog/how-to-deploy-clawdbot-on-northflank-sandbox-microvm) - a practical walkthrough of running an agent with sandbox environments on Northflank
- [Talk to an engineer](https://cal.com/team/northflank/northflank-demo) - if you have specific infrastructure or compliance requirements and want to talk through how Northflank fits your setup

</InfoBox>

## What should you look for in a persistent sandbox platform?

If you're evaluating platforms for persistent sandbox support, here are the things worth scrutinising:

- **How does persistence work?**
    
    Some platforms snapshot filesystem state and restore it on the next call. Others keep the environment running as a long-lived process. Others let you pause and resume. The underlying mechanism affects performance, cost, and what kinds of state persist (filesystem vs process vs memory).
    
- **What's the isolation model?**
    
    Persistence introduces a new surface area for security concerns, because long-lived environments accumulate state over time. You want MicroVM-level isolation, something like Firecracker or Kata Containers, or gVisor for user-space kernel sandboxing, rather than just container-level isolation, especially if you're running untrusted code or serving multiple tenants.
    
- **How fast does a new environment come up?**
    
    Even if you're using persistent environments for most workloads, you'll still need to spin up new ones. Pay attention to cold start time for the full environment creation path, not just component-level benchmarks.
    
- **Can you run the full workload on one platform?**
    
    Agents aren't just code execution. They need storage, APIs, background workers, and databases. If your sandbox platform only handles code execution, you end up stitching together multiple services. Look for platforms like Northflank that support full workload runtimes.
    
- **What does deployment look like in an enterprise context?**
    
    If you're building for an organisation with data residency or compliance requirements, confirm the platform supports deployment inside your own cloud or VPC. SOC 2 Type 2 compliance is also worth verifying. See [Northflank's security page](https://northflank.com/security) for its compliance posture.
    
- **Do you get GPU access?**
    
    For inference-heavy agent workloads, GPU availability and the ability to provision it on-demand without quota requests is worth checking.
    

## How does Northflank handle persistent sandboxes?

[Northflank](https://northflank.com/) is a full workload runtime that supports both persistent and ephemeral sandbox environments as first-class primitives.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

Here's what that looks like in practice:

- **Persistent environments:** Stateful services backed by persistent volumes, where filesystem state survives between executions
- **Ephemeral environments:** Short-lived execution pools suited to stateless or one-shot workloads
- **Per-workload flexibility:** Environment type is configured per workload; both can run on the same platform alongside the rest of your agent infrastructure, including APIs, workers, databases, and background jobs
- **Isolation:** MicroVM-based using Kata Containers, Firecracker, and gVisor, depending on workload characteristics, for secure execution of untrusted code
- **Spin-up time:** New environments come up in around 1–2 seconds end-to-end
- **GPU access:** On-demand GPUs, self-service provisioning, no quota requests
- **BYOC:** Bring your own cloud (BYOC) deployment across your own cloud accounts, on-premises, and bare metal infrastructure, fully self-serve
- **Access:** API, CLI, and SSH access
- **Compliance:** SOC 2 Type 2 certified. See the [security page](https://northflank.com/security) for full details
- **Pricing:** CPU at $0.01667/vCPU/hour, memory at $0.00833/GB/hour. GPU pricing on the [pricing page](https://northflank.com/pricing)
- **Production track record:** In use since 2021 across startups, public companies, and government deployments

<InfoBox className="BodyStyle">

[**Get started with Northflank**](https://app.northflank.com/signup) or [talk to an engineer](https://cal.com/team/northflank/northflank-demo) if you want to discuss your company's specific infrastructure requirements.

</InfoBox>

## When should you use persistent sandboxes vs ephemeral?

Neither approach is universally better. The right choice depends on what your workload needs.

| Use case | Recommended environment |
| --- | --- |
| Agent resuming a task across executions | Persistent |
| Accumulating filesystem state (cloning repos, installing packages) | Persistent |
| Long-lived services or background processes | Persistent |
| Environment that behaves like a continuous workspace | Persistent |
| Stateless, one-shot code execution | Ephemeral |
| Guaranteed environment cleanup after each run | Ephemeral |
| Many parallel short-lived tasks with no continuity needed | Ephemeral |
| Short-burst workloads where cost efficiency is a priority | Ephemeral |

For most production AI agent architectures, you'll want both available. The workloads that benefit from persistence and the workloads that benefit from ephemerality often coexist in the same system.

## FAQ: persistent sandboxes

### What is a persistent sandbox?

A persistent sandbox is an isolated execution environment that retains its filesystem state, including files and installed packages, between separate sessions or invocations. Unlike ephemeral sandboxes that are destroyed after each run, a persistent sandbox retains its filesystem state across executions, even with no active connection.

### What's the difference between a persistent sandbox and a container?

Containers can be either persistent or ephemeral, depending on how they're managed. The term "persistent sandbox" specifically refers to a sandboxed (isolated) environment designed to maintain state over time, usually with additional security primitives like MicroVM isolation on top of the container layer.

### Do AI agents need persistent sandboxes?

It depends on the agent's task. Agents doing multi-step work, such as writing code across multiple executions, maintaining a working directory, or running background processes, benefit from persistent sandboxes. Agents doing stateless one-shot tasks can work fine with ephemeral environments.

### Are persistent sandboxes less secure than ephemeral ones?

Not inherently, but they require more careful security design. Because state accumulates over time, persistent environments need robust isolation (MicroVM-level, not just container-level) and clear lifecycle management policies to limit exposure. Ephemeral environments get a degree of automatic cleanup that persistent ones don't.

### How do persistent sandboxes handle state between sessions?

It varies by platform. Some keep the environment process running continuously. Others snapshot and restore filesystem state on reconnection. The mechanism affects what kinds of state persist (filesystem, in-memory, running processes) and the latency of resuming a session.

### Can I use persistent and ephemeral sandboxes on the same platform?

Yes. Platforms like Northflank support both as first-class primitives, so you can choose the right model per workload without switching providers.

## Related articles on persistent sandboxes and sandbox infrastructure

- [Ephemeral sandbox environments](https://northflank.com/blog/ephemeral-sandbox-environments): The direct counterpart to this article. Covers how ephemeral sandboxes work and when they're the right choice.
- [Ephemeral execution environments for AI agents](https://northflank.com/blog/ephemeral-execution-environments-ai-agents): Goes deeper on ephemeral execution for agent workloads, including the security model behind short-lived environments.
- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox): A foundational explainer on sandbox environments in the context of AI, covering isolation models and use cases.
- [Best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents): A platform comparison for agent builders evaluating code execution sandbox options.
- [How to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents): A practical guide to sandboxing agent workloads, including isolation strategies and platform setup.
- [Self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes): Covers the self-hosted and BYOC route for teams with compliance or data residency requirements.
- [Top AI sandbox platforms for code execution](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution): An overview of sandbox platforms for code execution, covering isolation, performance, and scalability.
- [Remote code execution sandbox](https://northflank.com/blog/remote-code-execution-sandbox): How remote code execution sandboxes work and the security considerations involved.
- [Best sandboxes for coding agents](https://northflank.com/blog/best-sandboxes-for-coding-agents): Focused on coding agent use cases and what they need from a sandbox environment.
- [Code execution environment for autonomous agents](https://northflank.com/blog/code-execution-environment-for-autonomous-agents): What autonomous agents need from a code execution environment, including state and continuity requirements.]]>
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</item><item>
  <title>Top Beam Cloud sandboxes alternatives in 2026</title>
  <link>https://northflank.com/blog/beam-cloud-sandboxes-alternatives</link>
  <pubDate>2026-03-10T16:45:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the top Beam Cloud sandboxes alternatives in 2026: Northflank, E2B, Modal, Fly.io Sprites, and Microsandbox. Evaluate isolation, persistence, and deployment scope.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/beam_cloud_sandboxes_alternatives_84a04b295c.png" alt="Top Beam Cloud sandboxes alternatives in 2026" /><InfoBox className="BodyStyle">

## TL;DR: Top Beam Cloud sandboxes alternatives in 2026

Beam Cloud sandboxes offer Python-native ephemeral environments with 1–3 second cold boots, GPU support, and snapshot-based state management. Teams typically look for alternatives when they need stronger isolation guarantees, support for languages beyond Python, persistent environments, or deployment infrastructure beyond the sandbox itself.

- **Northflank** - Best for teams that need both secure sandboxed execution and full production infrastructure in one platform, including BYOC (Bring Your Own Cloud) deployment into your own cloud
- **E2B** - Isolated sandboxes for AI code execution with Python and JavaScript/TypeScript SDKs
- **Modal** - Serverless compute platform with a sandbox interface for running untrusted code
- **Fly.io Sprites** - Persistent, hardware-isolated Linux environments that go idle when not in use
- **Microsandbox** -  Open-source self-hosted sandboxes using libkrun microVMs; experimental software

> If you need a platform that goes beyond sandbox execution, [Northflank](https://northflank.com/product/sandboxes) covers GPU workloads, persistent and ephemeral environments, full production deployments, and compliance-grade isolation in one place. [BYOC](https://northflank.com/product/bring-your-own-cloud) is available self-serve across AWS, GCP, Azure, Civo, CoreWeave, Oracle Cloud, and on-premises and bare-metal infrastructure. Northflank has been in production since 2021, used by organisations from early-stage startups to public companies and government deployments.
> 

</InfoBox>

## What should you evaluate when comparing alternatives to Beam Cloud sandboxes?

Before choosing a platform, clarify your requirements across these four dimensions:

- **Isolation model:** Beam Cloud uses container-based environments. If you're running user-generated or AI-generated code at scale, the underlying isolation model matters. MicroVM technologies like Firecracker, gVisor, and Kata Containers provide hardware-level kernel separation that container namespaces alone do not.
- **Persistence vs. ephemerality:** Beam sandboxes are designed around session-scoped workloads, with snapshots for state recovery. Some use cases, such as AI agents that build up context or long-running dev environments, require environments that hold full state between sessions without manual snapshot logic.
- **Language and SDK support:** Beam's primary SDK is Python, with TypeScript in beta. If your stack is polyglot or your team builds TypeScript-first, evaluate whether the SDK surface of each alternative matches your workflow.
- **Infrastructure scope:** A sandbox API is not the same as a deployment platform. If you need the same platform to handle databases, background workers, GPU inference, and production services alongside sandboxed execution, evaluate platforms that cover the full stack beyond ephemeral execution.

## Best Beam Cloud sandboxes alternatives compared (2026)

Each platform takes a different approach to sandbox execution. Here's what they offer and where they fit.

### 1. Northflank

[Northflank](https://northflank.com/product/sandboxes) is a full-stack cloud platform that provides secure sandbox execution alongside production infrastructure: deployments, databases, GPU workloads, background jobs, and preview environments. It is the only option in this list that covers the full path from sandboxed code execution through to production deployment, without platform migration.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

**Key capabilities:**

- MicroVM isolation applied per workload (Kata Containers, gVisor, or Firecracker) based on workload security requirements
- Both ephemeral and persistent environments on one platform
- Sandbox creation in approximately 1–2 seconds
- On-demand GPU access, self-serve (no quota requests)
- [BYOC (Bring Your Own Cloud)](https://northflank.com/product/bring-your-own-cloud) deployment is available self-serve into AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, and on-premises and bare-metal infrastructure (Northflank manages the orchestration layer while workloads run inside your own cloud account)
- Northflank's BYOC (Bring your own cloud) model is designed for teams that need their workloads running inside their own cloud account, with managed orchestration handling the operational layer
- API, CLI, and SSH access
- Managed Kubernetes orchestration, scaling, and Day 2 operations handled by the platform
- SOC 2 Type 2 compliant; in production since 2021 across startups, public companies, and government deployments
- CPU from $0.01667/vCPU/hour, memory from $0.00833/GB/hour (See [Northflank pricing details](https://northflank.com/pricing))

<InfoBox className="BodyStyle">

Northflank is the right choice when you need isolation guarantees beyond containers, want to avoid managing separate infrastructure for execution and production, or require data to stay within your own cloud under compliance constraints, without the operational overhead of running orchestration yourself.

Next steps:

- [Get started with Northflank](https://app.northflank.com/signup)
- [Northflank Sandboxes](https://northflank.com/product/sandboxes)
- [Hands-on guide: spin up a secure sandbox and microVM in seconds](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
- [Book a demo with a Northflank engineer](https://cal.com/team/northflank/northflank-demo) (if you'd prefer to speak with an engineer about your organisation's needs)

</InfoBox>

### 2. E2B

E2B provides isolated sandbox environments for AI agents and code execution, with SDKs for Python and JavaScript/TypeScript.

**Key capabilities:**

- Isolated Linux VMs created on demand via API
- Sandbox pause and resume with full state preserved (filesystem and memory)
- Snapshots for restoring sandbox state
- Configurable timeout; sandboxes run up to 24 hours (Pro) or 1 hour (Base), with pause/resume for longer workloads
- SSH access and interactive terminal
- Internet access, proxy tunneling, and custom domain support
- Git integration and cloud storage bucket connectivity
- MCP gateway with available and custom MCP servers

### 3. Modal

Modal is a serverless compute platform with a dedicated sandbox interface for executing untrusted or dynamically defined code.

**Key capabilities:**

- Sandboxes defined and spawned at runtime with custom container images
- GPU access configurable per sandbox
- Idle timeout and maximum lifetime up to 24 hours (with Filesystem Snapshots for longer workloads)
- Named sandboxes with uniqueness enforcement per app
- Volumes, cloud bucket mounts, and secrets support
- Filesystem snapshots for state preservation and restoration
- Python SDK (primary), JavaScript/TypeScript and Go SDKs (beta)

### 4. Fly.io Sprites

Sprites are persistent, hardware-isolated Linux environments built on Fly.io's infrastructure, aimed at use cases that require state between runs.

**Key capabilities:**

- Hardware-level isolation via dedicated microVM per Sprite
- Persistent ext4 filesystem backed by NVMe storage during execution and durable object storage at rest
- Automatic idle behavior: compute charges stop when idle, filesystem preserved
- Unique HTTPS URL per Sprite for exposing web services or APIs
- Warm and cold wake states; warm Sprites resume quickly from hibernation
- CLI-based management

### 5. Microsandbox

Microsandbox is an open-source, self-hosted sandbox project using libkrun microVMs, intended for AI agent workflows.

**Key capabilities:**

- Hardware-level VM isolation via libkrun microVMs
- OCI-compatible: runs standard container images
- MCP integration for agent workflows
- Python, Rust, and JavaScript SDKs
- Apache 2.0 licensed; single binary installation

**Important:** Microsandbox is explicitly marked as experimental software by its maintainers. Expect breaking changes, missing features, and rough edges. It is not recommended for production use without significant engineering investment in stability and operational tooling.

## Which Beam Cloud sandboxes alternative should you choose?

Use the table below to match your primary requirement to the platform best suited for it:

| Your requirement | Recommended alternative | Why |
| --- | --- | --- |
| Full production platform, not just sandboxes | Northflank | Covers execution, deployment, databases, GPUs, and preview environments in one platform |
| Data must stay in your own cloud | Northflank BYOC ([Bring Your Own Cloud](https://northflank.com/product/bring-your-own-cloud)) | Self-serve BYOC into AWS, GCP, Azure, Civo, CoreWeave, Oracle Cloud, and on-premises; managed orchestration keeps data in your infrastructure |
| MicroVM-level isolation | Northflank, E2B | **Northflank**: Kata Containers, gVisor, or Firecracker applied per workload. **E2B**: Firecracker microVMs |
| AI agent code execution via API | E2B, Northflank | **Northflank**: full platform with sandbox API, GPU support, and BYOC. **E2B**: purpose-built sandbox API with Python and JS/TS SDKs |
| Persistent state between sessions | Northflank, Fly.io Sprites | **Northflank**: both ephemeral and persistent environments, no session limits. **Fly.io Sprites**: persistent ext4 filesystem, wakes from hibernation |
| Python-native serverless with GPUs | Modal | Modal's function and sandbox model is built around Python ML workflows |
| Maximum isolation, self-hosted, experimental | Microsandbox | Open-source libkrun microVMs; not yet production-ready |
| GPU sandboxes with no quota requests | Northflank | On-demand GPUs, self-serve |
| Self-hosted with managed orchestration | Northflank BYOC | Northflank deploys into your own infrastructure and handles orchestration |

## FAQ: Beam Cloud sandboxes alternatives

### What isolation technology should I look for in a Beam Cloud alternative?

Container-based isolation is the baseline. For untrusted or AI-generated code, hardware-level microVM isolation provides dedicated kernels per sandbox, preventing kernel-level exploits from affecting other workloads or the host. Northflank applies Firecracker, gVisor, or Kata Containers per workload based on security requirements. Other alternatives in this list also offer microVM-based isolation, but differ in production maturity, deployment flexibility, and platform scope.

### Can I self-host a Beam Cloud sandboxes alternative?

Yes. Northflank offers both BYOC (Bring your own cloud) deployment and on-premises/bare-metal deployment into your own infrastructure, with managed orchestration so you don't need to run the platform layer yourself. E2B supports self-hosting via Terraform and Nomad. Microsandbox is fully self-hosted but experimental. Fly.io Sprites runs on Fly.io's infrastructure only.

### Which alternative is best for compliance-sensitive workloads?

Northflank's [Bring Your Own Cloud (BYOC)](https://northflank.com/product/bring-your-own-cloud) deployment is built for this: workloads run inside your own VPC across AWS, GCP, Azure, Civo, CoreWeave, Oracle Cloud, and on-premises and bare-metal infrastructure, while Northflank manages the orchestration layer. This keeps data within your infrastructure without requiring your team to build and operate sandbox infrastructure from scratch.

## Related articles

Further reading on sandbox platforms, isolation technologies, and secure AI code execution infrastructure.

- [Best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents) - A comprehensive breakdown of how to evaluate sandbox platforms for AI agent workflows, covering isolation, latency, and SDK requirements.
- [Top AI sandbox platforms for code execution](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution) - A ranked comparison of the leading platforms used to execute AI-generated code.
- [Self-hostable alternatives to E2B for AI agents](https://northflank.com/blog/self-hostable-alternatives-to-e2b-for-ai-agents) - Options for teams that need AI code execution infrastructure within their own cloud, including BYOC (Bring Your Own Cloud) and open-source approaches.
- [Top Fly.io Sprites alternatives for secure AI code execution](https://northflank.com/blog/top-fly-io-sprites-alternatives-for-secure-ai-code-execution-and-sandboxed-environments) - A direct comparison for teams evaluating Sprites against other persistent, isolated environment options.
- [Top Modal Sandboxes alternatives for secure AI code execution](https://northflank.com/blog/top-modal-sandboxes-alternatives-for-secure-ai-code-execution) - A direct comparison for teams evaluating Modal Sandboxes, covering isolation models, OCI image support, BYOC options, and platform scope.
- [E2B vs Modal vs Fly.io Sprites](https://northflank.com/blog/e2b-vs-modal-vs-fly-io-sprites) - A head-to-head comparison of three commonly evaluated sandbox platforms, covering isolation models, persistence, and SDK support.
- [Best sandboxes for coding agents](https://northflank.com/blog/best-sandboxes-for-coding-agents) - Focused on the specific requirements of coding agents: isolation, tool access, filesystem persistence, and execution speed.
- [Ephemeral sandbox environments](https://northflank.com/blog/ephemeral-sandbox-environments) - Explains the tradeoffs between ephemeral and persistent sandbox models, and when each approach fits the workload.]]>
  </content:encoded>
</item><item>
  <title>Ephemeral execution environments for AI agents in 2026</title>
  <link>https://northflank.com/blog/ephemeral-execution-environments-ai-agents</link>
  <pubDate>2026-03-09T17:00:00.000Z</pubDate>
  <description>
    <![CDATA[A guide to ephemeral execution environments for AI agents in 2026: isolation models, state management, operational challenges, and how to run them at scale.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/ephemeral_execution_environments_ai_agents_74615fe5c2.png" alt="Ephemeral execution environments for AI agents in 2026" />Ephemeral execution environments for AI agents have moved from an architectural nice-to-have to a production requirement as agent workloads scale.

This article covers why the ephemeral pattern is critical for agents, how to implement it, the operational challenges you might encounter, and how platforms like [Northflank](https://northflank.com/product/sandboxes) handle it in production.

<InfoBox className="BodyStyle">

## TL;DR: Key takeaways on ephemeral execution environments for AI agents

Ephemeral execution environments for AI agents are short-lived, isolated runtimes created per agent session or task and destroyed automatically once execution is complete. Unlike persistent environments, they carry no state between runs and reduce residual access to host infrastructure.

Three things determine if your ephemeral execution strategy holds up in production:

- How deep your isolation model needs to go: for untrusted or AI-generated code, process-level separation carries meaningful kernel-level risk that microVM-based isolation addresses.
- How you manage the tension between the stateless nature of ephemeral environments and the stateful execution patterns many agent workflows require.
- How fast and automated your environment creation and teardown is, to match the throughput of agent task pipelines at scale.

> [Northflank](https://northflank.com/product/sandboxes) provides microVM-backed ephemeral and persistent execution environments for AI agent workloads, with Firecracker, gVisor, and Kata Containers isolation, and bring-your-own-cloud support across AWS, GCP, Azure, Civo, CoreWeave, Oracle Cloud, and on-premises and bare-metal infrastructure, available self-serve. The platform is used across a range of organisations, from early-stage startups to public companies and government deployments.
> 

</InfoBox>

## Why do AI agents need ephemeral execution environments?

AI agents generate and execute code dynamically, without a human reviewing every run. That changes your threat model fundamentally compared to standard developer workflows, where the code running inside an environment is authored by your own engineers.

In an agent execution environment, the code is produced by a model at runtime. Unlike code your engineers write, it is generated dynamically and cannot be fully predicted or controlled in advance.

Ephemeral environments address this directly:

- **Clean lifecycle per session:** The environment is created fresh for each task and destroyed after, so a compromised session cannot persist access or affect subsequent runs.
- **Hard isolation boundary:** Combined with the right isolation model, what happens inside the environment is contained within the sandbox boundary.
- **No state accumulation:** Persistent shared environments accumulate state across sessions, meaning a malformed or malicious run earlier in the lifecycle can influence behaviour later. Ephemeral environments address that risk by design.

For multi-tenant agent platforms where multiple users' agents run on shared infrastructure, ephemeral environments with proper isolation are a hard requirement.

*If you want a broader look at isolation strategies, see these guides on [how to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents) and [ephemeral sandbox environments](https://northflank.com/blog/ephemeral-sandbox-environments).*

## What makes agent execution environments different from standard ephemeral sandboxes?

Standard ephemeral sandboxes are designed around discrete, one-shot execution: a task runs, produces output, and the environment is destroyed. Agent workloads break that assumption in several ways you need to plan for upfront:

- **Execution is multi-step:** A single agent session can involve dozens or hundreds of code steps, each shaped by what ran before it. Your environment needs to support stateful execution within a session while remaining ephemeral across sessions.
- **Tool use adds network complexity:** Agents call external APIs as part of normal operation. You need scoped outbound networking, open access creates exfiltration risk, and full lockdown breaks legitimate tool calls.
- **Concurrent sessions require per-session isolation:** When multiple agents run simultaneously on behalf of different users, one session must have no visibility into or impact on another. Application-level separation alone is not a reliable boundary when agents run arbitrary generated code.
- **Throughput requirements are high:** Agent pipelines can create and destroy hundreds to thousands of environments per hour. Creation latency, pool management, and teardown reliability become performance variables, not just operational concerns.

*If you need a deeper look at these requirements, see this guide on [code execution environments for autonomous agents](https://northflank.com/blog/code-execution-environment-for-autonomous-agents).*

## What isolation model should you use?

Container-level isolation carries meaningful risk for agent workloads executing untrusted code. Containers share the host kernel, and a kernel vulnerability can expose the host node and other sessions running on it.

The three models suitable for production agent execution are:

- **Firecracker microVMs:** Each session runs in a lightweight VM with its own guest kernel via KVM. Designed for high-density, multi-tenant workloads.
- **gVisor:** Runs a userspace kernel (the Sentry) that handles syscalls from guest applications, reducing the attack surface on the host kernel. Lower overhead than a full microVM, but there are syscall compatibility gaps with some applications, and I/O-heavy workloads carry additional latency.
- **Kata Containers:** Runs OCI containers inside lightweight VMs via a pluggable VMM layer (Firecracker, Cloud Hypervisor, or QEMU). Hardware-level isolation with Kubernetes-native orchestration.

For most production multi-tenant agent platforms, Firecracker or Kata Containers is the right choice. gVisor is a reasonable middle ground for compute-heavy workloads where full VM overhead is not justified, and your application is compatible with its syscall coverage.

*See how they compare in detail: [Kata Containers vs Firecracker vs gVisor](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor).*

<InfoBox className="BodyStyle">

**Get started with ephemeral execution environments for AI agents**

Northflank is self-serve. You can spin up microVM-backed sandbox infrastructure with ephemeral and persistent execution modes and BYOC ([Bring Your Own Cloud](https://northflank.com/product/bring-your-own-cloud)) support across major clouds and on-premises infrastructure.

[Get started with Northflank](https://app.northflank.com/signup) to spin up your first sandbox in seconds. If you'd prefer to talk through your setup first, you can [schedule a demo](https://cal.com/team/northflank/northflank-demo) with an engineer.

See the following resources:

- [Northflank Sandboxes](https://northflank.com/product/sandboxes)
- [How to spin up a secure code sandbox and microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
- [Code execution environments for autonomous agents](https://northflank.com/blog/code-execution-environment-for-autonomous-agents)

</InfoBox>

## How do you handle state in ephemeral agent environments?

Pure ephemeral execution, where every session starts with a clean filesystem and no prior context, works for stateless tasks. Many agent workflows are not stateless, and this is where the ephemeral pattern gets complicated.

The practical approach is to separate session state from environment lifecycle:

- **Ephemeral filesystem:** The execution environment is destroyed after each session with no state leaking into subsequent runs.
- **External state:** Agent memory, execution history, intermediate outputs, and working data are written to attached volumes, object storage, or a database outside the sandbox boundary.
- **Session continuity:** If a session needs to resume, you create a new ephemeral environment and re-attach the external state, rather than keeping the original environment alive.

This gives you the security guarantees of ephemeral execution while supporting the stateful patterns agent workflows require. Teardown is also clean by design: the environment has nothing to clean up because all meaningful state lives outside it.

## What are the operational challenges of ephemeral agent execution at scale?

Running ephemeral agent environments at scale introduces challenges that go beyond building the execution environment itself. See the main challenges below:

- **Cold start latency:** Full initialization including networking and runtime setup adds overhead beyond VM boot time alone. Pre-warmed execution pools reduce perceived latency but require pool sizing logic, drain and refill orchestration, and idle resource cost management.
- **Pool management:** Pre-warming means paying for idle capacity. Under-provisioning causes latency spikes under load. Getting the balance right requires continuously monitoring actual utilization patterns, which adds operational overhead.
- **Lifecycle leakage:** Environments not torn down correctly leave dangling resources that accumulate cost and can hold residual state longer than intended. Automated garbage collection is essential at any meaningful scale.
- **Network policy management:** Each environment needs scoped outbound access. Managing per-environment network policies at scale requires automation.

## How Northflank runs ephemeral execution environments for AI agents

[Northflank](https://northflank.com/) is a developer platform for running full workload environments at scale, covering services, databases, background jobs, and agents. Its [Sandboxes](https://northflank.com/product/sandboxes) product provides secure, isolated execution environments for running untrusted code, AI agent workloads, and multi-tenant pipelines at scale.

The platform is used across a range of organisations, from early-stage startups to public companies and government deployments.

![northflank-full-homepage.png](https://assets.northflank.com/northflank_full_homepage_7e43a6b554.png)

Here is what it provides across the full stack:

### Ephemeral and persistent execution modes

Short-lived sessions are destroyed after each run with no state leakage. Persistent sessions support attached volumes, S3-compatible object storage, and stateful databases including PostgreSQL, Redis, MySQL, and MongoDB for agent memory and execution history. Both modes are available in the same platform.

### Isolation runtimes

Every agent session runs in its own microVM. Northflank supports Kata Containers, Firecracker, and gVisor, each applied based on your workload requirements and threat model.

### Environment creation and lifecycle

You can create environments in roughly 1-2 seconds end-to-end, covering the full creation lifecycle including networking and service initialization. Trigger environments via the API, CLI, or programmatically as part of your agent pipeline, with configurable lifecycle rules for automatic teardown.

### Bring your own cloud

You can deploy sandbox infrastructure inside your own VPC on AWS, GCP, Azure, Civo, CoreWeave, Oracle Cloud, or on-premises and bare-metal infrastructure. Northflank handles orchestration while your data stays within your network boundary. BYOC ([Bring Your Own Cloud](https://northflank.com/product/bring-your-own-cloud)) is self-serve.

### Full workload support

Your execution environment is not limited to the sandbox itself. You can run agents, background workers, APIs, and supporting databases together in the same platform, with CPU and on-demand GPU support. GPUs are available without quota requests.

*To spin up isolated microVM environments on Northflank step by step, see this guide on [how to spin up a secure code sandbox and microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh).*

### Pricing

Usage is billed at $0.01667 per vCPU per hour and $0.00833 per GB of memory per hour, with GPU pricing on the [Northflank pricing page](https://northflank.com/pricing).

<InfoBox className="BodyStyle">

**Run ephemeral agent execution environments on Northflank**

Northflank provides the full stack for running ephemeral agent execution environments in production: microVM isolation, BYOC ([Bring Your Own Cloud](https://northflank.com/product/bring-your-own-cloud)) deployment across major clouds and on-premises infrastructure, both CPU workloads and on-demand GPUs, and both ephemeral and persistent execution modes, all in one platform.

[Get started with Northflank](https://app.northflank.com/signup) or [schedule a demo](https://cal.com/team/northflank/northflank-demo) if you'd prefer to talk through your setup with an engineer first.

**Related resources:**

- [Northflank Sandboxes](https://northflank.com/product/sandboxes)
- [How to spin up a secure code sandbox and microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
- [Code execution environments for autonomous agents](https://northflank.com/blog/code-execution-environment-for-autonomous-agents)
- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox)

</InfoBox>

## What should you prioritize when choosing an ephemeral execution environment for AI agents?

The right configuration depends on your trust model, throughput requirements, and compliance constraints. Use this as a starting point:

| Situation | Recommended approach |
| --- | --- |
| Internal agents, trusted code | Hardened containers with resource limits and network restrictions |
| External users, moderate trust | gVisor or Kata Containers, ephemeral by default |
| LLM-generated code, multi-tenant | Firecracker or Kata, ephemeral sessions, default-deny networking, external state storage |
| Compliance or data residency requirements | MicroVM isolation with BYOC deployment inside your own VPC |
| High-throughput agent pipelines | Pre-warmed execution pools with automated lifecycle management |

## FAQ: Ephemeral execution environments for AI agents

### What is an ephemeral execution environment for AI agents?

A short-lived, isolated runtime created per agent session or task and destroyed automatically once execution is complete. It carries no state between runs and gives agent-generated code no access to host infrastructure or other sessions.

### Do ephemeral agent environments support stateful workflows?

Yes, by separating session state from environment lifecycle. The execution environment is ephemeral, but agent memory, working data, and execution history are written to external storage (volumes, object storage, or a database) that persists independently of the environment.

### Is container-level isolation sufficient for AI agent execution?

For untrusted or AI-generated code, container-level isolation alone carries meaningful kernel-level risk. You need microVM-based isolation to enforce a harder boundary between agent code and the host system. [Northflank](https://northflank.com/product/sandboxes) supports Firecracker, gVisor, and Kata Containers for exactly this purpose.

### How do pre-warmed execution pools work?

Pre-warmed pools keep a set of initialized environments ready to accept workloads immediately, reducing perceived creation latency for incoming agent tasks. The trade-off is idle resource cost for environments sitting in the pool. Pool size needs to be calibrated against your actual throughput patterns.

## Related articles on ephemeral execution environments for AI agents

These articles go deeper on specific aspects covered here.

- [Ephemeral sandbox environments](https://northflank.com/blog/ephemeral-sandbox-environments): the broad concept, all isolation models, and key considerations for production use.
- [Code execution environments for autonomous agents](https://northflank.com/blog/code-execution-environment-for-autonomous-agents): runtime requirements, session management, and what production-grade agent execution looks like.
- [How to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents): microVM and gVisor isolation strategies specific to agent workloads.
- [Secure runtime for codegen tools: microVMs, sandboxing, and execution at scale](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale): execution at scale for code generation pipelines.
- [Best sandboxes for coding agents](https://northflank.com/blog/best-sandboxes-for-coding-agents): platform comparison with isolation and operational trade-offs.
- [Best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents): options for different agent workload types and use cases.
- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox): what AI sandboxes are and how isolation requirements differ from standard dev environments.]]>
  </content:encoded>
</item><item>
  <title>Ephemeral sandbox environments [2026 guide]</title>
  <link>https://northflank.com/blog/ephemeral-sandbox-environments</link>
  <pubDate>2026-03-06T17:45:00.000Z</pubDate>
  <description>
    <![CDATA[A guide to ephemeral sandbox environments in 2026: isolation models, operational challenges, decision framework, and how Northflank implements them at scale.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/ephemeral_sandbox_environments_29925c1594.png" alt="Ephemeral sandbox environments [2026 guide]" />Ephemeral sandbox environments have become a core part of how you ship software and run AI agent workloads safely.

This article covers how they work, the main isolation models, key considerations for production use, and how platforms like [Northflank](https://northflank.com/product/sandboxes) can simplify running them at scale.


<InfoBox className="BodyStyle">

## TL;DR: Key takeaways on ephemeral sandbox environments

Ephemeral sandbox environments are short-lived, isolated execution contexts that spin up on demand and are destroyed once their purpose is served. They replace long-lived shared test environments with per-task, per-PR, or per-request environments that start clean every time and leave no lingering state.

Three variables determine if your ephemeral sandbox strategy works in production:

- How deep your isolation needs to be versus how fast environments need to start up.
- How closely environments need to match production versus what that costs at scale.
- How much of the lifecycle you can automate versus the operational overhead that introduces.

The right isolation model depends on your threat model, beyond latency alone.

> Platforms like [Northflank](https://northflank.com/product/sandboxes) provide both ephemeral and persistent sandbox infrastructure with microVM-based isolation (Firecracker, gVisor, Kata), bring-your-own-cloud support, and environment creation in roughly 1-2 seconds, letting you run sandboxed workloads inside your own VPC rather than a third-party managed cloud.
*Northflank is used across a range of organisations, from early-stage startups to public companies and government deployments*.
> 

</InfoBox>


## What are ephemeral sandbox environments?

An ephemeral sandbox environment is an isolated runtime that you create, use, and destroy within a defined lifecycle, typically triggered by a specific event like a pull request, a function call, or an agent task. Unlike persistent environments, ephemeral sandboxes carry no long-term state and impose no cleanup burden on your team.

The "sandbox" refers to the isolation model: code running inside the environment has no access to external systems, other tenants' data, or your production infrastructure unless you explicitly allow it. The "ephemeral" part means the environment exists only as long as it needs to.

In practice, you'll encounter ephemeral sandbox environments in two contexts:

- **Development and testing:** Preview environments, per-PR deployments, integration test runners, and short-lived staging replicas.
- **Code execution for AI:** Running LLM-generated or agent-authored code in isolated runtimes where the code cannot be trusted by default.

Both use cases share the same infrastructure requirements: fast creation, deep isolation, predictable resource usage, and reliable teardown. Where they diverge is in isolation depth and latency requirements.

*For a deeper look at how preview environments work in practice, see [the what and why of ephemeral preview environments on Kubernetes](https://northflank.com/blog/the-what-and-why-of-ephemeral-preview-environments-on-kubernetes-sandbox-testing).*

## What does ephemeral mean in DevOps?

In DevOps, "ephemeral" refers to infrastructure with a lifecycle tied to a specific task rather than a calendar. You create an environment when you need it, it runs for a defined duration, and it's destroyed automatically when the task is complete.

This contrasts with the traditional model of maintaining a small number of long-lived shared environments (dev, QA, staging, production). Those environments accumulate stale state, become configuration drift hazards, and create bottlenecks when multiple developers need to test simultaneously.

Ephemeral environments solve the bottleneck problem by making environment creation cheap enough to do per-PR or per-request. The trade-off is that creation time and infrastructure overhead now become variables you need to optimize actively.

## What are the main types of ephemeral sandbox environments?

There is no single implementation model for ephemeral sandboxes. The right approach depends on your workload type, your isolation requirements, and your existing infrastructure. Here are the four primary models in use in 2026.

### Container-based environments

Containers using Linux namespaces and cgroups are the default starting point for most teams: fast to create, cheap to run, and compatible with existing Kubernetes clusters. The limitation is kernel sharing. All containers on a host share the same OS kernel, so a kernel vulnerability can break isolation entirely. Use this model only for internal workflows where the code running inside is trusted.

### MicroVM-based sandboxes

MicroVMs sit between containers and full VMs, giving each sandbox its own kernel boundary without the startup overhead of a full VM. The three runtimes you'll encounter most:

- **Firecracker:** lightweight VM via KVM, designed for serverless and multi-tenant workloads.
- **gVisor:** runs a userspace kernel that handles syscalls from guest applications, reducing the attack surface on the host kernel.
- **Kata Containers:** OCI containers inside lightweight VMs, compatible with existing container tooling.

This is the current standard for running untrusted or AI-generated code at scale.

### Full VM isolation

Full VMs give each sandbox a separate guest OS and kernel. Reserve this for malware analysis or compliance workloads requiring complete kernel-level separation. Startup times and memory overhead make it impractical at scale.

### Preview environments

Each pull request or feature branch gets a complete, production-like deployment: services, databases, networking, and configuration, spun up automatically and torn down on merge or close. Teams running microservices architectures often need 10-30 services per environment, which is where lifecycle management becomes non-trivial fast.

<InfoBox className="BodyStyle">

**Run ephemeral sandbox environments on Northflank**

Northflank supports all four models above, from container-based preview environments to microVM-isolated code execution, with both ephemeral and persistent modes, BYOC support, and environment creation in roughly 1-2 seconds.

[Get started with Northflank](https://app.northflank.com/signup) or [schedule a demo](https://cal.com/team/northflank/northflank-demo).

**Go deeper:**

- [Northflank sandboxes](https://northflank.com/product/sandboxes)
- [Northflank preview environments](https://northflank.com/product/preview-environments)
- [Set up a preview environment](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment)
- [What is a sandbox environment?](https://northflank.com/blog/what-is-a-sandbox-environment)
- [The what and why of ephemeral preview environments on Kubernetes](https://northflank.com/blog/the-what-and-why-of-ephemeral-preview-environments-on-kubernetes-sandbox-testing)
- [Best sandboxes for coding agents](https://northflank.com/blog/best-sandboxes-for-coding-agents)

</InfoBox>

## How do the main ephemeral sandbox models compare?

The right model depends on what you're protecting against and what latency you can tolerate. Here's how they compare at a glance:

| Model | Isolation boundary | Best for | Key limitation |
| --- | --- | --- | --- |
| Containers | Shared kernel (namespaces + cgroups) | Trusted internal code, dev workflows | Kernel vulnerability breaks isolation |
| gVisor | Userspace kernel (syscall interception) | Untrusted code, multi-tenant workloads | Incomplete syscall compatibility with some applications |
| Firecracker microVM | Separate kernel via KVM | AI agent execution, serverless, multi-tenancy | Requires KVM support on host |
| Kata Containers | Separate kernel via lightweight VM | Regulated workloads, OCI-compatible pipelines | Higher per-sandbox overhead than Firecracker |
| Full VM | Separate kernel via hypervisor | Malware analysis, hardware-level compliance | Cost and startup latency make it impractical at scale |

## What are the key considerations when managing ephemeral sandbox environments?

Before you commit to an implementation model, these are the variables that will determine whether your strategy holds up in production.

- **Isolation depth:** Containers are sufficient for trusted internal code. For AI-generated, third-party, or external user code, you need at minimum gVisor or Firecracker-level isolation.
- **Creation latency:** Vendors often quote VM boot time, not full end-to-end time, which also includes image pulls, network setup, and service initialization. Know which metric applies to your use case before benchmarking.
- **Environment accuracy:** Run the same container images, database versions, and configuration as production. A preview environment that omits your background job workers will not catch integration bugs involving those workers.
- **Cost controls:** Ephemeral environments accrue cost even when idle. Scale-to-zero policies, auto-shutdown timers, and per-environment resource limits are essential, not optional.
- **Lifecycle and secrets management:** Sandboxes not torn down correctly leave dangling resources and can hold onto sensitive data longer than intended. Each environment also needs the correct secrets for its context. Reusing production secrets in ephemeral environments is a common misconfiguration with real security consequences.

## How Northflank implements ephemeral sandbox environments

[Northflank](https://northflank.com/) is a developer platform for running full workload environments at scale, covering services, databases, background jobs, and agents.

Among its features, it includes [Sandboxes](https://northflank.com/product/sandboxes) for running isolated, microVM-backed execution environments and [Preview Environments](https://northflank.com/product/preview-environments) for spinning up full-stack PR-based deployments automatically.

![northflank-full-homepage.png](https://assets.northflank.com/northflank_full_homepage_7e43a6b554.png)

If you need to run ephemeral sandboxes in production, here is what it provides across the full stack:

### Environment creation and lifecycle

You can create environments in roughly 1-2 seconds end-to-end, covering the full creation lifecycle including networking and service initialization. You can trigger environments via the API, CLI, or Git integration for PR-based preview environments, and configure automatic teardown based on lifecycle rules you define.

Both ephemeral and persistent modes are supported. Short-lived execution pools handle per-request workloads. Long-running stateful services handle workloads that need to maintain state across sessions.

### Isolation runtimes

For workloads requiring deeper isolation, Northflank supports microVM-based runtimes: Firecracker, gVisor, and Kata Containers, selected based on your workload requirements. This makes it practical to run untrusted or AI-generated code safely in production.

*For a detailed breakdown of how to configure each runtime, see [how to spin up a secure code sandbox and microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh).*

### Bring your own cloud

Most sandbox platforms host your workloads on their own managed cloud. With Northflank, you can deploy sandbox infrastructure inside your own VPC on AWS, GCP, Azure, or on-premises infrastructure.

This matters if you're in a regulated industry where workloads cannot leave a controlled network boundary, or if you simply prefer to keep compute inside your own infrastructure. BYOC ([Bring Your Own Cloud](https://northflank.com/product/bring-your-own-cloud)) on Northflank is self-serve.

### Full workload support

Your environment is not limited to single containers or functions. You can run agents, workers, APIs, databases, and background jobs together in a single environment, with both CPU and GPU support.

On-demand GPUs are available without quota requests or manual provisioning, which is relevant for AI agent pipelines that require GPU-accelerated inference alongside code execution.

*For more on sandboxing AI agent workloads specifically, see [code execution environments for autonomous agents](https://northflank.com/blog/code-execution-environment-for-autonomous-agents) and [best sandboxes for coding agents](https://northflank.com/blog/best-sandboxes-for-coding-agents).*

### Pricing

Usage is billed at $0.01667 per vCPU per hour and $0.00833 per GB of memory per hour, with GPU pricing on the [Northflank pricing page](https://northflank.com/pricing). Northflank is used across a range of organisations, from early-stage startups to public companies and government deployments.

<InfoBox className="BodyStyle">

**Get started with ephemeral sandbox environments**

Northflank provides sandbox infrastructure with microVM isolation, BYOC (Bring your own cloud) support, and both ephemeral and persistent execution modes.

[Get started with Northflank](https://app.northflank.com/signup) or [schedule a demo](https://cal.com/team/northflank/northflank-demo).

**Related resources:**

- [What is a sandbox environment?](https://northflank.com/blog/what-is-a-sandbox-environment)
- [Preview environment platforms](https://northflank.com/blog/preview-environment-platforms)
- [Secure runtime for codegen tools: microVMs, sandboxing, and execution at scale](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale)

</InfoBox>

## FAQ: ephemeral sandbox environments

### What are ephemeral sandbox environments?

Ephemeral sandbox environments are temporary, isolated infrastructure instances created on demand for a specific task and destroyed automatically when that task is complete. You'll encounter them in developer workflows (preview environments, integration testing, CI/CD pipelines) and AI agent systems (isolated code execution).

### What is the difference between a sandbox environment and a preview environment?

A sandbox environment is any isolated execution context. A preview environment is a specific type of sandbox used in developer workflows: a full-stack deployment created per pull request or branch for testing and stakeholder review. All preview environments are sandboxes, but not all sandboxes are preview environments.

### What is the difference between ephemeral and persistent sandbox environments?

Ephemeral sandboxes are destroyed after use and carry no persistent state. Persistent sandboxes maintain state across sessions, retaining filesystem contents, network identity, and configuration. The right choice depends on whether your workload needs state continuity across multiple interactions.

### Are ephemeral sandbox environments suitable for AI agent use cases?

Yes, but container-level isolation is insufficient for running untrusted AI-generated code. AI agent execution pipelines require microVM-based isolation (Firecracker, gVisor, or Kata Containers) to enforce a meaningful security boundary between generated code and the host system. For more detail, see [what is an AI sandbox](https://northflank.com/blog/what-is-an-ai-sandbox) and [best code execution sandboxes for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents).

## Related articles on ephemeral sandbox environments

If you want to go deeper on any of the topics covered in this article, these resources are a good next step.

- [The what and why of ephemeral preview environments on Kubernetes](https://northflank.com/blog/the-what-and-why-of-ephemeral-preview-environments-on-kubernetes-sandbox-testing): how preview environments work in Kubernetes-based stacks and what makes them challenging for full-stack apps.
- [What is a sandbox environment?](https://northflank.com/blog/what-is-a-sandbox-environment): sandbox isolation models, use cases, and how to choose between them in 2026.
- [What is a staging environment and how to set one up](https://northflank.com/blog/what-is-a-staging-environment-how-to-set-one-up): how staging environments differ from ephemeral sandboxes and when you need both.
- [Preview environment platforms](https://northflank.com/blog/preview-environment-platforms): a comparison of platforms for running PR-based preview environments at scale.
- [Remote code execution sandbox](https://northflank.com/blog/remote-code-execution-sandbox): infrastructure requirements for running code execution sandboxes securely.
- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox): what AI sandboxes are and how isolation requirements differ from standard dev environments.
- [Best cloud sandboxes](https://northflank.com/blog/best-cloud-sandboxes): cloud sandbox options for different workload types and use cases.]]>
  </content:encoded>
</item><item>
  <title>Best cloud sandboxes in 2026</title>
  <link>https://northflank.com/blog/best-cloud-sandboxes</link>
  <pubDate>2026-03-05T18:00:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the best cloud sandboxes in 2026. Expert guide to Northflank, E2B, Modal, Fly.io Sprites, Vercel Sandbox, and Together for isolated cloud environments.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/best_cloud_sandboxes_64d310cf5c.png" alt="Best cloud sandboxes in 2026" /><InfoBox className="BodyStyle">

## TL;DR

Cloud sandboxes give you isolated, on-demand environments for running untrusted code, testing new features, or executing agent workloads without touching production. Choosing the wrong one costs you security, developer velocity, or both.

The best cloud sandbox platforms and tools in 2026:

1. **Northflank** - Provides [secure cloud sandboxes](https://northflank.com/product/sandboxes) that run on Northflank's managed cloud or deploy inside your own infrastructure (AWS, GCP, Azure, Oracle, Civo, CoreWeave, on-premises, or bare-metal) with microVM-based isolation (Kata Containers, Firecracker, and gVisor) and support for both ephemeral and persistent environments.
    
    > **Note:** Northflank sandboxes run alongside APIs, workers, databases, and CPU or GPU workloads in the same control plane. BYOC ([Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud)) is available self-serve. Northflank has been in production since 2021 across startups, public companies, and government deployments.
    > 
2. **E2B** - Open-source sandbox runtime with Firecracker microVMs and Python, JavaScript, and TypeScript SDKs for AI agent workflows
3. **Modal** - gVisor-isolated sandboxes on a serverless compute fabric with GPU support and granular networking controls
4. **Fly.io Sprites** - Persistent, KVM/Firecracker-backed Linux VMs with checkpoint/restore
5. **Vercel Sandbox** - Firecracker-based ephemeral sandboxes integrated with the Vercel platform
6. **Together Code Sandbox** - MicroVM-backed sandboxes with memory snapshotting, built for AI development environments

If you need a cloud sandbox platform that handles agents, APIs, databases, and GPU workloads in one place with self-serve BYOC that works in production, [Northflank](https://northflank.com/product/sandboxes) is the strongest option in this list.

</InfoBox>

## What are cloud sandboxes?

Cloud sandboxes are isolated compute environments that run in the cloud, separated from your production systems by design. They let you spin up short-lived or long-running environments for executing untrusted code, running tests, previewing features, or giving AI agents a safe place to work.

Unlike traditional virtual machines or shared developer environments, cloud sandboxes are built for rapid provisioning, hard multi-tenant isolation, and automatic teardown. Depending on the implementation, environments can be ready in under a second or within a few seconds, used, then discarded or kept running as long as your workload requires.

The category has grown significantly in recent years. The rise of AI coding assistants and autonomous agents has driven demand for sandboxes that can handle not only developer test runs, but thousands of concurrent AI-generated code executions in parallel.

## What should you consider when evaluating cloud sandbox tools and platforms?

Not all cloud sandboxes are built the same. Before choosing, it helps to understand the key technical dimensions that separate them in practice.

- **Isolation technology:** MicroVMs (Firecracker, Kata Containers, KVM) give each workload a dedicated kernel. Standard containers share the host kernel - a kernel exploit can escape the sandbox. gVisor intercepts syscalls in user space for a middle ground. Your threat model determines the right choice.
- **Session duration:** Some platforms cap sessions at minutes or hours; others impose no limits. For agents maintaining state across long interactions, session limits force architectural workarounds.
- **Ephemeral vs. persistent:** Ephemeral sandboxes are destroyed after each run. Persistent sandboxes retain state via attached volumes or snapshots. Some platforms support both; others are designed for one model only.
- **BYOC:** Required when execution must stay inside your network boundary - common in regulated industries and enterprise SaaS. Not all platforms support it, and among those that do, the supported clouds and operational model vary.
- **Platform scope:** Some products are sandbox-only; others include databases, APIs, GPU workloads, and CI/CD in the same control plane. If your application grows beyond code execution, you will need to add vendors or migrate.
- **Cold start latency:** Many platforms publish a headline boot time that measures only the microVM start step. Full environment readiness - network attachment, filesystem mount, process initialization - takes longer. Evaluate end-to-end, not just the advertised figure.

## Which are the best cloud sandboxes in 2026?

The platforms and tools below cover the main approaches to cloud sandbox infrastructure available today. They are ordered from most comprehensive to most specialized.

### 1. Northflank

[Northflank](https://northflank.com/) is a workload platform that provides [secure cloud sandboxes](https://northflank.com/product/sandboxes) as a first-class product. Sandboxes run on Northflank's managed infrastructure or inside your own cloud account or VPC. Northflank has been operating microVMs at scale since 2021 across startups, public companies, and government deployments.

> What distinguishes Northflank from point-solution sandbox tools is that isolation is one part of a broader platform. Agents, APIs, workers, databases, GPU workloads, CI/CD, and persistent storage all run in the same control plane, with the same security model.
> 

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

**Key features for secure cloud sandboxes:**

- **MicroVM-based isolation (Kata Containers, Firecracker, and gVisor):** Workloads run in isolated environments using Kata Containers, Firecracker, or gVisor, depending on workload type and security requirements. The isolation technology is matched to the workload automatically.
- **Ephemeral and persistent environments:** Sandboxes run ephemerally for stateless workloads or persist with attached volumes for state that survives restarts. No forced session time limits.
- **Self-serve BYOC:** Deploy inside your own AWS, GCP, Azure, Oracle, CoreWeave, or on-premises infrastructure. Available self-serve.
- **Full workload runtime:** Run agents, workers, APIs, databases (Postgres, Redis, MySQL, MongoDB), and GPU workloads alongside sandboxes in one platform.
- **On-demand GPUs:** Self-service GPU provisioning with no quota requests. CPU and GPU sandbox environments managed through the same control plane.
- **API, CLI, and SSH access:** Full programmatic control for automation and integration into agent frameworks.
- **1-2 second cold starts:** Full environment readiness including network and filesystem, not just the microVM boot step.
- **Pricing:** CPU at $0.01667/vCPU/hour, memory at $0.00833/GB/hour. See [Northflank's pricing](https://northflank.com/pricing) for more details.

**Best for:** Teams building AI products where sandboxes, databases, and APIs need to run in one platform; engineering orgs in regulated industries requiring VPC deployment and data residency guarantees; multi-tenant SaaS platforms that need workload isolation at scale.

<InfoBox className="BodyStyle">

BYOC for secure cloud sandboxes is a recurring blocker for teams in regulated industries or building enterprise AI products. Northflank's self-serve BYOC runs inside your own VPC with full infrastructure control and the same APIs and experience as the managed cloud offering.

If you want to see how it works in practice, the [how to spin up a secure code sandbox and microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh) guide is a hands-on walkthrough covering microVM setup, multi-tenancy, and deployment from any OCI image. It is the fastest way to understand what running secure sandboxes on Northflank actually looks like.

[Get started on Northflank](https://app.northflank.com/signup) or [book a demo with an engineer](https://cal.com/team/northflank/northflank-demo) to see how it fits your workload.

</InfoBox>

### 2. E2B

E2B is an open-source runtime for running AI-generated code in secure cloud sandboxes. It uses Firecracker microVM isolation and provides Python, JavaScript, and TypeScript SDKs designed for AI agent workflows.

**Key features:**

- **Firecracker microVM isolation:** Dedicated kernel per sandbox with hardware-level separation
- **Python, JavaScript, and TypeScript SDKs:** Designed for AI agent frameworks and LLM orchestration libraries
- **Filesystem API:** File read/write operations within sandboxes for agent state workflows
- **Open-source core:** The runtime is open-source and self-hostable
- **Custom sandbox templates:** Define and reuse environment snapshots across sessions

**Best for:** AI agent developers who need Firecracker isolation and SDK support for AI agent frameworks; teams where open-source transparency of the execution layer is a requirement.

**Session limits and BYOC:** The free Hobby plan caps sessions at 1 hour with 20 concurrent sandboxes. The Pro plan extends sessions to 24 hours. BYOC is available on the Enterprise plan only and is not self-serve.

### 3. Modal

Modal provides sandboxes as part of a serverless compute platform. Sandboxes run on Modal's infrastructure using gVisor - the container runtime developed at Google and used in Google Cloud Run and GKE. 

**Key features:**

- **gVisor isolation:** Syscall interception via a user-space guest kernel, reducing host kernel attack surface
- **Python, JavaScript, and Go SDKs:** Code-first developer experience with no YAML
- **Granular networking controls:** Port tunneling, CIDR-based egress allowlists, and a block-all network mode for fully air-gapped execution
- **Filesystem and memory snapshots:** Save and restore sandbox state for agent workflow continuity
- **GPU support:** On-demand GPU access within sandboxes via Modal's GPU fleet

**Best for:** Python-centric AI and ML teams that want to run sandboxes within a broader serverless compute platform.

**No BYOC:** All execution runs on Modal's infrastructure. There is no on-premises or bring-your-own-cloud deployment option.

*For teams that need VPC-level isolation or execution inside their own cloud account, [Northflank](https://northflank.com/product/sandboxes) runs the execution plane inside your own infrastructure with the same APIs.*

### 4. Fly.io Sprites

Fly.io Sprites are persistent, hardware-isolated Linux VMs backed by KVM/Firecracker. They go idle when inactive and retain their full filesystem state on object storage between sessions. Checkpoint and restore lets agents resume from a saved state rather than rebuilding their environment from scratch on every invocation. No Dockerfiles or OCI images are required.

**Key features:**

- **KVM/Firecracker hardware isolation:** Hardware-level VM separation per workload
- **Checkpoint and restore:** Save full VM state and resume it, including filesystem and memory
- **Persistent storage:** 100GB starting partition backed by S3-compatible object storage, retained when idle
- **REST API with TypeScript and Go SDKs:** Programmatic lifecycle control; Python SDK in development

**Best for:** Coding agent workflows where persistent environments reduce per-invocation setup time; use cases that benefit from long-lived, resumable environments.

*For teams that also need GPU support, BYOC, or OCI-based image workflows alongside persistent sandboxes, [Northflank](https://northflank.com/product/sandboxes) supports all three.*

### 5. Vercel Sandbox

Vercel Sandbox provides on-demand Firecracker microVMs exposed through an SDK and CLI. Each sandbox runs Amazon Linux 2023 with Node.js 22/24 and Python 3.13 available by default. Environments are ephemeral by design and shut down automatically when the task completes.

**Key features:**

- **Firecracker microVM isolation:** Each sandbox has a dedicated kernel and isolated filesystem, network, and process space
- **Open-source SDK and CLI:** TypeScript SDK with OIDC-based authentication
- **Sudo access and package managers:** Install packages and run arbitrary Linux commands

**Best for:** Teams with existing Vercel deployments that need co-located, short-lived sandboxed code execution without introducing a separate vendor.

*For agents that need to run beyond 5 hours, or for teams that require BYOC, [Northflank](https://northflank.com/product/sandboxes) imposes no session time limits and supports VPC deployment.*

### 6. Together Code Sandbox

Together Code Sandbox provides microVM-backed sandbox environments built on CodeSandbox infrastructure, which is a Together company. Sandboxes support memory snapshot and restore for fast hibernate and resume from a warm state.

**Key features:**

- **Memory snapshot and restore:** Hibernate and resume sandbox state from a warm state
- **Git-versioned filesystem:** Persistent storage with version control for environment state
- **Built-in dev tooling:** Terminal access, task runner, preview hosting, and session management
- **Together AI integration:** Sandboxes run alongside Together's inference APIs and fine-tuning products

**Best for:** Teams using Together AI's inference APIs who want co-located code execution; AI IDE and SaaS products that need full development environments with memory-snapshotted resume.

*Teams that need self-serve access to BYOC or GPU-enabled sandboxes within a single platform should evaluate [Northflank](https://northflank.com/product/sandboxes), which supports both.*

## How do you choose the right cloud sandbox?

The right cloud sandbox depends primarily on where sandboxes sit in your architecture: core product infrastructure or a supplementary capability. Use the table below to narrow down your options.

| Factor | What to consider | Recommended options |
| --- | --- | --- |
| Isolation strength | Kernel-level isolation for untrusted or AI-generated code | Northflank (Kata Containers, Firecracker, gVisor), E2B (Firecracker), Modal (gVisor), Vercel (Firecracker), Fly.io Sprites (KVM/Firecracker) |
| BYOC / VPC deployment | Execution must stay inside your own network boundary | Northflank (self-serve, multiple clouds and on-prem), E2B (Enterprise only) |
| Platform completeness | Need databases, APIs, GPUs, and sandboxes in one control plane | Northflank |
| Session duration | Long-running agents that need state for days or weeks | Northflank (no forced limits), Fly.io Sprites (persistent with idle sleep) |
| Python-native serverless | Python-first team wanting tight SDK integration with serverless compute | Modal |
| Vercel ecosystem | Already on Vercel, need co-located short-lived execution | Vercel Sandbox |
| GPU alongside sandboxes | Need GPU inference and code execution in one platform | Northflank, Modal |
| Open-source runtime | Need to inspect or self-host the execution layer | E2B |
| Snapshot-based resume | Full dev environments with fast warm-state resume | Together Code Sandbox, Fly.io Sprites |

If sandboxes are a core part of your product - you are building a coding assistant, an agent platform, or a multi-tenant SaaS where users execute code - you need a platform with a full control plane. If sandboxes are a secondary capability used occasionally, a more narrowly scoped tool may be sufficient to start.

### How do cloud sandbox platforms compare on pricing?

Pricing as of April 2026. Billing models differ across platforms (some bill based on active CPU usage only, others bill for the entire duration the sandbox is running). Verify current rates on each platform's pricing page before making cost decisions.

| Platform | CPU | Memory | Storage | GPU | Billing model |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | $0.01667/vCPU-hr | $0.00833/GB-hr | $0.15/GB-month | L4: $0.80/hr, A100 40GB: $1.42/hr, A100 80GB: $1.76/hr, H100: $2.74/hr, H200: $3.14/hr | Per second |
| **E2B** | $0.0504/vCPU-hr | $0.0162/GiB-hr | 10–20GB included free | Do not provide GPU compute | Per second |
| **Fly.io Sprites** | $0.07/CPU-hr | $0.04375/GB-hr | $0.00068/GB-hr (hot NVMe) | Do not provide GPU compute | Per second, actual cgroup usage. No charge when idle |
| **Vercel Sandbox** | $0.128/vCPU-hr | $0.0212/GB-hr | $0.023/GB-month (snapshots) | Do not provide GPU compute | Active CPU only |
| **Modal Sandboxes** | $0.1419/physical core-hr (2 vCPU) | $0.0242/GiB-hr | — | L4: $0.80/hr, A100 40GB: $2.10/hr, A100 80GB: $2.50/hr, H100: $3.95/hr, H200: $4.54/hr | Per second |
| **Together Code Sandbox** | Not publicly listed | Not publicly listed | Not publicly listed | Not publicly listed | — |

### BYOC support across cloud sandbox platforms

The table below shows how each platform handles BYOC deployment, which clouds are supported, and whether it requires a sales process.

| Platform | BYOC available | Clouds supported | Access model | Pricing model |
| --- | --- | --- | --- | --- |
| **Northflank** | Yes, fully self-serve | AWS, GCP, Azure, Oracle, CoreWeave, any neoclouds, Civo, bare-metal, on-premises | Self-serve, enterprise contracts available for larger commits (with bulk discounts) | Your existing cloud bill, CPU $0.01389/vCPU-hr and Memory $0.00139/GB-hr |
| **E2B** | Yes, limited and not self-serve | AWS and GCP only | Not publicly disclosed, need to contact sales | Starts at $50/sandbox/month, on top of your existing cloud bill |
| **Modal** | No | Managed only | — | — |
| **Fly.io Sprites** | No | Managed only | — | — |
| **Vercel Sandbox** | No | Managed only (iad1 region only) | — | — |
| **Together Code Sandbox** | No | Managed only | — | — |

## FAQ: cloud sandboxes

Answers to the questions engineers most commonly ask when evaluating cloud sandbox options.

**What is a cloud sandbox?**

A cloud sandbox is an isolated compute environment in the cloud, separated from production systems by hard security boundaries. It lets teams execute untrusted code, run tests, or give AI agents a safe workspace. Cloud sandboxes use container or microVM isolation, provision in seconds, and can be ephemeral or persistent depending on the platform.

**What is the best cloud sandbox platform in 2026?**

For teams building AI products or running multi-tenant workloads, Northflank is the strongest option. It combines microVM-based isolation (Kata Containers, Firecracker, gVisor), self-serve BYOC across multiple clouds and on-premises, and a full workload runtime for agents, databases, and GPUs. For Python-focused teams without BYOC requirements, Modal is an alternative. For persistent coding agent environments, Fly.io Sprites is an option.

**What is the difference between a cloud sandbox and a container?**

Containers share the host OS kernel. A cloud sandbox using microVM technology (Firecracker, Kata Containers, KVM) gives each workload a dedicated kernel, creating a much stronger isolation boundary. A kernel exploit in a container can potentially escape to the host; a microVM-based sandbox contains the blast radius to a single virtual machine.

**Do I need BYOC for a cloud sandbox?**

You need BYOC if sandbox workloads must access private services, comply with data residency requirements, or stay within your network perimeter. This applies in regulated industries and enterprise SaaS products. Among the options in this list, Northflank is the only one offering self-serve BYOC across multiple cloud providers and on-premises infrastructure without an enterprise-tier prerequisite.

**How do cloud sandbox platforms handle multi-tenancy?**

Strong multi-tenant implementations use microVM isolation (dedicated kernel per workload) combined with network policies preventing cross-tenant communication. Weaker implementations rely on container namespacing, which shares the host kernel. For AI platforms serving multiple customers, microVM-level multi-tenancy is the appropriate security baseline.

**What should I look at when comparing cloud sandbox tools?**

The key criteria are isolation technology, session duration limits, BYOC support, platform completeness, and cold start latency measured to full environment readiness - not just VM boot time. For production AI workloads, also verify the vendor's track record at scale and what happens when your workload outgrows the sandbox layer alone.

## Related cloud sandbox articles

Further reading to help you evaluate and implement the right cloud sandbox infrastructure for your use case.

- [Top AI sandbox platforms, ranked](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution)
- [Top BYOC AI sandboxes for running untrusted code](https://northflank.com/blog/top-byoc-ai-sandboxes)
- [What is a sandbox environment?](https://northflank.com/blog/what-is-a-sandbox-environment)
- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox)
- [How to spin up a secure code sandbox and microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
- [E2B vs Modal vs Fly.io Sprites for AI code execution sandboxes](https://northflank.com/blog/e2b-vs-modal-vs-fly-io-sprites)
- [Top Fly.io Sprites alternatives for secure AI code execution](https://northflank.com/blog/top-fly-io-sprites-alternatives-for-secure-ai-code-execution-and-sandboxed-environments)
- [Best alternatives to E2B.dev for running untrusted code in secure sandboxes](https://northflank.com/blog/best-alternatives-to-e2b-dev-for-running-untrusted-code-in-secure-sandboxes)
- [How to sandbox AI agents: microVMs, gVisor and isolation strategies](https://northflank.com/blog/how-to-sandbox-ai-agents)
- [Self-hosted AI sandboxes: guide to secure code execution](https://northflank.com/blog/self-hosted-ai-sandboxes)]]>
  </content:encoded>
</item><item>
  <title>Top 7 AI agent runtime tools and platforms in 2026</title>
  <link>https://northflank.com/blog/top-ai-agent-runtime-tools</link>
  <pubDate>2026-03-04T17:00:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the best AI agent runtime tools and platforms in 2026. Expert breakdown of Northflank, E2B, Modal, Fly.io, Cloudflare Workers, and more for scalable, secure agent execution.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/top_ai_agent_runtime_tools_333d8adbbb.png" alt="Top 7 AI agent runtime tools and platforms in 2026" /><InfoBox className="BodyStyle">

## TL;DR: Top AI agent runtime tools and platforms at a glance

AI agent runtime tools are the infrastructure layer that lets your agents actually run: isolated, scalable, and without compromising your production environment. The decision usually comes down to **workload scope**, **isolation model**, **session lifecycle**, **GPU requirements**, and **deployment model**.

**Top AI agent runtime tools and platforms (compared):**

1. **Northflank** - Full-stack cloud platform for running AI agents, APIs, databases, background workers, and isolated sandbox environments in a single control plane. Supports microVM-based isolation (Kata Containers, Firecracker, and gVisor), on-demand GPUs, self-service BYOC across AWS, GCP, Azure, Oracle, CoreWeave, on-premises, and bare-metal, and both ephemeral and persistent environments with no forced time limits. In production since 2021 across startups, public companies, and government deployments.
2. **E2B** - Purpose-built sandbox tool for AI agents and LLM apps. Firecracker microVMs, Python and TypeScript SDKs, sessions up to 24 hours on paid tiers.
3. **Modal** - Serverless Python-first platform for GPU-accelerated ML workloads and agent sandboxing. gVisor isolation, elastic GPU scaling with no reserved capacity required.
4. **Fly.io Machines** - API-driven KVM hardware-isolated VMs that accept any OCI container. Good general-purpose agent runtime for polyglot teams.
5. **Together AI Sandbox** - Managed microVM sandbox infrastructure built on CodeSandbox's stack. Best for teams already using Together's model inference.
6. **Cloudflare Workers** - V8 isolate-based edge execution across a global network. Stateless by design, JavaScript and TypeScript native.
7. **Vercel Sandbox** - Firecracker-based sandboxes running on Fluid compute, integrated into the Vercel deployment platform. Best for frontend-adjacent AI workloads on Vercel.

> **If you need a complete AI agent runtime, not just sandboxes:** Prioritize platforms where agents, persistent services, databases, and sandboxes share a single control plane. [Northflank](https://northflank.com/product/sandboxes) supports self-serve BYOC across major clouds and on-premises infrastructure, microVM-based isolation (Kata Containers, Firecracker, and gVisor), on-demand GPUs, and both ephemeral and long-running environments with no forced time limits.
> 

</InfoBox>

## What is an AI agent runtime?

An AI agent runtime is the compute infrastructure that executes the code, tools, and processes your agent invokes during a task. It is not the LLM and it is not the orchestration framework. It is what happens when your agent writes and runs a Python script, spins up a subprocess, executes a terminal command, or calls an external API in an isolated environment.

Runtime platforms handle multiple workload types under one control plane: ephemeral sandboxes, long-running stateful services, databases, and background workers together. Specialized sandbox tools focus narrowly on isolated code execution and hand off everything else to you. The distinction matters when you are choosing infrastructure for agents that need to maintain state, access GPUs, or run inside your own cloud.

## What to look for when evaluating AI agent runtime tools

Not every tool addresses every dimension of the problem. Before choosing, verify each option against these requirements:

- **Isolation model:** Does it use microVMs (Firecracker, Kata Containers, gVisor) or container-level isolation? MicroVMs provide stronger tenant separation for untrusted code.
- **Ephemeral and persistent support:** Can it run both short-lived sandboxes and long-running stateful services, or only one? Agents with memory and state need persistence.
- **Session limits:** Does the platform impose time limits that would break long-horizon agent tasks? Some platforms cap sessions at 24 hours or less.
- **GPU availability:** Does your agent need GPU-accelerated tools or inference? Only a subset of platforms in this category support it without separate infrastructure.
- **BYOC and deployment model:** Enterprise customers frequently require execution inside their own VPC. Most managed-only platforms do not support this.
- **Language and SDK support:** Is your team Python-first, TypeScript-first, or polyglot? Some platforms are tied to specific language ecosystems.

## The top AI agent runtime tools and platforms in 2026

The tools and platforms below cover the full range of the category, from purpose-built sandboxes to full-stack production runtimes. Each has a distinct set of trade-offs worth understanding before committing to infrastructure.

### 1. Northflank

Northflank is a production infrastructure platform that runs the complete stack an AI product needs: agents, APIs, background workers, databases, cron jobs, and isolated sandbox execution in one place. Unlike purpose-built sandbox tools that cover only code execution, Northflank handles the entire operational surface, from provisioning to scaling to BYOC enterprise deployment.

> What separates Northflank from single-purpose sandbox tools is that secure execution is one feature of a comprehensive runtime, not the whole product. Teams running AI agents in production need more than ephemeral sandboxes. They need persistent services for memory and state, databases for storage, background workers for async tasks, and GPUs for inference, and they need all of it to work together under a single control plane. That is what Northflank provides.
> 

Northflank uses microVM-based isolation with Kata Containers, Firecracker, and gVisor depending on workload type, giving teams the ability to tune the security and performance trade-off per use case. Environment creation takes 1-2 seconds end-to-end, accounting for the full orchestration cycle.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

**Key features:**

- **MicroVM isolation:** Kata Containers, Firecracker, and gVisor options applied per workload type
- **Full workload runtime:** Run agents, APIs, databases, background workers, and cron jobs inside a single platform, CPU and GPU supported
- **Ephemeral and persistent environments:** Short-lived execution pools for isolated code runs alongside long-running stateful services for memory and agent state
- **Bring your own cloud:** Deploy inside your own AWS, GCP, Azure, Oracle, CoreWeave, on-premises, or bare-metal infrastructure with full feature parity, available self-serve.
- **On-demand GPUs:** Self-service GPU provisioning without quota requests or reservation overhead
- **API, CLI, and SSH access:** Multiple access modes for operational flexibility across automated pipelines and direct access

**Best for:**

- Teams building multi-tenant AI products that need both sandbox execution and persistent infrastructure in one platform
- Enterprise deployments where data residency, VPC isolation, or compliance requirements make fully managed external platforms non-viable
- AI workloads that combine code execution sandboxes with long-running stateful agents, APIs, and databases
- Teams that want GPU access without managing quotas or reservations


<InfoBox className="BodyStyle">

**Northflank in production**

Northflank has been running production workloads since 2021 across startups, public companies, and government deployments.

[Get started on Northflank](https://northflank.com/) or [book a demo with an engineer](https://cal.com/team/northflank/northflank-demo) to see if the platform fits your agent infrastructure requirements.

</InfoBox>

### 2. E2B

E2B is an open-source cloud sandbox platform built for AI agents and LLM applications. It runs isolated environments using Firecracker microVMs, providing kernel-level isolation per sandbox, with Python and TypeScript SDKs for integration into agent workflows.

**Key features:**

- **Firecracker microVM isolation:** Kernel-level isolation per sandbox
- **Python and TypeScript SDKs:** Clean APIs for programmatic sandbox lifecycle management
- **Code Interpreter Sandbox:** Pre-built execution environment with a running Jupyter server for code generation agents
- **Open-source core:** Self-hostable alongside a managed SaaS tier
- **Fast startup:** Firecracker-based environments start quickly for interactive agent use cases
- **Persistent filesystem:** State within a session persists across commands

**Best for:**

- Teams building code-execution features inside AI applications who need reliable, low-setup sandboxing with Python or TypeScript SDKs

### 3. Modal

Modal is a serverless compute platform built for data and ML teams. Developers define compute requirements through Python decorators, and Modal handles container builds, scheduling, and scaling automatically. It uses gVisor isolation across all workloads and supports GPU types from T4 through B200 without long-term reservations.

**Key features:**

- **Python-native infrastructure-as-code:** Define hardware requirements with Python decorators, no YAML required
- **Fast cold starts:** Custom Rust runtime and lazy-loading filesystem enable fast container initialization
- **Elastic GPU scaling:** Scale from zero to many GPUs across multiple GPU types without quotas or reservations
- **Sandboxes for untrusted code:** Containers with configurable TTL, dynamically defined at runtime, for agent code execution
- **gVisor isolation:** User-space kernel interception applied across all container workloads
- **Filesystem and memory snapshots:** Save and restore sandbox state for agent persistence

**Best for:** ML engineers and agent teams running Python workloads who need GPU access, fast sandboxes, and elastic scale without managing infrastructure

### 4. Fly.io Machines

Fly.io Machines are KVM hardware-isolated VMs controlled through a REST API, accepting any OCI-compliant container image across multiple global regions. The Machines API supports per-user isolated environments, ephemeral sandboxes for agent-generated code, and persistent VM instances for stateful agents.

**Key features:**

- **KVM hardware isolation:** Hardware-assisted virtualization giving strong tenant separation
- **OCI-compatible:** Any Docker or Kubernetes image runs without modification
- **Fast VM startup:** API-driven lifecycle with fast boot times for agent session creation
- **Multi-language execution:** Runs JavaScript, Python, Go, or any language inside standard containers
- **Ephemeral and persistent modes:** Clean-slate ephemeral machines or persistent machines with volume storage for stateful agents
- **Global region placement:** Multiple regions for latency-optimized agent deployment

**Best for:** Teams that need hardware-isolated, OCI-compatible agent environments without platform-specific SDK lock-in

### 5. Together AI Sandbox

Together AI Sandbox provides managed microVM sandbox environments for code execution, built on CodeSandbox's infrastructure. It covers two use cases on the same stack: Together Code Sandbox for full-scale development environments, and Together Code Interpreter for session-based Python execution via API.

**Key features:**

- **Fast VM snapshot resume:** Resume from a paused sandbox state quickly for repeated agent sessions
- **Sandbox forking:** Clone a running sandbox including its active processes, not just the filesystem
- **Hot-swappable VM sizing:** Resize compute without tearing down the environment
- **Code Interpreter API:** Session-based Python execution for agentic and RL workflows
- **Git-versioned storage:** Repository-style versioning for agent workspace state
- **Live preview hosts:** Running services can be exposed via preview URLs during execution

**Best for:** Teams already using Together AI for model inference who want code execution capability on the same platform

### 6. Cloudflare Workers

Cloudflare Workers uses V8 isolates to run agent code at the network edge across a globally distributed network. Workers are stateless by design, which makes them well-suited for stateless tool calls, API proxies, and short-lived agent actions. Durable Objects extend this with optional persistent state, though the programming model differs significantly from traditional server-based runtimes.

**Key features:**

- **V8 isolate-based execution:** JavaScript and TypeScript native edge runtime with very fast cold starts
- **Global edge network:** Execution close to users without manual region configuration
- **Stateless by default:** Clean execution per invocation with no residual state between requests
- **Durable Objects for state:** Optional persistent state with strong consistency guarantees
- **WebAssembly support:** Compile other languages to WASM for edge execution
- **Cloudflare ecosystem integration:** Native integration with R2 storage, KV, and AI Gateway

**Best for:** JavaScript and TypeScript teams building stateless agent tool calls or API proxy layers where global low latency matters most

### 7. Vercel Sandbox

Vercel Sandbox provides Firecracker-based isolated execution environments for AI-generated code, running on Vercel's Fluid compute infrastructure. It integrates directly with the Vercel AI SDK and Vercel deployment platform. Node.js and Python runtimes are available by default, and sandboxes are billed only when code is actively running.

**Key features:**

- **Firecracker microVM isolation:** MicroVM-level isolation per sandbox on Fluid compute
- **Vercel AI SDK integration:** Works natively with the AI SDK's agent and tool abstractions
- **Node.js and Python runtimes:** Available by default with package installation support
- **Active CPU pricing:** Billed only when code is actively running, not during idle or I/O wait
- **Port exposure:** Running services can be accessed via sandbox preview URLs

**Best for:** Teams already deploying on Vercel who need lightweight sandbox execution for frontend-adjacent AI workloads

## How to choose the right AI agent runtime tool or platform

Use this table as a starting framework, then validate against your actual requirements:

| Factor | What to consider | Recommended options |
| --- | --- | --- |
| Workload type | Do you need only sandboxes, or a full stack including persistent services and databases? | Full stack: Northflank. Sandboxes only: E2B, Modal |
| GPU requirements | Does your agent need GPU-accelerated inference or ML tools? | Northflank, Modal, Together AI Sandbox |
| Session duration | Do your agents run for hours or days, or just seconds to minutes? | Northflank (no limits), Fly.io persistent machines (long-lived); avoid Vercel for long-running sessions |
| Enterprise/compliance | Do you need deployment inside your own VPC? | Northflank (self-service BYOC across all major clouds and on-prem) |
| Language requirements | Is your team Python-first, TypeScript-first, or polyglot? | Modal (Python-first), E2B (Python and TypeScript), Fly.io (any OCI), Cloudflare (JS/WASM) |
| Existing ecosystem | Are you already committed to a cloud or platform? | Together AI (if using their models), Vercel (if deploying on Vercel), Cloudflare (if on Workers) |

## FAQ

**What is an AI agent runtime tool?**

An AI agent runtime tool is infrastructure that executes the code, commands, and processes an AI agent invokes during a task. It provides isolation so agent-generated code cannot affect production systems, scaling so many agent sessions can run concurrently, and lifecycle management for spinning environments up and tearing them down automatically.

**What is the difference between an AI sandbox and a full agent runtime platform?**

An AI sandbox focuses specifically on isolated code execution. A full agent runtime platform handles sandboxes alongside persistent services, databases, background workers, GPUs, and deployment infrastructure. Sandboxes solve one problem; runtime platforms solve the complete operational challenge of running production AI agents at scale.

**Do I need BYOC for running AI agents in enterprise environments?**

For enterprise customers with data residency requirements, compliance needs (SOC 2, HIPAA, FedRAMP), or internal security policies that prohibit third-party execution environments, BYOC deployment inside their own VPC is typically non-negotiable. Northflank provides self-service BYOC with full feature parity across AWS, GCP, Azure, Oracle, CoreWeave, on-premises, and bare-metal.

**Can AI agents run GPU workloads in these platforms?**

Yes, but only a subset of platforms support it. Northflank, Modal, and Together AI Sandbox all provide GPU-backed execution environments. Fly.io has limited GPU availability. E2B, Cloudflare Workers, and Vercel Sandbox do not support GPU workloads.

**Which runtime is best for multi-tenant AI products?**

For multi-tenant products where each user or session needs an isolated environment, Northflank's multi-tenant architecture, Fly.io's per-user VM model, and E2B's programmatic sandbox management are all viable. Northflank is the most complete option if you also need persistent services, databases, and BYOC for enterprise accounts within the same platform.

## Related articles

- [How to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents)
- [Code execution environment for autonomous agents](https://northflank.com/blog/code-execution-environment-for-autonomous-agents)
- [Best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents)
- [Secure runtime for codegen tools: microVMs, sandboxing, and execution at scale](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale)
- [Top AI sandbox platforms for code execution](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution)
- [Top BYOC AI sandboxes](https://northflank.com/blog/top-byoc-ai-sandboxes)
- [AI infrastructure](https://northflank.com/blog/ai-infrastructure)
- [Top AI PaaS platforms](https://northflank.com/blog/top-ai-paas-platforms)]]>
  </content:encoded>
</item><item>
  <title>Code execution environment for autonomous agents in 2026</title>
  <link>https://northflank.com/blog/code-execution-environment-for-autonomous-agents</link>
  <pubDate>2026-03-03T16:45:00.000Z</pubDate>
  <description>
    <![CDATA[Code execution environments for autonomous agents in 2026: isolation models, requirements, operational challenges, and what a production-ready platform looks like.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/code_execution_environment_for_autonomous_agents_515d9092d9.png" alt="Code execution environment for autonomous agents in 2026" />Autonomous agents require a dedicated code execution environment to run generated tool calls, shell commands, and scripts safely without exposing host infrastructure or adjacent workloads.

This guide covers what makes agent execution environments distinct, what they require in production, how to evaluate them, and what a production-ready platform looks like.

<InfoBox className="BodyStyle">

## TL;DR: Key considerations for agent code execution environments

A code execution environment for autonomous agents is an isolated runtime where agent-generated code executes without access to the host system, other tenants, or sensitive infrastructure.

A production-grade environment enforces:

- **Per-session isolation**: Each agent session runs in its own boundary
- **Scoped network access**: Outbound connectivity limited to known endpoints, not open by default
- **Resource limits**: CPU, memory, and I/O caps per agent session
- **Ephemeral or persistent execution**: Depending on whether the agent needs state across steps
- **Audit logging**: Every execution is traceable for debugging and compliance

> [Northflank](https://northflank.com/product/sandboxes) provides microVM-backed execution environments for agent workloads, with both ephemeral and persistent modes and [BYOC (Bring Your Own Cloud)](https://northflank.com/product/bring-your-own-cloud) support across AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, and on-premise or bare metal.
> 

</InfoBox>

## What is a code execution environment for autonomous agents?

A code execution environment for autonomous agents is the runtime layer where an agent executes code it generates or receives as part of its reasoning loop.

The code is produced by a model, not submitted by a human, and it runs immediately as part of an automated workflow.

The key distinction from a standard remote code execution sandbox is continuity: a sandbox handles discrete, independent executions, while an agent execution environment handles sessions that evolve over time.

## Why do autonomous agents need dedicated execution environments?

Standard sandbox infrastructure is designed around one-shot execution. Agent workloads break that assumption in ways that matter at the infrastructure level:

- **Execution is multi-step**: A single session can involve dozens or hundreds of code steps, each shaped by what ran before it
- **State compounds risk**: A compromised or malformed step early in a session can influence every subsequent step
- **Code is unpredictable by design**: You cannot whitelist what will run because the model decides at runtime

Beyond execution itself, tool use adds another layer of complexity. Agents call external APIs as part of normal operation, which creates a real tension between giving agents the connectivity they need and preventing exfiltration.

And when multiple agents run concurrently on behalf of different users, tenant isolation becomes as critical as workload isolation. One agent's execution should have no visibility into, or impact on, another's.

<InfoBox className="BodyStyle">

If you are running agent workloads in standard containers today, your containers aren't as isolated as you think: containers share the host kernel, and a successful escape gives an attacker access to the host node and potentially adjacent workloads running on it.

This guide on [microVMs, VMMs, and container isolation](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation) breaks down why that is a problem for multi-tenant agent workloads and how microVMs and VMMs close that gap.

</InfoBox>

## What are the core requirements of an agent execution environment?

Running agent workloads safely requires controls across isolation, state management, networking, and observability. Take a look at the key requirements below:

- **Per-session isolation**: Each agent session runs in a dedicated boundary, preventing interference between sessions
- **Stateful execution support**: Agents that maintain context across steps need persistent storage, not just ephemeral filesystems
- **Scoped outbound networking**: Tool calls require connectivity, but access should be limited to known endpoints with default-deny policies everywhere else
- **Resource limits per session**: Runaway agents can exhaust CPU, memory, or I/O and affect other tenants without hard limits
- **Clean teardown**: Ephemeral sessions must be fully destroyed after completion, with no state leaking into subsequent sessions
- **Audit logging**: Every execution step should be traceable, including what ran, what it produced, and what resources it consumed

## What isolation models are suitable for agent code execution?

Isolation models for agent workloads are the same as for general sandbox execution, but the tradeoffs shift when execution is multi-step and stateful.

*For a full breakdown of isolation primitives, see this guide on [remote code execution sandbox](https://northflank.com/blog/remote-code-execution-sandbox).*

The summary as it applies to agents:

- **Hardened containers**: Acceptable for internal agents running bounded, low-risk tasks. The shared kernel boundary is a meaningful risk when agents execute LLM-generated code on behalf of external users
- **gVisor**: A reasonable middle ground. Syscalls are intercepted before reaching the host kernel, but there are latency costs, kernel feature compatibility gaps, and an additional attack surface from the interception layer
- **MicroVMs (Firecracker/Kata)**: The standard choice for production multi-tenant agent platforms. Each session gets its own guest kernel. A guest kernel compromise does not directly expose the host kernel, but the hypervisor remains part of the attack surface

<InfoBox className="BodyStyle">

 If you are evaluating isolation for agent code execution environments, see the following guides:

- [How to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents): microVMs, gVisor, and isolation strategies specific to agent workloads
- [Secure runtimes for codegen tools](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale): execution at scale for code generation pipelines
- [Best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents): platform comparison with isolation and operational tradeoffs

</InfoBox>

## What are the operational challenges of running agent execution environments at scale?

Building the execution environment is only part of the problem: operating it reliably across concurrent agent sessions introduces a different set of challenges. See the most common challenges below:

- **Cold start latency**: MicroVM initialization takes longer than container startup, and full initialization including networking and runtime setup adds overhead beyond VMM boot alone. Pre-warmed pools reduce perceived latency but require pool sizing logic, drain and refill orchestration, and idle resource cost management.
- **State management across steps**: Persistent sessions need attached volumes or databases; ephemeral sessions need guaranteed clean teardown after every step
- **Concurrent session scaling**: Hundreds of simultaneous agent sessions require autoscaling, bin-packing, and load balancing that accounts for in-progress workload state, not just request count
- **Multi-tenant isolation at scale**: Tenant boundaries must be enforced at the infrastructure level. Application-level separation is not sufficient when agents run arbitrary generated code (see this guide on [What is multitenancy?](https://northflank.com/blog/what-is-multitenancy) if you want a deeper breakdown of multi-tenant architecture and its risks).
- **Observability constraints**: Monitoring inside a sandboxed agent session is deliberately limited. External log collection and tracing infrastructure needs to be designed carefully to avoid creating side channels between tenants
- **Dependency and image management**: Agent environments often require specific runtimes, packages, or tools. Base image management, vulnerability scanning, and environment versioning add ongoing operational overhead
- **Access model**: Full API, CLI, and SSH access for programmatic control and debugging.

## How Northflank handles agent execution environments in production

[Northflank](https://northflank.com/product/sandboxes) provides infrastructure designed for production agent workloads, combining microVM-based isolation with full workload orchestration.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

Here's what Northflank offers:

- **MicroVM isolation**: Every agent session runs in its own microVM using Kata Containers, Firecracker, or gVisor, selectable depending on workload requirements.
- **Ephemeral and persistent execution**: Short-lived sessions are destroyed after each run. Persistent sessions support attached volumes starting at 4GB, S3-compatible object storage, and stateful databases including PostgreSQL, Redis, MySQL, and MongoDB for agent memory and execution history
- **Bring your own cloud**: Support for running inside your own VPC across AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, or on-premise and bare metal. Production-ready and self-serve.
- **Full workload runtime**: Agents, background workers, APIs, and supporting databases run in the same platform alongside sandbox execution, reducing architectural fragmentation
- **GPU support**: On-demand CPU and GPU provisioning without manual quota requests, relevant for teams running [inference or training workloads](https://northflank.com/product/gpu-paas) alongside agent execution
- **Pricing**: CPU at $0.01667 per vCPU per hour and memory at $0.00833 per GB per hour, with full details on the [Northflank pricing page](https://northflank.com/pricing)

<InfoBox className="BodyStyle">

**Next steps for your agent execution environment**

If you'd like a step-by-step walkthrough of spinning up isolated microVM environments, see [how to spin up a secure code sandbox and microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh).

You can review deployment models and sandbox capabilities on [Northflank](https://northflank.com/product/sandboxes). And if you want to talk through your organization's specific compliance, networking, GPU, or BYOC requirements, you can [book a demo to speak with an engineer](https://cal.com/team/northflank/northflank-demo).

</InfoBox>

## What should you prioritize when choosing an agent execution environment?

The right choice depends on your trust model, scale, and operational capacity.

| Situation | Recommended approach |
| --- | --- |
| Internal agents, low-risk tasks | Hardened containers with seccomp and resource limits |
| External users, moderate trust | gVisor or Kata Containers |
| LLM-generated code, multi-tenant | MicroVMs, ephemeral by default, default-deny networking |
| Compliance requirements | MicroVMs with BYOC deployment inside your own VPC |
| Scale with limited infra team | Managed platform with built-in orchestration and autoscaling |

## FAQ: common questions about code execution environments for autonomous agents

### How is agent code execution different from running user-submitted scripts?

User-submitted scripts are discrete, one-shot executions. Agent code execution is multi-step and stateful, with each step potentially influenced by previous outputs and the code itself generated dynamically rather than reviewed before running.

### Can an agent escape its execution environment?

Escape risk depends on the isolation model. Container-based environments share the host kernel, making kernel exploits a realistic path. MicroVM-based environments give each session its own guest kernel, significantly raising the cost of a successful escape. No isolation model provides an unconditional guarantee.

### Should agent execution environments be ephemeral or persistent?

It depends on the workload. Stateless tool calls and one-shot tasks benefit from ephemeral environments that reset between runs. Agents that maintain memory, write artifacts, or run across multiple sessions require persistent storage alongside their execution environment. For example, [Northflank](https://northflank.com/product/sandboxes) supports both modes (ephemeral and persistent) within the same platform, so you are not forced to choose one architecture over the other.

### How do you isolate multiple agents running concurrently?

Each agent session should run in its own isolated boundary, enforced at the infrastructure level. Application-level separation is insufficient when agents execute arbitrary generated code. MicroVM-based isolation with per-session guest kernels is the standard approach for production multi-tenant agent platforms. Platforms like [Northflank](https://northflank.com/product/sandboxes) enforce this by default, running every workload in its own microVM with Kata Containers or gVisor.]]>
  </content:encoded>
</item><item>
  <title>What comes after the code? Event recap: Northflank x Augment Code x Zed</title>
  <link>https://northflank.com/blog/event-recap-northflank-augment-code-zed</link>
  <pubDate>2026-03-03T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[In January we brought together engineers and founders from Augment Code, Zed, and Human Layer for an evening in San Francisco, co-hosted with our friends at Kindred Ventures.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/After_4_2_0481f81da1.png" alt="What comes after the code? Event recap: Northflank x Augment Code x Zed" />In January we brought together engineers and founders from [Augment Code](https://www.augmentcode.com/), [Zed](https://zed.dev/), and [Human Layer](https://www.humanlayer.dev/) for an evening in San Francisco, co-hosted with our friends at [Kindred Ventures](https://kindredventures.com/). 

The panel, which was moderated by [Steve Jang](https://www.linkedin.com/in/stevejang1/), covered what coding agents can realistically do today, what the new developer workflows look like when they’re in the loop, and where the tooling still falls short. Then [Will](https://www.linkedin.com/in/william-j-stewart/), [Northflank](https://northflank.com/)’s co-founder/CEO talked about what Northflank is seeing from the infrastructure side.

You can watch the full recording below. This post covers the ground we covered on the night.

[![DSC_5718.jpg](https://assets.northflank.com/DSC_5718_4a7bab900d.jpg)
](https://www.youtube.com/watch?v=xRzYmNTSXE8)

## Agents are doing the typing, but good engineers are still doing the thinking

The opening question was a familiar one: when will agents write all the software? Nobody on stage took the bait. The more useful framing, which came from [Dex](https://www.linkedin.com/in/dexterihorthy/) at Human Layer, is the distinction between typing code and writing code. Models have been doing most of his typing since late last year. But typing is not the same as deciding what to build, what the API should feel like, or what trade-off is right for a particular system. Those decisions still belong to the engineer.

![BWT_1022.jpg](https://assets.northflank.com/BWT_1022_b25a7c91d6.jpg)

[Chris](https://www.linkedin.com/in/amateurhuman/) from Augment Code put it in terms of the underlying technology. An LLM is a function that takes context and tells you what the next likely token is. The job of the engineer is to constrain the probability space aggressively enough, through tests, type systems, good prompts, and well-scoped tasks, that the only possible next token is the right one. 

[Mikayla](https://www.linkedin.com/in/mikayla-maki/) from Zed added a point that reframed a lot of the hype. The reason someone could build a programming language in a few weeks with an agent is that programming languages are highly verifiable. You run it and it either works or it does not. A product that a human has to navigate and form an opinion about is much harder for an agent to validate on its own. The more a system can check its own output, the more autonomy you can safely give it.

## What you can trust agents to do (for now)

Chris described his workflow: give the agent a reasonably scoped task, review the diff in source control, stage the changes that look right, go back to the agent for the next step. Repeat until you have a commit worth making. It is not the version of agentic development that gets shared on social media. But it produces code he can vouch for.

The shared view across the panel was that scope matters more than almost anything else. Large, ambiguous tasks fail. Well-specified, bounded tasks with clear verification criteria tend to work. The agents that perform best are the ones with access to the same tools a good engineer would use to check their own work: linters, type checkers, test suites, CI logs. As Dex said, this is just good engineering. None of it is new.

What is new is how badly it is missed when it is not there.

## Building a coding assistant on top of foundation models is not a losing position

Steve polled the room on tooling. Claude Code had a strong showing, yet very few people seemed to be using Cursor.

Chris made the case for why third-party coding tools are not simply waiting to be acquired or undercut. The model is not the whole product. Context is. Getting the right information from a large codebase into the system before the agent starts working is a hard problem, and retrieval alone does not solve it. Augment built a context engine from the ground up, which is why their evals beat Claude Code on the same tasks, using the same underlying model, at lower token cost.

What they cannot compete on is price. Anthropic subsidises Claude Code's inference because the usage data feeds back into training. That creates a structural cost advantage that is very difficult to match. It has also set a price expectation in the market that pressures every tool built on top of API pricing. For now that tension is manageable. It is worth watching.

## Code generation is roughly 30% of the software development lifecycle. Northflank automates the other 70%

Northflank started out as a game server hosting platform. Now we help teams [deploy](https://northflank.com/product/deployments) and run their most critical production software. The connection between those two things is very direct.

Will framed it like this: if you think of writing code as gaming, then these LLMs and coding tools have made everyone into an eSports professional. StarCraft players are measured in clicks per second. The words per minute in a codebase have gone up by an order of magnitude. But words per minute is not the same as shipping something that works.

Code generation is roughly 30% of the software development lifecycle. Northflank lives in the other 70%: build, deploy, release, autoscaling, disaster recovery, metrics, alerting. The part that runs when you are not looking at it. That part has not changed. It has just been asked to move much faster than it was designed to.

## Every agent-generated PR is untrusted code

![BWT_1291.jpg](https://assets.northflank.com/BWT_1291_1ec4a977a5.jpg)

When you are not reviewing every line, and most people are not, you are deploying untrusted code to production. The fact that a model wrote it does not change that. In some ways it makes it harder, because the code can look fluent and still be wrong.

Northflank's approach to this problem grew out of the game server era. Workloads run in micro VMs, isolated from the node and the cluster, so that a compromised or buggy workload cannot affect anything outside its boundary. It turns out to be exactly what teams deploying agent-generated code need.

One government agency customer has built a workflow that reflects where a lot of teams are heading. Engineers write software using Claude Code inside [Northflank sandboxes](https://northflank.com/product/sandboxes), push to version control, and loop back in. The sandbox is the safe surface. The agent operates within it. That separation lets the team move fast without the security exposure that comes from pointing an agent at a live environment.

## Startups are moving fast, enterprises are moving carefully, both have the same underlying problem

![BWT_1040.jpg](https://assets.northflank.com/BWT_1040_f2debe16a0.jpg)

On the startup side, some teams do not even have dev environments. They push from agent output to production and iterate when something breaks. It works until it does not, and when it does not, it tends to be very visible.

On the enterprise side, most deployments require a manual approval from a senior engineer before anything touches production. The cost of a bad deployment is too high. What is changing is the volume of things queued up for that approval. Agents generate pull requests faster than any review process was designed to handle, and that mismatch is only going to grow.

The gap between those two worlds is where most of the interesting infrastructure problems are right now. How do you move fast without removing the checkpoints that catch what a code review missed? [Preview environments](https://northflank.com/product/preview-environments), canary deployments, test runners that can tell you whether the deployed thing actually does what the ticket described. These are not glamorous. They are what makes high-velocity development sustainable rather than just fast.

And is what the Northflank platform excels at.

## The economics of self-hosting are changing

There is a compliance case for running models inside [your own VPC](https://northflank.com/product/bring-your-own-cloud) that most [enterprise](https://northflank.com/enterprise) teams already understand. Sending your codebase to a third-party API is a risk that would have triggered an incident five years ago. The fact that it became normal does not mean it is a good idea.

But there is a cost case emerging too. H100s that were around $0.90 an hour not long ago are now trading at $2 to $3 depending on region. Data centre capacity takes years to build. The subsidised API pricing that the major labs are currently absorbing is not a permanent feature of the market. Will's read is that real compute costs are more likely to go up through 2027 than down, once that subsidy starts to compress.

For teams thinking about [self-hosting](https://northflank.com/product/gpu-paas), whether that is DeepSeek, Qwen, something fine-tuned on their own codebase, or a combination, the question is no longer purely technical. It is starting to be a cost question too.

## LLMs are becoming part of the infrastructure stack

The way Northflank thinks about this: LLMs are not a special category of thing. They are a component of an infrastructure stack, like a database or a job runner. You should be able to deploy a model, a Postgres instance, a GPU workload, and a Node service from the same control plane, with the same observability, in the same place.

![BWT_1234.jpg](https://assets.northflank.com/BWT_1234_bc59db4c0b.jpg)

We see customers move in that direction naturally. They come to Northflank for one thing, a deployment or a database, and within a few weeks they are running GPU workloads alongside it, then self-hosting a model, then asking about preview environments for their agent-generated PRs. The stack keeps getting wider. The control plane needs to keep up.

## What we're working on

The two infrastructure primitives that matter most for teams building with agents right now are sandboxes and preview environments. They solve different parts of the same problem: how do you run a lot of agent-generated code safely, and how do you know whether it works before it reaches production.

![DSC_5529.jpg](https://assets.northflank.com/DSC_5529_a0ac066e95.jpg)

On the sandbox side, the thing Northflank is built for is **scale** and **isolation**. We can run 100,000+ concurrent sandboxes, in your VPC or ours, each fully isolated at the micro VM level. That isolation is not a configuration option or a best-effort boundary. It is the foundation the whole thing is built on. An agent operating inside a [Northflank sandbox](https://northflank.com/product/sandboxes) cannot reach anything outside it, which means you can run thousands of them in parallel without the security surface growing with the number of agents.

[Northflank Sandboxes](https://northflank.com/product/sandboxes) boot fast, which matters when agents are spinning up environments on demand. This is obviously important, but the part that tends to surprise people is the concurrency. Most teams do not start thinking about running agents at that scale until they are already blocked on it.

Preview environments are the other piece. The question they answer is whether the thing works the way a real user would encounter it, connected to real services, running in an environment that reflects production. For teams with complex multi-service setups, getting that right has historically been painful. That is where we spend a lot of our time.

## Closing remarks

![DSC_5884 (1).jpg](https://assets.northflank.com/DSC_5884_1_4aca1c0daa.jpg)

Thanks to Dex, Chris, and Mikayla for a great panel, and to Steve and the whole Kindred Ventures team for co-hosting and keeping the conversation smart. 

If any of the infrastructure questions here are ones your team is working through, [we would be glad to talk.](https://cal.com/team/northflank/northflank-demo?duration=30)]]>
  </content:encoded>
</item><item>
  <title>Best sandboxes for coding agents in 2026</title>
  <link>https://northflank.com/blog/best-sandboxes-for-coding-agents</link>
  <pubDate>2026-03-02T23:45:00.000Z</pubDate>
  <description>
    <![CDATA[Sandboxes for coding agents in 2026: compare Northflank, E2B, CodeSandbox, Modal, and Fly.io Sprites on isolation, session limits, BYOC support, and pricing to find the right fit for your stack.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/digitalocean_gpu_paperspace_alternatives_1_464d0bcf39.png" alt="Best sandboxes for coding agents in 2026" /><InfoBox className="BodyStyle">

## TL;DR: What are the best sandboxes for coding agents in 2026?

Coding agents generate and execute code without human review on every run. That makes sandboxing not just a nice-to-have but a hard requirement for any production deployment. The right sandbox needs strong isolation, a lifecycle that matches how agents actually work, and enough infrastructure around it to handle what comes after the code runs.

- [**Northflank**](https://northflank.com/) – Full-stack AI infrastructure platform with managed cloud and [BYOC deployment](https://northflank.com/product/bring-your-own-cloud) into AWS, GCP, Azure, or bare-metal. [Production-grade microVM sandboxes](https://northflank.com/product/sandboxes) with Kata Containers, Firecracker, and gVisor isolation, unlimited sessions, databases, GPUs, CI/CD, and observability all in one place.
- **E2B** – Developer-friendly sandbox infrastructure built specifically for AI agents, with Python and TypeScript SDKs and Firecracker microVM isolation.
- **CodeSandbox** – Snapshot and forking-first sandbox platform backed by Together AI, well-suited for parallel agent runs and web-focused coding tools.
- **Modal** – Python-first serverless compute with gVisor isolation and deep GPU support, built for ML-heavy agent workloads.
- **Fly.io Sprites** – Stateful sandbox environments on Firecracker microVMs with persistent storage, designed for long-running coding agent sessions.

</InfoBox>

## Why coding agents need sandboxes

When a coding agent runs, it executes code you have not reviewed. That code can access credentials, consume unbounded resources, make external requests, or escape container boundaries through bugs, hallucinations, or prompt injection. Traditional containers are not enough because they share the host kernel. A kernel vulnerability lets untrusted code break out entirely. Purpose-built sandboxes use microVMs or user-space kernel interception to put a hard boundary between agent code and everything else.
Beyond security, the sandbox you pick affects what you can actually build. Session length, cold start speed, state persistence, and whether execution runs inside your own infrastructure all matter in production. Here is how the leading options compare.

## What are the best sandboxes for coding agents?

### 1. Northflank - Full-stack AI sandbox and agent infra platform

[Northflank](https://northflank.com/) is the most complete option on this list for teams taking coding agents to production. While other platforms focus on the sandbox itself, Northflank gives you the full execution layer: [microVM sandboxes](https://northflank.com/product/sandboxes) alongside databases, APIs, workers, CI/CD pipelines, and GPU workloads, all in one control plane. That matters when your coding agent does more than just run code.

On isolation, Northflank supports Kata Containers with Cloud Hypervisor, Firecracker, and gVisor, applied per workload based on your threat model. This is the strongest isolation lineup available from any sandbox platform. Northflank's engineering team actively contributes to the Kata Containers, QEMU, and Cloud Hypervisor open-source projects, which means the isolation layer is not a third-party bolt-on.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

Sessions run indefinitely with no forced time limits. You can run a sandbox for seconds or keep it alive for weeks without worrying about a platform-imposed cutoff. Both ephemeral and persistent environments are supported, so short-lived execution pools and long-running stateful agent sessions can coexist in the same platform.

For teams with compliance requirements, [BYOC](https://northflank.com/product/bring-your-own-cloud) deployment runs sandbox execution inside your own AWS, GCP, Azure, Oracle, CoreWeave, or bare-metal infrastructure. Northflank handles orchestration while your data never leaves your VPC. That is available self-serve, with no enterprise-only gatekeeping. Northflank has been running microVM workloads at scale in production since 2021 across startups, public companies, and government deployments.

[cto.new](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) migrated their entire sandbox infrastructure to Northflank in two days after EC2 metal instances made scaling costs unpredictable, going from unworkable provisioning to thousands of daily deployments with linear, per-second billing.

**Best for:** Production coding agents, compliance-sensitive workloads, teams that need more than just a sandbox, and anyone who wants BYOC without going through enterprise sales.

**Pricing:** $0.01667/vCPU-hour, $0.00833/GB-hour, H100 GPU at $2.74/hour all-inclusive. BYOC deployments bill against your own cloud account.

### 2. E2B

E2B is purpose-built for AI agent code execution. The Python and TypeScript SDKs are well-documented, boot times sit around 150ms, and Firecracker microVM isolation handles workload separation at the hypervisor level. It integrates cleanly with LangChain, OpenAI, and Anthropic tooling, which makes it one of the fastest ways to add sandboxed execution to an existing agent stack

The main constraint is the session cap: 24 hours on Pro and one hour on Base. Self-hosting exists but is not production-ready for most teams, and BYOC is limited to AWS enterprise customers only.

**Best for:** Teams building AI coding agents or Code Interpreter-style tools who want a fast integration path and do not need sessions longer than 24 hours.

**Pricing:** Free tier available with a $100 one-time credit. Pro plan at $150/month with 24-hour sessions and configurable CPU and RAM.

### 3. CodeSandbox

Backed by Together AI, CodeSandbox brings snapshotting and environment forking to coding agent infrastructure. You can branch from the same base state, run agents in parallel, and restore any snapshot in under two seconds, which is genuinely useful for testing pipelines and iterative agent workflows. It accepts Dev Container images and a range of standard environment formats, and state persists across sessions so agents can resume without rebuilding from scratch. There is no BYOC option, and it skews toward web-focused use cases.

**Best for:** Web-focused coding agents, educational coding tools, and teams where parallel environments and forking are core to the product.

**Pricing:** The community plan is free. Production workloads bill at $0.0446/vCPU-hour plus $0.0149/GB-RAM-hour.

### 4. Modal

Modal is a Python-first serverless platform where sandboxes sit inside a broader ML infrastructure stack. It scales to 20,000 concurrent containers with sub-second cold starts, uses gVisor for isolation, and supports GPU workloads alongside code execution. Companies like Lovable and Quora run millions of executions through it. The tradeoff is the SDK model: environments are defined through Modal's Python library rather than arbitrary container images, which limits flexibility. There is no BYOC option.

**Best for:** Python-heavy coding agents running alongside ML workloads, data analysis pipelines, and teams already using Modal for inference or training.

**Pricing:** Usage-based per second. CPU from around $0.047/vCPU-hour. GPU billed separately from CPU and RAM.

### 5. Fly.io Sprites

Sprites runs on Firecracker microVMs with 100GB persistent NVMe storage per sandbox and checkpoint/restore in around 300ms. The idle billing model stops charging when the environment is not in use, which works well for coding agents that need a warm environment between sessions without paying for always-on compute. It is a clean fit if you are already on Fly.io. If you are not, sandbox creation times of one to twelve seconds and no BYOC make it a harder sell, and the platform is still early-stage compared to the other options here.

**Best for:** Individual developers building coding agents, teams already on [Fly.io](http://fly.io/), and Claude Code-style persistent development environment use cases.

**Pricing:** Pay-per-use based on CPU, memory, and storage.

## Which sandbox should you choose for your coding agent?

If you are running user-generated or untrusted code in a multi-tenant system, microVM isolation is worth the small overhead. Northflank, E2B, and Fly.io Sprites all provide this out of the box. If you are running internal automation where you control the code, gVisor from Modal is sufficient.

If you need the sandbox to coexist with databases, GPUs, APIs, or CI/CD pipelines without adding another platform, Northflank is the only option here that handles all of it in one place.

| Platform | Isolation | BYOC | Session limit | GPU support |
| --- | --- | --- | --- | --- |
| [**Northflank**](https://northflank.com/) | [Kata Containers, Firecracker, gVisor](https://northflank.com/product/sandboxes) | [Yes (AWS, GCP, Azure, bare-metal)](https://northflank.com/product/bring-your-own-cloud) | Unlimited | Yes |
| **E2B** | Firecracker | AWS only, enterprise only | 24 hours | No |
| **CodeSandbox** | microVM | No | None | No |
| **Modal** | gVisor | No | None | Yes, |
| **[Fly.io](http://fly.io/) Sprites** | Firecracker | No | None | No |

### How do sandboxes for coding agents compare on pricing?

Pricing as of April 2026. Billing models differ across platforms (some bill based on active CPU usage only, others bill for the entire duration the sandbox is running). Verify current rates on each platform's pricing page before making cost decisions.

| Platform | CPU | Memory | Storage | GPU | Billing model |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | $0.01667/vCPU-hr | $0.00833/GB-hr | $0.15/GB-month | L4: $0.80/hr, A100 40GB: $1.42/hr, A100 80GB: $1.76/hr, H100: $2.74/hr, H200: $3.14/hr | Per second |
| **E2B** | $0.0504/vCPU-hr | $0.0162/GiB-hr | 10–20GB included free | Do not provide GPU compute | Per second |
| **Fly.io Sprites** | $0.07/CPU-hr | $0.04375/GB-hr | $0.00068/GB-hr (hot NVMe) | Do not provide GPU compute | Per second, actual cgroup usage. No charge when idle |
| **CodeSandbox** | $0.075/core-hr (credit-based: $0.015/credit) | Bundled with VM tier | Included | Do not provide GPU compute | Credit-based ($0.015/credit) |
| **Modal Sandboxes** | $0.1419/physical core-hr (2 vCPU) | $0.0242/GiB-hr | — | L4: $0.80/hr, A100 40GB: $2.10/hr, A100 80GB: $2.50/hr, H100: $3.95/hr, H200: $4.54/hr | Per second |

### BYOC support across coding agent sandbox platforms

The table below shows how each platform handles BYOC deployment, which clouds are supported, and whether it requires a sales process.

| Platform | BYOC available | Clouds supported | Access model | Pricing model |
| --- | --- | --- | --- | --- |
| **Northflank** | Yes, fully self-serve | AWS, GCP, Azure, Oracle, CoreWeave, any neoclouds, Civo, bare-metal, on-premises | Self-serve, enterprise contracts available for larger commits (with bulk discounts) | Your existing cloud bill, CPU $0.01389/vCPU-hr and Memory $0.00139/GB-hr |
| **E2B** | Yes, limited and not self-serve | AWS and GCP only | Not publicly disclosed, need to contact sales | Starts at $50/sandbox/month, on top of your existing cloud bill |
| **Modal** | No | Managed only | — | — |
| **Fly.io Sprites** | No | Managed only | — | — |
| **CodeSandbox** | Enterprise only | Custom dedicated cluster | Enterprise plan, contact sales | Custom |

## FAQ: sandboxes for coding agents

### Why do coding agents need a sandbox?

Coding agents execute code they generate autonomously, often without human review of each run. Without a sandbox, that code runs with your system permissions and can access credentials, make external requests, or escape to the host. A sandbox puts a hard isolation boundary around execution so a misbehaving or compromised agent cannot affect the rest of your infrastructure.

### What is the difference between container isolation and microVM isolation?

Containers share the host kernel using Linux namespaces and cgroups. A kernel vulnerability or misconfiguration can allow container escape. MicroVMs like Firecracker and Kata Containers run each workload with its own dedicated kernel inside a lightweight virtual machine. The hardware boundary prevents entire classes of kernel-based attacks that container isolation cannot stop.

### What is prompt injection and why does it matter for coding agents?

Prompt injection is when untrusted content in an agent's environment sneaks instructions into the agent's context. A README, a webpage, or a code comment could instruct your agent to exfiltrate credentials or perform operations you never authorized. Because the agent cannot reliably distinguish its original instructions from injected ones, sandboxing the execution environment limits the blast radius when this happens.

### Which sandbox has the strongest isolation for untrusted code?

Northflank supports Kata Containers with Cloud Hypervisor, Firecracker, and gVisor, giving you the broadest range of isolation options. For the strongest default isolation for untrusted code, Kata Containers and Firecracker both provide hardware-level separation. E2B and [Fly.io](http://fly.io/) Sprites use Firecracker by default. Modal uses gVisor.

### Do I need BYOC for a coding agent sandbox?

Not always. You need BYOC when sandbox execution must happen inside your own infrastructure, such as when agents access private APIs, internal databases, or regulated data that cannot leave your VPC. For public-facing coding tools with no private data access, a managed sandbox is fine. Northflank is the only platform here with self-serve BYOC across multiple cloud providers.

### How long can a coding agent sandbox session run?

It depends on the platform. Northflank supports unlimited session lengths. E2B caps at 24 hours on Pro. CodeSandbox and Fly.io Sprites support long-running sessions. For agents that need to maintain state across multi-day workflows or keep a development environment warm between uses, choose a platform without an artificial time limit.

## Conclusion

Coding agents are moving fast, and the infrastructure decisions you make now will shape what you can build later. The sandbox is the most critical part of that infrastructure. It determines whether your agents can run safely in production, how much you pay at scale, and whether you can meet compliance requirements as your product grows.

For most teams taking a coding agent to production, [Northflank](https://northflank.com/) is the platform worth evaluating first. The microVM isolation is production-grade, the session model is flexible, and everything else your agent needs can run in the same place. The other platforms here each do something well. Northflank is the one built to grow with you.

<InfoBox className="BodyStyle">

You can [get started for free on Northflank](https://app.northflank.com/signup) or [talk to the team](https://cal.com/team/northflank/northflank-demo?duration=30) if you have specific infrastructure requirements for your coding agent.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Remote code execution sandbox: secure isolation at scale (2026 guide)</title>
  <link>https://northflank.com/blog/remote-code-execution-sandbox</link>
  <pubDate>2026-03-02T17:15:00.000Z</pubDate>
  <description>
    <![CDATA[A guide to remote code execution sandbox design in 2026: isolation models, microVMs, security controls, operational challenges, and how to choose the right approach.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/remote_code_execution_sandbox_517cc56c95.png" alt="Remote code execution sandbox: secure isolation at scale (2026 guide)" />Running untrusted code is now a core requirement for AI agents, developer tools, automation platforms, CI systems, and user-defined workflows. If your product allows external input to execute logic, you are operating a remote code execution surface.

This guide explains what a remote code execution sandbox is, how isolation models differ, what security controls are required in production, and how to evaluate platforms without oversimplifying the tradeoffs.

<InfoBox className="BodyStyle">

## TL;DR: key considerations for a remote code execution sandbox

A remote code execution sandbox is an isolated runtime environment that allows untrusted or user-submitted code to execute without exposing the host system, adjacent workloads, or sensitive infrastructure.

A production-grade sandbox enforces:

- **Filesystem isolation**: Prevents access to host files and secrets.
- **Process isolation**: Stops interference with other workloads.
- **Network isolation**: Restricts outbound and internal connectivity.
- **Kernel isolation**: Reduces the blast radius of privilege escalation attempts.

Isolation can be implemented using hardened containers, syscall interception such as gVisor, or microVM-based virtualization such as Firecracker and Kata Containers.

> [Northflank](https://northflank.com/product/sandboxes) provides microVM-backed remote code execution sandboxes using Firecracker, gVisor, and Kata, with both ephemeral and persistent execution modes and bring-your-own-cloud support across AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, and on-premise or bare metal deployments.
>
 
</InfoBox>

## What is a remote code execution sandbox?

A remote code execution sandbox is a controlled execution boundary where code originating outside your trusted system runs inside restricted limits.

“Remote” refers to the source of the code. It may come from:

- End users submitting scripts
- AI agents generating tool calls
- API consumers uploading logic
- CI/CD pipelines running builds from external contributors or unreviewed sources
- Plugin ecosystems extending your application

The sandbox enforces boundaries so that this code cannot:

- Read sensitive files from the host
- Access internal services
- Escalate privileges
- Persist malicious changes across sessions

This is distinct from a remote code execution (RCE) vulnerability, which is an unintended exploit path. A remote code execution sandbox is intentional execution with controlled isolation.

## Why is running untrusted code without a sandbox dangerous?

Executing user-submitted or generated code directly on shared infrastructure exposes your system to multiple failure modes.

The required isolation level scales with the trust level of the code source. A pipeline running version-controlled internal code carries a different risk profile than a platform executing LLM-generated or user-submitted scripts.

Without isolation:

- **Filesystem access:** Code can read environment variables, credentials, or configuration files.
- **Network access**: Code can exfiltrate data or access internal metadata endpoints.
- **Kernel exposure**: Standard containers share the host kernel, which expands the impact of kernel-level exploits.
- **Persistence**: Reused environments allow state to survive across executions.
- **Resource exhaustion**: CPU, memory, and I/O abuse can disrupt other tenants.

These risks compound in multi-tenant systems or AI-driven platforms where code is generated dynamically.

## What isolation models are used in remote code execution sandboxes?

Different isolation approaches provide different kernel boundaries and operational tradeoffs.

### Hardened containers

Containers isolate workloads using namespaces and cgroups, but share the host kernel.

A hardened container configuration includes:

- **Syscall filtering**: Restrictive seccomp profiles reduce the available syscall surface.
- **Capability reduction:** Remove elevated Linux capabilities unless explicitly required.
- **Read-only root filesystem**: Prevent base image mutation.
- **cgroup enforcement**: Apply CPU and memory constraints.
- **Network restrictions**: Enforce default-deny egress policies.

Containers are efficient and widely supported. The architectural limitation is kernel sharing, which increases the impact if a kernel vulnerability is exploited.

### Syscall interception with gVisor

gVisor intercepts syscalls in user space, reducing direct interaction with the host kernel.

Characteristics include:

- Reduced kernel exposure compared to standard containers.
- Compatibility with existing container workflows.
- Increased syscall latency, kernel feature compatibility gaps, and an additional attack surface from the interception layer.

This approach fits environments where container isolation is insufficient, but full virtualization per workload is not required.

### MicroVM-based isolation

MicroVMs use hardware virtualization to provide a separate guest kernel per workload.

Technologies such as Firecracker and Kata Containers create lightweight virtual machines designed for high-density workloads.

With microVM-based sandboxes:

- Each execution runs with its own guest kernel.
- Virtualization boundaries isolate workloads at the hypervisor level.
- A guest kernel compromise does not directly expose the host kernel, but the hypervisor remains part of the attack surface.

For multi-tenant SaaS, LLM-generated code execution, and compliance-sensitive systems, microVM-based isolation is frequently chosen as the default boundary.

<InfoBox className="BodyStyle">

If you are evaluating remote code execution sandbox isolation for AI-generated or code-generation workloads, see the following guides:

- [How to spin up a secure code sandbox and microVM using Firecracker, gVisor, and Kata](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh): covers architecture decisions and isolation layers, not just surface configuration.
- [Secure runtimes for codegen tools: microVMs, sandboxing, and execution at scale](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale)
- [Best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents)

</InfoBox>

## How do isolation models compare in a remote code execution sandbox?

Isolation models differ primarily in how they enforce kernel boundaries and limit blast radius.

| Model | Kernel boundary | Isolation mechanism | Host kernel exposure | Typical use case |
| --- | --- | --- | --- | --- |
| Hardened containers | Shared | Namespaces + cgroups + seccomp | Direct | Internal or semi-trusted workloads |
| gVisor | Shared but intercepted | User-space syscall interception | Indirect | Moderately untrusted multi-tenant |
| MicroVM (Firecracker/Kata) | Separate guest kernel | Hardware virtualization | Indirect (via hypervisor boundary) | Untrusted or adversarial multi-tenant |

The deeper the kernel boundary, the smaller the blast radius if a workload is compromised. No isolation model eliminates risk entirely; isolation reduces blast radius and raises the cost of compromise.

## What security controls are required in a production-grade sandbox?

Isolation mechanisms are only one layer of protection. A production-grade remote code execution sandbox relies on layered controls.

Core controls include:

- Syscall filtering: Reduce high-risk kernel interactions.
- Capability reduction: Remove unnecessary runtime privileges.
- Network isolation: Enforce default-deny outbound policies.
- Ephemeral execution: Reset environments between runs.
- Resource limits: Apply CPU, memory, and I/O quotas.

The objective is consistent enforcement of least privilege across runtime, network, and storage layers.

## Should you choose ephemeral or persistent sandbox environments?

Sandbox workloads vary. Some require clean, single-use execution, while others need long-running state.

- **Ephemeral environments:** Short-lived execution pools destroyed after each run. Suitable for user-submitted scripts and AI-generated tool calls where isolation between executions is critical.
- **Persistent environments:** Long-running stateful services with attached volumes, databases, or background workers. Suitable for agents, APIs, and orchestration layers that maintain state over time.

Modern platforms like Northflank increasingly support both models.

<InfoBox className="BodyStyle">

[Northflank](https://northflank.com/product/sandboxes) supports short-lived execution pools as well as long-running stateful services within the same platform, allowing teams to run isolated sandbox executions alongside persistent agents, APIs, and supporting infrastructure. Supporting both modes reduces architectural fragmentation as systems evolve from simple execution pipelines to full application runtimes.

</InfoBox>

## What should you look for in a remote code execution sandbox platform?

Selecting a sandbox platform requires architectural and operational evaluation.

Key considerations include:

- **Kernel boundary**: Does the platform provide microVM-level isolation?
- **Execution model**: Are both ephemeral pools and persistent services supported?
- **Bring your own cloud**: Can you deploy inside your own VPC across major cloud providers such as AWS, GCP, Azure, or on-premise?
- **Workload scope**: Can agents, APIs, workers, and databases run alongside sandboxes?
- **Multi-tenant design**: Is tenant isolation enforced by default?
- **Access model**: Are API, CLI, and SSH interfaces available?

## What does a production-ready remote code execution sandbox platform look like?

A production-ready remote code execution sandbox platform goes beyond basic workload isolation. It must provide durable isolation boundaries, support multi-tenant execution, integrate with surrounding infrastructure, and operate reliably at scale.

[Northflank](https://northflank.com/product/sandboxes) provides this model, combining microVM-based isolation with workload orchestration and bring-your-own-cloud deployment.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

Key characteristics of Northflank sandboxes include:

- **MicroVM isolation:** Separate guest kernels for untrusted workloads using technologies such as Firecracker, Kata Containers, or gVisor.
- **Ephemeral execution pools:** Short-lived environments destroyed after each run to prevent state leakage.
- **Persistent services:** Long-running stateful workloads with attached volumes, databases, background workers, and APIs.
- **Bring-your-own-cloud deployment:** Support for running inside your own VPC across AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, or on-premise and bare metal environments.
- **Full workload runtime:** Ability to run agents, APIs, workers, and supporting services alongside sandbox execution.
- **GPU support:** On-demand CPU and GPU provisioning without manual quota requests, relevant for teams running [inference or training workloads](https://northflank.com/product/gpu-paas) alongside sandbox execution

<InfoBox className="BodyStyle">

**Next steps for your remote code execution sandbox architecture**

If you’d like a step-by-step architectural walkthrough, see  [how to spin up a secure code sandbox and microVM](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh).

You can review deployment models and secure sandbox capabilities directly on [Northflank](https://northflank.com/product/sandboxes). For teams with specific compliance, networking, GPU, or bring-your-own-cloud requirements, you can also [book a demo to speak with an engineer](https://cal.com/team/northflank/northflank-demo).

</InfoBox>

## FAQ: what are the most common remote code execution sandbox questions?

This FAQ addresses common questions related to remote code execution sandbox architecture.

### What is sandboxed code execution?

Sandboxed code execution is the practice of running code inside a restricted environment that limits its access to the host filesystem, network, processes, and kernel interfaces.

### What is the difference between an RCE vulnerability and a remote code execution sandbox?

An RCE vulnerability is an unintended exploit that allows attackers to execute code. A remote code execution sandbox is an intentional execution boundary with enforced isolation controls.

### Can malware escape a sandbox?

Escape risk depends on the isolation model. Container-based sandboxes share the host kernel. MicroVM-based sandboxes use separate guest kernels, which reduces host exposure if a guest environment is compromised.

### Is container isolation sufficient for multi-tenant workloads?

For internal or low-risk systems, hardened containers may be acceptable. For adversarial multi-tenant environments, microVM-level isolation is commonly preferred.]]>
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</item><item>
  <title>Top Cloudflare Sandboxes alternatives for secure AI code execution in 2026</title>
  <link>https://northflank.com/blog/top-cloudflare-sandboxes-alternatives</link>
  <pubDate>2026-03-02T15:30:00.000Z</pubDate>
  <description>
    <![CDATA[Explore the top Cloudflare Sandboxes alternatives in 2026. Compare Northflank, E2B, CodeSandbox, Modal, Daytona, and Fly.io for AI sandboxing, long sessions, BYOC, and GPU workloads.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/cloudflare_sandboxes_alternatives_683d1dbe7b.png" alt="Top Cloudflare Sandboxes alternatives for secure AI code execution in 2026" /><InfoBox className="BodyStyle">

## TL;DR: What are the top Cloudflare Sandboxes alternatives in 2026?

Cloudflare Sandboxes is a fast, edge-native sandbox for running untrusted code close to users globally. It works well for TypeScript-first teams already on Cloudflare Workers, but falls short when you need [BYOC](https://northflank.com/product/bring-your-own-cloud), [persistent long-running sessions](https://northflank.com/product/sandboxes), [GPU workloads](https://northflank.com/product/gpu-paas), or a full infrastructure stack. [Northflank](https://northflank.com/) is the strongest alternative built for production.

- [**Northflank**](https://northflank.com/) – Full-stack AI infrastructure platform with managed cloud and [BYOC deployment](https://northflank.com/product/bring-your-own-cloud) into AWS, GCP, Azure, or bare-metal. Production-grade microVM sandboxes with Kata Containers, Firecracker, and gVisor isolation, unlimited sessions, databases, GPUs, CI/CD, and observability all in one place.
- **E2B** – Developer-friendly AI sandbox with polished SDKs and Firecracker microVMs, best for teams that need quick integration
- **CodeSandbox** – Browser-based sandboxing with snapshot and forking support, now backed by Together AI
- **Modal** – Serverless compute platform purpose-built for Python/ML workloads with massive autoscaling
- **Daytona** – Fastest cold starts in the market; pivoted from dev environments to AI code execution in 2025
- **Fly.io Sprites** – Stateful sandbox environments built on Firecracker microVMs, designed for AI coding agents

</InfoBox>

Cloudflare Sandboxes launched as part of Cloudflare's broader push into AI infrastructure, built on top of Cloudflare Containers and running across its global network. Sandboxes start in milliseconds, integrate natively with Workers, and let teams run untrusted Python or JavaScript code at the edge without managing any infrastructure. For TypeScript-first teams already deep in the Cloudflare ecosystem, that is a genuinely compelling offer.

The constraints show up fast in production. Sessions are optimized for short-lived execution and can lose state when containers go idle. There is no BYOC option, no GPU support, and no persistent state beyond what you manage yourself. For early-stage apps running short-lived code close to users, Cloudflare Sandboxes is solid. Once you need compliance controls, long-running agents, or a platform that handles more than just code execution, the alternatives start to look a lot more interesting. Here are the top alternatives worth your time.

## What are the top alternatives to Cloudflare Sandboxes?

### 1. Northflank - Full-stack AI sandbox and agent infra platform

[Northflank](https://northflank.com/) is the most complete platform on this list. While Cloudflare Sandboxes focuses on edge-native code execution within the Workers ecosystem, Northflank gives you the full infrastructure stack: microVM sandboxes, databases, APIs, CI/CD pipelines, GPU workloads, and observability. Deploy into your own cloud account or use Northflank's managed cloud.

The biggest differentiator is production-grade [BYOC support](https://northflank.com/product/bring-your-own-cloud). You can deploy into AWS, GCP, Azure, Oracle, CoreWeave, Civo, or bare-metal, and Northflank handles the orchestration while your data never leaves your VPC. For teams in fintech, healthcare, or any regulated industry, that distinction often determines whether a platform makes it past a security review. Cloudflare Sandboxes has no equivalent offering.

On [sandboxes](https://northflank.com/product/sandboxes) specifically, Northflank supports both Kata Containers with Cloud Hypervisor and gVisor, giving you flexibility based on your threat model. Sessions run indefinitely with no artificial caps. Cloudflare Sandboxes is optimized for short-lived execution and relies on idle containers, which rules it out for any agent that needs to hold state across a real user session.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

Northflank also accepts any OCI-compliant image from any registry without modifications, which means your existing Docker workflows port over without a rewrite. GPU pricing is all-inclusive, covering CPU and RAM, roughly 62% cheaper than platforms billing GPU, CPU, and RAM separately.

Teams like [cto.new moved to Northflank](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) when managed sandbox costs became unsustainable at scale. With thousands of daily deployments, they needed cost predictability and infrastructure that could grow with them — Northflank's BYOC model gave them both.

**Best for:** Teams that need full infrastructure control, compliance-sensitive workloads, long-running stateful agents, or anyone who wants one platform instead of stitching together multiple point solutions.

**Pricing:** $0.01667/vCPU-hour, $0.00833/GB-hour, H100 GPU at $2.74/hour all-inclusive. BYOC deployments run on your own cloud billing.

### 2. E2B

E2B has clean Python and TypeScript SDKs and Firecracker microVM isolation, making it one of the fastest ways to add sandboxed code execution to an AI agent. Boot times sit around 150ms and it integrates well with LangChain, OpenAI, and Anthropic tooling. It supports longer sessions than Cloudflare Sandboxes, though the Pro plan still caps at 24 hours and there is no production-ready self-hosting option.

**Best for:** Developers building AI coding agents or Code Interpreter-style experiences who don't need sessions longer than 24 hours.

**Pricing:** Free tier with $100 one-time credit. Pro at $150/month with 24-hour sessions and configurable CPU and RAM.

### 3. CodeSandbox

Now backed by Together AI, CodeSandbox brings snapshot and forking to AI agent infrastructure. You can branch environments from the same base state, run agents in parallel, and restore VMs in under two seconds. Unlike Cloudflare Sandboxes where sessions are ephemeral by design, CodeSandbox persists environment state and lets you resume from exactly where you left off.

**Best for:** Web-focused coding agents, educational platforms, and use cases where parallel environments and forking are central to the product.

**Pricing:** Community plan is free. Production workloads are usage-based at $0.0446/vCPU-hour plus $0.0149/GB-RAM-hour.

### 4. Modal

Modal is a Python-first serverless compute platform where sandboxes are one feature within a broader ML-focused fabric. It scales to 20,000 concurrent containers with sub-second cold starts, and teams like Lovable and Quora run millions of executions through it. Unlike Cloudflare Sandboxes, Modal supports GPU workloads and unlimited session durations, making it suitable for heavier agent workloads. The tradeoff is that you must define environments through Modal's Python SDK with no BYOC option.

**Best for:** Python-centric ML teams running batch jobs, model inference, and data pipelines who want sandboxing integrated with their existing Modal setup.

**Pricing:** Usage-based per second. CPU from around $0.047/vCPU-hour. GPU billed separately from CPU and RAM.

### 5. Daytona

Daytona pivoted to AI agent infrastructure in early 2025 and leads on cold-start speed, with sub-90ms provisioning and some configurations hitting 27ms. That edges out even Cloudflare Sandboxes on raw startup latency, and Daytona supports full Linux, Windows, and macOS virtual desktops for computer-use agents. The tradeoff is isolation: Docker containers by default, with Kata Containers available but not the out-of-the-box experience. Its BYOC option is also limited compared to more mature offerings like Northflank.

**Best for:** Teams where raw cold-start speed is the priority, or computer-use agent workloads.

**Pricing:** Usage-based with $200 in free compute credits. Around $0.067/hour for a 1 vCPU, 1 GiB RAM sandbox while running.

### 6. Fly.io Sprites

Sprites launched in January 2026 as Fly.io's purpose-built sandbox for AI coding agents. It runs on Firecracker microVMs with a 100GB persistent NVMe filesystem, checkpoint/restore in around 300ms, and automatic idle billing. Where Cloudflare Sandboxes is designed for short-lived, edge-native execution, Sprites persists environment state indefinitely. It is a good fit if you are already on Fly.io. If you are not, sandbox creation times of one to twelve seconds and the absence of BYOC make it harder to justify outside that ecosystem.

**Best for:** Individual developers building coding agents, teams already on Fly.io, and Claude Code-style persistent environment use cases.

**Pricing:** Pay-per-use based on CPU, memory, and storage.

## Which Cloudflare Sandboxes alternative should you choose?

Most of the platforms here solve one problem well. Northflank solves the whole thing. It is the only option on this list that gives you production-grade microVM sandboxes, BYOC deployment into your own cloud account, unlimited session lengths, GPU support, databases, CI/CD, and observability under one roof. If you are building something that needs to scale, stay compliant, and not fall apart when you outgrow a point solution, Northflank is where teams end up.

| Platform | Best for | BYOC | Session limit | Isolation |
| --- | --- | --- | --- | --- |
| **Northflank** | Production AI infra, compliance, full stack | Yes (AWS, GCP, Azure, bare-metal) | Unlimited | microVMs (Kata Containers), gVisor |
| **E2B** | Quick integration, AI agent prototypes | Experimental only | 24 hours | Firecracker |
| **CodeSandbox** | Forking, parallel agents, web tooling | No | None | microVM |
| **Modal** | Python ML, inference, batch jobs | No | None | gVisor |
| **Daytona** | Speed-first, computer-use agents | Limited | None | Docker (default) |
| **Fly.io Sprites** | Fly.io users, persistent dev environments | No | None | Firecracker |

---

## FAQ: Cloudflare Sandboxes alternatives

### What is Cloudflare Sandboxes?

Cloudflare Sandboxes is a secure code execution platform built on Cloudflare Containers, designed for running AI-generated or untrusted code at the edge. It integrates natively with Cloudflare Workers and supports Python and JavaScript execution with millisecond startup times.

### What are the main limitations of Cloudflare Sandboxes?

Sessions are optimized for short-lived execution, there is no BYOC option, and GPU workloads are not supported. It is also tightly coupled to the Cloudflare ecosystem, which makes it a weaker fit for teams not already on Workers.

### Which platform has the best session persistence?

Northflank supports unlimited session lengths with no artificial caps. E2B allows up to 24 hours on Pro. Daytona, CodeSandbox, and Fly.io Sprites also support persistent sessions. Sessions are optimized for short-lived execution and can lose state when containers go idle, which is the most restrictive of any platform here.

### Which platform is best for GPU workloads?

Cloudflare Sandboxes does not support GPU workloads. Modal has deep GPU support for ML workloads. Northflank supports NVIDIA H100 and A100 with all-inclusive pricing that runs roughly 62% cheaper than platforms billing GPU, CPU, and RAM separately.

### Can I use Cloudflare Sandboxes with BYOC?

No. Cloudflare Sandboxes is managed-only and runs on Cloudflare's infrastructure. If you need workloads running inside your own cloud account, Northflank is the most production-ready BYOC option available.

### What is the difference between Cloudflare Sandboxes and Northflank?

Cloudflare Sandboxes is a focused, edge-native code execution tool tightly integrated with Workers. Northflank is a full infrastructure platform that includes microVM sandboxes, BYOC deployment, databases, CI/CD, GPUs, and observability. Cloudflare Sandboxes is a good starting point. Northflank is where teams go when they need more.

## Conclusion

Cloudflare Sandboxes is a sharp, well-executed tool for teams already in the Workers ecosystem who need fast, ephemeral code execution at the edge. The millisecond startup times and global distribution are real strengths. But its short-lived execution model, no BYOC support, and lack of GPU workloads are hard limits that push most production AI teams toward alternatives.

If you are building something that needs to last, run inside your own cloud account, and handle more than just code execution, [Northflank](https://northflank.com/) is the platform worth evaluating. The rest of the options here each do one thing well. Northflank is the one built to do it all.

<InfoBox className="BodyStyle">

If Northflank sounds like the right fit, you can [get started for free](https://app.northflank.com/signup) or [talk to the team](https://cal.com/team/northflank/northflank-demo?duration=30) to see how it fits your stack.

</InfoBox>]]>
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</item><item>
  <title>What is a cloud migration strategy? Process and steps</title>
  <link>https://northflank.com/blog/what-is-cloud-migration-strategy</link>
  <pubDate>2026-02-28T15:45:00.000Z</pubDate>
  <description>
    <![CDATA[What is a cloud migration strategy? Learn the 6 R's, planning steps, and best practices. Northflank helps you migrate with confidence.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/what_is_cloud_migration_strategy_ce81d5c608.png" alt="What is a cloud migration strategy? Process and steps" /><InfoBox className="BodyStyle">

## TL;DR: what is a cloud migration strategy?
 
- A cloud migration strategy defines the approach, sequencing, and risk management plan for moving workloads to the cloud.
- The 6 R's framework (Rehost, Replatform, Refactor, Repurchase, Retire, Retain) provides a structured way to categorize each workload and select the right migration approach.
- Most migrations use a combination of approaches: lift-and-shift for applications that work well as-is, replatforming for workloads that benefit from managed services, and refactoring for applications where cloud-native architecture delivers significant long-term value.
- The most common failure modes are undiscovered application dependencies, cost overruns from over-provisioned resources, and security gaps from misconfigured cloud controls.
> [Northflank](https://northflank.com/) is a full-stack cloud platform that supports cloud migration from on-premises, Heroku, AWS, Azure, GCP, and other environments. Deploy applications, managed databases, background workers, and GPU workloads without managing Kubernetes or cloud infrastructure directly. [Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30).

</InfoBox>

*A cloud migration strategy is a plan that defines how an organization moves applications, data, and infrastructure from on-premises systems or existing cloud environments to a target cloud environment. It covers which workloads to migrate, which migration approach to apply to each, how to sequence the work, and how to manage risk and downtime during the transition.*

Without a defined strategy, cloud migrations routinely face cost overruns, security gaps, and extended timelines. This article covers what a cloud migration strategy includes, the six migration approaches, the key steps to build one, and how platforms like Northflank reduce the operational overhead of executing it.

## What is a cloud migration strategy?

A cloud migration strategy is a documented plan covering which workloads to move, in what order, using which migration approach, with defined success criteria and rollback procedures for each. It answers the key questions an organization needs to resolve before migration begins: which applications migrate first, what changes are needed to each, how data security is maintained during transition, and what happens if a migration fails.
 
A strategy differs from a migration plan in scope. A plan covers the tactical execution of a specific workload. A strategy covers the full portfolio: assessment of the current environment, prioritization of workloads, selection of migration approaches, cloud provider selection, team readiness, and post-migration operations. Without the strategy layer, individual migrations succeed but the overall program loses direction as complexity compounds.

## What are the 6 R's of cloud migration?

The 6 R's framework gives you six distinct approaches to migrating your applications. Each "R" represents a different strategy, and you'll likely use multiple approaches across your migration project.

Most organizations start with rehosting for speed, then replatform or refactor high-value workloads once they are stable in the cloud.

### 1. Rehost (lift and shift)

Rehosting means moving your applications to the cloud without making any changes. You're essentially lifting them from your current environment and shifting them to cloud infrastructure.

This approach is the fastest and least complex. It's ideal when you need to migrate quickly or when your applications already work well and don't need cloud-specific optimizations. However, you won't immediately benefit from cloud-native features like auto-scaling or serverless computing.

### 2. Replatform (lift, tinker, and shift)

With replatforming, you make minor optimizations during migration without changing your application's core architecture. You might switch to a managed database service or containerize your application for better portability.

This approach gives you some cloud benefits without the complexity of a full redesign. It's a middle ground that works well when you want to improve performance or reduce operational overhead without a complete overhaul. If you're looking to [migrate from on-premise to cloud](https://northflank.com/blog/on-premise-to-cloud-migration), replatforming often provides the best balance of effort and benefit.

### 3. Refactor (re-architect)

Refactoring involves redesigning your applications to take full advantage of cloud-native features. You might break down a monolithic application into microservices or adopt serverless architectures.

This approach requires the most effort and technical expertise, but it delivers the greatest long-term benefits. You'll get better scalability, performance, and cost efficiency. Consider refactoring for your most critical applications where cloud-native capabilities will provide significant competitive advantages.

### 4. Repurchase (drop and shop)

Repurchasing means replacing your existing application with a cloud-based alternative, typically a SaaS solution. Instead of migrating your on-premises CRM, for example, you might switch to Salesforce.

This approach reduces maintenance overhead and gives you immediate access to modern features. However, you'll need to manage data migration and ensure the new solution meets your requirements. It's particularly effective for commodity applications where custom solutions don't provide competitive advantage.

### 5. Retire

Not everything needs to migrate. Some applications have outlived their usefulness or have been replaced by newer systems. Retiring these applications reduces complexity and cuts costs.

Use your migration project as an opportunity to audit your entire application portfolio. You might discover that 20% of your applications are rarely used or provide duplicate functionality. Removing these cuts down what you need to maintain going forward.

### 6. Retain (revisit)

Some applications should stay where they are, at least for now. Maybe they have compliance requirements that make cloud migration complex, or perhaps they're scheduled for replacement soon.

Retaining doesn't mean never migrating, it means waiting until the timing is right. You might revisit these applications after your primary migration is complete or when regulatory requirements change. This approach prevents your migration from stalling on edge cases.

## How to create a cloud migration strategy

Follow these steps to build a plan that works for your organization.

1. **Start with a comprehensive assessment**: Document every application, database, and dependency in your current environment. Understand what you have before deciding what to migrate. This assessment reveals hidden dependencies that could cause problems later.
2. **Define your business objectives**: Are you migrating to reduce costs, improve scalability, or enhance disaster recovery? Your objectives will guide every decision you make. If cost reduction is your priority, you'll favor different approaches than if you're optimizing for performance.
3. **Choose your cloud provider**: Different providers specialize in different areas. If you're considering [AWS](https://northflank.com/blog/aws-cloud-migration-guide), [Azure](https://northflank.com/blog/azure-cloud-migration-strategy-migrate), or [Google Cloud](https://northflank.com/blog/complete-guide-for-google-cloud-gcp-migration), evaluate them based on your specific requirements. You might even adopt a [multi-cloud or hybrid cloud approach](https://northflank.com/blog/multi-cloud-vs-hybrid-cloud) for different workloads.
4. **Prioritize your workloads**: Not everything should migrate at once. Start with low-risk applications that deliver quick wins. This builds momentum and helps your team gain cloud expertise before tackling complex migrations.
5. **Select your migration approach**: Use the 6 R's framework to determine the right approach for each application. Your strategy will likely combine multiple approaches based on each application's requirements and business value.
6. **Plan for security and compliance**: Identify data residency requirements, encryption needs, and compliance obligations before you migrate. Address these upfront rather than discovering them mid-migration when they're harder and more expensive to fix.
7. **Prepare your team**: Cloud migration requires new skills and processes. Invest in training and consider bringing in expertise for specialized areas. Your team's readiness is just as important as your technical plan.

<InfoBox className="BodyStyle">

Northflank handles many of these steps by providing a unified platform for deploying and managing your cloud infrastructure. You can migrate applications gradually, test them in cloud environments, and manage both legacy and cloud-native workloads from a single interface.

[Get started with Northflank for free](https://app.northflank.com/signup) or [book a demo with our expert engineers](https://cal.com/team/northflank/northflank-intro) to discuss your specific use case and migration challenges.

</InfoBox>

## What are the common cloud migration challenges?

Even with a well-planned strategy, you'll face challenges during migration. Understanding them helps you prepare.

### Cost overruns

Cost overruns happen when you don't optimize cloud resources properly. Without proper planning, you might over-provision resources or leave unused instances running. Monitor your spending closely and right-size your resources based on actual usage patterns.

### Application dependencies

Application dependencies often surprise teams during migration. An application you thought was standalone might depend on services, databases, or network configurations you didn't know about. Your assessment phase should map all dependencies, but be prepared for discoveries during migration.

### Performance issues

Performance issues can emerge after migration if you don't optimize for cloud architectures. An application that performed well on-premises might struggle in the cloud if it wasn't designed for distributed environments. Test thoroughly before cutting over production traffic.

### Security gaps

Security gaps appear when you don't properly configure [cloud security](https://www.aikido.dev/platform/cloud) controls. The shared responsibility model means you're responsible for securing your applications and data even though the cloud provider secures the underlying infrastructure. Don't assume default configurations are secure enough for your needs.

### Skills gaps

Skills gaps slow migrations when your team lacks cloud expertise. Cloud platforms work differently from traditional data centers. Budget for training or consider working with partners who can accelerate your migration.

<InfoBox className="BodyStyle">

If you're dealing with a complex environment, like [migrating from Heroku](https://northflank.com/blog/how-to-migrate-from-heroku-a-step-by-step-guide), you'll want a platform that handles the complexity for you while giving you control over the migration process.

</InfoBox>

## FAQ: cloud migration strategy
 
### What is the difference between a cloud migration strategy and a migration plan?
 
A cloud migration strategy covers the full program: assessment, workload prioritization, approach selection, provider selection, team readiness, and post-migration operations. A migration plan covers the tactical execution of a specific workload or set of workloads. The strategy provides the framework within which individual migration plans are executed.
 
### Which of the 6 R's is most commonly used?
 
Rehosting (lift and shift) is the most common approach for initial migrations because it carries the least risk and moves the fastest. Most organizations then replatform or refactor applications in a subsequent optimization phase once the initial migration is complete and the team has cloud operational experience.
 
### How long does a cloud migration take?
 
Migration timelines vary significantly based on the number of applications, their complexity, and the migration approaches used. Small application portfolios with straightforward workloads can complete in weeks. Large enterprise migrations with hundreds of applications and complex compliance requirements typically take one to three years for the full program, with ongoing optimization continuing afterward.
 
### How do you handle data during cloud migration?
 
Data migration requires careful planning around downtime tolerance, consistency requirements, and compliance obligations. Common approaches include bulk migration with a cutover window, continuous replication with a short cutover, and dual-write patterns where data writes go to both old and new environments during transition. The right approach depends on the application's tolerance for downtime and data inconsistency.
 
### What is the shared responsibility model in cloud security?
 
The shared responsibility model defines which security obligations belong to the cloud provider and which belong to the customer. Cloud providers secure the physical infrastructure, hypervisor, and managed services. Customers are responsible for securing operating systems, applications, access controls, network configuration, and data. The boundary varies by service type: IaaS, PaaS, and SaaS have different shared responsibility boundaries.
 
### Can Northflank be used during a phased cloud migration?
 
Yes. Northflank supports running workloads on managed cloud and on customer-owned infrastructure simultaneously via BYOC. Teams executing phased migrations can move workloads to Northflank incrementally while maintaining existing infrastructure, validating each workload in the new environment before cutting over.

## Conclusion

Your cloud migration strategy is the foundation for a successful transition. Take the time to plan thoroughly, involve stakeholders from across your organization, and remain flexible as you learn what works for your specific environment.

Remember that cloud migration isn't a one-time project, it's the beginning of your cloud journey. Your strategy should adapt as your organization grows and as cloud technologies advance.

<InfoBox className="BodyStyle">

To start your migration, Northflank provides the tools and infrastructure you need to migrate confidently.

If you're moving from on-premises systems, migrating between cloud providers, or even considering [migrating from cloud to on-premise](https://northflank.com/blog/how-to-migrate-from-cloud-to-on-premise), our platform adapts to your needs and helps you manage complex migrations with less risk and overhead.

[Get started with Northflank for free](https://app.northflank.com/signup) or [book a demo with our expert engineers](https://cal.com/team/northflank/northflank-intro) to discuss your specific use case and migration challenges.

</InfoBox>

## Related articles
 
- [**On-premises to cloud migration: guide and steps**](https://northflank.com/blog/on-premise-to-cloud-migration): Covers the specific challenges and steps involved in moving from on-premises infrastructure to cloud environments.
- [**AWS cloud migration guide**](https://northflank.com/blog/aws-cloud-migration-guide): Step-by-step guidance for migrating workloads to AWS, including provider-specific tooling and best practices.
- [**Azure cloud migration strategy**](https://northflank.com/blog/azure-cloud-migration-strategy-migrate): Covers the Azure-specific migration path including tooling, compliance considerations, and sequencing.
- [**How to migrate from Heroku: step-by-step guide**](https://northflank.com/blog/how-to-migrate-from-heroku-a-step-by-step-guide): Practical guide for teams moving off Heroku to a more flexible deployment platform.]]>
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</item><item>
  <title>January &amp; February 2026 | Product releases</title>
  <link>https://northflank.com/changelog/january-and-february-2026</link>
  <pubDate>2026-02-28T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[We shipped major upgrades across observability, networking, and developer workflows.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Group_1410119489_4faced61dd.png" alt="January &amp; February 2026 | Product releases" />## Observability & Metrics

The metrics system has been substantially rebuilt.

**New grid layout with collapsible categories.** Metrics are now organised into named, collapsible sections. The collapsed/expanded state and any custom focus selection you make are persisted between page visits.

**Per-pod and per-container breakdowns.** CPU and memory charts now break down data by individual pod and container, making it easier to identify outliers across multi-replica deployments.

**Deployment view metrics.** The deployment overview now shows live CPU and memory charts inline, consistent with individual pod views.

**Volume charts.** Volume metrics charts have been added and improved.

**Chart quality improvements:**

- Y-axis scaling can now be toggled between capacity-based and data-based
- Charts with very small values (sub-1 units) no longer collapse to a flat line
- Stats table legend and tooltip are now visually consistent; series can be toggled by clicking
- Some charts now offer additional context via description text

**Available GPUs visibility.** Northflank regions with GPUs now list which GPU SKUs are available in the UI and if there is available capacity or if workloads will be queued and spawned as soon as capacity is available. 

## Compute & Deployments

**Consistent replica routing for internal traffic.** Internal traffic can now be routed to a consistent replica rather than load-balanced, useful for session-affinity requirements on internal endpoints.

## Networking

**Static Egress IPs**. You can now provision dedicated egress IPs directly from the UI or API, without contacting Northflank. This gives your services a stable outbound address you can allowlist in external firewalls, databases, and third-party APIs that restrict access by IP.

**Dedicated Load Balancers.** You can now provision dedicated load balancers directly from the UI or API, without contacting Northflank. This lets you serve TCP and UDP traffic independently of your HTTP/HTTPS ingress, or bring your own HTTPS load balancer for advanced routing requirements useful for databases, game servers, or custom traffic configurations that go beyond standard web ingress.

## Templates & Pipelines

**Workflow and Preview Blueprint APIs.** New API endpoints for workflows and preview blueprints are now available, following the shape of existing pipeline endpoints.

**Template navigation context.** Templates now correctly return to project or team context after viewing, depending on where you navigated from.

**Template run logs.** Build nodes and job run nodes now display relevant logs when viewing a template run. Nodes can be clicked to open a dedicated logs pane.

## Developer Experience

## Workload Identities

Workload identities allow your Northflank services and jobs to authenticate directly to external cloud resources, such as AWS S3, GCP storage, or other cloud APIs, without storing long-lived credentials as secrets. Northflank acts as an OIDC identity provider, issuing short-lived tokens that your workload uses to assume a role in the target cloud. The workload identity management UI is available as a dedicated sub-page on services and jobs, showing identity metadata and current status.

## SSH Identities

SSH identities let you attach SSH public keys to Northflank services and jobs, enabling direct SSH access to running workloads. Key changes take effect without redeployment. SSH identities can also be added as nodes in templates, enabling automated provisioning of SSH access as part of environment setup. You can now easily use coding agents or IDEs with Northflank’s SSH capability and other tools built on the SSH protocol like: scp and rysnc. This complements Northflank’s existing exec feature.

**Environment editor rebuild.** The environment variable editor has been fully rebuilt. Local changes are no longer lost when switching between views.

**OpenTofu: Azure provider.** Azure is now available as an OpenTofu provider.

**OpenTofu: cross-account AWS roles.** Cross-account role assumption (STS AssumeRole) is now supported for AWS OpenTofu providers.

**Microsoft Teams Workflows notification integration.** Microsoft Teams Workflows (`TEAMS_WORKFLOWS`) is now a supported notification integration type.

**CLI login timeout fix.** Improved HTTP error handling during the CLI login flow.

**Redesigned project dashboard.** The project dashboard has been redesigned to display a unified resource list with more useful information, as well as a new project activity log.

## Permissions & RBAC

**Team RBAC role refactor.** Team roles and permissions have been substantially refactored. Legacy permission mappings have been consolidated and API token creation flows updated throughout.

**API token permission view.** The token detail page now shows permissions pulled from the associated role, with a direct link to that role.

## Addons

**PostgreSQL v18 support.** PostgreSQL 18 is now available. New addons default to `scram-sha-256` password encryption.

**New MySQL versions.** Additional MySQL versions have been added.

**PostgreSQL: replication lag metric.** A missing replication lag metric has been added to PostgreSQL addon monitoring.

**RabbitMQ cluster size hint.** The RabbitMQ creation form now shows a cluster size hint.

**PgPooler external access fix.** Fixed an issue with PgPooler and external access configuration.

**MongoDB fork-of-fork fix.** Fixed restoring a fork of a forked MongoDB backup.

**Team-level backups UI.** Added a new view of backups across all projects and addons at the team level for better visibility. 

**Import backup UI improvements.** When uploading backups from your machine, the UI now displays useful information like upload speed and estimated time remaining.

## BYOC

**CoreWeave: periodic CA cert regeneration.** CoreWeave clusters automatically regenerate their CA certificate on a schedule to prevent expiry-related failures.

## Stack Templates

New one-click templates added: **[Docuseal](https://northflank.com/stacks/deploy-docuseal)**, **[Notifuse](https://northflank.com/stacks/deploy-notifuse)**, **[Dify](https://northflank.com/stacks/deploy-dify)**, and **[openclaw](https://northflank.com/stacks/deploy-openclaw)**.]]>
  </content:encoded>
</item><item>
  <title>What is a sandbox environment? [2026 guide]</title>
  <link>https://northflank.com/blog/what-is-a-sandbox-environment</link>
  <pubDate>2026-02-27T17:15:00.000Z</pubDate>
  <description>
    <![CDATA[A sandbox environment is an isolated runtime for executing untrusted code safely. Learn the isolation models, trade-offs, and how to choose the right approach in 2026.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/what_is_a_sandbox_environment_2b4cc349ea.png" alt="What is a sandbox environment? [2026 guide]" />A sandbox environment is one of those concepts that sounds simple until you need to implement it at scale. The core idea is consistent: give code a place to run that cannot affect anything outside its defined boundary.

What varies is the implementation, and the right implementation depends heavily on what you're sandboxing and why.

This article covers what a sandbox environment is, the main isolation models available in 2026, how to choose between them, the operational challenges you'll run into in production, and a recommended platform for running sandboxes at scale. 

<InfoBox className="BodyStyle">

## TL;DR: Key considerations for a sandbox environment

- A sandbox environment is an isolated runtime that lets you execute code, test changes, or run untrusted workloads without affecting your production systems or other users' data.
- Sandboxes exist on a spectrum. At one end, you have lightweight process-level isolation for quick integration testing. At the other, you have microVM-based runtimes with hard resource limits and network restrictions, which are now the standard for safely executing AI-generated code.
- The key considerations are isolation strength, cold start latency, support for both ephemeral and persistent workloads, and whether you can run sandboxes inside your own infrastructure.

> Platforms like [Northflank](https://northflank.com/product/sandboxes) provide secure sandbox infrastructure with microVM and advanced runtime isolation (Firecracker, gVisor, Kata), both ephemeral and persistent execution modes, and bring-your-own-cloud support, so you can run sandboxed workloads inside your own VPC rather than a third-party managed cloud.
> 

</InfoBox>

## What is a sandbox environment?

A sandbox environment is an isolated execution context where code runs without access to resources, data, or network segments outside its defined scope, unless you explicitly allow it.

You'll encounter sandboxes across several distinct use cases:

- **Development and testing:** Run feature branches against a copy of production services without touching live data.
- **Security research:** Execute suspicious files or code in an isolated VM to observe behavior safely.
- **Multi-tenant platforms:** Isolate each customer's workload so one tenant's code or resource usage cannot affect another's.
- **AI agent execution:** Autonomous agents generate and run code dynamically. That code is untrusted by default, even if your own model wrote it.

All of these share the same requirement: enforceable boundaries between what runs inside and what exists outside.

## How does a sandbox environment work?

Isolation is implemented at different layers of the stack, and the layer you choose determines both your security guarantees and your operational overhead.

### Process-level isolation

Containers use Linux namespaces and cgroups to enforce boundaries at the OS level. They're fast to start and cheap to run, but they share the host kernel. A kernel vulnerability can break the isolation boundary entirely.

### Advanced isolation runtimes

Advanced isolation runtimes sit between containers and full VMs. Firecracker and Kata use microVM-based isolation, while gVisor takes a different approach entirely, running a userspace kernel that intercepts all syscalls before they reach the host.

- **Firecracker** boots a lightweight VM using KVM with significantly lower overhead than a full VM.
- **gVisor** intercepts syscalls in userspace before they reach the host kernel
- **Kata Containers** run containers inside lightweight VMs with separate kernels

These runtimes are the current standard for running untrusted code at scale. The isolation is strong enough to mitigate most kernel exploits, and the startup overhead is acceptable for production workloads.

### Full VM isolation

Full VMs run each sandbox in a separate guest OS with its own kernel. Isolation is strongest here, but cold start times run into seconds, and memory overhead is significant. This is the right choice for malware analysis or workloads with hardware-level isolation as a compliance requirement.

<InfoBox className="BodyStyle">

**Run sandboxes in your own infrastructure**

If you're building on AI agents or running multi-tenant workloads, you need sandbox infrastructure that fits inside your existing stack.

Northflank provides [secure sandboxes](https://northflank.com/product/sandboxes) for running untrusted code at scale, with microVM and advanced runtime isolation (Firecracker, gVisor, Kata), both ephemeral and persistent execution modes, and the option to deploy entirely inside your own VPC.

[Get started with Northflank](https://northflank.com/product/sandboxes) or [schedule a demo](https://cal.com/team/northflank/northflank-demo).

**Related resources:**

- [How to spin up a secure code sandbox and microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
- [How to sandbox AI agents in 2026: microVMs, gVisor, and isolation strategies](https://northflank.com/blog/how-to-sandbox-ai-agents)
- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox)

</InfoBox>

## What are the main types of sandbox environments?

Choosing the right sandbox type comes down to your threat model and your tolerance for overhead.

| Isolation type | Mechanism | Security boundary | Typical use case |
| --- | --- | --- | --- |
| Container | Linux namespaces + cgroups | Shared kernel | Dev/test, low-risk workloads |
| gVisor | Userspace kernel | Userspace kernel boundary | Untrusted code, multi-tenancy |
| Firecracker microVM | KVM lightweight VM | Separate kernel | AI agent execution |
| Kata Containers | Container in lightweight VM | Separate kernel | Regulated workloads |
| Full VM | Hypervisor | Separate kernel + hardware | Malware analysis |

## What are the limitations of a sandbox environment?

Sandboxes introduce real trade-offs you need to plan for, both upfront and in production.

- **Cold start latency variance:** Even microVMs have startup overhead. Quoted creation times often measure only the VM boot phase and exclude image pulls, network setup, and process initialization. Your real end-to-end time will be higher, and it will vary under load.
- **Resource overhead at scale:** Each sandbox carries baseline memory and CPU costs. At high concurrency, this compounds fast. You need precise resource limits and efficient scheduling to keep costs manageable.
- **Network restrictions vs. functionality:** Sandboxes work best with minimal network access, but many real workloads need outbound access to install packages or call APIs. Every allowlisted endpoint is a potential escape path.
- **Persistent state complexity:** Ephemeral sandboxes are simple: destroy them after use. Persistent sandboxes that maintain state across sessions require careful management of storage volumes, network identity, and lifecycle, and zombie environments that aren't properly garbage collected will accumulate and consume compute.
- **Escape risks:** No isolation model is unbreakable. Kernel vulnerabilities have been exploited to break out of containers. MicroVMs significantly raise the difficulty, but resource limits, network restrictions, and least-privilege configuration all reduce your attack surface.

## How Northflank implements sandbox environments

[Northflank](https://northflank.com/product/sandboxes) provides secure sandboxes for running untrusted code at scale with microVM and advanced runtime isolation (Kata Containers, Firecracker, gVisor), supporting both ephemeral and persistent environments in managed cloud or your own VPC.

> Northflank has been running secure sandboxes sandboxes in production since 2021 across startups, public companies, and government deployments. If you need GPUs, workers, APIs, or databases running alongside your sandboxes, they run in the same platform.
> 

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

Here's what Northflank provides out of the box:

### Isolation and runtime

Northflank supports Firecracker, gVisor, and Kata Containers. You choose the isolation model based on your workload's security requirements, and Northflank handles the orchestration. End-to-end sandbox creation runs at 1-2 seconds, covering the full stack.

### Ephemeral and persistent environments

You get both. Ephemeral sandboxes for short-lived execution and persistent environments for stateful workloads like development environments, long-running agents, or user sessions that need to survive beyond a single request.

### Bring your own cloud

Most enterprise teams deploying sandboxed workloads can't accept their code or data leaving their own infrastructure. Northflank supports [bring-your-own-cloud](https://northflank.com/product/bring-your-own-cloud) deployment inside your own VPC on AWS, GCP, Azure, Oracle Cloud, CoreWeave, Civo, bare-metal, and on-premises, and it's available self-serve.

### Full workload runtime

You can run APIs, background workers, databases, and AI agent infrastructure alongside your sandbox pool on the same platform. [GPU workloads](https://northflank.com/product/gpu-paas) are supported with on-demand provisioning and no quota requests.

### Access and pricing

Sandboxes are accessible via API, CLI, and SSH. CPU at $0.01667/vCPU-hour, memory at $0.00833/GB-hour. Full pricing, including GPUs, is on the [Northflank pricing page](https://northflank.com/pricing).

## What should you prioritize when choosing a sandbox environment?

The right sandbox approach depends on your threat model and workload type. Work through these questions:

- **Who controls the code?** If it's trusted code from your own engineers, container-level isolation is likely sufficient. If it's AI-generated or user-submitted, use microVM isolation. The overhead is worth it.
- **Ephemeral or persistent?** If your use case requires session state across requests, confirm your platform supports persistent sandboxes, not just fire-and-forget execution.
- **Where does the code run?** If you have data residency requirements or can't send code to a third-party cloud, BYOC deployment is a hard requirement.
- **What's your scale?** At low request volume, a simple container pool works. At high concurrency with burst traffic, you need pre-warming, efficient scheduling, and per-sandbox resource enforcement.

## Frequently asked questions about sandbox environments

### What is an example of a sandbox environment?

A Firecracker microVM that spins up when an AI agent needs to execute generated Python code, runs it with no outbound network access and a defined memory cap, returns the output, and is destroyed after the session. Another example: a per-developer environment that mirrors production services but has no access to production data.

### Why is it called a sandbox?

The term comes from the physical concept of a sandbox: a bounded area where you can experiment freely without the mess spreading to the surrounding environment. In software, it maps to an isolated execution context where changes and side effects are contained.

### What is the difference between a sandbox and a test environment?

A test environment is a deployment stage used for running automated tests before releasing to production. A sandbox is about execution isolation for safety or tenancy reasons. You can run tests inside a sandbox, but a staging environment running trusted code is not a sandbox in the security sense.

### What are the different types of sandboxes?

The main types are process-isolated containers, gVisor (userspace kernel), microVMs (Firecracker, Kata), and full VMs. Each trades isolation strength for startup speed and resource overhead. MicroVMs are the current standard for untrusted code execution.

### Can malware escape a sandbox?

Yes. Kernel vulnerabilities have been used to break out of container-based sandboxes. MicroVMs significantly raise the difficulty by providing a separate kernel, but no isolation model is unbreakable. Defense-in-depth, combining strong isolation with network restrictions, resource limits, and least-privilege config, is the right approach.

## Related articles on sandbox environments and secure code execution

- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox)
- [How to sandbox AI agents: microVMs, gVisor, and isolation strategies](https://northflank.com/blog/how-to-sandbox-ai-agents)
- [Best alternatives to E2B.dev for running untrusted code in secure sandboxes](https://northflank.com/blog/best-alternatives-to-e2b-dev-for-running-untrusted-code-in-secure-sandboxes)
- [Top AI sandbox platforms, ranked](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution)
- [Self-hosted AI sandboxes: guide to secure code execution](https://northflank.com/blog/self-hosted-ai-sandboxes)
- [10 best CodeSandbox alternatives](https://northflank.com/blog/codesandbox-alternatives)]]>
  </content:encoded>
</item><item>
  <title>Top BYOC AI sandboxes for running untrusted code in 2026</title>
  <link>https://northflank.com/blog/top-byoc-ai-sandboxes</link>
  <pubDate>2026-02-26T17:00:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the top BYOC AI sandboxes (Northflank, Daytona, and E2B) across deployment model, microVM isolation, lifecycle design, and operational overhead. Find the right fit for running untrusted code inside your VPC.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/top_byoc_ai_sandboxes_cb0ad90e4c.png" alt="Top BYOC AI sandboxes for running untrusted code in 2026" />AI agents and code-executing developer tools need a safe place to run untrusted code without breaking security or networking boundaries.

This guide compares the top **bring your own cloud (BYOC) AI sandboxes** and shows what to evaluate when execution must run inside your VPC.

<InfoBox className="BodyStyle">

## TL;DR: Top BYOC AI sandboxes at a glance

If sandbox workloads must run inside your own cloud account or VPC, the decision usually comes down to **deployment model**, **isolation**, **lifecycle design**, and operational overhead.

**Top BYOC AI sandboxes (compared):**

1. **Northflank** – Provides secure sandboxes that can run on Northflank's managed cloud or deploy inside your own infrastructure (AWS, GCP, Azure, Oracle, CoreWeave, on-premises, or bare-metal) with microVM-based isolation options (Kata Containers, Firecracker, and gVisor) and support for both ephemeral and persistent environments.
    
    > **Note**: [Northflank Sandboxes](https://northflank.com/product/sandboxes) can run alongside APIs, workers, databases, and CPU or GPU workloads in the same control plane. BYOC is available self-serve. Northflank has been in production since 2021 across startups, public companies, and government deployments.
    > 
2. **Daytona** – Sandbox environments for AI agent and code execution workflows that can run on customer-managed compute inside your own cloud or on-prem, with Daytona providing the control plane.
3. **E2B** – API-driven sandbox sessions for agent execution with a BYOC deployment option that runs sandboxes inside your own VPC. Currently available for only AWS and offered to enterprise customers only.

> **If BYOC is the non-negotiable requirement:** Prioritize platforms where the execution plane runs inside your cloud, then compare isolation, lifecycle, and networking controls. [Northflank](https://northflank.com/product/sandboxes) supports self-serve BYOC across AWS, GCP, Azure, Oracle, CoreWeave, and on-premises infrastructure, with microVM-based isolation (Kata Containers, Firecracker, and gVisor), and both ephemeral and persistent environments, with platform-managed orchestration.
> 

</InfoBox>

## What is a BYOC AI sandbox?

A BYOC AI sandbox is a programmable execution environment for running untrusted code where the **execution plane runs inside infrastructure you control**, such as your cloud account or VPC, while the platform may still provide APIs, lifecycle automation, and orchestration.

This becomes relevant when sandbox workloads must access private services, comply with internal security policies, or remain inside regulated infrastructure boundaries. Instead of routing execution through vendor infrastructure, you keep compute where your systems and data already live.

## When do you need a BYOC sandbox?

You typically start evaluating BYOC sandboxes when sandbox execution can no longer happen outside your infrastructure boundary.

Common triggers include agent workloads needing private API access, internal data processing requirements, strict network egress policies, or organizational constraints around data residency and infrastructure ownership. In these cases, the sandbox platform must integrate with your network rather than sit in front of it.

## What should you evaluate in a BYOC AI sandbox?

When you compare BYOC sandbox platforms, most decisions come down to a consistent set of technical dimensions:

- **Deployment model:** whether sandbox execution runs inside your infrastructure and how control plane separation works
- **Isolation model:** microVM-based isolation versus container isolation and the associated security posture
- **Lifecycle design:** ephemeral sessions, persistent environments, warm pools, and state management patterns
- **Networking controls:** outbound restriction, inbound posture, and private connectivity integration
- **Interfaces:** API, SDK, CLI, and SSH ergonomics for automation and integration
- **Operational overhead:** what infrastructure components you must operate when using BYOC

## What are the top BYOC AI sandboxes?

The platforms below represent the current set of sandbox solutions that support execution inside customer infrastructure.

### 1. Northflank

[Northflank](https://northflank.com/product/sandboxes) provides microVM-backed sandbox environments that run inside your own infrastructure (across AWS, GCP, Azure, Oracle, CoreWeave, on-premises, or bare-metal) while remaining part of a full workload runtime platform.

> This is particularly relevant when sandbox workloads must run alongside production services, databases, and GPU workloads without requiring a separate platform. Northflank has been operating microVMs at scale in production since 2021 across startups, public companies, and government deployments.
> 

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

**Key characteristics:**

- **Deployment model:** Supports BYOC deployment into your own AWS, GCP, Azure, Oracle, CoreWeave, on-premises, or bare-metal infrastructure, allowing sandbox execution to run inside infrastructure you control, including customer VPCs, while Northflank manages orchestration. Available self-serve, with no enterprise-only gatekeeping.
- **Isolation:** Uses microVM-based isolation (Kata Containers, Firecracker, and gVisor) applied based on workload type, enabling strong VM-level isolation suited to untrusted code execution across multi-tenant environments.
- **Lifecycle:** No forced time limits (run sandboxes for seconds or weeks). Supports both ephemeral and persistent environments, allowing teams to combine short-lived execution pools with long-running stateful services. Persistent volumes, S3-compatible storage, and stateful databases can be attached and run in the same platform.
- **Interfaces:** Provides UI, API, CLI, SSH, and GitOps access for creating, managing, and interacting with sandbox environments as part of automated workflows or agent pipelines.
- **Operational considerations:** Infrastructure ownership and networking remain in your cloud or on-prem environment. Northflank abstracts scheduling, orchestration, autoscaling, bin-packing, CI/CD, and lifecycle management, including microVM provisioning and multi-tenant isolation, so you don't have to build or maintain that stack.
- **Workload scope:** Sandbox environments run alongside APIs, workers, databases, and CPU or GPU workloads in the same control plane. On-demand GPUs (H100s and others) are available without quota requests at $2.74/hour (up to 62% cheaper than major cloud providers). CPU is priced at $0.01667/vCPU/hour (up to 65% cheaper than major cloud providers), reducing the need for separate runtime systems as workload requirements grow.

<InfoBox className="BodyStyle">

Understand how Northflank sandboxes run inside your infrastructure and how BYOC deployments work:

- How Northflank sandboxes are provisioned and used for secure code execution - https://northflank.com/product/sandboxes
- How bring your own cloud deployments allow workloads to run inside your cloud accounts - https://northflank.com/product/bring-your-own-cloud
- How sandbox workloads can be deployed directly into customer VPC environments - https://northflank.com/product/customer-vpc-deployments
- How Northflank operates within your infrastructure boundaries and deployment architecture - https://northflank.com/features/bring-your-own-cloud
- How microVM sandbox environments are created using Firecracker, gVisor, and Kata Containers - https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh

</InfoBox>

### 2. Daytona

Daytona provides stateful, isolated sandbox environments designed primarily for AI agent and code execution workflows, with a customer-managed compute option that allows sandboxes to run inside your own cloud or on-prem infrastructure while Daytona retains control plane management.

**Key characteristics:**

- **Deployment model:** Supports a customer-managed compute deployment pattern where sandboxes run on isolated infrastructure inside your cloud or on-prem, with Daytona providing the control plane.
- **Isolation:** Docker-based sandbox environments with support for standard Docker images, Dockerfile configurations, Docker Compose, and Docker-in-Docker, providing container-level isolation for AI-generated code.
- **Lifecycle:** Stateful by design, with sandboxes that run indefinitely and support environment snapshots that can be saved, restored, and resumed.
- **Interfaces:** SDK, API, and CLI-driven workflows for environment creation, lifecycle control, and integration into automation pipelines.
- **Operational considerations:** Requires operating and scaling the infrastructure layer that hosts sandbox environments when deployed in BYOC mode.

### 3. E2B

E2B provides API-driven sandbox sessions designed for agent execution workflows with a BYOC deployment option (only available for AWS and enterprise customers) that deploys sandboxes inside the customer's own VPC.

**Key characteristics:**

- **Deployment model:** Supports a BYOC deployment pattern where sandboxes are deployed inside the customer's VPC, with E2B retaining control plane management. Currently available on AWS only. BYOC is offered to enterprise customers only.
- **Isolation:** microVM-based isolation powered by Firecracker, designed to execute untrusted agent-generated code safely with full workload isolation.
- **Lifecycle:** Programmatic sandbox lifecycle with configurable timeouts, up to 24 hours on the Pro tier (1 hour on the Base tier), and support for sandbox persistence and snapshots. Sandboxes are created, managed, and terminated via SDK or API.
- **Interfaces:** SDK-first interaction model (Python and JavaScript/TypeScript), with REST API, CLI, and SSH access, designed for integration with agent frameworks and orchestration layers.
- **Operational considerations:** In BYOC deployments, the customer manages the VPC, AWS account, and compute nodes (orchestrators and edge controllers). E2B manages the control plane.

## How to choose the right BYOC AI sandbox

Use this framework to map your requirements to the platform characteristics and solutions that typically drive the decision.

| If your priority is… | Focus on evaluating… | Platform | Fit |
| --- | --- | --- | --- |
| Running sandboxes inside your VPC | Deployment model | Northflank | Self-serve BYOC across AWS, GCP, Azure, Oracle, CoreWeave, on-prem, and bare-metal |
|  |  | Daytona | Customer-managed compute, cloud or on-prem |
|  |  | E2B | Customer VPC, AWS only, enterprise only |
| Strong isolation for untrusted code | Isolation model | Northflank | Kata Containers, Firecracker, and gVisor, applied per workload |
|  |  | Daytona | Docker-based isolation |
|  |  | E2B | Firecracker microVM isolation |
| Mixing short-lived and long-running workloads | Lifecycle model | Northflank | Ephemeral and persistent, no time limits |
|  |  | Daytona | Stateful, runs indefinitely |
|  |  | E2B | Up to 24 hours, persistence supported |
| Accessing private services or datasets | Networking | Northflank | Inside your VPC across any cloud, on-prem, or bare-metal |
|  |  | Daytona | Customer-managed compute, cloud or on-prem |
|  |  | E2B | Inside customer VPC, AWS only, enterprise only |
| Minimizing infrastructure overhead | Operational responsibility | Northflank | Platform-managed orchestration, autoscaling, and microVM provisioning; in production since 2021 |
|  |  | Daytona | Customer operates the infrastructure layer |
|  |  | E2B | Customer manages compute nodes |
| Running other workloads alongside sandboxes | Workload scope | Northflank | Sandboxes, services, jobs, databases, and CPU/GPU in one control plane |
|  |  | Daytona | Sandbox-focused |
|  |  | E2B | Sandbox-focused |

## Frequently asked questions about BYOC AI sandboxes

Common questions about how BYOC sandbox platforms work and what to consider when evaluating them.

### What does BYOC mean for AI sandboxes?

BYOC (bring your own cloud) means sandbox execution runs inside infrastructure you control, such as your cloud account or VPC, while the platform handles orchestration, APIs, and lifecycle management.

### How is a BYOC sandbox different from a self-hosted sandbox?

Self-hosted sandboxes require you to operate the full runtime stack yourself. BYOC separates control plane and execution plane responsibilities, so execution runs in your infrastructure while the platform manages orchestration. Some platforms, such as Northflank, extend this to on-premises and air-gapped environments for regulated industries and government deployments.

### Why do AI agent systems often require BYOC sandboxes?

Agent systems frequently execute untrusted code while interacting with internal APIs or private services. Running sandboxes inside your infrastructure enables secure connectivity to those systems while maintaining workload isolation.

### Do BYOC sandboxes support both ephemeral and persistent environments?

Support varies by platform. Some cap session length (for example, E2B's Pro tier limits sessions to 24 hours). Others, like Northflank, support both ephemeral and persistent environments with no forced time limits.

### Does BYOC increase operational complexity?

It depends on the platform model. Some approaches require you to manage and scale the infrastructure layer directly. Others, like Northflank, abstract orchestration and microVM provisioning while still keeping execution inside your infrastructure.

## Related guides on AI sandboxes and BYOC deployment

If you’re evaluating sandbox platforms or designing secure execution infrastructure, these guides expand on adjacent decisions and architectural tradeoffs.

- [**Top AI sandbox platforms for code execution**](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution): Compare leading sandbox platforms across isolation models, lifecycle design, and operational responsibility.
- [**Self-hosted AI sandboxes**](https://northflank.com/blog/self-hosted-ai-sandboxes): Understand how DIY, self-hosted, and BYOC sandbox approaches differ and what changes operationally.
- [**Best code execution sandbox for AI agents**](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents): Learn which runtime characteristics matter most for agent workloads, including lifecycle and network access.
- [**How to sandbox AI agents**](https://northflank.com/blog/how-to-sandbox-ai-agents): Learn how different isolation strategies reduce risk when agents execute generated code.
- [**How to spin up a secure code sandbox and microVM in seconds with Northflank**](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh): A step-by-step guide to deploying microVM-backed services using Firecracker, gVisor, and Kata Containers inside a secure multi-tenant project]]>
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  <title>Daytona vs Modal: comparing AI code execution sandboxes in 2026</title>
  <link>https://northflank.com/blog/daytona-vs-modal</link>
  <pubDate>2026-02-25T18:00:00.000Z</pubDate>
  <description>
    <![CDATA[Daytona vs Modal in 2026: compare sandbox lifecycle, isolation model, networking controls, and when to use each for secure untrusted code execution.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/daytona_vs_modal_9e9e5970f8.png" alt="Daytona vs Modal: comparing AI code execution sandboxes in 2026" /><InfoBox className="BodyStyle">

## TL;DR: Daytona vs. Modal in 2026, key differences at a glance

Both platforms provide isolated sandboxes for running untrusted code, but they approach the problem differently.

- **Daytona** is built around SDK-managed sandbox lifecycle. You create sandboxes from snapshots, configure auto-stop and cleanup behavior, and reuse warm sandboxes for fast startup.
- **Modal** is an AI infrastructure platform that includes sandboxes as one product. Sandboxes run on gVisor and are defined at runtime.
- **The core difference is environment and lifecycle design**: Daytona focuses on sandbox objects with lifecycle automation (auto-stop, auto-archive, auto-delete) and warm-start behavior, while Modal focuses on runtime-defined containers with gVisor-based isolation and a deny-by-default posture for inbound connections.

> [**Northflank**](https://northflank.com/product/sandboxes) provides secure sandboxes for running untrusted code at scale with microVM isolation (Kata Containers, Firecracker, gVisor), supporting both ephemeral and persistent environments in managed cloud or your own VPC. It also removes the ceiling: if you need GPUs, workers, APIs, or databases running alongside your sandboxes, they run in the same platform.
> 

</InfoBox>

## What is Daytona?

Daytona provides sandboxes as isolated runtime environments managed via SDKs. You can create sandboxes from snapshots, configure lifecycle behavior, and control how sandboxes stop, archive, and delete over time.

A few concrete behaviors define the runtime model:

- **Warm starts via pools:** Daytona keeps a pool of warm sandboxes using default snapshots, so sandboxes can launch within milliseconds rather than cold-booting.
- **Lifecycle automation:** sandboxes auto-stop by default after inactivity, and you can configure auto-archive and auto-delete intervals.
- **Network controls:** Daytona includes configurable network firewall controls when creating a sandbox.

## What is Modal?

Modal Sandboxes are secure containers for executing untrusted user or agent code. They are created dynamically through Modal’s SDKs, and their lifecycle is governed by timeouts.

Two traits are especially relevant for sandbox workloads:

- **Timeout-based sessions:** sandboxes have a default maximum lifetime of 5 minutes, configurable up to 24 hours. For runs beyond 24 hours, Modal recommends preserving state via filesystem snapshots and restoring into a new sandbox.
- **Security model and networking controls:** a default sandbox cannot accept incoming connections and cannot access your Modal resources; outbound network can be blocked entirely or restricted with CIDR allowlists. Sandboxes run on gVisor.

## Quick comparison: Daytona vs. Modal vs. Northflank

Here's how the three platforms stack up across the dimensions that typically drive the decision.

|  | Daytona | Modal | Northflank |
| --- | --- | --- | --- |
| Primary use case | SDK-managed sandbox lifecycle and reuse via snapshots / warm pools | AI infrastructure platform with sandboxes as one product | Secure microVM sandboxes at scale, with full workload runtime |
| Isolation | Container-based (OCI / Docker images) | gVisor (syscall interception) | microVM isolation via Kata Containers, Firecracker, gVisor |
| Persistence model | Snapshot-based reuse plus lifecycle archive/delete controls | Session-scoped (timeout up to 24h); filesystem snapshots for state preservation | Both ephemeral and persistent, same platform |
| Networking | Firewall controls, allowlist/block networking, preview URLs and SSH access | Block all network, restrict outbound via CIDR allowlist; connect tokens and port forwarding | Runs in your VPC, microVM-per-workload isolation |
| Lifecycle controls | Auto-stop default 15 minutes inactivity; auto-archive default 7 days; auto-delete disabled by default | Default max lifetime 5 minutes; configurable up to 24 hours | Ephemeral pools or long-running stateful services |
| SDK / access | Python, TypeScript, Ruby, Go SDKs; API and CLI | SDK-first (Python, JavaScript, Go) | API, CLI, SSH |

## How do Daytona and Modal compare?

The differences that drive the decision come down to isolation, lifecycle, environment definition, and networking.

### Isolation and security model (Daytona vs. Modal)

Modal sandboxes run on gVisor, which intercepts system calls to reduce host kernel exposure. A default sandbox also cannot accept inbound connections and is not authorized to access other Modal workspace resources.

Daytona sandboxes run as container-based environments created from OCI/Docker images, with configurable firewall controls for managing network access.

### Lifecycle and time boundaries (Daytona vs. Modal)

Daytona is built around inactivity-based lifecycle automation, including auto-stop, auto-archive, and auto-delete policies.

Modal is built around timeout-bounded sessions. Sandboxes have a short default maximum lifetime, can be configured up to 24 hours, and longer workflows use snapshots and restore into a new sandbox.

### Environment definition and reuse (Daytona vs. Modal)

Daytona emphasizes snapshot reuse and warm-start behavior, which fits repeated execution in consistent environments.

Modal emphasizes runtime-defined environments, where the container configuration is assembled in code at sandbox creation time.

### Networking and connectivity (Daytona vs. Modal)

Modal exposes explicit network restriction controls, including the ability to block network access entirely or restrict outbound access.

Daytona focuses on firewall configuration and sandbox lifecycle controls as the main way you shape connectivity and exposure.

## When should you use Daytona?

Daytona fits when you want sandbox lifecycle controls to be the primary interface. Use it when:

- You want inactivity-based lifecycle automation (auto-stop) and cleanup controls (auto-archive and auto-delete) as part of the sandbox object model.
- Your workloads benefit from snapshot-based reuse and warm-start behavior for repeated sandbox creation.
- You want configurable firewall controls at sandbox creation time.
- Your agent workload often needs to keep a sandbox alive indefinitely by disabling auto-stop.

## When should you use Modal?

Modal fits when sandboxes are one part of a broader compute platform, and you want runtime-defined containers with explicit networking controls. Use it when:

- You want gVisor-based isolation for sandboxes, with a clearly described security model and default restrictions on inbound connections.
- You need explicit “no network” or “restricted outbound only” behavior directly in the sandbox API.
- Your execution model fits session timeouts (default 5 minutes, configurable up to 24 hours) and snapshot/restore for longer workflows.
- You want built-in patterns for authenticated inbound access using connect tokens, plus port-forwarding options when you need raw TCP exposure.

## How Northflank handles secure sandbox execution, BYOC, and the infrastructure around it

Northflank’s [Secure Sandboxes](https://northflank.com/product/sandboxes) run each workload in its own microVM (Kata Containers, Firecracker, gVisor) in your VPC. It supports both ephemeral and persistent environments in the same control plane.

> Where it goes further is in what surrounds the sandboxes: the same platform also runs APIs, workers, background jobs, databases, and CPU or GPU workloads, so teams do not need a separate system as requirements grow.
> 

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

Here's how it compares:

- **MicroVM sandboxes:** Kata Containers, Firecracker, and gVisor isolation depending on workload. Sub-second cold starts. Built for running untrusted, LLM-generated code safely at scale with true multi-tenant isolation.
- **Ephemeral and persistent, same control plane:** Short-lived execution pools and long-running stateful services run together. No need to choose one model or stitch two tools.
- **Self-serve BYOC:** Deploy in your own infrastructure on AWS, GCP, Azure, Oracle, Civo, CoreWeave, or on-premises. Enterprises can run sandboxes entirely within their own infrastructure, which is important for teams with compliance or data residency requirements.
- **On-demand GPUs without quota requests:** Self-service provisioning for inference, training, and compute-heavy agent work. No waiting on allocations.
- **Full workload runtime alongside sandboxes:** Agents, APIs, workers, background jobs, databases, and inference run in the same platform. Teams that outgrow sandbox-only tools don't need to migrate.
- **End-to-end sandbox creation in 1-2 seconds:** The full creation process, not just VM boot.
- **In production since 2021:** Multi-tenant microVM workloads across startups, public companies, and government deployments. For a concrete example, [cto.new](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) uses Northflank's microVMs to scale secure sandboxes in production.
- **Pricing:** CPU at $0.01667/vCPU-hour, memory at $0.00833/GB-hour. Full details on the [Northflank pricing page](https://northflank.com/pricing).

### Cost comparison at scale
To make the pricing difference concrete, here is what 200 sandboxes costs across providers under the same conditions.

_Based on 200 sandboxes, plan: nf-compute-100-4, infra node: m7i.2xlarge_

| Model | Provider | Cloud | Sandbox vendor | Total |
| --- | --- | --- | --- | --- |
| PaaS | Northflank | — | $7,200.00 | $7,200.00 |
| PaaS | Daytona | — | $16,819.20 | $16,819.20 |
| PaaS | Modal | — | $24,491.50 | $24,491.50 |
| BYOC (0.2 overcommit)* | Northflank | $1,500.00 | $560.00 | $2,060.00 |

*Through Northflank's plans on BYOC, there's a default overcommit which allows a customer to spawn more services and sandboxes on the same amount of compute. A request modifier of 0.2 means each sandbox only requests 20% of its plan's resources as a guaranteed minimum, but can burst up to the full plan limit if there's available capacity on the node. So instead of fitting 8 sandboxes per node, you could fit 40 on the same hardware, reducing both infrastructure cost and the Northflank management fee.

<InfoBox className="BodyStyle">

[Northflank sandboxes](https://northflank.com/product/sandboxes) run untrusted code at scale with microVM isolation, in managed cloud or your own infrastructure. Ephemeral or persistent, CPU or GPU, with full workload orchestration alongside. [Get started on Northflank](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo) with an engineer if you have specific requirements for your organization.

</InfoBox>

## Frequently asked questions about Daytona vs. Modal

### What is Daytona used for?

Daytona is used to provision and manage isolated sandboxes through SDKs, with lifecycle controls like auto-stop, auto-archive, and auto-delete. It also supports snapshot-based reuse and warm pools, which is useful when you are spinning up many similar sandboxes repeatedly.

### What is Modal and how does it differ from Daytona?

Modal is an AI infrastructure platform that includes sandboxes as one product. Modal Sandboxes run on gVisor and come with explicit networking restriction controls and an inbound deny-by-default posture. Daytona is more centered on sandbox object lifecycle and reuse via snapshots and warm starts.

### Which is better for strict outbound network restriction?

Modal exposes direct controls to block all network access or restrict outbound traffic using CIDR allowlists. If outbound restriction needs to be part of your sandbox API contract, this maps cleanly to that requirement.

### How long can a sandbox run?

Modal sandboxes have a default maximum lifetime of 5 minutes and can be configured up to 24 hours. Daytona sandboxes auto-stop by default after 15 minutes of inactivity, and you can disable auto-stop by setting the interval to 0.

### Do Daytona sandboxes stop while work is running?

Daytona’s inactivity timer can trigger auto-stop even if internal processes are running. Background scripts and long-running tasks without active interaction do not reset the timer, so a long-running job can be stopped mid-process under the default settings unless you adjust the auto-stop interval.

### When does Northflank become relevant in a sandbox decision?

Northflank becomes relevant when you want microVM-based isolation and you expect your system to include more than sandboxes, such as APIs, workers, databases, and GPU workloads, within the same runtime and your VPC.

## Further reading on AI sandboxes and microVM execution

If you're evaluating sandbox platforms or comparing isolation approaches, these articles cover adjacent decisions and trade-offs:

- [Daytona vs E2B: AI code execution sandboxes](https://northflank.com/blog/daytona-vs-e2b-ai-code-execution-sandboxes) - Compares Daytona and E2B across lifecycle design, environment reuse, and isolation approach for agent workloads.
- [Top Daytona.io alternatives for running AI code in secure sandboxed environments](https://northflank.com/blog/top-daytona-io-alternatives-for-running-ai-code-in-secure-sandboxed-environments) - Reviews strong alternatives to Daytona with a focus on isolation, persistence, and deployment flexibility.
- [Top Modal Sandboxes alternatives for secure AI code execution](https://northflank.com/blog/top-modal-sandboxes-alternatives-for-secure-ai-code-execution) - Covers alternative platforms to Modal Sandboxes for teams evaluating other options for secure code execution.
- [E2B vs Modal: comparing AI code execution sandboxes](https://northflank.com/blog/e2b-vs-modal) - A direct comparison of two widely discussed sandbox platforms across isolation model, persistence, and environment definition.
- [E2B vs Sprites dev: comparing AI code execution sandboxes](https://northflank.com/blog/e2b-vs-sprites-dev) - A comparison of E2B and Sprites dev across persistence model, isolation approach, and developer experience.
- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox) - Explains what AI sandboxes are, why isolation is required, and how different approaches compare.
- [How to sandbox AI agents: MicroVMs, gVisor and isolation strategies](https://northflank.com/blog/how-to-sandbox-ai-agents) - Explains isolation approaches and trade-offs between Firecracker, gVisor, and Kata Containers for agent workloads.
- [How to spin up a secure code sandbox and microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh) - A practical walkthrough of launching a Northflank microVM sandbox using Firecracker, gVisor, or Kata Containers.]]>
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</item><item>
  <title>Top Cloudflare Containers alternatives in 2026</title>
  <link>https://northflank.com/blog/top-cloudflare-containers-alternatives</link>
  <pubDate>2026-02-25T17:30:00.000Z</pubDate>
  <description>
    <![CDATA[Cloudflare Containers is a new edge compute service that lets you run Docker containers on Cloudflare’s global network.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/cloudflare_containers_e396efbc1c.png" alt="Top Cloudflare Containers alternatives in 2026" /><InfoBox className='BodyStyle'>

## 📌 **TL;DR**

Cloudflare Containers is an edge compute service that lets you run Docker containers on Cloudflare’s global network alongside Workers.

Cloudflare recently raised Containers account-level limits to approximately 6 TiB of memory, 1,500 vCPU, and 30 TB of disk for concurrent workloads, enabling much larger container fleets and higher aggregate resource usage.

Despite higher limits, Cloudflare Containers currently relies on programmatic scaling rather than native autoscaling or load balancing, and container instances use ephemeral storage and lifecycle behavior that limits support for long-running, stateful services.

> For teams that need full container orchestration, persistent services, secure sandboxing, and GPU-powered AI, [Northflank](https://northflank.com/) is the most complete Cloudflare Containers alternative.

</InfoBox>

## What are Cloudflare Containers?

Cloudflare Containers expands on the company’s [Workers](https://northflank.com/blog/best-cloudflare-workers-alternatives) platform by introducing full container support. 

Instead of being limited to JavaScript, TypeScript, or WebAssembly in V8 isolates, you can now run standard Docker images. This means you can deploy applications and services in any language or runtime directly to Cloudflare’s edge network.

The key difference from traditional container platforms is placement. With Cloudflare Containers, your workloads run in the same global edge locations as Workers, giving them low-latency access to users worldwide. You still define and control your container via a Worker, which routes traffic and manages the lifecycle. Deployment is done with the familiar `wrangler deploy` workflow.

## How Cloudflare Containers work

When you deploy a Cloudflare Container, the Worker acts as the entry point and orchestrator. It can start a container, pass it traffic, and shut it down when idle. Containers can be used for on-demand compute, background tasks, or to extend a Worker with capabilities that aren’t possible inside an isolate.

Recent platform updates increased aggregate account limits for CPU, memory, and disk, making it possible to run thousands of concurrent containers. This shifts Cloudflare Containers from purely small edge extensions toward fleet-scale ephemeral compute scenarios.

Examples of what Cloudflare Containers enable:

- Running services in languages not supported by Workers
- Porting existing Linux applications to the edge
- Processing jobs with libraries and binaries unavailable in the isolate runtime
- Creating APIs that respond with heavy computation at low latency to the user’s region

Because they run close to users, Cloudflare Containers are well-suited for scenarios like media transformation, low-latency APIs, or localized AI inference.

## Pricing for Cloudflare Containers

Cloudflare Containers is billed on a usage basis. The Workers Paid Plan ($5/month) is required to deploy containers. That plan includes:

- 25 GiB-hours of memory/month
- 375 vCPU-minutes/month
- 200 GB-hours of disk/month

Beyond the included amounts, pricing is:

- Memory: $0.0000025 per GiB-second
- CPU: $0.000020 per vCPU-second
- Disk: $0.00000007 per GB-second

Billing is in 10 ms increments, which is efficient for short-lived tasks.

**Cloudflare Containers pricing vs Northflank Pricing**

| Resource / Plan | **Cloudflare Containers** | **Northflank** |
| --- | --- | --- |
| **Base plan** | Requires Workers Paid Plan: $5/month | No base fee – free tier available |
| **Included usage** | 25 GiB-hours memory, 375 vCPU-minutes, 200 GB-hours disk | Free tier: 2 services, 2 jobs, 1 add-on, 1 BYOC cluster |
| **CPU pricing** | $0.000020 per vCPU-second (~$0.072/hr per vCPU) | $0.01667/hr per vCPU |
| **Memory pricing** | $0.0000025 per GiB-second (~$0.009/hr per GiB) | $0.00833/hr per GiB |
| **Disk storage** | $0.00000007 per GB-second (~$0.18/GB/month) | $0.30/GB/month |
| **GPU pricing** | Not available | H100, B200, and other GPUs priced per hour (e.g., H100 ~ $2.74/hr) |
| **Billing granularity** | 10 ms increments | Per-second billing (pay-as-you-go) |

Cloudflare Containers has fine-grained billing in 10 ms increments, which can be cost-efficient for short-lived tasks. Northflank offers lower hourly rates for CPU and memory, a free tier without a mandatory base fee, and GPU availability, which Cloudflare Containers does not currently offer.

**Northflank’s vCPU pricing is roughly three-quarters cheaper than Cloudflare Containers, and memory is slightly cheaper. Cloudflare has a lower disk rate, but Northflank offers GPUs and avoids the $5/month base fee, making it better value for most continuous or AI workloads.**

## Why look for Cloudflare Containers alternatives?

While Cloudflare Containers adds important flexibility compared to Workers, it’s still in beta and has notable gaps.

Cloudflare has recently increased Containers platform limits substantially, enabling significantly higher aggregate memory, CPU, and disk usage across concurrent containers. This makes it possible to run large fleets of ephemeral workloads on the platform.

However, capabilities such as built-in orchestration, persistence primitives, and lifecycle management tooling remain limited.

- No built-in autoscaling or load balancing, you scale manually in code
- No support for long-running or persistent containers
- Limited orchestration tools for multi-service deployments
- No GPU access for AI workloads
- Early-stage dashboard and tooling

These limitations mean that if you’re planning a production workload with complex networking, stateful components, or high performance AI inference, you’ll quickly need features that Cloudflare Containers doesn’t yet provide.

## The best Cloudflare Containers alternative: Northflank

![image - 2025-08-08T123011.085.png](https://assets.northflank.com/image_2025_08_08_T123011_085_d279fa8e00.png)

For teams that like the flexibility of Docker but need more than what Cloudflare Containers offers today, **Northflank** is the clear choice. 

It’s a container-first platform with full orchestration, persistent infrastructure, and the ability to run anything from small microservices to GPU-heavy AI training jobs.

**Northflank capabilities compared to Cloudflare Containers:**

- **Full Linux environments**: Any runtime, any dependencies, persistent storage
- **Secure sandboxing**: Isolated microVMs for untrusted code execution
- **Autoscaling**: Both horizontal and vertical, with zero-downtime deploys
- **AI and GPU support**: Run any model with your choice of GPU, including H100s and B200s
- **Stateful services**: Managed databases, message queues, and caching layers
- **Private networking**: VPC peering, private service-to-service communication
- **Transparent pricing**: Pay-as-you-go with no mandatory base fee, starting at ~$0.0038/hour for small containers

Where Cloudflare Containers gives you global edge placement, Northflank gives you everything you expect from a mature container orchestration platform.

## Conclusion

Cloudflare Containers is a significant step for the Cloudflare ecosystem, moving beyond serverless isolates into full Docker workloads at the edge. It opens up new possibilities for developers who want to run diverse workloads close to their users.

However, despite recent increases in platform limits that enable larger concurrent fleets, the service still lacks key capabilities such as orchestration primitives, native autoscaling, persistent workloads, and GPU support.

If you need those capabilities today, alongside secure sandboxing and persistent services, **Northflank** is the most complete and production-ready alternative. You can deploy any container, scale it automatically, and run workloads that Cloudflare Containers can’t handle yet.

[Start building on Northflank](https://northflank.com/) and see what’s possible when you combine full container freedom with modern orchestration.]]>
  </content:encoded>
</item><item>
  <title>E2B vs Modal: comparing AI code execution sandboxes in 2026</title>
  <link>https://northflank.com/blog/e2b-vs-modal</link>
  <pubDate>2026-02-24T16:45:00.000Z</pubDate>
  <description>
    <![CDATA[Compare E2B and Modal sandboxes in 2026. See how isolation models, environment definitions, and use cases differ to choose the right sandbox for your AI workload.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/e2b_vs_modal_c46850f61f.png" alt="E2B vs Modal: comparing AI code execution sandboxes in 2026" /><InfoBox className="BodyStyle">

## TL;DR: E2B vs Modal in 2026, key differences at a glance

Both platforms provide isolated sandboxes for running untrusted code, but they approach the problem differently.

- **E2B** is built for AI agent code execution. Sandboxes are session-scoped, defined via custom templates, and managed via a Python or JS/TS SDK. Focused specifically on sandboxing.
- Modal is an AI infrastructure platform that includes sandboxes as one of its products. Sandboxes run on gVisor and are dynamically defined at runtime.
- **The core difference** is scope and isolation model: E2B sandboxes use microVM isolation and are purpose-built for untrusted code execution; Modal sandboxes use gVisor-based isolation and sit inside a wider platform covering inference, training, and batch compute.

> [Northflank](https://northflank.com/product/sandboxes) provides secure sandboxes for running untrusted code at scale with microVM isolation (Kata Containers, Firecracker, gVisor), supporting both ephemeral and persistent environments in managed cloud or your own infrastructure. It also removes the ceiling: if you need GPUs, workers, APIs, or databases running alongside your sandboxes, they're in the same platform.
> 

</InfoBox>

## What is E2B?

E2B provides isolated Linux microVM sandboxes for AI agents to execute code safely. You define an environment via a custom template, and your agent provisions sandboxes on demand via a Python or JavaScript/TypeScript SDK. Each sandbox has a defined lifecycle: created, used, then torn down.

The platform exposes SSH access, an interactive terminal (PTY), lifecycle webhooks, and the ability to connect to running sandboxes. Common use cases include coding agents, computer use agents, and CI/CD pipelines. A Bring Your Own Cloud option is available, currently limited to AWS and enterprise customers only.

## What is Modal?

Modal is an AI infrastructure platform covering inference, training, batch processing, notebooks, and sandboxes. Modal Sandboxes are specifically for running untrusted or agent-generated code, isolated using gVisor.

What makes Modal Sandboxes distinct is how environments are defined. Rather than pre-built templates, you define the container image, dependencies, and configuration in code at runtime; the environment is assembled at the point of sandbox creation.

## Quick comparison: E2B vs Modal vs Northflank

Here's how the three platforms stack up across the dimensions that typically drive the decision.

|  | E2B | Modal | Northflank |
| --- | --- | --- | --- |
| Primary use case | AI agent code execution | AI infrastructure platform with sandbox, inference, and training products | Secure microVM sandboxes at scale, with full workload runtime |
| Isolation | MicroVM (Firecracker) | gVisor (syscall interception) | MicroVM (Kata Containers, Firecracker, gVisor) |
| Persistence model | Session-scoped (up to 24h) | Session-scoped (up to 24h); filesystem snapshots for state preservation | Both ephemeral and persistent, same platform |
| Filesystem | Ephemeral within session, bucket storage available | Ephemeral within session; snapshots save and restore filesystem and memory state | Persistent volumes (4GB to 64TB), S3-compatible object storage, ephemeral by default |
| Hibernation | Auto-pause available in beta | Idle timeout terminates sandbox | Ephemeral pools or long-running stateful services |
| SDK / access | Python, JS/TS SDKs; CLI and SSH also available | Python, JS, Go SDKs | API, CLI, SSH |
| Self-hosted / BYOC | BYOC on AWS only; enterprise customers only | Managed service only | Self-serve BYOC; deploy in your own infrastructure on AWS, GCP, Azure, Oracle, Civo, CoreWeave, or on-premises |
| GPU support | CPU-focused | GPU sandboxes available | Both CPU and GPU workloads supported; on-demand GPUs, no quota requests |
| Full runtime (APIs, DBs, workers) | Sandboxes only | Yes - inference, training, batch, notebooks alongside sandboxes | Yes - agents, APIs, workers, background jobs, databases, GPU inference, and training alongside sandboxes |
| Templates | SDK-defined custom templates | Dynamically defined at runtime; any container image | Reusable templates, any language or framework |

## How do E2B and Modal compare?

The table above captures the what. Here's the why behind the differences that actually drive decisions.

### Isolation: microVM vs gVisor (E2B vs Modal)

E2B runs sandboxes inside Firecracker microVMs, providing hardware-level isolation between workloads and the host. Each sandbox runs in its own VM with a separate kernel. Modal Sandboxes run on gVisor, a container runtime by Google that intercepts system calls to prevent malicious code from reaching the host kernel.

Both approaches are stronger than standard container isolation. The practical difference is in the mechanism: microVMs provide hardware-level VM boundaries per sandbox; gVisor intercepts system calls. Teams with strict compliance requirements or specific threat models should evaluate both directly.

### Persistence and state (E2B vs Modal)

|  | E2B | Modal |
| --- | --- | --- |
| State survives between runs | Ephemeral by default; pause/resume available in beta | No; snapshots allow save and restore into a new sandbox |
| Idle behavior | Auto-pause available in beta | Idle timeout terminates sandbox |
| Use case fit | Fresh environment per agent run | Fresh environment per run; snapshots for checkpoint/restore workflows |

Both are primarily session-scoped. Modal's snapshot feature lets you save and restore filesystem and memory state, but restoring creates a new sandbox from that snapshot rather than resuming the original.

### Environment definition (E2B vs Modal)

E2B uses SDK-defined custom templates: you build a template with the required dependencies, version and cache it, and sandboxes spawn from that template consistently.

Modal takes a different approach: environments are defined dynamically in code at the point of sandbox creation. You can pass any valid container image, including ones assembled from requirements at runtime. This means the environment definition can itself be generated by an LLM.

Both approaches are SDK-driven; the difference is when the environment is assembled: at template-build time for E2B, or at runtime for Modal.

### Developer experience (E2B vs Modal)

|  | E2B | Modal |
| --- | --- | --- |
| Primary interface | SDK-first (Python, JS/TS) | SDK-first (Python, JS, Go) |
| Environment definition | SDK-defined custom templates, versioned and cached | Dynamically defined at runtime; any container image |
| Reproducibility | High: same template, same environment every time | Depends on how the image is defined |
| Observability | Lifecycle webhooks, metrics | Native observability dashboard; per-sandbox metrics and logs |
| Best for | Agent pipelines with consistent, versioned environments | High-scale execution; LLMs defining their own environments at runtime |

## When should you use E2B?

E2B fits when you need microVM-isolated, reproducible execution environments for agent workloads. Use it when:

- Your agents generate code that needs a fresh, hardware-isolated Linux environment each time
- You want SDK-driven sandbox creation with consistent, versioned templates
- You need SSH access, PTY, or lifecycle webhooks for sandbox observability and control
- Each task is stateless or self-contained within a session
- You need a BYOC option for AWS (enterprise customers)

## When should you use Modal?

Modal fits when sandboxes are one part of a wider ML compute stack, or when you need very high concurrency. Use it when:

- You need to scale to very high concurrency of simultaneous sandbox sessions
- Your agent or LLM needs to define its own execution environment dynamically at runtime
- You want inference, training, batch processing, and sandboxes in a single platform
- gVisor-based isolation is sufficient for your threat model
- You need GPU sandboxes alongside other GPU workloads

## How Northflank handles secure sandbox execution, BYOC, and the infrastructure around it

Northflank's [Secure Sandboxes](https://northflank.com/product/sandboxes) provide microVM-based isolation for running untrusted code safely, with both ephemeral and persistent environments, in managed cloud or your own infrastructure.

> Where it goes further is in what surrounds the sandboxes: the same platform also runs agents, APIs, workers, databases, and both CPU and GPU workloads, so teams don't need a separate system as their requirements grow.
> 

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

Here's how it compares:

- **MicroVM sandboxes:** Kata Containers, Firecracker, and gVisor isolation depending on workload. Sub-second cold starts. Built for running untrusted, LLM-generated code safely at scale with true multi-tenant isolation.
- **Ephemeral and persistent, same control plane:** Short-lived execution pools and long-running stateful services run together. No need to choose one model or stitch two tools.
- **Self-serve BYOC:** Deploy in your own infrastructure on AWS, GCP, Azure, Oracle, Civo, CoreWeave, or on-premises. Enterprises can run sandboxes entirely within their own infrastructure, which is important for teams with compliance or data residency requirements.
- **On-demand GPUs without quota requests:** Self-service provisioning for inference, training, and compute-heavy agent work. No waiting on allocations.
- **Full workload runtime alongside sandboxes:** Agents, APIs, workers, background jobs, databases, and inference run in the same platform. Teams that outgrow sandbox-only tools don't need to migrate.
- **End-to-end sandbox creation in 1-2 seconds:** The full creation process, not just VM boot.
- **In production since 2021:** Multi-tenant microVM workloads across startups, public companies, and government deployments. For a concrete example, [cto.new](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) uses Northflank's microVMs to scale secure sandboxes in production.
- **Pricing:** CPU at $0.01667/vCPU-hour, memory at $0.00833/GB-hour. Full details on the [Northflank pricing page](https://northflank.com/pricing).

### Cost comparison at scale
To make the pricing difference concrete, here is what 200 sandboxes costs across providers under the same conditions.

_Based on 200 sandboxes, plan: nf-compute-100-4, infra node: m7i.2xlarge_

| Model | Provider | Cloud | Sandbox vendor | Total |
| --- | --- | --- | --- | --- |
| PaaS | Northflank | — | $7,200.00 | $7,200.00 |
| PaaS | E2B | — | $16,819.20 | $16,819.20 |
| PaaS | Modal | — | $24,491.50 | $24,491.50 |
| BYOC (0.2 overcommit)* | Northflank | $1,500.00 | $560.00 | $2,060.00 |
| BYOC | E2B | $1,500.00 | $10,000.00 | $11,500.00 |

*Through Northflank's plans on BYOC, there's a default overcommit which allows a customer to spawn more services and sandboxes on the same amount of compute. A request modifier of 0.2 means each sandbox only requests 20% of its plan's resources as a guaranteed minimum, but can burst up to the full plan limit if there's available capacity on the node. So instead of fitting 8 sandboxes per node, you could fit 40 on the same hardware, reducing both infrastructure cost and the Northflank management fee.


<InfoBox className="BodyStyle">

[Northflank sandboxes](https://northflank.com/product/sandboxes) run untrusted code at scale with microVM isolation, in managed cloud or your own infrastructure. Ephemeral or persistent, CPU or GPU, with full workload orchestration alongside. [Get started on Northflank](https://app.northflank.com/signup) or [book a demo with an engineer](https://cal.com/team/northflank/northflank-demo) if you have specific requirements for your organization.

</InfoBox>

## Frequently asked questions about E2B vs Modal

### What is E2B used for?

E2B provides on-demand Linux microVM sandboxes for AI agents to execute code safely. Common use cases include coding agents, computer use agents, data analysis pipelines, and CI/CD workflows where each job needs an isolated execution environment, managed via Python or JavaScript/TypeScript SDKs.

### What is Modal and how does it differ from E2B?

Modal is an AI infrastructure platform covering inference, training, batch processing, notebooks, and sandboxes. Unlike E2B, which is focused specifically on sandboxing, Modal Sandboxes are one product within a broader ML platform. The other key difference is isolation: E2B uses microVM-based isolation; Modal uses gVisor, a container runtime that intercepts system calls for stronger-than-standard container isolation.

### Does Modal support the same environment templating as E2B?

Modal does not use pre-built templates in the same way as E2B. Instead, environments are defined dynamically in code at runtime: you specify a container image and configuration when creating the sandbox. E2B uses SDK-defined custom templates that are built, versioned, and cached ahead of time, so each sandbox spawns from a consistent, pre-warmed environment.

### Which provides stronger isolation for running untrusted code?

Modal runs sandboxes on gVisor, which provides stronger isolation than standard containers by intercepting system calls. E2B uses Firecracker microVMs, which provide hardware-level VM boundaries with a separate kernel per sandbox. Both are stronger than standard container isolation; the difference is in the mechanism.

### Can either platform be self-hosted or deployed in a private VPC?

E2B offers a BYOC option on AWS for enterprise customers only. Modal is a managed service. Northflank offers self-serve BYOC across AWS, GCP, Azure, Oracle, Civo, CoreWeave, and on-premises.

### What should I look for when evaluating sandbox platforms for production scale?

Beyond isolation model, look at whether the platform supports your deployment model (managed vs. your own infrastructure), whether you need ephemeral, persistent, or both environment types, GPU availability, and whether you'll need additional infrastructure running alongside sandboxes. Platforms like [Northflank](https://northflank.com/product/sandboxes) combine microVM-based sandboxes with a full production runtime, reducing the number of tools you need as requirements grow.

## Further reading on AI sandboxes and microVM execution

If you're evaluating sandbox platforms or digging deeper into the architecture, these articles cover the adjacent decisions and trade-offs:

- [Top Modal Sandboxes alternatives for secure AI code execution](https://northflank.com/blog/top-modal-sandboxes-alternatives-for-secure-ai-code-execution) - Covers the strongest alternatives to Modal Sandboxes for teams evaluating other options for secure code execution.
- [E2B vs Sprites dev: comparing AI code execution sandboxes](https://northflank.com/blog/e2b-vs-sprites-dev) - A direct comparison of E2B and Sprites dev across isolation model, persistence, and developer experience.
- [E2B vs Modal vs Fly.io Sprites for AI code execution sandboxes](https://northflank.com/blog/e2b-vs-modal-vs-fly-io-sprites) - A three-way comparison across three of the most discussed platforms in the AI sandbox space.
- [The best alternatives to E2B.dev for running untrusted code in secure sandboxes](https://northflank.com/blog/best-alternatives-to-e2b-dev-for-running-untrusted-code-in-secure-sandboxes) - If E2B isn't the right fit, this covers the strongest alternatives with a focus on isolation and security.
- [Top Fly.io Sprites alternatives for secure AI code execution and sandboxed environments](https://northflank.com/blog/top-fly-io-sprites-alternatives-for-secure-ai-code-execution-and-sandboxed-environments) - Covers platforms with a similar persistent microVM model to Sprites.
- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox) - A foundational explainer on what sandboxes are, why isolation is required, and how different approaches compare.
- [How to spin up a secure code sandbox and microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh) - A practical walkthrough of launching a Northflank microVM sandbox using Firecracker, gVisor, or Kata Containers.
- [Top AI sandbox platforms, ranked](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution) - A broader ranked overview of the AI sandbox market as it stands in 2026.
- [How to sandbox AI agents: MicroVMs, gVisor and isolation strategies](https://northflank.com/blog/how-to-sandbox-ai-agents) - A technical deep-dive into isolation approaches and trade-offs between Firecracker, gVisor, and Kata Containers for agent workloads.
- [Self-hosted AI sandboxes: guide to secure code execution](https://northflank.com/blog/self-hosted-ai-sandboxes) - Useful if you're evaluating whether to run sandbox infrastructure inside your own infrastructure rather than using a managed service.
- [What's the best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents) - A decision-focused guide for teams actively choosing a sandbox platform for their AI agent stack.
- [Secure runtime for codegen tools: microVMs, sandboxing, and execution at scale](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale) - Covers the infrastructure considerations for code generation tools that need to run LLM-generated code at scale.]]>
  </content:encoded>
</item><item>
  <title>E2B vs Sprites dev: comparing AI code execution sandboxes in 2026</title>
  <link>https://northflank.com/blog/e2b-vs-sprites-dev</link>
  <pubDate>2026-02-23T16:45:00.000Z</pubDate>
  <description>
    <![CDATA[Compare E2B and Sprites dev in 2026. Understand how their isolation models, persistence approaches, and use cases differ to choose the right sandbox for your AI workload.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/e2b_vs_sprites_dev_a61c589f19.png" alt="E2B vs Sprites dev: comparing AI code execution sandboxes in 2026" /><InfoBox className="BodyStyle">

## TL;DR: E2B vs Sprites dev in 2026, key differences at a glance

Both platforms provide microVM-based isolation for running untrusted code, but they're optimized for different problems.

- **E2B** is built for AI agent code execution. Sandboxes are session-scoped, defined by custom templates, and managed via a Python or JS/TS SDK.
- **Sprites** (by Fly.io) are persistent Linux microVMs that hibernate when idle and resume on demand. Their filesystem persists indefinitely, backed by object storage.

> [Northflank](https://northflank.com/product/sandboxes) provides secure sandboxes for running untrusted code at scale with microVM isolation (Kata Containers, Firecracker, gVisor), supporting both ephemeral and persistent environments in managed cloud or your own VPC. It also removes the ceiling: if you need GPUs, workers, APIs, or databases running alongside your sandboxes, they're in the same platform.
> 

</InfoBox>

## What is E2B?

[E2B](https://northflank.com/blog/e2b-vs-modal-vs-fly-io-sprites#what-is-e2b) provides isolated Linux microVM sandboxes for AI agents to execute code safely. You define an environment via a custom template, and your agent provisions sandboxes on demand via a Python or JavaScript/TypeScript SDK. Each sandbox has a defined lifecycle: created, used, then torn down.

The platform exposes SSH access, an interactive terminal (PTY), lifecycle webhooks, and the ability to connect to running sandboxes. Common use cases include coding agents, computer use agents, and CI/CD pipelines.

## What is Sprites (Fly.io)?

[Sprites](https://northflank.com/blog/e2b-vs-modal-vs-fly-io-sprites#what-is-flyio-sprites), built by Fly.io, are persistent, hardware-isolated Linux microVM environments. Unlike short-lived sandboxes, Sprites maintain their full filesystem state between executions. When idle, a Sprite hibernates automatically. When you need it, it wakes up. Warm Sprites resume near-instantly; cold Sprites take a bit longer.

What makes them architecturally distinct is how storage works. Sprites back their 100GB durable filesystem with object storage rather than host-attached NVMe. NVMe acts as a read-through cache.

## Quick comparison: E2B vs Sprites vs Northflank

Here's how the three platforms stack up across the dimensions that typically drive the decision.

|  | E2B | Sprites | Northflank |
| --- | --- | --- | --- |
| Primary use case | AI agent code execution | Persistent stateful compute environments | Secure microVM sandboxes at scale, with full workload runtime |
| Isolation | MicroVM (Firecracker) | MicroVM (Firecracker via Fly Machines) | MicroVM (Kata Containers, Firecracker, gVisor) |
| Persistence model | Session-scoped (up to 24h) | Indefinite, persists between runs | Both ephemeral and persistent, same platform |
| Filesystem | Ephemeral within session, bucket storage available | Full ext4, 100GB durable, backed by object storage | Persistent volumes (4GB to 64TB), S3-compatible object storage, ephemeral by default |
| Hibernation | Auto-pause available in beta | Core feature: auto-sleep when idle | Ephemeral pools or long-running stateful services |
| SDK / access | Python, JS/TS SDK;  CLI and SSH also available | CLI, REST API, JS/Go | API, CLI, SSH |
| HTTP access | Via proxy tunneling; custom domains via self-managed proxy setup | Every Sprite gets a unique public URL | Built-in routing and load balancing |
| Self-hosted / BYOC | BYOC on AWS only; enterprise customers only | Managed service (Fly.io infrastructure) | Self-serve BYOC; deploy in your own infrastructure on AWS, GCP, Azure, Oracle, Civo, CoreWeave or on-premises |
| GPU support | CPU-focused | CPU-focused | Both CPU and GPU workloads supported; on-demand GPUs, no quota requests |
| Full runtime (APIs, DBs, workers) | Sandboxes only | Sandboxes only | Yes, full platform |
| Templates | SDK-defined custom templates | Standardized Linux environment | Reusable templates, any language or framework |

## How do E2B and Sprites compare?

The table above captures the what. Here's the why behind the four differences that drive decisions.

### Persistence: session-scoped vs indefinite (E2B vs Sprites)

|  | E2B | Sprites |
| --- | --- | --- |
| State survives between runs | Ephemeral by default; pause/resume available in beta | Yes (filesystem persists indefinitely) |
| Auto-hibernation | Not the primary model | Yes, auto-sleep when idle |
| Use case fit | Fresh environment per agent run | Stateful environments you return to repeatedly |

If your agent needs a clean slate for every execution, E2B's session model fits. If you need state that accumulates between runs, a long-running service that sleeps when idle, or a persistent dev environment, Sprites are designed for that.

### Which provides stronger isolation? (E2B vs Sprites)

Both platforms use Firecracker-based microVM isolation, providing hardware-level separation appropriate for running untrusted or AI-generated code. This is meaningfully stronger than container-based isolation.

Sprites add an inner container layer within the VM, separating user code from the root namespace where orchestration services run. This means the platform can restart the inner environment without rebooting the full VM, including on checkpoint restores. E2B provides VM-level isolation with a secured access mode and proxy tunneling for controlled network access.

*The meaningful practical difference: E2B offers a BYOC option on AWS only and for only enterprise customers. Sprites run on Fly.io's managed infrastructure only.*

> **Note**: Northflank supports [BYOC](https://northflank.com/features/bring-your-own-cloud) (Bring Your Own Cloud) self-serve across AWS, GCP, Azure, Oracle, Civo, CoreWeave and on-premises. (or use Northflank's [managed cloud](https://northflank.com/features/managed-cloud) if you'd rather not manage your own infrastructure.)
> 

### What does the developer experience look like? (E2B vs Sprites)

|  | E2B | Sprites |
| --- | --- | --- |
| Primary interface | SDK-first (Python, JS/TS) | CLI-first |
| Environment definition | SDK-defined custom templates, versioned and cached | Standardized Linux base; configure inside the running Sprite |
| Reproducibility | High: same template, same environment every time | Lower: environment is configured manually, state persists |
| Integrations | Claude Code, Codex, OpenCode, agent frameworks | Claude, Gemini, Codex pre-installed |
| Best for | Agent pipelines provisioning sandboxes programmatically | Interactive or persistent environments you set up once |

## When should you use E2B?

E2B fits when you need a code-defined, reproducible execution environment per agent run. Use it when:

- Your agents generate code that needs a fresh, isolated Linux environment each time
- You want SDK-driven sandbox creation embedded directly in an agent framework
- You need custom-based templates for consistent, versioned environments
- Each task is stateless or self-contained within a session
- You want lifecycle webhooks and metrics for sandbox observability

## When should you use Sprites?

Sprites fit when the persistence of the environment is itself the feature. Use them when:

- You want a development environment that retains state between sessions
- Your environment accumulates configuration, dependencies, or data over time
- You want instant HTTP access to services running inside via a unique URL
- You're already working within Fly.io's infrastructure and ecosystem

## How Northflank handles secure sandbox execution, BYOC, and the infrastructure around it

Northflank's [Secure Sandboxes](https://northflank.com/product/sandboxes) provide microVM-based isolation for running untrusted code safely, with both ephemeral and persistent environments, in managed cloud or your own infrastructure.

> Where it goes further is in what surrounds the sandboxes: the same platform also runs agents, APIs, workers, databases, and both CPU and GPU workloads, so teams don't need a separate system as their requirements grow.
> 

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

Here's how it compares:

- **MicroVM sandboxes:** Kata Containers, Firecracker, and gVisor isolation depending on workload. Sub-second cold starts. Built for running untrusted, LLM-generated code safely at scale with true multi-tenant isolation.
- **Ephemeral and persistent, same control plane:** Short-lived execution pools and long-running stateful services run together. No need to choose one model or stitch two tools.
- **Self-serve BYOC:** Deploy in your own infrastructure on AWS, GCP, Azure, Oracle, Civo, CoreWeave, or on-premises. Enterprises can run sandboxes entirely within their own infrastructure, which is important for teams with compliance or data residency requirements.
- **On-demand GPUs without quota requests:** Self-service provisioning for inference, training, and compute-heavy agent work. No waiting on allocations.
- **Full workload runtime alongside sandboxes:** Agents, APIs, workers, background jobs, databases, and inference run in the same platform. Teams that outgrow sandbox-only tools don't need to migrate.
- **Provisioning reflects full workload readiness:** End-to-end sandbox creation in 1-2 seconds: The full creation process, not just VM boot.
- **In production since 2021:** Multi-tenant microVM workloads across startups, public companies, and government deployments. For a concrete example, [cto.new](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) uses Northflank's microVMs to scale secure sandboxes in production.
- **Pricing:** CPU at $0.01667/vCPU-hour, memory at $0.00833/GB-hour. Full details on the [Northflank pricing page](https://northflank.com/pricing).

### Cost comparison at scale
To make the pricing difference concrete, here is what 200 sandboxes costs across providers under the same conditions.

_Based on 200 sandboxes, plan: nf-compute-100-4, infra node: m7i.2xlarge_

| Model | Provider | Cloud | Sandbox vendor | Total |
| --- | --- | --- | --- | --- |
| PaaS | Northflank | — | $7,200.00 | $7,200.00 |
| PaaS | E2B | — | $16,819.20 | $16,819.20 |
| PaaS | Fly Sprites | — | $35,770.00 | $35,770.00 |
| BYOC (0.2 overcommit)* | Northflank | $1,500.00 | $560.00 | $2,060.00 |
| BYOC | E2B | $1,500.00 | $10,000.00 | $11,500.00 |

*Through Northflank's plans on BYOC, there's a default overcommit which allows a customer to spawn more services and sandboxes on the same amount of compute. A request modifier of 0.2 means each sandbox only requests 20% of its plan's resources as a guaranteed minimum, but can burst up to the full plan limit if there's available capacity on the node. So instead of fitting 8 sandboxes per node, you could fit 40 on the same hardware, reducing both infrastructure cost and the Northflank management fee.

<InfoBox className="BodyStyle">

[Northflank sandboxes](https://northflank.com/product/sandboxes) run untrusted code at scale with microVM isolation, in managed cloud or your own infrastructure. Ephemeral or persistent, CPU or GPU, with full workload orchestration alongside. [Get started on Northflank](https://app.northflank.com/signup) or [book a demo with an engineer](https://cal.com/team/northflank/northflank-demo) if you have specific requirements for your organization.

</InfoBox>

## Frequently asked questions about E2B vs Sprites dev

### What is E2B used for?

E2B provides on-demand Linux microVM sandboxes for AI agents to execute code safely. Common use cases include coding agents, computer use agents, data analysis pipelines, and CI/CD workflows where each job needs an isolated execution environment, managed via Python or JavaScript/TypeScript SDKs.

### What is Sprites dev and how does it differ from E2B?

Sprites are persistent, hardware-isolated Linux microVMs built by Fly.io. Unlike E2B, which is optimized for session-scoped agent execution, Sprites maintain full filesystem state indefinitely between runs and hibernate automatically when idle. The core difference is persistence model: E2B sandboxes are session-scoped; Sprites are long-lived.

### Does Sprites support custom environments like E2B templates?

Sprites use a standardized Linux base rather than custom templates. You configure the environment directly inside a running Sprite, and the persistent filesystem means you only do it once. E2B uses SDK-defined custom templates that provision a fresh, reproducible environment for each sandbox.

### Which is better for running untrusted AI-generated code?

Both platforms provide hardware-level microVM isolation appropriate for executing untrusted code. E2B fits pipeline-driven, session-scoped execution where each run needs a clean environment. Sprites fit when execution state needs to persist. For teams with VPC or compliance requirements, E2B offers a BYOC option on AWS for enterprise customers; Sprites run on Fly.io's managed infrastructure only.

### What should I look for when evaluating sandbox platforms for production scale?

Beyond isolation, look at whether the platform supports your deployment model (managed vs. your own VPC), whether you need ephemeral, persistent, or both environment types, GPU availability, and whether you'll need additional infrastructure around the sandboxes. Platforms like [Northflank](https://northflank.com/product/sandboxes) combine microVM-based sandboxes with a full production runtime, reducing the number of tools you need as requirements grow.

## Further reading on AI sandboxes and microVM execution

If you're evaluating sandbox platforms or digging deeper into the architecture, these articles cover the adjacent decisions and trade-offs:

- [E2B vs Modal vs Fly.io Sprites for AI code execution sandboxes](https://northflank.com/blog/e2b-vs-modal-vs-fly-io-sprites) - A three-way comparison across three of the most discussed platforms in the AI sandbox space.
- [The best alternatives to E2B.dev for running untrusted code in secure sandboxes](https://northflank.com/blog/best-alternatives-to-e2b-dev-for-running-untrusted-code-in-secure-sandboxes) - If E2B isn't the right fit, this covers the strongest alternatives with a focus on isolation and security.
- [Top Fly.io Sprites alternatives for secure AI code execution and sandboxed environments](https://northflank.com/blog/top-fly-io-sprites-alternatives-for-secure-ai-code-execution-and-sandboxed-environments) - Covers platforms with a similar persistent microVM model to Sprites.
- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox) - A foundational explainer on what sandboxes are, why isolation is required, and how different approaches compare.
- [How to spin up a secure code sandbox and microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh) - A practical walkthrough of launching a Northflank microVM sandbox using Firecracker, gVisor, or Kata Containers.
- [Top AI sandbox platforms in 2026, ranked](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution) - A broader ranked overview of the AI sandbox market as it stands in 2026.
- [How to sandbox AI agents in 2026: MicroVMs, gVisor and isolation strategies](https://northflank.com/blog/how-to-sandbox-ai-agents) - A technical deep-dive into isolation approaches and trade-offs between Firecracker, gVisor, and Kata Containers for agent workloads.
- [Self-hosted AI sandboxes: guide to secure code execution in 2026](https://northflank.com/blog/self-hosted-ai-sandboxes) — Useful if you're evaluating whether to run sandbox infrastructure inside your own VPC rather than using a managed service.
- [What's the best code execution sandbox for AI agents in 2026?](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents) - A decision-focused guide for teams actively choosing a sandbox platform for their AI agent stack.
- [Secure runtime for codegen tools: microVMs, sandboxing, and execution at scale](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale) - Covers the infrastructure considerations for code generation tools that need to run LLM-generated code at scale.]]>
  </content:encoded>
</item><item>
  <title>Top alternatives to Railway preview environments in 2026</title>
  <link>https://northflank.com/blog/alternatives-railway-preview-environments</link>
  <pubDate>2026-02-20T17:30:00.000Z</pubDate>
  <description>
    <![CDATA[Compare alternatives to Railway preview environments for backend and full-stack teams. Evaluate platforms offering database forking, teardown automation, and infrastructure control for production-like testing.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/alternatives_railway_preview_environments_71433fb4b6.png" alt="Top alternatives to Railway preview environments in 2026" />Railway provides PR environments that duplicate services from your base environment when you open a pull request. Teams needing database support, bring-your-own-cloud deployment, or automated teardown scheduling often evaluate alternatives.

It's also worth noting that as of mid-2026, Railway has had a recurring pattern of outages and degraded performance, including a December 2025 incident that paused builds across all plan tiers in their EU West region. This is a consideration for teams that depend on preview environments staying live during active development cycles. Northflank has historically maintained 99.99% uptime, contractually guaranteed under enterprise SLAs.

<InfoBox className="BodyStyle">

## TL;DR: Top alternatives to Railway preview environments

Railway offers PR environments that duplicate services and configuration from your base environment.

**Alternatives to Railway preview environments:**

1. **Northflank** – Provides ephemeral, full-stack preview environments for every pull request. Automatic database cloning from production, built-in secret injection, teardown scheduling, and BYOC support (AWS, GCP, Azure, Civo, Oracle, CoreWeave, on-premises). Orchestrates services, databases, and jobs together. [Learn more about Northflank's preview environments](https://northflank.com/product/preview-environments)
2. **Render:** Blueprint-based preview environments with automatic PR deployment. Requires Preview Environment Initialization for database seeding. Professional workspace plan required.
3. **Coolify:** Preview deployments with scoped environment variables and configurable deployment triggers. Preview URLs use wildcard domains with pull request IDs or random subdomains.
4. **Fly.io:** GitHub Actions-based review apps with custom workflow configuration. Default setup creates a single application; data stores require custom workflow configuration.

**For teams outgrowing Railway:** If you need production-like data in preview environments, infrastructure control through BYOC deployment, or cost control through scheduled teardowns, [Northflank](https://northflank.com/product/preview-environments) provides ephemeral, full-stack preview environments with database forking, teardown scheduling, and multi-cloud support.

</InfoBox>

## What should you evaluate when comparing alternatives to Railway preview environments?

Consider how each platform handles database management, cost control, and infrastructure deployment.

### Database forking and seeding

Evaluate whether the platform provides automatic database forking or cloning from production for preview environments.

### Teardown automation

Consider whether preview environments can be automatically deleted based on idle time, working hours, or PR lifecycle. Platforms offering idle detection and schedule-based teardown help control infrastructure costs.

### Infrastructure deployment options

Determine whether you can deploy on your own cloud infrastructure. BYOC platforms let you deploy preview environments in your AWS, GCP, or Azure accounts.

### Developer experience

Evaluate whether the platform uses visual templates, YAML configuration, or infrastructure-as-code. Platforms with visual template builders reduce context-switching between configuration files and services.

## What are the top alternatives to Railway preview environments?

The following platforms provide preview environment capabilities with different approaches to database management, orchestration, and infrastructure control.

### 1. Northflank

[Northflank](https://northflank.com/product/preview-environments) provides preview environments designed for full-stack applications requiring automated database management and complete stack orchestration.

- **Preview environment capabilities:** Preview environments include databases (PostgreSQL, MySQL, MongoDB, Redis), microservices, and background jobs. Each preview is configured using visual templates in pipelines.
- **Database forking:** You can back up and create a fork of an existing database. The original database in your permanent environment is unaffected by changes in the preview branch. Use the latest backup to create a new addon as a fork of the existing addon.
- **Secret management:** Secrets and environment variables automatically scope per preview environment. Secret groups are restricted by the preview environment's tag so only resources in the environment inherit variables from it. Teams define secrets once using arguments and overrides.
- **Full-stack templates:** Define complete application stacks including services, databases, jobs, and cron tasks in visual templates. Templates use references and arguments to programmatically provision resources.
- **Teardown scheduling and cost control:** Configure preview environments to be torn down after a certain amount of time. Set preview environments to only be created automatically during certain hours. Duration timers can be reset if the environment is updated with new commits.
- **BYOC support:** Deploy preview environments on AWS, GCP, Azure, Civo, Oracle Cloud Infrastructure, CoreWeave, or on-premises infrastructure while Northflank manages orchestration.
- **Git-triggered automation:** Configure automatic preview creation on PR events with branch pattern matching. Supports GitHub, GitLab, and Bitbucket with configurable trigger conditions.
- **Visual templates:** Define complete application stacks including services, databases, jobs, and cron tasks in visual templates. Templates use references and arguments to programmatically provision resources.

<InfoBox className="BodyStyle">

**How Northflank addresses common pain points**

For engineering managers concerned about preview environment costs at scale, teardown scheduling and idle detection prevent resource waste. Preview environments can shut down during off-hours and restart when needed, reducing infrastructure costs.

For platform engineers managing multi-environment YAML configurations, visual templates provide configuration without maintaining separate Blueprint files. Changes apply across all previews consistently.

For backend teams needing production parity, database forking provides realistic data for testing migrations, queries, and application logic.

> [Learn more about Northflank's preview environment capabilities](https://northflank.com/product/preview-environments), [see how to set up preview environments with full-stack orchestration](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), and [compare Kubernetes preview environment platforms](https://northflank.com/blog/kubernetes-preview-environments-comparison).
> 

</InfoBox>

### 2. Render

Render provides preview environments that automatically create fresh copies of your production environment on every pull request. Preview environments are defined using Blueprint YAML files synchronized in the Render Dashboard.

**Preview environment capabilities:**

- Automatic preview environment creation for pull requests
- Creates new instances of services and databases defined in Blueprint
- Preview Environment Initialization for database seeding
- Automatic deletion when pull request merged or closed
- Expiry time configuration for automatic cleanup after inactivity

### 3. Coolify

Coolify provides preview deployments for GitHub repositories. When you enable preview deployments, Coolify automatically deploys new versions when someone opens a pull request. Preview deployments are deleted when the pull request is merged or closed.

**Preview deployment capabilities:**

- Scoped environment variables separate from production
- Configurable deployment triggers (repository members, collaborators, contributors)
- Automated deployment status comments on pull requests
- Wildcard domain configuration for unique preview URLs
- GitHub App or Webhook-based setup

### 4. Fly.io

Fly.io provides review apps through GitHub Actions. The default workflow creates a single application. Teams can customize the GitHub Actions workflow to add data stores like Redis and Memcached.

**Review app capabilities:**

- GitHub Actions-based review app deployment
- Single application deployment by default
- Custom workflow configuration for data stores (Redis, Memcached)
- Resource specifications via workflow inputs (vmsize, cpu, memory)
- Secrets and environment variables via GitHub repository configuration

## How to choose the right alternative to Railway preview environments

| Your requirement | Recommended alternative | Why |
| --- | --- | --- |
| Automatic database forking from production | Northflank | Forks databases via backup and restore |
| Full-stack orchestration (services, databases, jobs) | Northflank | Complete application stacks with database support |
| BYOC deployment on your infrastructure | Northflank | Supports AWS, GCP, Azure, Civo, Oracle, CoreWeave, on-premises |
| Self-hosted with complete infrastructure control | Coolify | Runs on your own servers |
| GitHub Actions-based workflows | Fly.io | Custom workflows with configurable resources |
| Automated teardown scheduling | Northflank | Idle detection, schedule-based cleanup, duration limits |
| Blueprint-based preview environments  | Render | Creates fresh instances with initialization scripts |

## What do teams ask about alternatives to Railway preview environments?

### How do platforms handle database forking for preview environments?

Northflank forks production databases into preview environments using backup and restore. Render creates fresh database instances and requires Preview Environment Initialization for database seeding. Coolify and Fly.io require custom configuration for database provisioning in preview environments.

### What happens to preview environments after PR merge?

Railway, Render, and Coolify automatically delete preview environments when PRs merge or close. Fly.io's GitHub Actions workflow destroys review apps when PRs close. Northflank provides additional teardown options including idle detection and schedule-based cleanup.

### Which alternatives support bring-your-own-cloud workflows?

Northflank deploys preview environments on your AWS, GCP, Azure, Civo, Oracle Cloud Infrastructure, CoreWeave, or on-premises infrastructure while managing orchestration. Railway, Render, and Fly.io run on their managed infrastructure.

### Can preview environments scale with team size?

Preview environments scale with the number of active pull requests. Platforms offering automatic teardown and cost tracking help control infrastructure spending as teams grow.

## Which alternative to Railway preview environments fits your needs?

For teams building full-stack applications, preview environments should provide production-like testing without manual configuration overhead. [Learn about Northflank's preview environment capabilities](https://northflank.com/product/preview-environments) for automated database forking, full-stack orchestration, and flexible infrastructure deployment.

For broader context on preview environments, see [what and why of preview environments](https://northflank.com/blog/the-what-and-why-of-ephemeral-preview-environments-on-kubernetes-sandbox-testing) and [compare preview environment platforms](https://northflank.com/blog/preview-environment-platforms).]]>
  </content:encoded>
</item><item>
  <title>Top alternatives to Render preview environments in 2026</title>
  <link>https://northflank.com/blog/alternatives-to-render-preview-environments</link>
  <pubDate>2026-02-19T17:45:00.000Z</pubDate>
  <description>
    <![CDATA[Compare alternatives to Render preview environments for backend and full-stack teams. Evaluate platforms offering database cloning, automated teardown, and BYOC support for production-like testing.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/alternatives_to_render_preview_environments_5fe49945dd.png" alt="Top alternatives to Render preview environments in 2026" />If you're managing backend or full-stack applications and need automated database cloning, full-stack orchestration, or bring-your-own-cloud deployment, we'll cover alternatives to Render preview environments.

This guide compares platforms offering automated database management, complete stack orchestration, and infrastructure flexibility.

<InfoBox className="BodyStyle">

## TL;DR: Top alternatives to Render preview environments

Render preview environments work well for web application deployments and provide automatic PR-based previews with Blueprint configuration. Teams managing complex backend applications with databases, background jobs, and secrets sometimes need platforms with different automation approaches.

**Alternatives to Render preview environments:**

1. **Northflank** – Provides ephemeral, full-stack preview environments for every pull request. Automatic database cloning from production, built-in secret injection, teardown scheduling, and BYOC support (AWS, GCP, Azure, Civo, Oracle, CoreWeave, on-premises). Orchestrates services, databases, and jobs together. [Learn more about Northflank's preview environments](https://northflank.com/product/preview-environments)
2. **Qovery** – Preview environments that clone applications, databases, and configuration from blueprint environments
3. **Fly.io** – Review apps via GitHub Actions with custom workflow configuration for data stores
4. **Porter** – Preview environments for applications on your cluster

> **For teams outgrowing Render:** If your team requires production-like data in preview environments, automated secret management across multiple previews, or cost control through scheduled teardowns, [Northflank](https://northflank.com/product/preview-environments) provides ephemeral, full-stack preview environments with database cloning, built-in secret injection, and teardown scheduling.
> 

</InfoBox>

## What should you evaluate when comparing alternatives to Render preview environments?

When evaluating alternatives to Render preview environments, consider how each platform handles database management, secret injection, and complete application stacks.

- **Database management:** Some platforms clone production databases automatically into preview environments. Others provision fresh databases that require seeding.
- **Secret and configuration management:** Platforms differ in how they handle environment-specific secrets. Some inject secrets automatically per preview. Others require manual configuration for each environment.
- **Stack orchestration:** Full-stack applications include services, databases, background jobs, and scheduled tasks. Evaluate whether the platform provisions all components together or requires separate configuration.
- **Teardown and cost control:** Preview environment costs scale with active pull requests. Platforms offering idle detection, schedule-based shutdown, and automatic teardown help control infrastructure spending.
- **Infrastructure deployment options:** Some platforms offer BYOC for deploying on your existing AWS, GCP, or Azure accounts. Others provide fully managed infrastructure.

## What are the top alternatives to Render preview environments?

The following platforms provide preview environment capabilities with different approaches to database management, orchestration, and infrastructure control.

### 1. Northflank

[Northflank](https://northflank.com/product/preview-environments) provides preview environments designed for full-stack applications requiring automated database management and complete stack orchestration.

- **Preview environment capabilities:** Preview environments include databases (PostgreSQL, MySQL, MongoDB, Redis), microservices, and background jobs. Each preview is configured using visual templates in pipelines.
- **Database forking:** You can back up and create a fork of an existing database using backup and restore. The original database in your permanent environment is unaffected by changes in the preview branch.
- **Secret management:** Secrets and environment variables automatically scope per preview environment. Secret groups are restricted by the preview environment's tag so only resources in the environment inherit variables from it.
- **Teardown scheduling and cost control:** Configure preview environments to be torn down after a certain amount of time. Set preview environments to only be created automatically during certain hours. Duration timers can be reset if the environment is updated with new commits.
- **BYOC support:** Deploy preview environments on AWS, GCP, Azure, Civo, Oracle Cloud Infrastructure, CoreWeave, or on-premises infrastructure while Northflank manages orchestration.
- **Git-triggered automation:** Configure automatic preview creation on PR events with branch pattern matching. Supports GitHub, GitLab, and Bitbucket with configurable trigger conditions.
- **Full-stack templates:** Define complete application stacks including services, databases, jobs, and cron tasks in visual templates. Templates use references and arguments to programmatically provision resources.

<InfoBox className="BodyStyle">

**How Northflank addresses common pain points**

For engineering managers concerned about preview environment costs at scale, teardown scheduling and idle detection prevent resource waste. Preview environments can shut down during off-hours and restart when needed, reducing infrastructure costs.

For platform engineers managing multi-environment YAML configurations, visual templates provide configuration without maintaining separate Blueprint files. Changes apply across all previews consistently.

For backend teams needing production parity, database forking provides realistic data for testing migrations, queries, and application logic.

[Learn more about Northflank's preview environment capabilities](https://northflank.com/product/preview-environments), [see how to set up preview environments with full-stack orchestration](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment). and [compare Kubernetes preview environment platforms](https://northflank.com/blog/kubernetes-preview-environments-comparison).

</InfoBox>

### 2. Qovery

Qovery provides preview environments that automatically clone production environments including applications, databases, and configuration.

**Preview environment capabilities:**

- Automatic preview environment creation from blueprint environments
- Clones applications, databases (PostgreSQL, Redis), and configuration
- Environment variables and secrets included in clones
- Auto-stop and start for preview environments
- Expiry time configuration for automatic cleanup after inactivity

### 3. Fly.io

Fly.io provides review apps through GitHub Actions. The default workflow creates a single application. Teams can customize the GitHub Actions workflow to add data stores like Redis and Memcached.

**Review app capabilities:**

- GitHub Actions-based review app deployment
- Single application deployment by default
- Custom workflow configuration for data stores (Redis, Memcached)
- Resource specifications via workflow inputs (vmsize, cpu, memory)
- Secrets and environment variables via GitHub repository configuration

### 4. Porter

Porter provides preview environments for applications deployed on your cluster. Creates preview environments automatically for every pull request. Each preview environment is created in isolation on your cluster and destroyed when the pull request is closed or merged.

**Preview environment capabilities:**

- Automatic preview environment creation for pull requests
- Preview environments created in isolation on your cluster
- Addons provisioned alongside preview applications (databases, custom helm charts)
- Configuration overrides for services and environment variables
- Multiple applications can deploy to the same preview environment
- GitHub Actions workflow-based

## How to choose alternatives to Render preview environments

Select an alternative based on your technical requirements and infrastructure preferences.

| Your requirement | Recommended alternative | Why |
| --- | --- | --- |
| Automatic database cloning from production | Northflank | Clones production databases automatically |
| Full-stack orchestration (services, databases, jobs) | Northflank | Northflank provisions complete application stacks together. Qovery clones applications, databases, and configuration from blueprint environments |
| BYOC deployment on your infrastructure | Northflank | Deploy on your AWS, GCP, or Azure accounts |
| Multi-cloud deployment | Northflank or Qovery | Northflank works across AWS, GCP, Azure, Civo, Oracle, CoreWeave, on-premises. Qovery works across AWS, GCP, Azure |
| Existing cluster infrastructure | Porter, Northflank | Creates isolated preview environments on your cluster |
| GitHub Actions-based workflows | Fly.io | Custom review app workflows with configurable resources |

<InfoBox className="BodyStyle">

**For teams moving from Render:**

Teams needing automated database cloning benefit from platforms that fork production data automatically. Teams managing environment-specific secrets across multiple previews benefit from built-in secret injection. Teams concerned about preview environment costs benefit from teardown scheduling and idle detection.

Northflank clones production databases automatically and handles environment-specific secret injection and teardown scheduling alongside BYOC support and full-stack orchestration.

[See how to create and manage preview environments on Northflank](https://northflank.com/docs/v1/application/release/create-and-manage-previews).

</InfoBox>

## What do teams ask about alternatives to Render preview environments?

### What plan is required for Render preview environments?

Preview environments are available starting with Render's Professional workspace plan. The Hobby plan does not include preview environment functionality.

### How do platforms handle database cloning for preview environments?

Northflank forks production databases into preview environments using backup and restore. Qovery clones applications, databases, and configuration from blueprint environments. Render provisions fresh database instances. Fly.io and Porter require custom configuration for database provisioning in preview environments.

### What happens to preview environments after PR merge?

Render, Qovery, and Porter automatically delete preview environments when PRs merge or close. Northflank provides additional teardown options including idle detection and schedule-based cleanup.

### Do alternatives support the same Git providers as Render?

Most platforms support GitHub, GitLab, and Bitbucket. Verify specific Git provider support in each platform's documentation.

### How does BYOC affect preview environment workflows?

Northflank deploys preview environments on your AWS, GCP, Azure, Civo, Oracle, CoreWeave, or on-premises infrastructure while managing orchestration. Porter creates preview environments on your existing cluster infrastructure. Data remains in your infrastructure with your security policies. Fully managed platforms handle infrastructure entirely.

### Can preview environments scale with team size?

Preview environments scale with the number of active pull requests. Platforms offering automatic teardown and cost tracking help control infrastructure spending as teams grow.

## Which alternative to Render preview environments fits your needs?

For teams building full-stack applications, preview environments should provide production-like testing without manual configuration overhead. [Learn about Northflank's preview environment capabilities](https://northflank.com/product/preview-environments) for automated database cloning, full-stack orchestration, and flexible infrastructure deployment.

For broader context on preview environments, see [what and why of preview environments](https://northflank.com/blog/the-what-and-why-of-ephemeral-preview-environments-on-kubernetes-sandbox-testing) and [compare preview environment platforms](https://northflank.com/blog/preview-environment-platforms).]]>
  </content:encoded>
</item><item>
  <title>E2B vs Modal vs Fly.io Sprites for AI code execution sandboxes</title>
  <link>https://northflank.com/blog/e2b-vs-modal-vs-fly-io-sprites</link>
  <pubDate>2026-02-18T17:15:00.000Z</pubDate>
  <description>
    <![CDATA[Compare E2B, Modal, and Fly.io Sprites for AI code execution sandboxes. See how isolation, persistence, GPU support, and deployment options differ across all three platforms.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/e2b_vs_modal_vs_fly_io_sprites_fbc10c116f.png" alt="E2B vs Modal vs Fly.io Sprites for AI code execution sandboxes" /><InfoBox className="BodyStyle">

## TL;DR: E2B vs Modal vs Fly.io Sprites for AI code execution sandboxes

All three platforms solve the same core problem: where does your AI agent safely run code? They each make very different trade-offs:

- **E2B** - open-source, Firecracker microVM isolation, purpose-built for AI agents and LLM code execution. Sessions have a maximum length that varies by plan. Operates as a managed service.
- **Modal** - serverless cloud infrastructure with sandbox capabilities, gVisor isolation, Python-first. Scales to a large number of concurrent sessions. Operates as a managed service.
- **Fly.io Sprites** - stateful, persistent Linux VMs with checkpoint/restore, Firecracker isolation. CPU-only. Does not use Docker or OCI container images by design. Operates on Fly.io's infrastructure.

> **Note:** If you need to run sandboxes inside your own cloud or VPC, [Northflank Sandboxes](https://northflank.com/product/sandboxes) offers [bring-your-own-cloud deployment](https://northflank.com/features/bring-your-own-cloud) across AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, and on-premise, self-serve and production-ready. It also supports both ephemeral and persistent environments, both CPU and GPU workloads, any OCI container image, and a full workload runtime beyond just sandboxes.
> 

</InfoBox>

When you're building AI agents or platforms that execute untrusted code, choosing the right sandbox comes down to three things: how isolation works, whether your environments need to persist between sessions, and where your code actually runs.

E2B, Modal, and Fly.io Sprites each answer these questions differently. This guide breaks down the architectural differences between them so you can choose based on your use case, not just marketing claims.

If you're new to sandboxing concepts, [what is an AI sandbox](https://northflank.com/blog/what-is-an-ai-sandbox) is a good starting point.

## What is E2B?

E2B is an open-source cloud platform built specifically for running AI-generated code in secure sandboxes.

Each sandbox runs inside a Firecracker microVM, giving every execution its own dedicated kernel.

E2B provides Python and JavaScript/TypeScript SDKs, supports custom sandbox templates, and offers pause-and-resume for long-running sessions. Sessions have a maximum lifetime that varies by plan.

## What is Modal?

Modal is a serverless cloud infrastructure platform built for data and ML workloads, with sandboxes as part of its broader offering.

Modal Sandboxes run inside gVisor containers - Google's user-space kernel that intercepts system calls to reduce the host attack surface.

You define sandbox environments dynamically in Python, and Modal handles scaling. GPU support is available across Modal's full infrastructure including sandboxes.

## What is Fly.io Sprites?

Fly.io Sprites is a stateful sandbox product from Fly.io. Unlike E2B and Modal, Sprites are designed to be persistent Linux computers rather than disposable execution environments.

Each Sprite runs inside a Firecracker microVM with an NVMe-backed filesystem that persists between sessions.

When a Sprite goes inactive, compute is removed and billing stops - but the filesystem stays intact and is restored when the Sprite resumes. Sprites support checkpoint/restore, which captures the entire disk state and can be rolled back to in under a second.

Sprites do not use Docker or OCI container images by design, and are CPU-only.

## How does isolation work across E2B, Modal, and Fly.io Sprites?

Isolation is the foundation of any sandbox. It determines whether untrusted code can escape its environment and affect your host system or other workloads.

| Platform | Isolation technology | Dedicated kernel per sandbox |
| --- | --- | --- |
| E2B | Firecracker microVM | Yes |
| Modal | gVisor (user-space kernel) | No |
| Fly.io Sprites | Firecracker microVM | Yes |

**E2B and Fly.io Sprites both use Firecracker microVMs.** Each sandbox gets its own Linux kernel. A compromised sandbox cannot exploit shared kernel vulnerabilities to escape to the host or affect other sandboxes.

**Modal uses gVisor**, which runs a user-space kernel that intercepts system calls rather than passing them directly to the host. This provides meaningful isolation without the full overhead of a dedicated VM, but the isolation boundary sits at the syscall interception layer rather than at hardware virtualization.

For running untrusted code where escape prevention is a priority, microVM isolation provides a harder boundary. For trusted ML pipelines where scaling speed takes precedence, gVisor is a reasonable trade-off.

<InfoBox className="BodyStyle">

**Worth knowing:** [Northflank Sandboxes](https://northflank.com/product/sandboxes) uses microVM-based isolation with Kata Containers, Firecracker, and gVisor depending on the workload, so every workload gets the right level of isolation.

You can read more about these isolation differences in our guide on [how to spin up a secure code sandbox with microVMs](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh).

</InfoBox>

## Ephemeral or persistent: what does your AI agent actually need?

This is the most consequential architectural question in this comparison, and where the three platforms diverge most clearly.

- **E2B** is built around ephemeral execution. Sandboxes are created, run code, and are shut down. Sessions can be paused and resumed within their maximum allowed duration, suited for discrete tasks like generating code, executing it, and cleaning up.
- **Modal** leans toward task-based, serverless execution. Sandboxes have a configurable timeout with a short default that you can extend, and persistent storage is available via network filesystems and volumes.
- **Fly.io Sprites** are built around persistence as a first principle. Your Sprite's filesystem survives indefinitely between sessions - installed packages, created files, database state, and running services all remain exactly as you left them. The checkpoint/restore feature lets you snapshot your environment at any point and roll it back in under a second.

> **Note:** [Northflank Sandboxes](https://northflank.com/product/sandboxes) supports both ephemeral execution pools and long-running stateful environments from the same platform, so you are not forced to choose one model or manage two separate tools depending on your agent architecture. See the [best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents) guide for a broader comparison.
> 

## What deployment options do E2B, Modal, and Fly.io Sprites offer?

All three platforms operate as managed services, meaning your sandboxes run on their infrastructure.

- **E2B** operates as a managed cloud service. BYOC deployment is available for enterprise customers, currently on AWS, with support for additional cloud providers listed as in progress.
- **Modal** is a serverless AI infrastructure platform built for data and ML workloads, with sandboxes as part of its broader offering
- **Fly.io Sprites** run on Fly.io's global infrastructure.

<InfoBox className="BodyStyle">

**If your team needs to run sandboxes inside your own cloud or VPC,** [Northflank](https://northflank.com/product/sandboxes) offers both a [managed cloud](https://northflank.com/features/managed-cloud) and [bring-your-own-cloud deployment](https://northflank.com/features/bring-your-own-cloud) across AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, and on-premise. Most enterprise customers deploy inside their own VPC, and unlike most platforms, BYOC is self-serve and production-ready.

</InfoBox>

For teams with data residency requirements, compliance mandates, or a preference for running workloads inside their own cloud accounts, our [self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes) guide covers your options in more detail.

## Which of these platforms supports GPU workloads?

GPU support varies significantly across the three platforms and is worth checking early if your agents require it.

- **E2B** does not currently offer GPU support in its sandbox offering.
- **Modal** supports GPUs across its full infrastructure including sandboxes, with access to a range of NVIDIA GPUs. GPU and CPU workloads are priced separately.
- **Fly.io Sprites** are CPU-only. Fly.io offers GPUs on its Fly Machines product, but Sprites specifically do not support GPU workloads.

> **Note**: [Northflank](https://northflank.com/product/sandboxes) supports **both CPU and on-demand GPU** workloads. GPUs are available with self-service provisioning and without quota requests, with all-inclusive pricing. See the [Northflank pricing page](https://northflank.com/pricing) for details.
> 

## Which AI code execution sandbox should you choose?

| If you need | Consider |
| --- | --- |
| Open-source SDKs for AI agent code execution with session pause/resume | E2B |
| GPU-accelerated ML pipelines, Python-first workloads at scale | Modal |
| Persistent stateful environments with checkpoint/restore | Fly.io Sprites |
| Both ephemeral and persistent in one platform | Northflank |
| OCI/Docker image support in sandboxes | E2B or Modal |
| Self-serve BYOC into your own cloud or VPC | Northflank |
| GPU sandboxes + BYOC + any OCI image | Northflank |

## How does Northflank compare to E2B, Modal, and Fly.io Sprites?

If your requirements go beyond what these three platforms offer, such as deploying inside your own cloud account, running GPU workloads, using existing OCI container images, or needing both ephemeral and persistent environments on the same platform, [Northflank Sandboxes](https://northflank.com/product/sandboxes) is worth evaluating.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

Here is what you get:

- **Both ephemeral and persistent environments**: Run short-lived execution pools or long-running stateful services from the same platform, depending on what your agent architecture needs.
- **Any OCI-compliant container image**: Bring images from any registry without a proprietary image format or SDK-defined build process.
- **Multiple isolation layers**: Kata Containers with Cloud Hypervisor, gVisor, and Firecracker, applied per workload based on your security and performance requirements.
- **Self-serve BYOC**: Deploy into your own AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, or on-premise infrastructure, self-serve and production-ready.
- **On-demand GPUs**: NVIDIA L4, A100, H100, H200, and [more](https://northflank.com/product/gpu-paas) available for sandboxed workloads, with self-service provisioning and no quota requests. ([Request your GPU cluster](https://northflank.com/request/gpu))
- **Full workload runtime**: Run agents, APIs, databases, background jobs, and cron jobs alongside sandboxes on the same platform.
- **API, CLI, and SSH access:** Connect to your environments through your preferred interface.
- **Environment creation in around 1-2 seconds:** Accounting for full environment readiness, not just boot time.
- **In production since 2021:** Running across startups, public companies, and government deployments.

### Cost comparison at scale
To make the pricing difference concrete, here is what 200 sandboxes costs across providers under the same conditions.

_Based on 200 sandboxes, plan: nf-compute-100-4, infra node: m7i.2xlarge_

| Model | Provider | Cloud | Sandbox vendor | Total |
| --- | --- | --- | --- | --- |
| PaaS | Northflank | — | $7,200.00 | $7,200.00 |
| PaaS | E2B | — | $16,819.20 | $16,819.20 |
| PaaS | Modal | — | $24,491.50 | $24,491.50 |
| PaaS | Fly Sprites | — | $35,770.00 | $35,770.00 |
| BYOC (0.2 overcommit)* | Northflank | $1,500.00 | $560.00 | $2,060.00 |
| BYOC | E2B | $1,500.00 | $10,000.00 | $11,500.00 |

*Through Northflank's plans on BYOC, there's a default overcommit which allows a customer to spawn more services and sandboxes on the same amount of compute. A request modifier of 0.2 means each sandbox only requests 20% of its plan's resources as a guaranteed minimum, but can burst up to the full plan limit if there's available capacity on the node. So instead of fitting 8 sandboxes per node, you could fit 40 on the same hardware, reducing both infrastructure cost and the Northflank management fee.

<InfoBox className="BodyStyle">

You can see how Northflank compares directly to each platform: [vs Modal](https://northflank.com/blog/top-modal-sandboxes-alternatives-for-secure-ai-code-execution), [vs E2B](https://northflank.com/blog/best-alternatives-to-e2b-dev-for-running-untrusted-code-in-secure-sandboxes), [vs Fly.io Sprites](https://northflank.com/blog/top-fly-io-sprites-alternatives-for-secure-ai-code-execution-and-sandboxed-environments). Pricing is on the [Northflank pricing page](https://northflank.com/pricing).

[Get started on Northflank](https://app.northflank.com/signup) or [book a demo with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) if you have specific requirements for your organization.

</InfoBox>

## FAQ: E2B vs Modal vs Fly.io Sprites

**What is the main difference between E2B and Fly.io Sprites?**
E2B is built for ephemeral, session-based code execution with a maximum session length per plan. Fly.io Sprites are persistent Linux computers: your filesystem, installed packages, and environment survive between sessions. Both use Firecracker microVMs for isolation, so the security model is similar, but the execution philosophy is fundamentally different.

**Does Modal support microVM isolation?**
No. Modal Sandboxes use gVisor, which provides isolation via a user-space kernel that intercepts system calls. This is stronger than standard containers but does not provide a dedicated kernel per sandbox the way Firecracker microVMs do in E2B and Fly.io Sprites. Northflank supports both microVM isolation (Kata Containers with Cloud Hypervisor and Firecracker) and gVisor, applied per workload.

**Can I use Docker images with Fly.io Sprites?**
No. Sprites do not use Docker or OCI container images by design. You start from a base Linux environment and install dependencies manually or restore from a checkpoint. Fly.io describes this as a deliberate choice to keep creation times fast.

**Which platforms support GPU workloads inside sandboxes?**
E2B's sandbox offering is CPU-only, and Fly.io Sprites are also CPU-only. Modal supports GPUs across its infrastructure including sandboxes. Northflank supports **both CPU and on-demand GPU workloads**, including NVIDIA L4, A100, H100, H200, and [more](https://northflank.com/gpu). GPUs are available with self-service provisioning without quota requests. See the [Northflank pricing page](https://northflank.com/pricing) for details.

**Is E2B open source?**
Yes. The core E2B infrastructure and SDKs are open source and available on GitHub. The hosted cloud service is commercial with a free tier.

**Where can I learn more about sandboxing options beyond these three?**
The [top AI sandbox platforms](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution) for code execution guide covers a broader set of platforms and how they compare.]]>
  </content:encoded>
</item><item>
  <title>Top Blaxel alternatives for AI sandbox and agent infrastructure in 2026</title>
  <link>https://northflank.com/blog/top-blaxel-alternatives-for-ai-sandbox-and-agent-infrastructure</link>
  <pubDate>2026-02-18T15:15:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the top Blaxel alternatives for AI agent infrastructure in 2026, including Northflank, E2B, Modal, Daytona, and more with pricing, BYOC support, and sandbox isolation breakdown.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/self_hostable_alternatives_to_daytona_3c53fca4b4.png" alt="Top Blaxel alternatives for AI sandbox and agent infrastructure in 2026" /><InfoBox className="BodyStyle">
## TL;DR: What are the top Blaxel alternatives in 2026?

Blaxel is a managed AI agent infrastructure platform with fast sandbox resume times and serverless agent hosting. It works well for teams that just want sandboxes, but it falls short when you need [BYOC](https://northflank.com/product/bring-your-own-cloud), compliance, GPUs, or a full stack (databases, pipelines, and observability in your own VPC). [Northflank](https://northflank.com/) is the strongest alternative built for it.

- [**Northflank**](https://northflank.com/) – Full-stack AI infrastructure platform with managed cloud and BYOC deployment into AWS, GCP, Azure, or bare-metal. Production-grade microVM sandboxes with Kata Containers, Firecracker, and gVisor isolation, unlimited sessions, databases, GPUs, CI/CD, and observability all in one place.
- **E2B** – Developer-friendly AI sandbox with polished SDKs and Firecracker microVMs, best for teams that need quick integration
- **CodeSandbox** – Browser-based sandboxing with snapshot and forking support, now backed by Together AI
- **Modal** – Serverless compute platform purpose-built for Python/ML workloads with massive autoscaling
- **Daytona** – Fastest cold starts in the market; pivoted from dev environments to AI code execution in 2025
- **Fly.io Sprites** – Stateful sandbox environments built on Firecracker microVMs, designed for AI coding agents
</InfoBox>

Blaxel came out of YC's Spring 2025 batch with a clear thesis: the cloud wasn't built for AI agents. Its perpetual sandbox platform keeps environments on standby indefinitely, resumes in under 25ms, and co-locates agent APIs alongside sandboxes to cut latency. For teams that just need sandboxes, that's a solid pitch. But costs can escalate quickly at scale, and once compliance, BYOC, GPUs, or a full infrastructure stack enter the picture, the alternatives start to look a lot more interesting. Here are the top alternatives worth your time.

## What are the top alternatives to Blaxel?

### 1. Northflank - Full-stack AI sandbox and agent infra platform

[Northflank](https://northflank.com/) is the most complete platform on this list. While Blaxel focuses on agent hosting and code execution, Northflank provides the full infrastructure stack: microVM sandboxes, databases, APIs, CI/CD pipelines, GPU workloads, and observability, all running either in your own cloud account or in Northflank's managed cloud.

The biggest differentiator is production-grade [BYOC support](https://northflank.com/product/bring-your-own-cloud). You can deploy into AWS, GCP, Azure, Oracle, CoreWeave, Civo, or bare-metal, and Northflank handles the orchestration while your data never leaves your VPC. For teams in fintech, healthcare, or any regulated industry, that distinction often determines whether a platform even makes it past a security review.

On [sandboxes](https://northflank.com/product/sandboxes) specifically, Northflank supports both Kata Containers with Cloud Hypervisor and gVisor, giving you flexibility based on your threat model. Sessions run indefinitely with no artificial caps, which matters more than most teams realize until they're debugging why a production agent died mid-task.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

Northflank also accepts any OCI-compliant image from any registry without modifications, which means your existing Docker workflows port over without a rewrite. GPU pricing is all-inclusive covering CPU and RAM, which works out roughly 62% cheaper than **sandbox products** billing GPU, CPU, and RAM separately.

**Best for:** Teams that need full infrastructure control, compliance-sensitive workloads, long-running stateful agents, or anyone who wants one platform instead of stitching together five point solutions.

**Pricing:** Transparent usage-based pricing with no mandatory minimums. CPU at $0.01667/vCPU-hour, RAM at $0.00833/GB-hour, and H100 GPU at $2.74/hour all-inclusive (CPU and RAM included). BYOC deployments run on your own cloud billing.

### 2. E2B

E2B has clean Python and TypeScript SDKs and Firecracker microVM isolation, making it one of the fastest ways to add sandboxed code execution to an AI agent. Boot times sit around 150ms and it integrates well with LangChain, OpenAI, and Anthropic tooling. The ceiling is session duration: 24 hours on Pro, with no production-ready self-hosting option if you need data to stay in your own infrastructure.

**Best for:** Developers building AI coding agents, data analysis tools, or Code Interpreter-style experiences who don't need sessions longer than 24 hours.

**Pricing:** Free tier with $100 one-time credit. Pro at $150/month with 24-hour sessions and configurable CPU and RAM.

### 3. CodeSandbox

Now backed by Together AI, CodeSandbox brings snapshot and forking to AI agent infrastructure. You can branch environments from the same base state, run agents in parallel, and restore VMs in under two seconds. It uses Dev Container images rather than arbitrary Docker images, so there is some convention to work within, but the SDK is solid and the pricing is competitive.

**Best for:** Teams already on CodeSandbox, web-focused coding agents, educational platforms, or use cases where snapshot and forking are central to the product.

**Pricing:** The community plan is free. Production workloads are usage-based at $0.0446 per vCPU per hour plus $0.0149 per GB-RAM per hour.

### 4. Modal

Modal is a Python-first serverless compute platform where sandboxes are one feature within a broader ML-focused fabric. It scales to 20,000 concurrent containers with sub-second cold starts, and teams like Lovable and Quora run millions of executions through it. The constraints are significant though: you must define environments through Modal's Python SDK, there is no BYOC option, and GPU, CPU, and RAM are billed separately.

**Best for:** Python-centric ML teams running batch jobs, model inference, and data pipelines who want sandboxing integrated with their existing Modal setup.

**Pricing:** Usage-based per second. CPU from around $0.047/vCPU-hour. GPU billed separately from CPU and RAM.

### 5. Daytona

Daytona pivoted to AI agent infrastructure in early 2025 and leads on cold-start speed, with sub-90ms provisioning and some configurations hitting 27ms. It also supports full Linux, Windows, and macOS virtual desktops for computer-use agents. The tradeoff is isolation: Docker containers by default, with Kata Containers available but not the out-of-the-box experience. Daytona does offer a BYOC option, though it is still limited compared to more mature offerings like Northflank.

**Best for:** Teams where raw cold-start speed is the priority, computer-use agent workloads, or cases where Docker-level isolation is acceptable.

**Pricing:** Usage-based with $200 in free compute credits. Around $0.067/hour for a 1 vCPU, 1 GiB RAM sandbox while running.

### 6. Fly.io Sprites

Sprites launched in January 2026 as Fly.io's purpose-built sandbox for AI coding agents. It runs on Firecracker microVMs with a 100GB persistent NVMe filesystem, checkpoint/restore in around 300ms, and automatic idle billing. It is a good fit if you are already on Fly.io. If you are not, sandbox creation times of one to twelve seconds and the absence of BYOC make it harder to justify outside that ecosystem.

**Best for:** Individual developers building coding agents, teams already on Fly.io, and Claude Code-style persistent environment use cases.

**Pricing:** Pay-per-use based on CPU, memory, and storage.

## Which Blaxel alternative should you choose?

Most of the platforms here solve one problem well. Northflank solves the whole thing. It is the only option on this list that gives you production-grade microVM sandboxes, BYOC deployment into your own cloud account, unlimited session lengths, GPU support, databases, CI/CD, and observability under one roof. If you are building something that needs to scale, stay compliant, and not fall apart when you outgrow a point solution, Northflank is where teams end up.

| Platform | Best for | BYOC | Session limit | Isolation |
| --- | --- | --- | --- | --- |
| **Northflank** | Production AI infra, compliance, full stack | Yes (AWS, GCP, Azure, bare-metal) | Unlimited | microVMs (Kata Containers), gVisor |
| **E2B** | Quick integration, AI agent prototypes | Experimental only | 24 hours | Firecracker |
| **CodeSandbox** | Forking, parallel agents, web tooling | No | None | microVM |
| **Modal** | Python ML, inference, batch jobs | No | None | gVisor |
| **Daytona** | Speed-first, computer-use agents | No | None | Docker (default) |
| **Fly.io Sprites** | Fly.io users, persistent dev environments | No | None | Firecracker |

## FAQ: Blaxel alternatives

### What makes Blaxel different from other AI sandbox platforms?

Blaxel keeps sandboxes on standby indefinitely and resumes them in under 25ms, with agent APIs co-located in the same network to cut round-trip latency. Most competitors either expire sessions or require full cold starts.

### Is Blaxel suitable for enterprise deployments?

Blaxel has SOC2 and HIPAA compliance and supports data residency policies by region. That said, it is managed-only, so enterprises that need workloads running inside their own cloud accounts will need a BYOC platform like Northflank instead.

### Which sandbox platform has the best cold start performance?

Daytona leads with sub-90ms provisioning and some configurations hitting 27ms. Blaxel resumes from standby in 25ms. E2B cold boots in around 150ms. Northflank is competitive for production workloads across both Kata Containers and gVisor.

### Can I self-host any of these platforms?

Northflank is the most production-ready BYOC option, deploying into your AWS, GCP, Azure, or bare-metal infrastructure while managing the control plane for you. E2B is open source but self-hosting at scale means running their control plane yourself. Modal, Blaxel, and Fly.io Sprites are managed-only.

### Which platform is best for GPU workloads?

Modal has deep GPU support for ML workloads. Northflank supports NVIDIA H100 A100, and more, with [all-inclusive pricing](https://northflank.com/pricing) that runs roughly 62% cheaper than other products billing GPU, CPU, and RAM separately. The other platforms on this list do not currently prioritize GPU workloads.

### What is the difference between ephemeral and persistent sandboxes?

Ephemeral sandboxes execute code and disappear. Persistent sandboxes hold state across sessions so agents can pick up where they left off. Northflank supports unlimited persistent sessions. E2B caps at 24 hours. Daytona and Fly.io Sprites also support persistence.

## Conclusion

Blaxel is well-built for teams that want fast sandboxes without the infrastructure overhead. But managed-only means your data leaves your VPC, the platform stops at agent execution, and when you need databases, CI/CD, compliance controls, or GPUs alongside your sandboxes, you are back to stitching tools together. 

If you are building something that needs to last and scale inside your own cloud account, [Northflank](https://northflank.com/pricing) is the platform worth evaluating. The rest of the options here each do one thing well. Northflank is the one built to do it all.

<InfoBox className="BodyStyle">
If Northflank sounds like the right fit, you can [get started for free](https://app.northflank.com/signup) or [talk to the team](https://cal.com/team/northflank/northflank-demo?duration=30) to see how it fits your stack.
</InfoBox>
]]>
  </content:encoded>
</item><item>
  <title>Render vs Vercel (2026): Which platform suits your app architecture better?</title>
  <link>https://northflank.com/blog/render-vs-vercel</link>
  <pubDate>2026-02-17T18:17:00.000Z</pubDate>
  <description>
    <![CDATA[Comparing Render and Vercel in 2026? This guide breaks down backend support, pricing, background jobs, free tiers, and production readiness, so you can choose what fits your workload.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/render_vs_vercel_7f0fc2e029.png" alt="Render vs Vercel (2026): Which platform suits your app architecture better?" />Knowing which one to go for: Render vs Vercel comes down to two things:

1. What kind of app are you building?
2. How much backend control do you need?

If you’re working with a frontend framework like Next.js, both platforms can get you up and running fast. But once you go beyond static sites or edge functions, say, you need background workers, cron jobs, or a persistent database, you’ll start to run into limitations that impact how you build and scale your app.

I’ll break down where Render and Vercel fit best depending on your architecture, how they handle jobs and databases, and what limitations you’ll need to think about as your project scales.

> *And if you want more than what Render or Vercel is offering, scroll down to this section: “Want more than what Render or Vercel is offering?”.*
> 

<InfoBox className='BodyStyle'>

    ### Quick look: Render vs Vercel vs Northflank
    
    Here’s a quick summary of what each platform focuses on:
    
    1. [**Vercel**](https://vercel.com/) – Frontend-first with a serverless core, built around frameworks like Next.js.
    2. [**Render**](https://render.com/) – Backend-friendly with support for long-running services, job runners, and managed databases.
    3. [**Northflank**](https://northflank.com/) – Handles fullstack apps with built-in CI/CD, job types, databases, and optional [Bring Your Own Cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) — all in one workflow.

</InfoBox>
 

### TL;DR: Render vs Vercel vs Northflank

If you're deciding between Vercel and Render or wondering how Northflank compares, this table gives you a quick overview. I've laid out how each platform handles backend support, jobs, CI/CD, and more, so you can choose based on what your app needs.

| Feature | [**Vercel**](https://vercel.com/) | [**Render**](https://render.com/) | [**Northflank**](https://northflank.com/) |
| --- | --- | --- | --- |
| **Deployment model** | Serverless-first, edge functions | Persistent services, Docker containers | Containers with fine-grained service types (jobs, cron, services) |
| **Backend support** | Serverless functions with short timeouts, no persistent processes | Long-running services, custom Docker support | Stateful and stateless services, support for service dependencies |
| **Background jobs** | Needs external schedulers or third-party queues | Native background workers and cron jobs | Native support for scheduled jobs, workers, and queue-based processing |
| **Free tier scope** | Generous for frontend, usage-based limits, no free database | Free web services and cron jobs, limited bandwidth | Free container-based services with CI/CD and job support |
| **Built-in databases** | Not included (use external DBs like Neon or PlanetScale) | Managed PostgreSQL and Redis available | Managed PostgreSQL support, or bring your own database |
| **CI/CD support** | Git-based deployments, minimal customization | Git deployments with Docker or buildpacks | Native CI/CD pipelines with full customization and service linking |
| **Bring Your Own Cloud (BYOC)** | Not supported | Not supported | Supported for AWS, GCP, Azure, ideal for enterprise/cloud control |
| **Preview environments** | Automatic for frontend and serverless APIs | Available but manual setup for some services | Automatic per-branch previews across services and jobs |
| **Static IP / custom networking** | No static IP support | Static IP available for services | Static IP, private networking, custom DNS supported |
| **Best use case** | Fast frontend deployments with Next.js and edge functions | Simple fullstack apps with background tasks and databases | Fullstack apps needing CI, jobs, preview envs, and custom infra setup |

## What to know before choosing between Render and Vercel

Choosing between Vercel and Render isn’t about which platform is better; it comes down to how much control and flexibility your app needs.

Let’s talk.

### What you get out of the box

Vercel is a go-to for frontend developers, especially if you’re working with Next.js. It’s fast, serverless by design, and handles most of the heavy lifting around CDN, routing, and builds.

But it comes with some backend limitations. See what I mean:

1. You won’t get persistent backend services
2. You’ll likely need external schedulers or workarounds for long-running jobs

Developers have noted these constraints, particularly when scaling applications:

> “If you go over the limits, your projects will be paused. If you want to unpause them, then you can upgrade to a paid plan (Pro).”
> 
> 
> — [u/lrobinson2011](https://www.reddit.com/r/nextjs/comments/1cfxuz1/what_happens_when_you_outspend_limits_on_hobby/)
> 

![vercel-vs-render-reddit1.png](https://assets.northflank.com/vercel_vs_render_reddit1_8cf7c54c06.png)

This highlights the importance of monitoring usage, especially on the free tier.

### When you need more backend flexibility

Render gives you more backend freedom. You can:

- Run long-lived services
- Spin up background workers
- Use built-in databases without extra tooling

But this flexibility comes with its own considerations.

You’ll need to manage a bit more infrastructure, think custom build commands, service types, and occasional YAML configuration. It’s not overly complex, but it asks for more setup than a purely serverless approach.

Some developers have shared their experiences:

> “Trying render out now. Very easy but I don't like their rather low bandwidth limits (and they charge a lot for exceeding them) and I don't like that my site is always available through the .onrender.com address in addition to my real domain name.”
> 
> 
> — [u/AGrimmInPortland](https://www.reddit.com/r/webdev/comments/1bc0eh0/what_do_you_like_and_dont_like_about_rendercom/)
> 

![vercel-vs-render-reddit2.png](https://assets.northflank.com/vercel_vs_render_reddit2_fe8b41c815.png)

This highlights the need to be aware of bandwidth limitations and domain configurations when using Render.

So the main question is:

*Are you shipping a frontend-first app that benefits from a fast, minimal setup? Or are you building something more involved, maybe with queues, job runners, or a custom API layer?*

<InfoBox>
💡If you’re leaning toward both, say, you want CI/CD, background jobs, and full control over where and how your services run, then it’s worth looking at platforms like [Northflank](https://northflank.com/). It’s built for full-stack teams who want everything in one place without giving up control.
</InfoBox>

Also, if you’re running into limits with serverless timeouts or external job schedulers, this [Vercel vs Heroku comparison](https://northflank.com/blog/vercel-vs-heroku) breaks down what to expect when shifting toward platforms with more backend flexibility.

## How much backend support do you get on Render vs Vercel?

Now, let’s talk backend.

This is usually where the main constraints show up.

### Vercel’s backend limitations

If you’re using Vercel, you’ll be working with serverless functions that are stateless and short-lived. I know they’re great for quick APIs or basic logic, but they:

- Timeout after 10 seconds on the Hobby plan (and 60 seconds on Pro)
- Don’t support persistent connections or stateful workloads
- Require external schedulers or services if you need background jobs, queues, or cron-like behavior.

This setup can work for frontend-focused apps or marketing sites. But once your project involves jobs that run longer, or multiple services making inter-service calls, it becomes harder to manage.

See what one developer experienced:

> “I am getting a 504 time out on serverless functions in vercel and it's causing errors in my app. Probably 1 out of every 10 requests. It is a 10 second limit error, but when I look at the logs of my server, I don't even see the request from vercel...”
> 
> 
> — [u/christo9090](https://www.reddit.com/r/nextjs/comments/1f83iqv/constant_504_timeouts_on_serverless_functions/)
> 

![vercel-vs-render-reddit3.png](https://assets.northflank.com/vercel_vs_render_reddit3_a98551f5bd.png)

### Render’s backend flexibility

What about Render?

Render gives you the backend flexibility you’d expect from a platform aimed at full-stack apps. You can:

- Spin up background workers for jobs that need to run asynchronously
- Deploy long-running services (with persistent memory and state)
- Schedule cron jobs directly in the platform
- Connect to built-in PostgreSQL without external setup

But one thing to note: Render doesn’t support Bring Your Own Cloud (BYOC), so you won’t be able to deploy into your own AWS, GCP, or Azure account.

So, if you need backend APIs, task runners, or supporting services, Render is better suited out of the box.

See what one developer shared:

> “I use Render for a few side projects and it’s been solid. I run a long-lived Django backend with Celery + Redis workers, a cron job, and a Postgres DB. Haven’t had any issues.”
> 
> 
> — [u/magenta-wolf](https://www.reddit.com/r/webdev/comments/1h5hxjp/what_has_happened_to_render/kc5zuxh/)
> 

![vercel-vs-render-reddit4.png](https://assets.northflank.com/vercel_vs_render_reddit4_65e407ea25.png)

<InfoBox>
💡And for teams that need even more, like handling background jobs with clear dependencies, queueing logic, or persistent services across regions, some look at platforms like [Northflank](https://northflank.com/). It supports native job types, service dependencies, and persistent workloads without forcing a serverless model.

This kind of architectural flexibility matters when you’re building something beyond the basics. It saves you from patching together external schedulers, queues, or multiple deployment layers as your project scales.
</InfoBox>

## Render vs Vercel pricing: what’s free and what scales with you?

We’ve talked about backend flexibility. Now let’s look at how pricing plays out when your app starts getting traction or you’re collaborating with a team.

### Vercel’s usage-based pricing

Vercel is free to start, but the moment you go past the limits, like bandwidth, serverless function execution time, or team features, you’ll need to upgrade. And because their model is usage-based, your monthly bill can increase quickly depending on how often your app is used.

A few things to keep in mind:

- **Functions timeout** after 10 seconds on Hobby and can be configured up to 60 seconds
- **Bandwidth is capped** at 100 GB on the free tier
- **Collaborators** are limited unless you’re on a Team plan
- You get **100,000 serverless function invocations per month** on Hobby

This pricing structure is fine for solo projects or early testing. But for apps with growing usage or team-based collaboration, you’ll start running into upgrade prompts quickly.

See how one developer described the change:

> “Got an email from v0 today about their new ‘improved pricing.’ It’s only ‘improved’ for vercel, not us. [...] Also these tokens you have to buy now expire if you don’t use them fast enough. And the included usage does not roll over month-to-month. [...] This is ridiculous.”
> 
> 
> — [u/atiaa11](https://www.reddit.com/r/vercel/comments/1km67co/new_improved_pricing_from_messagebased_to/)
> 

![vercel-vs-render-reddit5.png](https://assets.northflank.com/vercel_vs_render_reddit5_50b94f431f.png)

> For a breakdown of how Vercel compares to another frontend-focused platform, see this [Vercel vs Netlify comparison](https://northflank.com/blog/vercel-vs-netlify-choosing-the-deployment-platform-in-2025).
> 

### Render’s more predictable service pricing

Render takes a service-based approach. You pay per type, like web services, background workers, cron jobs, and databases, and scale each one independently.

There’s a free tier for web services and static sites (with usage caps), but pricing for other services is clearly defined by resources used. This allows you to plan ahead when running persistent services or workloads, such as cron jobs.

It’s not without limitations, though. Developers have pointed out how some free tier behaviors might impact reliability or require manual workarounds:

> “The app goes to sleep when unused for 15 minutes, and will often be quite slow to boot back up. [...] Either setup a cron that pings your app every 5–10 minutes [...] or start coughing up a couple dollars a month.”
> 
> 
> — [u/flexiiflex](https://www.reddit.com/r/webdev/comments/1h5hxjp/what_has_happened_to_render/)
> 

![vercel-vs-render-reddit6.png](https://assets.northflank.com/vercel_vs_render_reddit6_c8c6c0b579.png)

<InfoBox>
💡Some platforms like [Northflank](https://northflank.com/) also use [usage-based pricing](https://northflank.com/pricing), but they add flexibility, like the ability to [Bring Your Own Cloud (BYOC)](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes). You can define where your workloads run, reduce vendor lock-in, and only pay for the compute and services you use.

This setup is useful if you're managing costs more closely or scaling projects across multiple teams or regions.
</InfoBox>

## What’s the deployment experience like on Render vs Vercel?

Once you’ve figured out pricing and backend fit, the next thing that matters is how deployments work, especially if you’re pushing updates frequently or working across teams.

### Vercel’s Git-based workflow is made for frontend teams

Vercel keeps things tight and fast. Every time you push to Git, it auto-deploys, meaning no additional configuration is required. For frontend projects (especially with Next.js), this workflow is hard to beat. It:

- Hooks into GitHub, GitLab, or Bitbucket with zero setup
- Spins up preview deployments for every pull request
- Gives you custom domain previews so stakeholders can test before merging
- Supports instant rollbacks to earlier deploys if something goes wrong

This setup works best when your workflow is Git-driven and your focus is speed over fine-grained deploy control.

One developer shared their experience:

> "Vercel gives you also nice and easy deployment flows as well as the ability to preview every branch you push to them in a live URL to debug."
> 
> 
> — [u/Accomplished-Gap-748](https://www.reddit.com/r/nextjs/comments/1j14ahh/vercel_isnt_enough_anymore_cheap_hosting_providers/)([Reddit](https://www.reddit.com/r/nextjs/comments/1j14ahh/vercel_isnt_enough_anymore_cheap_hosting_providers/?utm_source=chatgpt.com))
> 

![vercel-vs-render-reddit7.png](https://assets.northflank.com/vercel_vs_render_reddit7_396b17f10f.png)

### Render supports more service types, but you’ll define more services manually

Render also integrates with Git, but gives you more control over what gets deployed and how. You can:

- Choose different service types (static sites, web services, workers, cron jobs)
- Manually trigger deploys or use auto-deploy
- Set up custom build and start commands for each service
- Use the dashboard to manage environments and see logs per service

So, what’s different compared to Vercel? You’ll need to define each service separately, and the UI isn’t as minimal as Vercel’s, but it’s much more backend-friendly.

A developer shared their experience:([docs.render.com](https://docs.render.com/troubleshooting-deploys?utm_source=chatgpt.com))

> "Deployment and setup was extremely easy for me with setting up a Next app alongside an Express API, Postgres DB, Redis cache, and a periodic cron job. Their web services fit my preferred concept of keeping everything as separate entities on the chance that I need to scale one of them. Their internal routing is nice to keep latencies low between services."
> 
> 
> — [u/ratbiscuits](https://www.reddit.com/r/webdev/comments/1bc0eh0/what_do_you_like_and_dont_like_about_rendercom/)
> 

![vercel-vs-render-reddit8.png](https://assets.northflank.com/vercel_vs_render_reddit8_a84c98d677.png)

<InfoBox>
💡Some teams that need visual pipelines, rollback logic, or more advanced release workflows tend to explore platforms like [Northflank](https://northflank.com/), which lets you build full deployment flows and control how services update in staging or production.

For a comparison of another platform focused on production deployments, see this breakdown of [Fly.io vs Render](https://northflank.com/blog/flyio-vs-render).
</InfoBox>

## Want more than what Render or Vercel is offering?

So, what if you’ve gotten to a point where the basics are working, but you need more than what Render or Vercel is offering you or your team?

I mean things like:

1. You can deploy your app, but you can’t customize the job schedules.
2. You can scale services, but you can’t pin a static IP.
3. You get builds, but no visibility into what’s happening between commit and container.
4. And you still have to work around limited networking, no background job support, and rigid infrastructure options.

Now, this is where a platform like [Northflank](https://northflank.com/) comes in to give you more flexibility, more control, and fewer limitations.

Let’s break it down:

### 1. You define the pipeline, not the platform

If you're working around CI/CD limits right now, maybe trying to link external actions to a deploy webhook, Northflank lets you move everything into a single, connected pipeline.

You can [set up your own CI/CD workflows](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) that tie directly into your GitHub, GitLab, or Bitbucket repos. Every step is defined visually or in config, so you can:

- Trigger builds from branches, tags, or PRs
- Add custom build steps (like cache warming or artifact reuse)
- Connect jobs and services in a release pipeline
- View the full pipeline history with logs at each stage

You’re not forced into one deploy pattern; you can ship code the way your team already works.

See what that visibility looks like in an actual project:

![Northflank service overview showing deployment status, branch, container logs, and linked Git repo](https://assets.northflank.com/combined_service_overview_f476cc028c.webp)*This is what a connected CI/CD pipeline looks like in Northflank, with your deployments, commits, ports, and container logs all visible in one place.*

### 2. You run background jobs, cron, and workers in one place

Need to run scheduled jobs or long-running workers? Northflank has native support for that, so no hacks, no extra tooling.

From the same interface, you can:

- [Schedule recurring jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs) using cron syntax
- Launch background workers as standalone containers
- Configure retries, restart policies, and secrets
- Use shared environment variables across services and jobs
- Observe logs and metrics for every job execution

And because these jobs run in your Kubernetes cluster (or your BYOC environment), you don’t have to manually connect separate tools for deployment and scheduling.

See what it looks like when you schedule and monitor a recurring job directly in your Kubernetes cluster using Northflank:

![Screenshot of a recurring job in Northflank showing cron schedule, recent job runs, and associated commits](https://assets.northflank.com/cron_jobs_northflank1_ba3d09a5af.webp)*Native cron job support in Northflank lets you handle recurring tasks without integrating third-party schedulers.*

### 3. You get full control over networking

When you need predictable IPs or isolated networking, platforms like Render or Vercel don’t go far enough. With Northflank, you can [assign static outbound IPs](https://northflank.com/docs/v1/networking/static-ip) to services and jobs, configure private DNS, and route traffic securely across clusters.

You also get:

- Custom domains and DNS routing
- VPC-native deployments (if using [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud))
- Internal networking between services and jobs
- TLS/SSL termination and built-in HTTPS support

This makes it easier to integrate with firewalled APIs, corporate networks, or compliance-heavy services.

See how a service with public and private access looks when configured on Northflank:

![Screenshot of networking settings in Northflank showing port exposure, custom domain, and HTTP configuration](https://assets.northflank.com/networking_northflank_8ae187b240.png)*Example of port 80 exposed on `web.acme.org` with both public and internal networking configured.*

### 4. You can deploy into your own cloud (BYOC)

Sometimes you’re not allowed to run on a third-party cloud. Or maybe you just want your workloads inside your own VPC, under your governance policies.

With Northflank’s [Bring Your Own Cloud](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes), you can:

- Provision and manage Kubernetes clusters in **AWS**, **GCP**, **Azure**, **Civo**, or **Oracle**
- Use your own cloud credentials, billing, and networking setup
- Choose regions from 60+ supported zones
- Access advanced features like static IPs, autoscaling, and [multi-cloud failover](https://northflank.com/features/bring-your-own-cloud)
- Still manage everything from one interface: builds, pipelines, jobs, and monitoring

This gives you the benefits of a platform-as-a-service, without giving up control over where and how your software runs.

See what it looks like when you set up a cluster in your own AWS account through Northflank:

![Northflank interface for creating a Kubernetes cluster in a user’s own AWS account, with options to create or use an existing integration](https://assets.northflank.com/byoc_northflank_1_762e90fc58.png)*Northflank lets you deploy into your own cloud by provisioning clusters using your own credentials, integrations, and network configuration.*

## FAQ: common questions about Render and Vercel

Still unsure how these platforms stack up for your specific needs? Let’s tackle a few of the most common questions developers ask when comparing Render and Vercel.

### 1. Is Render better than Vercel?

It depends on what you’re deploying. Vercel is optimized for frontend frameworks like Next.js, React, and static sites, perfect for teams focused on performance, preview URLs, and global CDN delivery.

Render gives you more backend support out of the box. You can deploy web services, background workers, cron jobs, and PostgreSQL databases, which makes it a better fit for full-stack applications.

If you need CI/CD pipelines, static IPs, or full control over infrastructure, platforms like [Northflank](https://northflank.com/) are worth considering instead.

### 2. Can I deploy a backend on Vercel?

Technically, yes, you can deploy serverless functions that handle API logic. But you don’t get long-running services, stateful apps, or background job support like you would with Render or platforms like [Northflank](https://northflank.com/features/run).

For anything beyond lightweight endpoints, Vercel tends to push backend logic to external services.

### 3. Is Vercel free for commercial use?

Vercel has a free tier, but it’s not meant for production use at scale. The Hobby plan includes limited build minutes, bandwidth, and serverless execution time. For commercial or team use, you’ll need to upgrade to the Pro or Enterprise plan, which includes team collaboration features, more resources, and support.

### 4. What are the disadvantages of Vercel?

- Limited support for stateful or long-running processes
- No background jobs or native database hosting
- Locked into Vercel's infrastructure, no BYOC or VPC control
- Custom networking and private IPs aren’t supported
- Deployments work best with Vercel-optimized frameworks (like Next.js)

It’s great for frontend teams, but you’ll reach constraints fast if you’re building anything more complex.

## So which platform makes the most sense for your app?

It all comes down to what you're building, and how much control you need.

**→ Go with Vercel** if you're focused on frontend performance, edge functions, and shipping static or serverless apps quickly. It's built for frameworks like Next.js and React, and the developer experience (DX) is hard to beat for frontend-first teams.

**→ Go with Render** if you want a middle ground: full-stack apps, background workers, persistent services, and databases, all on one platform. It works well for small backend workloads and APIs that don’t need advanced orchestration or infrastructure-level control.

**→ Go with [Northflank](https://northflank.com/)** if you're building something more complex, like CI pipelines, stateful services, background jobs, or need static IPs, private networking, or BYOC. It gives you the flexibility of Kubernetes and the simplicity of a platform-as-a-service, all in one UI.

> Still deciding? [Go through the docs](https://northflank.com/docs) or [launch a project](https://app.northflank.com/signup) to see how Northflank fits your workflow.
>]]>
  </content:encoded>
</item><item>
  <title>Top self-hostable alternatives to Daytona for AI code execution</title>
  <link>https://northflank.com/blog/self-hostable-alternatives-to-daytona</link>
  <pubDate>2026-02-17T17:00:00.000Z</pubDate>
  <description>
    <![CDATA[Compare top self-hostable alternatives to Daytona including Coder, DevPod, and Microsandbox. Deploy secure AI code execution environments in your own infrastructure.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/self_hostable_alternatives_to_daytona_218b550f27.png" alt="Top self-hostable alternatives to Daytona for AI code execution" /><InfoBox className="BodyStyle">

## TL;DR: Top self-hostable alternatives to Daytona in 2026

- Daytona is an open-source infrastructure for running AI-generated code in isolated sandbox environments
- **Top self-hostable alternatives:** Coder (enterprise Terraform-based CDEs), DevPod (client-only), Microsandbox (hardware isolation)
- **Two deployment approaches:** DIY open-source (maximum control, high complexity), BYOC platforms (managed orchestration in your infrastructure)
- **Key decision factors:** Operational capacity, isolation requirements, whether you want client-only tools or full platforms

> **Note**: [Northflank Sandboxes](https://northflank.com/product/sandboxes) lets you run untrusted code at scale with microVMs, either on Northflank's infrastructure or in your VPC. For teams needing self-hosted control, Northflank offers [BYOC deployment](https://northflank.com/features/bring-your-own-cloud) into your AWS, GCP, Azure, Civo, Oracle, CoreWeave, or on-premise infrastructure, handling orchestration, scaling, and microVM management. Alternatively, Northflank's [managed PaaS](https://northflank.com/features/managed-cloud) provides instant deployment without any infrastructure setup.
> 

</InfoBox>

Self-hostable alternatives to Daytona give you infrastructure control for running AI agent code execution while meeting compliance requirements and managing costs at scale.

This guide compares the top self-hostable options to help you choose based on operational complexity, isolation technology, and deployment model.

## Why do teams need self-hostable alternatives to Daytona?

When your AI agents execute code, where that code runs determines your compliance posture, cost structure, and operational control. See the following:

- **Data sovereignty and compliance requirements:** Processing financial transactions, patient health records, or customer PII requires code execution within your own VPC. Third-party APIs introduce additional data processors into your compliance chain, complicating audits and potentially disqualifying you from enterprise contracts that mandate data residency.
- **Cost predictability at scale:** Managed services charge per execution or per compute minute. Running millions of code executions monthly makes per-unit costs accumulate quickly. Self-hosting lets you pay for underlying infrastructure directly with more predictable economics.
- **Infrastructure control and customization:** You need custom network policies, observability stack integration, or specific isolation technologies. Managed services don't offer the configuration flexibility your security policies require. Self-hosting gives you complete control over sandbox configuration.
- **Air-gapped environments:** Organizations with strict security requirements need to deploy in networks without external internet access. Self-hosted solutions can run in completely isolated environments.

> **Alternative approach:** Platforms like [Northflank](https://northflank.com/product/sandboxes) offer [BYOC deployment](https://northflank.com/features/bring-your-own-cloud), which keeps data in your infrastructure while providing managed orchestration. This addresses self-hosting requirements without the operational complexity of managing sandbox infrastructure yourself.
> 

## What are the best self-hostable alternatives to Daytona?

When evaluating self-hostable Daytona alternatives, you're choosing between different tradeoffs in deployment complexity, operational requirements, isolation strength, and whether you need server infrastructure at all. Here are the top self-hostable options.

### 1. Coder

Coder is an open-source platform for self-hosted cloud development environments, used across industries including automotive, finance, government, and technology sectors.

**Key characteristics:**

- Terraform infrastructure-as-code for workspace provisioning
- Self-hosted on Docker, Kubernetes, or air-gapped deployments
- Governed workspaces and access controls for AI agents and developers

**When to choose Coder:**
Need enterprise-grade infrastructure-as-code, use Terraform already, want to run both human developers and AI agents in the same platform.

**When to consider alternatives:**
Want simpler deployment without Terraform complexity, prefer client-only tools, or don't need enterprise governance features.

### 2. DevPod

DevPod is a client-only tool that creates reproducible development environments using the DevContainer standard.

**Key characteristics:**

- Client-only tool using the DevContainer standard
- Works with local Docker, Kubernetes, and major cloud providers

**When to choose DevPod:**
Want client-only development environments, need flexibility to run locally or in multiple clouds, or prefer client-side tools over centralized platforms.

**When to consider alternatives:**
Need centralized workspace management, require enterprise governance, or want managed orchestration.

### 3. Microsandbox

Microsandbox is an open-source project providing secure execution of untrusted code using libkrun microVMs.

**Important:** Microsandbox is explicitly marked as experimental software by its developers. Expect breaking changes, missing features, and rough edges.

**Key characteristics:**

- libkrun microVM isolation with dedicated kernels
- OCI-compatible (runs standard container images)

**When to choose Microsandbox:**
Security is your top priority, you have infrastructure engineering capacity, and you're comfortable working with experimental software.

**When to consider alternatives:**
Need production-proven infrastructure with stability guarantees, enterprise support, or managed operations.

## How does Northflank compare to self-hostable Daytona alternatives?

[Northflank Sandboxes](https://northflank.com/product/sandboxes) lets you run untrusted code at scale with microVMs. The platform offers two deployment options: [managed PaaS](https://northflank.com/features/managed-cloud) for teams wanting zero infrastructure management, and [BYOC](https://northflank.com/features/bring-your-own-cloud) for teams requiring self-hosted control with data in their own cloud.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

For self-hosting requirements, Northflank's BYOC option provides a different approach than traditional self-hostable alternatives. Rather than downloading software and managing it yourself, Northflank deploys into your infrastructure while handling orchestration, scaling, and operations.

**What Northflank's BYOC deployment provides:**

- **Deployment flexibility:** BYOC deployment to AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, or on-premise infrastructure. Northflank manages the orchestration layer while workloads run in your cloud account. Most enterprise customers choose BYOC, and unlike many platforms where BYOC is not fully production-ready, Northflank's BYOC is self-serve and production-proven.
- **Isolation technology:** Kata Containers, Firecracker, or gVisor isolation depending on your workload requirements. All three provide stronger isolation than standard containers.
- **Managed Kubernetes orchestration:** Northflank handles cluster management, scaling, updates, and Day 2 operations. You get Kubernetes' power without operating it yourself.
- **Production track record:** Northflank has been in production since 2021 across startups, public companies, and government deployments.
- **Enterprise observability:** Built-in monitoring, logging, and debugging capabilities without building your own observability stack.
- **Ephemeral and persistent environments:** Short-lived execution pools or long-running stateful services, depending on your workflow needs.

<InfoBox className="BodyStyle">

**When Northflank's BYOC fits your requirements:**

Choose Northflank when you need self-hosted control (data stays in your infrastructure) but don't want to build and maintain sandbox orchestration yourself. This fits teams where compliance requires data in their VPC, but dedicating engineering resources to infrastructure management doesn't make business sense.

If you need faster deployment than building infrastructure from scratch, want production-grade microVM isolation without the operational burden, or your team focuses on application development rather than platform engineering, Northflank's BYOC model addresses these constraints.

</InfoBox>

Learn more about [Northflank Sandboxes](https://northflank.com/product/sandboxes) or read our guide on [self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes).

## Which self-hostable Daytona alternative should you choose?

| If you need | Choose | Why |
| --- | --- | --- |
| **Enterprise CDE with AI agent support** | Coder | Terraform-based provisioning, governance features |
| **Client-only development environments** | DevPod | Works with local Docker, Kubernetes, and major cloud providers |
| **Secure execution of untrusted code** | Microsandbox | Hardware-isolated microVMs with dedicated kernels (experimental) |
| **Infrastructure control without operational burden** | Northflank BYOC | Managed orchestration in your cloud account |
| **Air-gapped deployment** | Coder | Supports offline deployment in air-gapped environments |
| **Both ephemeral and persistent environments** | Northflank BYOC | Short-lived execution pools or long-running stateful services in one platform |

## FAQ: Self-hostable Daytona alternatives

### What is the easiest self-hostable alternative to Daytona?

DevPod offers the simplest deployment as a client-only tool. Microsandbox provides simple installation with a CLI tool, but you'll build monitoring and operational tooling yourself. Coder requires more setup but provides enterprise features out of the box.

### Which self-hostable alternative has the strongest isolation?

Microsandbox provides hardware-level microVM isolation with dedicated kernels per sandbox, preventing kernel-level exploits from affecting other sandboxes or the host. Northflank BYOC offers microVM-level isolation with Kata Containers, Firecracker, or gVisor depending on workload.

### Do self-hostable alternatives support AI agents?

Coder supports AI agents with governed workspaces, access controls, and audit logging. Microsandbox is designed for secure AI code execution with its microVM isolation. DevPod provides development environments that can run AI workflows but doesn't have AI-specific features.

### Can self-hostable alternatives meet compliance requirements?

Self-hosting keeps data in your infrastructure, which helps meet compliance requirements like HIPAA, SOC2, and GDPR. You control data residency, security policies, and audit logging. With Northflank's BYOC deployment, data stays in your infrastructure while Northflank handles orchestration, helping you meet those requirements without the full operational burden of self-hosting.

### What's the difference between self-hosting and BYOC?

Self-hosting means you deploy and manage the entire platform yourself. BYOC means the platform deploys into your infrastructure but the vendor manages orchestration and operations. Self-hosting gives maximum control but requires operational expertise. BYOC provides infrastructure control with managed complexity.

For more on sandbox security and compliance, see our guide on [how to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents).

## Choose the right self-hostable Daytona alternative for your needs

Self-hostable alternatives to Daytona give you infrastructure control, data sovereignty, and deployment flexibility for running AI agent code execution.

Your choice depends on operational capacity, isolation requirements, and whether you want client-only tools or full platforms.

<InfoBox className="BodyStyle">

For teams wanting self-hosted control without infrastructure burden, Northflank offers BYOC deployment into your AWS, GCP, Azure, Civo, Oracle, CoreWeave, or on-premise infrastructure with production-ready microVM isolation and managed orchestration. Get started with [Northflank Sandboxes](https://northflank.com/product/sandboxes) or see more [alternatives to Daytona](https://northflank.com/blog/top-daytona-io-alternatives-for-running-ai-code-in-secure-sandboxed-environments) based on your requirements.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Top self-hostable alternatives to E2B for AI agents in 2026</title>
  <link>https://northflank.com/blog/self-hostable-alternatives-to-e2b-for-ai-agents</link>
  <pubDate>2026-02-16T16:30:00.000Z</pubDate>
  <description>
    <![CDATA[Compare top self-hostable E2B alternatives like Daytona, Microsandbox, and BYOC platforms. Deploy secure sandboxes in your infrastructure for AI agents.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/self_hostable_alternatives_to_e2b_for_ai_agents_42ab08704a.png" alt="Top self-hostable alternatives to E2B for AI agents in 2026" /><InfoBox className="BodyStyle">

## TL;DR: Top self-hostable alternatives to E2B in 2026

- E2B offers self-hosting via Terraform but requires Nomad orchestration expertise and significant infrastructure management
- **Top self-hostable alternatives:** Daytona (persistent workspaces), Microsandbox (hardware isolation), DifySandbox (Dify integration)
- **Three deployment approaches:** DIY open-source (maximum control, high complexity), E2B Terraform (official but complex), BYOC platforms (managed orchestration in your infrastructure)
- **Key decision factors:** Isolation technology needs, team capacity, compliance requirements, operational complexity tolerance

> **Note**: [Northflank Sandboxes](https://northflank.com/product/sandboxes) lets you run untrusted code at scale with microVMs, either on Northflank's infrastructure or in your VPC. For teams needing self-hosted control, Northflank offers [BYOC deployment](https://northflank.com/features/bring-your-own-cloud) into your AWS, GCP, Azure, Civo, Oracle, CoreWeave, or on-premise infrastructure, handling orchestration, scaling, and microVM management. Alternatively, Northflank's [managed PaaS](https://northflank.com/features/managed-cloud) provides instant deployment without any infrastructure setup.
> 

</InfoBox>

Self-hostable alternatives to E2B give you infrastructure control for running AI agent code execution while meeting compliance requirements and managing costs at scale.

This guide compares the top self-hostable options to help you choose based on isolation technology, deployment complexity, and team capacity.

## Why do teams need self-hostable E2B alternatives?

When your AI agents execute code generated by LLMs, where that code runs determines your compliance posture, cost structure, and operational control. E2B's managed service routes code execution through external infrastructure, which creates barriers for many production deployments.

- **Data sovereignty and compliance requirements:** Processing financial transactions, patient health records, or customer PII requires code execution within your own VPC. Third-party APIs introduce additional data processors into your compliance chain, complicating audits and potentially disqualifying you from enterprise contracts that mandate data residency.
- **Cost predictability at scale:** Managed services charge per execution or per compute minute. Running millions of code executions monthly makes per-unit costs accumulate quickly. Self-hosting lets you pay for underlying infrastructure directly with more predictable economics.
- **Infrastructure control and customization:** You need custom network policies, observability stack integration, or specific isolation technologies. Managed services don't offer the configuration flexibility your security policies require. Self-hosting gives you complete control over sandbox configuration.
- **Latency requirements:** Network round-trips to external sandbox APIs add latency to code execution. Self-hosting sandboxes on the same network as your LLM infrastructure reduces this overhead.

E2B does provide self-hosting through Terraform and Nomad, but this approach requires infrastructure expertise and ongoing operational management. Teams look for alternatives when they need simpler deployment models, different isolation technologies, or managed orchestration that handles Day 2 operations without requiring dedicated platform engineering resources.

> **Alternative approach:** Platforms like [Northflank](https://northflank.com/product/sandboxes) offer [BYOC deployment](https://northflank.com/features/bring-your-own-cloud), which keeps data in your infrastructure while providing managed orchestration. This addresses self-hosting requirements without the operational complexity of managing sandbox infrastructure yourself.
> 

## What are the best self-hostable alternatives to E2B?

When evaluating self-hostable E2B alternatives, you're choosing between different tradeoffs in isolation strength, deployment complexity, persistence models, and operational maturity. Here are the top self-hostable options.

### 1. Daytona

Daytona is a development environment platform that focuses on persistent workspaces where AI agents can build up state over multiple sessions.

**Key characteristics:**

- Container-based isolation (Docker default, Kata optional)
- Persistent environments where dependencies and files remain across sessions
- Custom orchestration built specifically for AI agents

**When to choose Daytona:**
Building AI agents that need persistent workspaces where state accumulates over time.

**When to consider alternatives:**
Need microVM isolation or want managed orchestration for your infrastructure.

For more context, see our [Daytona vs E2B comparison](https://northflank.com/blog/daytona-vs-e2b-ai-code-execution-sandboxes).

### 2. Microsandbox

Microsandbox is an open-source project providing maximum security for untrusted code execution using libkrun microVMs.

**Important:** Microsandbox is explicitly marked as experimental software by its developers. Expect breaking changes, missing features, and rough edges.

**Key characteristics:**

- libkrun microVM isolation (hardware-level security)
- OCI-compatible (runs standard container images)
- Simple binary installation

**When to choose Microsandbox:**
Security is your top priority, you have infrastructure engineering capacity, and you're comfortable working with experimental software.

**When to consider alternatives:**
Need production-proven infrastructure with stability guarantees, enterprise support, and managed operations.

### 3. DifySandbox

DifySandbox is the code execution engine built into the Dify AI framework.

**Key characteristics:**

- Seccomp filters and Linux namespaces for isolation
- Native integration with Dify framework
- Lightweight (no VM overhead)

**When to choose DifySandbox:**
Already building with the Dify framework. Native integration makes it the natural choice within that ecosystem.

**When to consider alternatives:**
Not using Dify, building standalone AI infrastructure, or need stronger isolation than namespaces provide.

## How does Northflank compare to self-hostable E2B alternatives?

[Northflank Sandboxes](https://northflank.com/product/sandboxes) lets you run untrusted code at scale with microVMs. The platform offers two deployment options: [managed PaaS](https://northflank.com/features/managed-cloud) for teams wanting zero infrastructure management, and [BYOC](https://northflank.com/features/bring-your-own-cloud) for teams requiring self-hosted control with data in their own cloud.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

For self-hosting requirements, Northflank's BYOC option provides a different approach than traditional self-hostable alternatives. Rather than downloading software and managing it yourself, Northflank deploys into your infrastructure while handling orchestration, scaling, and operations.

**What Northflank's BYOC deployment provides:**

- **Deployment flexibility:** Self-serve BYOC deployment to AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, or on-premise infrastructure. Northflank manages the orchestration layer while workloads run in your cloud account.
- **Isolation technology:** Kata Containers with Cloud Hypervisor, gVisor, or Firecracker microVMs based on your security requirements. All three provide stronger isolation than standard containers.
- **Configurable persistence:** Set session duration and state management based on your workflow needs. You're not locked into short-lived sessions or forced into permanent persistence.
- **Managed Kubernetes orchestration:** Northflank handles cluster management, scaling, updates, and Day 2 operations. You get Kubernetes' power without operating it yourself.
- **Production track record:** Northflank has been in production since 2021 across startups, public companies, and government deployments.
- **Enterprise observability:** Built-in monitoring, logging, and debugging capabilities without building your own observability stack.

<InfoBox className="BodyStyle">

**When Northflank's BYOC fits your requirements:**

Choose Northflank when you need self-hosted control (data stays in your infrastructure) but don't want to build and maintain sandbox orchestration yourself. This fits teams where compliance requires data in their VPC, but dedicating engineering resources to infrastructure management doesn't make business sense.

If you need faster deployment than building infrastructure from scratch, want production-grade microVM isolation without the operational burden, or your team focuses on application development rather than platform engineering, Northflank's BYOC model addresses these constraints.

</InfoBox>

Learn more about [Northflank Sandboxes](https://northflank.com/product/sandboxes) or read our guide on [self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes).

## Which self-hostable E2B alternative should you choose?

| If you need | Choose | Why |
| --- | --- | --- |
| **Maximum security with hardware isolation** | Microsandbox, Northflank BYOC, or E2B self-hosted | MicroVM isolation provides dedicated kernels per sandbox, preventing kernel-level exploits |
| **Persistent workspaces for long-running agents** | Daytona or Northflank | State persists across sessions, agents can build up environments over time |
| **Both ephemeral and persistent environments** | Northflank | Short-lived execution pools or long-running stateful services in one platform |
| **Fastest deployment with managed operations** | Northflank BYOC | Managed orchestration in your infrastructure |
| **Already using Dify framework** | DifySandbox | Native integration with Dify workflows |
| **Simple installation, maximum control** | Microsandbox | Single binary, no Kubernetes required, but you build operational tooling |
| **Production-proven infrastructure** | Northflank BYOC | Operational maturity, enterprise support available |
| **Compliance requires data in your VPC** | Any option works, but Northflank BYOC simplifies operations | All keep data in your infrastructure, BYOC reduces operational burden |

For more guidance on choosing sandbox platforms, see our analysis of [the best code execution sandboxes for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents).

## FAQ: Self-hostable E2B alternatives

### What is the easiest self-hostable alternative to E2B?

Microsandbox offers the simplest installation with a single binary, but you'll need to build monitoring and operational tooling around it. Northflank's BYOC provides the fastest path to production-ready sandboxes with managed orchestration already in place. DifySandbox is easiest if you're already using the Dify framework.

### Which self-hostable alternative has the strongest isolation?

Microsandbox, E2B self-hosted, and Northflank BYOC all provide microVM-level isolation with dedicated kernels per sandbox. This is stronger than container-based isolation used by Daytona in default configuration. MicroVM isolation prevents kernel-level exploits from affecting other sandboxes or the host.

### Do self-hostable E2B alternatives support microVMs?

Yes, several do. Microsandbox uses libkrun microVMs, E2B self-hosted uses Firecracker, Northflank BYOC offers Kata Containers with Cloud Hypervisor, gVisor, or Firecracker. Daytona optionally supports Kata Containers. DifySandbox uses namespace-based isolation.

### How do self-hostable alternatives compare to E2B's managed service?

Self-hostable alternatives give you infrastructure control, data sovereignty, and cost predictability. E2B's managed service offers faster initial setup but your code executes on E2B's infrastructure. Self-hosted options require more operational work unless you choose BYOC platforms that handle orchestration while keeping data in your infrastructure.

### Can self-hostable E2B alternatives meet compliance requirements?

Yes. Self-hosting keeps data in your VPC, which helps meet compliance requirements like HIPAA, SOC2, and GDPR. You maintain full control over data residency, security policies, and audit logging. BYOC platforms like Northflank simplify compliance by managing infrastructure operations while ensuring data never leaves your cloud account.

For more on sandbox security and compliance, see our guide on [how to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents).

## Choose the right self-hostable E2B alternative for your needs

Self-hostable E2B alternatives give you infrastructure control, data sovereignty, and cost predictability for running AI agent code execution.

Your choice depends on team capacity, security requirements, and how much operational management you want to handle.

<InfoBox className="BodyStyle">

For teams wanting self-hosted control without infrastructure burden, Northflank offers BYOC deployment into your AWS, GCP, Azure, Civo, Oracle, CoreWeave, or on-premise infrastructure with production-ready microVM isolation and managed orchestration. Get started with [Northflank Sandboxes](https://northflank.com/product/sandboxes) or look at more [alternatives to E2B](https://northflank.com/blog/best-alternatives-to-e2b-dev-for-running-untrusted-code-in-secure-sandboxes) based on your requirements.

</InfoBox>]]>
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  <title>Daytona vs E2B in 2026: which sandbox for AI code execution?</title>
  <link>https://northflank.com/blog/daytona-vs-e2b-ai-code-execution-sandboxes</link>
  <pubDate>2026-02-11T18:30:00.000Z</pubDate>
  <description>
    <![CDATA[Compare Daytona and E2B in 2026 for AI code sandboxing. Learn the differences in isolation, persistence, and performance to choose the right platform for your needs.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/daytona_vs_e2b_ai_code_execution_sandboxes_2acc0389ff.png" alt="Daytona vs E2B in 2026: which sandbox for AI code execution?" /><InfoBox className="BodyStyle">

## TL;DR: Top Daytona vs E2B comparison for AI code execution sandboxes

- **Daytona** provides persistent workspaces with container-based isolation and fast cold start performance
- **E2B** uses Firecracker microVMs for hardware-level isolation with session-based execution
- **Core trade-off**: Daytona uses Docker containers (faster startup, shared kernel) while E2B uses microVMs (dedicated kernel per session, hardware-level isolation)
- Both platforms operate as managed services where you send your code to their infrastructure

> **Note:** If you need the speed of containers with the security of microVMs, or must keep code execution within your own cloud (AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, or on-premise) for compliance, [Northflank Sandboxes](https://northflank.com/product/sandboxes) provides [bring-your-own-cloud deployment](https://northflank.com/features/bring-your-own-cloud) with Kata and gVisor microVM options and configurable persistence. Unlike managed-only platforms limited to specific cloud providers, Northflank has supported multi-cloud BYOC deployment since inception, giving you full control over where your code executes.
> 

</InfoBox>

## Why compare Daytona and E2B sandboxes for AI code execution?

When you're building AI agents that execute code, the sandbox platform you choose determines how you balance security, performance, and persistence.

These platforms represent two different philosophies for handling AI-generated code:

- **Daytona** approaches sandboxing from a developer workspace perspective, focusing on persistent environments where your agent can build up state over time
- **E2B** focuses specifically on running untrusted code from LLMs with hardware-level isolation boundaries

Understanding these architectural differences helps you choose based on your isolation requirements, session persistence needs, and deployment constraints.

If you're new to sandboxing concepts, this guide on [what an AI sandbox is](https://northflank.com/blog/what-is-an-ai-sandbox) provides essential context for evaluating these platforms.

## What is Daytona?

Daytona is a development environment platform that provides workspaces for developers and AI agents.

The platform focuses on persistent environments where code, dependencies, and files remain available across sessions.

Daytona uses Docker containers as its isolation technology, prioritizing startup speed and resource efficiency.

## What is E2B?

E2B is a platform built specifically for executing untrusted code from AI agents.

The platform uses Firecracker microVMs, the same virtualization technology that powers AWS Lambda.

Each code execution runs in its own kernel, providing isolation at the hardware level rather than just the process level.

## What is the difference between Daytona and E2B sandboxes?

The fundamental difference lies in how each platform approaches isolation and persistence.

**Daytona** provides stateful workspaces where your AI agent can install dependencies, create files, and return to the same environment later. The platform uses containers that share the host operating system kernel.

**E2B** provides execution environments that run in dedicated microVMs. Each execution gets its own kernel, preventing kernel-level exploits from affecting other executions or the host system.

| Feature | Daytona | E2B |
| --- | --- | --- |
| **Isolation technology** | Docker containers | Firecracker microVMs |
| **Kernel isolation** | Shared host kernel | Dedicated kernel per session |
| **Session persistence** | Configurable, long-term | Session-based |
| **Primary use case** | Persistent development workspaces | Ephemeral code execution |
| **Deployment model** | Managed or self-hosted | Managed service |

<InfoBox className="BodyStyle">

**Need deployment flexibility, speed, and security?**

Both Daytona and E2B operate as managed services where your code executes on their infrastructure.

If you need to keep code execution within your own infrastructure or require hardware-level isolation beyond standard containers, [Northflank Sandboxes](https://northflank.com/product/sandboxes) supports [bring-your-own-cloud deployment](https://northflank.com/features/bring-your-own-cloud) to AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, or on-premise servers.

</InfoBox>

## How does Daytona handle persistent AI agent workspaces?

Daytona treats sandboxes as long-lived workspaces rather than disposable execution environments.

When your AI agent installs a Python package or creates a configuration file in a Daytona workspace, that change persists. The next time your agent connects to the same workspace, the installed packages and created files remain available.

**This persistence model suits AI agents that need to:**

- Build up a working environment over multiple interactions
- Store generated code across sessions
- Maintain installed dependencies without reinstalling them
- Keep project files and artifacts available

An AI coding assistant that helps refactor a large codebase might work across multiple sessions, building on previous changes each time. Teams building AI agents that manage projects or maintain state across multiple user interactions typically need persistence.

Understanding [how to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents) helps you design these architectures based on your requirements.

## How does E2B run untrusted code with microVMs?

E2B uses Firecracker microVMs to provide hardware-level isolation for each code execution.

Each code execution runs in its own microVM, which means it gets a dedicated Linux kernel. This kernel-level isolation prevents exploits that rely on shared kernel vulnerabilities, such as container escape attacks that target the host operating system.

When you run code generated by an LLM, you're executing potentially malicious instructions. **The code might attempt to:**

- Access sensitive files on the host system
- Exploit kernel vulnerabilities to escalate privileges
- Attack other processes running on the same machine
- Exfiltrate data from adjacent workloads

Firecracker microVMs prevent these attacks by isolating each execution at the virtualization layer. This isolation level meets compliance requirements for organizations handling sensitive data.

When evaluating the [best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents), compliance teams often require hardware-level isolation rather than container-level boundaries.

## How do Daytona and E2B isolation layers compare for AI sandboxes?

The isolation technologies operate at different layers of the system stack.

**Docker containers** (used by Daytona) isolate processes using Linux kernel features like namespaces and cgroups. Multiple containers share the same kernel, which means kernel vulnerabilities could potentially allow an attacker to escape container boundaries.

**Firecracker microVMs** (used by E2B) provide each execution with its own kernel. Kernel exploits inside one microVM cannot affect other microVMs or the host because the isolation happens at the hardware virtualization layer.

| Isolation aspect | Docker containers (Daytona) | Firecracker microVMs (E2B) |
| --- | --- | --- |
| **Kernel sharing** | Shared across containers | Dedicated per microVM |
| **Escape prevention** | Relies on kernel security | Hardware-enforced boundaries |
| **Boot overhead** | Minimal | Higher |
| **Security boundary** | Process-level | Hardware-level |
| **Compliance suitability** | Depends on requirements | Meets strict requirements |

<InfoBox className="BodyStyle">

If you're implementing your own isolation stack, this guide on [how to spin up a secure code sandbox with Firecracker, gVisor, and Kata](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh) shows you the technical requirements.

</InfoBox>

## Which platform offers faster cold starts for AI code execution?

Cold start performance affects the responsiveness of your AI applications.

Daytona's container-based approach provides faster cold starts because containers only need to initialize namespaces and mount filesystems. E2B's microVMs need to boot a minimal Linux kernel, which takes longer despite Firecracker's optimizations.

For most AI code execution use cases, both platforms provide acceptable performance. The performance difference matters most for applications that create thousands of short-lived sandboxes per minute.

<InfoBox className="BodyStyle">

**Need both speed and security?**

Running untrusted code in production often requires hardware-level isolation beyond what standard containers provide.

If you need microVM security (Kata or gVisor) with configurable persistence and deployment options, [Northflank Sandboxes](https://northflank.com/product/sandboxes) offers isolation technologies that you can run in your own cloud environment, giving you the security benefits of microVMs while maintaining control over your infrastructure.

</InfoBox>

## How do Daytona and E2B APIs differ for AI code execution?

Both platforms provide SDKs with different design philosophies.

| Aspect | Daytona SDK | E2B SDK |
| --- | --- | --- |
| **Primary focus** | Workspace lifecycle management | Code execution with minimal setup |
| **Language support** | TypeScript/JavaScript, Ruby, Go | Python, TypeScript/JavaScript |
| **Best suited for** | Complex workflows with dependencies, multiple commands, state maintenance | Straightforward code execution without workspace management |

## Can you run Daytona or E2B on Kubernetes for AI sandboxes?

Kubernetes integration affects how these platforms fit into your existing infrastructure.

**Daytona and Kubernetes:**
Daytona provides Helm charts for Kubernetes deployment, but the setup uses Docker containers (not microVMs) and requires manual cluster management. While some BYOC options may exist, they're not publicly documented or self-serve.

**E2B and Kubernetes:**
E2B operates primarily as a managed service. Their self-hosting uses Nomad for orchestration and supports AWS and Google Cloud environments. Like Daytona, any BYOC capabilities are not easily accessible or self-serve.

For teams that need self-serve bring-your-own-cloud deployment without sales negotiations, this guide on [top AI sandbox platforms for code execution](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution) compares options designed for accessible BYOC.

## Which AI code execution sandbox should you choose?

Your choice depends on how you balance persistence, isolation, and deployment requirements.

| Use case | Daytona | E2B | Northflank Sandboxes |
| --- | --- | --- | --- |
| **Session persistence** | Extended workspace duration, multi-session projects | Session-based execution | Configurable persistence based on workflow needs |
| **Isolation technology** | Docker containers (shared kernel) | Firecracker microVMs (dedicated kernel) | Choice of Kata, gVisor, or Firecracker microVMs |
| **Performance** | Fast cold starts | Optimized microVM boot times | Configurable based on isolation choice |
| **Deployment model** | Managed or Helm charts (Docker only, BYOC not self-serve) | Managed service (BYOC not self-serve, AWS/GCP) | [Managed PaaS](https://northflank.com/features/managed-cloud), self-serve [BYOC](https://northflank.com/features/bring-your-own-cloud) (AWS, GCP, Azure, Civo, Oracle, CoreWeave), or on-premise |
| **Kubernetes orchestration** | Helm charts available, requires manual setup | Not supported (uses Nomad) | Self-serve BYOC with managed K8s |
| **Infrastructure control** | Limited, manual configuration required | AWS/GCP managed infrastructure | Full control in your own environment or managed |
| **Best for** | Long-running AI agent projects, stateful development | AI chat code interpreters, untrusted LLM code | Compliance-focused teams, data sovereignty, enterprise requirements |

## How Northflank addresses AI code execution sandbox requirements

If you need to keep AI code execution within your own infrastructure or require flexibility beyond what Daytona and E2B provide, Northflank addresses these constraints.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_dd9fa07375.png)

**What Northflank offers:**

- **Deployment flexibility**: Choose between [managed PaaS](https://northflank.com/features/managed-cloud) or [bring-your-own-cloud](https://northflank.com/features/bring-your-own-cloud) deployment to AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, or on-premise servers
- **Advanced isolation options**: Select between Kata Containers, gVisor, or Firecracker microVMs based on your security requirements
- **Configurable persistence**: Set session duration based on your workflow requirements without platform-imposed limits
- **Kubernetes-native orchestration**: Integrates with your existing K8s clusters, security policies, and RBAC

Northflank targets compliance-focused teams and organizations with data sovereignty requirements that prevent using external managed services.

### Cost comparison at scale
To make the pricing difference concrete, here is what 200 sandboxes costs across providers under the same conditions.

_Based on 200 sandboxes, plan: nf-compute-100-4, infra node: m7i.2xlarge_

| Model | Provider | Cloud | Sandbox vendor | Total |
| --- | --- | --- | --- | --- |
| PaaS | Northflank | — | $7,200.00 | $7,200.00 |
| PaaS | Daytona | — | $16,819.20 | $16,819.20 |
| PaaS | E2B | — | $16,819.20 | $16,819.20 |
| BYOC (0.2 overcommit)* | Northflank | $1,500.00 | $560.00 | $2,060.00 |
| BYOC | E2B | $1,500.00 | $10,000.00 | $11,500.00 |

*Through Northflank's plans on BYOC, there's a default overcommit which allows a customer to spawn more services and sandboxes on the same amount of compute. A request modifier of 0.2 means each sandbox only requests 20% of its plan's resources as a guaranteed minimum, but can burst up to the full plan limit if there's available capacity on the node. So instead of fitting 8 sandboxes per node, you could fit 40 on the same hardware, reducing both infrastructure cost and the Northflank management fee.

<InfoBox className="BodyStyle">

**Get started with Northflank:**

Learn more about [Northflank Sandboxes](https://northflank.com/product/sandboxes) or read our guide on [self-hosted AI sandboxes](https://northflank.com/blog/self-hosted-ai-sandboxes) for your infrastructure. For production workloads, see how to implement [secure AI code execution at scale](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale).

Compare [best alternatives to E2B](https://northflank.com/blog/best-alternatives-to-e2b-dev-for-running-untrusted-code-in-secure-sandboxes) and [top Daytona alternatives](https://northflank.com/blog/top-daytona-io-alternatives-for-running-ai-code-in-secure-sandboxed-environments) based on your specific requirements.

</InfoBox>

## FAQ: Daytona vs E2B sandboxes for AI code execution

### What is the main difference between Daytona and E2B?

Daytona provides persistent container-based workspaces, while E2B provides Firecracker microVM environments. Daytona prioritizes stateful development environments while E2B prioritizes hardware-level security. For teams needing both, [Northflank Sandboxes](https://northflank.com/product/sandboxes) offers configurable options.

### Which sandbox provides stronger isolation for untrusted AI code?

E2B provides stronger isolation through Firecracker microVMs with dedicated kernels. Daytona uses Docker containers with shared host kernels. MicroVM isolation reduces risk when running LLM-generated code.

### Can you self-host Daytona or E2B sandboxes?

Both platforms offer self-hosting with limitations. Daytona's Helm charts deploy Docker containers and require manual Kubernetes management. E2B's self-hosting uses Nomad and supports only AWS and Google Cloud. While both may offer BYOC options, these are not easily accessible or self-serve. [Northflank](https://northflank.com/product/sandboxes) provides self-serve, production-ready BYOC deployment with managed Kubernetes orchestration across AWS, GCP, Azure, Civo, Oracle Cloud, CoreWeave, and on-premise infrastructure.

### What isolation technology meets production security requirements?

Container isolation suits trusted code in non-critical applications. MicroVM isolation (Firecracker, Kata, gVisor) is required for untrusted code in production, especially when meeting compliance standards like SOC2 or HIPAA.


## Related guides on AI code execution sandboxes

- [Best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents)
- [How to sandbox AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents)
- [Top AI sandbox platforms for code execution](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution)
- [Top Modal sandboxes alternatives](https://northflank.com/blog/top-modal-sandboxes-alternatives-for-secure-ai-code-execution)
- [Top Vercel sandbox alternatives](https://northflank.com/blog/top-vercel-sandbox-alternatives-for-secure-ai-code-execution-and-sandbox-environments)
- [CodeSandbox alternatives](https://northflank.com/blog/codesandbox-alternatives)]]>
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</item><item>
  <title>Self-hosted AI sandboxes: Guide to secure code execution in 2026</title>
  <link>https://northflank.com/blog/self-hosted-ai-sandboxes</link>
  <pubDate>2026-02-10T16:30:00.000Z</pubDate>
  <description>
    <![CDATA[Learn how self-hosted AI sandboxes provide secure code execution in your infrastructure. Compare BYOC, managed, and DIY options for compliance and scale.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/self_hosted_ai_sandboxes_9fe17784bf.png" alt="Self-hosted AI sandboxes: Guide to secure code execution in 2026" /><InfoBox className="BodyStyle">

## TL;DR: Self-hosted AI sandboxes overview

Self-hosted AI sandboxes are isolated execution environments that run AI-generated code within your own infrastructure rather than relying on third-party managed services.

**Why companies choose self-hosted sandboxes:**

- Maintain data sovereignty and meet compliance requirements (GDPR, HIPAA, SOC2)
- Reduce latency by running sandboxes on the same network as LLM infrastructure
- Control costs at scale as managed sandbox pricing becomes unsustainable
- Keep sensitive data within their own security perimeter

**Three paths to self-hosted sandboxes:**

- **BYOC (Bring Your Own Cloud) platforms** like [Northflank](https://northflank.com/product/sandboxes): Managed orchestration deploying directly into your AWS, GCP, Azure, Oracle, Civo, CoreWeave, or bare-metal infrastructure with production-ready microVM isolation
- **Fully managed services** (E2B, Modal): Quick start but data leaves your infrastructure
- **Open-source DIY** (Firecracker, Kata Containers): Maximum control but requires months of engineering investment

Most enterprises find [BYOC](https://northflank.com/features/bring-your-own-cloud) offers the ideal balance: you get self-hosted infrastructure control with sovereignty guarantees, without the operational burden of building and maintaining complex sandbox systems from scratch.

</InfoBox>

Companies move from managed sandbox services to self-hosted AI sandboxes to maintain control over their infrastructure, data, and costs. This guide covers the three deployment options, decision criteria for each, and what implementation involves.

## What are self-hosted AI sandboxes?

Self-hosted AI sandboxes are secure, isolated environments for executing AI-generated code that run on infrastructure you own or control, rather than on a vendor's shared multi-tenant platform. 

Unlike managed sandbox services where your code executes on someone else's servers, self-hosted sandboxes deploy directly into your cloud account, on-premises data center, or private infrastructure.

The core difference comes down to where the compute runs and who controls the data:

- **Self-hosted sandboxes with BYOC:** Platforms like [Northflank](https://northflank.com/product/sandboxes) manage the control plane (orchestration, monitoring, updates) while the data plane (actual compute and execution) runs in your infrastructure. You get managed operations with self-hosted control.
- **Managed AI sandboxes:** Code-execution-as-a-service running in vendor's shared multi-tenant infrastructure. Best for prototyping and low-security workloads where compliance isn't a concern.
- **Self-hosted AI sandboxes:** Sovereign execution runtimes with isolation technology (Firecracker microVMs, gVisor, Kata Containers) running in your infrastructure. Best for production-scale agents, PII-handling, and regulated industries where data cannot leave your VPC.

This isn't just about running containers on your own servers. [AI sandboxes](https://northflank.com/blog/what-is-an-ai-sandbox) require isolation beyond standard containers to safely execute untrusted code. Standard Docker containers share the host kernel, creating security vulnerabilities when running code generated by LLMs that might contain bugs, hallucinations, or prompt-injection attacks.

## Why are companies shifting to self-hosted AI sandboxes?

Three critical barriers are forcing engineering teams to move sandbox infrastructure in-house:

### Compliance requirements

For fintech, healthcare, and government sectors, regulatory demands make managed sandboxes non-viable. When your AI agent processes customer financial data or patient health records, that data cannot leave your VPC without triggering GDPR, HIPAA, or SOC2 violations.

Managed sandbox APIs act as third-party data processors, requiring complex data processing agreements and often disqualifying you from certain enterprise contracts.

Managed sandbox APIs also run in shared multi-tenant environments where your workloads execute alongside other customers' code, creating potential cross-tenant data exposure risks that compliance auditors scrutinize. Self-hosted sandboxes keep PII within your security perimeter, simplifying compliance audits and maintaining data sovereignty.

### Latency constraints

Real-time AI applications can't afford the round-trip time to external sandbox services. When your agent needs to execute code to answer a user question, 200-500ms of network latency to a managed API breaks the conversational flow.

Self-hosting sandboxes on the same network as your LLM inference reduces execution latency to near-zero. For AI coding assistants, data analysis tools, or autonomous agents making rapid decisions, this performance difference is the gap between "feels instant" and "feels broken."

### Cost pressures at scale

Managed providers charge premium pricing for convenience. Early-stage usage costs are manageable, but as you grow to millions of code executions monthly, the markup becomes unsustainable.

For instance, [cto.new hit this inflection point](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) during their launch week. Thousands of daily deployments made managed sandbox costs prohibitive. By moving to [self-hosted infrastructure with Northflank's BYOC platform](https://northflank.com/product/sandboxes), they gained cost predictability and economics that scaled with their growth.

### Managed vs self-hosted: Key differences

| Factor | Managed sandboxes | Self-hosted / BYOC |
| --- | --- | --- |
| **Compliance** | Third-party processor (high risk) | In-VPC residency (low risk) |
| **Latency** | Network round-trip (200ms+) | Local network (near-zero) |
| **Cost at scale** | Per-execution pricing (expensive) | Infrastructure-based (predictable) |
| **Data control** | Vendor infrastructure | Your infrastructure |

As we've covered in our analysis of [the best code execution sandboxes for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents), the choice isn't just about features. It's about where your trust boundary lies and who controls your infrastructure.

## What are your self-hosted sandbox options?

When evaluating self-hosted AI sandbox solutions, you're choosing between three approaches, each with distinct tradeoffs:

| Approach | Infrastructure control | Operational burden | Best for |
| --- | --- | --- | --- |
| **BYOC (Bring Your Own Cloud) Platform** (Northflank) | High (your cloud account) | Low (managed control plane) | Production scale, compliance-driven, enterprise |
| **Managed SaaS** (E2B, Modal, Daytona) | Low (vendor's infrastructure) | None | Early-stage, testing, proof-of-concept |
| **Open-Source DIY** (Firecracker, microsandbox) | Total (you manage everything) | Very High | Unique requirements, extreme customization |

## How do BYOC, managed, and DIY self-hosted AI sandboxes differ?

The three paths to self-hosted AI sandboxes differ in the level of infrastructure control you get versus the amount of operational work you take on.

### Path 1: BYOC (Bring Your Own Cloud) platforms

BYOC represents the pragmatic middle ground for self-hosted sandboxes. Platforms like [Northflank](https://northflank.com/product/sandboxes) provide managed orchestration while deploying compute into your AWS, GCP, Azure, Oracle, Civo, CoreWeave, or on-premises infrastructure.

You get production-ready sandbox infrastructure with [microVM isolation technologies](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh) (Kata Containers with Cloud Hypervisor, gVisor, Firecracker) running in your VPC. Data never leaves your infrastructure, thereby meeting compliance requirements, while Northflank handles orchestration, networking, scaling, and Day 2 operations.

This approach solves the self-hosting dilemma: you maintain sovereignty without building and maintaining complex sandbox infrastructure from scratch.

### Path 2: Managed SaaS sandboxes

Platforms like E2B, Modal, and Daytona handle all infrastructure, offering simple APIs for code execution. You trade control for convenience. Great for validating product-market fit, but the barriers mentioned above eventually force migration.

### Path 3: Open-source DIY solutions

For teams with specific requirements that no platform addresses, open-source tools offer maximum flexibility:

- **Firecracker**: AWS's microVM technology, sub-200ms boot times, hardware isolation
- **Microsandbox**: Experimental self-hosted platform with MicroVM support and MCP integration

The reality: building production-grade self-hosted sandbox infrastructure requires 6-12 months of dedicated engineering work. You're responsible for isolation technology, orchestration, networking security, monitoring, patching, and scaling. Most teams underestimate this complexity.

## When should you self-host AI sandboxes?

Not every team needs self-hosted infrastructure. Use this framework to determine if self-hosting is right for your situation:

| Scenario | Self-host now | Consider self-hosting | Stay with managed |
| --- | --- | --- | --- |
| **Compliance** | HIPAA, GDPR, FedRAMP requirements mandate data in your VPC | Enterprise customers asking security questions | No regulatory requirements |
| **Data Sensitivity** | Processing customer PII, financial records, health data | Handling proprietary business logic | Public or non-sensitive data |
| **Scale** | Over 1 million monthly executions | 100k to 1 million monthly executions | Under 100k monthly executions |
| **Latency** | Need under 50ms response times for real-time agents | 100 to 200ms acceptable | Over 500ms acceptable |
| **Infrastructure** | Have dedicated platform engineering team | Can allocate 1 to 2 engineers | No infrastructure capacity |
| **Deployment** | Enterprise requires on-premises or private cloud | Prefer infrastructure control | Speed to market critical |

## What does implementing self-hosted sandboxes involve?

If you're building self-hosted AI sandbox infrastructure from scratch, understanding the full scope prevents costly surprises down the line.

### Core architectural components

- **Isolation layer**: Choose between Firecracker microVMs (strongest isolation, AWS-proven), gVisor (user-space kernel interception, Google-developed), or Kata Containers (container UX with VM security). This isn't just running Docker. You need dedicated kernels per execution.
- **Orchestration system**: Something must manage thousands of ephemeral sandbox lifecycles, handle scheduling, and ensure resource efficiency. Kubernetes with Kata runtime classes works, but requires significant hardening for untrusted code.
- **Networking security**: Implement default-deny egress policies so AI agents can't exfiltrate data or scan internal networks. You'll need granular controls for which sandboxes can access external APIs versus remaining completely air-gapped.
- **API gateway**: Your LLM application needs secure methods to submit code, stream execution output, retrieve results, and handle errors. This layer manages authentication, rate limiting, and routing to available sandbox capacity.
- **Monitoring and observability**: When a sandbox execution fails or gets compromised, you need detailed logging, metrics, and tracing to diagnose issues without exposing sensitive data.

<InfoBox className="BodyStyle">

The DIY path could demand 2-3 senior infrastructure engineers working 3-6 months minimum, plus ongoing maintenance. [BYOC platforms like Northflank](https://northflank.com/features/bring-your-own-cloud) handle this complexity while giving you infrastructure control. You get production-ready self-hosted sandboxes in weeks instead of months.

For technical implementation details, see our guides on [spinning up secure microVMs](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh) and [sandboxing AI agents](https://northflank.com/blog/how-to-sandbox-ai-agents).

</InfoBox>

## How Northflank simplifies self-hosted sandboxes

As AI models become more capable and generate increasingly complex code, the security and compliance risks of third-party code execution grow proportionally. Enterprises building serious AI applications can't afford to send sensitive data to external sandbox APIs.

![northflank-sandbox-page.png](https://assets.northflank.com/northflank_sandbox_page_b7ccb3b65d.png)

Self-hosted AI sandboxes, through BYOC platforms or DIY infrastructure, ensure your innovation never compromises your security. The question isn't if you'll need self-hosted sandboxes, but when the transition makes strategic and economic sense for your team.

<InfoBox className="BodyStyle">

Get started with self-hosted sandbox infrastructure through [Northflank's BYOC deployment](https://northflank.com/product/sandboxes) to get production-grade self-hosted sandboxes running in your cloud account, or go into our technical guide on [secure sandbox architecture and implementation](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale).

</InfoBox>

## Frequently asked questions about self-hosted sandboxes

### Can I run self-hosted AI sandboxes on Kubernetes?

Yes, but standard Kubernetes pods aren't secure for untrusted AI code. You need runtime classes like Kata Containers for VM-level isolation. Self-hosted sandbox platforms on Kubernetes require specialized runtimes, resource quotas, and network policies to prevent sandbox escape.

### What's the difference between self-hosted and BYOC sandboxes?

BYOC (Bring Your Own Cloud) is a type of self-hosted deployment where the vendor manages the control plane while sandboxes run in your infrastructure. Pure self-hosting means you operate everything. Platforms like [Northflank](https://northflank.com/product/sandboxes) use BYOC to give you data sovereignty while handling orchestration and operations.

### How much does self-hosting AI sandboxes cost compared to managed services?

Self-hosted costs depend on your approach. DIY requires months of engineering work plus ongoing maintenance. BYOC platforms like [Northflank](https://northflank.com/product/sandboxes) remove this upfront work. You pay for compute resources while the platform manages infrastructure. At scale, self-hosted options typically cost less than managed per-execution pricing.

### What isolation technology should I use for self-hosted sandboxes?

MicroVMs (Firecracker, Kata Containers) provide the strongest isolation with dedicated kernels. gVisor offers good security with lower overhead. Standard Docker containers aren't sufficient due to shared kernel vulnerabilities. Choose based on your security needs.

### Can self-hosted sandboxes meet HIPAA and SOC2 compliance?

Yes. Self-hosted sandboxes keep data in your VPC with proper isolation, network policies, and audit logging. However, compliance also requires documented security policies, access controls, encryption, and regular audits.

### How do I prevent self-hosted sandboxes from consuming all my cloud resources?

Implement strict resource quotas: CPU limits, memory caps, disk I/O restrictions, and timeouts. [Northflank's architecture](https://northflank.com/product/sandboxes) makes these limits configurable per sandbox. DIY implementations need resource restrictions and monitoring.]]>
  </content:encoded>
</item><item>
  <title>Top 5 Kubernetes preview environments comparison for 2026</title>
  <link>https://northflank.com/blog/kubernetes-preview-environments-comparison</link>
  <pubDate>2026-02-09T17:15:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the best K8s preview environments: Northflank, Okteto, Namespace, Qovery, and Bunnyshell. Evaluate full-stack support, BYOC integration, and resource management for your team.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/kubernetes_preview_environments_comparison_cf63c9d9c8.png" alt="Top 5 Kubernetes preview environments comparison for 2026" /><InfoBox className="BodyStyle">

## TL;DR: Top Kubernetes preview environments comparison

The best Kubernetes preview environment platforms in 2026 go beyond simple namespace isolation to offer full-stack ephemeral orchestration with managed databases, secrets, and production-like configurations.

Choosing the right platform depends on your infrastructure model (managed vs BYOC), team size, and if you need just containers or complete application stacks.

**Key evaluation criteria for K8s preview environments:**

- Cluster architecture (managed vs. bring-your-own-cluster)
- Isolation strategy (namespace vs. virtual cluster vs. dedicated cluster)
- Workload support (frontend-only vs. full-stack with databases)
- Resource management (quotas, autoscaling, cost efficiency)
- Deployment workflow (Helm, Kustomize, GitOps compatibility)

**Top 5 platforms compared:**

1. **Northflank**: Managed + BYOC hybrid with full-stack orchestration, native database provisioning, production promotion workflows, and teardown scheduling for cost efficiency
2. **Okteto**: Live code synchronization for inner-loop development
3. **Namespace**: Ephemeral cluster provisioning with BYOC
4. **Qovery**: Cloud-agnostic platform abstraction
5. **Bunnyshell**: Environment-as-code configuration templates

> Northflank provides production-ready preview environments that handle the entire application lifecycle, not just containers. You get managed databases, automated secrets, job scheduling, and native [Kubernetes deployment patterns](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) with both managed infrastructure and BYOC options. The platform's teardown scheduling and resource management reduce costs as you scale to dozens or hundreds of concurrent previews.
> 

</InfoBox>

## Why evaluate Kubernetes preview environments differently?

In 2026, the best Kubernetes preview environments offer full-stack ephemeral orchestration beyond namespace isolation. Choosing between platforms like Northflank, Okteto, and Namespace depends on your infrastructure model, cost-scaling needs, and whether you require managed databases alongside containers.

If you're running Kubernetes in production, adding preview environments requires evaluating how they integrate with your cluster infrastructure, not just developer productivity features.

**Infrastructure-level concerns that change your evaluation:**

- **Cluster operations**: Does the platform respect your existing K8s RBAC, network policies, and security boundaries, or does it require workarounds?
- **Resource management**: Can you prevent preview environment sprawl from consuming production cluster resources through quotas and autoscaling?
- **Deployment compatibility**: Will your existing Helm charts, Kustomize overlays, and GitOps workflows work without modification?
- **Multi-tenancy strategy**: How does the platform handle isolation between teams and projects at scale?
- **Production parity**: Can previews include databases, jobs, secrets, and other stateful components, not just application containers?

<InfoBox className="BodyStyle">

Platforms that provide full-stack orchestration (like Northflank) solve these by treating the preview as a complete application lifecycle event, rather than just a temporary container deployment.

</InfoBox>

This comparison focuses on these Kubernetes infrastructure capabilities rather than general preview environment features. For a broader context on [why ephemeral preview environments are important](https://northflank.com/blog/the-what-and-why-of-ephemeral-preview-environments-on-kubernetes-sandbox-testing), see our foundational guide.

## What criteria should you use to compare Kubernetes preview environment platforms?

You need a framework based on K8s infrastructure priorities before evaluating specific platforms.

### 1. Cluster architecture model

Does the platform manage clusters for you or integrate with your existing infrastructure?

- **Managed clusters:** Platform provisions and operates K8s infrastructure. Reduced operational overhead but limited cluster-level customization.
- **BYOC (Bring Your Own Cloud):** You maintain full control over cluster infrastructure. Requires K8s expertise and cluster admin permissions. Maximum flexibility for networking and security.
- **Hybrid:** Both [managed and BYOC options](https://northflank.com/product/preview-environments), letting you choose based on project requirements.

### 2. Workload and isolation strategy

How does the platform handle multi-tenancy and environment isolation?

- **Namespace-per-preview:** Dedicated namespace for each preview. Clear isolation and simpler quota management.
- **Virtual clusters:** Lightweight K8s clusters within your main cluster. Better isolation than namespaces with lower overhead.
- **Dedicated clusters:** Full cluster per preview. Maximum isolation but higher costs.

### 3. Full-stack vs container-only support

Does the platform provision complete application stacks or just containers?

- **Container-only:** Deploys application code but requires external services for databases. Simpler but may not match production.
- **Full-stack:** [Provisions databases, jobs, and secrets automatically](https://northflank.com/docs/v1/application/release/create-and-manage-previews) per preview. Production parity for testing.

### 4. Resource management and cost efficiency

How does the platform control costs as you scale preview environments?

- **Critical capabilities:** Per-environment quotas, automatic idle shutdown, [teardown scheduling, and cost tracking](https://northflank.com/blog/preview-environment-platforms) per team or project.

## How do the top 5 Kubernetes preview environment platforms compare?

Here's a side-by-side comparison of how each platform handles the key K8s infrastructure criteria that impact your deployment workflow and operational costs.

| Platform | Cluster Model | Isolation Strategy | Workload Support | Deployment Patterns | Cost Management | Best For |
| --- | --- | --- | --- | --- | --- | --- |
| **Northflank** | Managed + BYOC | Namespace per project | Full-stack (containers + databases + jobs) | Helm, Kustomize, manifests, GitOps | Teardown scheduling, auto-shutdown, detailed tracking | Full-stack teams requiring production parity (databases + jobs) with enterprise-grade BYOC control |
| **Okteto** | Managed + BYOC | Namespace per preview | Container-focused with external services | Helm, manifests, live sync | Configurable sleep/wake policies | Inner-loop development and live code sync |
| **Namespace** | BYOC | Ephemeral clusters | Container-focused | Helm, Kustomize, manifests | Branch-based cleanup | High-performance compute needs |
| **Qovery** | Managed (your cloud) | Namespace per environment | Containers with database provisioning | Helm, manifests | Auto-cleanup on merge | Multi-cloud deployments |
| **Bunnyshell** | Managed + BYOC | Namespace per environment | Full-stack with templates | Helm, manifests, templates | Template-defined limits | Complex microservices with dependencies |

<InfoBox className="BodyStyle">

While many platforms focus on the container layer, Northflank is designed to mirror your entire production environment. This includes [managed databases and add-ons](https://northflank.com/docs/v1/application/release/create-and-manage-previews) that are automatically provisioned, seeded, and destroyed alongside your code, ensuring that your preview environments are as reliable as your production cluster.

</InfoBox>

## What are the key differences between these Kubernetes preview platforms?
Each platform takes a different approach to K8s preview environments based on their target use case and infrastructure philosophy.

### 1. Northflank

Northflank provides production-ready Kubernetes infrastructure with comprehensive [preview environment](https://northflank.com/product/preview-environments) capabilities for teams wanting full-stack orchestration with operational flexibility.

![northflank-previews.png](https://assets.northflank.com/northflank_previews_b051ec41ef.png)

**Key features of Northflank:**

- **Cluster architecture:** You get both [managed Kubernetes](https://northflank.com/features/managed-cloud) and BYOC ([Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud)) options. With managed clusters, Northflank handles operations, upgrades, and scaling while giving you namespace-level control. BYOC lets you connect existing clusters when you need specific configurations or compliance requirements.
- **Full-stack orchestration:** Unlike container-only platforms, Northflank provisions managed databases (PostgreSQL, MySQL, MongoDB, Redis), scheduled jobs, and secrets automatically for each preview. This creates production-like environments where you can test complete application behavior including database migrations and background workers.
- **Kubernetes deployment flexibility:** Native support for Helm charts, Kustomize, and raw Kubernetes manifests without conversion or rewriting. Works with your existing deployment configurations and [GitOps workflows](https://northflank.com/docs/v1/application/release/create-and-manage-previews), including ArgoCD integration.
- **Resource management:** Per-environment resource quotas with automatic teardown scheduling. Preview environments shut down after configurable idle periods, and you get detailed cost tracking showing resource consumption per preview, project, or team. This cost efficiency becomes significant when you're running 50+ concurrent previews.
- **Production promotion:** Built-in workflows for promoting tested preview configurations to staging and production environments, maintaining consistency across your deployment pipeline.

**Best for:** Teams requiring full-stack preview environments with the flexibility to choose between managed infrastructure and BYOC based on project needs. Particularly valuable when you need databases, jobs, and secrets automatically provisioned alongside containers.

<InfoBox className="BodyStyle">

Learn more about [Northflank's preview environment capabilities](https://northflank.com/product/preview-environments) and follow this guide to [set up a preview environment](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment)

</InfoBox>

### 2. Okteto

Okteto focuses on live code synchronization in Kubernetes clusters.

**Key features of Okteto:**

- **Cluster architecture:** Okteto Cloud provides managed clusters, or you can install into your existing K8s infrastructure.
- **Development workflow:** Live code synchronization where you can code directly in remote Kubernetes clusters.
- **Kubernetes capabilities:** Supports existing manifests and Helm charts with namespace isolation.

**Best for:** Teams prioritizing inner-loop development and live code sync.

### 3. Namespace

Namespace provides ephemeral cluster provisioning with a BYOC approach.

**Key features of Namespace:**

- **Cluster architecture:** BYOC model where you install into existing clusters.
- **Performance focus:** Fast environment provisioning through caching and resource reuse. Creates ephemeral clusters rather than namespaces.

**Best for:** Platform engineers managing their own Kubernetes infrastructure.

### 4. Qovery

Qovery offers managed Kubernetes deployment across multiple cloud providers.

**Key features of Qovery:**

- **Cluster architecture:** Managed approach where Qovery provisions clusters in your AWS, GCP, or Azure accounts.
- **Cloud abstraction:** No-ops deployment without YAML management.

**Best for:** Teams running multi-cloud Kubernetes.

### 5. Bunnyshell

Bunnyshell uses environment-as-code templates for preview environments.

**Key features of Bunnyshell:**

- **Cluster architecture:** Offers both managed Kubernetes and existing cluster integration.
- **Template-driven environments:** Environments defined in YAML templates specifying components and dependencies.

**Best for:** Teams with multi-service applications requiring environment templates.

## Which Kubernetes preview environment platform fits your needs?

Your decision depends on infrastructure maturity, team expertise, and workload requirements.

| Your situation | Recommended platform | Why |
| --- | --- | --- |
| Need full-stack previews (databases + containers + jobs) with managed or BYOC flexibility | **Northflank** | Platform offering complete application stack provisioning with infrastructure choice |
| Have existing K8s clusters and want minimal platform overhead | **Namespace** | Lightweight integration with existing infrastructure |
| Prioritize developer inner-loop and live code synchronization | **Okteto** | Built for rapid iteration with hot reload |
| Running multi-cloud Kubernetes across AWS/GCP/Azure | **Qovery** | Unified management across cloud providers |
| Complex microservices with templated dependency requirements | **Bunnyshell** | Environment-as-code approach for consistency |
| Scaling from 10 to 100+ concurrent preview environments | **Northflank** | Teardown scheduling and cost tracking prevent resource sprawl |

## Choose Northflank for production-ready Kubernetes preview environments

If you need full-stack preview environments that include managed databases, scheduled jobs, and secrets alongside containers, [Northflank's preview environments](https://northflank.com/product/preview-environments) provide production-ready infrastructure with the flexibility to choose between managed and BYOC models.

Northflank respects your existing Kubernetes investments rather than forcing deployment configuration rewrites. Your [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) should accelerate development without creating new operational overhead or forcing you to maintain separate configurations from production.

Start with [setting up your first preview environment](https://northflank.com/docs/v1/application/release/create-and-manage-previews) to see how production-like ephemeral environments integrate with your existing workflows.

## Frequently asked questions about Kubernetes preview environments

### Can I use my existing Helm charts?

Yes. Northflank, Okteto, Namespace, and Bunnyshell support existing Helm charts without modification.

### How are preview environments cleaned up?

Most platforms automatically delete preview environments when Git branches are merged or deleted. Northflank also supports idle timeout policies.

### What about database data in preview environments?

Northflank provisions fresh managed databases per preview with optional seeding and automatic teardown, ensuring no data remnants or extra costs after the PR is closed. Other platforms may require connecting to shared staging databases.

### Do I need separate clusters for previews?

Not necessarily. BYOC platforms install into existing clusters using namespace isolation. Northflank's hybrid approach lets you choose.

### How much do preview environments cost at scale?

Platforms with automatic teardown scheduling and idle shutdown (like Northflank) provide better cost efficiency when scaling to 50+ concurrent previews.]]>
  </content:encoded>
</item><item>
  <title>Top 10 Kubernetes management tools and platforms in 2026</title>
  <link>https://northflank.com/blog/tools-for-managing-kubernetes-clusters</link>
  <pubDate>2026-02-06T12:45:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the best Kubernetes management tools and platforms in 2026. Expert guide to Northflank, Rancher, Lens, K9s and more for simplified cluster operations.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/tools_for_managing_kubernetes_clusters_1_ea13b22fc6.png" alt="Top 10 Kubernetes management tools and platforms in 2026" />Kubernetes has become the standard for container orchestration, but managing Kubernetes clusters can be overwhelming. That's where Kubernetes management tools and platforms come in, providing the automation, visibility, and control teams need to operate clusters effectively.

Between monitoring dozens of pods, debugging networking issues, managing configurations across multiple clusters, and ensuring security compliance, operations teams often find themselves drowning in complexity.

You don't have to do it all manually. The Kubernetes ecosystem has matured significantly, and there are now tools that simplify cluster management, improve visibility, and reduce operational overhead.

<InfoBox className="BodyStyle">

## Quick overview: Top tools for managing Kubernetes clusters at a glance

Here's a quick look at the 10 best tools for managing Kubernetes clusters. We'll cover each in detail below.

1. [**Northflank**](https://northflank.com/) - Developer-first platform that abstracts Kubernetes complexity without sacrificing flexibility. Built-in CI/CD, databases, and multi-cloud support without vendor lock-in. Best for teams who need to manage Kubernetes clusters but want to ship fast without dealing with infrastructure complexity.
    
    > What makes Northflank stand out: it solves the management problem at a higher level. While most tools require you to interact directly with Kubernetes resources (pods, deployments, YAML files), Northflank manages your Kubernetes clusters for you through a single unified interface focused on services, databases, and deployments, handling the underlying Kubernetes complexity automatically.
    > 
2. **Rancher** - Open-source multi-cluster management platform. Ideal for managing large numbers of clusters across hybrid cloud environments.
3. **Lens (OpenLens)** - Desktop "Kubernetes IDE" with real-time monitoring and intuitive resource management.
4. **Platform9** - Fully managed Kubernetes as a service with self-healing clusters. Minimizes operational overhead.
5. **K9s** - Terminal-based UI for fast cluster navigation and troubleshooting. Great for engineers who live in the command line.
6. **Portainer** - Container management platform with Kubernetes support. Good for teams transitioning from Docker or managing edge deployments.
7. **Kubevious** - Visual cluster representation with configuration validation. Helps prevent errors before deployment.
8. **Cyclops** - Developer-friendly interface that hides Kubernetes complexity. Reduces the learning curve for teams new to Kubernetes.
9. **kOps** - Command-line tool for infrastructure-as-code cluster management on AWS. Popular for automated cluster provisioning.
10. **DevSpace** - Development workflow optimization with hot-reloading and fast iteration cycles. Built for active development on Kubernetes.

</InfoBox>


## What are Kubernetes management tools and platforms?

Kubernetes management tools and platforms help teams operate clusters more efficiently by automating routine tasks, improving visibility, and reducing complexity. However, not all solutions are created equal, so, understanding the difference between tools and platforms helps you choose the right approach.

**Kubernetes management platforms** (like Northflank, Rancher, and Platform9) provide unified control planes that handle multiple aspects of cluster operations, from deployment and scaling to monitoring and security, through a single integrated interface. These platforms are ideal for teams who want comprehensive management without piecing together multiple solutions.

**Kubernetes management tools** (like Lens, K9s, and Kubevious) are specialized utilities that excel at specific tasks such as visualization, command-line management, or configuration validation. Many teams use these tools alongside a primary platform to handle specialized workflows.

In practice, most organizations adopt a hybrid approach: a core Kubernetes management platform for day-to-day operations, supplemented with specialized tools for debugging, development, or specific operational needs.


## Why you need Kubernetes management tools

Managing Kubernetes clusters manually is incredibly challenging, which is why Kubernetes management tools have become essential for modern operations teams. Here's why these tools are critical:

### 1. Steep learning curve

Kubernetes has a rich vocabulary of concepts: Pods, Deployments, StatefulSets, Services, Ingresses, ConfigMaps, Secrets, and more. Each has its own YAML configuration format, and understanding how they interact requires significant investment.

For teams new to Kubernetes, even basic tasks like exposing a service or debugging a failed deployment can take hours.

### 2. Operational complexity

Managing production Kubernetes clusters involves constant maintenance: scaling workloads up and down, rolling out updates safely, handling rollbacks when things go wrong, managing persistent storage, configuring networking and service discovery, and keeping everything secure.

Without the right tools, these operational tasks consume enormous amounts of time and increase the risk of human error.

### 3. Multi-cluster management difficulties

Most organizations don't run just one Kubernetes cluster. They typically have separate clusters for development, staging, and production, and often multiple production clusters across different regions or cloud providers.

Keeping configurations consistent across all these clusters while avoiding configuration drift is a major challenge. Managing access controls, monitoring, and logging across multiple clusters adds another layer of complexity.

### 4. Security and compliance

Kubernetes security is multi-layered and complex. You need to manage role-based access control (RBAC), network policies, pod security standards, secrets management, image scanning, and admission control. Ensuring all deployments follow security best practices and comply with regulatory requirements requires constant vigilance and proper tooling.

### 5. Visibility gaps

Understanding what's happening inside your clusters can be difficult. Logs are distributed across hundreds of containers. Metrics come from multiple sources. Tracking down the root cause of performance issues or application failures often feels like detective work. Without centralized observability, you're flying blind.

## The 10 best Kubernetes management tools and platforms

These Kubernetes management tools and platforms address the challenges above in different ways. Some provide comprehensive platforms for complete cluster management, while others are specialized tools that excel at specific tasks like visualization, debugging, or development workflows.

### 1. Northflank
**Category:** Kubernetes management platform

As a Kubernetes management platform, [Northflank](https://northflank.com/) takes a fundamentally different approach. Rather than giving you another dashboard to manage raw Kubernetes resources, it provides a developer-first platform that handles the complexity while still giving you the power of Kubernetes underneath.

<InfoBox className="BodyStyle">

What makes Northflank stand out: it solves the management problem at a higher level. While most tools require you to interact directly with Kubernetes resources (pods, deployments, YAML files), Northflank manages your Kubernetes clusters for you through a single unified interface focused on services, databases, and deployments, handling the underlying Kubernetes complexity automatically.

</InfoBox>

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

**Key features:**

- **Intuitive UI and developer experience**: Manage deployments, services, databases, and jobs through a clean, modern interface that doesn't require YAML expertise
- **Built-in CI/CD**: Integrated pipelines mean you can go from Git push to production without stitching together multiple tools ([See how](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank))
- **Multi-cloud support**: Deploy to your own cloud accounts ([AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), [Oracle](https://northflank.com/cloud/oci)) without vendor lock-in
- **Real-time logs and monitoring**: Centralized logging and metrics without complex setup ([See how](https://northflank.com/docs/v1/application/observe/observability-on-northflank))
- **Preview environments**: Automatic environment creation for every pull request ([See how](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment))
- **Managed databases and add-ons**: Provision databases, caches, and other services alongside your applications ([See how](https://northflank.com/docs/v1/application/databases-and-persistence/configure-addons-for-high-availability))

**Best for:**

- Development teams who want Kubernetes power without Kubernetes complexity
- Startups and scale-ups building microservices architectures
- Teams looking to reduce time spent on DevOps and infrastructure management

<InfoBox className="BodyStyle">

**Why Northflank is the recommended choice:**

Most teams don't actually want to manage Kubernetes; they want to ship applications. Northflank recognizes this and provides a complete platform that handles everything from CI/CD to databases to monitoring, all with production-ready defaults for security and scalability.

The result is faster deployments, reduced operational burden, and developers who can focus on building features instead of learning Kubernetes internals. Teams typically reduce their time-to-deployment from days to minutes.

Unlike managed Kubernetes services that still require you to understand Kubernetes deeply, or tools that just add better UIs on top of the same complexity, Northflank provides an entirely different experience where Kubernetes power is available when you need it but invisible when you don't.

Try out [Northflank's free sandbox](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see if this approach fits your team's workflow and requirements.

</InfoBox>

**Potential drawbacks:**

- Teams that need direct access to low-level Kubernetes APIs might find it more opinionated than raw cluster management

### 2. Rancher
**Category:** Kubernetes management platform

Rancher is an open-source platform for managing Kubernetes clusters at scale. It provides centralized management for multiple clusters across different environments and cloud providers.

![rancher-homepage.png](https://assets.northflank.com/rancher_homepage_274dfd6470.png)

**Key features:**

- **Multi-cluster management**: Manage multiple Kubernetes clusters from a single control plane
- **Cluster provisioning**: Deploy new clusters across various infrastructure providers
- **Centralized authentication and RBAC**: Unified access control across all your clusters
- **Built-in monitoring and logging**: Integrated Prometheus and Grafana for observability
- **Application catalog**: Deploy applications with pre-configured Helm charts

**Best for:**

- Teams managing large numbers of Kubernetes clusters
- Organizations with hybrid cloud or multi-cloud strategies
- Platform teams building internal Kubernetes platforms

**Potential drawbacks:**

- Requires Kubernetes knowledge to configure effectively
- Can become complex to maintain as the number of managed clusters grows

### 3. Lens (OpenLens)
**Category:** Kubernetes IDE and management tool

Lens (now available as OpenLens) is often called the "Kubernetes IDE". It's a desktop application that provides an interface for working with Kubernetes clusters.

![lens-homepage.png](https://assets.northflank.com/lens_homepage_7bd155be3a.png)

**Key features:**

- **Context-aware UI**: Dynamic interface that adapts based on the selected resource
- **Real-time monitoring**: Live metrics and resource utilization
- **Multi-cluster management**: Switch between multiple clusters
- **Terminal integration**: Built-in terminal with kubectl access
- **Resource editing**: Edit Kubernetes resources with syntax highlighting and validation

**Best for:**

- Developers and operators who work with Kubernetes regularly
- Teams managing multiple clusters who need visibility
- Teams who prefer graphical interfaces over kubectl

**Potential drawbacks:**

- Desktop application means it's not accessible from anywhere
- Some advanced features require the paid Pro version

### 4. K9s
**Category:** Terminal-based management tool

K9s is a terminal-based UI for managing Kubernetes clusters. It provides real-time visibility and control over cluster resources from the command line.

![k9s-homepage.png](https://assets.northflank.com/k9s_homepage_0056f46f73.png)

**Key features:**

- **Real-time cluster monitoring**: Live view of all cluster resources with automatic refresh
- **Resource navigation**: Navigate between pods, deployments, services, and more
- **Log streaming**: View logs from multiple pods simultaneously
- **Resource editing**: Edit resources directly from the terminal
- **Custom resource support**: Works with CRDs and custom resources

**Best for:**

- DevOps engineers who prefer terminal-based workflows
- SSH-based cluster access scenarios
- Cluster troubleshooting and debugging

**Potential drawbacks:**

- Terminal-only interface isn't for everyone
- Steeper learning curve compared to graphical tools

### 5. Portainer
**Category:** Container management platform

Portainer is a container management platform that supports both Docker and Kubernetes.

![portainer-homepage.png](https://assets.northflank.com/portainer_homepage_2a6b6b9e25.png)

**Key features:**

- **Template library**: Deploy complex applications with pre-built templates
- **GitOps integration**: Automated deployments from Git repositories
- **Access control**: Fine-grained user and team permissions
- **Edge computing support**: Manage clusters at the edge alongside cloud deployments

**Best for:**

- Teams transitioning from Docker to Kubernetes
- Organizations that need access control and governance
- Edge computing and IoT deployments

**Potential drawbacks:**

- Some features require the paid Business Edition
- Less comprehensive than specialized Kubernetes tools

### 6. Kubevious
**Category:** Visualization and configuration tool

Kubevious provides an application-centric view of your Kubernetes clusters, helping you understand the relationships between resources and catch configuration issues before they become problems.

![kubevious-homepage.png](https://assets.northflank.com/kubevious_homepage_03c5dc54dd.png)

**Key features:**

- **Visual cluster representation**: See your applications and their dependencies graphically
- **Configuration validation**: Detect misconfigurations and potential issues automatically
- **Time-machine**: Review cluster state at any point in history
- **Full-text search**: Find resources across your entire cluster
- **Rule engine**: Create custom validation rules for your organization's standards

**Best for:**

- Understanding complex microservices architectures
- Preventing configuration errors before deployment
- Onboarding new team members to existing clusters

**Potential drawbacks:**

- Primarily focused on configuration and visualization rather than operational tasks
- Smaller community compared to more established tools

### 7. Cyclops
**Category:** Developer-focused management tool

Cyclops is designed to make Kubernetes more accessible to developers who don't want to become Kubernetes experts.

![cyclops-homepage.png](https://assets.northflank.com/cyclops_homepage_dc6abba20a.png)

**Key features:**

- **Developer-friendly interface**: Simplified UI that hides Kubernetes complexity
- **One-click deployments**: Deploy applications without writing YAML
- **Rollback support**: Rollback to previous versions when things go wrong
- **Custom validation rules**: Prevent common mistakes with pre-deployment checks
- **Troubleshooting tools**: Built-in debugging features to identify issues

**Best for:**

- Development teams with limited Kubernetes experience
- Organizations wanting to democratize access to Kubernetes
- Reducing the learning curve for new developers

**Potential drawbacks:**

- Newer tool with smaller community
- May lack advanced features needed by experienced operators

### 8. kOps (Kubernetes Operations)
**Category:** Infrastructure automation tool

kOps is a command-line tool for creating, destroying, upgrading, and maintaining production-grade Kubernetes clusters, particularly on AWS.

![kOps-homepage.png](https://assets.northflank.com/k_Ops_homepage_b8d726786f.png)

**Key features:**

- **Infrastructure as code**: Define entire clusters in declarative configuration files
- **Automated cluster lifecycle**: Create, update, and delete clusters with commands
- **High availability support**: Built-in support for HA control plane configurations
- **Rolling updates**: Update clusters with automated rolling updates
- **Terraform integration**: Generate Terraform configurations for your clusters

**Best for:**

- Platform teams managing Kubernetes infrastructure on AWS
- Organizations practicing infrastructure as code
- Automated cluster provisioning and management

**Potential drawbacks:**

- Command-line only, no GUI
- Primarily focused on AWS (though GCE support exists)

### 9. Platform9
**Category:** Managed Kubernetes platform

Platform9 offers fully managed Kubernetes as a SaaS service, handling cluster operations so you can focus on applications.

![platform9-homepage.png](https://assets.northflank.com/platform9_homepage_7b73874f21.png)

**Key features:**

- **Fully managed service**: Platform9 manages cluster operations, updates, and maintenance
- **Self-healing clusters**: Automatic detection and remediation of cluster issues
- **Multi-environment support**: Works across cloud, on-premises, and edge environments
- **Automated security patching**: Automated security updates and patching
- **99.9% SLA**: Enterprise-grade reliability guarantees

**Best for:**

- Organizations wanting to minimize operational overhead
- Teams without dedicated Kubernetes expertise
- Hybrid cloud and edge deployments

**Potential drawbacks:**

- SaaS model may not suit organizations with strict data residency requirements
- Less control compared to self-managed solutions

### 10. DevSpace
**Category:** Development workflow tool

DevSpace focuses on improving the inner development loop for Kubernetes, making it faster to build, test, and deploy changes during active development.

![devspace-homepage.png](https://assets.northflank.com/devspace_homepage_983a1e15da.png)

**Key features:**

- **Development workflows**: Hot-reloading of code changes in running containers
- **Integrated build pipeline**: Build, tag, and deploy in one workflow
- **Port forwarding**: Access to services running in your cluster
- **File sync**: Automatically sync local changes to running containers
- **Build caching**: Reduced build times with caching

**Best for:**

- Active development and iteration
- Teams with slow development cycles on Kubernetes
- Microservices development with multiple interdependent services

**Potential drawbacks:**

- Primarily focused on development workflows rather than production operations
- Requires configuration to set up optimal workflows

## How to choose the right Kubernetes management tool and platform for your team

When evaluating Kubernetes management tools and platforms, several key factors should guide your decision. Here's what to consider:
                                                                       
| Tool | Category | Best for |
| --- | --- | --- |
| Northflank | Platform | Complete management, developer experience, enterprise scale |
| Rancher | Platform | Multi-cluster, enterprise scale |
| Lens | Tool | Desktop IDE, visualization |
| K9s | Tool | Terminal-based debugging |
| Portainer | Platform | Docker migration, edge deployments |
| Kubevious | Tool | Configuration validation |
| Cyclops | Tool | Simplified deployments |
| kOps | Tool | Infrastructure as code (AWS) |
| Platform9 | Platform | Fully managed service |
| DevSpace | Tool | Development workflows |

## Finding the right management approach for your needs

Managing Kubernetes clusters doesn't have to be overwhelming. The right Kubernetes management tools and platforms can reduce complexity, improve visibility, and free your team to focus on building great products instead of struggling with infrastructure.

For most teams, the best approach is combining multiple Kubernetes management tools: a platform like Northflank for simplified deployments and operations, specialized tools like Lens or K9s for troubleshooting and debugging, and Rancher if you're managing many clusters at scale.

The Kubernetes ecosystem is mature enough now that there's truly a tool for every need and every team size. Start with the tools that address your biggest pain points, and expand from there as your needs grow.

<InfoBox className="BodyStyle">

To reduce the operational complexity of managing Kubernetes clusters, try [Northflank's free sandbox](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to discuss your specific setup.

</InfoBox>]]>
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</item><item>
  <title>How to sandbox AI agents in 2026: MicroVMs, gVisor &amp; isolation strategies</title>
  <link>https://northflank.com/blog/how-to-sandbox-ai-agents</link>
  <pubDate>2026-02-02T16:30:00.000Z</pubDate>
  <description>
    <![CDATA[Learn how to sandbox AI agents in 2026: Compare microVMs, gVisor, and containers for secure code execution. Complete guide to isolation technologies, security best practices, and implementation.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/how_to_sandbox_ai_agents_1_a3576a40ed.png" alt="How to sandbox AI agents in 2026: MicroVMs, gVisor &amp; isolation strategies" /><InfoBox className="BodyStyle">

## TL;DR: How to sandbox AI agents in 2026

- Sandboxing AI agents involves isolating code execution in secure environments to prevent unauthorized access, data breaches, and system compromise. Standard containers aren't sufficient for AI-generated code because they share the host kernel.
- The three main isolation approaches are microVMs (Firecracker, Kata Containers), gVisor (user-space kernel), and hardened containers. MicroVMs provide the strongest isolation with dedicated kernels per workload, gVisor offers syscall interception without full VMs, and containers work only for trusted code.
- Production AI agent sandboxing requires defense-in-depth: isolation boundaries, resource limits, network controls, permission scoping, and monitoring.

> Platforms like [Northflank](https://northflank.com/) provide production-ready sandbox infrastructure using Kata Containers and gVisor, processing isolated workloads at scale without operational overhead. See [how to spin up a secure code sandbox & microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
> 

</InfoBox>

## Why do AI agents need sandboxing?

AI agents are autonomous systems that generate and execute code, call APIs, access data, and make decisions without human oversight.

Unlike traditional applications, where developers write and review every line of code, AI agents produce code dynamically based on prompts, context, and objectives. This creates fundamental security challenges:

- AI agents generate code you haven't reviewed or audited
- Prompt injection attacks manipulate agent behavior to execute malicious actions
- Compromised agents abuse APIs and system access beyond intended scope
- Successful exploits enable data exfiltration and lateral movement across infrastructure
- Agents can become rogue insiders with programmatic access to critical systems

When 83% of companies plan to deploy AI agents, understanding sandboxing becomes essential for preventing security breaches that traditional cybersecurity tools weren't designed to handle.

## What is AI agent sandboxing?

AI agent sandboxing creates isolated execution environments where agents can run code without affecting the host system or other workloads.

A sandbox provides strict boundaries that limit what an agent can access, modify, or interact with. Effective sandboxing addresses multiple threat vectors simultaneously: code execution exploits, filesystem access, network communication, resource consumption, and privilege escalation.

The security model operates on zero-trust principles where all agent actions are explicitly allowed rather than implicitly permitted, treating all AI-generated code as potentially malicious.

## What isolation technologies exist for AI agents?

Different isolation technologies provide different security guarantees and performance characteristics for AI agent workloads.

### Standard Docker containers

Docker containers use Linux namespaces and cgroups to isolate processes while sharing the host kernel.

- **Security model**: Containers rely on kernel features for isolation. A kernel vulnerability or misconfiguration can allow container escape, giving attackers host access.
- **Performance**: Fast startup (milliseconds), minimal overhead, high density.
- **Use case**: Suitable only for trusted, vetted code in single-tenant environments.

### gVisor user-space kernel

gVisor implements a user-space kernel that intercepts system calls before they reach the host kernel.

When a container makes a syscall, gVisor's Sentry process handles it in user space, drastically reducing kernel attack surface. Instead of hundreds of syscalls reaching the host kernel, gVisor allows only a minimal, vetted subset.

- **Security model**: Syscall-level isolation. Stronger than containers, weaker than VMs.
- **Performance**: Some overhead on I/O-heavy workloads (10-30%), fast startup.
- **Use case**: Compute-heavy AI workloads where full VM isolation isn't justified.

### Firecracker microVMs

Firecracker creates lightweight virtual machines with minimal device emulation, running each microVM with its own Linux kernel inside KVM.

- **Security model**: Hardware-level isolation. Each workload has a dedicated kernel completely separated from the host. Attackers must escape both the guest kernel and the hypervisor.
- **Performance**: Boots in ~125ms, less than 5 MiB overhead per VM, up to 150 VMs per second per host.
- **Use case**: Multi-tenant AI agent execution, untrusted code, production environments.

### Kata Containers

Kata Containers orchestrates multiple VMMs (Firecracker, Cloud Hypervisor, QEMU) to provide microVM isolation through standard container APIs.

It integrates with Kubernetes, handling all operational complexity of running microVMs. From Kubernetes' perspective, it's a normal container. Under the hood, it's a full VM with hardware isolation.

- **Security model**: Same hardware-level isolation as Firecracker, with Kubernetes-native orchestration.
- **Performance**: Boots in ~200ms, minimal memory overhead.
- **Use case**: Production Kubernetes workloads needing VM-level security with container workflows.

S*ee the following related articles:*

- [*Firecracker vs gVisor: Which isolation technology should you use?*](https://northflank.com/blog/firecracker-vs-gvisor)
- [*Kata Containers vs Firecracker vs gVisor: Which container isolation tool should you use?*](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor)

## Which isolation technology should you use?

The right isolation technology depends on your threat model and workload characteristics. See the table below that summarizes the answer:

| Technology | Isolation level | Boot time | Security strength | Best for |
| --- | --- | --- | --- | --- |
| **Docker containers** | Process (shared kernel) | Milliseconds | Process-level isolation | Trusted workloads |
| **gVisor** | Syscall interception | Milliseconds | Interposed / syscall-level isolation | Multi-tenant SaaS, CI/CD pipelines |
| **Firecracker** | Hardware (dedicated kernel) | ~125ms | Hardware-enforced isolation | Serverless functions, AI inference, untrusted code execution |
| **Kata Containers** | Hardware (via VMM) | ~200ms | Hardware-enforced isolation | Regulated industries, multi-tenant Kubernetes, zero-trust environments |

In a nutshell:

- **For production AI agents executing untrusted code**: Use Firecracker microVMs or Kata Containers. The hardware boundary prevents entire classes of kernel-based attacks.
- **For compute-heavy agents with limited I/O**: gVisor provides strong isolation without full VM overhead.
- **For trusted internal automation**: Hardened containers with seccomp, AppArmor, and capability dropping work only when agents execute code you've reviewed and trust.

<InfoBox className="BodyStyle">

**Production-ready AI agent sandboxing without the operational complexity**

Building secure sandbox infrastructure requires managing kernel images, networking configuration, security hardening, and orchestration.

[Northflank](https://northflank.com/) provides microVM-backed sandboxes using Kata Containers and gVisor, handling all operational complexity. Deploy any OCI container image and get hardware-level isolation with standard container workflows. [Try Northflank](https://app.northflank.com/signup) or [talk to an engineer](https://cal.com/team/northflank/northflank-demo) about AI agent sandboxing.

> **See [how to spin up a secure code sandbox & microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)**
> 

Also, see:

- [Secure runtime for codegen tools: microVMs, sandboxing, and execution at scale](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale)
- [Your containers aren't isolated. Here's why that's a problem](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation)

</InfoBox>

## How do you implement resource limits for AI agents?

AI agents can consume excessive resources either accidentally or maliciously, requiring strict limits on CPU, memory, disk, and network usage.

- **CPU limits**: Prevent compute exhaustion by setting maximum CPU shares and throttling runaway processes.
- **Memory limits**: Stop memory bombs by defining hard limits that terminate processes exceeding allocation.
- **Disk quotas**: Block storage attacks by limiting filesystem usage and rate-limiting I/O operations.
- **Network bandwidth**: Prevent data exfiltration by rate-limiting outbound traffic and monitoring for unusual patterns.

## What network controls should AI agent sandboxes have?

AI agents should operate on a zero-trust network model where all connections are explicitly allowed rather than implicitly permitted.

- **Egress filtering**: Block all outbound connections by default. Whitelist only required API endpoints and services.
- **DNS restrictions**: Limit DNS resolution to prevent discovery attacks and command-and-control communication.
- **Network segmentation**: Isolate agent networks from production systems and sensitive data stores.

## How do you scope AI agent permissions?

Grant AI agents only the minimum permissions required for their specific tasks, following the principle of least privilege.

- **Short-lived credentials**: Issue temporary tokens with limited scope for each task. Expired credentials can't be reused if compromised.
- **Tool-specific permissions**: Different agent capabilities require different permission sets. Separate read-only from write access.
- **Human-in-the-loop gates**: Require explicit human approval for high-risk actions like financial transactions or data deletion.

## How do you monitor AI agent behavior?

Comprehensive logging and monitoring detect compromised agents before they cause damage.

- **Execution tracking**: Log all code execution attempts, tool calls, and API requests with immutable audit trails.
- **Anomaly detection**: Monitor for unexpected network connections, excessive API calls, and unusual resource consumption.
- **Failed access attempts**: Track permission denials and policy violations as indicators of compromise.

## What are common AI agent security vulnerabilities?

Understanding attack vectors helps you design better sandboxes for AI agent workloads.

- **Prompt injection attacks**: Attackers craft inputs that manipulate agent behavior, causing it to execute malicious actions or leak data. Mitigate with input validation, prompt filtering, output monitoring, and sandboxed tool execution.
- **Code generation exploits**: Agents generate code containing vulnerabilities or malicious logic. Mitigate with code execution sandboxing in isolated containers with no network access and minimal system privileges.
- **Context poisoning**: Attackers modify information agents rely on for continuity (dialog history, RAG knowledge bases), warping future reasoning. Mitigate with cryptographic verification of context data and immutable storage.
- **Tool abuse**: Agents misuse available tools with dangerous parameters. Mitigate with policy enforcement gates that vet agent plans before execution and human approval for critical operations.

## Should you build or use a sandbox platform?

Most teams face a choice between building custom sandbox infrastructure or using an existing platform.

**Building your own** gives full control over security policies but requires significant engineering investment (months of work), ongoing operational burden for patching and scaling, and expertise in virtualization, networking, and Kubernetes.

**Using a platform** provides production-ready infrastructure immediately, abstracts operational complexity, handles regular security updates and compliance, and lets engineering resources focus on agent capabilities rather than infrastructure.

Platforms like Northflank provide both Kata Containers and gVisor, choosing appropriate isolation based on workload requirements while processing isolated workloads at scale with automatic security hardening built in.

## How does Northflank sandbox AI agents?

[Northflank](https://northflank.com/) provides a secure runtime environment by default, isolating every container in the way that makes sense for your workload.

![northflank-sandbox.png](https://assets.northflank.com/northflank_sandbox_ac966e0f30.png)

**Infrastructure-adaptive isolation:**

- On infrastructure where nested virtualization is available: Northflank runs Kata Containers with Cloud Hypervisor for hardware-level isolation
- On environments where nested virtualization is unavailable: Northflank uses gVisor for syscall-level isolation

This becomes critical when you're working with AI agents that demand API tokens or environment variables. They might need your Cloudflare auth token, your Stripe secret key, or Postgres access. Without proper isolation, you're giving them the ability to become your infrastructure.

Enterprise customers run secure multi-tenant AI agent deployments processing thousands of code executions daily. The platform handles kernel image management, networking configuration, security hardening, and orchestration complexity automatically. You get VM-grade security with container-grade workflows on any cloud.

> [Try Northflank](https://app.northflank.com/signup) to sandbox your AI agents with production-ready infrastructure, or [talk to an engineer](https://cal.com/team/northflank/northflank-demo) about your specific isolation requirements.
> 

## What are AI agent sandboxing best practices?

Follow these practices when deploying AI agents in production environments.

- **Start with strong isolation**: Default to microVMs for untrusted code. Relax to gVisor or containers only when threat model justifies it.
- **Implement defense-in-depth**: Combine multiple security layers including sandboxing, monitoring, approval gates, and signed artifacts.
- **Limit agent scope**: Start with narrow, well-defined tasks where the blast radius of failures is contained. Expand capabilities gradually.
- **Validate failure modes**: Test what happens when agents behave maliciously. Can they delete files, exfiltrate data, or escalate privileges?
- **Monitor continuously**: Log all agent actions, tool calls, and resource usage. Set alerts for policy violations and anomalous behavior.
- **Plan for rapid change**: Best practices evolve monthly as new attack techniques emerge. What's adequate protection today may be insufficient next quarter.

<InfoBox className="BodyStyle">

**Related articles:**

- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox)
- [Top AI sandbox platforms in 2026](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution)
- [What's the best code execution sandbox for AI agents in 2026?](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents)

</InfoBox>

## Frequently asked questions about sandboxing AI agents

### What is the difference between sandboxing and containerization?

Containerization provides process-level isolation using Linux namespaces and cgroups. Sandboxing is a broader concept that includes containers but also encompasses stronger isolation technologies like microVMs and user-space kernels. For AI agents, standard containers alone don't provide sufficient isolation because they share the host kernel.

### Why can't I use Docker containers to sandbox AI agents?

Docker containers share the host kernel with all other containers. A kernel vulnerability or misconfiguration can allow container escape, giving attackers access to the host and other containers. AI agents generate unpredictable code that might exploit these vulnerabilities. MicroVMs provide dedicated kernels per workload, eliminating this entire attack vector.

### How much performance overhead does sandboxing add?

The overhead depends on the isolation technology. Firecracker microVMs boot in ~125ms with less than 5 MiB memory overhead. gVisor adds 10-30% overhead on I/O-heavy workloads but minimal overhead on compute-heavy tasks. For most AI agent workloads, the security benefits far outweigh the performance cost.

### What is the best sandbox technology for AI code execution?

For production environments running untrusted AI-generated code, Firecracker microVMs or Kata Containers provide the strongest isolation. They create hardware-enforced boundaries that prevent kernel-based exploits. gVisor is acceptable for compute workloads where you control the code. Standard containers are insufficient for untrusted code.

### How do I sandbox AI agents in Kubernetes?

Use Kata Containers with a RuntimeClass that specifies the kata-clh handler. Kata integrates with Kubernetes through CRI, automatically provisioning microVMs for pods that specify the Kata RuntimeClass. This provides VM-level isolation with standard Kubernetes workflows and APIs.

### Do I need to build my own sandbox infrastructure?

Most teams are better served using existing platforms rather than building custom infrastructure. Building sandbox infrastructure requires months of engineering work and ongoing operational burden. Platforms like Northflank provide production-ready sandbox infrastructure with Kata Containers and gVisor, handling all operational complexity so you can focus on agent capabilities.]]>
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  <title>Guide to Cloud Hypervisor in 2026: Modern VMM for cloud workloads</title>
  <link>https://northflank.com/blog/guide-to-cloud-hypervisor</link>
  <pubDate>2026-01-30T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[Complete guide to Cloud Hypervisor in 2026: Learn how this Rust-based VMM provides hardware-level isolation for cloud workloads with CPU hotplugging and Kata integration.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/guide_to_cloud_hypervisor_ba8ddd954b.png" alt="Guide to Cloud Hypervisor in 2026: Modern VMM for cloud workloads" /><InfoBox className="BodyStyle">

## TL;DR: Guide to Cloud Hypervisor in 2026

Cloud Hypervisor is an open-source Virtual Machine Monitor written in Rust for modern cloud workloads. It provides hardware-level isolation through lightweight VMs while supporting CPU and memory hotplugging, vhost-user devices, and Kata Containers integration.

It boots VMs in ~200ms and runs on KVM and Microsoft Hypervisor across x86-64 and AArch64 architectures. The project is part of the Linux Foundation and is commonly used with Kata Containers for cloud workloads.

> Platforms like [Northflank](https://northflank.com/) use Cloud Hypervisor as the primary VMM for Kata Containers, processing isolated workloads at scale in production.
> 

</InfoBox>

## What is Cloud Hypervisor?

Cloud Hypervisor is a Virtual Machine Monitor that creates and manages lightweight virtual machines for cloud workloads.

Unlike traditional hypervisors designed for flexibility and legacy hardware support, Cloud Hypervisor focuses exclusively on modern operating systems running in cloud environments. The project started as Intel's contribution to the Rust VMM ecosystem, building on lessons learned from both Firecracker and crosvm.

While Firecracker prioritizes minimalism for serverless workloads and QEMU prioritizes completeness for every possible use case, Cloud Hypervisor aims for the middle ground: enough features to handle production workloads without unnecessary complexity.

Cloud Hypervisor implements modern virtualization features that cloud applications actually need: paravirtualized I/O through virtio devices, CPU and memory hotplugging, device passthrough via VFIO, and integration with container orchestration platforms.

### Cloud Hypervisor architecture

Cloud Hypervisor is built on the Rust VMM project, sharing virtualization components with Firecracker and crosvm.

Key architectural choices include minimal device emulation (only 16 devices needed for modern workloads), paravirtualization through virtio for networking and storage, API-driven management via REST, and Rust's memory safety to prevent common vulnerabilities.

| Feature | Specification |
| --- | --- |
| Language | Rust (memory-safe) |
| Code size | ~50,000 lines |
| Boot time | ~200ms |
| Architectures | x86-64, AArch64 |
| Guest OS support | Linux, Windows 10/Server 2019 |
| Hypervisor backend | KVM, Microsoft Hypervisor (MSHV) |

## How does Cloud Hypervisor compare to other VMMs?

Understanding where Cloud Hypervisor fits relative to Firecracker and QEMU helps clarify its design trade-offs.

- **Firecracker comparison:** Cloud Hypervisor boots in ~200ms compared to Firecracker's ~125ms. The extra 75ms enables CPU and memory hotplugging, vhost-user devices, and broader hardware compatibility that Firecracker deliberately omits. Firecracker optimizes for ephemeral serverless functions, while Cloud Hypervisor targets longer-running workloads needing runtime flexibility.
- **QEMU comparison:** Cloud Hypervisor has ~50k lines of Rust versus QEMU's ~2 million lines of C. QEMU emulates 40+ devices including legacy hardware; Cloud Hypervisor implements 16 modern devices. Cloud Hypervisor boots significantly faster (~200ms vs several seconds for QEMU) with sensible defaults for cloud workloads, while QEMU requires extensive configuration.

| Factor | Cloud Hypervisor | Firecracker | QEMU |
| --- | --- | --- | --- |
| **Code size** | ~50k lines (Rust) | ~50k lines (Rust) | ~2M lines (C) |
| **Boot time** | ~200ms | ~125ms | Several seconds |
| **Hotplugging** | CPU, memory, devices | No | Yes (complex) |
| **GPU support** | Limited | No | Full (VFIO) |
| **Kata integration** | Supported (commonly selected in cloud platforms) | Supported | Supported (historically default |

<InfoBox className="BodyStyle">

**Use a platform that abstracts the complexity**

- You need production-grade isolation without infrastructure engineering
- You're running AI agents, code sandboxes, or untrusted workloads at scale
- You want infrastructure that grows beyond sandboxing (databases, APIs, orchestration)

Northflank provides Cloud Hypervisor through Kata Containers with operational complexity abstracted away. [Try Northflank](https://app.northflank.com/signup) or [talk to an engineer](https://cal.com/team/northflank/northflank-demo) about your isolation requirements.

</InfoBox>

## What are Cloud Hypervisor's key features?

Cloud Hypervisor provides several features that make it suitable for production cloud workloads.

### CPU and memory hotplugging

Cloud Hypervisor can add CPUs and memory to running VMs without restarting, enabling workloads to scale resources dynamically.

CPU hotplugging works by the hypervisor creating new vCPUs and advertising them to the guest kernel through ACPI. The guest OS then brings the new CPUs online. Memory hotplugging allocates additional memory on the host (in multiples of 128 MiB) and maps it into the guest's address space.

### Vhost-user device support

Vhost-user offloads device emulation to separate processes, improving performance and security.

Instead of handling I/O directly in the VMM process, Cloud Hypervisor delegates it to specialized daemons. This architecture enables higher I/O throughput by running device handlers on dedicated cores, better isolation by separating device logic from the VMM, and easier device implementation through standard vhost-user protocols.

### Virtio device model

Cloud Hypervisor uses paravirtualized virtio devices for all I/O operations.

This includes virtio-net for networking, virtio-blk for block storage, virtio-fs for filesystem sharing, virtio-vsock for host-guest communication, and virtio-pmem for persistent memory. Virtio avoids emulating real hardware, providing a clean interface that both guest and host understand, eliminating overhead and enabling better performance.

### VFIO device passthrough

Cloud Hypervisor supports passing through physical devices directly to VMs using VFIO.

This gives near-native performance for PCIe devices like network cards or accelerators that need direct hardware access. While QEMU has more mature VFIO support, Cloud Hypervisor provides the functionality needed for most cloud use cases.

## How does Cloud Hypervisor integrate with Kubernetes?

Kata Containers supports multiple VMMs (including QEMU and Cloud Hypervisor). Many cloud platforms run Kata with Cloud Hypervisor for Kubernetes microVM workloads.

When you deploy a container with Kata's Cloud Hypervisor runtime class, Kata handles all the orchestration: provisioning the VM, booting a minimal guest kernel, mounting your container image, managing networking between guest and host, and handling VM lifecycle. From Kubernetes' perspective, it's a normal container. Under the hood, it's a full VM with hardware isolation.

This integration makes Cloud Hypervisor accessible to teams who need VM-level security without building custom infrastructure. Platforms like [Northflank](https://northflank.com/) use this Kata Containers integration with Cloud Hypervisor to provide hardware-level isolation for production workloads. You deploy using standard Kubernetes YAML, and Kata handles the VMM complexity.

## What are Cloud Hypervisor's limitations?

Cloud Hypervisor makes deliberate trade-offs that create some limitations compared to full-featured hypervisors.

- **No legacy hardware support:** Cloud Hypervisor doesn't emulate legacy devices like floppy drives, PS/2 keyboards, or ISA buses. Modern cloud workloads don't need these devices, and emulating them adds complexity and attack surface.
- **Limited GPU support:** While Cloud Hypervisor supports some GPU passthrough scenarios via VFIO, QEMU's implementation is more mature and handles a wider range of GPUs and configurations.
- **Windows support is evolving:** Cloud Hypervisor supports Windows 10 and Windows Server 2019, but the implementation is less mature than Linux support. Most production deployments run Linux guests.
- **Snapshot stability:** Snapshot/restore and live migration features exist but aren't guaranteed stable across versions. Production deployments should test these features thoroughly before relying on them.

<InfoBox className="BodyStyle">

**Production-ready Cloud Hypervisor without the operational complexity**
Running Cloud Hypervisor requires managing VM lifecycles, networking configuration, and Kubernetes integration.

[Northflank](https://northflank.com/) uses Kata Containers with Cloud Hypervisor to provide hardware-level isolation without operational overhead. Deploy any OCI container image and get VM-level security with standard container workflows. [Try Northflank](https://app.northflank.com/signup) or [talk to an engineer](https://cal.com/team/northflank/northflank-demo).

</InfoBox>

## When should you use Cloud Hypervisor?

Understanding Cloud Hypervisor's ideal use cases helps you decide if it fits your requirements.

- **Production microVMs in Kubernetes:** Production microVMs in Kubernetes: Cloud Hypervisor is a commonly selected VMM for Kata Containers when running hardware-isolated container workloads.
- **Multi-tenant cloud workloads:** SaaS platforms, code execution environments, AI sandboxes, and customer deployments all benefit from Cloud Hypervisor's balance of security and functionality.
- **Workloads needing runtime flexibility:** If your applications need CPU/memory hotplugging, vhost-user devices, or broader hardware support than Firecracker provides, Cloud Hypervisor delivers these features without QEMU's complexity.
- **Memory safety and security:** Rust's memory safety prevents entire classes of vulnerabilities that affect C-based hypervisors. The smaller codebase means fewer potential attack vectors compared to QEMU.

## How does Northflank use Cloud Hypervisor in production?

Northflank uses Kata Containers with Cloud Hypervisor as the primary VMM for microVM isolation.

![northflank-sandbox.png](https://assets.northflank.com/northflank_sandbox_ac966e0f30.png)

Cloud Hypervisor was chosen for its strong runtime performance, broad workload compatibility, production stability, and active development community under the Linux Foundation. The platform processes isolated workloads at scale, providing VM-level security with container workflows.

Enterprise customers run secure multi-tenant workloads on Northflank's infrastructure. When companies need to provision thousands of secure sandboxes for untrusted code execution, Northflank's Cloud Hypervisor-based infrastructure handles the scale reliably. No kernel images to maintain, no networking configuration, no complex hypervisor setup.

> [Try Northflank](https://app.northflank.com/signup) or [talk to an engineer](https://cal.com/team/northflank/northflank-demo) about your isolation requirements.
> 

## How do you get started with Cloud Hypervisor?

Getting started with Cloud Hypervisor depends on whether you're using it standalone or through Kata Containers.

### Installing Cloud Hypervisor standalone

Build Cloud Hypervisor from source:

```bash
# Install dependencies
sudo apt install git build-essential

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Clone and build
git clone https://github.com/cloud-hypervisor/cloud-hypervisor.git
cd cloud-hypervisor
cargo build --release

# Grant network capabilities
sudo setcap cap_net_admin+ep ./target/release/cloud-hypervisor
```

### Using Cloud Hypervisor with Kata Containers

The easiest way to use Cloud Hypervisor in production is through Kata Containers on Kubernetes.

Install Kata Containers:

```bash
# Set the latest version and chart URL
export VERSION=$(curl -sSL https://api.github.com/repos/kata-containers/kata-containers/releases/latest | jq .tag_name | tr -d '"')
export CHART="oci://ghcr.io/kata-containers/kata-deploy-charts/kata-deploy"

# Install Kata Containers using the OCI Helm chart
helm install kata-deploy "${CHART}" \
  --namespace kube-system \
  --create-namespace \
  --version "${VERSION}"
```

Specify the Cloud Hypervisor runtime in your pod spec:

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: secure-workload
spec:
  runtimeClassName: kata-clh
  containers:
  - name: app
    image: your-image:latest

```

Kata handles all Cloud Hypervisor orchestration automatically.

## What are the trade-offs between VMMs?

Different VMMs make different trade-offs based on their target use cases.

| VMM | Design focus | Best suited for |
| --- | --- | --- |
| **Cloud Hypervisor** | Balance of features and minimalism | Production Kubernetes workloads needing VM isolation with runtime flexibility (hotplugging, vhost-user). Frequently used with Kata Containers for cloud workloads. |
| **Firecracker** | Maximum minimalism and speed | Serverless functions requiring fastest possible boot times and smallest footprint. AWS Lambda's foundation for ephemeral workloads. |
| **QEMU** | Maximum flexibility and compatibility | Full system emulation, GPU workloads, legacy hardware, desktop virtualization. Most flexible but largest attack surface. |

<InfoBox className="BodyStyle">

**Related articles:**

- [Kata Containers vs Firecracker vs gVisor: Which container isolation tool should you use?](https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor)
- [Firecracker vs gVisor: Which isolation technology should you use?](https://northflank.com/blog/firecracker-vs-gvisor)
- [Firecracker vs QEMU: Which one should you use?](https://northflank.com/blog/firecracker-vs-qemu)
- [Your containers aren't isolated. Here's why that's a problem](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation)
- [How to spin up a secure code sandbox and microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
- [Multi-tenant cloud deployment](https://northflank.com/blog/multi-tenant-cloud-deployment)
- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox)

</InfoBox>

## Frequently asked questions on Cloud Hypervisor

### What is the main difference between Cloud Hypervisor and Firecracker?

Cloud Hypervisor provides more features than Firecracker (CPU/memory hotplugging, vhost-user, broader device support) while maintaining a security-focused design. Firecracker boots faster (~125ms vs ~200ms) but deliberately omits features to stay minimal. Cloud Hypervisor targets longer-running cloud workloads, Firecracker targets ephemeral serverless functions.

### Is Cloud Hypervisor more secure than QEMU?

Cloud Hypervisor has a significantly smaller codebase (~50k lines of Rust vs ~2M lines of C) and is written in a memory-safe language, reducing potential vulnerabilities. However, QEMU has been battle-tested for decades and receives extensive security auditing. For modern cloud workloads, Cloud Hypervisor's smaller attack surface is generally advantageous.

### Does Cloud Hypervisor support GPU passthrough?

Cloud Hypervisor supports some GPU passthrough scenarios via VFIO, but the implementation is less mature than QEMU's. For production GPU workloads, thoroughly test your specific GPU and drivers with Cloud Hypervisor, or consider QEMU for more reliable GPU support.

### Can Cloud Hypervisor run Windows?

Yes, Cloud Hypervisor supports Windows 10 and Windows Server 2019. However, Linux guests receive more development focus and are more widely deployed in production. Test Windows workloads thoroughly before production deployment.

### How does Cloud Hypervisor integrate with Kata Containers?

Kata Containers supports multiple VMMs, including QEMU and Cloud Hypervisor. Some platforms configure Kata to use Cloud Hypervisor by default. When you specify the kata-clh runtime class in Kubernetes, Kata automatically uses Cloud Hypervisor to create and manage VMs. This integration handles all operational complexity, making Cloud Hypervisor accessible through standard Kubernetes APIs.

### Can I use Cloud Hypervisor outside of Kubernetes?

Yes, Cloud Hypervisor can run standalone. However, most production deployments use it through Kata Containers for Kubernetes integration. Running Cloud Hypervisor directly requires manual VM lifecycle management, networking configuration, and orchestration.]]>
  </content:encoded>
</item><item>
  <title>Firecracker vs gVisor: Which isolation technology should you use?</title>
  <link>https://northflank.com/blog/firecracker-vs-gvisor</link>
  <pubDate>2026-01-29T17:30:00.000Z</pubDate>
  <description>
    <![CDATA[Firecracker vs gVisor: Compare isolation technologies for secure container workloads. Learn which provides stronger security, better performance, and easier integration for your infrastructure.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/firecracker_vs_gvisor_59f90aae57.png" alt="Firecracker vs gVisor: Which isolation technology should you use?" /><InfoBox className="BodyStyle">

## TL;DR: Firecracker vs gVisor

**Firecracker** and **gVisor** solve the same problem (container isolation) using fundamentally different approaches.

- **Firecracker** creates lightweight virtual machines called microVMs. Built by AWS in Rust, it boots VMs in ~125ms with hardware-enforced isolation via KVM. Each workload gets its own dedicated kernel, completely separated from the host and other workloads. AWS Lambda and Fargate run on Firecracker, handling tens of trillions of function invocations. Strong isolation but requires running a guest OS inside each microVM.
- **gVisor** implements a user-space kernel that intercepts system calls. Created by Google, it runs as a sandbox between your containers and the host kernel, drastically reducing attack surface without full VM overhead. Faster integration with existing container workflows than Firecracker, but syscall interception adds performance overhead on I/O-heavy workloads.
- **Choose Firecracker if** you need the strongest possible isolation for untrusted code, are building serverless platforms, or running multi-tenant workloads where security trumps everything. Boot times and memory efficiency matter.
- **Choose gVisor if** you want enhanced container security without managing VMs, need easier integration with Docker and Kubernetes, or want to reduce kernel attack surface without hardware virtualization overhead.

> **Note**: Platforms like [Northflank](https://northflank.com/) use both technologies in production, processing isolated workloads at scale using Kata Containers (which orchestrates Firecracker and other VMMs) alongside gVisor, choosing the right isolation technology based on your workload and infrastructure requirements.
> 

</InfoBox>

## What problem are Firecracker and gVisor solving?

Traditional Docker containers share the host kernel, which creates a fundamental security problem: if an attacker breaks out of a container through a kernel exploit, they gain access to the host system and potentially every other container running on that host.

This is more critical now because:

- AI agents generate and execute code automatically without human review
- Developers install thousands of npm packages they've never audited
- Malicious packages get published to registries regularly
- Supply chain attacks spread laterally across shared infrastructure
- Running untrusted code in production containers is increasingly common

## How does Firecracker provide isolation?

[Firecracker](https://firecracker-microvm.github.io/) is a Virtual Machine Monitor written in Rust by AWS. It creates microVMs, which are lightweight virtual machines with minimal device emulation.

Each Firecracker microVM runs its own Linux kernel inside a KVM virtual machine. This provides hardware-enforced isolation. An attacker breaking out of your application still needs to escape a full VM boundary, which is significantly harder than escaping a container.

Firecracker implements only five devices:

- virtio-net for networking
- virtio-block for storage
- virtio-vsock for host-guest communication
- Serial console for debugging
- Minimal keyboard controller

This minimalism is intentional. Fewer devices mean less code, which means fewer potential vulnerabilities. Firecracker's entire codebase is roughly 50,000 lines of Rust, compared to QEMU's nearly 2 million lines of C.

### Firecracker technical specifications

| Metric | Performance |
| --- | --- |
| Boot time to userspace | ~125ms |
| Memory overhead per microVM | Less than 5 MiB |
| microVM creation rate | Up to 150 per second per host |
| Language | Rust (memory-safe) |
| Isolation mechanism | Hardware virtualization via KVM |

## How does gVisor provide isolation?

[gVisor](https://gvisor.dev/) takes a completely different approach. Instead of running VMs, it implements a user-space kernel written in Go.

When your container makes a system call, gVisor intercepts it. Rather than passing the syscall directly to the host kernel, gVisor handles it in a sandboxed process called the Sentry. This drastically reduces the kernel attack surface because your application never directly interacts with the host kernel.

gVisor supports multiple execution modes: **Systrap** uses seccomp to intercept syscalls with better performance, **KVM** uses virtualization for syscall isolation (fastest on bare-metal), and storage modes include **Directfs** for high-performance file operations.

| Feature | Implementation |
| --- | --- |
| Isolation type | User-space kernel (syscall interception) |
| Language | Go |
| Overhead | Low (no full VM) but syscall tax exists |
| Compatibility | Most Linux syscalls supported, some limitations |
| Integration | Works directly with Docker, containerd, Kubernetes |

## Firecracker vs gVisor: Quick comparison

Here's the comparison table summarizing the key technical differences between Firecracker and gVisor across isolation, performance, and operational complexity.

| Factor | Firecracker | gVisor |
| --- | --- | --- |
| **Isolation strength** | Hardware-level (VM boundary) | Syscall-level (user-space kernel) |
| **Boot time** | ~125ms | Milliseconds (faster) |
| **Runtime overhead** | Minimal (near-native) | Syscall tax (10-30% on I/O) |
| **Memory per workload** | Less than 5 MiB + guest kernel | Minimal |
| **Integration complexity** | High (requires VM orchestration) | Low (works with Docker/K8s) |
| **Compatibility** | Full Linux support | Most syscalls (some unsupported) |
| **Best for** | Untrusted code, serverless, multi-tenant | Enhanced container security, existing workflows |

## Which provides stronger security isolation? Firecracker or gVisor?

**Firecracker delivers stronger isolation.** Hardware virtualization creates a hard boundary that's significantly more difficult to breach than a software-based sandbox.

Each Firecracker microVM has a dedicated kernel completely isolated from the host, hardware-enforced memory isolation via KVM, and minimal attack surface with only 5 devices and 50k lines of Rust code. Breaking out requires escaping both the guest kernel AND the hypervisor layer.

**gVisor provides strong isolation but not VM-level.** It reduces kernel attack surface dramatically by intercepting syscalls, but your workload still shares some host resources. The Sentry process runs on the host, and while it's sandboxed, it's not a full hardware boundary.

For untrusted multi-tenant workloads where customers are actively adversarial, Firecracker's hardware isolation provides the strongest security guarantees.

<InfoBox className="BodyStyle">

**Production-ready isolation without the complexity**

Running Firecracker or gVisor at scale requires significant engineering investment: kernel image management, networking configuration, security hardening, and orchestration.

[Northflank](https://northflank.com/) abstracts this operational complexity, providing both isolation technologies based on your infrastructure and workload requirements. Deploy any OCI container image and get VM-level security with container workflows. Companies run secure multi-tenant workloads on Northflank's infrastructure. [Learn more about secure sandboxes](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh).

**Related articles:**

- [Your containers aren't isolated. Here's why that's a problem](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation)
- [How to spin up a secure code sandbox and microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
- [Multi-tenant cloud deployment](https://northflank.com/blog/multi-tenant-cloud-deployment)
- [CodeSandbox alternatives](https://northflank.com/blog/codesandbox-alternatives)
- [Firecracker vs QEMU: Which one should you use?](https://northflank.com/blog/firecracker-vs-qemu)
- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox)
- [Top AI sandbox platforms in 2026](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution)

</InfoBox>

## How do Firecracker and gVisor compare in performance?

Performance differences between Firecracker and gVisor depend on whether you're measuring startup time or runtime overhead.

- **Startup time:** Firecracker boots microVMs in approximately 125 milliseconds. gVisor starts faster since there's no VM boot process, typically just milliseconds. For workloads that spin up frequently, gVisor has the edge.
- **Runtime performance:** Firecracker provides near-native performance with minimal VM boundary overhead. gVisor's syscall interception adds latency, sometimes 10-30% slower on I/O-heavy workloads. For CPU-bound workloads, both perform reasonably well. For I/O-intensive applications, Firecracker maintains better throughput.

## How difficult is it to run Firecracker and gVisor?

The operational complexity differs significantly: gVisor works with existing container tooling, while Firecracker requires VM orchestration infrastructure.

- **gVisor** integrates directly with container runtimes. You install `runsc` and configure Docker or containerd to use it. For Kubernetes, create a RuntimeClass pointing to gVisor. Your existing container images work without modification. The trade-off: not every syscall is implemented.
- **Firecracker** requires preparing kernel images, creating root filesystems, configuring networking (TAP devices, routing), implementing the jailer for security hardening, and building VM lifecycle management. Running one microVM is straightforward. Running thousands requires significant engineering investment.

This is why projects like Kata Containers exist. Kata integrates Firecracker (and other VMMs) with Kubernetes, handling the complexity so you can use microVMs through standard container APIs.

## When should you use Firecracker?

Use Firecracker when you need hardware-level isolation for untrusted code. Running customer workloads, AI-generated code, or scenarios where code might be actively malicious requires the strongest possible isolation.

It's ideal for building serverless platforms (fast boot times, minimal memory overhead), running multi-tenant SaaS where tenant isolation is critical, and I/O workloads requiring consistent, predictable performance.

## When should you use gVisor?

Use gVisor when you want enhanced isolation without managing VMs. It provides syscall-level sandboxing with minimal operational overhead. If your threat model is "defense in depth" rather than "actively adversarial tenants," gVisor is sufficient.

It works well for existing container workflows you don't want to rewrite, compute-heavy workloads with limited syscalls, and infrastructure where nested virtualization isn't available.

## Which should you choose? Firecracker or gVisor?

- **Choose Firecracker if** you need the strongest possible isolation for untrusted code, are building serverless infrastructure, security is your top priority, or I/O performance matters.
- **Choose gVisor if** you want enhanced security over standard containers without full VMs, have existing container workflows, or need fast deployment with minimal infrastructure changes.

<InfoBox className="BodyStyle">

**Use a platform that abstracts the complexity if** you need production-grade isolation without building infrastructure, want flexibility to use both technologies based on workload requirements, or are running AI agents, code sandboxes, or other untrusted workloads at scale.

Northflank provides both isolation technologies with the operational complexity abstracted away. Companies run production workloads with VM-level security using standard container workflows. [Try Northflank](https://app.northflank.com/signup) or [talk to an engineer](https://cal.com/team/northflank/northflank-demo) about your isolation requirements.

</InfoBox>

## Frequently asked questions about Firecracker and gVisor

### What is the main difference between Firecracker and gVisor?

Firecracker creates lightweight VMs with hardware-enforced isolation through KVM. Each workload runs in its own virtual machine with a dedicated kernel. gVisor implements a user-space kernel that intercepts syscalls, providing isolation without full VMs. Firecracker offers stronger security, gVisor offers easier integration.

### Which is more secure, Firecracker or gVisor?

Firecracker provides stronger isolation through hardware virtualization. Each microVM has a dedicated kernel and hardware-enforced memory boundaries. gVisor reduces kernel attack surface significantly through syscall interception but doesn't provide full VM-level isolation. For actively adversarial workloads, Firecracker is more secure.

### Does gVisor work with Docker?

Yes. gVisor integrates with Docker through the `runsc` runtime. You configure Docker to use gVisor and your existing containers work with enhanced isolation. Most applications run without modification, though some syscalls aren't supported.

### How fast does Firecracker boot?

Firecracker microVMs boot to userspace in approximately 125 milliseconds. This makes them suitable for serverless functions that need to scale from zero quickly. Traditional VMs take seconds to boot, containers boot faster but without VM-level isolation.

### What is Kata Containers and how does it relate to Firecracker?

Kata Containers is an orchestration project that integrates multiple VMMs (including Firecracker, Cloud Hypervisor, and QEMU) with Kubernetes. It handles the complexity of running microVMs through container APIs. Kata makes Firecracker usable in Kubernetes without building custom infrastructure.

### Why would I choose gVisor over Firecracker?

Choose gVisor when you want enhanced container security without the complexity of managing VMs. gVisor integrates with existing container workflows, requires no guest OS management, and works on infrastructure where nested virtualization isn't available. If your threat model doesn't require hardware-level isolation, gVisor is simpler to operate.]]>
  </content:encoded>
</item><item>
  <title>Kata Containers vs Firecracker vs gVisor: Which container isolation tool should you use?</title>
  <link>https://northflank.com/blog/kata-containers-vs-firecracker-vs-gvisor</link>
  <pubDate>2026-01-29T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[Kata Containers vs Firecracker vs gVisor: Compare container isolation tools for secure workloads. Learn which provides the best security, performance, and Kubernetes integration for your infrastructure.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/kata_containers_vs_firecracker_vs_gvisor_b7865dc5d4.png" alt="Kata Containers vs Firecracker vs gVisor: Which container isolation tool should you use?" /><InfoBox className="BodyStyle">

## TL;DR

**Kata Containers**, **Firecracker**, and **gVisor** are three leading technologies for isolating container workloads, each taking a different architectural approach.

- **Kata Containers** is an orchestration framework that integrates multiple Virtual Machine Monitors (VMMs) like Firecracker, Cloud Hypervisor, and QEMU with Kubernetes. It provides hardware-level isolation through lightweight VMs while maintaining container-like workflows. Kata handles the operational complexity of running microVMs at scale, making it production-ready for Kubernetes environments.
- **Firecracker** is a lightweight VMM built by AWS in Rust. It creates microVMs that boot in ~100-200ms depending on configuration with hardware-enforced isolation via KVM. Each workload gets a dedicated kernel. Firecracker performs well for speed and security but requires significant orchestration infrastructure to run in production. AWS Lambda and Fargate use Firecracker directly.
- **gVisor** is a user-space kernel developed by Google that intercepts system calls. It provides strong isolation without full VMs by acting as a syscall proxy between containers and the host kernel. Easier to integrate than microVMs but adds syscall overhead on I/O-heavy workloads.
- **Choose Kata Containers if** you need production-ready microVM isolation in Kubernetes with minimal operational overhead. Kata abstracts the complexity of running Firecracker, Cloud Hypervisor, or QEMU.
- **Choose Firecracker directly if** you're building custom serverless infrastructure, have deep virtualization expertise, or need the absolute fastest boot times with full control over the VMM layer.
- **Choose gVisor if** you want enhanced container security without VMs, need the simplest integration path, or are running on infrastructure where nested virtualization isn't available.

> **Note**: Platforms like [Northflank](https://northflank.com/) use Kata Containers with Cloud Hypervisor as the primary approach for microVM isolation, processing isolated workloads at scale in production. [Read how Northflank uses Kata Containers in production](https://katacontainers.io/blog/kata-containers-northflank-case-study/).
> 

</InfoBox>

## What problem do Kata Containers, Firecracker, and gVisor solve?

Standard Docker containers share the host kernel, creating a security vulnerability: if an attacker exploits a kernel bug to break out of a container, they gain access to the host and potentially every other container on that host.

This security gap is increasingly critical because:

- AI agents automatically generate and execute code without human review
- Developers install thousands of unaudited dependencies through package managers
- Malicious packages regularly appear in npm, PyPI, and other registries
- Supply chain attacks spread across shared infrastructure
- Multi-tenant platforms run untrusted customer code in production

## What is Kata Containers?

Kata Containers is an open-source project that integrates lightweight virtual machines with container orchestration platforms like Kubernetes. Unlike Firecracker or gVisor, Kata isn't itself an isolation technology. It's an orchestration framework that makes microVMs work seamlessly with container workflows.

Kata supports multiple VMMs as backends: **Cloud Hypervisor** (default, best performance), **Firecracker** (AWS-optimized), and **QEMU** (maximum hardware support).

When you deploy a container with Kata, it automatically provisions a lightweight VM, boots a minimal guest kernel, and manages networking. From Kubernetes' perspective, it looks like a normal container. Under the hood, it's a full VM with hardware isolation.

| Feature | Implementation |
| --- | --- |
| Isolation type | Hardware virtualization (via VMM) |
| Integration | Native Kubernetes through CRI |
| Boot time | ~150-300ms depending on VMM and configuration |
| Orchestration | Built-in lifecycle management |
| VMM options | Cloud Hypervisor, Firecracker, QEMU |

The key advantage: you get VM-level security with container-level ease of use.

## What is Firecracker?

Firecracker is a Virtual Machine Monitor written in Rust by AWS specifically for serverless workloads. It creates microVMs with minimal device emulation and fast boot times.

Each Firecracker microVM runs its own Linux kernel inside a KVM virtual machine. Hardware-enforced isolation means an attacker must escape both the guest kernel and the hypervisor layer.

Firecracker implements only five virtio devices: networking, block storage, vsock for host-guest communication, serial console, and a minimal keyboard controller. This minimalism is intentional. The entire codebase is roughly 50,000 lines of Rust versus QEMU's nearly 2 million lines of C.

| Metric | Performance |
| --- | --- |
| Boot time to userspace | ~100-200ms depending on configuration |
| Memory overhead per microVM | less than 5 MiB |
| microVM creation rate | Up to 150 per second per host |
| Language | Rust (memory-safe) |
| Isolation mechanism | KVM virtualization |

**The operational challenge**: Firecracker doesn't include orchestration. You must build systems to manage kernel images, root filesystems, networking configuration, the jailer security layer, and VM lifecycle. This is why most teams use Firecracker through Kata Containers rather than directly.

## What is gVisor?

gVisor takes a fundamentally different approach. Instead of running VMs, it implements a user-space kernel written in Go.

When your container makes a system call, gVisor intercepts it through a component called the Sentry. The Sentry handles the syscall in user space rather than passing it to the host kernel. This drastically reduces kernel attack surface.

gVisor supports multiple execution modes: **Systrap** (seccomp-based syscall interception), **KVM** (virtualization-based isolation), and storage modes including **Directfs** for high-performance file operations.

| Feature | Implementation |
| --- | --- |
| Isolation type | User-space kernel (syscall interception) |
| Language | Go |
| Boot time | Milliseconds (no VM boot) |
| Overhead | Low memory, syscall tax on I/O |
| Integration | Docker, containerd, Kubernetes via RuntimeClass |

## Kata Containers vs Firecracker vs gVisor: Quick comparison

Here's how the three technologies compare across architecture, security, performance, and operational complexity.

| Factor | Kata Containers | Firecracker | gVisor |
| --- | --- | --- | --- |
| **What it is** | Orchestration framework | Virtual Machine Monitor | User-space kernel |
| **Isolation level** | Hardware (via VMM) | Hardware (KVM) | Syscall interception |
| **Boot time** | ~150-300ms depending on VMM and configuration | ~100-200ms depending on configuration | Milliseconds |
| **Memory overhead** | Less than 10 MiB + guest kernel | Less than 5 MiB + guest kernel | Minimal |
| **Kubernetes integration** | Native (CRI) | Requires orchestration | RuntimeClass |
| **Operational complexity** | Low (handles VMM) | High (DIY orchestration) | Low (container runtime) |
| **VMM flexibility** | Multiple options | N/A (is the VMM) | N/A (no VM) |
| **I/O performance** | Near-native | Near-native | 10-30% overhead |
| **Best for** | Production K8s workloads | Custom serverless platforms | Enhanced container security |

## Which provides the strongest security isolation?

**Kata Containers and Firecracker both provide hardware-level isolation** since Kata uses Firecracker or Cloud Hypervisor as its VMM backend. Each workload runs in a dedicated VM with its own kernel, hardware-enforced memory boundaries, and KVM isolation.

**gVisor provides strong but not VM-level isolation.** It reduces kernel attack surface significantly by intercepting syscalls in user space, but workloads still share some host resources.

For untrusted multi-tenant workloads where customers might be actively adversarial, Kata Containers or Firecracker provide stronger security guarantees. For defense-in-depth where you control the code, gVisor is often sufficient.

## How do they compare on performance?

Performance differences depend on whether you're measuring startup time or runtime overhead.

- **Startup time:** Firecracker boots fastest at ~100-200ms depending on configuration. Kata Containers takes ~150-300ms depending on VMM and configuration due to orchestration layers. gVisor starts in milliseconds with no VM boot process.
- **Runtime performance:** Kata Containers and Firecracker deliver near-native performance for CPU and I/O workloads. gVisor's syscall interception creates measurable overhead on I/O-heavy workloads, sometimes 10-30% slower than native containers. For CPU-bound workloads, all three perform reasonably well.

## How difficult is it to run each technology?

- **Kata Containers:** Low complexity. Integrates with Kubernetes through CRI. Install Kata, create a RuntimeClass, specify it in pod specs. Kata handles VMM provisioning, guest kernel management, networking, and lifecycle.
- **Firecracker:** High complexity. Requires building infrastructure for kernel images, root filesystems, networking configuration, jailer implementation, and lifecycle management. Most teams use Kata Containers to abstract this complexity.
- **gVisor:** Low complexity. Install `runsc`, configure Docker or containerd, create a RuntimeClass. Existing containers work with enhanced isolation, though not every syscall is supported.

<InfoBox className="BodyStyle">

**Production-grade isolation without the operational overhead**

Running microVMs at scale requires managing kernel images, networking, security hardening, and orchestration.

Northflank uses Kata Containers with Cloud Hypervisor to provide hardware-level isolation without the operational complexity. Deploy any OCI container image and get VM-level security with standard container workflows.

Enterprise customers run secure multi-tenant workloads on Northflank's infrastructure. [Try Northflank](https://app.northflank.com/signup) or [talk to an engineer](https://cal.com/team/northflank/northflank-demo).

</InfoBox>

## When should you use Kata Containers?

Use Kata Containers when you need microVM isolation in Kubernetes without building custom infrastructure. It provides the easiest path to hardware-level isolation for containerized workloads.

Kata is ideal for multi-tenant workloads requiring strong isolation (SaaS platforms, code execution environments, AI sandboxes), production-ready infrastructure today (handles complexity that would take months to build), and flexibility to switch VMMs based on infrastructure needs.

## When should you use Firecracker directly?

Use Firecracker directly when you're building custom serverless infrastructure with deep virtualization expertise, need absolute control over the VMM layer, or require the smallest possible footprint (5 MiB overhead, 50k lines of Rust).

Most organizations are better served using Firecracker through Kata Containers unless you have a dedicated team for building microVM orchestration.

## When should you use gVisor?

Use gVisor when you want enhanced container security without VMs, your infrastructure doesn't support nested virtualization, or you have existing container workflows you don't want to change.

gVisor works well for compute-heavy workloads with minimal I/O, where syscall overhead is negligible (machine learning inference, batch processing, CPU-bound workloads).

## How does Northflank use these technologies in production?

Northflank uses **Kata Containers with Cloud Hypervisor** as the primary approach for microVM isolation. Cloud Hypervisor was chosen for its strong runtime performance, broad workload compatibility, and stability in production.

On infrastructure where nested virtualization isn't available, Northflank falls back to **gVisor** for syscall-level isolation.

This multi-layered approach provides the strongest appropriate isolation for each workload. The platform abstracts all operational complexity: no kernel images to maintain, no networking configuration, no security hardening.

When cto.new launched their free AI coding platform to 30,000+ users, they needed to provision thousands of secure sandboxes daily. Northflank's Kata-based infrastructure handled the scale without issues. [Read the full case study](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes).

Northflank's engineering team actively contributes to Kata Containers, QEMU, containerd, and Cloud Hypervisor in the open-source community. [Read the Kata Containers case study on Northflank](https://katacontainers.io/blog/kata-containers-northflank-case-study/).

## Which should you choose?

| Technology | Best for |
| --- | --- |
| **Kata Containers** | Production Kubernetes workloads needing VM-level isolation with minimal operational overhead. Switch between VMMs (Cloud Hypervisor, Firecracker, QEMU) based on infrastructure. |
| **Firecracker directly** | Custom serverless platforms requiring absolute VMM control, smallest footprint, and fastest boot times. Requires deep virtualization expertise and orchestration engineering. |
| **gVisor** | Enhanced container security without VMs. Works where nested virtualization isn't available. Simpler integration with existing container workflows. |

<InfoBox className="BodyStyle">

**Use a platform that abstracts the complexity if:**

- You need production-grade isolation without infrastructure engineering
- You're running AI agents, code sandboxes, or untrusted workloads at scale
- You want infrastructure that grows beyond sandboxing (databases, APIs, orchestration)

Northflank provides Kata Containers and gVisor with operational complexity abstracted away. Companies run production workloads with VM-level security using standard container workflows. [Try Northflank](https://app.northflank.com/signup) or [talk to an engineer](https://cal.com/team/northflank/northflank-demo) about your isolation requirements.

**Related articles:**

- [Firecracker vs gVisor: Which isolation technology should you use?](https://northflank.com/blog/firecracker-vs-gvisor)
- [Your containers aren't isolated. Here's why that's a problem](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation)
- [How to spin up a secure code sandbox and microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
- [Multi-tenant cloud deployment](https://northflank.com/blog/multi-tenant-cloud-deployment)
- [Firecracker vs QEMU: Which one should you use?](https://northflank.com/blog/firecracker-vs-qemu)
- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox)
- [Top AI sandbox platforms in 2026](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution)

</InfoBox>

## Frequently asked questions

### What is the main difference between Kata Containers and Firecracker?

Kata Containers is an orchestration framework that integrates multiple VMMs (including Firecracker) with Kubernetes. Firecracker is a Virtual Machine Monitor that creates lightweight VMs. Kata uses Firecracker (or Cloud Hypervisor or QEMU) as its backend while handling all the operational complexity of running microVMs at scale.

### Is Kata Containers more secure than gVisor?

Kata Containers provides hardware-level isolation through VMs, which is stronger than gVisor's syscall interception. Each Kata workload runs in a dedicated VM with its own kernel. gVisor reduces kernel attack surface but workloads still share the host kernel. For actively adversarial workloads, Kata offers stronger security guarantees.

### How fast does Kata Containers boot compared to Firecracker?

Kata boots in approximately ~150-300ms depending on VMM and configuration versus Firecracker's ~100-200ms depending on configuration. The extra overhead comes from Kata's orchestration layers that handle Kubernetes integration. For most workloads, this 75ms difference is negligible compared to application startup time.

### Can I use Kata Containers with Cloud Hypervisor instead of Firecracker?

Yes. Kata supports Cloud Hypervisor, Firecracker, and QEMU as VMM backends. Cloud Hypervisor is actually the default and recommended option for most workloads due to its strong performance and broad compatibility. You select the VMM through Kata's configuration.

### Why would I use Kata Containers instead of running Firecracker directly?

Kata abstracts the operational complexity of running Firecracker in production. It handles kernel image management, networking configuration, storage mounting, VM lifecycle, and Kubernetes integration. Unless you have a dedicated team for microVM infrastructure, Kata provides production-ready orchestration that would otherwise take months to build.

### Can Kata Containers and gVisor run on the same Kubernetes cluster?

Yes. You can deploy both as different RuntimeClasses. Specify `kata-clh` for workloads needing VM-level isolation and `gvisor` for workloads where syscall filtering is sufficient. This gives you flexibility to choose the right isolation level per workload.]]>
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</item><item>
  <title>10 best CodeSandbox alternatives in 2026</title>
  <link>https://northflank.com/blog/codesandbox-alternatives</link>
  <pubDate>2026-01-28T18:30:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for CodeSandbox alternatives? Compare the 10 best platforms for frontend prototyping, cloud dev environments, and secure AI code execution in 2026.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/codesandbox_alternatives_9f3cc41f83.png" alt="10 best CodeSandbox alternatives in 2026" /><InfoBox className="BodyStyle">

## TL;DR: What are the best CodeSandbox alternatives in 2026?

CodeSandbox has been a go-to for browser-based prototyping, but teams are moving toward specialized environments that offer better performance, security, and persistence for production workloads.

1. **Northflank** (production infrastructure, not a browser IDE): Best for secure, persistent, and scalable code execution infrastructure. Unlike browser IDEs, Northflank provides the underlying platform for running untrusted AI code or production applications in [isolated microVMs](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale) without session timeouts.
2. **StackBlitz**: Best for browser-native Node.js development using WebContainers.
3. **GitHub Codespaces**: Best for managed VS Code environments integrated with GitHub repositories.
4. **Replit**: Best for collaborative full-stack building and AI-assisted coding.
5. **E2B**: Best for programmatic AI code execution sandboxes.
6. **Gitpod**: Best for automated, ephemeral dev environments.
7. **Glitch**: Best for lightweight web app deployment and remixing.
8. **Modal**: Best for serverless Python and ML execution.
9. **Val.town**: Best for small, social backend functions.
10. **CodePen**: Best for frontend UI and CSS experimentation.

</InfoBox>

The cloud development space has moved beyond simple frontend playgrounds.

This article evaluates the best CodeSandbox alternatives in 2026 based on their execution speed, security models, and suitability for both human developers and AI agents.

## What is CodeSandbox?

CodeSandbox is a browser-based development environment designed for writing and running web applications without local setup.

It combines a code editor with a preview window, making it a common choice for reproducible bug reports and sharing frontend components. The platform is built primarily for frontend prototyping with frameworks like React, Vue, and Angular.

## What is CodeSandbox used for?

CodeSandbox serves multiple purposes across different developer workflows:

- Creating shareable code examples for documentation and tutorials that others can view and fork
- Reproducing bugs in clean, isolated environments accessible via link
- Live coding during technical interviews or presentations where viewers see code and output simultaneously
- Prototyping AI-generated code in browser-based sandboxes
- Backend development through "Devboxes," though the platform's core strength remains frontend experimentation

## Why are developers and AI engineers searching for CodeSandbox alternatives?

It's no longer just developers looking for cloud editors. AI engineers and DevOps teams are searching for alternatives to address specific technical friction:

- **Cold starts and latency**: Large containers can take significant time to boot, which breaks the flow when you need instant feedback.
- **Session limitations**: Most browser-based environments terminate processes once the tab is closed. This is incompatible with long-running AI tasks or background jobs that need to persist beyond a single browser session.
- **Execution isolation**: Running untrusted code requires hardened security layers that basic sandboxes often lack. If you're building AI coding assistants or allowing users to execute arbitrary code, you need microVM-level isolation, not just containerization.

## Why are traditional cloud sandboxes limited?

Traditional sandboxes are built for ephemeral testing, which creates constraints for professional workflows:

- **Ephemeral storage**: Data is often lost between restarts, making stateful development difficult. If your application needs to maintain state or store user data, you'll hit these limits quickly.
- **Networking constraints**: Exposing multiple ports or managing complex microservice communication within a browser tab becomes challenging. Most sandboxes are designed for single-service prototypes, not distributed systems.
- **Resource overhead**: High CPU and memory overhead for simple tasks compared to optimized microVMs. Browser-based environments need to run the editor, preview, and your application simultaneously, which adds unnecessary load.

<InfoBox className="BodyStyle">

Need a secure runtime for AI code execution?

If you're building AI coding tools or need to execute untrusted code at scale, Northflank provides microVM isolation with Firecracker, gVisor, and Kata Containers. Learn more about [secure runtime environments for codegen tools](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale) or [see how to spin up secure sandboxes in seconds](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh).

</InfoBox>

## Which CodeSandbox alternative is best for frontend prototyping?

If you need instant feedback for UI experiments and component demos, these platforms offer the fastest path from idea to shareable prototype.

### 1. StackBlitz

StackBlitz runs Node.js in your browser using WebContainers, which eliminates the need for remote VMs. The platform supports offline work after the initial load.

**Best for:**

- Angular, React, and npm-based projects
- Frontend prototyping without infrastructure setup
- Sharing reproducible environments via URL
- Developers who need offline capability

### 2. Replit

Replit provides a collaborative full-stack environment with AI assistance through Ghostwriter. The platform includes multiplayer features for real-time collaboration.

**Best for:**

- Pair programming and teaching scenarios
- Full-stack applications with frontend and backend
- Teams using AI-assisted coding with collaboration
- Educational use cases and coding interviews

### 3. CodePen

CodePen is a frontend-focused playground for HTML, CSS, and JavaScript with real-time preview.

**Best for:**

- UI component demonstrations
- CSS animations and visual effects
- Shareable code demos
- Frontend-only projects and experiments

### 4. Glitch

Glitch focuses on community features and project remixing. You can fork public projects and the platform handles deployment automatically.

**Best for:**

- Community-driven projects and learning
- Web app prototypes with deployment
- Remixing existing projects
- Social coding features

## Which platforms offer the best cloud-native dev environments?

For teams that need full development environments with repository integration and standardized setups, these platforms provide VS Code-like experiences in the cloud.

### 5. GitHub Codespaces

Codespaces provides a VS Code instance in the cloud, connected to your GitHub repositories. Environment configuration uses devcontainer.json for team consistency.

**Best for:**

- Teams using GitHub for version control
- Projects requiring consistent development environments
- Workflows with GitHub Actions and pull requests
- Repository-to-environment workflows

### 6. Gitpod

Gitpod creates ephemeral dev environments based on Git branches and can generate preview environments for pull requests.

**Best for:**

- Review workflows requiring environment access
- Teams wanting automated preview environments
- Organizations needing self-hosted options
- Git-based development workflows

## How do I choose a sandbox for secure AI code execution?

If you're building AI coding assistants, educational platforms, or any service that needs to run user-generated code, you need more than a traditional sandbox. You need programmatic access, robust isolation, and APIs designed for automation. Learn more about [choosing the best code execution sandbox for AI agents](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents).

### 7. E2B

E2B provides isolated environments controlled via API with SDKs for Python, TypeScript, and JavaScript. The platform includes configurable timeout and resource limits.

**Best for:**

- AI coding assistants and agents
- Programmatic code execution via API
- Educational platforms requiring code sandboxing
- Applications needing isolated execution environments
- For more context on AI sandbox platforms, check out our guide on [top AI sandbox platforms for code execution](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution)

### 8. Modal

Modal handles Python workloads for ML and data processing. You define functions in Python and the platform manages infrastructure including GPU allocation and scaling.

**Best for:**

- ML inference and training jobs
- Batch processing and data pipelines
- Python-based API endpoints with GPU access
- Serverless Python workloads
- See how Modal compares in our [Modal sandboxes alternatives guide](https://northflank.com/blog/top-modal-sandboxes-alternatives-for-secure-ai-code-execution)

### 9. Val.town

Val.town turns functions into API endpoints or scheduled jobs. The platform includes social features for browsing and forking other users' code.

**Best for:**

- Scripts and personal automation projects
- Learning from community examples
- Function-based API endpoints
- Social coding and function sharing

## How does Northflank bridge the gap between secure sandboxing and production?

[Northflank](https://northflank.com/) is not a browser IDE. You write code in your preferred editor and use Northflank as the secure execution layer and production infrastructure.

![northflank-sandbox.png](https://assets.northflank.com/northflank_sandbox_ac966e0f30.png)

### Execution vs. editing

With CodeSandbox or StackBlitz, you write and run code in the same environment. Northflank separates these concerns:

- Your developers use local IDEs or cloud editors for writing code
- Northflank handles execution, particularly for untrusted or AI-generated code
- Code runs isolated at the VM level, not just process level
- Uses microVM technologies like Firecracker, gVisor, and Kata Containers

This architecture matters for AI coding tools. When your AI agent generates code, you need isolation at the VM level to prevent untrusted code from accessing your application logic.

### Security via isolation

Traditional containers share the kernel with the host OS, creating security risks. MicroVMs provide hardware-level isolation:

- Each sandbox gets its own kernel
- Arbitrary user code runs without risking your infrastructure
- Hardware-level isolation prevents container breakout attacks
- Critical for platforms allowing custom scripts, model training, or AI-generated code execution

Learn more about Northflank's [microVM implementation](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh) for secure sandboxing.

### Persistence and state

Northflank supports persistent volumes and managed databases for production workloads:

- Applications stay online 24/7 with production uptime guarantees
- No session limits or sleeping apps
- Persistent storage for stateful applications
- Managed databases integrated with your execution environment

You can start with a sandbox for testing AI-generated code, then promote the same environment to production without platform migration.

### From prototype to production

Northflank eliminates the distinction between prototyping and production infrastructure:

- Same platform for secure code execution and application hosting
- Supports databases, APIs, and frontend applications
- No need to rebuild on different platforms
- Single infrastructure for both sandbox testing and production deployment

<InfoBox className="BodyStyle">

**For teams building AI coding tools:** Offer users both a sandbox for testing and a deployment target for production code.

**For developers:** Learn one platform instead of separate tools for development and deployment.

Want to build secure code execution into your product? [Get started with Northflank](https://app.northflank.com/signup) or check out our guides on:

- [Secure runtime for codegen tools: microVMs, sandboxing, and execution at scale](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale)
- [How to spin up a secure code sandbox & microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox)
- [What’s the best code execution sandbox for AI agents in 2026?](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents)
- [Top AI sandbox platforms in 2026, ranked](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution)
- [Top Vercel Sandbox alternatives for secure AI code execution and sandbox environments](https://northflank.com/blog/top-vercel-sandbox-alternatives-for-secure-ai-code-execution-and-sandbox-environments)
- [Top Modal Sandboxes alternatives for secure AI code execution](https://northflank.com/blog/top-modal-sandboxes-alternatives-for-secure-ai-code-execution)

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>SST alternatives in 2026: What to use now that development has slowed</title>
  <link>https://northflank.com/blog/sst-alternatives-serverless-stack</link>
  <pubDate>2026-01-28T08:00:00.000Z</pubDate>
  <description>
    <![CDATA[SST development slowed in 2025 after the team shifted focus to OpenCode, their AI coding agent. The framework still works but is in maintenance mode. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/sst_alternative_a0625c9024.png" alt="SST alternatives in 2026: What to use now that development has slowed" /><InfoBox className="BodyStyle">

## 📌 TL;DR

SST development slowed in 2025 after the team shifted focus to OpenCode, their AI coding agent. The framework still works but is in maintenance mode. 

[**Northflank**](https://northflank.com/) is the strongest SST alternative for most teams:

- Supports infrastructure-as-code through templates with bidirectional GitOps
- Deploys to any cloud (AWS, GCP, Azure) or your own infrastructure via BYOC
- Runs any workload type beyond just serverless
- Avoids vendor lock-in entirely

Other SST alternatives include Pulumi (IaC without SST's abstraction), Render (simpler, less flexible), and Railway (fast prototyping, limited for production).

</InfoBox>

![CleanShot 2026-01-28 at 14.27.42@2x.png](https://assets.northflank.com/Clean_Shot_2026_01_28_at_14_27_42_2x_e2aeee5901.png)

## What happened to SST

SST (Serverless Stack) made AWS serverless development good. Live Lambda Development gave you sub-second feedback instead of CloudFormation waits. TypeScript configuration replaced YAML. High-level constructs handled the tedious coordination between Lambda, API Gateway, and DynamoDB.

In mid-2025, the SST team launched OpenCode, an AI coding agent that hit 650,000 monthly users within five months. SST's GitHub activity dropped and the organization restructured under Anomaly Co.

SST v3 works fine and will get maintenance updates, but active development has moved on. This matters because SST is an opinionated framework you buy into, and investing in a framework with uncertain development carries risk when SST alternatives exist.

## Northflank: Best SST alternative in 2025

If you're looking for an SST replacement, Northflank is the strongest option for most teams. It takes a different approach than SST. Instead of infrastructure-as-code abstractions over one cloud, it's a workload platform that handles infrastructure for you across any cloud provider.

### Bring Your Own Cloud

The key differentiator that makes Northflank the top SST alternative is [BYOC](https://northflank.com/features/bring-your-own-cloud). You can run workloads in your own AWS, GCP, or Azure account while using Northflank's interface, CI/CD, and observability.

This means you:

- Keep full infrastructure ownership
- Use existing cloud credits and enterprise agreements
- Maintain data in your own network for compliance
- Avoid lock-in to any platform's infrastructure

You can also deploy to Northflank's managed cloud if you'd rather not deal with cloud accounts. Same platform experience either way.

### ☁️ Cloud and workload agnostic

SST was designed around AWS serverless primitives, particularly Lambda. While SST v3 added 150+ providers via Pulumi, the constructs stayed serverless-first.

Northflank doesn't care what you run or where. Containers, scheduled jobs, databases, background workers, GPU workloads. All deploy through the same interface to whichever cloud fits your needs.

This matters as applications outgrow what serverless handles well. Long-running processes, stateful workloads, and compute-heavy tasks often fit containers better than Lambda. Switching paradigms mid-project is painful when your infrastructure is tied to serverless-specific constructs. As an SST alternative, Northflank avoids this limitation entirely.

### Infrastructure as Code with Templates

For teams migrating from SST who want the repeatability of infrastructure-as-code, Northflank offers a full template system.

You define your entire stack (services, jobs, databases, pipelines) as JSON templates. Edit them through a visual drag-and-drop interface or directly as code. Your choice.

Templates support bidirectional GitOps:

- Changes committed to your repo automatically update infrastructure on Northflank
- Changes made through the UI get committed back to your repo
- Git stays the single source of truth

You can make templates dynamic with variables and arguments, deploying the same stack across different environments, regions, or cloud providers. Northflank also provides pre-built stack templates for common setups like [PostHog](https://northflank.com/stacks/deploy-posthog) and [GrowthBook](https://northflank.com/stacks/deploy-growthbook).

### Developer Experience

- Preview environments spin up automatically per PR
- Deployments happen in seconds rather than CloudFormation minutes
- Logging and metrics built in
- Kubernetes abstracted without being hidden

You get production-grade orchestration without writing manifests.

### SST vs Northflank comparison

| Capability | SST | Northflank |
| --- | --- | --- |
| Cloud providers | AWS-first | AWS, GCP, Azure, or managed |
| BYOC | No | Yes |
| Workload types | Serverless-first | Any container workload |
| Infrastructure as code | TypeScript with Pulumi | JSON templates with GitOps |
| Development status | Maintenance mode | Active ($22M Series A) |
| Static IPs / Private networking | Via AWS VPC | Built-in |

## Other SST alternatives

### Pulumi

SST v3 uses Pulumi under the hood. If you want infrastructure-as-code without SST's abstractions, using Pulumi directly makes sense as an SST alternative. More verbose, but full multi-cloud support and active development. Best for teams with infrastructure expertise.

### Render

Render is a managed platform focused on simplicity. Git-based deploys, managed databases, automatic SSL. No BYOC, limited networking control, pricing scales poorly. As an SST alternative, Render works best for small teams with straightforward apps who don't need SST's flexibility.

### Railway

Railway is optimized for speed. Repo to deployed app in under a minute. Usage-based billing can surprise you, no BYOC, limited production features. Best as an SST alternative for prototypes and internal tools, not production workloads.

### Vercel and Netlify

Great for frontend-heavy apps on Next.js, Remix, or Astro. Not true SST alternatives since they don't address backend infrastructure, but worth mentioning for frontend-focused teams.

## Migrating from SST to Northflank

Lambda functions can usually be containerized with minimal changes. The business logic stays the same; only the runtime wrapper changes.

SST-deployed databases can stay in place while Northflank services point at them. This enables incremental migration from SST rather than a risky cutover.

With BYOC, you can run Northflank in your existing AWS account alongside SST resources and migrate services one at a time.

**You'll gain when switching from SST:**

- Faster deploys
- Built-in observability
- Preview environments
- GitOps-based infrastructure management
- Active maintenance

**You'll lose when leaving SST:**

- Lambda's pay-per-invocation pricing (containers run continuously)
- Tight integration with AWS-specific services like EventBridge
- TypeScript infrastructure definitions

## When to stick with SST

SST still makes sense if:

- Your current SST setup works and you're not planning changes
- Your architecture is specifically designed around Lambda
- Your team strongly prefers TypeScript for infrastructure
- You're using SST constructs without equivalents elsewhere

The framework won't stop working. No urgent need to find an SST alternative if your current setup meets your needs.

## FAQ

**Is SST dead?**
Maintenance mode, not dead. SST works fine and gets critical updates, but active development shifted to OpenCode.

**What is the best SST alternative?**
Northflank is the best SST alternative for most teams due to its BYOC support, multi-cloud deployment, and active development. Pulumi is better for teams wanting pure infrastructure-as-code.

**Can Northflank run in my AWS account?**
Yes. BYOC lets you deploy to your own AWS, GCP, or Azure while using Northflank for management and CI/CD. You keep infrastructure ownership and existing cloud credits.

**Does Northflank support infrastructure as code?**
Yes. Templates let you define your entire stack as JSON with bidirectional GitOps. Edit visually or as code.

**How does Northflank pricing compare to SST?**
SST is free (you pay AWS). Northflank has platform fees but can reduce total cost through better utilization and built-in observability.

**Should I migrate from SST?**
Only if you're hitting SST limitations or need features SST doesn't offer. If SST works for you, migration adds risk without clear benefit.

**Does Northflank support serverless like SST?**
Northflank is container-based, so workloads run continuously rather than scaling to zero. No cold starts and simpler debugging, but if pay-per-invocation matters, keep Lambda for those specific workloads.

**What's the difference between SST and Northflank?**
SST is an infrastructure-as-code framework focused on AWS serverless. Northflank is a multi-cloud workload platform with BYOC support. SST requires you to define infrastructure in TypeScript; Northflank manages infrastructure for you.]]>
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  <title>What is multi-tenant cloud deployment? A complete guide for 2026</title>
  <link>https://northflank.com/blog/multi-tenant-cloud-deployment</link>
  <pubDate>2026-01-27T17:45:00.000Z</pubDate>
  <description>
    <![CDATA[Learn multi-tenant cloud deployment in 2026: deploy multi-tenant applications with the right infrastructure models, isolation strategies, and production automation.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/multi_tenant_cloud_deployment_79cbad6714.png" alt="What is multi-tenant cloud deployment? A complete guide for 2026" /><InfoBox className="BodyStyle">

## TL;DR: What is multi-tenant cloud deployment and its key considerations?

Multi-tenant cloud deployment is the process of deploying applications that serve multiple customers (tenants) on shared cloud infrastructure while maintaining strict isolation between them.

Here's what you need to know:

- **The deployment challenge:** Unlike single-tenant systems, you need to handle automated tenant provisioning, resource isolation, per-tenant scaling, and compliance requirements, all while keeping operational overhead manageable.
- **Your deployment options:** You can deploy on shared infrastructure (cost-efficient), partitioned resources (balanced approach), or dedicated infrastructure per tenant (maximum isolation). Most production systems use a hybrid approach based on customer tier.
- **The operational complexity:** Managing tenant lifecycle, cost allocation, network isolation, and disaster recovery across dozens or hundreds of tenants requires significant automation and tooling.
- **How platforms help:** Solutions like Northflank automate multi-tenant deployment by providing instant tenant isolation, BYOC (Bring Your Own Cloud) support across cloud providers, and built-in security, letting you deploy production-ready multi-tenant environments in minutes instead of weeks.

</InfoBox>

Let's break down how multi-tenant cloud deployment works and what you need to get it right.

## What is multi-tenant cloud deployment?

You've designed your multi-tenant architecture with separate schemas, namespace isolation, or dedicated databases per tenant. Now you need to deploy this system to production cloud infrastructure.

Multi-tenant cloud deployment is the process of taking your multi-tenant application and deploying it across cloud infrastructure (AWS, GCP, Azure, or your own hardware) in a way that keeps tenants isolated while maximizing resource efficiency.

Here's what makes it different from deploying a regular application:

- **Tenant provisioning at scale:** You need automated systems to spin up new tenant environments (namespaces, databases, configs) without manual intervention
- **Infrastructure isolation decisions:** You're choosing between shared compute (cost-efficient but needs careful isolation), partitioned infrastructure (balanced approach), or fully dedicated resources per tenant (maximum isolation but higher cost).
- **Deployment across boundaries:** Your tenants might need to be deployed in different regions for data residency, or across multiple cloud providers for compliance requirements.

Getting the isolation right while avoiding the operational overhead of managing dozens of separate deployments manually is the core challenge.

## What are the main deployment models for multi-tenant cloud systems?

Your deployment model determines how you balance cost, isolation, and operational complexity. There are three main approaches, and most production systems use a combination of them.

### Shared infrastructure deployment

All tenants run on the same compute, networking, and storage resources. You're using namespaces, RBAC, and network policies to create logical boundaries between tenants.

This works well for internal teams or early-stage SaaS customers who don't need physical isolation. Your costs stay low because you're maximizing resource utilization.

### Partitioned infrastructure deployment

Each tenant gets dedicated resources within a shared environment: separate namespaces in Kubernetes with dedicated node pools, or VPC-per-tenant setups in AWS.

This middle-ground approach suits growing SaaS companies that need stronger security guarantees without the overhead of completely separate deployments.

### Dedicated infrastructure deployment

Every tenant gets completely separate infrastructure: their own cluster, VPC, and potentially even cloud account.

You'll use this for customers with strict compliance requirements (HIPAA, PCI-DSS) or large enterprise contracts that demand physical isolation. The trade-off: you're paying for duplicate control planes and managing multiple clusters.

### Hybrid and multi-cloud deployment

Real-world production systems often mix these models based on tenant tier. Your standard customers might share infrastructure, while enterprise customers get dedicated deployments.

| **Model** | **Best for** | **Cost efficiency** | **Isolation level** |
| --- | --- | --- | --- |
| Shared | Internal teams, early SaaS | High | Logical |
| Partitioned | Growing SaaS | Medium | Network + compute |
| Dedicated | Enterprise, compliance | Low | Physical |
| Hybrid | Mixed customer tiers | Variable | Flexible |

<InfoBox className="BodyStyle">

## Get started with multi-tenant cloud deployment in 2026

Northflank provides production-ready multi-tenant environments with built-in isolation, BYOC support, and automated tenant lifecycle management. Deploy multi-tenant applications without spending months on infrastructure complexity.

[Get started with Northflank](https://app.northflank.com/signup) or [schedule a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to see how we can simplify your multi-tenant deployment.

**Related resources:**

- [What is Multitenancy? Meaning, architecture, benefits & risks](https://northflank.com/blog/what-is-multitenancy)
- [Kubernetes multi-tenancy: A 2026 guide to secure shared infrastructure](https://northflank.com/blog/kubernetes-multi-tenancy)
- [Your containers aren't isolated. Here's why that's a problem.](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation)

</InfoBox>

## How do you handle tenant provisioning and lifecycle management in production?

Once you've chosen your deployment model, you need systems to manage the tenant lifecycle, from onboarding new customers to decommissioning old ones.

### Automated tenant onboarding

Manual tenant provisioning doesn't scale. You need infrastructure-as-code that creates all tenant-specific resources automatically: namespaces, databases, secrets, DNS records, and monitoring configs.

### Database deployment strategies per tenant

Your database strategy significantly impacts deployment complexity. Many production systems use a tiered approach: shared databases for smaller customers, dedicated instances for enterprise tenants.

### Configuration and secrets management

Each tenant needs isolated configuration: API keys, feature flags, and integration credentials. Modern platforms handle this through projects or workspace abstractions that automatically scope secrets to the right tenants.

### Tenant isolation at the network level

Your deployment must enforce network boundaries between tenants through network policies and service mesh configurations with mutual TLS. Platforms like Northflank automate this by provisioning Cilium-based network policies and automatic mTLS when you create new tenant projects.

## What are the operational challenges you'll face with multi-tenant cloud deployment?

Multi-tenant deployment introduces operational complexity that doesn't exist in single-tenant systems.

- **The blast radius problem:** One tenant's misconfiguration or resource spike can crash your entire cluster, requiring aggressive resource quotas and API rate limiting.
- **Upgrade and rollout coordination:** Deploying updates means coordinating across all tenants simultaneously, requiring canary rollouts and tenant-specific feature flags.
- **Cost allocation and chargeback:** You need per-tenant resource tracking to allocate costs for internal chargeback or customer billing.
- **Monitoring and debugging at scale:** You need centralized observability with tenant-scoped views and automated alerting for tenant-specific issues.
- **Disaster recovery becomes complex:** You need point-in-time recovery per tenant, tenant isolation during restore operations, and the ability to migrate tenants between clusters.

## How does Northflank simplify multi-tenant cloud deployment?

Building and operating multi-tenant deployment infrastructure from scratch typically takes 3-6 months of platform engineering work. Northflank provides production-ready multi-tenant deployment out of the box, letting you focus on your application instead of infrastructure complexity.

![northflank-paas-home-page.png](https://assets.northflank.com/northflank_paas_home_page_0cff0595d9.png)

### Instant tenant environments with built-in isolation

When you create a new project in Northflank, you get an automatically configured tenant environment: dedicated namespaces with network policies, RBAC rules scoped to project members, encrypted secrets storage, and isolated networking with automatic mutual TLS.

You're not writing YAML or configuring network policies manually. Everything is provisioned automatically with production-grade security defaults.

### Deploy across your own cloud infrastructure

Northflank's [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) model lets you deploy multi-tenant systems across AWS, GCP, Azure, Civo, CoreWeave, Oracle, or bare metal, all managed from one control plane.

Your data stays in your VPC, you meet compliance requirements for data residency, and you use existing cloud credits while Northflank handles cluster provisioning, tenant isolation, and ongoing operations.

This is particularly valuable for companies serving enterprise customers who demand data stays in specific regions or clouds.

### Built-in tenant lifecycle automation

Northflank handles tenant onboarding, database provisioning, and configuration management automatically. Developers get self-service access to create isolated environments for new customers or projects without waiting for platform team intervention.

[Preview environments](https://northflank.com/use-cases/preview-environments-backend-for-kubernetes), managed databases, and GitOps workflows work seamlessly across all tenant projects.

### Operational visibility across tenants

You get per-tenant cost tracking, resource usage monitoring, and audit logs out of the box. No need to build custom solutions for chargeback or debugging tenant-specific issues.

The platform provides unified observability while maintaining tenant isolation, so you can see what's happening across your entire multi-tenant deployment without compromising security.

## What should you prioritize when deploying multi-tenant systems to production in 2026?

Focus on these fundamentals before you deploy your first production tenant:

- **Start with clear isolation requirements:** Understand your compliance and security needs upfront. This determines whether you can use shared infrastructure or need dedicated deployments for certain tenants.
- **Automate tenant provisioning from day one:** Manual provisioning works for 5 tenants but breaks at 50. Build or adopt automation that handles the full tenant lifecycle before you scale.
- **Implement per-tenant monitoring early:** You need visibility into resource usage, costs, and performance per tenant. Adding this retroactively to a running multi-tenant system is painful.
- **Plan your scaling strategy:** Decide how you'll handle vertical vs horizontal scaling in a multi-tenant context, and whether you'll rebalance tenants across infrastructure as you grow.
- The teams that succeed with multi-tenant cloud deployment either build strong platform engineering teams to handle this complexity, or they adopt platforms that provide these capabilities out of the box.

## Frequently asked questions about multi-tenant cloud deployment in 2026

### What's the difference between SaaS and multi-tenancy?

SaaS is a software delivery model where you provide applications over the internet. Multi-tenancy is an architecture pattern where multiple customers share the same infrastructure. Most modern SaaS applications use multi-tenant deployment to serve customers cost-effectively.

### What are the best multi-tenant deployment patterns?

The most common patterns are namespace-per-tenant in Kubernetes, VPC-per-tenant for network isolation, and hybrid models that combine shared infrastructure for standard customers with dedicated deployments for enterprise accounts.

### How do you design a multi-tenant deployment system?

Start by defining your isolation requirements, choose your deployment model (shared, partitioned, or dedicated), implement automated tenant provisioning, configure network isolation, and build monitoring for per-tenant resource usage.

### What is an example of a multi-cloud deployment model?

A common multi-cloud pattern deploys tenants in different regions across AWS, GCP, and Azure based on data residency requirements. For example, European customers on GCP in europe-west1, US customers on AWS in us-east-1, and Asian customers on Azure in southeast-asia, all managed from a unified control plane.]]>
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</item><item>
  <title>Best tools to deploy backends in 2026</title>
  <link>https://northflank.com/blog/best-tools-to-deploy-backends</link>
  <pubDate>2026-01-26T19:30:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the best tools to deploy backends in 2026. Detailed guide covering PaaS platforms, cloud providers, and how to choose the right deployment solution.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/best_tools_to_deploy_backends_6827e0c781.png" alt="Best tools to deploy backends in 2026" /><InfoBox className="BodyStyle">

## TL;DR: What are the best tools for deploying backends in 2026?

Here's a quick comparison of the top backend deployment platforms you should consider this year.

1. **Northflank** - The most complete backend deployment platform, combining PaaS simplicity with Kubernetes flexibility. Supports any language, managed databases, BYOC (AWS/GCP/Azure/Oracle/Civo/Coreweave/Bare-metal), and GPU instances for AI workloads. Free tier available with per-second billing for paid plans.
2. **Railway** - A platform that works for smaller projects where you need basic deployment capabilities.
3. **Render** - Offers managed databases and automatic SSL certificates for standard backend deployments.
4. **Heroku** - A platform that handles deployments through Git integration with add-on support for databases and services.
5. **AWS App Runner** - Amazon's container deployment service for teams already using the AWS ecosystem.

</InfoBox>

In 2026, deploying your backend doesn't mean choosing between simplicity and control. Modern deployment platforms let you deploy backends with production-grade features without managing infrastructure, from APIs and SaaS applications to AI-powered services.

## Platform-as-a-service (PaaS)

PaaS platforms handle infrastructure management for you, letting you focus on building your application instead of configuring servers. In 2026, the best PaaS options provide production-grade features without sacrificing flexibility.

### 1. Northflank

[Northflank](https://northflank.com/) is a platform for deploying backend applications, APIs, databases, and AI workloads. You get the simplicity of traditional PaaS combined with the flexibility you need for production environments, without managing Kubernetes directly.

![northflank-platform.png](https://assets.northflank.com/northflank_platform_1f875d6f8a.png)

**Key features:**

- Deploy any backend stack (Node.js, Python, Go, Ruby, Java, or any containerized application)
- Managed databases: [PostgreSQL](https://northflank.com/dbaas/managed-postgresql), [MySQL](https://northflank.com/dbaas/managed-mysql), [MongoDB](https://northflank.com/dbaas/mongodb-on-northflank), [Redis](https://northflank.com/dbaas/managed-redis), and [MinIO](https://northflank.com/dbaas/managed-minio) with automated backups
- Bring Your Own Cloud (BYOC): Deploy to [Northflank's managed cloud](https://northflank.com/features/managed-cloud) or your own [AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), or [Oracle Cloud accounts](https://northflank.com/cloud/oci)
- GPU support: NVIDIA A100, H100, B200, and more [GPU types](https://northflank.com/gpu) for AI inference and training workloads
- Multiple interfaces: Web dashboard, [CLI](https://northflank.com/docs/v1/api/use-the-cli), [REST API](https://northflank.com/docs/v1/api/use-the-api), and [infrastructure-as-code](https://northflank.com/docs/v1/application/infrastructure-as-code/infrastructure-as-code) templates
- Git integration: GitHub, GitLab, and Bitbucket with automatic deployments and preview environments
- Production features: Horizontal/vertical [autoscaling](https://northflank.com/docs/v1/application/scale/scale-on-northflank), [health checks](https://northflank.com/docs/v1/application/observe/configure-health-checks) with automatic rollbacks, private [networking](https://northflank.com/docs/v1/application/network/networking-on-northflank), [RBAC with SSO](https://northflank.com/docs/v1/application/secure/use-role-based-access-control), [secrets management](https://northflank.com/docs/v1/application/secure/manage-secret-groups)
- One-click stack templates: Pre-configured stacks for FastAPI, Django, Flask, Express, Rails, Laravel, AI infrastructure (vLLM, Ollama, Langfuse), databases (Supabase, ClickHouse), and productivity tools (n8n, Temporal)
- Free Developer Sandbox plan with per-second billing for paid tiers ([See full pricing details](https://northflank.com/pricing))

**Best for:** Production backend applications requiring databases, BYOC flexibility, GPU support for AI workloads, and teams that need enterprise features without Kubernetes complexity.

<InfoBox className="BodyStyle">

Northflank provides a free Developer Sandbox plan where you can deploy and test workloads. You can [create an account](https://app.northflank.com/signup), [connect your Git repository](https://northflank.com/docs/v1/application/getting-started/link-your-git-account), and [deploy your first backend service](https://northflank.com/docs/v1/application/getting-started/build-and-deploy-your-code) in under a minute. The platform handles everything from build to production.

See some guides & one-click stack templates:

- [Deploy Node Express on Northflank](https://northflank.com/stacks/deploy-node-express)
- [How to deploy Flask app on Northflank](https://northflank.com/guides/deploy-flask-app-on-northflank)
- [Deploy Ruby on Rails on Northflank](https://northflank.com/stacks/deploy-ruby-on-rails)
- [Deploy FastAPI on Northflank](https://northflank.com/stacks/deploy-fastapi)
- [Deploy Django on Northflank](https://northflank.com/stacks/deploy-django)
- [Deploy Laravel on Northflank](https://northflank.com/stacks/deploy-laravel)
- [Deploy vLLM OpenAI on GCP on Northflank](https://northflank.com/stacks/deploy-vllm-gcp)
- [Deploy Ollama on Northflank](https://northflank.com/stacks/deploy-ollama)
- [Deploy Langfuse on Northflank](https://northflank.com/stacks/deploy-langfuse)
- [Deploy Supabase on Northflank](https://northflank.com/stacks/deploy-supabase)
- [Deploy ClickHouse on Northflank](https://northflank.com/stacks/deploy-clickhouse)
- [Deploy n8n on Northflank](https://northflank.com/stacks/deploy-n8n)
- [Deploy Temporal on Northflank](https://northflank.com/stacks/deploy-temporal)

</InfoBox>

### 2. Railway

Railway provides a platform where you can deploy containerized applications and connect databases. It includes basic CI/CD from Git repositories and handles SSL provisioning.

**Key features:**

- Container deployment with database connections
- Git-based CI/CD
- SSL certificate management
- Dashboard for service monitoring

**Best for:** Smaller projects with basic deployment needs.

### 3. Render

Render offers deployment for web services with managed PostgreSQL, Redis, and other databases. The platform includes automatic SSL certificate provisioning and background workers for handling asynchronous tasks.

**Key features:**

- Web service deployment from Git
- Managed databases (PostgreSQL, Redis)
- Automatic SSL certificates
- Background worker support

**Best for:** Standard backend deployments with common database requirements.

### 4. Heroku

Heroku pioneered the git-push deployment model that many platforms now use. The platform provides add-ons for databases and other services, though it doesn't offer the infrastructure flexibility found in newer platforms.

**Key features:**

- Git-push deployments
- Add-on marketplace for databases and services
- Buildpack support for multiple languages
- Process management (web, worker, scheduler)

**Best for:** Teams familiar with the Heroku workflow who don't need BYOC or advanced infrastructure options.

## Cloud provider solutions

Cloud providers give you access to their full ecosystem of services, which can be valuable if you need advanced networking, specific compliance requirements, or integration with other cloud-native tools. These options require more configuration than dedicated PaaS platforms.

### AWS App Runner

App Runner is Amazon's service for deploying containerized applications or source code directly. It integrates with other AWS services like RDS for databases and CloudWatch for monitoring.

**Key features:**

- Container and source code deployment
- Integration with AWS ecosystem (RDS, CloudWatch, IAM)
- Automatic scaling
- VPC connectivity

**Best for:** Teams already using AWS who need tight integration with other AWS services.

### Google Cloud Run

Cloud Run lets you deploy containerized applications that scale automatically from zero to handle traffic spikes. It integrates with Google Cloud's ecosystem including Cloud SQL for databases and Cloud Build for CI/CD.

**Key features:**

- Serverless container deployment
- Scale-to-zero capability
- Integration with GCP services (Cloud SQL, Cloud Build, Cloud Logging)
- Request-based scaling

**Best for:** Teams using Google Cloud Platform who need serverless container deployment.

### Azure Container Apps

Microsoft's Container Apps service provides deployment for containerized backends with automatic scaling. It connects to Azure's broader ecosystem including Azure SQL, Cosmos DB, and Azure Monitor.

**Key features:**

- Containerized application deployment
- Integration with Azure services (Azure SQL, Cosmos DB, Azure Monitor)
- Automatic scaling based on HTTP traffic or events
- VNET integration

**Best for:** Teams working within the Microsoft Azure ecosystem.

## How to choose the right backend deployment platform

Your choice depends on what you're building and where you are in your journey. Here's a comparison of common scenarios matched to the platforms that fit best.

| Use case | Recommended platform | Why |
| --- | --- | --- |
| Production backend with databases, background workers, and observability | Northflank | Most complete solution with PaaS simplicity, BYOC flexibility, managed databases, and enterprise features |
| AI-powered backends requiring GPU instances | Northflank | Native GPU support (A100, H100, B200) alongside traditional compute on one platform |
| Data residency requirements or existing cloud credits | Northflank | BYOC deployment to your own AWS, GCP, or Azure accounts while maintaining unified developer experience |
| Microservices with private networking and complex pipelines | Northflank | Abstracts Kubernetes complexity while preserving flexibility for service discovery and orchestration |
| Side projects with predictable traffic | Railway or Render | Basic deployment capabilities for smaller workloads |
| Deep AWS ecosystem integration | AWS App Runner | Native integration with RDS, Lambda, CloudWatch, and other AWS services |
| Google Cloud Platform integration | Google Cloud Run | Serverless containers with GCP service integration |
| Microsoft Azure integration | Azure Container Apps | Container deployment within Azure ecosystem |

The key question isn't just "which platform can deploy my code?" but "which platform supports my application's requirements?" Consider your database needs, whether you need GPU support, if you have existing cloud commitments, and how much operational complexity you're willing to manage.

## Getting started with backend deployment

Most platforms offer free tiers or developer plans that let you test deployments before committing.

Northflank provides a free Developer Sandbox plan where you can deploy and test workloads. You can [create an account](https://app.northflank.com/signup), [connect your Git repository](https://northflank.com/docs/v1/application/getting-started/link-your-git-account), and [deploy your first backend service](https://northflank.com/docs/v1/application/getting-started/build-and-deploy-your-code) in under a minute. The platform handles everything from build to production.

The fastest way to start is using [stack templates](https://northflank.com/stacks). Northflank's one-click templates deploy complete application stacks with all required services, databases, secrets, and networking pre-configured. Select a template for your framework (FastAPI, Django, Express, Rails, Laravel), connect your repository, and your backend deploys with production-ready configuration.

## Frequently asked questions: Best tools to deploy backends in 2026

**1. What is the best tool to deploy backends in 2026?**

The best tool depends on your specific requirements. For production backend applications requiring databases, BYOC flexibility, and GPU support for AI workloads, Northflank provides the most complete solution. For basic deployments on managed infrastructure, Railway and Render offer adequate functionality. For teams deeply integrated with AWS, App Runner provides native ecosystem integration.

**2. How much does it cost to deploy a backend in 2026?**

Backend deployment costs vary based on compute resources, storage, and traffic. Northflank offers a free Developer Sandbox plan for testing, with per-second billing for production workloads. A typical backend with a managed database costs approximately $15-50/month depending on your traffic and resource requirements. BYOC deployments use your existing cloud provider pricing with no markup.

**3. What is the easiest way to deploy a backend?**

The easiest approach is using a PaaS platform with one-click templates. Northflank provides stack templates for common frameworks including FastAPI, Django, Flask, Express, Rails, and Laravel. Select a template, connect your repository, and your backend deploys in under a minute with databases, secrets management, and networking pre-configured.

**4. Can I deploy a backend without managing servers?**

Yes. Modern deployment platforms abstract all server management. Northflank handles server provisioning, load balancing, auto-scaling, and failover automatically. You interact with your applications through a web dashboard, CLI, or API while the platform manages the underlying Kubernetes infrastructure.

**5. How do I deploy a Python backend?**

For Python backends (Django, Flask, FastAPI), add a requirements.txt to your repository along with either a Dockerfile or Procfile. Connect the repository to your deployment platform, and it builds and deploys your application. Northflank provides stack templates for Python frameworks that include web server configuration and optional PostgreSQL databases.

**6. How do I deploy a Node.js backend?**

Node.js backends need a package.json with a start script. Add a Dockerfile if you need custom configuration, then connect your repository to your deployment platform. The platform detects Node.js projects automatically, installs dependencies, builds the application, and deploys it with a public URL.

**7. What's the difference between PaaS and cloud provider solutions for backend deployment?**

PaaS platforms like Northflank, Railway, and Render abstract infrastructure management and provide streamlined developer experiences with built-in CI/CD and monitoring. Cloud provider solutions like AWS App Runner and Google Cloud Run give you access to broader cloud ecosystems but require more configuration. PaaS works best when you want to focus on building features. Cloud providers work when you need deep integration with specific cloud services.

**8. Do I need Kubernetes to deploy a backend?**

No. While Kubernetes powers many modern platforms, you don't need to learn or manage it directly. Platforms like Northflank abstract Kubernetes complexity while preserving its benefits. You get container orchestration, auto-scaling, and high availability without writing YAML configurations or managing clusters.]]>
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  <title>How to deploy OpenClaw (Clawdbot) on Northflank, securely</title>
  <link>https://northflank.com/blog/how-to-deploy-clawdbot-on-northflank-sandbox-microvm</link>
  <pubDate>2026-01-26T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[Deploy OpenClaw (Clawdbot), an open source AI automation and chatbot platform, on Northflank in minutes. Use the stack template to run a self hosted assistant with chat integrations, persistence, and zero server management.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/the_best_serverless_gpu_cloud_providers_7527a9ea1a.png" alt="How to deploy OpenClaw (Clawdbot) on Northflank, securely" /><InfoBox className='BodyStyle'>

## 📌 Sandboxes and microVMs are becoming the default runtime for AI agents

If an agent can generate code, run commands, install packages, or call internal tools, you need a real isolation boundary, not just a container, and definitely not “just run it locally.” MicroVM-style sandboxes give you a stronger security model while still being fast enough to spin up per task, per user, or per workflow.

In practice, “agents in prod” often ends up meaning “untrusted code in prod,” and sandboxes are what keeps that from turning into an incident. Every vibe-coded PR or auto-generated branch you merge without reviewing every line is basically a CVE lottery ticket. In the following guide we'll learn how to deploy a new agent called OpenClaw (Clawdbot) and self-service deploy onto a Northflank sandbox in seconds.

If you want to run OpenClaw (Clawdbot) safely in a real environment, Northflank provides the pieces you need to deploy it with isolation, storage and a remote runtime. Follow this guide to deploy OpenClaw (Clawdbot), and reach out if you’re looking to run other agent workloads or untrusted code on Northflank, either on our cloud or inside your own VPC.

</InfoBox>

## What is OpenClaw (Clawdbot)?

[OpenClaw (Clawdbot)](https://openclaw.ai/) is an open source AI automation and chat bot system that lets you run an assistant capable of actually doing things. It can manage inboxes, send emails, run workflows, and respond through chat apps like Telegram, Discord, and Slack. Clawdbot is commonly used for personal AI assistants, team automation bots, and chat driven workflows with persistent memory.

With [Northflank](https://northflank.com), you can deploy OpenClaw (Clawdbot) in minutes using the OpenClaw (Clawdbot) stack template. This prebuilt setup handles container builds, persistent storage, networking, and onboarding automatically, so you can focus on configuring your assistant instead of managing servers.

## What the template deploys

The OpenClaw (Clawdbot) stack template provisions everything needed for a production ready OpenClaw (Clawdbot) deployment on Northflank.

It includes:

- OpenClaw (Clawdbot) Gateway and Control UI exposed over HTTP
- A wrapper web service with a password protected setup wizard
- A persistent Northflank volume for configuration, credentials, conversations, and workspace data
- Secure environment variables for setup and gateway access

This setup follows production best practices while keeping the deployment simple and easy to maintain.

## How to get started with deploying OpenClaw (Clawdbot)

Getting OpenClaw (Clawdbot) running on Northflank takes only a few steps and requires no server side terminal access.

### Step 1: Deploy the stack template

1. Click [Deploy OpenClaw](https://northflank.com/stacks/deploy-openclaw) to open the template.
2. Create an [account on Northflank](https://app.northflank.com/signup) if you don’t already have one.
3. Click **`Deploy OpenClaw now`**.
4. Set the required environment variable `SETUP_PASSWORD`.
5. Click **`Deploy stack`** to build and run the OpenClaw template.
    
![image-65.png](https://assets.northflank.com/image_65_2476dccb82.png)

### Step 2: View deployed resources

Wait for the deployment to complete, then click **View resources**.

![image-66.png](https://assets.northflank.com/image_66_42918e3dbd.png)

### Step 3: Open the OpenClaw service

Select the OpenClaw service to access the public URL.

![image-67.png](https://assets.northflank.com/image_67_89e0156212.png)

### Step 4: Complete setup and open the Control UI

1. Open the public Clawdbot URL and complete setup at `/setup`.
2. Open the Control UI at `/openclaw`.

## Setup flow

1. Visit `https://<your-northflank-domain>/setup` and enter your `SETUP_PASSWORD`.
2. Choose a model/auth provider and paste your key.
3. (Optional) Add Telegram/Discord/Slack tokens.
4. Click **Run setup**.
5. Open the Control UI at `https://<your-northflank-domain>/openclaw`

## Key features

This stack template gives you a complete, self hosted OpenClaw (Clawdbot) environment:

- Run an AI assistant that interacts through Telegram, Discord, Slack, and other chat platforms
- Complete onboarding entirely in the browser with no server side terminal access
- Persist configuration, credentials, conversations, and workspace data across redeploys
- Secure sensitive values using environment variables and private volumes
- Scale the Clawdbot service as usage grows

It is suitable for personal assistants, internal team bots, and production chat based automation.

## How it works

Under the hood, the deployment follows a clear and secure architecture:

- Wrapper service runs on the Northflank public HTTP port and serves setup while reverse proxying all traffic
- Setup wizard runs clawdbot onboard non interactively inside the container
- Persistent volume stores Clawdbot state at data dot clawdbot and workspace at data workspace
- Clawdbot gateway runs internally and handles chat traffic and the Control UI
- Load balancer routes incoming HTTP and WebSocket traffic to keep the service responsive

All internal communication happens over private networking, with only the HTTP interface exposed publicly.

## Conclusion

Deploying OpenClaw (Clawdbot) on Northflank is one of the fastest ways to run a fully self hosted AI assistant that actually does things.

With this stack template, you get a ready to run OpenClaw (Clawdbot) environment with persistent storage, secure onboarding, and automated deployment. You can connect your chat apps, configure your assistant, and start automating real work immediately without managing servers, terminals, or complex infrastructure.]]>
  </content:encoded>
</item><item>
  <title>Top Fly.io Sprites alternatives for secure AI code execution and sandboxed environments</title>
  <link>https://northflank.com/blog/top-fly-io-sprites-alternatives-for-secure-ai-code-execution-and-sandboxed-environments</link>
  <pubDate>2026-01-26T08:00:00.000Z</pubDate>
  <description>
    <![CDATA[This guide examines the leading Fly.io Sprites alternatives, comparing isolation technologies, deployment options, pricing models, and production readiness.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/fly_io_sprites_alternatives_f7b915e431.png" alt="Top Fly.io Sprites alternatives for secure AI code execution and sandboxed environments" />If you're building AI agents, code interpreters, or platforms that execute untrusted code, Fly.io Sprites (and Fly.io Sprites alternatives) might be on your radar. But depending on your needs, BYOC deployment, GPU support, OCI container images, or enterprise features, you may need to explore alternatives.

This guide examines the leading Fly.io Sprites alternatives, comparing isolation technologies, deployment options, pricing models, and production readiness.

We wrote a detailed explanation of container isolation and everything you need to know about it [here](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation). Use it as a primer before going deeper into Fly.io Sprites alternatives.

<InfoBox className='BodyStyle'>

## 📌 TL;DR: Best Fly.io Sprites alternatives

[**Northflank**](https://northflank.com/) delivers production-proven microVM isolation (Kata Containers/CLH) plus gVisor, accepts any OCI container image, offers unlimited sandbox duration, BYOC deployment, and complete platform capabilities. Handles millions of workloads monthly.

- **E2B.dev** uses Firecracker microVMs with excellent AI agent SDKs but limits sessions to 24 hours
- **Modal** provides gVisor containers optimized for Python ML workloads, no BYOC options
- **Daytona.io** offers sub-90ms provisioning for AI workflows, Docker containers by default
- **Vercel Sandbox** leverages Firecracker for dev environments, 45-minute session limits

</InfoBox>

## What are Fly.io Sprites?

Fly.io Sprites launched in January 2026 as stateful sandbox environments for AI coding agents. Built on Firecracker microVMs, they offer:

- **Persistent 100GB root filesystem** using NVMe for fast local storage plus object storage for durability
- **Checkpoint/restore** that takes ~300ms and captures entire environment state
- **Scale-to-zero** after 30 seconds of inactivity
- **HTTP access** via unique URLs with automatic TLS
- **Network policies** for controlling egress

Sprites are designed for individual developers using Claude Code. They create in 1-12 seconds and automatically idle when inactive, billing only for actual CPU, memory, and storage usage.

Unlike standard Fly Machines, Sprites don't use Docker images. They use a custom storage stack where you start from a base Linux environment and install dependencies manually or via checkpoint/restore. This is a deliberate design choice, Fly.io argues that avoiding container image pulls enables faster creation times (1-2 seconds vs. potentially minutes for large images).

Note: Fly.io does offer GPUs (L40S, A100) for Fly Machines, but Sprites specifically are CPU-only. If you need GPU sandboxes, you'd use Fly Machines with Docker images, not Sprites.

## Why consider Fly.io Sprites alternatives?

Sprites solve a specific problem well: giving individual developers persistent sandboxes for Claude Code. But teams building production AI applications often need:

- **Any OCI image support**: Use existing containers without manual setup
- **BYOC deployment**: Run in your AWS/GCP/Azure accounts for compliance and data residency
- **GPU support in sandboxes**: Sprites are CPU-only; Fly GPUs require Fly Machines
- **Multi-region deployment**: Global distribution with predictable latency
- **Enterprise features**: Audit logs, SSO, RBAC, compliance tools
- **Multi-tenant isolation**: Platform-grade security for SaaS applications
- **Complete infrastructure**: Databases, APIs, and more beyond sandboxes

## At-a-glance comparison

| Platform | Isolation | Images | Persistence | Deploy options | Best for |
| --- | --- | --- | --- | --- | --- |
| [**Northflank**](https://northflank.com/) | microVM (Kata/CLH) & gVisor | Any OCI image | Unlimited | Managed or BYOC | Complete platform + sandboxes |
| **Fly.io Sprites** | microVM (Firecracker) | Base Linux (no Docker) | Unlimited (scale-to-zero) | Fly.io only | Individual dev workflows |
| **E2B.dev** | microVM (Firecracker) | Pre-built + custom | 24hr max | Managed only | AI agent tools |
| **Modal** | gVisor | SDK-defined only | Yes (network FS) | Managed only | Python ML workloads |
| **Daytona.io** | Docker/Kata | Docker images | Limited | Managed only | Quick AI demos |
| **Vercel Sandbox** | microVM (Firecracker) | Node.js/Python | 45 min max | Vercel only | Dev previews |

## 1. Northflank – Overall best Sprites alternative

[Northflank](https://northflank.com/) stands out by offering multiple isolation technologies and deployment flexibility. Since 2019, we've processed millions of workloads for companies like Writer, Sentry, and cto.new.

### Key advantages over Sprites:

- **Any OCI image**: Bring any container from Docker Hub, GitHub Container Registry, or private registries, no manual dependency installation required
- **Choice of isolation**: Kata Containers (microVM), gVisor, Firecracker, or Cloud Hypervisor based on your security requirements
- **True BYOC**: Deploy in your AWS, GCP, Azure, or bare-metal infrastructure with full control
- **GPU support in sandboxes**: NVIDIA L4, A100, H100, and H200 available for isolated workloads
- **Multi-region**: 330+ availability zones globally
- **Complete platform**: Run databases, APIs, cron jobs, and GPU workloads alongside sandboxes
- **Enterprise features**: SSO, RBAC, audit logging, SOC 2 compliance tools

### Why teams choose Northflank over Sprites

**Bring any container**: With Sprites, you start from a base Linux environment and install dependencies manually (or checkpoint a configured environment). This enables fast creation but means you can't directly deploy existing container images. Northflank accepts any OCI-compliant image without modification; deploy existing containers from any registry and integrate with CI/CD pipelines that produce Docker images.

**Stronger isolation options**: Sprites use Firecracker only. Northflank gives you Kata Containers with Cloud Hypervisor for true microVM isolation, gVisor for user-space kernel protection, or Firecracker for lightweight workloads. 

**Infrastructure flexibility**: Sprites run exclusively on Fly.io infrastructure. Northflank deploys in your cloud accounts, keeping data in your VPC for compliance and cost optimization. Use existing cloud commitments and savings plans.

**GPU support for sandboxes**: Sprites are CPU-only. While Fly.io offers GPUs for Fly Machines, those use Docker images and different orchestration. Northflank provides GPU-enabled sandboxes (L4, A100, H100, H200) with the same microVM isolation and API as CPU workloads.

**Production scale**: Northflank processes millions of isolated workloads monthly, powering multi-tenant platforms for public companies and governments. Sprites launched in January 2026 and are designed for individual developer workflows rather than platform-scale multi-tenancy.

## 🤑 Pricing comparison

**Northflank**

- CPU: $0.01667/vCPU/hour
- RAM: $0.00833/GB/hour
- NVIDIA H100: $2.74/hour (all-inclusive)

**Fly.io Sprites**

- CPU: $0.07/CPU-hour
- RAM: $0.04375/GB-hour
- Hot storage: $0.000683/GB-hour
- Cold storage: $0.000027/GB-hour
- GPUs: Not available for Sprites (Fly Machines required)

### Example: 4-hour coding session

**Sprites** (averaging 30% of 2 CPUs, 1.5GB RAM, 5GB storage):

- CPU (2.4 CPU-hrs): $0.17
- Memory (6 GB-hrs): $0.26
- Storage: $0.01
- **Total: ~$0.44**

**Northflank** (2 vCPU, 4GB RAM for 4 hours):

- Compute: $0.13
- **Total: ~$0.13**

For sustained workloads, Northflank's predictable per-second billing is more cost-effective than Sprites' usage-based model with separate CPU, memory, and storage charges.

### GPU workloads

Sprites are CPU-only. If you need GPU sandboxes on Fly.io, you'd use Fly Machines (which require Docker images and different tooling). Northflank provides GPU-enabled sandboxes with the same isolation and APIs as CPU workloads:

| GPU | Price (all-inclusive) |
| --- | --- |
| NVIDIA L4 24GB | $0.80/hour |
| NVIDIA A100 40GB | $1.42/hour |
| NVIDIA A100 80GB | $1.76/hour |
| NVIDIA H100 80GB | $2.74/hour |
| NVIDIA H200 141GB | $3.14/hour |

Northflank's GPU pricing includes CPU and RAM, approximately 62% cheaper than Modal for equivalent configurations.

## 2. E2B.dev

E2B specializes in AI code execution with Firecracker microVMs and polished SDKs. Great for hackathons and demos but lacks production features.

**Pros**: ~150ms cold starts, excellent Python/JavaScript SDKs, AI framework integrations (LangChain, OpenAI, Anthropic)

**Cons**: 24-hour session limit, no self-hosting, expensive at scale, sandbox-only platform

**Best for**: AI agent developers who need reliable sandboxes with excellent SDK design and don't require sessions longer than 24 hours.

## 3. Modal

Modal provides a serverless platform optimized for machine learning and data workloads, with sandboxing as one capability within a broader compute fabric.

**Pros**: Massive autoscaling (20,000+ concurrent containers), Python-first DX, built-in GPU support, snapshot primitives

**Cons**: gVisor only (no microVM isolation), SDK-defined images only, no BYOC, Python orchestration required

**Best for**: Python ML teams who want serverless simplicity and don't need infrastructure flexibility.

## 4. Daytona.io

Daytona pivoted to AI code execution in 2026, focusing on fast container starts with optional enhanced isolation.

**Pros**: Sub-90ms cold starts, Docker ecosystem compatibility

**Cons**: Docker containers by default (weaker isolation than microVMs), limited persistence, streaming stability issues reported

**Best for**: Quick prototypes and demos where speed matters more than isolation strength.

## 5. Vercel Sandbox

Vercel's beta sandbox offering provides Firecracker microVMs tightly integrated with their platform.

**Pros**: Great DX for Vercel users, Firecracker isolation, simple SDK

**Cons**: 45-minute session limit, Vercel ecosystem only, no BYOC, limited to Node.js and Python

**Best for**: Teams already on Vercel who need short-lived sandboxes for development workflows.

## Why teams choose Northflank

### 1. Bring any container

With Sprites, you start from scratch on every environment. Northflank accepts any OCI-compliant image from any registry, Docker Hub, GitHub Container Registry, your private registry, without modifications or SDK requirements.

### 2. Stronger isolation options

Sprites use Firecracker only. Northflank gives you:

- **Kata Containers**: Full microVM isolation with Cloud Hypervisor
- **gVisor**: User-space kernel with syscall interception
- **Firecracker**: Lightweight microVMs for ephemeral workloads
- **Cloud Hypervisor (CLH)**: High-performance VM isolation

### 3. Infrastructure flexibility

- **Your cloud**: Deploy in your AWS/GCP/Azure accounts
- **Compliance**: Keep data in your VPC for regulatory requirements
- **Hybrid**: Mix Northflank-managed and self-hosted deployments
- **Cost optimization**: Use existing cloud commitments and spot instances

### 4. Beyond sandboxes

Northflank runs your complete stack:

- Secure code execution
- Backend APIs with load balancing
- Databases (PostgreSQL, MySQL, MongoDB, Redis)
- Scheduled jobs and cron workloads
- GPU inference and training
- CI/CD pipelines with GitOps

### 5. Production scale

Since 2019, Northflank has solved the operational challenges others haven't:

- Multi-tenant isolation for SaaS platforms
- Resource quotas and autoscaling
- Audit logging and compliance tools
- Enterprise SSO and RBAC
- 330+ availability zones globally

## Making the right choice

**Choose Sprites if**: You're an individual developer using Claude Code who wants fast-creating persistent sandboxes with checkpoint/restore and don't need BYOC, GPUs, or OCI container support.

**Choose E2B if**: You need quick AI demos with polished SDKs and don't require sessions longer than 24 hours.

**Choose Modal if**: You're Python-first and comfortable with SDK-defined images for ML workloads.

**Choose Northflank if**: You need production-grade isolation, any OCI image support, BYOC deployment, GPU workloads, or a complete platform beyond just sandboxes.

## Get started with secure sandboxes

Specialized sandboxing tools have their place, but modern AI applications need more than just isolated code execution.

Northflank leads because it's the only platform that combines:

- Enterprise-grade microVM isolation (Kata Containers using CLH)
- Any OCI container image support
- True BYOC deployment (AWS, GCP, Azure, bare metal)
- GPU support with all-inclusive pricing
- A complete platform for all your workloads
- Production scale
- Transparent, predictable pricing

With Northflank, secure AI execution is just one part of a comprehensive infrastructure solution that grows with your needs.

[Try Northflank today](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo) with a Northflank engineer.

## FAQs

### Can I migrate from Fly.io Sprites to Northflank?

Yes. While Sprites don't use standard container images, you can containerize your environment and deploy it directly on Northflank. Northflank accepts any OCI-compliant image, making migration straightforward once you've packaged your dependencies.

### Does Northflank support checkpoint/restore like Sprites?

Northflank uses persistent volumes that maintain state across sessions. While the mechanism differs from Sprites' checkpoint/restore approach, the practical outcome (preserving environment state indefinitely) is the same. Sandboxes persist until you terminate them.

### What's the difference between Firecracker and Kata Containers?

Firecracker (used by Sprites, E2B, Vercel) is a lightweight VMM designed for fast boot times. Kata Containers (available on Northflank) provides OCI-compatible containers running in lightweight VMs with Cloud Hypervisor, offering stronger isolation with broader compatibility. Both provide hardware-level isolation superior to container-only solutions.

### Does Northflank support GPU sandboxes?

Yes. Northflank supports NVIDIA L4, A100 (40GB and 80GB), H100, and H200 GPUs with all-inclusive pricing and the same microVM isolation as CPU workloads. Sprites are CPU-only; if you need GPUs on Fly.io, you'd use Fly Machines (which require Docker images and different tooling than Sprites).

### Can I run Northflank in my own AWS/GCP/Azure account?

Yes. Northflank's BYOC (Bring Your Own Cloud) deployment runs in your VPC with full infrastructure control. Same APIs, same experience, your cloud credits and commitments. Sprites run exclusively on Fly.io infrastructure.

### How does Northflank's pricing compare to Fly.io Sprites for long-running workloads?

For sustained workloads, Northflank's predictable per-second billing ($0.01667/vCPU/hour, $0.00833/GB/hour) is typically more cost-effective than Sprites' separate CPU ($0.07/CPU-hour), memory ($0.04375/GB-hour), and storage charges. Sprites' scale-to-zero is advantageous for intermittent usage; Northflank is better for sustained or predictable workloads.

### Is self-hosting available for Fly.io Sprites alternatives?

Northflank offers true production-ready BYOC, letting you deploy in your AWS, GCP, Azure, or bare-metal infrastructure. E2B's self-hosting is experimental. Sprites, Modal, and Vercel are managed-only.

### Can I use Northflank for Claude Code like Sprites?

Yes. Northflank's microVM isolation provides the same security guarantees as Sprites for running AI coding agents. You can run Claude Code, Codex, or any AI agent in isolated environments with full network control and persistent storage.]]>
  </content:encoded>
</item><item>
  <title>What is cloud repatriation and why are companies doing it in 2026?</title>
  <link>https://northflank.com/blog/cloud-repatriation</link>
  <pubDate>2026-01-23T18:30:00.000Z</pubDate>
  <description>
    <![CDATA[Learn what cloud repatriation is and why companies are moving from AWS, Azure, and GCP to save 30-60% on infrastructure costs. Discover how to repatriate workloads while maintaining developer experience.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/cloud_repatriation_f03977508e.png" alt="What is cloud repatriation and why are companies doing it in 2026?" />> Cloud repatriation has become one of the most significant infrastructure trends of 2026. Companies that migrated aggressively to public cloud providers like AWS, Azure, and GCP are now moving workloads back to private infrastructure, on-premises data centers, or cheaper cloud alternatives.
> 

The primary driver? Cost. Organizations are discovering they can reduce infrastructure spending by 30-60% through strategic repatriation while maintaining the performance and reliability their applications need.

But here's the challenge most companies face: traditional repatriation means losing the developer experience, automation, and platform capabilities that made public cloud attractive in the first place. You're forced to choose between cost savings and operational efficiency.

*You shouldn't have to make that trade-off.*

This guide explains what cloud repatriation means, why it's accelerating in 2026, and how modern platform approaches let you capture cost savings without sacrificing developer velocity or operational capabilities.

<InfoBox className="BodyStyle">

## TL;DR: Cloud repatriation in 2026

**What it is:** Moving workloads from public cloud providers (AWS, Azure, GCP) back to private infrastructure, on-premises data centers, or lower-cost cloud alternatives like Hetzner, OVH, or Civo.

**Why companies do it:**

- Reduce infrastructure costs by 30-60%
- Gain better control over data and compliance
- Improve performance for specific workloads
- Avoid unpredictable cloud billing

**The challenge:** Traditional repatriation means losing platform automation, developer self-service, and operational simplicity.

**The solution:** Use platforms like [Northflank](https://northflank.com/) that let you [bring your own cloud infrastructure](https://northflank.com/features/bring-your-own-cloud) while maintaining AWS-like developer experience and automation. You get the cost savings of cheaper infrastructure with the operational benefits of a modern platform.

**Key takeaway:** Cloud repatriation isn't about abandoning cloud principles. It's about choosing infrastructure that delivers better economics while preserving the capabilities your engineering teams need.

</InfoBox>

## What is cloud repatriation?

Cloud repatriation is the process of moving applications, data, and workloads from public cloud providers back to private infrastructure, on-premises data centers, or alternative cloud environments.

Companies typically repatriate workloads for three main reasons: reducing costs, improving control over data and infrastructure, or optimizing performance for specific use cases.

Modern repatriation isn't necessarily a return to traditional on-premises infrastructure. Many companies are moving to lower-cost cloud providers, colocation facilities, or hybrid architectures that combine multiple infrastructure sources.

Cloud repatriation doesn't mean abandoning cloud principles like automation, scalability, and self-service. The goal is to maintain these capabilities while choosing infrastructure that better aligns with your economic and operational requirements.

## Why are companies repatriating workloads from the cloud?

The reasons driving cloud repatriation in 2026 reflect both the maturity of cloud adoption and changing economic realities.

### Cost optimization and predictability

Infrastructure costs are the primary driver of repatriation. Public cloud pricing models that seemed attractive initially often become expensive at scale. Companies with stable workloads discover they're paying a premium for flexibility they don't use. AI and data-intensive workloads intensify this challenge, with GPU costs making alternative infrastructure economically necessary.

### Control and compliance requirements

Some organizations need direct control over their infrastructure for regulatory, security, or governance reasons. Certain industries face data residency requirements that are simpler to satisfy with infrastructure you control directly. Control also lets you tune hardware, networking, and storage configurations precisely for your workload characteristics.

### Performance optimization for specific workloads

Certain applications perform better on dedicated infrastructure. Database workloads with consistent high I/O demands, real-time processing systems, and applications requiring low-latency access to large datasets often benefit from infrastructure optimized specifically for those patterns.

## How much can companies save with cloud repatriation?

Cost savings from cloud repatriation typically range from 30-60% of infrastructure spending, with the actual amount depending on workload characteristics, scale, and implementation approach.

The savings come from several sources:

- No data transfer fees (often 15-20% of cloud bills)
- Avoided premium pricing for on-demand flexibility
- Infrastructure precisely matched to your needs

For compute-intensive workloads, the economics are particularly compelling. A dedicated server from providers like Hetzner costs a fraction of equivalent AWS or GCP instances. GPU infrastructure shows similar patterns, with alternatives often costing 50-70% less for equivalent performance.

<InfoBox className="BodyStyle">

However, these raw infrastructure savings must account for operational costs. Managing your own infrastructure requires expertise, monitoring, maintenance, and tooling.

This is where platform approaches become valuable. [Northflank](https://northflank.com/) lets you capture infrastructure cost savings while automating operational complexity through [bring-your-own-cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud), [built-in autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments), [ephemeral preview environments](https://northflank.com/use-cases/preview-environments-backend-for-kubernetes), [spot instance orchestration](https://northflank.com/blog/what-are-spot-gpus-guide), and developer self-service capabilities.

</InfoBox>

## What are the risks of cloud repatriation?

Cloud repatriation introduces several challenges that organizations must address for successful execution.

- **Loss of operational capabilities**: Losing the developer experience and automation that made public cloud attractive creates friction. Platform solutions that work across infrastructure providers help maintain capabilities while changing economics.
- **Capacity planning and scaling challenges**: Planning capacity without elastic scaling means accepting that growth requires lead time. Hybrid architectures can handle occasional burst capacity needs.
- **Technical complexity and expertise requirements**: Managing infrastructure means networking, storage, and security become your responsibility. Organizations either build platform teams or use platforms that abstract this complexity.
- **Migration execution risk**: Moving production workloads carries risk from provider-specific dependencies and data migration complexity. Most companies repatriate incrementally, starting with non-critical workloads.

## How does cloud repatriation work?

Cloud repatriation follows a structured process that minimizes risk while maximizing the benefits of infrastructure change.

- **Assessment and planning phase**: Analyze your current cloud spending and workload characteristics. Identify which applications are driving costs and evaluate whether they're good candidates for repatriation. Calculate total cost of ownership for 3-5 years, including hardware, colocation, networking, and operational overhead.
- **Infrastructure selection and setup**: Choose infrastructure that matches your technical requirements and economic goals. Alternative cloud providers like Hetzner, OVH, or Civo offer compelling economics without requiring physical infrastructure management.
- **Application migration execution**: Migrate applications in phases based on risk and complexity. Start with development and staging environments, then move non-critical production workloads, and finally migrate increasingly important systems. Maintain parallel infrastructure during migration to enable quick rollback.
- **Operational optimization**: After migration, optimize your infrastructure for cost and performance. Build self-service capabilities so development teams can deploy and manage applications without manual intervention. Running platforms like Northflank on your own infrastructure gives you the automation and developer experience of public cloud while maintaining cost advantages.

## What is the best approach to cloud repatriation in 2026?

The most successful cloud repatriation strategies avoid the false choice between cost savings and operational capabilities.

### Platform-enabled repatriation

Modern platforms let you maintain AWS-like developer experience while running on infrastructure you control. This approach captures cost savings without forcing teams back to manual infrastructure management.

[Northflank's bring-your-own-cloud model](https://northflank.com/features/bring-your-own-cloud) demonstrates this pattern. You connect your own Kubernetes clusters running on Hetzner, bare metal, or any infrastructure provider. Northflank provides the platform layer: deployment automation, [built-in autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments), monitoring, [ephemeral preview environments](https://northflank.com/use-cases/preview-environments-backend-for-kubernetes), and developer self-service.

Your developers interact with a polished platform interface while you capture 30-60% infrastructure cost savings without changing deployment workflows.

![northflank--platform.png](https://assets.northflank.com/northflank_platform_d625d79568.png)

### Hybrid and multi-cloud strategies

Rather than repatriating everything, many companies adopt hybrid architectures. Baseline workloads run on cost-effective infrastructure while variable workloads use public cloud flexibility. Platforms that work across infrastructure providers maintain consistent deployment processes and developer experience.

### Incremental migration with validation

Successful repatriation happens in stages, not big-bang migrations. Start with workloads that have stable resource requirements, high cost, and minimal dependencies on provider-specific services. Validate each phase by measuring performance, cost savings, and operational overhead before proceeding.

### Focus on total cost of ownership

Compare infrastructure costs and operational overhead honestly. Repatriation delivers value when total cost of ownership is lower, not when raw infrastructure is cheaper but operational costs explode. Platforms reduce operational overhead by automating tasks and improving developer productivity.

<InfoBox className="BodyStyle">

**Start your cloud repatriation with Northflank**

See how Northflank's [bring-your-own-cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud), [built-in autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments), [ephemeral preview environments](https://northflank.com/use-cases/preview-environments-backend-for-kubernetes), and [spot instance orchestration](https://northflank.com/blog/what-are-spot-gpus-guide) let you capture infrastructure cost savings while maintaining platform automation and developer experience.

[Try Northflank](https://app.northflank.com/signup) or [talk to our team](https://cal.com/team/northflank/northflank-intro) about your repatriation strategy.

</InfoBox>

## Is cloud repatriation right for your company?

Cloud repatriation makes sense for specific situations and workload types.

### When repatriation delivers value

You're a good candidate if you're running stable, predictable workloads at meaningful scale. Companies spending $50,000+ monthly on cloud infrastructure typically find compelling economics in alternative approaches.

Repatriation works well when you have technical expertise in-house or are willing to use platforms that handle operational complexity. Data-intensive applications and AI/ML workloads often benefit significantly when GPU costs, storage volumes, or data transfer fees dominate your cloud bill.

### When to stay on public cloud

Early-stage startups and small teams should usually stick with public cloud. The operational overhead makes sense only when costs justify the effort. If your workloads are genuinely variable and unpredictable, public cloud's elastic scaling may be worth the premium.

### The hybrid middle ground

Many organizations find the optimal approach combines public cloud and self-managed infrastructure. Use cost-effective infrastructure for baseline workloads while maintaining public cloud for specific use cases where it excels. Modern platforms make hybrid architectures practical with consistent deployment processes across infrastructure types.

## Frequently asked questions about cloud repatriation in 2026

### What does cloud repatriation mean?

Cloud repatriation means moving applications and data from public cloud providers like AWS, Azure, or GCP back to private infrastructure, on-premises data centers, or alternative cloud environments. Companies typically repatriate to reduce costs, improve control, or optimize performance.

### Why are companies moving away from public cloud?

Companies move away from public cloud primarily to reduce infrastructure costs. Other drivers include a desire for better control over data and infrastructure, compliance requirements, and performance optimization for specific workloads. Most organizations adopting repatriation are seeking 30-60% cost savings.

### How long does cloud repatriation take?

Cloud repatriation timelines vary widely based on workload complexity and scale. Simple applications can migrate in weeks while complex systems may take months. Most organizations adopt phased approaches, migrating incrementally over 6-18 months.

### Can you repatriate some workloads while keeping others in public cloud?

Yes, hybrid approaches are common and often optimal. Many companies repatriate baseline workloads to cost-effective infrastructure while maintaining public cloud for variable workloads or applications that benefit from provider-specific services.

### What happens to developer productivity during cloud repatriation?

Developer productivity can suffer if repatriation means losing platform automation and self-service capabilities. Organizations maintain productivity by using platforms that provide AWS-like developer experience on any infrastructure.

### How much technical expertise does cloud repatriation require?

Traditional repatriation requires significant infrastructure expertise: networking, storage management, Kubernetes operations, and security hardening. However, platforms like Northflank that abstract this complexity reduce expertise requirements substantially.

### How do you measure success of cloud repatriation?

Measure repatriation success through total cost of ownership (infrastructure plus operational costs), application performance metrics, developer productivity, and business outcomes. Successful repatriation reduces costs by 30-60% while maintaining or improving performance.]]>
  </content:encoded>
</item><item>
  <title>11 cloud cost optimization strategies and best practices for 2026</title>
  <link>https://northflank.com/blog/cloud-cost-optimization</link>
  <pubDate>2026-01-23T17:00:00.000Z</pubDate>
  <description>
    <![CDATA[Learn 11 proven cloud cost optimization strategies to reduce spending by 30-50% in 2026. Includes AI/ML cloud cost reduction. See how Northflank automates these optimizations.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/cloud_cost_optimization_2_db4e7cb51b.png" alt="11 cloud cost optimization strategies and best practices for 2026" />> *Cloud cost optimization or cloud cost reduction has become a major requirement now more than ever in 2026. You can go from a manageable $5,000 monthly cloud bill to a shocking $50,000 expense in a few quarters.*

If you lead an engineering team handling infrastructure, you've likely experienced this firsthand or watched costs spiral out of control.

Now the challenge has gone beyond rising numbers. You now need to maintain performance and reliability while keeping your expenses under control.

*You shouldn't have to choose between cost and capability.*

I'll show you 10 strategies that can help reduce your cloud spending by 30-50% without compromising the performance your applications need. If you're focused on cloud cost reduction or long-term optimization, these approaches will help you regain control.

You'll also see how platforms like [Northflank](https://northflank.com/) can automate many of these optimizations, without you having to do the manual work in managing cloud costs.

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## TL;DR - 11 cloud cost optimization strategies in 2026

Let's take a quick look at the 11 most effective cloud cost optimization strategies:

1. **Implement autoscaling** - Scale resources up and down based on actual demand
2. **Use ephemeral environments** - Spin up temporary environments that shut down when not needed
3. **Right-size your instances** - Match compute resources to actual usage patterns
4. **Leverage spot instances and preemptible VMs** - Use discounted excess capacity for non-critical workloads, especially for AI/ML
5. **Optimize storage costs** - Choose appropriate storage tiers and clean up unused data
6. **Monitor and shut down idle resources** - Identify and remove resources that aren't being used
7. **Implement proper resource tagging** - Track costs by team, project, or environment
8. **Use reserved instances strategically** - Lock in discounts for predictable workloads
9. **Optimize data transfer costs** - Minimize cross-region and egress charges
10. **Establish cost governance and budgets** - Set spending limits and alerts to prevent overruns
11. **Optimize AI/ML infrastructure costs** - Manage GPU expenses, vector databases, and model lifecycle costs

> **How Northflank helps:**
Northflank's platform includes [built-in autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments), ephemeral [preview environments](https://northflank.com/use-cases/preview-environments-backend-for-kubernetes) that automatically shut down, [spot GPU orchestration](https://northflank.com/blog/what-are-spot-gpus-guide), and [bring-your-own-cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) options that let you maintain cost control while leveraging advanced developer tools. The platform automates many manual optimization tasks while providing the flexibility to run workloads efficiently across any cloud provider.

*Note: If you want to see how these optimizations work in practice, you can [try the platform directly](https://app.northflank.com/signup) or [talk to our engineering team](https://cal.com/team/northflank/northflank-intro).*

</InfoBox>

## What is cloud cost optimization?

Cloud cost optimization is the ongoing process of reducing your overall cloud computing expenses while maintaining or improving performance, security, and reliability.

*It's about finding the right balance between cost efficiency and operational performance.*

For instance, it’s like tuning a high-performance car. You want maximum speed and reliability, but you also want to optimize fuel consumption.

So, cloud cost optimization works similarly because you're fine-tuning your infrastructure to reduce waste, right-size resources, and leverage cost-effective alternatives without compromising the performance your applications need.

However, doing this manually is challenging because it requires dealing with the complexity of cloud environments.

For example, you have hundreds of services, multiple pricing models, and your workloads constantly scale up and down. Trying to manually optimize your cloud costs in such scenarios becomes nearly impossible.

This is why successful cloud cost optimization requires combining strategic planning with automated tools and continuous monitoring.

## Why is cloud cost optimization important for your business in 2026?

Cloud waste remains stubbornly high even as tools and practices mature. Organizations still waste 30-50% of their cloud spending on unused or over-provisioned resources, with AI and ML workloads now representing a growing share of that inefficiency.

For a company spending $100,000 monthly on cloud infrastructure, that's potentially $30,000-50,000 in waste every month. Over the course of a year, that waste could fund multiple engineering hires, a complete AI infrastructure buildout, or critical business initiatives.

In 2026, this challenge has intensified as GPU costs, high-performance storage, and data-intensive AI pipelines push cloud bills into new territory. Organizations that fail to optimize are finding their AI ambitions constrained by budget reality, while those with disciplined cost management are accelerating innovation.

Beyond the obvious financial benefits, cloud cost optimization provides strategic advantages that matter more than ever:

1. **Your infrastructure becomes more effective:**
    
    It scales with your actual needs rather than perceived requirements. This leads to better performance and reliability, as right-sized resources are less likely to experience bottlenecks or failures.
    
2. **Your spending patterns become clearer:**
    
    This helps you understand which projects, teams, or features are driving costs. This data becomes invaluable for making informed decisions about resource allocation and product development priorities.
    
3. **Your competitive position strengthens:**
    
    You can deliver the same or better performance at lower costs. This allows you to price products more competitively or reinvest savings into innovation and growth.
    
4. **Your budget planning becomes more predictable:**
    
    This reduces the risk of budget overruns and surprise bills. When you have control over your cloud costs, you can plan more accurately and avoid the scramble to cut expenses when bills exceed expectations.
    
<InfoBox className="BodyStyle">

And as someone who leads an engineering team, cloud cost optimization also improves team productivity.

When developers have access to well-optimized infrastructure through platforms like [Northflank](https://northflank.com/), they spend less time waiting for deployments and more time building features that matter to customers.

</InfoBox>

## What are the 11 cloud cost optimization strategies and best practices for 2026?

These strategies address the most common cost drains that engineering teams face when managing cloud infrastructure at scale.

### 1. Implement autoscaling

You're likely over-provisioning resources to handle peak traffic, which means you're paying for idle capacity during off-hours. Autoscaling automatically adjusts your compute resources based on actual demand.

Set up scaling policies that match your workload patterns. Use aggressive scale-down for development environments and more conservative settings for production. Schedule automatic scaling for predictable patterns like shutting down non-production environments overnight.

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*See how [Northflank's built-in autoscaling](https://www.notion.so/northflank/link) handles this automatically without complex configuration.*

</InfoBox>

### 2. Use ephemeral environments

Your development and staging environments probably run 24/7 even though your team uses them maybe 8 hours a day. Ephemeral environments spin up when needed and automatically shut down when idle.

This alone can cut your development infrastructure costs by 70-80%. Set up ephemeral environments for pull request previews and feature testing. Most modern platforms (like Northflank’s [ephemeral preview environments](https://northflank.com/use-cases/preview-environments-backend-for-kubernetes)) can create these from Git branches and tear them down when branches merge.

<InfoBox className="BodyStyle">

*Learn more about implementing this strategy in “[The what and why of ephemeral preview environments on Kubernetes](https://northflank.com/blog/the-what-and-why-of-ephemeral-preview-environments-on-kubernetes-sandbox-testing)” and see this guide on “[Setting up a preview environment](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment)” for implementation details.*

</InfoBox>

### 3. Right-size your instances

You're probably running instances that are 50-100% larger than needed because it's easier to over-provision than to analyze actual requirements. Start by reviewing your CPU, memory, and network utilization over the past few months.

Look beyond average utilization and consider your performance requirements. Sometimes a slightly larger instance offers better price-performance or includes features that take out additional service costs.

### 4. Leverage spot instances and preemptible VMs (especially for AI/ML workloads)

Spot instances and preemptible VMs offer 50-90% discounts in exchange for potential interruption, making them ideal for your CI/CD pipelines, batch processing, ML training, and any fault-tolerant workloads.

**In 2026, this strategy has become essential for AI/ML cost management.** GPU-backed spot instances can reduce training costs by 70-80% compared to on-demand pricing, transforming economics for organizations scaling AI initiatives. The key is designing workloads that handle interruptions gracefully through checkpointing and orchestration.

**Best practices for spot instance success:**

- **ML training workloads**: Implement checkpointing every 15-30 minutes so training can resume from interruption points
- **Inference serving**: Use mixed fleets (spot + on-demand) with automatic failover to maintain availability
- **Batch processing**: Design jobs as small, stateless tasks that can restart independently
- **GPU workloads**: Target less popular GPU types (e.g., A10G vs A100) for better availability

Use orchestration tools like [Northflank](https://northflank.com/) that automatically move workloads when spot instances terminate, maintaining reliability while reducing costs by 30-50% for standard workloads and up to 70-80% for GPU-intensive AI operations.

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**Helpful resources for spot optimization:**

- [How you can use spot instances in Northflank](https://northflank.com/docs/v1/application/bring-your-own-cloud/deploy-workloads-to-your-cluster#use-spot-instances)
- [What are AWS Spot Instances? Guide to lower cloud costs and avoid downtime](http://northflank.com/blog/spot-instances)
- [What are spot GPUs? Complete guide to cost-effective AI infrastructure](https://northflank.com/blog/what-are-spot-gpus-guide)
- [See how Weights reduced GPU costs by 70% with spot instance optimization](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)

</InfoBox>

### 5. Optimize storage costs

Storage costs add up quickly, especially if you're not managing data lifecycle properly. Set up automatic policies to move older data to cheaper storage tiers and regularly clean up unused volumes and snapshots.

Audit your storage monthly. Delete orphaned volumes from terminated instances and implement automated cleanup for temporary files and logs. This can reduce storage costs by 50-80% for older data.

### 6. Monitor and shut down idle resources

You likely have 15-25% of resources sitting completely idle - stopped instances still incurring charges, unused load balancers, forgotten databases. Set up monitoring to identify these systematically.

Create automated shutdown schedules for development environments and require approval to keep idle production resources running. Use resource tagging to track ownership so you know what's safe to terminate.

<InfoBox className="BodyStyle">

*See how [Northflank's monitoring and alerts](https://northflank.com/docs/v1/application/observe/observability-on-northflank) help you track resource utilization and identify idle workloads.*

</InfoBox>

### 7. Implement proper resource tagging

Without proper tagging, you can't track which teams or projects are driving your costs. Establish consistent tags for environment, team, project, and cost center across all resources.

Automate tagging wherever possible since manual tagging gets forgotten. When teams can see their actual spending, they naturally become more cost-conscious about resource usage.

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*See [how tagging works in Northflank](https://northflank.com/docs/v1/application/release/tag-workloads-and-resources) for implementation details*

</InfoBox>

### 8. Use reserved instances strategically

Reserved instances offer 30-60% discounts for 1-3 year commitments, but only buy them for stable, predictable workloads. Analyze your usage patterns to identify baseline capacity that runs consistently.

Use reserved instances for your foundation and on-demand or spot instances for variable demand. This gives you cost savings while maintaining flexibility for growth.

### 9. Optimize data transfer costs

Data transfer charges can surprise you, especially with poor architectural decisions. Keep related services in the same region and use CDNs to cache content closer to users.

Review your architecture for unnecessary cross-region transfers. Sometimes paying slightly more for compute in the right region saves significant data transfer costs.

### 10. Establish cost governance and budgets

Without governance, your optimization efforts will fade as teams focus on other priorities. Set up budgets and alerts at multiple levels with both warning thresholds and hard limits.

Assign cost ownership to specific teams and hold regular cost reviews. When someone is responsible for monitoring expenses in each area, optimization becomes part of the regular workflow.

### 11. Optimize AI/ML infrastructure costs specifically

AI and machine learning workloads have become the fastest-growing cost category in cloud infrastructure, requiring dedicated optimization approaches beyond traditional strategies.

**GPU and compute optimization:**
- Use lower-cost GPU types for development, testing, and model experimentation (T4, A10G) and reserve premium instances (A100, H100) only for production training
- Implement multi-instance GPU training to maximize utilization across multiple smaller GPUs rather than single large instances
- Shut down notebook environments and training jobs automatically when idle, even 4 hours of forgotten GPU time costs $50-200

**Data and storage strategies:**
- Audit vector databases monthly and implement retention policies, vector embeddings can consume terabytes faster than traditional data
- Use tiered storage for training datasets: hot tier for active experiments, cool tier for completed projects
- Implement data versioning cleanup, ML teams often accumulate dozens of dataset versions that never get deleted

**Model lifecycle management:**
- Track cost-per-inference and cost-per-training-run as key metrics alongside model performance
- Prune or archive unused models, organizations often run 10x more models than actively used
- Right-size inference serving: batch similar request patterns, use autoscaling based on inference latency

**Development environment controls:**
- Enforce automatic shutdown for ML notebooks after 2-4 hours of inactivity
- Use ephemeral environments for model experimentation that tear down automatically
- Share GPU resources across data science teams rather than dedicated allocations

Northflank's spot GPU orchestration and automatic resource management helps organizations reduce AI infrastructure costs by 50-70% while maintaining the performance data scientists need for rapid experimentation.

<InfoBox className="BodyStyle">

*Learn more: [Spot GPU optimization guide](https://northflank.com/blog/what-are-spot-gpus-guide) and [AI infrastructure cost management](https://northflank.com/docs/v1/application/bring-your-own-cloud/deploy-workloads-to-your-cluster#use-spot-instances)*

</InfoBox>

## How can Northflank help optimize your cloud costs in 2026?

You've seen the strategies that can reduce your cloud spending by 30-50%. The challenge is implementing them without turning your team into full-time infrastructure managers.

Let's see how [Northflank](https://northflank.com/) automates these optimizations so your team can focus on building products:

| **Feature** | **What it solves** | **Impact** |
| --- | --- | --- |
| [**Built-in autoscaling**](https://northflank.com/docs/v1/application/scale/autoscale-deployments) | No more paying for idle capacity or manual scaling policies | Automatic scale-down during quiet periods, scale-up for demand spikes |
| [**Ephemeral preview environments**](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) | Always-on development environments draining your budget | 70-80% reduction in development costs, auto-shutdown when merged |
| [**Bring-your-own-cloud (BYOC)**](https://northflank.com/features/bring-your-own-cloud) | Losing existing cloud discounts when adopting new platforms | Keep your commitments and discounts while gaining automation |
| [**Spot instance orchestration**](https://northflank.com/blog/what-are-spot-gpus-guide#how-northflank-cuts-spot-gpu-costs-with-automated-orchestration) | Complex management of discounted compute for AI/ML workloads | 50-80% compute cost reduction with automatic interruption handling |
| [**Template-driven deployments**](https://northflank.com/docs/v1/application/infrastructure-as-code/write-a-template) | Over-provisioning from manual resource creation | Right-sized configurations from day one based on proven patterns |

> The result is that your team ships features faster while your cloud bills decrease. You get the cost optimization without the operational complexity.
> 

<InfoBox className="BodyStyle">

***See how Weights scaled to millions of users using these optimization strategies without hiring a DevOps team.***

***If you're facing similar scaling challenges, you can [try the platform directly](https://app.northflank.com/signup) or [discuss your specific setup with our engineering team](https://cal.com/team/northflank/northflank-intro).***

</InfoBox>

## Frequently Asked Questions about cloud cost optimization in 2026

### How much can we realistically save through cloud cost optimization in 2026?

Most organizations can reduce cloud spending by 20-40% through systematic optimization, with some achieving 50%+ savings in the first year. Organizations with no existing optimization typically see larger gains (40-60%), while those with some practices may see 15-25% additional savings. AI/ML-heavy workloads often present the biggest opportunities, with GPU cost reductions of 60-80% possible.

### Should we optimize cloud costs ourselves or hire consultants?

Start with quick wins you can implement internally: idle resource cleanup, auto-shutdown schedules, and basic rightsizing. Consider specialized platforms like Northflank when you need automated optimization at scale, expertise in complex multi-cloud environments, or AI/ML infrastructure optimization. The best approach combines internal ownership with external expertise.

### What's the difference between cloud cost optimization and FinOps?

Cloud cost optimization refers to specific strategies and tactics used to reduce cloud spending (like rightsizing, spot instances, and storage tiering). FinOps (Financial Operations) is the broader organizational practice that makes optimization sustainable, including team structures, governance processes, and accountability frameworks.

### How often should we review cloud costs and optimization opportunities?

Implement continuous monitoring with automated alerts for anomalies and budget thresholds (daily/weekly). Conduct structured cost reviews monthly to assess trends and identify new opportunities. Perform comprehensive optimization audits quarterly, especially after major deployments or architecture changes.

### What are the biggest cloud cost optimization mistakes to avoid in 2026?

The most damaging mistakes include: (1) Optimizing for cost alone without considering performance impacts, (2) Making one-time optimizations without establishing ongoing governance, (3) Over-committing to reserved instances before understanding actual usage patterns, (4) Neglecting to tag resources properly, (5) Ignoring AI/ML cost growth.

### How do we optimize cloud costs without slowing down development teams?

Build optimization into your platform and workflows rather than adding manual steps. Use automated policies (like auto-shutdown for non-production environments), provide self-service tools that default to right-sized resources, and implement cost visibility in developer dashboards. Platforms like Northflank automate many optimizations so developers maintain velocity while costs stay controlled.

### What cloud cost optimization strategies work best for AI/ML workloads in 2026?

AI/ML workloads require specialized approaches: (1) Aggressive use of spot instances for training (70-80% cost reduction), (2) Separating training and inference infrastructure, (3) Implementing automatic shutdown for notebooks and development environments, (4) Right-sizing vector databases and implementing data retention policies, (5) Using lower-cost GPU types for development, reserving premium GPUs for production training.

### How does multi-cloud strategy impact cost optimization?

Multi-cloud adds complexity because each provider has different pricing models, discount structures, and cost management tools. However, it creates opportunities to optimize workload placement based on price-performance, negotiate better pricing, and avoid vendor lock-in. The key is implementing unified cost visibility and tagging across all clouds.

### When should we start implementing cloud cost optimization?

Start immediately, even during early cloud adoption phases. The patterns you establish early become embedded in your architecture and team culture. Begin with foundational practices: resource tagging, basic monitoring and alerting, auto-shutdown for non-production environments, and cost visibility dashboards.

### What cloud cost optimization metrics should we track in 2026?

Essential metrics include: (1) Cloud spend as percentage of revenue (typically 5-15% for SaaS companies), (2) Wasted spend percentage (target: under 15%), (3) Cost per customer/transaction, (4) Month-over-month cost growth rate, (5) Reserved instance/savings plan utilization (target: >70%), (6) Spot instance adoption rate, (7) Average resource utilization, (8) For AI/ML: cost-per-model-training-run and cost-per-inference.]]>
  </content:encoded>
</item><item>
  <title>Top 10 Azure cost optimization tools and strategies in 2026</title>
  <link>https://northflank.com/blog/azure-cost-optimization</link>
  <pubDate>2026-01-21T18:00:00.000Z</pubDate>
  <description>
    <![CDATA[Learn 10 Azure cost optimization tools and strategies to reduce your Microsoft Azure bill. Cut costs with spot VMs, reserved instances, and automated platforms.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/azure_cost_optimization_dc5120e9de.png" alt="Top 10 Azure cost optimization tools and strategies in 2026" />You're overspending on Microsoft Azure, and you know it. In this guide, you'll learn 10 proven tools and strategies to reduce your Azure costs significantly without sacrificing performance or reliability.

<InfoBox className="BodyStyle">

### TL;DR: Azure cost optimization tools and strategies at a glance

Here's what you need to know to start cutting your Azure bill today:

- Use Azure Spot VMs and right-sizing for compute workloads to cut costs by up to 90%
- Implement reserved instances or savings plans for predictable workloads running continuously
- Enable storage lifecycle policies to automatically tier data between hot, cool, and archive storage
- Optimize Azure Kubernetes Service with autoscaling and spot node pools
- Leverage Azure native tools like Cost Management + Billing and Azure Advisor for visibility
- Consider platforms like Northflank that handle optimization while deploying in your own Azure account

> **A recommended solution for Azure cost optimization:** Instead of managing Azure infrastructure manually, Northflank's [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) approach deploys directly into your Azure subscription and AKS clusters. You keep your reserved instances and credits while Northflank handles spot VM orchestration, right-sizing, and autoscaling. Teams typically see significant cost reductions within the first month.
> 

</InfoBox>

## What is Azure cost optimization?

Azure cost optimization is the process of reducing your Microsoft Azure spending while maintaining the performance and reliability your applications need.

It involves identifying wasted resources, right-sizing infrastructure to match actual usage, and selecting cost-effective pricing models for your workloads.

This includes finding idle virtual machines, over-provisioned databases, unnecessary storage in expensive tiers, and opportunities to use discount pricing like reserved instances or spot VMs.

Effective optimization happens continuously through automation rather than quarterly manual reviews, ensuring every dollar you spend on Azure supports your business objectives.

## How does Azure pricing work?

Understanding how Microsoft Azure prices its services helps you identify where you're overspending and which optimization strategies will deliver the biggest impact.

**Key pricing models:**

- **Pay-as-you-go:** The default pricing model with maximum flexibility but highest cost, billed per second for most compute services (with a one-minute minimum for VMs)
- **Reserved instances:** Commit to one or three years for up to 72% discount on VMs, databases, and other services
- **Azure savings plans:** Commit to hourly spending for one or three years and get up to 65% discount with more flexibility than reserved instances
- **Spot VMs:** Up to 90% less than standard pricing with 30-second termination notice when capacity is needed elsewhere
- **Azure Hybrid Benefit:** Use existing on-premises Windows Server and SQL Server licenses on Azure for up to 85% savings

Understanding these pricing models helps you select the right approach for each workload instead of defaulting to expensive pay-as-you-go pricing.

## Why is Azure cost optimization important?

Your uncontrolled Azure costs are forcing difficult budget conversations and limiting what your team can accomplish.

**Common waste patterns:**

- Idle virtual machines running 24/7 when only needed during business hours
- Over-provisioned SQL databases using a fraction of their capacity
- Forgotten storage in hot tiers when it should be in cool or archive
- Unoptimized queries scanning unnecessary data in Azure services

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**The business impact:**

Wasted spend reduces your budget for hiring and building features. Your team spends time managing infrastructure instead of focusing on work that drives business value.

Effective cost optimization frees up budget for innovation, improves productivity, and gives you clear visibility into spending.

This is where platforms like [Northflank](https://northflank.com/) help by handling optimization continuously while you maintain full control of your Azure subscription, ensuring your investment delivers maximum value.

</InfoBox>

## What are the common challenges in Azure cost optimization?

Even with Azure's transparent pricing, you're likely struggling with optimization challenges that prevent you from reducing costs effectively.

**Common challenges:**

- **Complexity of pricing models:** Understanding when to use spot VMs versus reserved instances and which savings plan fits different workloads creates decision paralysis
- **Lack of visibility:** Managing multiple subscriptions makes it nearly impossible to pinpoint which teams or applications drive your costs without proper tagging
- **Manual optimization doesn't scale:** Reviewing Azure Advisor suggestions and right-sizing resources consistently across your infrastructure while shipping features isn't realistic

These challenges are why the right tools make such a significant difference in achieving sustained cost reduction.

## What factors should you consider when choosing an Azure cost optimization tool?

Not all cost optimization tools work the same way, and choosing the wrong approach can create more problems than it solves.

**Key factors to evaluate:**

- **Maintains your Azure relationship:** Deploy in your own Azure subscription to keep reserved instances, credits, and your existing Microsoft relationship
- **Level of automation:** Decide between monitoring tools with manual recommendations versus platforms that handle optimizations continuously
- **Visibility and reporting:** Ensure clear visibility into actions taken and savings achieved with detailed cost allocation
- **Ease of implementation:** Consider setup time and expertise required, some tools need weeks while others deploy quickly

Choose based on your team's capacity for ongoing infrastructure management and how much time you want to spend on optimization versus building your product.

## What are the top 10 Azure cost optimization tools and strategies?

Here are the 10 most impactful ways to reduce your Azure costs, from quick wins you can implement today to automated solutions that deliver ongoing savings.

### 1. How can you reduce compute costs with spot VMs and right-sizing?

Your Azure virtual machines likely represent the largest portion of your bill.

- **Spot VMs:** Microsoft Azure's interruptible instances cost up to 90% less than standard VMs. Spot VMs provide 30-second termination notice when capacity is needed elsewhere, making them perfect for batch processing, CI/CD pipelines, machine learning training, and fault-tolerant systems.
- **Right-sizing VMs:** Use Azure Advisor for machine learning-based resizing suggestions based on actual usage. Most teams over-provision for peak capacity rather than typical usage, wasting money on unused resources.

Platforms like Northflank handle both spot VM management and right-sizing with instant failover and continuous optimization.

### 2. What are reserved instances and savings plans and when should you use them?

Reserved instances and savings plans provide substantial discounts when you commit to using specific resources for one or three years.

- **Reserved instances:** Commit to specific VM sizes and regions for up to 72% discount. Best for predictable workloads like production databases and core services.
- **Savings plans:** Commit to hourly spending with more flexibility than reserved instances for up to 65% discount. You can use any VM size within your committed spending limit.

Use these for workloads running continuously. Avoid them for development environments where usage might change. Start with one-year commitments before committing to three years.

### 3. How do you optimize Azure Storage costs?

Azure Storage costs compound quickly when storing terabytes of data.

- **Storage tiers:** Hot for frequent access, Cool for monthly access, Cold for quarterly access, and Archive for yearly access. Each tier costs progressively less with higher retrieval fees.
- **Lifecycle policies:** Automatically transition blobs between tiers or delete them based on age. Move to Cool after 30 days, Cold after 90 days, and Archive after one year.

Use regional storage when global distribution isn't necessary and clean up incomplete multipart uploads.

### 4. Can Azure Kubernetes Service optimization reduce your costs?

Manual AKS capacity management leads to overspending when teams over-provision node pools for peak capacity.

- **Cluster autoscaler:** Automatically adds or removes nodes based on pod requirements, eliminating idle nodes during low-traffic periods.
- **Spot node pools:** Use spot VMs for fault-tolerant workloads while maintaining standard nodes for critical services.
- **Pod resource limits:** Set accurate CPU and memory requests so AKS can pack pods efficiently.

Northflank deploys into your AKS clusters and handles spot node orchestration, right-sizing, and intelligent autoscaling.

### 5. What is autoscaling and how does it reduce costs?

Without autoscaling, you're paying for peak capacity continuously even during low traffic.

- **Horizontal autoscaling:** Scales the number of VM instances or pods based on demand.
- **Vertical autoscaling:** Adjusts CPU and memory allocations based on actual usage.

Configure autoscaling policies carefully to avoid aggressive scaling. Use meaningful metrics like request count or response time for intelligent scaling decisions.

### 6. How can you optimize Azure SQL and database costs?

Azure databases are among the most expensive resources, making optimization essential.

- **Select the right pricing model:** Choose between vCore (constant workloads) and serverless compute (workloads with long inactive periods).
- **Elastic pools:** Share resources between databases with flexible demand instead of dedicating them separately.
- **Savings plans:** Apply savings plans to database workloads for substantial discounts on predictable usage.

Monitor query performance to identify expensive operations that can be optimized.

### 7. Should you deallocate idle resources?

Forgotten resources cost money while delivering zero value.

Use Azure Advisor to identify idle VMs, unattached disks, old snapshots, and unused static IP addresses.

Schedule automatic shutdown for development and staging environments during nights and weekends. Delete snapshots older than compliance requirements. Clean up resources systematically.

### 8. How do tags help with cost allocation?

Without proper tagging, you can't identify which teams, projects, or environments drive your costs.

Label all resources with environment, team owner, project name, and cost center. Use Cost Management + Billing with tags to analyze spending and create accountability.

Make tagging part of your infrastructure-as-code templates so it happens automatically.

### 9. What Azure native tools help with cost optimization?

Microsoft Azure provides free native tools to monitor and reduce spending.

- **Cost Management + Billing:** Track costs across subscriptions, services, and resources with detailed analysis and budgeting.
- **Azure Advisor:** Get machine learning-based recommendations for right-sizing, reserved instances, and identifying idle resources.
- **Azure Pricing Calculator:** Estimate costs before deploying resources to plan budgets and compare configurations.
- **Azure Monitor:** Track resource utilization metrics to identify waste.

These tools identify problems but you still need to implement fixes manually.

### 10. How does platform automation optimize Azure costs?

The challenge isn't knowing what to do; it's doing it consistently while your team focuses on building products.

Platform solutions handle the optimizations you should implement but don't have time for. Find one that deploys in your Azure subscription so you keep your existing reserved instances and credits.

Northflank's [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) approach deploys into your AKS clusters, handling optimization for spot VMs, right-sizing, and autoscaling while you focus on features that drive business value.

## How does Northflank help with Azure cost optimization?

You've learned 10 strategies for reducing Azure costs, but implementing them consistently while shipping features requires either a dedicated team or the right automation.

### The Bring Your Own Cloud (BYOC) approach

![northflank-azure.png](https://assets.northflank.com/northflank_azure_35f8917c44.png)

Northflank deploys directly into your own Azure subscription and AKS clusters, so you're not migrating infrastructure or changing cloud providers.

You're adding an intelligent automation layer that handles optimization while you maintain complete control over your infrastructure.

**What you keep:**

- Your Azure subscription and relationship with Microsoft
- Reserved instances and savings plans
- Azure credits or enterprise agreements
- Your VNet, security posture, and compliance certifications
- Full visibility into all resources and costs in your Azure portal

### What Northflank handles for you

- **Spot VM orchestration with zero-downtime failover:** Northflank manages spot VMs across multiple zones and machine types. When Azure sends a termination notice, it instantly fails over to standard instances so your applications stay running while you capture spot savings.
- **Continuous right-sizing:** Instead of quarterly reviews that quickly become outdated, Northflank monitors your actual resource usage in real-time and adjusts allocations automatically as your needs change.
- **Intelligent autoscaling:** The platform learns your traffic patterns and scales resources to match real demand, not guesses about capacity you might need.
- **Automated resource cleanup:** Northflank identifies and removes unused resources like old snapshots and unattached disks before they accumulate into significant waste.
- **Multi-cloud optionality:** Start with Azure and expand to AWS or GCP later without vendor lock-in. Learn more about [cloud cost optimization across multiple providers](https://northflank.com/blog/cloud-cost-optimization) to maintain flexibility as your needs evolve.

<InfoBox className="BodyStyle">

You'll see the biggest impact if you're spending significant amounts monthly on Azure, have a small DevOps team stretched across multiple priorities, run variable workloads with fluctuating traffic patterns, or are running workloads on AKS that need continuous optimization.

Calculate your potential savings at [northflank.com/pricing](https://northflank.com/pricing) or check out how Northflank works with Azure at [northflank.com/cloud/azure](https://northflank.com/cloud/azure).

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Top 10 GCP cost optimization tools and strategies in 2026</title>
  <link>https://northflank.com/blog/gcp-cost-optimization</link>
  <pubDate>2026-01-21T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[Learn 10 GCP cost optimization tools and strategies to reduce your Google Cloud bill. Cut costs with spot VMs, committed use discounts, and automated platforms.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/gcp_cost_optimization_384320e8d4.png" alt="Top 10 GCP cost optimization tools and strategies in 2026" />You're overspending on Google Cloud Platform, and you know it. In this guide, you'll learn 10 proven tools and strategies to reduce your GCP costs significantly without sacrificing performance or reliability.

<InfoBox className="BodyStyle">

### TL;DR: Best GCP cost optimization tools and strategies at a glance

Here's what you need to know to start cutting your GCP bill today:

- Use spot VMs and preemptible instances for fault-tolerant workloads to cut compute costs by up to 91%
- Implement committed use discounts for predictable workloads running continuously
- Enable Cloud Storage lifecycle policies to automatically tier data based on access patterns
- Optimize GKE (Google Kubernetes Engine) with autoscaling and spot node pools
- Leverage GCP native tools like Recommender Hub and Cloud Billing Reports for visibility
- Consider platforms like Northflank that handle optimization while deploying in your own GCP account

> **A recommended solution for GCP cost optimization:** Instead of managing GCP infrastructure manually, Northflank's [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) approach deploys directly into your GCP account and GKE clusters. You keep your committed use discounts and credits while Northflank handles spot VM orchestration, right-sizing, and autoscaling automatically. Teams typically see significant cost reductions within the first month.
> 

</InfoBox>

## What is GCP cost optimization?

GCP cost optimization is the process of reducing your Google Cloud Platform spending while maintaining the performance and reliability your applications need.

It involves identifying wasted resources, right-sizing infrastructure to match actual usage, and selecting cost-effective pricing models.

Effective optimization happens continuously through automation rather than quarterly manual reviews, ensuring every dollar you spend supports your business objectives.

## How does GCP pricing work?

Understanding how Google Cloud prices its services helps you identify where you're overspending and which optimization strategies will deliver the biggest impact.

**Key pricing models:**

- **Per-second billing:** GCP bills per second for most services (with a one-minute minimum for Compute Engine), so you pay for exactly what you use
- **Sustained use discounts:** Automatically applied when Compute Engine resources run for more than 25% of a month, providing up to 30% discount with no commitment required
- **Committed use discounts:** Commit to one or three years for up to 70% discount. Choose resource-based (locked to specific machine families and regions) or spend-based (flexible, minimum hourly spend across services)
- **Spot and preemptible VMs:** Up to 91% less than standard instances with 30-second termination notice. Spot VMs have no maximum runtime, while preemptible VMs run up to 24 hours

Understanding these pricing models helps you select the right approach for each workload instead of defaulting to expensive on-demand pricing.

## Why is GCP cost optimization important?

Your uncontrolled GCP costs are forcing difficult budget conversations and limiting what your team can accomplish.

**Common waste patterns:**

- Idle Compute Engine instances running 24/7 when only needed during business hours
- Over-provisioned Cloud SQL databases using a fraction of their capacity
- Forgotten storage in expensive hot tiers when it should be in archive
- Unoptimized BigQuery queries scanning unnecessary data

<InfoBox className="BodyStyle">

**The business impact:**

Wasted spend reduces your budget for hiring and building features. Your team spends time managing infrastructure instead of focusing on work that drives business value.

Effective cost optimization frees up budget for innovation, improves productivity, and gives you clear visibility into spending.

This is where platforms like [Northflank](https://northflank.com/) help by handling optimization continuously while you maintain full control of your GCP account, ensuring your investment delivers maximum value.

</InfoBox>

## What are common challenges in GCP cost optimization?

Even with GCP's relatively transparent pricing, you're likely struggling with optimization challenges that prevent you from reducing costs effectively. Some common ones include:

- **Complexity of pricing models:** Understanding when to use spot VMs versus committed use discounts and which storage tier fits different data creates decision paralysis
- **Lack of visibility:** Managing dozens or hundreds of projects makes it nearly impossible to pinpoint which teams or applications drive your costs without proper labeling
- **Manual optimization doesn't scale:** Reviewing Recommender suggestions and right-sizing instances consistently across your infrastructure while shipping features isn't realistic

These challenges are why the right tools make such a significant difference in achieving sustained cost reduction.

## What factors should you consider when choosing a GCP cost optimization tool?

Not all cost optimization tools work the same way, and choosing the wrong approach can create more problems than it solves.

**Key factors to evaluate:**

- **Maintains your GCP relationship:** Deploy in your own GCP account to keep committed use discounts, startup credits, and your existing Google Cloud relationship
- **Level of automation:** Decide between monitoring tools that provide manual recommendations versus platforms that handle optimizations continuously
- **Visibility and reporting:** Ensure clear visibility into actions taken and savings achieved with detailed cost allocation reports
- **Ease of implementation:** Consider setup time and expertise required; some tools need weeks of configuration, while others deploy quickly

Choose based on your team's capacity for ongoing infrastructure management and how much time you want to spend on optimization versus building your product.

## What are the top 10 GCP cost optimization tools and strategies?

Here are the 10 most impactful ways to reduce your GCP costs, from quick wins you can implement today to automated solutions that deliver ongoing savings.

### 1. How can you reduce compute costs with spot VMs and right-sizing?

Your Compute Engine instances likely represent the largest portion of your GCP bill.

- **Spot VMs and preemptible instances:** Google Cloud's interruptible instances cost up to 91% less than standard instances. Spot VMs run longer than 24 hours with a 30-second termination notice, while preemptible VMs run up to 24 hours before termination.
    
    Use them for batch processing, CI/CD pipelines, machine learning training, and fault-tolerant systems. The massive savings are attractive, but manual management creates operational complexity.
    
- **Right-sizing instances:** Use Recommender Hub for machine learning-based resizing suggestions based on actual usage. Most teams over-provision for peak capacity rather than typical usage, wasting money on unused resources.

Platforms like Northflank handle both spot VM management and right-sizing with instant failover and continuous optimization.

### 2. What are committed use discounts and when should you use them?

Committed use discounts (CUDs) provide substantial savings when you commit to using specific resources for one or three years.

GCP offers resource-based CUDs (commit to minimum resources in a region) and spend-based CUDs (commit to minimum hourly spending with more flexibility).

Use them for workloads running continuously, like production databases and core services. Avoid them for development environments where usage might change. Start with one-year commitments before committing to three years.

### 3. How do you optimize Cloud Storage costs?

Cloud Storage costs compound quickly when storing terabytes of data.

- **Storage classes:** Standard for frequent access, Nearline for monthly access, Coldline for quarterly access, and Archive for yearly access. Each tier costs progressively less with higher retrieval fees.
- **Lifecycle policies:** Automatically transition objects between classes or delete them based on age. Move to Nearline after 30 days, Coldline after 90 days, and Archive after one year.

Enable compression for text files and logs, use regional buckets when global distribution isn't necessary, and clean up incomplete multipart uploads.

### 4. Can GKE optimization reduce your Kubernetes costs?

Manual GKE capacity management leads to overspending when teams over-provision node pools for peak capacity.

- **Cluster autoscaler:** Automatically adds or removes nodes based on pod requirements, eliminating idle nodes during low-traffic periods.
- **Spot node pools:** Use spot VMs for fault-tolerant workloads while maintaining standard nodes for critical services.
- **Pod resource limits:** Set accurate CPU and memory requests so GKE can pack pods efficiently.
- **GKE Autopilot:** Google handles node provisioning automatically while you only pay for pod resource requests.

Northflank deploys into your GKE clusters and handles spot node orchestration, right-sizing, and intelligent autoscaling.

### 5. What is autoscaling and how does it reduce costs?

Without autoscaling, you're paying for peak capacity continuously even during low traffic.

- **Horizontal Pod Autoscaler:** Scales pods based on CPU utilization or custom metrics.
- **Vertical Pod Autoscaler:** Adjusts CPU and memory requests based on actual usage.
- **Cluster Autoscaler:** Adds or removes nodes based on pending pods.

Configure policies carefully to avoid aggressive scaling. Use meaningful metrics like request count or response time for intelligent scaling decisions.

### 6. How can you optimize BigQuery costs?

BigQuery's pay-per-query model backfires when unoptimized queries scan terabytes of data.

- **Query optimization:** Use partitioning and clustering on large tables. Avoid SELECT * and specify only needed columns. Set daily spending limits to prevent runaway costs.
- **Slot reservations:** Purchase slot reservations for predictable workloads instead of on-demand pricing.
- **BI Engine:** Cache frequently accessed data to reduce costs for dashboards and reports.

Monitor BigQuery costs to identify which queries or users drive spending.

### 7. Should you delete idle resources and unused volumes?

Forgotten resources cost money while delivering zero value.

Use Recommender Hub to identify idle Compute Engine instances, unattached persistent disks, old snapshots, and unused static IP addresses.

Schedule automatic shutdown for development and staging instances during nights and weekends. Delete snapshots older than compliance requirements. Clean up resources systematically.

### 8. How do labels help with cost allocation?

Without proper labeling, you can't identify which teams, projects, or environments drive your costs.

Label all resources with environment, team owner, project name, and cost center. Use Cloud Billing Reports with labels to analyze spending and create accountability.

Make labeling part of your infrastructure-as-code templates so it happens automatically.

### 9. What GCP native tools help with cost optimization?

Google Cloud provides free native tools to monitor and reduce spending.

- **Cloud Billing Reports:** Track costs across projects, services, and resources.
- **Recommender Hub:** Get machine learning-based recommendations for right-sizing, committed use discounts, and identifying idle resources.
- **Cloud Monitoring:** Track resource utilization metrics to identify waste.
- **GCP Pricing Calculator:** Estimate costs before deploying resources.

These tools identify problems but you still need to implement fixes manually.

### 10. How does platform automation optimize GCP costs?

The challenge isn't knowing what to do; it's doing it consistently while your team focuses on building products.

Platform solutions handle the optimizations you should implement but don't have time for. Find one that deploys in your GCP account so you keep your existing credits and committed use discounts.

Northflank's [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) approach deploys into your GKE clusters, handling optimization for spot VMs, right-sizing, and autoscaling while you focus on features that drive business value.

## How does Northflank help with GCP cost optimization?

You've learned 10 strategies for reducing GCP costs, but implementing them consistently while shipping features requires either a dedicated team or the right automation.

### The Bring Your Own Cloud (BYOC) approach

![northflank-gcp.png](https://assets.northflank.com/northflank_gcp_cd050b4b7e.png)

Northflank deploys directly into your own GCP account and GKE clusters, so you're not migrating infrastructure or changing cloud providers.

You're adding an intelligent automation layer that handles optimization while you maintain complete control over your infrastructure.

**What you keep:**

- Your GCP account and relationship with Google Cloud
- Committed use discounts and sustained use discounts
- GCP startup credits or committed spend agreements
- Your VPC, security posture, and compliance certifications
- Full visibility into all resources and costs in your GCP console

### What Northflank handles for you

- **Spot VM orchestration with zero-downtime failover:** Northflank manages spot VMs across multiple zones and machine types. When Google Cloud sends a termination notice, it instantly fails over to standard instances so your applications stay running while you capture spot savings.
- **Continuous right-sizing:** Instead of quarterly reviews that quickly become outdated, Northflank monitors your actual resource usage in real-time and adjusts allocations automatically as your needs change.
- **Intelligent autoscaling:** The platform learns your traffic patterns and scales resources to match real demand, not guesses about what capacity you might need.
- **Automated resource cleanup:** Northflank identifies and removes unused resources like old snapshots and unattached disks before they accumulate into significant waste.
- **Multi-cloud optionality:** Start with GCP and expand to AWS or Azure later without vendor lock-in. Learn more about [cloud cost optimization across multiple providers](https://northflank.com/blog/cloud-cost-optimization) to maintain flexibility as your needs evolve.

<InfoBox className="BodyStyle">

You'll see the biggest impact if you're spending significant amounts monthly on GCP, have a small DevOps team stretched across multiple priorities, run variable workloads with fluctuating traffic patterns, or are running workloads on GKE that need continuous optimization.

Calculate your potential savings at [northflank.com/pricing](https://northflank.com/pricing) or check out how Northflank works with GCP at [northflank.com/cloud/gcp](https://northflank.com/cloud/gcp).

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Top 12 AWS cost optimization tools and strategies in 2026</title>
  <link>https://northflank.com/blog/aws-cost-optimization</link>
  <pubDate>2026-01-20T18:00:00.000Z</pubDate>
  <description>
    <![CDATA[Learn 12 AWS cost optimization tools and strategies to reduce your cloud bill. Right-size EC2, use spot instances, and automate savings with smart platforms.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/aws_cost_optimization_6d3bce7e81.png" alt="Top 12 AWS cost optimization tools and strategies in 2026" />AWS cost optimization helps you reduce your cloud bill by removing waste and right-sizing resources. In this guide, you'll learn 12 proven strategies to optimize your AWS costs significantly without sacrificing performance.

<InfoBox className="BodyStyle">

### TL;DR: AWS cost optimization tools and strategies at a glance

- Right-size EC2 instances based on actual usage patterns to avoid over-provisioning
- Use spot instances for fault-tolerant workloads with automated failover to avoid downtime
- Implement S3 Intelligent-Tiering and lifecycle policies for automatic storage optimization
- Deploy auto-scaling to match resources with real demand instead of peak capacity
- Consider platform solutions like Northflank that handle cost optimization while letting you keep your AWS account and existing credits

> **A recommended solution for AWS cost optimization:** Instead of choosing between AWS complexity and expensive Platform-as-a-Service solutions, Northflank's [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) approach lets you deploy into your own AWS account. You keep your enterprise agreements and reserved instance discounts while Northflank handles spot instance orchestration, auto-scaling, and right-sizing automatically. Teams typically see significant cost reductions within the first month without sacrificing control.
> 

</InfoBox>

## What is AWS cost optimization?

AWS cost optimization is the process of reducing your cloud spending while maintaining performance and reliability. It involves identifying and removing wasted resources, right-sizing infrastructure to match actual usage, and choosing cost-effective pricing models for your workloads.

The practice includes finding idle EC2 instances, over-provisioned databases, forgotten snapshots, and opportunities to use discounted pricing like Reserved Instances or spot instances. Effective optimization happens continuously through automation rather than periodic manual reviews.

The goal is ensuring every dollar spent on AWS infrastructure supports your business objectives, freeing up budget for innovation instead of wasted capacity.

## What are the four pillars of cost optimization in AWS?

AWS defines four key pillars in its Well-Architected Framework for cost optimization:

1. **Right-sizing:** Match your resource capacity to actual workload requirements instead of over-provisioning for peak capacity.
2. **Increasing elasticity:** Scale resources up during demand spikes and down during quiet periods instead of running maximum capacity continuously.
3. **Choosing optimal pricing models:** Use Reserved Instances, Savings Plans, or spot instances based on your workload patterns instead of defaulting to on-demand pricing.
4. **Optimizing over time:** Continuously monitor usage patterns and adjust resources as your needs evolve instead of treating optimization as a one-time project.

These pillars form the foundation of the strategies covered in this guide.

## Why AWS cost optimization is important

AWS's pay-as-you-go model gives you flexibility, but it also makes overspending easy. Without active cost management, expenses spiral quickly.

**The typical cost waste patterns:**

Most organizations waste significant cloud budget on idle EC2 instances running 24/7, over-provisioned databases using a fraction of their capacity, forgotten EBS snapshots accumulating monthly charges, and hidden data transfer fees that aren't as visible as compute costs.

Effective cost optimization frees up budget, improves team productivity, and ensures every dollar spent on AWS directly supports your business objectives.

<InfoBox className="BodyStyle">

Uncontrolled AWS costs force difficult budget conversations with finance. Wasted spend reduces your budget for innovation and hiring. Your team spends valuable time manually managing infrastructure instead of building features that drive business value.

This is where platform automation helps. Instead of manually implementing every optimization, platforms like [Northflank](https://northflank.com/) automate these decisions while you maintain full control of your AWS account.

</InfoBox>

## What are the essential AWS cost optimization tools?

AWS provides several free native tools to help you monitor and reduce your cloud spending:

- **AWS Cost Explorer**: shows your spending patterns over the past 13 months and forecasts future costs. Use it to create custom views and identify areas where you're overspending.
- **AWS Budgets**: lets you set spending limits and receive alerts when costs approach or exceed your thresholds. Configure budgets for specific services, teams, or projects.
- **AWS Cost Optimization Hub**: consolidates recommendations from Compute Optimizer, Trusted Advisor, and other services into one dashboard.
- **AWS Compute Optimizer**: analyzes your resource usage and recommends right-sizing opportunities for EC2 instances, Auto Scaling Groups, and EBS volumes.

While these tools provide valuable insights, they only identify problems. The strategies below show you how to actually implement fixes and automate ongoing optimization.

## What are the top 12 AWS cost optimization tools and strategies?

These 12 strategies cover the most impactful ways to reduce your AWS costs, from quick wins you can implement today to automated solutions that deliver ongoing savings.

### 1. Right-size your EC2 instances to prevent over-provisioning

Your EC2 instances likely represent the largest portion of your AWS bill. Right-sizing means matching your instance types and sizes to actual workload requirements, not peak capacity fears.

Use AWS Compute Optimizer to get recommendations based on your CloudWatch metrics. Look for instances with consistently low CPU utilization or memory usage well below what you've provisioned. Consider switching to AWS Graviton processors for better price-performance.

### 2. Use spot instances with automated failover

Spot instances let you use spare EC2 capacity at steep discounts compared to on-demand pricing. AWS can reclaim them with short notice when capacity is needed elsewhere.

Manual spot management is complex because you need interruption handling, fallback logic, and continuous monitoring. Most teams abandon spot instances after their first production incident.

Automated platforms like Northflank handle spot orchestration with instant failover to on-demand instances, giving you the savings without the operational risk.

### 3. Enable S3 Intelligent-Tiering for automatic storage optimization

Your S3 storage costs compound quickly when you're storing terabytes of data. S3 Intelligent-Tiering automatically moves objects between access tiers based on usage patterns.

Frequently accessed data stays in the frequent access tier, while objects untouched for 30 days move to infrequent access and objects untouched for 90 days [move to archive tiers](https://www.archondatastore.com/blog/storage-tiering-and-data-tiering/). Set lifecycle rules to delete incomplete multipart uploads after seven days for immediate savings.

### 4. Implement auto-scaling to match demand

Without auto-scaling, you're paying for peak capacity all the time even when traffic is low. Configure your Auto Scaling Groups to add instances during peaks and remove them during lulls.

Use meaningful metrics like request count or response time, not just CPU utilization. Avoid scaling too aggressively, which creates constant instance churn and actually increases costs through repeated launches and terminations.

Platforms like Northflank use intelligent algorithms that learn your traffic patterns and scale appropriately without manual tuning.

### 5. Purchase Reserved Instances or Savings Plans for steady workloads

If you run workloads continuously, Reserved Instances or Savings Plans can significantly reduce your costs compared to on-demand pricing.

Reserved Instances lock you into specific instance types for one or three years and work best for databases and core services. Savings Plans give you more flexibility across instance types and regions with the same commitment model.

### 6. Delete idle resources and unused volumes

Forgotten resources cost you money while delivering zero value. Use AWS Cost Explorer to find unattached EBS volumes, old snapshots, idle instances, and unused Elastic IPs.

Schedule automatic shutdown for your development instances during nights and weekends. They don't need to run continuously when nobody's using them.

### 7. Optimize data transfer costs

Data transfer fees often surprise you because they're less visible than compute charges. Egress fees vary by destination and region.

Keep resources in the same availability zone when possible, use CloudFront for caching, and leverage VPC endpoints to avoid NAT gateway fees. Cross-AZ transfers also add up at scale.

### 8. Implement tagging and cost allocation

Without proper tagging, you can't identify which teams or projects drive your costs. Tag all resources with environment, team owner, project name, and cost center.

Use AWS Cost Explorer with cost allocation tags to analyze spending by dimension and make informed optimization decisions.

### 9. Use AWS Cost Optimization Hub for recommendations

Cost Optimization Hub consolidates suggestions from Compute Optimizer, Trusted Advisor, and other services into one dashboard. It identifies right-sizing opportunities, idle resources, and Reserved Instance recommendations.

These tools show you problems but don't fix them automatically. Implementation still requires manual work.

### 10. Optimize database costs with right-sizing

Your RDS instances often run over-provisioned. Check your CloudWatch metrics for CPU and memory utilization patterns.

If usage stays consistently low, downsize to a smaller instance class. Use General Purpose SSD instead of Provisioned IOPS unless you truly need guaranteed IOPS. Stop development databases during off-hours instead of running them continuously.

### 11. Clean up old snapshots and backups

Snapshots and backups accumulate over time and increase your storage costs. Set appropriate retention periods based on your compliance requirements.

Delete old manual snapshots you no longer need. These costs add up across hundreds of snapshots over months and years.

### 12. Deploy platform automation for ongoing optimization

Manual implementation of these strategies requires significant time. Your team likely spends a substantial portion of infrastructure time on cloud management instead of building features.

Platform solutions automate the optimizations you know you should do but don't have time for. The key is finding one that works in your AWS account so you keep your existing discounts and credits. Check out the [best cloud hosting platforms](https://northflank.com/blog/best-cloud-hosting-platforms) to find one that works in your AWS account so you keep your existing discounts and credits.

For instance, Northflank's Bring Your Own Cloud approach deploys into your AWS account, letting you maintain your existing discounts while adding automated optimization.

## How does Northflank help with AWS cost optimization?

You've learned 12 strategies for reducing AWS costs. The challenge isn't knowing what to do, it's doing it consistently while your team focuses on building products.

### The Bring Your Own Cloud (BYOC) approach

![aws-on-northflank.png](https://assets.northflank.com/aws_on_northflank_366e066ee8.png)

Northflank deploys into your own AWS account. You're not migrating infrastructure or changing cloud providers. You're adding an intelligent automation layer that handles optimization while you maintain complete control.

**What you keep:**

- Your AWS account and relationship with AWS
- Enterprise Agreement discounts and volume pricing
- Reserved Instances and Savings Plans you've purchased
- AWS startup credits or committed spend (learn [how to get free AWS credits for your startup](https://northflank.com/blog/how-to-get-free-aws-credits-for-your-startup))
- Your VPC, security posture, and compliance certifications
- Full visibility into all resources and costs

### What Northflank helps with

- **Spot instance orchestration with zero-downtime failover:** Northflank manages spot instances across multiple availability zones and instance types. When AWS sends an interruption notice, it instantly fails over to on-demand instances. Your applications stay running while you capture spot savings. Learn more about [how AWS spot instances work](https://northflank.com/blog/spot-instances) to lower your cloud costs and avoid downtime.
- **Continuous right-sizing:** Instead of quarterly reviews, Northflank monitors your actual resource usage in real-time and adjusts allocations automatically.
- **Intelligent auto-scaling:** The platform learns your traffic patterns and scales resources to match real demand, not guesses.
- **Automated resource cleanup:** Northflank identifies and removes unused resources like old snapshots and unattached volumes.
- **Multi-cloud optionality:** Start with AWS and expand to GCP or Azure later. Learn more about [cloud cost optimization across multiple providers](https://northflank.com/blog/cloud-cost-optimization) to avoid vendor lock-in.

<InfoBox className="BodyStyle">

**Real cost comparison:**

Running similar workloads, Northflank's BYOC approach typically costs significantly less than traditional setups because of intelligent spot usage, continuous right-sizing, and efficient allocation. You get transparent, per-second billing with no hidden fees.

**Who benefits most:**

You'll see the biggest impact if you're spending significant amounts monthly on AWS, have a small DevOps team, run variable workloads with fluctuating traffic, or are considering multi-cloud to avoid vendor lock-in.

Calculate your potential savings at [northflank.com/pricing](https://northflank.com/pricing) or explore how Northflank works with AWS at [northflank.com/cloud/aws](https://northflank.com/cloud/aws).

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Kubernetes multi-tenancy: A 2026 guide to secure shared infrastructure</title>
  <link>https://northflank.com/blog/kubernetes-multi-tenancy</link>
  <pubDate>2026-01-19T17:30:00.000Z</pubDate>
  <description>
    <![CDATA[Master Kubernetes multi-tenancy in 2026. Learn how to implement secure isolation, reduce cloud costs, and scale your platform with proven patterns and modern tooling.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/kubernetes_multi_tenancy_06e191058c.png" alt="Kubernetes multi-tenancy: A 2026 guide to secure shared infrastructure" /><InfoBox className="BodyStyle">

## Key takeaways on Kubernetes multi-tenancy

- **Kubernetes multi-tenancy** is the practice of sharing a single cluster's resources across multiple teams or customers while maintaining strict security and performance isolation. Instead of managing dozens of separate clusters, you can consolidate infrastructure, cut costs, and give teams self-service access to isolated environments.
- **The challenge:** Kubernetes wasn't designed for multi-tenancy out of the box. You need to carefully configure namespaces, RBAC, network policies, and resource quotas to prevent tenants from affecting each other.
- **The solution:** Modern platforms like [Northflank](https://northflank.com/) automate this complexity by providing hardened isolation through secure runtimes (gVisor, Kata Containers), automated network policies (Cilium), and built-in governance, thereby turning weeks of manual configuration into instant, production-ready environments.

</InfoBox>

This guide shows you how multi-tenancy works, when to use it, and how to implement it securely. You'll learn the three main approaches to multi-tenancy, from lightweight namespace isolation to hardened virtual clusters, and understand the tradeoffs between cost, security, and operational complexity.

By the end, you'll know if you should build multi-tenancy from scratch or adopt a platform that provides it out of the box, and understand how modern tooling can turn weeks of manual configuration into instant, production-ready environments.

## What is Kubernetes multi-tenancy?

Kubernetes multi-tenancy means sharing one cluster among multiple independent users or teams, called "tenants", while keeping their workloads isolated from each other.

Similar to a hotel situation, everyone gets a secure, private room in the same building, rather than each person owning their own house. You share the infrastructure, but your space remains completely yours.

A tenant can be an internal team, a specific project, or an external customer. Each tenant gets their own isolated environment where they can deploy workloads without seeing or interfering with other tenants.

<InfoBox className="BodyStyle">

**Related resources on multi-tenancy**

- [Your containers aren't isolated. Here's why that's a problem.](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation)
- [How to spin up a secure code sandbox & microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
- [What is Multitenancy? Meaning, architecture, benefits & risks](https://northflank.com/blog/what-is-multitenancy)

</InfoBox>

## What are the core benefits of implementing Kubernetes multi-tenancy?

Multi-tenancy solves three critical problems that affect organizations running multiple Kubernetes clusters: escalating costs, operational burden, and slow developer velocity:

- **Infrastructure cost optimization**:
    
    You're currently running separate clusters, each with its own control plane, over-provisioned resources, and dedicated load balancers. Multi-tenancy lets you share these costs across all tenants, with organizations typically seeing significant savings by consolidating from 10+ clusters to 2-3 multi-tenant clusters.
    
- **Operational efficiency**:
    
    Instead of applying security patches 50 times and monitoring 50 different control planes, you update once and affect all tenants. You monitor from a single control plane, centralize logging and alerting, and maintain consistent policies. Your platform team spends less time on operations and more time building features.
    
- **Developer autonomy and speed**:
    
    Your developers currently wait days or weeks for new environments, submitting tickets and dealing with approval processes. Multi-tenancy enables "Namespace-as-a-Service" where they provision isolated environments instantly with self-service access. Teams ship features faster because infrastructure isn't the bottleneck.
    

## What are the different models for Kubernetes multi-tenancy?

There are three main approaches to implementing multi-tenancy, each with different isolation guarantees and complexity levels.

### Soft multi-tenancy: Logical isolation

Uses native Kubernetes features like namespaces, RBAC, network policies, and resource quotas to separate tenants logically. Works well for internal teams where tenants trust each other and cost is a primary concern.

The limitation: tenants share the same control plane and kernel, so a Kubernetes vulnerability could potentially affect other tenants.

### Hard multi-tenancy: Virtual clusters

Virtual cluster solutions give each tenant their own API server with separate control over CRDs and cluster-scoped resources. Suits external customers, untrusted workloads, or compliance requirements needing stronger isolation.

The tradeoff is more resource overhead and complexity.

### Physical isolation: Dedicated node pools

Complete physical separation using node taints, tolerations, and dedicated hardware per tenant. Necessary for high-compliance workloads (HIPAA, PCI-DSS), GPU workloads, or performance-critical applications.

The tradeoff: you reduce resource-sharing benefits and increase costs significantly.

## How do you securely isolate tenants in a shared Kubernetes cluster?

Security in multi-tenancy requires multiple layers of defense. Here are the four critical mechanisms you need:

- **Network isolation** through Network Policies prevents tenants from accessing each other's services or moving laterally after a compromise. Modern implementations like Cilium add Layer 7 filtering and observability on top of basic Kubernetes policies.
- **Resource quotas** stop one tenant from consuming all cluster resources and starving others. You set limits on CPU, memory, and pod counts per namespace to prevent the "noisy neighbor" problem.
- **RBAC (Role-Based Access Control)** ensures tenants only access their own resources. Start with deny-all by default, grant minimum necessary permissions, and use groups instead of individual users.
- **Sandboxed runtimes** like gVisor and Kata Containers add a security boundary that traditional containers lack. They prevent container breakouts from compromising the entire node by implementing user-space kernels or lightweight VMs.

Getting all of these mechanisms configured correctly takes significant expertise and ongoing maintenance.

## What are the common challenges and disadvantages of Kubernetes multi-tenancy?

While multi-tenancy offers significant benefits, you might encounter several technical and operational challenges that require careful planning to avoid:

- **The "blast radius" problem**: A single tenant's misconfiguration can crash the entire cluster by overwhelming etcd (Kubernetes' key-value store), starving the control plane, or misconfiguring webhooks. You need strict resource quotas, API rate-limiting, and separate control plane monitoring.
- **Managing complexity at scale**: As you add more tenants, you'll struggle with hundreds of YAML files, replicated network policies, and out-of-sync RBAC configurations. GitOps and policy automation tools become essential to maintain consistency across tenants.
- **The chargeback dilemma**: Traditional cloud billing shows one bill for the entire cluster with no breakdown by namespace or team, making cost allocation impossible. You need tooling that tracks resource usage at the namespace level.
- **Cluster-wide resources**: Custom Resource Definitions, admission webhooks, and ingress controllers affect the entire cluster and can conflict between tenants. Reserve cluster-scoped resources for administrators only.

## How does Northflank simplify building a multi-tenant Kubernetes platform?

Building multi-tenancy from scratch takes months of engineering effort. You need to configure namespaces, network policies, RBAC, quotas, monitoring, and security, then maintain them all as Kubernetes evolves.

[Northflank](https://northflank.com/) provides production-ready multi-tenancy out of the box, letting you focus on your applications instead of infrastructure complexity. Let’s see some of the ways Northflank helps:

- **Instant project isolation**:
    
    Northflank's "Project" abstraction automatically configures dedicated namespaces with network policies, RBAC rules scoped to project members, resource quotas, encrypted secret storage, and isolated networking with automatic mTLS. No YAML required, you just create a project and start deploying.
    
- **Hardened security by default**:
    
    You get gVisor and Kata Containers as runtime options to prevent container breakout attacks, Cilium-based network policies with mutual TLS encryption between services, and SOC 2 certified infrastructure with audit logs and SSO support. Enterprise-grade security without hiring a security engineering team.
    
- **Bring Your Own Cloud (BYOC)**:
    
    Manage multi-tenant environments across your own AWS, GCP, Azure, Civo, CoreWeave, Oracle, or bare-metal infrastructure from a single control plane. Your data stays in your VPC, you meet compliance requirements for data residency, and you use existing cloud credits while Northflank provisions and manages Kubernetes clusters in your infrastructure.
    
- **Self-service developer experience**:
    
    Developers get automatically created preview environments for each pull request, one-click managed databases (Postgres, Redis, MySQL), and GitOps workflows with automatic deployments on push. Teams move faster because infrastructure adapts to their development workflow.
    
- **Transparent cost tracking**:
    
    Track CPU and memory usage per project, storage costs per tenant, and network egress by environment with historical trends and forecasting. See exactly which teams or customers are consuming resources and at what cost.
    

## Get started with secure Kubernetes multi-tenancy today

Organizations are consolidating dozens of clusters into well-governed, multi-tenant platforms that give developers self-service capabilities while maintaining enterprise security and compliance.

Multi-tenancy is no longer optional; it's a requirement for cost-effective scaling. The choice isn't if you'll implement it, but how: build it yourself with months of engineering effort, or adopt a platform that provides it out of the box.

<InfoBox className="BodyStyle">

If you're ready to consolidate cluster sprawl and give your teams instant, secure environments, [get started with Northflank](https://app.northflank.com/signup) today.

</InfoBox>

See our guides on:

- [Your containers aren't isolated. Here's why that's a problem.](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation)
- [How to spin up a secure code sandbox & microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
- [What is Multitenancy? Meaning, architecture, benefits & risks](https://northflank.com/blog/what-is-multitenancy)]]>
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  <title>Firecracker vs QEMU: Which one should you use?</title>
  <link>https://northflank.com/blog/firecracker-vs-qemu</link>
  <pubDate>2026-01-19T08:00:00.000Z</pubDate>
  <description>
    <![CDATA[Firecracker and QEMU are both KVM-based virtualization technologies, but they're built for different purposes.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/firecracker_vs_qemu_fe34fcc0a5.png" alt="Firecracker vs QEMU: Which one should you use?" /><InfoBox className='BodyStyle'>

## 📌 TL;DR

**Firecracker** and **QEMU** are both KVM-based virtualization technologies, but they're built for different purposes.

**Firecracker** is a lightweight Virtual Machine Monitor (VMM) designed for serverless and ephemeral workloads. It boots microVMs in ~ 125ms, uses less than 5 MiB memory overhead, and prioritizes security through a minimal codebase (~ 50k lines of Rust). AWS built it to power Lambda and Fargate.

**QEMU** is a general-purpose emulator and virtualizer with nearly 2 million lines of code. It supports extensive hardware emulation, multiple CPU architectures, GPU passthrough, and legacy devices. It's slower to boot and has a larger attack surface, but offers unmatched flexibility.

**Choose Firecracker** for serverless functions, AI code sandboxes, multi-tenant isolation, and any workload where speed and security matter more than hardware flexibility.

**Choose QEMU** for full system emulation, GPU workloads, legacy hardware support, desktop virtualization, or when you need device passthrough.

Platforms like [**Northflank**](https://northflank.com/) use microVM technology (via Kata Containers with Cloud Hypervisor) to provide Firecracker-grade isolation without requiring you to operate the infrastructure directly.

</InfoBox>

## What is Firecracker?

**Firecracker** is an open-source Virtual Machine Monitor (VMM) developed by AWS for running serverless workloads. Written in Rust, it creates lightweight virtual machines called **microVMs** that combine VM-level security isolation with near-container efficiency.

AWS released Firecracker in 2018 after building it internally to power **AWS Lambda** and **AWS Fargate**. Lambda now handles tens of trillions of function invocations using Firecracker for customer isolation.

Firecracker's design philosophy is minimalism. It implements only the bare essentials needed to run a modern Linux kernel:

- **virtio-net** for networking
- **virtio-block** for storage
- **virtio-vsock** for host-guest communication
- **Serial console** for debugging
- **Minimal keyboard controller** for boot

### Firecracker key specifications

| Metric | Value |
| --- | --- |
| Boot time | ~ 125ms to user space |
| Memory overhead | less than 5 MiB per microVM |
| Creation rate | Up to 150 microVMs/second/host |
| Codebase | ~ 50,000 lines of Rust |
| Language | Rust (memory-safe) |

## Relevant reads

- [What is AWS Firecracker?](https://northflank.com/blog/what-is-aws-firecracker)
- [Top AI sandbox platforms in 2026](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution)
- [What’s the best code execution sandbox for AI agents in 2026?](https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents)

## What Is QEMU?

**QEMU** (Quick EMUlator) is a generic, open-source machine emulator and virtualizer. It's been in development since 2003 and has become the Swiss Army knife of virtualization, capable of emulating entire systems across different CPU architectures.

QEMU operates in two primary modes:

**Full system emulation:** QEMU emulates an entire machine, including CPU, memory, and devices. This allows running operating systems built for one architecture (like ARM) on a different architecture (like x86). Useful for development and testing but slower than native execution.

**KVM acceleration:** When the guest and host architectures match, QEMU can use Linux's KVM hypervisor for near-native CPU performance. QEMU handles device emulation while KVM handles CPU virtualization. This is how most production QEMU deployments work.

QEMU's strength is its comprehensive hardware support. It emulates everything from ancient floppy drives to modern NVMe storage, from serial ports to USB devices, from VGA graphics to GPU passthrough via VFIO.

### QEMU key characteristics

| Metric | Value |
| --- | --- |
| Boot time | Several seconds (varies by configuration) |
| Memory overhead | Hundreds of MiB (varies by configuration) |
| Codebase | ~ 2 million lines of C |
| Device support | Extensive (legacy and modern) |
| Architecture support | x86, ARM, RISC-V, PowerPC, s390x, and more |

## Firecracker vs QEMU: Direct comparison

### Security and attack surface

**Firecracker** was designed with security as a primary goal. Its minimal codebase (~50k lines of Rust) means fewer potential vulnerabilities. Rust's memory safety eliminates entire classes of bugs (buffer overflows, use-after-free) that plague C codebases. Firecracker also includes a **jailer** component that applies cgroups, namespaces, seccomp filters, and chroot isolation as defense-in-depth.

**QEMU** has a much larger attack surface. With ~2 million lines of C code and extensive device emulation, it has accumulated a significant history of CVEs. Each emulated device is potential attack surface. While QEMU can be hardened, achieving Firecracker-level security requires careful configuration and ongoing vigilance.

**Winner for security:** Firecracker

### Boot time and performance

**Firecracker** boots microVMs in approximately 125 milliseconds, fast enough for serverless functions that need to scale from zero. It can create up to 150 microVMs per second on a single host.

**QEMU** typically takes several seconds to boot, depending on configuration. Even QEMU's MicroVM mode (a stripped-down configuration) boots roughly 3x slower than Firecracker.

For ephemeral workloads where VMs spin up and down frequently, this difference is significant. For long-running VMs, boot time matters less.

**Winner for boot time:** Firecracker

### Resource efficiency

**Firecracker** microVMs consume less than 5 MiB of memory overhead each. This enables running thousands of microVMs on a single host, which is essential for multi-tenant serverless platforms.

**QEMU** VMs require significantly more memory overhead, typically hundreds of megabytes depending on emulated devices. This limits density when running many small workloads.

**Winner for resource efficiency:** Firecracker

### Hardware and device support

**QEMU** supports an enormous range of hardware:

- Multiple CPU architectures (x86, ARM, RISC-V, PowerPC, s390x, MIPS, SPARC)
- GPU passthrough via VFIO
- USB device emulation and passthrough
- Legacy devices (floppy drives, PS/2 keyboards, IDE controllers)
- Network interface cards, sound cards, and graphics adapters
- TPM emulation, NVRAM, RTC

**Firecracker** supports only five virtio devices. No GPU passthrough (PCIe support work was paused in 2025), no USB, no legacy hardware, no architecture emulation.

If your workload requires a GPU, specific hardware, or cross-architecture emulation, QEMU is your only option.

**Winner for hardware support:** QEMU

### Flexibility and use cases

**QEMU** can do almost anything:

- Run Windows, Linux, BSD, or exotic operating systems
- Emulate ARM on x86 for mobile development
- Provide GPU-accelerated VMs for ML training
- Support legacy applications requiring specific hardware
- Power desktop virtualization with full graphics

**Firecracker** does one thing well:

- Run lightweight Linux workloads with strong isolation

Firecracker's constraints are intentional. But if you need flexibility, QEMU provides it.

**Winner for flexibility:** QEMU

### Ecosystem and tooling

**QEMU** has two decades of ecosystem development:

- libvirt for VM management
- virt-manager for GUI administration
- Extensive documentation and community support
- Integration with every major orchestration system
- Broad adoption across cloud providers and enterprises

**Firecracker** has a growing but smaller ecosystem:

- Simple REST API for management
- firecracker-containerd for container integration
- Kata Containers support (as one of several VMM backends)
- Adopted by AWS Lambda, Fargate, Fly.io, Northflank, and others

**Winner for ecosystem:** QEMU

## Firecracker vs QEMU: Comparison summary

| Factor | Firecracker | QEMU |
| --- | --- | --- |
| **Boot time** | ~ 125ms | Seconds |
| **Memory overhead** | less than 5 MiB | Hundreds of MiB |
| **Codebase size** | ~ 50k lines (Rust) | ~ 2M lines (C) |
| **Security posture** | Minimal attack surface | Large attack surface |
| **GPU support** | No | Yes (VFIO passthrough) |
| **Legacy devices** | No | Yes |
| **Cross-arch emulation** | No | Yes |
| **Best for** | Serverless, sandboxing, multi-tenant | Full VMs, GPU, legacy, flexibility |

## When to use Firecracker

**Serverless and FaaS platforms:** If you're building a function-as-a-service platform where workloads spin up and down rapidly, Firecracker's boot time and density are essential. This is literally what AWS built it for.

**AI code execution sandboxes:** Running LLM-generated code requires strong isolation. Firecracker microVMs provide hardware-level isolation that containers cannot match, with startup times fast enough for interactive use.

**Multi-tenant workload isolation:** SaaS platforms running customer code benefit from Firecracker's security model. Each tenant gets their own microVM with dedicated kernel, no shared-kernel vulnerabilities.

**Edge computing:** Firecracker's minimal resource footprint makes it suitable for resource-constrained edge deployments.

**CI/CD build isolation:** Running untrusted builds in Firecracker microVMs prevents build-time attacks from affecting other builds or the host.

## When to use QEMU

**GPU workloads:** Machine learning training, rendering, or any workload requiring GPU access needs QEMU's VFIO passthrough capabilities. Firecracker cannot do this.

**Desktop virtualization:** Running Windows, macOS, or full Linux desktops with graphics requires QEMU's display emulation.

**Cross-architecture development:** Building and testing ARM software on x86 hardware (or vice versa) requires QEMU's emulation capabilities.

**Legacy system support:** Applications requiring specific hardware (floppy drives, parallel ports, specific network cards) need QEMU's extensive device emulation.

**Long-running, feature-rich VMs:** When VMs run for extended periods and boot time doesn't matter, QEMU's flexibility may be more valuable than Firecracker's speed.

## Running microVM workloads without the operational complexity

Operating Firecracker or QEMU directly requires significant engineering investment:

- Configuring KVM and host security
- Managing kernel images and root filesystems
- Implementing networking (TAP devices, bridges, firewall rules)
- Setting up the jailer security model
- Building orchestration for provisioning and lifecycle management
- Handling monitoring, logging, and debugging

For most teams, using a platform that abstracts this complexity makes more sense than building microVM infrastructure from scratch.

[**Northflank**](https://northflank.com/) provides production-ready microVM isolation using Kata Containers with **Cloud Hypervisor** as its primary VMM. Cloud Hypervisor was chosen for its broader workload compatibility, excellent runtime performance (faster CPU, disk, and memory operations), and stability. For edge cases where specific workloads require it, Northflank can fall back to QEMU or Firecracker. GPU workloads run on gVisor for containerized isolation. The platform processes over 2 million isolated workloads monthly.

### **Getting started with Northflank**

1. **Sign up at [northflank.com](https://app.northflank.com/signup)** 
2. **Create a project** — choose your region or connect your own cloud (AWS, GCP, Azure) for BYOC deployment
3. **Deploy a service** — use any OCI container image
4. **Get microVM isolation automatically** — Northflank provisions isolated infrastructure without manual VMM configuration

Northflank handles the use cases where microVMs excel—AI code sandboxes, multi-tenant isolation, secure workload execution—while also providing databases, APIs, GPU workloads, and CI/CD in a unified platform.

For teams with specific requirements, [**book a demo**](https://cal.com/team/northflank/northflank-intro) with Northflank's engineering team.

## Conclusion

**Firecracker** excels at ephemeral, security-sensitive workloads: serverless functions, AI sandboxes, multi-tenant isolation, and any scenario where you need to spin up thousands of isolated environments quickly. Its minimal design makes it secure and efficient but inflexible.

**QEMU** excels at flexibility: GPU workloads, legacy systems, cross-architecture emulation, desktop virtualization, and any scenario requiring specific hardware. Its comprehensive feature set comes with complexity and a larger attack surface.

For teams building AI applications, SaaS platforms, or developer tools that need to execute untrusted code, microVM isolation is increasingly essential. [**Northflank**](https://northflank.com/) provides this isolation through Kata Containers and Cloud Hypervisor, handling the operational complexity so you can focus on your application rather than virtualization infrastructure.

[**Get started with Northflank**](https://app.northflank.com/signup) or [**talk to our engineering team**](https://cal.com/team/northflank/northflank-intro) about your isolation requirements.

## FAQs

### What is the main difference between Firecracker and QEMU?

**Firecracker** is a minimal VMM designed for speed and security, supporting only essential virtio devices. **QEMU** is a full-featured emulator supporting extensive hardware, multiple architectures, and GPU passthrough. Firecracker boots in ~ 125ms with less than 5 MiB overhead; QEMU takes seconds and uses more resources but offers far more flexibility.

### Is Firecracker more secure than QEMU?

Generally, yes. Firecracker's minimal codebase (~ 50k lines of Rust) has a much smaller attack surface than QEMU's ~ 2 million lines of C. Firecracker also includes the jailer for defense-in-depth. However, a carefully configured, minimal QEMU setup can also be secure, it just requires more effort.

### Can Firecracker run Windows?

No. Firecracker only supports Linux guests (and OSv). Its minimal device model lacks the hardware emulation Windows requires. For Windows VMs, use QEMU.

### Does Firecracker support GPUs?

No. Firecracker does not support PCIe or GPU passthrough. Work on PCIe support was paused in 2025. For GPU workloads, use QEMU with VFIO passthrough or Cloud Hypervisor.

### What is a microVM?

A **microVM** is a lightweight virtual machine optimized for fast boot times and minimal resource overhead. MicroVMs provide hardware-level isolation (dedicated kernel per workload) like traditional VMs, but with startup times and density approaching containers. Firecracker, Cloud Hypervisor, and QEMU's MicroVM mode all create microVMs.

### Should I use Firecracker or QEMU for AI code execution?

For AI code execution sandboxes, **Firecracker-style microVMs** are typically the better choice. They provide strong isolation for untrusted code with fast startup times suitable for interactive use. Platforms like Northflank use microVM technology for this exact purpose. Only use QEMU if your AI workloads require GPU access.]]>
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</item><item>
  <title>16 best DevOps automation tools in 2026</title>
  <link>https://northflank.com/blog/devops-automation-tools</link>
  <pubDate>2026-01-18T19:06:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for the best DevOps automation tools? This guide covers 16 tools to help with CI/CD, infrastructure automation, and cloud deployments.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/16_Dev_Ops_automation_tools_1_9f53b0da97.png" alt="16 best DevOps automation tools in 2026" />DevOps automation tools replace manual deployment, infrastructure provisioning, and configuration management tasks with structured, repeatable processes. The right combination reduces deployment failures, speeds up release cycles, and lets engineering teams scale without adding operational headcount.
 
Most teams use several tools together: a CI/CD platform to build and deploy, an infrastructure tool to provision cloud resources, an orchestration layer to manage containers, and a monitoring tool to observe what is running. This guide covers the best DevOps automation tools in each category, what they do, and how to choose between them.

<InfoBox className="BodyStyle">

### TL;DR: 16 best DevOps automation tools in 2026

The right DevOps automation tool depends on what part of the workflow you are automating and how much you want to manage yourself.
 
1. **[Northflank](https://northflank.com/)** – CI/CD, infrastructure, and scaling in one platform. Best for teams that want to automate the full deployment workflow without managing separate tools for each layer.
2. **Terraform** – Infrastructure as code. Best for teams that need to provision and manage cloud resources across multiple providers.
3. **Kubernetes** – Container orchestration. Best for teams running containerized applications at scale that need automated scheduling, scaling, and self-healing.
4. **Jenkins** – Self-hosted CI/CD. Best for teams that need highly customizable pipelines and are willing to manage the infrastructure themselves.
5. **GitHub Actions** – Repository-integrated CI/CD. Best for teams already on GitHub that want CI/CD without a separate platform.
6. **ArgoCD** – GitOps for Kubernetes. Best for teams managing Kubernetes deployments that want declarative, Git-driven configuration.
7. **Ansible** – Configuration management. Best for teams managing configuration across many servers or hybrid environments.
8. **Pulumi** – Infrastructure as code using general-purpose languages. Best for developer teams that prefer Python, TypeScript, or Go over HCL.
9. **Prometheus** – Metrics and alerting. Best for teams that need custom metrics collection and alerting for cloud-native applications.
10. **CircleCI** – Managed CI/CD. Best for teams that want a hosted CI/CD platform without self-hosting Jenkins.
11. **Portainer** – Container management UI. Best for teams managing Docker and Kubernetes that want a visual interface over CLI commands.
12. **Spinnaker** – Multi-cloud continuous delivery. Best for large organizations deploying across multiple cloud providers with complex rollout strategies.
13. **Docker** – Container packaging. Best for any team building applications that need to run consistently across environments.
14. **Selenium** – Browser test automation. Best for teams with web applications that need automated UI testing in CI/CD pipelines.
15. **Chef** – Infrastructure and configuration automation. Best for large-scale environments that need policy enforcement and compliance automation.
16. **Raygun** – Error and performance monitoring. Best for teams that need to trace production errors to the exact line of code.

> [Northflank](https://northflank.com/) automates CI/CD, infrastructure provisioning, and scaling in one platform. Build pipelines, manage services, run managed databases, and deploy to your own cloud account without managing separate tools for each layer. [Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30).

</InfoBox>

## What are DevOps automation tools?

DevOps automation tools handle the repetitive, error-prone tasks that slow down engineering teams: provisioning servers, running tests, building container images, deploying applications, and monitoring production. Without automation, each of these steps requires manual intervention, which introduces inconsistency and creates bottlenecks as teams and codebases scale.
 
The tools in this guide cover four main categories. CI/CD tools automate the build, test, and deploy pipeline so every code change goes through a consistent process. Infrastructure tools provision and configure cloud resources from code rather than through manual console operations. Container orchestration tools manage where and how containerized applications run in production. Monitoring tools collect metrics and alert on anomalies so teams know when something needs attention before users report it.

### How DevOps automation tools help with CI/CD, infrastructure, cloud, and workflow automation

You don’t have to spend time fixing the same deployment issues over and over. You can automate different parts of your DevOps workflow to increase speed and reliability. Let’s see how:

- **CI/CD automation**: Automates builds, tests, and deployments without delays. Tools like Jenkins, GitHub Actions, and Northflank manage versioning, testing, and delivery pipelines so you don’t have to troubleshoot broken releases manually.
- **Infrastructure automation**: Provisions and configures servers and cloud environments consistently. Terraform and Pulumi replace manual setup with reusable configurations to keep deployments predictable.
- **Cloud automation**: Allocates resources and scales applications based on demand. Kubernetes and Portainer simplify deployment and maintenance for cloud-native workloads. In some workflows, automation can extend beyond code deployments to include operational messaging and alerts. For example, integrating an [SMS blast](https://clerk.chat/features/text-blast-service/) system or [native alerting via a webhook or your internal messaging tool](https://northflank.com/docs/v1/application/observe/set-infrastructure-alerts) into your DevOps pipeline can notify teams or customers instantly during major updates or incidents.
- **Workflow automation**: Keeps [development and operations](https://www.designrush.com/agency/software-development/trends/software-development-statistics) aligned by managing deployments, enforcing policies, and maintaining environment consistency. ArgoCD, Ansible, and Spinnaker handle orchestration, security rules, and infrastructure changes.

## Benefits of using DevOps automation tools

We’ve seen how automation keeps deployments running without constant intervention. Now let’s look at what that means for your team in practice:

 ![Benefits of using DevOps automation tools](https://assets.northflank.com/Benefits_of_using_Dev_Ops_automation_tools_5b5ac940f9.png) 

### Faster deployments with fewer errors

CI/CD pipelines catch failures before they reach production. Tools like Jenkins and Northflank automatically run builds and tests, so broken code doesn’t delay releases. If a deployment fails, automated rollbacks keep services running without manual intervention.

### Better teamwork between development and operations

Infrastructure as Code (IaC) standardizes configurations so environments stay consistent across teams. With Terraform, developers and platform engineers work from the same setup, no last-minute surprises when moving from staging to production.

### Cost savings and optimized resource usage

Cloud automation scales infrastructure based on demand. Kubernetes and Portainer adjust workloads dynamically, so you’re not paying for unused resources or scrambling to add capacity when traffic spikes.

### Security and compliance automation

Security checks need to happen at every stage, not just before production. Tools like Ansible and Spinnaker enforce access controls, apply patches, and check compliance automatically. Rather than slowing down releases, security becomes part of the pipeline.

## 16 best DevOps automation tools

Knowing the most suitable DevOps automation tools for your workflow can save your team time and reduce deployment failures. Let’s go through the best options and how they help.

### 1. Northflank (Automate CI/CD, infrastructure, and scaling in one place)

 ![](https://assets.northflank.com/northflank_s_home_page_156eb12b11.png) 

Managing CI/CD, infrastructure, and scaling separately slows down deployments and creates unnecessary complexity. [Northflank](https://northflank.com/) automates these processes in one platform, so your team doesn’t have to manage multiple tools just to deploy and maintain applications.

Your pipelines can trigger builds, tests, and deployments automatically, reducing manual steps and lowering failure rates. Scaling is built-in, so applications adjust to demand without extra configuration.

If your team is working with microservices or cloud-native applications, this kind of automation keeps everything connected. Push code, and Northflank handles the rest.

*See [how Weights uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### 2. Terraform (Define infrastructure as code and deploy anywhere)

 ![](https://assets.northflank.com/terraform_s_website_b20e36ec1c.png) 

Provisioning infrastructure manually takes time, and misconfigurations can break deployments. [Terraform](https://www.terraform.io/) lets you define infrastructure as code, so everything is predictable and version-controlled.

Your team can spin up cloud instances, set up networking, and configure Kubernetes clusters using declarative configurations. Deployments are consistent across environments, reducing last-minute surprises.

If you’re running applications across AWS, Google Cloud, or on-prem servers, Terraform makes it easier to manage everything without logging into different platforms.

### 3. Docker (Package and run applications consistently)

 ![](https://assets.northflank.com/docker_s_website_be5c7da50b.png) 

Running applications across different environments often leads to dependency issues. [Docker](https://www.docker.com/) solves this by packaging applications with everything they need so they work the same everywhere.

Your team can create lightweight, portable containers that run on any system without needing extra setup. Local development, CI/CD, and production environments stay in sync, reducing the stress of deployment.

If you’re onboarding new developers, they can spin up a complete environment in seconds rather than spending hours manually installing dependencies.

*Learn about [Docker Build and Buildx best practices for optimized builds](https://northflank.com/blog/docker-build-and-buildx-best-practices-for-optimized-builds)*

### 4. Kubernetes (Orchestrate containerized applications at scale)

 ![](https://assets.northflank.com/kubernetes_website_0e2fd7f2ea.png) 

Running containers in production without orchestration leads to downtime and scaling issues. [Kubernetes](https://kubernetes.io/) automates deployment, scaling, and management, so applications stay available even when workloads shift.

You can deploy microservices, manage rollouts, and balance traffic without manually adjusting configurations. Kubernetes ensures applications recover from failures automatically, reducing manual intervention.

For teams handling cloud-native applications, this makes deployments far more reliable.

*See this guide “[Kubernetes alternatives: finding the right fit for your team](https://northflank.com/blog/kubernetes-alternatives-finding-the-right-fit-for-your-team)"*

### 5. Portainer (Manage Docker and Kubernetes through a UI)

 ![](https://assets.northflank.com/portainer_s_website_6d796a7015.png) 

Setting up and managing containers can feel overwhelming without a centralized interface. [Portainer](https://www.portainer.io/) provides a visual dashboard for deploying, monitoring, and troubleshooting containers and Kubernetes clusters.

Rather than running CLI commands, you can configure networking, manage access controls, and track deployments through an interactive UI. This is useful if your team is adopting Kubernetes but wants a simpler way to manage it.

If your company is running multiple environments, Portainer makes container management more accessible without sacrificing control.

*See “[5 best Portainer alternatives for enterprise Kubernetes and Docker management](https://northflank.com/blog/portainer-alternatives)”*

### 6. Jenkins (Automate CI/CD workflows with custom pipelines)

 ![](https://assets.northflank.com/jenkins_website_19b83d7001.png) 

Waiting on manual deployments slows down development. [Jenkins](https://www.jenkins.io/) automates build, test, and deployment pipelines so that every commit triggers a reliable workflow.

It integrates with cloud providers, container platforms, and infrastructure tools, making it adaptable to different workflows. No matter if your team needs a simple CI/CD setup or complex multi-stage pipelines, Jenkins provides flexibility.

For larger teams working across multiple services, Jenkins helps coordinate deployments across different environments with minimal manual effort.

*See “[Jenkins alternatives in 2026: CI/CD tools that won’t frustrate DevOps engineers](https://northflank.com/blog/jenkins-alternatives-2025)”*

### 7. GitHub Actions (Run CI/CD directly in your repository)

 ![](https://assets.northflank.com/Git_Hub_Actions_35bd3e0dbe.png) 

CI/CD should be tightly integrated with your codebase. [GitHub Actions](https://github.com/features/actions) lets you define workflows inside your repository, so builds and tests run automatically with every commit.

Rather than using external CI/CD tools, workflows trigger based on events like pull requests or releases. Your team can automate testing, integrate modern practices like [AI testing](https://testgrid.io/blog/ai-testing/), handle container builds, and manage deployments without leaving GitHub.

For teams already managing code on GitHub, this simplifies automation.

*Learn [how to use a GitHub Action to deploy to Northflank](https://northflank.com/guides/use-a-git-hub-action-to-deploy-to-northflank)*

### 8. Ansible (Automate configuration management and deployments)

 ![](https://assets.northflank.com/ansible_s_website_7950302603.png) 

Manually configuring servers and environments leads to inconsistency and downtime. [Ansible](https://www.redhat.com/en/technologies/management/ansible) automates configuration management, software installation, and system updates across multiple machines.

Your team can define desired system states using YAML-based playbooks. Once applied, Ansible ensures all machines are configured correctly. This reduces errors in infrastructure and speeds up provisioning.

If your company manages multiple cloud environments or hybrid infrastructure, Ansible keeps everything in sync.

### 9. ArgoCD (Automate GitOps for Kubernetes)

 ![](https://assets.northflank.com/argocd_cfe5642e7d.png) 

Deploying applications in Kubernetes can be complex without automation. [ArgoCD](https://argoproj.github.io/cd/) automates GitOps workflows, so your infrastructure and applications stay in sync with your repository.

Your team can define deployment configurations in Git, and ArgoCD continuously enforces these states. If any configuration drifts, ArgoCD detects and corrects it automatically.

This ensures Kubernetes deployments remain stable without requiring manual intervention.

See [Argo CD alternatives that don’t give you brain damage and simplify DX for GitOps, clusters & deployments](https://northflank.com/blog/argo-cd-alternatives-northflank-developer-platform-git-ops-self-service)

### 10. Spinnaker (Manage multi-cloud deployments effortlessly)

 ![](https://assets.northflank.com/spinnaker_website_ef3cbed98e.png) 

Deploying applications across different cloud providers can be challenging. [Spinnaker](https://spinnaker.io/) simplifies multi-cloud deployments by providing automated release management and rollout strategies.

Your team can define continuous delivery pipelines that work across AWS, GCP, Kubernetes, and more. Canary deployments and rollback features make releases safer.

For companies using hybrid or multi-cloud environments, Spinnaker makes deployments manageable.

### 11. Prometheus (Monitor applications and infrastructure in real-time)

 ![](https://assets.northflank.com/prometheus_website_2f622cad6c.png) 

Tracking performance and identifying issues manually is impractical. [Prometheus](https://prometheus.io/) collects metrics and alerts on anomalies and provides real-time monitoring for applications and infrastructure.

Your team can set up custom metrics, analyze trends, and trigger alerts when thresholds are exceeded.

For DevOps teams managing large-scale applications, Prometheus helps detect failures before they impact users.

*See [Application Performance Monitoring on Northflank with Autometrics (which is built on Prometheus)](https://northflank.com/blog/performance-testing-for-core-dns)*

### 12. Selenium (Automate testing for web applications)

 ![](https://assets.northflank.com/selenium_website_2f2c5b0ff7.png) 

Manually testing every deployment slows down releases and increases the chance of missed bugs. [Selenium](https://www.selenium.dev/) automates browser testing, so your team can run UI tests across multiple environments.

You can integrate Selenium into your CI/CD pipeline to catch regressions before they reach production.

For teams building web applications, automated testing with Selenium reduces the risk of broken features.

### 13. Pulumi (Define infrastructure using familiar programming languages)

 ![](https://assets.northflank.com/Pulumi_website_c8b5d0ebdc.png) 

If writing YAML or JSON for infrastructure feels restrictive, [Pulumi](https://www.pulumi.com/) lets you define cloud infrastructure using Python, TypeScript, Go, and more.

Your team can provision, update, and manage infrastructure with the same programming skills used for application development.

For developers looking to integrate infrastructure directly into their codebase, Pulumi provides a modern alternative.

### 14. Raygun (Track errors and performance issues in real-time)

 ![](https://assets.northflank.com/raygun_website_9f4f877989.png) 

Your application might be running, but users could experience crashes or slow response times without proper monitoring. [Raygun](https://raygun.com/) provides real-time error tracking and performance diagnostics.

Your team can trace issues to the exact line of code, fix performance bottlenecks, and improve response times.

### 15. Chef (Automate infrastructure and system configurations)

 ![](https://assets.northflank.com/chef_automate_website_727780614c.png) 

Keeping infrastructure and application environments consistent across multiple deployments is difficult without automation. [Chef](https://www.chef.io/products/chef-automate) automates system configuration, application setup, and policy enforcement using code-driven workflows.

Your team can define infrastructure as code and apply configurations across multiple servers without manually updating each one. This is useful for managing large-scale environments and automatically enforcing security policies.

If your organization needs a way to standardize deployments across cloud and on-premise environments, Chef makes it easier to maintain consistency.

### 16. CircleCI (Automate testing and delivery pipelines)

 ![](https://assets.northflank.com/circleci_dashboard_952037ef18.png) 

CI/CD should be reliable and fast. [CircleCI](https://circleci.com/) automates builds, testing, and deployments with optimized pipelines that speed up releases.

Your team can integrate with Docker, Kubernetes, and cloud providers, ensuring every commit goes through automated validation before deployment.

## How to choose the right DevOps automation tool

You’ve seen the best DevOps automation tools, but knowing which one fits your workflow is the next step. The right choice depends on what you’re automating, how your team works, and the level of control you need.

> A great example is [Clock](https://clock.co.uk/), a digital agency managing deployments for brands like Riot Games, Epic Games, and Times Plus. They had over 70 environments but struggled with long staging times, manual scaling, and unpredictable costs. Deployments took weeks, and scaling required hands-on coordination from the ops team. After switching to **Northflank**, they cut provisioning times from weeks to hours, automated scaling to handle 20,000 requests per second, and gained full cost transparency. You can read the full [case study.](https://northflank.com/blog/scaling-30-000-deployments-with-100-uptime-how-clock-uses-northflank-to-simplify-infrastructure)
> 

If you’re making a similar decision, a few key factors can help:

→ **Integration**: Does it work with your existing stack? Tools like GitHub Actions make sense if your code is already on GitHub, while Terraform integrates well with cloud providers for infrastructure automation.

**→ Scalability**: Can it handle growth? **Kubernetes** is built for scaling applications, while Spinnaker supports multi-cloud deployments.

**→ Security**: Does it support policies, access controls, and compliance? Ansible automates security patching, and ArgoCD enforces GitOps policies to keep environments consistent.

**→ Ease of use**: Does your team need a UI-based tool or full automation with scripts? Portainer simplifies container management visually, while Jenkins and CircleCI give you more flexibility for complex pipelines.

**→ Cost and maintenance**: Is it open-source or a managed service? Pulumi and Chef give you infrastructure as code, but [managed platforms](https://northflank.com/features/managed-cloud) like Northflank reduce the overhead of self-hosting.

## Conclusion

We've covered how DevOps automation tools help your team move faster, reduce errors, and scale without the manual overhead. Choosing the right tool depends on how well it fits into your workflow, simplifies deployments, and keeps operations running reliably.

Now, it's time to put DevOps automation into action. If you're looking for a CI/CD and infrastructure automation platform that takes out deployment roadblocks, Northflank makes it easy to set up pipelines, manage services, and scale applications. You can [get started for free](https://app.northflank.com/signup).]]>
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</item><item>
  <title>What is AWS Firecracker? The microVM technology, explained</title>
  <link>https://northflank.com/blog/what-is-aws-firecracker</link>
  <pubDate>2026-01-18T08:00:00.000Z</pubDate>
  <description>
    <![CDATA[AWS Firecracker is an open-source virtual machine monitor (VMM) that creates and manages lightweight virtual machines called microVMs. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/aws_firecracker_4c8d23c590.png" alt="What is AWS Firecracker? The microVM technology, explained" /><InfoBox className='BodyStyle'>

## 📌 TL;DR

**AWS Firecracker** is an open-source virtual machine monitor (VMM) that creates and manages lightweight virtual machines called **microVMs**. Developed by Amazon Web Services, Firecracker combines the security isolation of traditional VMs with the speed and resource efficiency of containers.

Firecracker powers **AWS Lambda** and **AWS Fargate**, handling trillions of function executions monthly. It boots microVMs in as little as 125 milliseconds, consumes less than 5 MiB of memory overhead per VM, and supports creating up to 150 microVMs per second on a single host.

The technology is open-source under the Apache 2.0 license and has been adopted by platforms including [**Northflank**](https://northflank.com/), and others, for workloads ranging from serverless functions to AI code execution sandboxes.

</InfoBox>

<aside>

Relevant reads:

- [What is an AI sandbox?](https://northflank.com/blog/what-is-an-ai-sandbox)
- [Top AI sandbox platforms in 2026, ranked](https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution)

</aside>

## What is Firecracker?

**Firecracker** is a Virtual Machine Monitor (VMM) built specifically for serverless and container workloads. Written in Rust, it uses Linux's Kernel-based Virtual Machine (KVM) to create **microVMs,** which are lightweight virtual machines stripped down to the essentials needed for running modern cloud workloads.

AWS released Firecracker as open-source software in November 2018 after developing it internally to power Lambda and Fargate. The project emerged from a realization that existing virtualization technologies weren’t optimized for the event-driven, often short-lived nature of serverless workloads.

Traditional VMMs like QEMU were designed for general-purpose virtualization, including desktop and server use cases. They emulate a full range of hardware devices (USB controllers, displays, sound cards, PCI buses, BIOS) that serverless functions simply don't need. This unnecessary complexity increases the attack surface and resource overhead.

Firecracker takes the opposite approach: minimalism. It implements only five emulated devices (virtio-net, virtio-block, virtio-vsock, serial console, and a minimal keyboard controller) and communicates with guest kernels through optimized virtio interfaces rather than simulating traditional hardware.

## How Firecracker works

### The MicroVM architecture

A **Firecracker microVM** is a lightweight virtual machine that provides hardware-level isolation with minimal overhead. Each microVM runs its own guest kernel, completely separate from other microVMs and the host system.

![download.jpeg](https://assets.northflank.com/download_cb3c2d3e7b.jpeg)

When you start a Firecracker microVM, the process looks like this:

1. The Firecracker VMM process starts and exposes a RESTful API endpoint
2. You configure the microVM via API calls, setting vCPUs, memory, network interfaces, and block devices
3. You provide a Linux kernel image and root filesystem
4. Firecracker boots the guest kernel and launches user-space code

The entire process completes in approximately 125 milliseconds. For comparison, traditional VMs typically take seconds to minutes to boot.

### Key technical specifications

**Boot time:** Firecracker initiates user-space code in as little as 125ms, with ongoing work to reduce this further.

**Memory overhead:** Less than 5 MiB per microVM, enabling thousands of VMs on a single host.

**Creation rate:** Up to 150 microVMs per second per host.

**Supported architectures:** 64-bit Intel, AMD, and ARM processors with hardware virtualization support.

**Host requirements:** Linux with KVM enabled, kernel version 4.14 or above.

### The Jailer Security Model

Firecracker includes a companion program called the **jailer** that provides defense-in-depth security. The jailer applies:

- **cgroup isolation** to limit resource consumption
- **namespace isolation** to restrict visibility of system resources
- **seccomp filters** to limit available system calls
- **chroot** to restrict filesystem access

This creates multiple security boundaries. Even if an attacker somehow escaped the virtualization barrier, they would still face the jailer's containment measures.

## Firecracker vs. traditional VMs vs. containers

Understanding Firecracker requires understanding the trade-offs between traditional approaches:

### Traditional Virtual Machines

Traditional VMs provide strong isolation, each VM has its own kernel and cannot access other VMs or the host directly. However, they carry significant overhead: large memory footprints, slow boot times, and resource inefficiency when running many small workloads.

### Containers

Containers (Docker, containerd) share the host kernel, making them lightweight and fast to start. However, kernel sharing means a vulnerability in the kernel could potentially allow one container to affect others. For truly untrusted workloads, this shared-kernel model presents risk.

### Firecracker MicroVMs

Firecracker occupies the middle ground. MicroVMs provide the security isolation of VMs (dedicated kernel per workload) with startup times and resource efficiency approaching containers. Each microVM is completely isolated at the hardware virtualization level, but without the bloat of traditional virtualization.

| Attribute | Traditional VM | Container | Firecracker MicroVM |
| --- | --- | --- | --- |
| Isolation | Strong (dedicated kernel) | Weak (shared kernel) | Strong (dedicated kernel) |
| Boot time | Seconds to minutes | Milliseconds | ~125 milliseconds |
| Memory overhead | Hundreds of MiB | Minimal | < 5 MiB |
| Density | Low | High | High |
| Attack surface | Large | Medium | Minimal |

## Firecracker vs. other MicroVM technologies

Firecracker isn't the only microVM technology available. Here's how it compares to alternatives:

### Firecracker vs. QEMU

QEMU is a general-purpose emulator and virtualizer supporting a vast range of hardware and use cases. Firecracker is purpose-built for serverless. It sacrifices QEMU's flexibility for a dramatically smaller attack surface and lower overhead. If you need USB passthrough, GPU support, or Windows guests, use QEMU. If you need to run thousands of isolated Linux workloads efficiently, Firecracker is the better choice.

### Firecracker vs. Cloud Hypervisor

**Cloud Hypervisor** is another Rust-based VMM focused on cloud workloads. It offers more features than Firecracker (including GPU passthrough and live migration support) while maintaining a relatively small footprint. Cloud Hypervisor and Firecracker share similar design philosophies and similar boot times (~100-150ms). Cloud Hypervisor may be preferable when you need features Firecracker doesn't support.

### Firecracker vs. Kata Containers

**Kata Containers** is a container runtime that runs OCI-compatible containers inside lightweight VMs. It can use multiple hypervisors as backends, including QEMU, Cloud Hypervisor, and Firecracker itself. Kata Containers is the integration layer; Firecracker (or another VMM) provides the actual isolation. Many platforms use Kata Containers with Cloud Hypervisor or Firecracker as the underlying VMM.

### Firecracker vs. gVisor

**gVisor** takes a fundamentally different approach. Rather than running workloads in a VM with a dedicated kernel, gVisor intercepts system calls in user space using a component called Sentry. This provides isolation without the overhead of full virtualization but doesn't offer the same hardware-level isolation guarantees as Firecracker. gVisor has lower overhead for some workloads but weaker isolation boundaries.

## Use cases for Firecracker

### Serverless Computing (FaaS)

The canonical Firecracker use case. AWS Lambda runs each function invocation in its own Firecracker microVM, providing strong isolation between customers while maintaining fast cold starts and high density. When a Lambda function executes, it runs inside a microVM with its own kernel, no shared kernel vulnerabilities can cross function boundaries.

### Container isolation

For organizations running containers in multi-tenant environments, Firecracker (often via Kata Containers) provides stronger isolation than standard Docker containers. Each container runs in its own microVM, eliminating shared-kernel risks. AWS Fargate uses this approach for running customer containers securely.

### AI and Code Execution Sandboxes

Running untrusted or AI-generated code requires strong isolation. Platforms like [**Northflank**](https://northflank.com/) use Firecracker microVMs to sandbox code execution. Each execution session gets its own microVM that's destroyed when complete, ensuring no data leakage between sessions and no persistent compromise if code behaves maliciously.

### Edge Computing

Firecracker's minimal resource requirements make it suitable for edge deployments where compute resources are constrained. Its fast boot times enable responsive scaling at edge locations.

### CI/CD and Build Systems

Build systems executing untrusted code (user-submitted builds, open-source project CI) benefit from Firecracker's isolation. Each build runs in a fresh microVM, preventing build-time attacks from affecting other builds or the host system.

## Who Uses Firecracker?

### AWS Services

**AWS Lambda:** Processes trillions of function executions monthly using Firecracker for customer isolation. Each function invocation runs in its own microVM.

**AWS Fargate:** Runs customer containers inside Firecracker microVMs rather than dedicated EC2 instances, improving density and reducing costs while maintaining isolation.

### Cloud Platforms and Infrastructure Providers

According to the official Firecracker documentation, the technology has been adopted by:

- **Northflank** — cloud platform for deploying applications and AI workloads
- **Kata Containers** — as one of several supported VMM backends
- **Koyeb** — serverless platform
- **Fly.io** — global application platform
- **OpenNebula** — cloud computing platform
- **Qovery** — deployment platform
- **webapp.io** — preview environments

### Open Source Projects

- **containerd** via firecracker-containerd integration
- **Weave Ignite** (now archived) — GitOps for Firecracker
- **microvm.nix** — NixOS on Firecracker

## Limitations of Firecracker

Firecracker's minimalist design means it intentionally doesn't support features that other VMMs provide:

1. **No GPU passthrough:** Applications requiring GPU access cannot use Firecracker. Use QEMU or Cloud Hypervisor instead.
2. **No live migration:** You cannot migrate running Firecracker microVMs between hosts. Workloads must be stateless or handle migration at the application layer.
3. **Linux guests only:** Firecracker supports Linux and OSv guests. Windows is not supported.
4. **No USB or arbitrary device passthrough:** Only the five emulated virtio devices are available.
5. **Bare metal or nested virtualization required:** Firecracker requires direct KVM access. On AWS, this means running on .metal instances or instances with nested virtualization enabled.

These limitations are intentional trade-offs. Every feature not implemented is attack surface that doesn't exist.

## Getting started with Firecracker on Northflank

Building and operating Firecracker infrastructure directly requires significant engineering investment: configuring KVM, managing kernel images, setting up networking, implementing the jailer security model, and handling orchestration at scale. Most teams use Firecracker through a platform that abstracts this complexity.

[**Northflank**](https://northflank.com/) provides production-ready microVM infrastructure powered by Kata Containers and Cloud Hypervisor, giving you Firecracker-grade isolation without the operational burden. The platform processes over 2 million isolated workloads monthly, with the engineering team actively contributing to Kata Containers, QEMU, containerd, and Cloud Hypervisor.

To run isolated workloads on Northflank:

1. **Sign up at [northflank.com](https://app.northflank.com/signup)** 
2. **Create a project** — select your region or connect your own cloud account (AWS, GCP, Azure) for BYOC deployment
3. **Deploy a service** — use any OCI container image from any registry
4. **Run with microVM isolation** — Northflank provisions isolated infrastructure automatically

Northflank is particularly suited for:

**AI code execution sandboxes:** When AI agents generate and execute code, that code runs in isolated microVM-backed environments. Each execution is contained, preventing malicious or buggy code from affecting other workloads.

**Multi-tenant deployments:** SaaS platforms and developer tools running workloads on behalf of customers get strict isolation boundaries between tenants.

**Untrusted workload execution:** Any scenario involving code you didn't write benefits from hardware-level isolation.

For teams with specific requirements, [**book a demo**](https://cal.com/team/northflank/northflank-intro) with Northflank's engineering team to discuss microVM configurations, compliance needs, or enterprise pricing.

## 💭 FAQs

### What is a Firecracker microVM?

A **Firecracker microVM** is a lightweight virtual machine created by the Firecracker VMM. It provides hardware-level isolation with minimal overhead—booting in ~125ms and consuming less than 5 MiB of memory. Each microVM runs its own Linux kernel, completely isolated from other microVMs and the host.

### Is AWS Firecracker free?

Yes. Firecracker is open-source software released under the Apache 2.0 license. You can use, modify, and distribute it freely. AWS developed it but released it to the community.

### What is the difference between Firecracker and Docker?

Docker containers share the host kernel, providing process-level isolation. Firecracker microVMs each have their own kernel, providing hardware-level isolation. Docker is faster to start and has lower overhead but provides weaker isolation. Firecracker provides stronger security boundaries at the cost of slightly more overhead.

### Can Firecracker run Windows?

No. Firecracker supports only Linux guests (and OSv). Its minimalist design intentionally excludes the device emulation required for Windows. For Windows workloads, use QEMU or Hyper-V.

### What is the difference between Firecracker and Kata Containers?

Firecracker is a VMM, it creates and manages virtual machines. Kata Containers is a container runtime that can use various VMMs (including Firecracker, QEMU, or Cloud Hypervisor) as backends. Kata Containers provides the OCI/Kubernetes integration layer; Firecracker (or another VMM) provides the actual isolation.

### Does Firecracker support GPUs?

No. Firecracker does not support GPU passthrough or any device passthrough beyond its five emulated virtio devices. For GPU workloads, use QEMU or Cloud Hypervisor.]]>
  </content:encoded>
</item><item>
  <title>Top AI sandbox platforms in 2026, ranked</title>
  <link>https://northflank.com/blog/top-ai-sandbox-platforms-for-code-execution</link>
  <pubDate>2026-01-17T08:00:00.000Z</pubDate>
  <description>
    <![CDATA[An AI sandbox platform provides isolated environments for executing code generated by large language models and AI agents. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Top_AI_sandbox_platforms_in_2026_ranked_d731a6bd98.png" alt="Top AI sandbox platforms in 2026, ranked" /><InfoBox className='BodyStyle'>

## 📌 TL;DR

An **AI sandbox platform** provides isolated environments for executing code generated by large language models and AI agents. As AI-generated code volumes surge (i.e. Cursor alone produces nearly a billion lines of accepted code daily) sandbox products have become essential infrastructure for any team building AI applications.

This guide ranks the **top AI sandbox platforms** and **code execution products** for 2026:

1. [**Northflank**](https://northflank.com/): Best overall AI sandbox platform. MicroVM isolation via Kata Containers and gVisor, unlimited session duration, any OCI image, BYOC deployment. Processes 2M+ isolated workloads monthly.
2. **E2B:** Best sandbox product for AI-first SDKs. Firecracker microVMs, 150ms startup, but 24-hour session limits.
3. **Modal:** Best AI sandbox runner for Python ML. gVisor isolation, no BYOC option.
4. **Daytona:** Fastest AI sandbox platform. Sub-90ms cold starts, Docker isolation by default.
5. **Together Code Sandbox:** Best for Together AI users. 500ms snapshot resume, VM-style pricing.
6. **Vercel Sandbox:** Best sandbox product for Vercel ecosystem. Firecracker isolation, 45-minute to 5-hour limits.

For teams that need a complete **AI sandbox platform,** not just ephemeral code execution but databases, APIs, GPUs, and enterprise controls, **Northflank** delivers production-grade infrastructure with the flexibility to run in your cloud or ours.

</InfoBox>

## What Is an AI Sandbox platform?

An **AI sandbox platform** is infrastructure designed to safely execute code produced by AI systems, whether from LLM-powered coding assistants, autonomous agents, or code generation APIs. These **sandbox products** isolate untrusted code execution from your production environment, preventing AI-generated code from accessing secrets, consuming excessive resources, or compromising your infrastructure.

The core function of any **AI sandbox runner** is containment. When an AI agent generates Python to analyze data, JavaScript to render a visualization, or shell commands to install dependencies, that code runs inside an isolated environment with strict boundaries. If the code behaves maliciously or unexpectedly, the blast radius is limited to that single sandbox.

### Why AI Sandbox platforms are important now, more than before

Traditional sandboxing has existed for decades, but **AI sandbox products** address challenges specific to LLM-generated code. AI outputs can contain bugs, hallucinations, or prompt-injected instructions, and unlike human-written code, they often execute immediately without review. An **AI code sandbox platform** assumes all code is potentially dangerous.

Scale compounds the challenge. AI applications spawn thousands of concurrent sessions, and sandbox runners must provision and tear down environments in milliseconds. When building AI products for multiple users, each execution must be completely isolated, a core requirement for any AI sandbox platform.

## How to evaluate AI sandbox platforms

When comparing AI sandbox products and code execution platforms, evaluate these factors:

### Isolation technology

The security of an **AI sandbox runner** depends on its isolation method:

- **Standard containers** (Docker) share the host kernel. Fast but weaker isolation, kernel exploits can escape the sandbox.
- **gVisor** intercepts system calls in user space, reducing kernel attack surface. Used by Modal and available on Northflank.
- **MicroVMs** (Firecracker, Kata Containers, Cloud Hypervisor) provide dedicated kernels per workload. Strongest isolation for AI code execution platforms. Used by Northflank, E2B, and Vercel.

For truly untrusted AI-generated code, sandbox platforms with microVM isolation provide the strongest security guarantees.

### Startup latency

How fast can the AI sandbox product provision new environments? Cold start times range from sub-90ms (Daytona) to several seconds. For responsive AI agents, faster sandbox runners keep interactions fluid.

### Session duration

Many **AI sandbox platforms** impose strict time limits:

- Vercel Sandbox: 45 minutes to 5 hours
- E2B: 24 hours maximum
- Northflank: Unlimited

For AI agents maintaining state across extended user interactions, session limits force complex workarounds. The best **AI code sandbox platforms** offer flexible or unlimited durations.

### Runtime flexibility

Can you run any container image, or must you use SDK-defined environments? Some **sandbox products** lock you into specific languages or image formats. Platforms accepting any OCI image provide maximum flexibility for diverse AI workloads.

### Infrastructure options

Where can the **AI sandbox platform** deploy?

- **Managed only:** The vendor controls all infrastructure (Modal, Daytona, Vercel, Together)
- **BYOC (Bring Your Own Cloud):** Run in your AWS, GCP, or Azure account while the vendor manages orchestration (Northflank)
- **Self-hosted:** You operate everything, including the control plane (E2B experimental)

For regulated industries or data-sensitive AI applications, BYOC capability in an **AI sandbox product** is often mandatory.

### Platform scope

Is the **sandbox runner** a standalone tool, or part of a broader platform? Sandbox-only products require you to stitch together separate solutions for databases, APIs, GPU workloads, and CI/CD. Complete **AI sandbox platforms** provide unified infrastructure.

## Top AI sandbox platforms, ranked

### 1. Northflank: Best overall AI sandbox platform

![CleanShot 2025-11-21 at 13.36.22@2x.png](https://assets.northflank.com/Clean_Shot_2025_11_21_at_13_36_22_2x_13dc910e87.png)

[**Northflank**](https://northflank.com/) ranks as the top **AI sandbox platform** for teams requiring production-grade isolation, infrastructure flexibility, and capabilities beyond ephemeral code execution.

Operating since 2019, Northflank processes over 2 million isolated workloads monthly. The engineering team actively contributes to open-source projects powering the platform: Kata Containers, QEMU, containerd, and Cloud Hypervisor.

### Why Northflank is the top AI sandbox product

<aside>

- **Strongest isolation options:** Northflank is the only **AI sandbox platform** offering both Kata Containers (microVM isolation via Cloud Hypervisor) and gVisor. Choose isolation technology based on your security requirements, no other **sandbox product** provides this flexibility.

</aside>

- **Any OCI container image:** Unlike **AI sandbox runners** requiring proprietary formats or SDK-defined images, Northflank accepts any container from Docker Hub, GitHub Container Registry, or private registries. Existing images work without modification.

- **Unlimited sessions:** While competing **sandbox platforms** cap sessions at 24 hours or less, Northflank environments persist indefinitely. Essential for AI agents maintaining state across days or weeks of user interactions.

- **Production-ready BYOC:** Deploy the **AI sandbox platform** in your AWS, GCP, Azure, or bare-metal infrastructure. Northflank handles orchestration while your data stays in your VPC. No other major **AI code execution platform** offers mature bring-your-own-cloud.

- **Complete infrastructure:** Beyond sandboxed code execution, Northflank runs databases, backend APIs, scheduled jobs, and GPU workloads, all with consistent security. As AI applications grow beyond simple **sandbox runners**, infrastructure scales accordingly.

- **Enterprise proven:** Companies including Sentry and governments run multi-tenant AI deployments on Northflank. When cto.new [launched](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) to 30,000+ users, Northflank's **sandbox platform** handled thousands of daily code executions without issues.

### Northflank [pricing](https://northflank.com/pricing)

Transparent usage-based [pricing](https://northflank.com/pricing):

- CPU: $0.01667/vCPU-hour
- RAM: $0.00833/GB-hour
- GPU (H100): $2.74/hour all-inclusive

### Cost comparison at scale
To make the pricing difference concrete, here is what 200 sandboxes costs across providers under the same conditions.

_Based on 200 sandboxes, plan: nf-compute-100-4, infra node: m7i.2xlarge_

| Model | Provider | Cloud | Sandbox vendor | Total |
| --- | --- | --- | --- | --- |
| PaaS | Northflank | — | $7,200.00 | $7,200.00 |
| PaaS | E2B | — | $16,819.20 | $16,819.20 |
| PaaS | Modal | — | $24,491.50 | $24,491.50 |
| PaaS | Vercel Sandbox | — | $31,068.80 | $31,068.80 |
| BYOC (0.2 overcommit)* | Northflank | $1,500.00 | $560.00 | $2,060.00 |
| BYOC | E2B | $1,500.00 | $10,000.00 | $11,500.00 |

*Through Northflank's plans on BYOC, there's a default overcommit which allows a customer to spawn more services and sandboxes on the same amount of compute. A request modifier of 0.2 means each sandbox only requests 20% of its plan's resources as a guaranteed minimum, but can burst up to the full plan limit if there's available capacity on the node. So instead of fitting 8 sandboxes per node, you could fit 40 on the same hardware, reducing both infrastructure cost and the Northflank management fee.

Northflank's [GPU](https://northflank.com/request/gpu) pricing includes CPU and RAM, approximately 62% cheaper than comparable AI sandbox products charging separately.

Plus, of course, you get the whole platform included.

![CleanShot 2026-01-17 at 10.30.40@2x.png](https://assets.northflank.com/Clean_Shot_2026_01_17_at_10_30_40_2x_d9f3c29923.png)

### Best for

Teams seeking the top **AI sandbox platform** with enterprise-grade isolation, BYOC deployment, and unified infrastructure for complete AI applications.

### 2. E2B: Best AI Sandbox Product for SDK Design

**E2B** built its **sandbox platform** specifically for AI agent developers, offering polished Python and JavaScript SDKs for programmatic code execution.

### Strengths

- **Firecracker microVM isolation:** Each sandbox in this **AI code execution product** runs in a dedicated lightweight VM
- **150ms cold starts:** Fast environment provisioning for responsive **AI sandbox runners**
- **Session persistence:** Pause and resume sandboxes from saved state

### Weaknesses

- **24-hour session cap:** Even Pro plans limit this **sandbox product** to day-long sessions
- **Self-hosting complexity:** Scaling the **AI sandbox platform** past hundreds of concurrent environments requires operating E2B's control plane
- **No network policies:** Lacks granular egress controls for **AI code execution**
- **Docker image requirements:** Custom environments require building and pushing images

### E2B Pricing

- Hobby: Free with $100 credit, 1-hour sessions, 20 concurrent sandboxes
- Pro: $150/month, 24-hour sessions, configurable resources
- Usage: ~$0.05/hour per 1 vCPU sandbox

### 3. Modal: Best AI Sandbox Runner for Python ML

**Modal** provides a serverless compute platform optimized for machine learning, with **AI sandbox** capabilities integrated into a broader Python-centric infrastructure.

### Strengths

- **Massive autoscaling:** This **sandbox runner** scales from zero to 20,000+ concurrent containers with sub-second cold starts
- **Python-first experience:** Define **AI sandbox** environments in Python code
- **Built-in networking:** Tunneling and egress policies for **code execution platform** connectivity
- **Snapshot primitives:** Save and restore **sandbox** state efficiently
- **GPU access:** Full range of NVIDIA GPUs for ML workloads

### Weaknesses

- **No BYOC:** This **AI sandbox platform** offers managed deployment only, no option to run in your cloud
- **SDK-defined images:** Cannot bring arbitrary OCI containers to this **sandbox product**
- **Python-centric:** JavaScript and Go SDKs exist but the **code execution platform** optimizes for Python
- **gVisor only:** No microVM option for stronger isolation in this **AI sandbox runner**

### Modal Pricing

- CPU: $0.047/vCPU-hour
- RAM: $0.008/GB-hour
- H100 GPU: $3.95/hour (plus CPU and RAM charges)
- $30/month free credits

<InfoBox type="success" title="Customer story">
    <p>cto.new uses Northflank’s microVMs to scale secure sandboxes without sacrificing speed or cost. Read more about their use case running Northflank secure sandboxes [here](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes).</p>
</InfoBox>

### 4. Daytona: Fastest AI Sandbox Platform

**Daytona** pivoted in early 2025 from development environments to AI agent infrastructure, positioning as the fastest **sandbox product** for code execution.

### Strengths

- **Sub-90ms cold starts:** The fastest **AI sandbox runner** available—critical for high-volume agent workflows
- **Docker compatibility:** Standard container workflows function without proprietary formats on this **sandbox platform**
- **Stateful execution:** Filesystem, environment variables, and process memory persist across interactions

### Weaknesses

- **Docker isolation default:** This **AI sandbox product** uses standard containers by default—weaker than microVMs. Kata Containers available but not default.
- **Maturing platform:** Feature parity with established **sandbox platforms** still developing
- **Limited networking:** No first-class tunneling or egress policies in this **code execution platform**
- **Sandbox-only scope:** No broader infrastructure for databases, APIs, or GPUs beyond the **AI sandbox runner**

### Daytona Pricing

- $200 free compute credit
- Pay-per-use after credits
- Startup program: up to $50k credits

### 5. Together Code Sandbox: Best AI Sandbox Product for Together Users

**Together AI** extended their GPU cloud with **sandbox platform** capabilities, providing integrated code execution for teams already using Together's inference infrastructure.

### Strengths

- **500ms snapshot resume:** This **AI sandbox product** resumes VMs from snapshot with memory pre-loaded
- **Hot-swappable sizing:** Scale from 2 to 64 vCPUs dynamically on this **code execution platform**
- **Together AI integration:** Seamless connection between model inference and **sandbox** execution

### Weaknesses

- **Slower cold starts:** 2.7 seconds for fresh **sandbox** creation versus sub-second competitors
- **VM-style pricing:** Per vCPU and GB-RAM billing less attractive for bursty **AI code execution**
- **No tunneling:** Lacks network tunneling features found in other **sandbox platforms**
- **Dev container format:** Must use Docker-based dev container images for this **AI sandbox runner**

### Together Pricing

- ~$0.089/vCPU-hour
- Billed per vCPU and GB-RAM per minute

### 6. Vercel Sandboxes: Best AI Sandbox Product for Vercel Ecosystem

**Vercel** launched their **sandbox platform** in beta, offering Firecracker-based isolation tightly coupled with Vercel's deployment infrastructure.

### Strengths

- **Firecracker microVMs:** True VM-level isolation for this **AI code execution product**
- **Vercel integration:** Seamless experience for teams using Vercel's **platform**
- **Active CPU billing:** Charges only when code actively executes in the **sandbox runner**

### Weaknesses

- **Strict time limits:** 45 minutes (Hobby) to 5 hours (Pro/Enterprise) maximum for this **AI sandbox platform**
- **Limited runtimes:** Only Node.js and Python supported in this **sandbox product**
- **Single region:** Only iad1 available for this **AI code execution platform**
- **Vercel dependency:** Designed for Vercel ecosystem—limited standalone utility as a **sandbox runner**
- **Beta status:** Production readiness timeline unclear for this **AI sandbox product**

### Vercel Pricing

- Hobby: 5 CPU hours, 420 GB-hours memory, 5,000 sandbox creations free
- Pro: $0.128/CPU-hour, $0.0106/GB-hour memory, $0.60/million creations

### How do AI sandbox platforms compare on pricing?

_Pricing as of April 2026. Billing models differ across platforms (some bill based on active CPU usage only, others bill for the entire duration the sandbox is running). Verify current rates on each platform's pricing page before making cost decisions._

| Platform | CPU | Memory | Storage | GPU | Billing model |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | $0.01667/vCPU-hr | $0.00833/GB-hr | $0.15/GB-month | L4: $0.80/hr, A100 40GB: $1.42/hr, A100 80GB: $1.76/hr, H100: $2.74/hr, H200: $3.14/hr | Per second |
| **E2B** | $0.0504/vCPU-hr | $0.0162/GiB-hr | 10–20GB included free | Do not provide GPU compute | Per second |
| **Daytona** | $0.0504/vCPU-hr | $0.0162/GiB-hr | $0.000108/GiB-hr (5GB free) | Do not provide GPU compute | Per second |
| **Vercel Sandbox** | $0.128/vCPU-hr | $0.0212/GB-hr | $0.023/GB-month (snapshots) | Do not provide GPU compute | Active CPU only |
| **Modal Sandboxes** | $0.1419/physical core-hr (2 vCPU) | $0.0242/GiB-hr | — | L4: $0.80/hr, A100 40GB: $2.10/hr, A100 80GB: $2.50/hr, H100: $3.95/hr, H200: $4.54/hr | Per second |

### BYOC support across AI sandbox platforms
The table below shows how each platform handles BYOC deployment, which clouds are supported, and whether it requires a sales process.

| Platform | BYOC available | Clouds supported | Access model | Pricing model |
| --- | --- | --- | --- | --- |
| **Northflank** | Yes, fully self-serve | AWS, GCP, Azure, Oracle, CoreWeave, any neoclouds, Civo, bare-metal, on-premises | Self-serve, enterprise contracts available for larger commits (with bulk discounts) | Your existing cloud bill, CPU $0.01389/vCPU-hr and Memory $0.00139/GB-hr |
| **E2B** | Yes, limited and not self-serve | AWS and GCP only | Not publicly disclosed, need to contact sales | Starts at $50/sandbox/month, on top of your existing cloud bill |
| **Daytona** | Yes, limited and not self-serve | Not publicly disclosed | You operate the infrastructure layer; Daytona provides the control plane | Not publicly disclosed |
| **Modal** | No | Managed only | — | — |
| **Vercel Sandbox** | No | Managed only (iad1 region only) | — | — |
| **Together Code Sandbox** | No | Managed only | — | — |

## How to choose the right AI sandbox platform

**Choose Northflank if:**

- You need the strongest isolation options (microVM + gVisor) in an **AI sandbox platform**
- Sessions must persist longer than 24 hours
- BYOC deployment is required for compliance or data residency
- You want unified infrastructure beyond just **sandbox runners,** databases, APIs, GPUs included

**Choose E2B if:**

- SDK quality is your top priority for **AI code execution**
- 24-hour sessions are sufficient
- You prefer open-source foundations in your **sandbox product**

**Choose Modal if:**

- Your team is Python-focused for ML workloads
- You need massive autoscaling in an **AI sandbox runner**
- gVisor isolation meets your security requirements

**Choose Daytona if:**

- Cold start speed is the critical factor for your **sandbox platform**
- Docker-level isolation is acceptable for your **AI code execution** needs
- You're running high-volume, short-duration agent workflows

**Choose Together if:**

- You already use Together AI for model inference
- Integrated **AI sandbox** and inference simplifies your architecture

**Choose Vercel if:**

- You're deeply invested in Vercel's ecosystem
- Short session limits (under 5 hours) work for your **sandbox product** needs

## Getting started with the top AI sandbox platform

To start using the leading **AI sandbox platform**:

1. **Sign up at [northflank.com](https://app.northflank.com/signup)** 
2. **Create a project,** select your region or connect your cloud account for BYOC
3. **Deploy a service,** choose any container image from any registry
4. **Configure isolation,** Northflank provisions microVM-backed infrastructure automatically

For enterprise requirements, [**schedule a demo**](https://cal.com/team/northflank/northflank-intro) with Northflank's engineering team to discuss custom **AI sandbox platform** configurations, compliance needs, or volume pricing.

## 💭 FAQs: AI Sandbox Platforms

### What is an AI sandbox platform?

An **AI sandbox platform** is infrastructure providing isolated environments for executing code generated by AI systems. These **sandbox products** prevent untrusted AI-generated code from accessing production resources, leaking data, or compromising host systems. The platform handles provisioning, isolation, networking, and teardown of **code execution** environments.

### Which AI sandbox product has the strongest isolation?

**Sandbox platforms** using microVMs (Firecracker, Kata Containers) provide stronger isolation than container-based solutions because each workload receives a dedicated kernel. Northflank offers both Kata Containers and gVisor, making it the most flexible **AI sandbox platform** for security requirements. E2B and Vercel also use Firecracker microVMs.

### How do session limits affect AI sandbox platform selection?

Many **AI sandbox products** impose time limits: Vercel caps at 5 hours, E2B at 24 hours. For AI agents maintaining state across extended interactions, these limits require complex state serialization. Northflank's **sandbox platform** offers unlimited sessions, avoiding this architectural overhead.

### What's the difference between BYOC and self-hosting for AI sandbox runners?

[**BYOC (Bring Your Own Cloud)**](https://northflank.com/features/bring-your-own-cloud) means the **sandbox platform** vendor manages the control plane while provisioning resources in your cloud account, you get managed operations with data in your VPC. **Self-hosting** means operating everything yourself. Northflank offers production-ready BYOC; E2B's self-hosting remains experimental.

### Which AI sandbox platform is most cost-effective?

Pricing varies by workload pattern. For CPU-intensive **AI code execution**, Northflank ($0.01667/vCPU-hour) costs approximately 65% less than Modal ($0.047/vCPU-hour). For [GPU](https://northflank.com/request/gpu) workloads, Northflank's all-inclusive pricing ($2.74/hour for H100) runs approximately 62% cheaper than **sandbox products** billing GPU, CPU, and RAM separately.

### Can AI sandbox platforms run GPU workloads?

Some **AI sandbox products** support GPU-accelerated code execution. Northflank offers NVIDIA H100, A100, and other GPUs with all-inclusive pricing. Modal also provides GPU access but charges separately for GPU, CPU, and RAM. Verify your **sandbox platform** supports required GPU types before committing.]]>
  </content:encoded>
</item><item>
  <title>What is an AI sandbox?</title>
  <link>https://northflank.com/blog/what-is-an-ai-sandbox</link>
  <pubDate>2026-01-17T08:00:00.000Z</pubDate>
  <description>
    <![CDATA[An AI sandbox is an isolated environment designed to safely execute code generated by large language models (LLMs) and AI agents. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/ai_sandbox_1_e70bf66fd5.png" alt="What is an AI sandbox?" /><InfoBox className='BodyStyle'>

## 📌 TL;DR

An AI sandbox is an isolated environment designed to safely execute code generated by large language models (LLMs) and AI agents. It prevents untrusted, AI-generated code from accessing your host system, leaking sensitive data, or causing damage to production infrastructure.

As AI coding assistants like Cursor, GitHub Copilot, and autonomous agents become standard tools in software development, sandboxing has shifted from a nice-to-have security measure to an essential requirement. Recent vulnerabilities, including remote code execution flaws in popular AI tools, demonstrate why every AI agent needs a sandbox.

[**Northflank**](https://northflank.com/) provides production-grade AI sandboxing through microVM isolation (Kata Containers, Cloud Hypervisor) and gVisor, processing over 2 million isolated workloads monthly. Unlike ephemeral sandbox-only solutions, Northflank offers unlimited session duration, any OCI container image support, bring-your-own-cloud (BYOC) deployment, and a complete platform for running databases, APIs, and GPU workloads alongside your sandboxed code execution.

</InfoBox>

## What is an AI Sandbox?

An AI sandbox is a controlled, isolated environment specifically designed for running code generated by artificial intelligence systems, particularly LLMs and AI agents, in a secure manner.

The core principle is straightforward: treat all AI-generated code as untrusted. Even the most sophisticated language models can produce code that accidentally (or through prompt injection) attempts to access sensitive files, make unauthorized network requests, escalate privileges, or execute malicious operations.

An AI sandbox contains this risk by establishing strict boundaries around code execution. The sandbox provides tools like interpreters, compilers, and useful libraries, while preventing the executed code from affecting anything outside its designated environment.

### How AI Sandboxes differ from traditional sandboxing

Traditional sandboxing has existed since the 1980s, when Unix systems introduced chroot to restrict process access to specific directories. Modern containerization (Docker, Kubernetes) evolved from these concepts and became the dominant paradigm for application isolation.

AI sandboxes build on these foundations but address specific challenges unique to AI-generated code:

**Ephemeral execution:** AI sandboxes often spin up for seconds or minutes to execute a single code snippet, then terminate, unlike traditional containers that might run for days or months.

**Untrusted input by default:** Traditional containers often run trusted, internally-developed code. AI sandboxes assume all code is potentially dangerous, whether generated by an LLM responding to user prompts or produced through autonomous agent workflows.

**Multi-tenant isolation:** When building AI products, you're often executing code on behalf of thousands of different users simultaneously. Each execution needs complete isolation from every other.

**Code interpreter integration:** AI sandboxes typically expose APIs for submitting code and retrieving results, integrating directly with LLM workflows rather than functioning as standalone runtime environments.

<GetStartedCta 
title="Try Northflank's Sandboxes"
secondaryButton={<Button>Book a call</Button>}
/>

## Why AI Sandboxes are all the rage now

The explosion of AI-assisted coding has created a security challenge that didn't exist at this scale even two years ago. Consider this:

AI coding assistants like Cursor, GitHub Copilot, and Windsurf now integrate directly into developer IDEs, generating code that gets executed immediately. Code generation platforms let users prompt for entire applications. AI agents autonomously write, test, and deploy code with minimal human oversight.

This creates attack surface that security teams hadn’t previously encountered. Recent security research has catalogued numerous vulnerabilities in AI coding tools:

- **Remote code execution in OpenAI Codex CLI** allowed attackers to execute arbitrary commands
- **Cursor vulnerabilities** enabled data exfiltration through malicious Jira tickets via prompt injection
- **Claude Code data exfiltration** via DNS lookup demonstrated how AI agents can be tricked into leaking sensitive information
- **n8n automation platform vulnerabilities** showed how sandbox escape through expression injection could compromise entire enterprise environments

## Isolation technologies: Containers vs. MicroVMs

Not all sandboxes provide equal security. The isolation technology you choose fundamentally affects your security posture.

### Container-based isolation (Docker, gVisor)

Standard Docker containers share the host kernel with other containers. While convenient and fast, this creates a larger attack surface, kernel exploits can potentially escape container boundaries.

**gVisor** addresses this by intercepting system calls through a user-space kernel. Rather than passing syscalls directly to the host kernel, gVisor handles them in a sandboxed process, significantly reducing the attack surface. Modal and some Northflank configurations use gVisor for this reason.

**Best for:** Lower-latency workloads where the overhead of full VM isolation isn't justified, or where you control the code being executed.

![ai sandbox 1.png](https://assets.northflank.com/ai_sandbox_1_c3ee47fb24.png)

### MicroVM isolation (Firecracker, Kata Containers, Cloud Hypervisor)

MicroVMs provide VM-grade isolation with near-container startup speed. Each workload runs in its own lightweight virtual machine with a dedicated kernel, completely isolated from other workloads and the host system.

**Firecracker**, developed by AWS, powers Lambda and Fargate. It boots in under 200 milliseconds and provides strong isolation guarantees.

**Kata Containers** combines the speed of containers with the security of VMs, running OCI-compliant containers inside lightweight virtual machines.

**Cloud Hypervisor (CLH)** is a modern, Rust-based VMM designed for cloud-native workloads.

**Best for:** Executing truly untrusted code from unknown sources, multi-tenant environments where user isolation is critical, and any scenario where security takes precedence over minimal latency.

## Common use cases for AI Sandboxes

### 1. AI coding assistants and IDEs

When an AI coding assistant generates code, that code needs to execute somewhere. Running it directly on the developer's machine introduces risk, the AI might inadvertently run destructive commands or be manipulated through prompt injection.

### 2. Code interpreters in LLM applications

ChatGPT's code interpreter, Google's Gemini code execution, and similar features rely on sandboxes to run user-requested computations safely. When you ask an LLM to analyze a dataset or generate a visualization, it writes and executes code in an isolated environment.

### 3. Multi-tenant SaaS platforms

If your product executes code on behalf of customers, whether for automation, data processing, or custom logic, you need isolation between tenants. A bug or malicious input from one customer shouldn't affect others.

### 4. Reinforcement learning training

Training code-generating AI models through reinforcement learning requires running thousands of code executions in parallel. Each execution needs isolation to prevent interference and ensure consistent evaluation.

### 5. Code review and testing platforms

Platforms that run tests on submitted code (whether human-written or AI-generated) need sandboxes to prevent malicious submissions from compromising the testing infrastructure.

## Northflank: Production-grade AI Sandboxing

![ai sandbox 2.png](https://assets.northflank.com/ai_sandbox_2_3071568c79.png)

[Northflank](https://northflank.com/) delivers enterprise-ready AI sandboxing through a complete cloud platform designed for modern software and AI companies. Since 2019, Northflank has processed over 2,000,000 microVM workloads monthly, powering secure multi-tenant deployments for companies like Sentry and [cto.new](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes).

### What sets Northflank apart

![CleanShot 2026-01-17 at 08.52.45@2x.png](https://assets.northflank.com/Clean_Shot_2026_01_17_at_08_52_45_2x_ae97c56e45.png)

**Multiple isolation options:** Choose between Kata Containers (microVM isolation via Cloud Hypervisor), gVisor, or Firecracker based on your security requirements. No other platform offers this flexibility.

**Any OCI container image:** Unlike platforms requiring SDK-defined images or specific runtimes, Northflank accepts any container from Docker Hub, GitHub Container Registry, or private registries, without modification.

**Unlimited session duration:** While competitors limit sessions to 45 minutes (Vercel) or 24 hours (E2B), Northflank sandboxes persist until you terminate them. Critical for AI agents that maintain state across user interactions.

**Bring Your Own Cloud (BYOC):** Deploy sandboxes in your AWS, GCP, Azure, or bare-metal infrastructure. Keep sensitive data in your VPC while Northflank handles orchestration.

**Complete platform:** Northflank allows you to deploy databases, backend APIs, GPU workloads, CI/CD, observability, and scheduled jobs with consistent security across everything. As your AI application grows beyond isolated code execution, your infrastructure grows with you.

**Production-proven scale:** cto.new migrated their entire sandboxing infrastructure to Northflank in days, handling thousands of daily deployments during their launch week without issues. When they needed to scale from testing to 30,000+ users, Northflank's per-second billing and API-driven provisioning made it economically viable.

### How Northflank Sandboxing works

Northflank's sandboxing architecture is built on isolation technologies that the engineering team actively maintains and contributes to in the open-source community, including Kata Containers, QEMU, containerd, and Cloud Hypervisor.

**Project-level isolation:** Every sandbox runs within a Northflank project, which acts as a strict namespace providing runtime and network separation. This multi-tenant architecture ensures that workloads from different users or customers never share resources or have visibility into each other's environments.

**MicroVM-backed containers:** When you deploy a sandbox, Northflank provisions a microVM that pulls your container image and runs it with complete kernel-level isolation. Each workload gets its own dedicated kernel and virtual network interface, there's no shared kernel attack surface between tenants.

**Flexible runtime configuration:** Configure CPU, memory, and disk resources per sandbox. Enable persistent or ephemeral storage depending on whether you need state to survive across sessions. Set network policies to control what external resources your sandboxed code can access.

**API-driven provisioning:** Northflank exposes a full REST API, CLI, and JavaScript client for programmatic sandbox management. Spin up sandboxes on-demand, execute code, retrieve results, and tear down environments, all through API calls that integrate directly into your AI agent workflows.

### Getting started with Northflank Sandboxes

The fastest path to running secure AI sandboxes:

1. **Sign up at [northflank.com](https://app.northflank.com/signup)**
2. **Create a project,** choose your region or connect your own cloud account for BYOC deployment
3. **Deploy a service,** select any container image from Docker Hub, GitHub Container Registry, or your private registry
4. **Configure isolation,** Northflank automatically provisions microVM-backed infrastructure with secure defaults

For teams with specific requirements, [**book a demo**](https://cal.com/team/northflank/northflank-intro) with Northflank's engineering team to discuss custom configurations, enterprise features, or high-volume pricing.

For detailed implementation guides, see:

- [How to spin up a secure code sandbox & microVM in seconds](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
- [Secure runtime for codegen tools](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale)

## Build secure AI applications with Northflank

AI sandboxes are no longer optional (or, in our opinion, ever were). They're now fundamental to building safe, scalable AI applications. As AI agents become more autonomous and code execution becomes more prevalent, proper isolation is a must for any production-ready products.

Northflank provides the **complete infrastructure for modern AI companies**: microVM isolation for secure code execution, plus databases, APIs, GPU workloads, and CI/CD, all with transparent pricing and the flexibility to run in your cloud, ours, or your customer’s.

[**Try Northflank for free**](https://app.northflank.com/signup) or [**book a demo**](https://cal.com/team/northflank/northflank-intro) with our engineering team to discuss your sandboxing requirements.

## FAQs

### What's the difference between an AI sandbox and a regular Docker container?

Standard Docker containers share the host kernel and provide process-level isolation. AI sandboxes typically add additional security layers, either through user-space kernels (gVisor) or full microVM isolation (Firecracker, Kata Containers), and are specifically designed for ephemeral execution of untrusted code with APIs for code submission and result retrieval.

### Can AI sandboxes prevent all security risks from AI-generated code?

No security measure is absolute. Sandboxes significantly reduce risk by containing the blast radius of any compromise, but proper sandbox configuration is critical. Sandboxes must implement both filesystem and network isolation to be effective.

### How fast do AI sandboxes start up?

Modern microVM sandboxes boot in under 200 milliseconds. Container-based sandboxes can start even faster. For most AI code execution use cases, sandbox startup time is negligible compared to LLM inference latency.

### Do I need AI sandboxes if I'm just generating code for users to copy-paste?

If users execute the generated code themselves, your security risk is lower (they're running it on their own machines). However, if your platform runs code on users' behalf, for testing, previewing, or producing results, sandboxing becomes essential.

### What isolation technology does Northflank use?

Northflank offers multiple isolation options: Kata Containers with Cloud Hypervisor for microVM isolation, gVisor for user-space kernel isolation, and Firecracker support. You choose the appropriate technology based on your security requirements and performance needs.

### Can I self-host AI sandboxes instead of using a managed platform?

Yes, but building production-ready sandbox infrastructure is substantial engineering work. Projects like Kata Containers, Firecracker, and gVisor are open source, but operating them reliably at scale, with proper orchestration, networking, security hardening, and observability, typically requires months of dedicated effort. Platforms like Northflank handle this complexity so you can focus on your product.]]>
  </content:encoded>
</item><item>
  <title>What’s the best code execution sandbox for AI agents in 2026?</title>
  <link>https://northflank.com/blog/best-code-execution-sandbox-for-ai-agents</link>
  <pubDate>2026-01-17T08:00:00.000Z</pubDate>
  <description>
    <![CDATA[Northflank is the best choice for teams that need production-grade microVM isolation, unlimited session duration, bring-your-own-cloud deployment, and a complete platform beyond just sandboxes. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/best_code_execution_sandbox_for_ai_agents_4ae1c3572a.png" alt="What’s the best code execution sandbox for AI agents in 2026?" /><InfoBox className='BodyStyle'>

## 📌 TL;DR

AI agents are generating billions of lines of code daily. Running that code safely requires a purpose-built **code execution sandbox for AI agents:** infrastructure that isolates execution, scales elastically, and integrates directly with AI agent workflows.

The best code sandbox for AI agents depends on your requirements:

- [**Northflank**](https://northflank.com/) is the best choice for teams that need production-grade microVM isolation, unlimited session duration, bring-your-own-cloud deployment, and a complete platform beyond just sandboxes. Northflank processes over 2 million isolated workloads monthly using Kata Containers and gVisor.
- **E2B** excels at AI-first SDK design with Firecracker microVMs, but limits sessions to 24 hours and requires you to manage scaling at higher volumes.
- **Modal** offers strong Python-centric workflows with gVisor isolation and massive autoscaling, but lacks BYOC options and on-prem deployment.
- **Daytona** delivers the fastest cold starts (sub-90ms) but uses Docker containers by default, weaker isolation than microVMs.

For teams evaluating the **best sandbox for AI code execution**, this guide compares isolation strength, session limits, pricing, and platform completeness across the top providers.

</InfoBox>

## Why AI Agents need a dedicated code execution sandbox

Cursor alone generates nearly a billion lines of accepted code each day. AI coding assistants, autonomous agents, and LLM-powered applications are producing unprecedented volumes of code that needs a secure AI code sandbox to execute safely.

Running AI-generated code directly on your application servers, without a proper code execution sandbox**,** creates serious risks: it can expose secrets, overwhelm resources, escape container boundaries, or execute malicious operations, whether through bugs, hallucinations, or prompt injection attacks.

Purpose-built AI sandboxes solve three problems simultaneously:

**Security isolation:** Containers or microVMs cut the blast radius of malicious or buggy code. A compromised sandbox can't access your production databases or leak API keys.

**Ephemeral scale:** Thousands of agent sessions can spin up and tear down in seconds without leaving idle infrastructure running up your bill.

**Observability and guardrails:** Good sandbox platforms expose granular logs, metrics, and network controls so you can monitor and constrain what AI-generated code does.

## What to look for in the best AI code execution sandbox

When evaluating sandbox providers for AI agent workloads, focus on these criteria:

1. **Isolation technology:** Does the AI sandbox platform use standard containers (shared kernel, weaker isolation), gVisor (user-space kernel interception), or microVMs like Firecracker and Kata Containers (dedicated kernel per workload)? For truly untrusted code, microVM isolation is essential.
2. **Startup latency:** How fast can sandboxes spin up? Sub-second cold starts keep your agents responsive. Some platforms offer snapshot-based resume for even faster warm starts.
3. **Session duration:** Can AI code execution sandboxes run for minutes, hours, or indefinitely? Many platforms impose strict time limits that break long-running agent workflows.
4. **Language and runtime flexibility:** Are you locked to Python and JavaScript, or can you run any containerized workload? Can you bring custom images or must you use SDK-defined environments?
5. **Infrastructure flexibility:** Can you deploy in your own cloud (BYOC), on-premises, or only on the provider's managed infrastructure? For regulated industries or data-sensitive applications, this matters.
6. **Networking controls:** Can you define egress policies, set up tunnels for database connections, or lock down outbound access entirely?
7. **Platform completeness:** Do you need just sandboxes, or will your AI application also require databases, backend APIs, GPU inference, and CI/CD? Starting with a complete platform avoids painful migrations.

## The best code execution sandboxes for AI Agents, compared

| Platform | Isolation | Cold start | Max session | BYOC | Languages | Best for |
| --- | --- | --- | --- | --- | --- | --- |
| **Northflank** | MicroVM (Kata/CLH) + gVisor | Seconds, depending on whether the container image is pulled or not | Unlimited | Yes | Any OCI image | Complete AI infrastructure |
| **E2B** | MicroVM (Firecracker) | ~150ms | 24 hours | Experimental | Any Linux runtime | AI agent SDKs |
| **Modal** | gVisor containers | Sub-second | Configurable | No | Python-first | ML/data workloads |
| **Daytona** | Docker (Kata optional) | ~90ms | Stateful | No | Docker images | Fast agent iterations |
| **Together** | MicroVM | 500ms (resume) | Configurable | No | Dev containers | Together AI users |
| **Vercel Sandbox** | MicroVM (Firecracker) | Sub-second | 45 min–5 hr | No | Node.js, Python | Vercel ecosystem |

### How do code execution sandboxes compare on pricing?
Pricing as of April 2026. Billing models differ across platforms (some bill based on active CPU usage only, others bill for the entire duration the sandbox is running). Verify current rates on each platform's pricing page before making cost decisions.

| Platform | CPU | Memory | Storage | GPU | Billing model |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | $0.01667/vCPU-hr | $0.00833/GB-hr | $0.15/GB-month | L4: $0.80/hr, A100 40GB: $1.42/hr, A100 80GB: $1.76/hr, H100: $2.74/hr, H200: $3.14/hr | Per second |
| **E2B** | $0.0504/vCPU-hr | $0.0162/GiB-hr | 10–20GB included free | Do not provide GPU compute | Per second |
| **Daytona** | $0.0504/vCPU-hr | $0.0162/GiB-hr | $0.000108/GiB-hr (5GB free) | Do not provide GPU compute | Per second |
| **Vercel Sandbox** | $0.128/vCPU-hr | $0.0212/GB-hr | $0.023/GB-month (snapshots) | Do not provide GPU compute | Active CPU only |
| **Modal Sandboxes** | $0.1419/physical core-hr (2 vCPU) | $0.0242/GiB-hr | — | L4: $0.80/hr, A100 40GB: $2.10/hr, A100 80GB: $2.50/hr, H100: $3.95/hr, H200: $4.54/hr | Per second |


## 1. Northflank: Best overall code execution sandbox platform for AI agents (and generally, AI infrastructure)

Northflank has operated secure sandboxing infrastructure since 2019, processing over 2 million isolated workloads monthly. Unlike sandbox-only tools, Northflank is a complete cloud platform where **secure AI code sandbox execution** is one capability among many, making it the **best sandbox for AI agents** that need production-grade infrastructure.

### Why Northflank leads

![ai sandbox 1.png](https://assets.northflank.com/ai_sandbox_1_c3ee47fb24.png)

- **Multiple isolation technologies:** Choose between Kata Containers with Cloud Hypervisor for true microVM isolation, or gVisor for user-space kernel protection. No other AI code execution sandbox platform offers this flexibility. Northflank's engineering team actively contributes to Kata Containers, QEMU, containerd, and Cloud Hypervisor in the open-source community.

- **Any OCI container image:** Unlike platforms requiring SDK-defined images or proprietary formats, Northflank accepts any container from Docker Hub, GitHub Container Registry, or private registries, without modification. Your existing images work immediately.

- **Unlimited session duration:** While E2B caps sessions at 24 hours and Vercel at 45 minutes to 5 hours, Northflank sandboxes persist until you terminate them. Critical for AI agents that maintain state across user interactions over days or weeks.

- **True BYOC deployment:** Deploy sandboxes in your AWS, GCP, Azure, or bare-metal infrastructure. Keep sensitive data in your VPC while Northflank handles orchestration. No other major sandbox platform offers production-ready bring-your-own-cloud.

- **Complete platform:** Northflank runs your entire AI application stack: sandboxed code execution, backend APIs, databases, scheduled jobs, and GPU workloads, with consistent security and orchestration. As your application grows beyond just sandboxes, your infrastructure grows with you.

- **Production-proven at scale:** Companies like Writer, Sentry, and cto.new run multi-tenant customer deployments for untrusted code on Northflank. When cto.new launched to 30,000+ users, their Northflank-powered sandbox infrastructure handled thousands of daily deployments without issues.

### [Northflank Pricing](https://northflank.com/pricing)

![CleanShot 2026-01-17 at 10.30.40@2x.png](https://assets.northflank.com/Clean_Shot_2026_01_17_at_10_30_40_2x_d9f3c29923.png)

Transparent usage-based pricing with no hidden fees:

- CPU: $0.01667/vCPU-hour
- RAM: $0.00833/GB-hour
- GPU (H100): $2.74/hour all-inclusive

Northflank's GPU pricing includes CPU and RAM, approximately 62% cheaper than Modal and other providers of GPUs.

### Best for

Teams building production AI applications that need enterprise-grade isolation, infrastructure flexibility, and a platform that handles more than just ephemeral code execution.

## 2. E2B: Best AI-First SDK Design

E2B built its platform specifically for AI agent workflows, with clean Python and JavaScript SDKs that make it easy to spin up sandboxes programmatically.

### Strengths

- **Firecracker microVM isolation:** Each sandbox runs in its own lightweight VM with a dedicated kernel
- **Fast startup:** Sandboxes boot in approximately 150ms
- **Session persistence:** Pause sandboxes and resume them later from the same state
- **Open source:** Core infrastructure is open source with self-hosting options
- **AI framework integrations:** Works seamlessly with LangChain, OpenAI, Anthropic, and other LLM providers

### Weaknesses

- **24-hour session limit:** Even on Pro plans, sandboxes can't run longer than 24 hours
- **Self-hosting complexity:** Scaling past a few hundred sandboxes means running the E2B control plane yourself
- **No built-in network policies:** Lacks granular egress controls and IP filtering
- **Custom images require Docker builds:** You must craft and push a Docker image for every custom environment

### E2B Pricing

- Hobby: Free with $100 one-time credit, 1-hour sessions, 20 concurrent sandboxes
- Pro: $150/month with 24-hour sessions, custom CPU/RAM
- Usage: ~$0.05/hour for 1 vCPU sandbox

### Best for

AI agent developers who need reliable sandboxes with excellent SDK design and don't require sessions longer than 24 hours or infrastructure beyond code execution.

<InfoBox type="success" title="Customer story">
    <p>cto.new uses Northflank’s microVMs to scale secure sandboxes without sacrificing speed or cost. Read more about their use case running Northflank secure sandboxes [here](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes).</p>
</InfoBox>

## 3. Modal: Best for Python ML workflows

Modal provides a serverless platform optimized for machine learning and data workloads, with sandboxing as one capability within a broader compute fabric.

### Strengths

- **Massive autoscaling:** Scale from zero to 20,000+ concurrent containers with sub-second cold starts
- **Python-first DX:** Define sandboxes in Python code, no YAML or Kubernetes manifests
- **Built-in networking:** Tunneling for external connections and granular egress policies
- **Snapshot primitives:** Save and restore sandbox state efficiently
- **GPU support:** Access to the full range of NVIDIA GPUs for ML workloads

### Weaknesses

- **No BYOC or on-prem:** Managed-only deployment with no option to run in your own cloud
- **SDK-defined images:** Can't bring arbitrary OCI images; must define through Modal's SDK
- **Python-centric:** While JavaScript and Go SDKs exist, the platform is heavily optimized for Python
- **gVisor isolation only:** No microVM option for stronger isolation guarantees

### Modal Pricing

- CPU: $0.047/vCPU-hour
- RAM: $0.008/GB-hour
- H100 GPU: $3.95/hour (plus separate CPU and RAM charges)
- $30/month free credits

### Best for

Python-focused ML teams running batch jobs, model inference, and data pipelines who want sandboxing integrated with their existing Modal workflows.

## 4. Daytona: Fastest cold starts

Daytona pivoted in early 2025 from development environments to [AI agent infrastructure](https://www.azilen.com/blog/ai-agent-architecture/), focusing on the fastest possible sandbox provisioning.

### Strengths

- **Sub-90ms cold starts:** The fastest sandbox creation in the market, critical when provisioning thousands of environments
- **Native Docker compatibility:** Standard container workflows work without proprietary formats
- **Stateful sandboxes:** Filesystem, environment variables, and process memory persist across agent interactions
- **Built-in LSP support:** Language server protocol integration for code intelligence
- **Desktop environments:** Linux, Windows, and macOS virtual desktops for computer-use agents

### Weaknesses

- **Docker isolation by default:** Standard containers share the host kernel, weaker security than microVMs. Kata Containers available but not default.
- **Young platform:** Feature parity with established players still evolving
- **Limited networking controls:** No first-class tunneling or granular egress policies yet
- **Sandbox-only focus:** No broader infrastructure capabilities for databases, APIs, or GPU workloads

### Daytona Pricing

- $200 free compute credit to start
- Pay-per-use after credits
- Startup program with up to $50k in credits

### Best For

Teams optimizing for startup speed above all else, particularly for rapid agent iteration workflows where milliseconds matter.

## BYOC support across code execution sandbox platforms
The table below shows how each platform handles BYOC deployment, which clouds are supported, and whether it requires a sales process.

| Platform | BYOC available | Clouds supported | Access model | Pricing model |
| --- | --- | --- | --- | --- |
| **Northflank** | Yes, fully self-serve | AWS, GCP, Azure, Oracle, CoreWeave, any neoclouds, Civo, bare-metal, on-premises | Self-serve, enterprise contracts available for larger commits (with bulk discounts) | Your existing cloud bill, CPU $0.01389/vCPU-hr and Memory $0.00139/GB-hr |
| **E2B** | Yes, limited and not self-serve | AWS and GCP only | Not publicly disclosed, need to contact sales | Starts at $50/sandbox/month, on top of your existing cloud bill |
| **Daytona** | Yes, limited and not self-serve | Not publicly disclosed | You operate the infrastructure layer; Daytona provides the control plane | Not publicly disclosed |
| **Modal** | No | Managed only | — | — |
| **Vercel Sandbox** | No | Managed only (iad1 region only) | — | — |
| **Together Code Sandbox** | No | Managed only | — | — |

## Why Northflank is the best choice for serious AI Infrastructure

The fundamental difference between Northflank and sandbox-only tools is scope and production readiness.

### Beyond ephemeral sandboxes

While other platforms solve the narrow problem of isolated code execution, Northflank provides complete infrastructure for AI applications:

- **Persistent AI agents** that maintain state across user sessions for days or weeks
- **Backend APIs and databases** running with the same security guarantees as your sandboxes
- **GPU workloads** for model inference and training alongside code execution
- **CI/CD pipelines** and preview environments integrated with your development workflow
- **Enterprise controls** including SSO, RBAC, audit logging, and compliance tools

### Production-proven security

Northflank's isolation technology has been battle-tested across millions of workloads since 2019. The engineering team actively maintains and contributes to the open-source projects that power this infrastructure: Kata Containers, QEMU, containerd, and Cloud Hypervisor.

### True infrastructure flexibility

No other sandbox platform offers Northflank's deployment options:

- **Managed cloud:** Zero-setup deployment on Northflank's infrastructure
- **BYOC:** Run in your AWS, GCP, Azure, or bare-metal with full control
- **Multi-region:** Deploy globally with consistent APIs and security
- **Any runtime:** Not locked to specific languages, frameworks, or image formats

### Cost comparison at scale
To make the pricing difference concrete, here is what 200 sandboxes costs across providers under the same conditions.

*Based on 200 sandboxes, plan: nf-compute-100-4, infra node: m7i.2xlarge*

| Model | Provider | Cloud | Sandbox vendor | Total |
| --- | --- | --- | --- | --- |
| PaaS | Northflank | — | $7,200.00 | $7,200.00 |
| PaaS | E2B | — | $16,819.20 | $16,819.20 |
| PaaS | Modal | — | $24,491.50 | $24,491.50 |
| PaaS | Vercel Sandbox | — | $31,068.80 | $31,068.80 |
| BYOC (0.2 overcommit)* | Northflank | $1,500.00 | $560.00 | $2,060.00 |
| BYOC | E2B | $1,500.00 | $10,000.00 | $11,500.00 |

*Through Northflank's plans on BYOC, there's a default overcommit which allows a customer to spawn more services and sandboxes on the same amount of compute. A request modifier of 0.2 means each sandbox only requests 20% of its plan's resources as a guaranteed minimum, but can burst up to the full plan limit if there's available capacity on the node. So instead of fitting 8 sandboxes per node, you could fit 40 on the same hardware, reducing both infrastructure cost and the Northflank management fee.

## Getting started with Northflank Sandboxes

The fastest path to running secure AI sandboxes:

1. **Sign up at [northflank.com](https://app.northflank.com/signup)** 
2. **Create a project,** choose your region or connect your own cloud account
3. **Deploy a service,** select any container image from any registry
4. **Configure isolation,** Northflank automatically provisions microVM-backed infrastructure

For teams with specific requirements, [**book a demo**](https://cal.com/team/northflank/northflank-intro) with Northflank's engineering team to discuss custom configurations, enterprise features, or high-volume pricing.

## Build Your AI Agent Infrastructure on Northflank

The sandbox market is crowded with tools that solve narrow problems. But AI applications need more than ephemeral code execution, they need databases, APIs, GPU inference, enterprise controls, and infrastructure flexibility.

Northflank provides all of this in one platform, with the strongest isolation options in the market and the flexibility to run anywhere: our cloud, your cloud, or bare metal.

[**Start building for free**](https://app.northflank.com/signup) or [**talk to our engineering team**](https://cal.com/team/northflank/northflank-intro) about your AI infrastructure requirements.

## 💭 FAQs: Best code sandbox for AI agents

### What isolation technology is most secure for an AI code execution sandbox?

MicroVMs (Firecracker, Kata Containers) provide the strongest isolation because each workload gets its own dedicated kernel. Standard containers share the host kernel, creating potential escape vectors. gVisor sits in between, intercepting syscalls in user space. For truly untrusted AI-generated code, microVM isolation is recommended.

### How do sandbox session limits affect AI agent workflows?

Many AI agents need to maintain state across extended user interactions, hours, days, or even weeks. Session limits of 45 minutes (Vercel) or 24 hours (E2B) force you to implement complex state serialization and restoration. Platforms with unlimited session duration like Northflank avoid this architectural complexity.

### Can I run GPU workloads in AI sandboxes?

Some platforms support GPU-accelerated sandboxes for AI inference. Northflank offers NVIDIA H100, A100, and other GPUs with all-inclusive pricing. Modal also supports GPUs but charges separately for GPU, CPU, and RAM. Check whether your sandbox provider supports the specific GPU types your workloads require.

### What's the difference between BYOC and self-hosting?

BYOC (Bring Your Own Cloud) means the platform runs its control plane but provisions resources in your cloud account, you get the operational benefits of a managed platform while keeping data in your VPC. Self-hosting means you run everything yourself, including the control plane. Northflank offers true BYOC; E2B's self-hosting is still experimental.

### How do I choose between an AI code sandbox platform and DIY with Kubernetes?

Building sandbox infrastructure with Kubernetes, gVisor, or Firecracker is possible but requires significant engineering investment, typically months of work, plus ongoing operational burden. For most teams, a purpose-built platform provides better security, faster time-to-market, and lower total cost of ownership than rolling your own.

### Which sandbox platform has the best pricing for high-volume workloads?

Pricing varies significantly based on workload patterns. For CPU-intensive workloads, Northflank's pricing ($0.01667/vCPU-hour) is approximately 65% cheaper than Modal ($0.047/vCPU-hour). For GPU workloads, Northflank's all-inclusive pricing ($2.74/hour for H100) is approximately 62% cheaper than Modal's separate billing for GPU, CPU, and RAM.]]>
  </content:encoded>
</item><item>
  <title>Top 6 Supabase Alternatives in 2026</title>
  <link>https://northflank.com/blog/supabase-alternative</link>
  <pubDate>2026-01-13T17:15:00.000Z</pubDate>
  <description>
    <![CDATA[Supabase alternatives compared: Northflank offers BYOC and managed Postgres. Compare Firebase, Appwrite, Nhost, PocketBase, Directus, and Backendless.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/supabase_alternatives_8ca9a807c3.png" alt="Top 6 Supabase Alternatives in 2026" />Supabase alternatives offer different approaches to backend infrastructure, from traditional BaaS platforms to infrastructure solutions where you can deploy Supabase itself or build custom backends.

If you need different deployment options, more infrastructure control, or alternatives to Supabase's managed service, we'll cover six options.

We'll review them based on deployment flexibility, infrastructure control, pricing transparency, and production readiness to help you find the right fit for your project.

<InfoBox type="info">

## TL;DR: Best Supabase alternatives at a glance

See a quick list of the top Supabase alternatives and what makes each one stand out:

1. **Northflank** – Infrastructure platform with Bring Your Own Cloud (BYOC) support and built-in CI/CD for deploying applications, databases, and workloads. 
    
    > Northflank offers managed Postgres databases and enables you to deploy a complete Supabase stack via our one-click template in your own infrastructure.
    
    Provides production-grade Kubernetes infrastructure, developer-friendly workflows, and multi-cloud deployment options (including AWS, GCP, Azure, CoreWeave, Civo, Oracle, and bare-metal) with no vendor lock-in.
    
    Suitable for solo developers, startups, and enterprises that need infrastructure control for running backend services.
    > 
2. **Firebase** – Google's BaaS platform with real-time capabilities and mobile SDKs, supporting both NoSQL and relational databases.
3. **Appwrite** – Open-source, self-hostable backend platform with multi-language SDK support, particularly suited for mobile and cross-platform development.
4. **Nhost** – Open-source backend platform built on PostgreSQL and Hasura, providing GraphQL-first APIs with real-time subscriptions for JAMstack applications.
5. **PocketBase** – Lightweight, open-source backend delivered as a single executable file with SQLite database, requiring zero configuration for deployment.
6. **Directus** – Open-source data platform that connects to existing SQL databases and generates REST and GraphQL APIs with a no-code admin interface.
7. **Backendless** – Visual development platform combining backend services with low-code/no-code tools for building applications with minimal coding.

</InfoBox>

## The 6 best Supabase alternatives

Here's a detailed look at each platform, what they offer, and who they're best suited for.

### 1. Northflank

[Northflank](https://northflank.com/) is a developer platform built on Kubernetes that enables teams to deploy and scale applications, databases, jobs, and AI workloads across any cloud, including Northflank's managed infrastructure or your own AWS, GCP, Azure, Oracle, Civo, CoreWeave, bare-metal, or on-premises accounts.

While Northflank is not a direct backend-as-a-service alternative to Supabase, it provides the infrastructure layer where you can either deploy a complete Supabase stack using our one-click template or build custom backends with managed Postgres databases. You get Git-based CI/CD pipelines, preview environments from pull requests, and the ability to run everything from APIs to GPU workloads, all without managing Kubernetes directly.

![deploy-supabase.png](https://assets.northflank.com/deploy_supabase_f7c1fd43af.png)

**Key features:**

- **Deploy Supabase or build with managed Postgres** – Use our one-click template to deploy a complete Supabase stack in your own infrastructure, or build custom backends with our managed Postgres, MySQL, Redis, and MongoDB databases.
- **Bring Your Own Cloud (BYOC)** – Deploy in your [AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), [Oracle](https://northflank.com/cloud/oci), [Civo](https://northflank.com/cloud/civo), [CoreWeave](https://northflank.com/cloud/coreweave), bare-metal, or on-premises accounts. Maintain data residency, use existing cloud credits, and avoid vendor lock-in. Available across all plans.
- **Full-stack deployment** – Run containers, databases ([Postgres](https://northflank.com/dbaas/managed-postgresql), [MySQL](https://northflank.com/dbaas/managed-mysql), [Redis](https://northflank.com/dbaas/managed-redis), [MongoDB](https://northflank.com/dbaas/mongodb-on-northflank)), [scheduled jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs), and [GPU workloads](https://northflank.com/gpu) on the same platform. No need to stitch together multiple services.
- **Built-in CI/CD & GitOps** – Connect your [GitHub](https://northflank.com/docs/v1/application/getting-started/link-your-git-account), [GitLab](https://northflank.com/docs/v1/application/getting-started/link-your-git-account#link-your-gitlab-account), or [Bitbucket](https://northflank.com/docs/v1/application/getting-started/link-your-git-account#link-your-bitbucket-account) repository and deploy automatically on every push. Create preview environments for each pull request.
- **Multi-tenancy support** – Securely isolate customer environments with sandbox containers and microVMs for code execution workloads ([spin up a secure code sandbox & microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)).
- **GPU support** – Deploy AI models, inference workloads, and ML training jobs with fractional GPU allocation and time slicing across multiple cloud providers (see [GPU Workloads on Northflank](https://northflank.com/gpu) or [request your high-performance GPU cluster](https://northflank.com/request/gpu)).
- **Templates & Infrastructure as Code** – Create reusable [templates](https://northflank.com/docs/v1/application/infrastructure-as-code/create-a-template) for your entire stack. Share configurations across teams and deploy complex architectures in seconds (see [Infrastructure as code on Northflank](https://northflank.com/docs/v1/application/infrastructure-as-code/infrastructure-as-code)).
- **Preview environments** – Automatically create isolated environments for each pull request to test changes before merging to production (see a guide on [setting up a preview environment on Northflank](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment)).
- **CI/CD pipelines** – Build and deploy directly from your Git repository with automatic deployments on every push (see [Continuous integration and delivery on Northflank](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank))
- **Observability tools** – Built-in [logs](https://northflank.com/docs/v1/application/observe/view-logs), [metrics](https://northflank.com/docs/v1/application/observe/view-metrics), and [monitoring](https://northflank.com/docs/v1/application/observe/monitor-containers) to track application performance and diagnose issues (see [Observability on Northflank](https://northflank.com/docs/v1/application/observe/observability-on-northflank)).
- **Background workers and cron jobs** – Run scheduled tasks and async job processing alongside your applications
- **Autoscaling** – Automatically adjust resources based on demand to handle traffic spikes and optimize costs
- **Secrets management** – Securely store and inject environment variables, API keys, and credentials into your applications
- **Automated backups** – Regular database backups with point-in-time recovery for data protection
- **Health checks** – Continuous monitoring of service health with automatic restarts for failed containers
- **Rollbacks** – Instantly revert to previous deployments when issues are detected

<InfoBox type="warning">

💰 Northflank pricing

**Sandbox tier**

- Free resources to test workloads
- 2 free services, 1 free database, 2 free cron jobs
- Always-on compute with no sleeping

**Pay-as-you-go**

- Per-second billing for compute (CPU and GPU), memory, and storage
- No seat-based pricing or commitments
- Deploy on Northflank's managed cloud (6+ regions) or bring your own cloud (600 BYOC regions across AWS, GCP, Azure, Civo, CoreWeave, Oracle, and bare-metal)
- GPU pricing: NVIDIA A100 40GB at $1.42/hour, A100 80GB at $1.76/hour, H100 at $2.74/hour, H200 at $3.14/hour, B200 at $5.87/hour
- Bulk discounts available for larger commitments

**Enterprise**

- Custom requirements with SLAs and dedicated support
- Invoice-based billing with volume discounts
- Hybrid cloud deployment across AWS, GCP, Azure
- Run in your own VPC with managed control plane
- Secure runtime and on-prem deployments
- Audit logs, Global back-ups and HA/DR
- 24/7 support and FDE onboarding

Use the [Northflank pricing calculator](https://northflank.com/pricing) for exact cost estimates based on your specific requirements, and see the pricing page for more details

</InfoBox>

**Ideal for:**

Teams that want to run Supabase in their own cloud infrastructure with full control over deployment and data residency. Organizations building custom backends who need managed Postgres databases alongside application hosting, CI/CD, and preview environments. Companies requiring BYOC for compliance or cost reasons. Startups and enterprises that want infrastructure flexibility without vendor lock-in while maintaining good developer experience.

Learn more about [Northflank's platform capabilities](https://northflank.com/features/platform) and [BYOC deployment options](https://northflank.com/features/bring-your-own-cloud).

<InfoBox type="info">

**Deploy Supabase on Northflank**
Looking for Supabase's features with more infrastructure control? You can deploy a complete, production-ready Supabase stack on Northflank using our one-click template.

This includes Postgres database, authentication, storage, real-time, and edge functions, all deployed in your own cloud infrastructure with Northflank's BYOC support, integrated CI/CD, and multi-cloud flexibility.

[Deploy Supabase on Northflank](https://northflank.com/stacks/deploy-supabase)

</InfoBox>

### 2. Firebase

Firebase is Google's backend-as-a-service platform. Built around a NoSQL database architecture (Firestore and Realtime Database), Firebase offers services including authentication, hosting, cloud functions, storage, and analytics.

**Key features:**

- NoSQL databases (Firestore and Realtime Database) optimized for document-based data structures
- Built-in authentication with support for email, OAuth, and custom providers
- Real-time data synchronization with automatic client-side caching and offline functionality
- Cloud Functions for serverless backend logic
- File storage and CDN delivery
- Built-in analytics and crash reporting
- Firebase Hosting for static and dynamic content
- Firebase Data Connect for relational use cases with Postgres and GraphQL

**Ideal for:**

Mobile-first applications requiring real-time synchronization. Teams already invested in the Google Cloud ecosystem. Projects that benefit from document-based data modeling. Applications requiring battle-tested infrastructure with extensive documentation and community support.

### 3. Appwrite

Appwrite is an open-source backend server designed for frontend and mobile developers. Built with Docker and supporting deployment on any infrastructure, Appwrite provides backend APIs for authentication, databases, storage, and functions while giving teams complete control over their data and hosting environment.

**Key features:**

- Self-hosted deployment via Docker with full infrastructure control
- Official SDKs for Web, Flutter, Apple, Android, and server-side languages
- Document-based database with collections and relationships
- User authentication with multiple providers and session management
- File storage with image manipulation and preview generation
- Cloud Functions supporting multiple runtimes (Node.js, Python, PHP, Ruby, Deno, .NET)
- Real-time API for live data updates
- Built-in web console for database, user, and storage management

**Ideal for:**

Mobile developers building with Flutter, Swift, or Kotlin who need backend services. Teams requiring full data sovereignty through self-hosting. Organizations with specific compliance requirements that necessitate on-premises or private cloud deployment. Developers who prefer working with document-based databases and want an open-source alternative.

### 4. Nhost

Nhost is an open-source backend platform built on PostgreSQL and Hasura that provides a GraphQL-first approach to application development. The platform combines managed Postgres with automatic GraphQL API generation, making it particularly suited for teams building modern JAMstack applications and real-time features.

**Key features:**

- PostgreSQL database with automatic GraphQL API generation via Hasura
- Built-in authentication with support for multiple providers and custom claims
- File storage with image transformation and manipulation
- Serverless functions for custom backend logic
- Real-time subscriptions through GraphQL subscriptions
- Self-hosting option for complete infrastructure control
- Database migrations and schema management tools
- Integration with popular frontend frameworks

**Ideal for:**

Development teams that prefer GraphQL over REST APIs. Projects built with modern JavaScript frameworks that benefit from type-safe GraphQL queries. Applications requiring real-time data synchronization through GraphQL subscriptions. Teams familiar with Hasura who want a managed solution without configuring infrastructure.

### 5. PocketBase

PocketBase is a lightweight, open-source backend solution delivered as a single executable file. Built with Go and using SQLite as its database, PocketBase provides a complete backend including database, authentication, file storage, and real-time subscriptions without requiring complex deployment infrastructure.

**Key features:**

- Single binary deployment with no dependencies or configuration required
- SQLite database with automatic admin UI for data management
- Built-in authentication supporting email/password and OAuth providers
- File storage with direct S3-compatible API
- Real-time subscriptions via Server-Sent Events (SSE)
- JavaScript and Dart SDKs for client-side integration
- Extendable with custom Go code or JavaScript hooks
- Completely free and open-source under MIT license

**Ideal for:**

Solo developers and small teams building MVPs or prototypes. Projects that need a self-hosted backend without infrastructure complexity. Applications with modest scale requirements that benefit from SQLite's simplicity. Developers who want complete control over their backend code and data without cloud dependencies.

<InfoBox type="info">

**Deploy PocketBase on Northflank**

You can deploy PocketBase on Northflank using our [one-click template](https://northflank.com/stacks/deploy-pocketbase), giving you a lightweight backend with the added benefits of Northflank's infrastructure control and multi-cloud deployment options.

</InfoBox>

### 6. Directus

Directus is an open-source data platform that wraps around your existing SQL database and instantly provides REST and GraphQL APIs, along with an intuitive admin app for non-technical users. Unlike traditional BaaS platforms that provide their own database, Directus connects to your existing database and provides the tooling layer on top.

**Key features:**

- Connects to existing SQL databases (PostgreSQL, MySQL, MariaDB, SQLite, MS SQL, Oracle, CockroachDB)
- Automatic REST and GraphQL API generation from your database schema
- No-code admin app (Data Studio) for content management and data editing
- Granular role-based access control at collection and field levels
- File storage with digital asset management and on-the-fly image transformations
- Flows automation engine for custom business logic, webhooks, and event triggers
- White-labelable dashboard for branded data management interfaces
- Available under Business Source License (BSL) 1.1, free for organizations with less than $5 million in annual revenue

**Ideal for:**

Teams that want to layer a modern API and UI over existing or legacy databases without migrating data. Organizations building internal tools or content management systems where non-technical staff need to edit data. Projects requiring a headless CMS with traditional backend features like authentication, file storage, and task automation. SaaS and app development that needs production-ready, scalable backend infrastructure while avoiding vendor lock-in by using standard SQL.

<InfoBox type="info">

**Deploy Directus on Northflank**

You can deploy Directus on Northflank using our [one-click template](https://northflank.com/stacks/deploy-directus), enabling you to run your data platform with full infrastructure control and BYOC support.

</InfoBox>

### 7. Backendless

Backendless is a visual development platform that combines backend services with low-code/no-code tools. The platform provides database management, user authentication, serverless functions, and visual UI development, allowing teams to build complete applications with minimal coding while retaining the ability to extend functionality with custom code.

**Key features:**

- Visual database designer with graphical schema management and relational data modeling
- Codeless logic builder using a visual interface to define backend APIs and workflows
- User management and authentication with support for social logins (Google, Apple) and role-based access controls
- Real-time database with pub/sub messaging system for live updates
- File storage with CDN delivery
- Geolocation services for location-based applications
- Visual UI builder for creating responsive web applications that can be packaged for mobile (iOS/Android)
- Cloud Code for server-side logic in JavaScript (Node.js) and Java
- Self-hosting option available with Pro version for deployment on your own servers or private cloud

**Ideal for:**

Teams combining developers and non-technical users who need to collaborate on application development. Organizations building internal tools, admin panels, or portals that benefit from visual development. Projects requiring rapid prototyping and MVP delivery with advanced control over data and logic. Businesses that need low-code capabilities with the option to self-host for complete infrastructure control.

## How to choose the best Supabase alternative

Use this decision framework to identify which platform aligns with your project requirements and organizational priorities.

| Your priority | Choose this platform | Why |
| --- | --- | --- |
| **Run Supabase in your own infrastructure** | Northflank | Deploy complete Supabase stack via template with BYOC support across AWS, GCP, Azure, Oracle, Civo, CoreWeave, and bare-metal |
| **Managed Postgres with full infrastructure control** | Northflank | Managed databases with CI/CD, preview environments, and multi-cloud deployment options |
| **Infrastructure control without vendor lock-in** | Northflank | BYOC available across all plans, deploy in your own cloud, no proprietary lock-in, Kubernetes-based |
| **Enterprise compliance and security** | Northflank | RBAC, audit logs, and deployment in your own cloud for data sovereignty |
| **Mobile-first with real-time sync** | Firebase | Mobile SDKs, offline support, real-time architecture |
| **Cross-platform mobile development** | Appwrite | Official SDKs for Flutter, Swift, Kotlin with self-hosted control |
| **GraphQL-first backend** | Nhost | PostgreSQL with automatic GraphQL API generation via Hasura, real-time subscriptions |
| **Lightweight self-hosted backend** | PocketBase | Single binary deployment, SQLite-based, zero configuration required |
| **Layer APIs over existing database** | Directus | Connects to existing SQL databases, generates REST and GraphQL APIs, no-code admin interface |
| **Visual development with low-code** | Backendless | Codeless logic builder, visual database designer, suitable for mixed-skill teams |
| **Self-hosting requirement** | Appwrite, PocketBase, or Directus | Open-source platforms with complete infrastructure control |
| **Predictable, transparent pricing** | Northflank | Usage-based pricing with no per-user fees, no separate charges for CI/CD or observability |

## Getting started with the right Supabase alternative

Choosing the right option depends on what you need. If you're looking for a direct BaaS replacement for Supabase, Firebase and Appwrite offer similar feature sets. If you want to run Supabase in your own infrastructure with full control, or build custom backends with managed Postgres, Northflank provides the infrastructure layer with BYOC support, integrated CI/CD, and multi-cloud flexibility.

**Ready to get started?**

- [Deploy your first project on Northflank](https://app.northflank.com/signup) – Free sandbox tier available
- [Deploy Supabase on Northflank](https://northflank.com/stacks/deploy-supabase) – Get Supabase running in your own infrastructure
- [Talk to our team](https://cal.com/team/northflank/northflank-demo?duration=30) – Schedule a demo to discuss your infrastructure needs

**Related resources:**

- [Northflank vs traditional PaaS platforms](https://northflank.com/blog/best-paas-that-runs-in-my-own-cloud-account-bypc-self-hosted-paas)
- [Bring Your Own Cloud deployment guide](https://northflank.com/docs/v1/application/bring-your-own-cloud/use-other-cloud-providers-with-northflank)
- [Kubernetes app platform overview](https://northflank.com/use-cases/app-platform-for-kubernetes)]]>
  </content:encoded>
</item><item>
  <title>Why CommonLit moved from Heroku Enterprise to Northflank for smoother deploys in their own cloud</title>
  <link>https://northflank.com/blog/commonlit-migrated-from-heroku-to-northflank</link>
  <pubDate>2026-01-12T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[They moved to Northflank to get smooth deploys, high-fidelity preview environments, strong observability, and bring-your-own-cloud (BYOC) support.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Group_1410119477_202f6a8ee7.png" alt="Why CommonLit moved from Heroku Enterprise to Northflank for smoother deploys in their own cloud" /><InfoBox className='BodyStyle'>

# ⌛  TL;DR

- CommonLit serves schools and districts across the US
- They run a Rails monolith with a small full-stack team and strict student data rules.
- After Heroku, they struggled with reliability, daytime deploys, and poor visibility on their previous PaaS.
- They moved to Northflank to get smooth deploys, high-fidelity preview environments, strong observability, and [bring-your-own-cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) support.
- Northflank now powers their production workloads inside their AWS VPC, with stable deploys, clear debugging, and previews for every PR.

</InfoBox>

[CommonLit](https://www.commonlit.org/) is a nonprofit that builds a rigorous literacy curriculum for schools focused on the English discipline. 

The product started as something teachers used for extra test prep, homework, or to support students whose first language at home was not English. Over time, it has become a core curriculum that districts adopt at scale. 

CommonLit is taking on textbook companies as its main competition.

They get heavy traffic during the school day, when students and teachers are active.

On the engineering side, they have a small, lean team. Everyone can deploy and everyone rotates on call.

The core product is a Ruby on Rails monolith with Sidekiq for background work. 

## Problem

When [Geoff](https://www.linkedin.com/in/geoffharcourt/) joined, CommonLit was on Heroku. They later became Heroku Private Spaces customers so they could keep their database inside the same VPC as their compute and avoid having a publicly accessible database.

After Heroku’s security incident in 2021, they moved to a different PaaS, where they had an “okay” time. The team was generous with them, but the custom changes needed to make everything work eventually created new issues.

Two things were especially painful and made them look for alternatives:

1. **Deploy reliability during the school day**
    
    CommonLit likes to deploy several times a day, including in the middle of the day when students are actively using the platform. On both Heroku and the other PaaS there were points where they had to stop doing this.
    
    Daytime rollouts caused visible blips. Even a small choke during a deploy hurt reliability and made shipping during business hours risky.
    
2. **Lack of visibility when things broke**
    
    On other platforms they tried, when a pod crashed they sometimes could see that it had crashed but had no insight into why. 
    
    The team felt blind when debugging and that raised stress levels around incidents.
    

There were a few more constraints the solution they were looking for had to satisfy:

- **High fidelity preview environments**
    
    On Heroku, preview environments were a “killer feature.” Geoff was very reluctant to move to any platform that could not provide a preview environment for every pull request.
    
    For CommonLit, a preview environment is not a simple static preview like you might see on Vercel. For each branch they need a full copy of the app, Redis, Memcache, Postgres, and  data to test with.
    
    
- **Strict handling of student data**
    
    Education in the US has tight rules around student data (i.e. you cannot put student data into QA and dev environments).
    
    CommonLit takes this seriously. They maintain synthetic districts with fake schools, teachers, and students for testing. Any preview setup has to support that pattern.
    
    
- **Run in their own AWS account and VPC**
    
    CommonLit uses Buildkite for CI and likes the model where you bring your own cloud resources and the platform orchestrates work on top. They applied the same idea to application hosting.
    
    They want to choose the exact compute instances they run and keep RDS in the same VPC as their application pods. 
    
    
- **Limited Kubernetes expertise on the team**
    
    The platform should hide that complexity and let a small team ship product.
    
    

<InfoBox className='BodyStyle'>

# 👀

In short, they were looking for a PaaS that could do:

- Smooth deploys with no mid-day blips.
- Strong observability.
- High fidelity preview environments that respect student data rules.
- Bring your own cloud with workloads inside their VPC.
- A simple interface that full stack engineers can use without becoming Kubernetes experts.

</InfoBox>

## Solution

CommonLit moved to Northflank and now runs their Rails monolith and Sidekiq workers on Northflank in their own AWS account, inside their own VPC. They continue to use AWS RDS for managed databases, now sitting in the same VPC as their Northflank pods.

Geoff describes Northflank as “fulfilling the promise” of the platform as a service setup he had been looking for since leaving Heroku. In his words, Northflank is:

- “Extremely easy to use.”
- “Extremely reliable.”
- A platform where his team can mostly focus on “building stuff and not on maintaining and operating stuff.”

What they ❤️ about Northflank:

### Preview environments for every pull request

Preview environments are one of the main reasons they chose Northflank.

For each pull request, they:

- Spin up a full preview application on a custom subdomain. The branch name is included in the subdomain so engineers always know which environment they are in.
- Run Redis, Memcache, and Postgres as part of the preview.
- Sync synthetic data from a reference database into the preview database.

Because each preview has its own independent Postgres database, engineers can run data model changes, alter columns, and generally exercise the full range of database operations as if they were in production.

### Staging environments and templates

CommonLit also runs five staging environments. These are needed for external tools that can only talk to a fixed subdomain, for example Google Classroom grade sync.

Northflank’s templating system lets them define environments and pipelines as templates stored in Git. Config changes are tracked as commits. When they update one environment, they can cascade the change to all the others that share the same template.

Before this, on past platforms, config changes could be made quietly and then either forgotten or rolled back by accident. Now they have a clear record of what changed and when, which their auditors also like.

### Bring your own cloud on AWS

Northflank runs in CommonLit’s AWS account and VPC.

This gives them:

- Control over which EC2 instance types they use.
- The ability to keep RDS and application pods in the same private network.
- A clear answer for auditors and school districts about the paths that lead to the database.

### Observability

Geoff calls out observability as an area where Northflank is “considerably better” than every other platform they tested.

On Northflank they can:

- See what is running.
- Understand why a pod crashed if something goes wrong.
- Inspect behavior without feeling blind.

This addresses one of the main complaints his team had about previous platforms, where they could see failures but could not see the cause.

## Results

### Reliable production, even at peak

Geoff says he is “hard pressed to remember” a production incident that was not caused by a mistake in their own code. Services run, and when something fails they know it failed and can see what happened.

This is a big change from earlier platforms where they had outages tied to deploys or platform changes.

### Smooth deploys in the middle of the day

On Northflank, they deploy several times a day, including during school hours. Deploys are “very smooth.” They do not see the request blips they had on Heroku and the other PaaS.

Given their high traffic, this is really important. They can deploy without worrying that a rollout will drop requests and interrupt classrooms.

### Less stress around operations

The combination of reliability, observability, and clear config history has had a direct effect on how the team feels about operating their infrastructure.

They know:

- Where to look if something fails.
- How changes are tracked.
- That they can test risky changes in isolated preview environments.

Geoff says they feel “a lot stronger” about understanding how things are working and are less stressed, even when nothing is broken, because they trust that if something did break, they would be able to see what is going on.

### Whole team can use the platform

Every engineer at CommonLit is full stack, can deploy, and takes on-call rotations. They interact with Northflank directly as operators of the cluster, without having deep Kubernetes knowledge.

This fits exactly what Geoff wanted: a PaaS that hides Kubernetes instead of turning his team into part-time cluster admins.

## Conclusion

CommonLit runs a Rails monolith with a small engineering team, strict rules around student data, and pressure to stay online during the busiest school hours of the year. They needed a platform that let them focus on building product instead of maintaining infrastructure and worrying about deploys.

Northflank gave them exactly that. 

> **Northflank fulfilled the promise of what I have been looking for in a platform-as-a-service setup since we left Heroku.**
> 

Deploys stay smooth even during peak traffic. Preview environments behave like production. Observability is strong enough that the team no longer feels blind when something breaks.

For CommonLit, the outcome is very direct: they can support students with a small team and stay confident that the platform underneath them is stable. 

As Geoff put it, they now operate on top of **a great product,** and he’s **really glad we’re operating it.**]]>
  </content:encoded>
</item><item>
  <title>7 best Pantheon alternatives for flexible cloud deployment</title>
  <link>https://northflank.com/blog/pantheon-alternatives</link>
  <pubDate>2026-01-09T14:00:00.000Z</pubDate>
  <description>
    <![CDATA[Pantheon alternatives: Compare Northflank, Kinsta, WP Engine, Render, and Heroku for flexible cloud deployment beyond WordPress/Drupal hosting.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/pantheon_alternatives_76cf30aa39.png" alt="7 best Pantheon alternatives for flexible cloud deployment" />Pantheon offers managed WordPress and Drupal hosting with automated workflows and Dev-Test-Live environments across Google Cloud infrastructure for teams managing content-heavy websites with enterprise requirements.

If you're looking for alternatives to Pantheon, it might be because of cost scaling concerns, a need for broader framework support beyond WordPress/Drupal, a desire to run infrastructure in your own cloud account, or the need for GPU support for AI workloads.

We'll cover some of the top Pantheon alternatives in this article.

<InfoBox className="BodyStyle">

## Top 7 Pantheon alternatives (Quick list)

For a quick overview of the 7 best Pantheon alternatives, here's the list based on their architectural approach and deployment flexibility:

1. **Northflank** – Kubernetes-native platform with BYOC (Bring Your Own Cloud) support (deploy in [Northflank's cloud](https://northflank.com/features/managed-cloud) or [bring your own](https://northflank.com/features/bring-your-own-cloud) infrastructure: [AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), [Oracle](https://northflank.com/cloud/oci), or bare-metal), managed databases (PostgreSQL, MySQL, Redis, MongoDB), GPU workloads, zero-downtime deployments, and autoscaling for teams needing infrastructure control without operational overhead
2. **Kinsta** – Managed WordPress hosting on Google Cloud Platform with C2 compute machines and edge caching
3. **WP Engine** – WordPress-specific hosting with staging environments, Git integration, and performance optimization
4. **Render** – Git-based deployments with managed Postgres, automated releases, and background workers for structured production workloads
5. **Heroku** – PaaS with extensive add-on marketplace and buildpack support
6. Upsun – Infrastructure-as-code PaaS supporting multiple languages and frameworks with Git branch environments
7. **Vercel** – Frontend-optimized platform with serverless functions and edge network for Jamstack applications

</InfoBox>

## What to look out for when evaluating Pantheon alternatives

When evaluating Pantheon alternatives, look out for the following capabilities:

- **Architecture flexibility** – Native container and Kubernetes support vs proprietary deployment models
- **Infrastructure control** – Ability to run in your own cloud account (BYOC) or bring your own Kubernetes clusters
- **Framework support** – Beyond WordPress/Drupal to include Node.js, Python, Go, and other modern stacks
- **Pricing transparency** – Usage-based models that scale predictably vs traffic-based automatic upgrades
- **Modern workload support** – GPU capabilities, microservices orchestration, and stateful applications
- **DevOps integration** – Native CI/CD, GitOps support, and external tool compatibility
- **Escape velocity** – Migration paths and avoidance of vendor lock-in through standard technologies

## 7 best Pantheon alternatives ranked and compared

We're evaluating these platforms based on their architectural approach, deployment flexibility, pricing models, and support for modern cloud-native workflows.

### 1. Northflank

Northflank provides a Kubernetes abstraction layer that delivers PaaS simplicity while maintaining the power and portability of container orchestration.

You can either deploy to Northflank's [managed Kubernetes cloud](https://northflank.com/features/managed-cloud) or connect your existing infrastructure ([AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), [Oracle](https://northflank.com/cloud/oci), or bare-metal).

You can run containerized applications, managed databases, [GPU workloads](https://northflank.com/gpu), scheduled jobs, and background workers on a single unified platform without requiring Kubernetes expertise.

![northflank--platform.png](https://assets.northflank.com/northflank_platform_d625d79568.png)

**Key capabilities:**

- **Kubernetes-native architecture with no vendor lock-in** – Built on K8s from the ground up with standard APIs and Dockerfiles, giving you container portability across any cluster or cloud provider without vendor-specific configuration formats
- **Bring Your Own Cloud (BYOC)** – Deploy to your [AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), [Oracle](https://northflank.com/cloud/oci), or bare-metal infrastructure while maintaining the same developer experience, addressing compliance and cost control requirements
- **Polyglot platform** – Run any language or framework beyond WordPress/Drupal, including Node.js, Python, Go, Ruby, and custom containers
- **Managed databases and persistent storage** – Provision [PostgreSQL](https://northflank.com/dbaas/managed-postgresql), [MySQL](https://northflank.com/dbaas/managed-mysql), [MongoDB](https://northflank.com/dbaas/mongodb-on-northflank), and [Redis](https://northflank.com/dbaas/managed-redis) with [automated backups](https://northflank.com/docs/v1/application/databases-and-persistence/backup-restore-and-import-data), [monitoring](https://northflank.com/docs/v1/application/observe/observability-on-northflank), and production-grade [persistent storage](https://northflank.com/docs/v1/application/production-workloads/persistent-storage-in-production)
- **GPU support** – Run AI inference, training, and LLM workloads with fractional GPU allocation and spot instance orchestration
- **Zero-downtime deployments with autoscaling** – Deploy production releases with automatic [health checks](https://northflank.com/docs/v1/application/observe/configure-health-checks), [rollback capabilities](https://northflank.com/docs/v1/application/release/run-and-manage-releases), and horizontal/vertical [scaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments) based on metrics
- **MicroVM isolation** – Secure code execution using Kata Containers for multi-tenant environments or untrusted code
- **Unified workflow management** – Deploy applications, [databases](https://northflank.com/docs/v1/application/databases-and-persistence/deploy-a-database), [scheduled jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs), background workers, and [release pipelines](https://northflank.com/docs/v1/application/release/create-a-pipeline-and-release-flow) through consistent UI, [CLI](https://northflank.com/docs/v1/api/use-the-cli), [API](https://northflank.com/docs/v1/api/introduction), or [GitOps](https://northflank.com/docs/v1/application/infrastructure-as-code/gitops-on-northflank) interfaces with external [CI/CD](https://northflank.com/docs/v1/application/release/manage-ci-cd) integration (GitHub Actions, GitLab CI)
- **Infrastructure as Code and preview environments** – [Template systems](https://northflank.com/features/templates) for standardizing deployments and Git branch deployments that create isolated [environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) with configurable data sharing
- **Usage-based pricing** – Pay by the second for CPU, memory, GPU, networking, and storage

**Best for:** Platform engineering teams building internal developer platforms, SaaS companies requiring multi-tenancy and GPU workloads, enterprises needing BYOC ([Bring Your Own Cloud)](https://northflank.com/features/bring-your-own-cloud) for compliance or cost optimization, teams running microservices architectures, organizations with AI/ML workloads requiring [GPU support](https://northflank.com/gpu), development teams wanting Kubernetes benefits without YAML complexity, and companies needing infrastructure control with data residency requirements.

<InfoBox className="BodyStyle">

[Start with Northflank's free sandbox](https://app.northflank.com/signup) to deploy your first application, or [book a demo](https://cal.com/team/northflank/northflank-intro) with our engineering team to discuss your specific infrastructure requirements and migration path. See [full pricing details](https://northflank.com/pricing).

</InfoBox>

### 2. Kinsta

Kinsta provides managed WordPress hosting built on Google Cloud Platform with compute-optimized C2 machines.

![kinsta-homepage.png](https://assets.northflank.com/kinsta_homepage_9b9f943358.png)

**Key capabilities:**

- Google Cloud Platform infrastructure with C2 machines
- Edge caching for reduced cached HTML serving time
- DevKinsta local development tool for cloning and developing projects locally
- Agency Partner Program with migrations included

**Best for:** WordPress-focused agencies and businesses migrating from Pantheon who want managed hosting and don't need Drupal or other framework support.

### 3. WP Engine

WP Engine offers WordPress-specific managed hosting with staging environments.

![wp-engine-homepage.png](https://assets.northflank.com/wp_engine_homepage_dde4e7b85b.png)

**Key capabilities:**

- Managed WordPress hosting with automated updates
- Staging environments and Git integration
- Performance optimization for WordPress
- WordPress support team
- Security scanning and threat blocking

**Best for:** WordPress developers and agencies seeking managed hosting with specialized WordPress tooling and support, without the need for Drupal or polyglot framework support.

### 4. Render

Render provides managed application hosting with Git-based deployments and integrated services.

![render's home page.png](https://assets.northflank.com/render_s_home_page_2982a329f2.png)

**Key capabilities:**

- Automatic deployments from Git with preview environments for pull requests
- Managed PostgreSQL with point-in-time recovery and automatic failover
- Support for background workers and scheduled tasks
- Deployments with health checks and rollbacks
- Private networking between services

**Best for:** Teams migrating from Heroku or Pantheon who need managed databases and background job processing without infrastructure management, supporting multiple frameworks beyond WordPress/Drupal.

<InfoBox className="BodyStyle">

See more:

- [7 Best Render alternatives for simple app hosting](https://northflank.com/blog/render-alternatives)
- [Render vs Vercel: Which platform suits your app architecture better?](https://northflank.com/blog/render-vs-vercel)
- [Render vs Heroku: Which platform-as-a-service is right for you](https://northflank.com/blog/render-vs-heroku)

</InfoBox>

### 5. Heroku

Heroku provides buildpack-based deployments with an add-on marketplace. The platform abstracts infrastructure management through dynos (containerized processes) and managed services.

![heroku.png](https://assets.northflank.com/heroku_092e1c7f09.png)

**Key capabilities:**

- Buildpack support for automatic language detection and dependency installation
- Add-on marketplace with integrations for databases, monitoring, and caching
- Heroku Postgres with automated backups and rollback capabilities
- Review apps for automated preview environments
- Git push deployment workflow

**Best for:** Organizations with existing Heroku deployments seeking migration paths, or teams wanting a PaaS with third-party integrations.

<InfoBox className="BodyStyle">

See more:

- [Heroku Pricing Comparison & Reduction](https://northflank.com/heroku-pricing-comparison-and-reduction)
- [Top Heroku alternatives](https://northflank.com/blog/top-heroku-alternatives)
- [[Documentation] Migrate from Heroku](https://northflank.com/docs/v1/application/migrate-from-heroku)
- [Heroku vs AWS: which cloud platform should you choose](https://northflank.com/blog/heroku-vs-aws)
- [Heroku Enterprise: capabilities, limitations, and alternatives](https://northflank.com/blog/heroku-enterprise-capabilities-limitations-and-alternatives)
- [How to migrate from Heroku: A step-by-step guide](https://northflank.com/blog/how-to-migrate-from-heroku-a-step-by-step-guide)

</InfoBox>

### 6. Upsun

Upsun (formerly Platform.sh) follows infrastructure-as-code principles supporting multiple languages and frameworks with YAML-based configuration.

![upsun-homepage.png](https://assets.northflank.com/upsun_homepage_46c389f24b.png)

**Key capabilities:**

- Support for multiple programming languages and frameworks
- Git branch environments for testing
- Infrastructure-as-code with YAML configuration files
- Automatic environment cloning with data
- Built-in services including databases, search, and caching

**Best for:** Teams wanting control over their stack and workflows with infrastructure-as-code flexibility, even if it means more configuration overhead compared to Pantheon's opinionated approach.

*See more: [7 best Upsun alternatives for flexible cloud deployment](https://northflank.com/blog/upsun-alternatives)*

### 7. Vercel

Vercel specializes in frontend frameworks with serverless function support and edge network distribution. The platform integrates with Next.js and other JavaScript frameworks.

![vercel-homepage.png](https://assets.northflank.com/vercel_homepage_7ecf227d81.png)

**Key capabilities:**

- Automatic preview deployments for every Git push
- Edge network with global locations
- Serverless Functions for API endpoints
- Image optimization and caching
- Analytics and Web Vitals monitoring

**Best for:** Frontend applications and Next.js projects requiring global CDN distribution and serverless backend capabilities, though limited for traditional CMS hosting or full-stack applications.

<InfoBox className="BodyStyle">

See more:

- [Can you use Vercel for backend? What works and when to use something else](https://northflank.com/blog/vercel-backend-limitations)
- [Best Vercel Alternatives for Scalable Deployments](https://northflank.com/blog/best-vercel-alternatives-for-scalable-deployments)
- [Vercel vs Netlify: Choosing the right one in 2026 (and what comes next)](https://northflank.com/blog/vercel-vs-netlify-choosing-the-deployment-platform-in-2025)
- [Vercel vs Heroku: Which platform fits your workflow best?](https://northflank.com/blog/vercel-vs-heroku)
- [Top Vercel Sandbox alternatives for secure AI code execution and sandbox environments](https://northflank.com/blog/top-vercel-sandbox-alternatives-for-secure-ai-code-execution-and-sandbox-environments)

</InfoBox>

## Match the 7 Pantheon alternatives to your requirements

Match the platform architecture to your application requirements and team capabilities rather than defaulting to the most feature-rich option.

| Your priority | Best fit | Why |
| --- | --- | --- |
| Infrastructure control & compliance | Northflank | BYOC ([Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud)) option lets you run in your own cloud account, including on-premises, with full control over networking, security, and data residency |
| Kubernetes without complexity | Northflank | K8s-native architecture with abstraction layer removes YAML management while preserving portability |
| GPU and AI workloads | Northflank | Native [GPU support](https://northflank.com/gpu) with spot instances and fractional allocation for cost optimization |
| Polyglot framework support | Northflank, Upsun | Run any language/framework beyond WordPress/Drupal including Node.js, Python, Go, Ruby |
| WordPress-only hosting | Kinsta, WP Engine | Specialized WordPress optimization with managed updates and support |
| Managed databases & workers | Render, Northflank | Managed database provisioning with background workers and scheduled jobs, plus monitoring and automated backups |
| Existing Heroku apps | Heroku | Migration path available if already using Heroku buildpacks and add-ons |
| Next.js & frontend focus | Vercel | Next.js integration with global CDN and serverless functions |

## Choosing the right Pantheon alternative

Northflank addresses the core limitations teams encounter with traditional PaaS platforms like Pantheon through Kubernetes-native architecture, infrastructure control via BYOC (Bring Your Own Cloud), and modern workload support, including GPU capabilities. The platform maintains developer experience simplicity while providing the flexibility required for complex production environments beyond WordPress and Drupal.

<InfoBox className="BodyStyle">

[Start with Northflank's free sandbox](https://app.northflank.com/signup) to deploy your first application, or [book a demo](https://cal.com/team/northflank/northflank-intro) with our engineering team to discuss your specific infrastructure requirements and migration path.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Travis CI vs Jenkins: which CI/CD tool should you choose in 2026?</title>
  <link>https://northflank.com/blog/travis-ci-vs-jenkins</link>
  <pubDate>2026-01-08T16:45:00.000Z</pubDate>
  <description>
    <![CDATA[Travis CI vs Jenkins: compare cloud-hosted vs self-hosted CI/CD in 2026. See why Northflank integrates builds, deployment &amp; infrastructure in one platform.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/travis_ci_vs_jenkins_3a0105f14b.png" alt="Travis CI vs Jenkins: which CI/CD tool should you choose in 2026?" />Choosing between Travis CI and Jenkins means understanding what each platform does well, where they differ, and what happens after your builds pass.

Both tools automate your build and test workflows, but they take fundamentally different approaches to setup, hosting, and customization.

This comparison reviews Travis CI and Jenkins in detail, including their features, configurations, use cases, and trade-offs, so you can make an informed decision based on your team's needs.

<InfoBox className="BodyStyle">

## TL;DR: What is the difference between Travis CI and Jenkins?

**Travis CI vs Jenkins comes down to cloud-hosted simplicity versus self-hosted control.**

Travis CI is a cloud-based CI platform where you connect your repository, add a `.travis.yml` file, and start building. It offers straightforward configuration with language-specific defaults, a build matrix, and multi-environment testing.

Jenkins is a self-hosted, open-source automation server that gives you complete control over your build environment. It requires installation and maintenance but offers extensive customization through 1,800+ plugins. While the software is free, you're responsible for infrastructure costs and ongoing maintenance.

> **Note: The major question isn't only about which tool builds your code better; it's what you need beyond CI/CD.**

Both platforms handle builds and tests, but shipping production software also requires deployment infrastructure, database management, environment orchestration, and observability.

**Solution**: Platforms like [Northflank](https://northflank.com/) provide CI/CD as part of a complete deployment workflow, so you don't need to piece together multiple services while giving you the flexibility to deploy to your own cloud infrastructure.
> 

</InfoBox>

## What is Travis CI?

Travis CI is a cloud-based continuous integration platform that automatically builds and tests code changes whenever you push commits or open pull requests. You define your build configuration in a `.travis.yml` file in your repository, and Travis CI executes those steps in clean, isolated environments.

![travis ci.png](https://assets.northflank.com/travis_ci_1225c7977f.png)

### What are the features of Travis CI?

Travis CI provides essential continuous integration capabilities with minimal configuration overhead:

- **Multi-language support** - Works with over 30 programming languages, including Python, JavaScript, Ruby, Java, Go, and PHP
- **Build matrix** - Test across multiple language versions, operating systems (Linux, macOS, Windows), and dependency combinations simultaneously
- **Built-in caching** - Speed up builds by reusing dependencies between runs
- **Version control integration** - Integration with GitHub, Bitbucket, and GitLab
- **Automated deployments** - Deploy directly to platforms like Heroku, AWS, and Google Cloud Platform

### What are the pros and cons of Travis CI?

**Pros:**

- Straightforward YAML configuration with sensible defaults
- Isolated build environments for every run
- Lower learning curve
- GitHub integration

**Cons:**

- Limited customization compared to self-hosted solutions
- Can become expensive for larger teams or private repositories
- Less control over the build environment
- Dependent on Travis CI's infrastructure availability

## What is Jenkins?

Jenkins is an open-source automation server that you install and run on your own infrastructure. It automates building, testing, and deploying software projects through pipelines you define using the Groovy-based Jenkins Pipeline DSL or through its web interface.

![jenkins x home page.png](https://assets.northflank.com/jenkins_x_home_page_ea832a2d5d.png)

### What are the features of Jenkins?

Jenkins delivers automation capabilities through its extensible architecture:

- **Extensive plugin ecosystem** - Over 1,800 plugins that integrate with most tools in the CI/CD toolchain
- **Pipeline as code** - Create complex pipelines using Jenkinsfiles for version-controlled build definitions
- **Distributed builds** - Distribute builds across multiple machines for parallel execution
- **Complete customization** - Customize every aspect of your build environment to match your requirements
- **Broad language support** - Works with most programming languages and version control systems
- **Build visualization** - Detailed monitoring capabilities and pipeline visualization

### What are the pros and cons of Jenkins?

**Pros:**

- Extensive customization through 1,800+ plugins
- Full control over infrastructure and security
- Large community with detailed documentation
- Scales to support thousands of jobs and users

**Cons:**

- Requires installation, configuration, and ongoing maintenance
- Steeper learning curve with more complex setup
- Can become resource-intensive for large projects
- UI feels dated compared to modern CI/CD platforms

## What are the differences between Travis CI and Jenkins?

Travis CI vs Jenkins represents the choice between cloud-hosted convenience and self-hosted flexibility. To give you a complete picture, we're also including Northflank in this comparison so you can see how an integrated platform approach differs from specialized CI/CD tools.

Below is a side-by-side comparison across the key factors to consider when selecting your build and deployment strategy:

|  | Travis CI | Jenkins | Northflank |
| --- | --- | --- | --- |
| **Setup** | No installation (sign up, connect repository, add `.travis.yml`) | Requires installation on server, configuring agents, and security setup | No installation (connect Git repository and deploy from the platform) |
| **Hosting** | Cloud-hosted SaaS managed by Travis CI | Self-hosted on your infrastructure (on-premises, cloud VMs, containers) | Managed cloud or deploy to your own cloud (AWS, GCP, Azure, Civo, on-premises Oracle) via BYOC (Bring your own cloud) |
| **Configuration** | YAML file (`.travis.yml`) with language defaults | Jenkinsfile using Groovy DSL or web-based configuration | UI-based, CLI, API or Infrastructure as Code with templates; no YAML for basic workflows |
| **Customization** | Limited to `.travis.yml` options | Extensive through 1,800+ plugins and custom scripts | Configurable through UI, CLI, API, or templates |
| **Learning curve** | Low (straightforward setup with good defaults) | Steep (extensive features require time to master) | Low (intuitive UI with straightforward setup; advanced features available when needed) |
| **Maintenance** | Fully managed (no maintenance burden) | Your team maintains updates, plugins, patches, and infrastructure | Fully managed on Northflank cloud; Bring Your Own Cloud (BYOC) option available (you control infrastructure, Northflank manages orchestration) |
| **Scalability** | Auto-scales based on plan's concurrent build limits | Manual scaling by adding build agents | Auto-scaling built-in based on traffic and demand |
| **Deployment** | Built-in providers for Heroku, AWS, GitHub Pages, etc. | Flexible deployment via plugins or custom scripts | Native infrastructure (services, databases, jobs run on platform; BYOC support) |
| **Beyond CI/CD** | Focuses solely on continuous integration and delivery | Focuses on CI/CD automation; separate tools needed for hosting | Complete platform: CI/CD, hosting, databases, preview environments, observability |

## Beyond CI/CD: complete deployment workflows with Northflank

Travis CI and Jenkins both handle building and testing code well. But after your tests pass, you still need infrastructure to run applications, databases for staging and production, environment orchestration, preview environments for pull requests, and observability for monitoring.

Most teams manage multiple platforms: Travis CI or Jenkins for builds, Heroku or AWS for hosting, a separate database provider, and monitoring tools. Each service brings its own configuration, billing, and integration overhead.

<InfoBox className="BodyStyle">

Northflank provides CI/CD as part of a complete platform. When you push code to your GitHub, GitLab, or Bitbucket repository, Northflank builds and deploys automatically. You get managed databases (PostgreSQL, MySQL, MongoDB, Redis), automatic full-stack preview environments for every pull request, and built-in observability with logs and metrics. With [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud), deploy to your AWS, GCP, Azure, Civo, Oracle, or bare-metal infrastructure while Northflank manages orchestration.

</InfoBox>

![northflank--platform.png](https://assets.northflank.com/northflank_platform_d625d79568.png)
**Northflank fits well if you're:**

- Consolidating separate CI/CD and hosting tools
- Building containerized applications or microservices
- Running AI/ML workloads needing [GPU support](https://northflank.com/gpu) (H100, B200, A100, e.t.c.)
- Avoiding vendor lock-in with BYOC (Bring your own cloud)
- Scaling without rebuilding your deployment workflow

If you only need builds and tests, Travis CI or Jenkins works. But if you're looking at your complete deployment workflow (from code push to production), Northflank provides an integrated platform that reduces toolchain complexity.

## Choosing the right platform for your workflow

Here's how to decide between Travis CI, Jenkins, and Northflank based on what you actually need:

| Choose this | If you need |
| --- | --- |
| **Northflank** | Complete deployment platform where CI/CD is integrated with infrastructure, databases, and orchestration. Instead of combining separate tools for builds, hosting, and databases, you get the full application lifecycle in one place with the flexibility to deploy to your own cloud. |
| **Travis CI** | Cloud-hosted CI with minimal setup and no infrastructure management. The build matrix makes multi-environment testing straightforward, and you want to avoid maintaining servers. |
| **Jenkins** | Maximum control and customization over your CI/CD pipelines. You have the technical resources to manage infrastructure, need extensive plugin integration, or have security/compliance requirements that mandate self-hosted solutions. |

**The decision extends beyond which tool builds your code; it's about how you want to manage your entire deployment workflow**. If you're evaluating CI/CD platforms, think about if you want specialized build tools that you'll integrate with other services, or a complete platform that handles everything from code push to production.

<InfoBox className="BodyStyle">

See how Northflank manages your application lifecycle with [our quickstart guide](https://northflank.com/docs/v1/application/getting-started), [try the platform with our free developer sandbox](https://app.northflank.com/signup), or [book a demo](https://cal.com/team/northflank/northflank-intro) to speak with an expert engineer about your specific deployment needs.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Best 5 Heroku Private Spaces alternatives in 2026</title>
  <link>https://northflank.com/blog/heroku-private-spaces-alternatives</link>
  <pubDate>2026-01-08T16:45:00.000Z</pubDate>
  <description>
    <![CDATA[Heroku Private Spaces alternatives: Compare Northflank, AWS VPC, Google Cloud, and more. Find secure, cost-effective private networking for your apps]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/heroku_private_spaces_alternatives_d68d71bc9f.png" alt="Best 5 Heroku Private Spaces alternatives in 2026" />Heroku Private Spaces are dedicated, network-isolated environments for running applications and data services that meet strict security and compliance requirements. These spaces provide organizations with enhanced network controls, stable outbound IP addresses, and the ability to connect securely with on-premise systems and other cloud services.

However, as development teams evaluate their infrastructure options, many are considering alternatives that offer similar security features with more flexibility, better pricing models, or additional capabilities.

If you're looking for more control over your infrastructure, want savings on Heroku pricing, or need features that Heroku Private Spaces doesn't provide, this guide covers the top alternatives available in 2026.

## What are Heroku Private Spaces?

Heroku Private Spaces are dedicated runtime environments that provide network-isolated infrastructure for running applications and data services. Each Private Space operates as a private network with its own dedicated dyno runtime, separate from Heroku's multi-tenant Common Runtime.

Private Spaces offer network controls including trusted IP ranges for restricting inbound access, stable outbound IP addresses for allowlisting with external services, and VPN connectivity for secure integration with on-premise infrastructure. Organizations can deploy Private Spaces in multiple global regions and configure custom network rules to meet security and compliance requirements.

## What to look for in a Heroku Private Spaces alternative

When evaluating alternatives to Heroku Private Spaces, keep these factors in mind:

- **Network isolation and security features:** The alternative should provide dedicated, isolated network environments with robust security controls. Look for features like private subnets, security groups, and network ACLs that let you control traffic at multiple layers.
- **VPN and private networking capabilities:** Your alternative should support secure connections to on-premise infrastructure through VPN or direct connect options, as well as private communication between services without traversing the public internet.
- **Compliance certifications:** For regulated industries, ensure the platform maintains relevant certifications like SOC 2, HIPAA, PCI-DSS, or GDPR compliance to meet your organization's requirements.
- **Pricing transparency:** Unlike Heroku's monthly caps, look for alternatives with clear, predictable pricing models. Pay-as-you-go options can be more cost-effective for varying workloads.
- **Performance and reliability:** Evaluate the platform's track record for uptime, the quality of its infrastructure, and whether it offers features like automatic failover and multi-region deployment.
- **Migration difficulty:** Consider how easily you can move your existing applications and data. Look for platforms that support your current tech stack and provide migration guides or tools.

## Top 5 Heroku Private Spaces alternatives

We'll review the best alternatives to Heroku Private Spaces based on their private networking capabilities, security features, compliance support, pricing models, and ease of migration.

### 1. Northflank

Northflank is a comprehensive cloud platform that combines private networking, security features, and developer-friendly orchestration without the complexity of managing Kubernetes directly.

![northflank-networking.png](https://assets.northflank.com/northflank_networking_faa5eafea6.png)

**Private networking features:**

- Flexible private and public networking for services, databases, and other addons ([See how](https://northflank.com/docs/v1/application/network/networking-on-northflank))
- Support for HTTP, HTTP/2, Websockets, gRPC, TCP, and UDP protocols ([See how](https://northflank.com/docs/v1/application/network/configure-ports))
- Services and databases can be deployed with private networking to limit connectivity by project namespace
- Connect to private endpoints locally using the Northflank CLI proxy
- Configure security policies for individual ports with IP-based allow/deny lists, basic authentication, and SSO
- Create granular security policies by subdomain path for greater control

**Security and compliance:**

- Enterprise-grade security with [role-based access control](https://northflank.com/docs/v1/application/secure/use-role-based-access-control) (RBAC) and [audit logs](https://northflank.com/docs/v1/application/observe/audit-logs)
- Deploy in your own cloud accounts across AWS, GCP, Azure, Oracle Cloud, Civo or bare-metal for complete data control ([See how](https://northflank.com/features/bring-your-own-cloud))
- Advanced private cluster and node networking options
- Cross-project private networking ([See how](https://northflank.com/docs/v1/application/network/enable-multi-project-networking))
- VPN Tailscale support ([See how](https://northflank.com/docs/v1/application/network/use-tailscale))
- Path-based routing capabilities ([See how](https://northflank.com/docs/v1/application/domains/use-path-based-routing))

**Pricing:**

- Free sandbox tier for getting started
- Pay-as-you-go plans for production workloads with per-second billing
- Enterprise plans for organizations with advanced requirements
- Pricing calculator and transparent pricing page for detailed cost estimates
- Significantly more cost-effective than Heroku Private Spaces' monthly minimum fees for smaller teams or variable workloads

([See full pricing details](https://northflank.com/pricing))

<InfoBox className="BodyStyle">

**Migration path:**

Northflank provides comprehensive [documentation](https://northflank.com/docs/v1/application/migrate-from-heroku) for migrating from Heroku, including guides for moving applications, databases, and environment variables. The platform supports both Docker containers and buildpacks, making it straightforward to transition existing Heroku applications.

**Related resources:**

- [How to migrate from Heroku: A step-by-step guide](https://northflank.com/blog/how-to-migrate-from-heroku-a-step-by-step-guide)
- [Migrate from Heroku documentation](https://northflank.com/docs/v1/application/migrate-from-heroku)
- [Top Heroku alternatives in 2026](https://northflank.com/blog/top-heroku-alternatives)
- [Heroku Enterprise: capabilities, limitations, and alternatives](https://northflank.com/blog/heroku-enterprise-capabilities-limitations-and-alternatives)
- [Heroku Pricing Comparison & Reduction](https://northflank.com/heroku-pricing-comparison-and-reduction)

</InfoBox>

### 2. AWS VPC (self-managed)

Amazon Virtual Private Cloud gives you complete control over your virtual networking environment, including resource placement, connectivity, and security. AWS VPC is the self-managed route for organizations that want maximum flexibility and are comfortable handling infrastructure operations.

![amazon-vpc.png](https://assets.northflank.com/amazon_vpc_6772cb340c.png)

**Features overview:**

- Isolated virtual networks with customizable IP address ranges and multiple subnets across availability zones
- Route tables, internet gateways, and NAT gateways for traffic management
- Security groups and Network ACLs for multi-layer traffic control
- VPN connections and AWS Direct Connect for hybrid cloud architectures
- AWS PrivateLink for private connectivity between VPCs and AWS services without exposing traffic to the public internet
- VPC peering to route traffic between multiple VPCs
- Transit Gateway as a central hub for interconnecting VPCs and on-premise networks
- VPC endpoints for accessing AWS services privately

**When it makes sense:**

AWS VPC is ideal for organizations with dedicated DevOps teams who need granular control over network architecture. It's particularly suitable for enterprise workloads with complex compliance requirements, hybrid cloud deployments connecting AWS with on-premise infrastructure, or teams already deeply invested in the AWS ecosystem who can leverage native integrations.

<InfoBox className="BodyStyle">

**Note**: For teams that need similar private networking capabilities without the operational complexity of managing VPCs, route tables, and security groups directly, **Northflank offers a managed alternative** that handles the infrastructure layer while providing enterprise-grade networking features.

</InfoBox>

### 3. Google Cloud Private Clusters

Google Cloud's Virtual Private Cloud provides networking functionality for Compute Engine instances, Google Kubernetes Engine clusters, and serverless workloads that is global, scalable, and flexible.

![google-cloud-private-clusters.png](https://assets.northflank.com/google_cloud_private_clusters_dab3a8e1a6.png)

**Similar approach to AWS:**

- Control over virtual networking in the cloud with customizable network architecture
- Global VPC networks consisting of regional subnets connected by Google's global wide area network
- VPC Network Peering for private connectivity between different VPC networks
- Cloud VPN tunnels and Cloud Interconnect for connecting to on-premise infrastructure
- Private Service Connect for accessing Google APIs and services privately
- Firewall rules and routes for traffic control and management

**Key differences from AWS:**

- VPCs are global by default, whereas AWS VPCs are regional constructs
- Easier multi-region deployment without complex peering configurations
- Shared VPC allows centralized network management across multiple projects in an organization
- Superior global network backbone with lower latency between regions
- Simpler subnet management with automatic subnet expansion

**When it makes sense:**

Google Cloud VPC is best suited for organizations already using Google Cloud services, teams building global applications that need consistent networking across regions, or those requiring integration with Google Workspace and other Google services. It's also a strong choice for data-intensive workloads that benefit from Google's high-performance global network backbone.

<InfoBox className="BodyStyle">

**Note**: For teams that want multi-cloud flexibility with the ability to deploy on GCP, AWS, Azure, or other providers, including on-premise, from a single platform, without needing Google Cloud-specific networking expertise, Northflank provides a unified approach to private networking across multiple cloud providers.

</InfoBox>

### 4. Render Private Networking

Render provides automated private networking where services in the same region can communicate over their shared private network without traversing the public internet.

![render-private-networking.png](https://assets.northflank.com/render_private_networking_f5702a8f34.png)

**Private networking features:**

- Unique hostname for each service on the private network
- Services can listen for traffic on almost any port using any protocol
- Stable internal hostnames and IPs that dynamically map to individual instance addresses
- Private services unreachable via public internet but accessible to other services on the same private network
- Fast, safe, and reliable communication without traversing the public internet
- Support for HTTP, TCP, and UDP protocols on private networks

**Best use cases:**

Render's private networking is excellent for simpler architectures where services need to communicate within a single region. It's ideal for startups and small teams building microservices applications without complex multi-region or hybrid cloud requirements. The simplicity and developer experience make it attractive for teams that want private networking without operational complexity.

<InfoBox className="BodyStyle">

**Note**: For applications that require cross-region private networking, app-level isolation, or need to scale beyond a single region deployment, Northflank provides these capabilities with a similarly straightforward developer experience.

</InfoBox>

### 5. Railway

Railway's private networking enables private communication between services in a project and environment, using encrypted Wireguard tunnels to create an IPv6 mesh network between all services.

![railway-private-networking.png](https://assets.northflank.com/railway_private_networking_c94d1a6417.png)

**Private networking approach:**

- Encrypted Wireguard tunnels creating IPv6 mesh network between services
- Internal DNS names under railway.internal domain for each service
- Automatic resolution to internal IPv6 addresses
- Support for any valid IPv6 traffic including UDP, TCP, and HTTP
- Automated service discovery and high-speed internal networking
- Automatic TLS encryption for all traffic from edge to applications

**When to consider Railway:**

Railway is best for development teams and startups building cloud-native applications that don't require hybrid connectivity. The platform excels at rapid deployment and iteration with excellent developer experience. It's particularly suitable for teams comfortable with modern IPv6 networking and those building applications entirely in the cloud without legacy on-premise dependencies.

<InfoBox className="BodyStyle">

**Note**: Teams that need IPv4 support, cross-environment networking, or enterprise-grade security controls alongside Railway's developer experience will find Northflank offers these features while maintaining ease of use.

</InfoBox>

## How to choose the right alternative for your team

Selecting the best Heroku Private Spaces alternative depends on your specific requirements, technical capabilities, and organizational priorities.

| Use case | Best alternative | Why |
| --- | --- | --- |
| Best developer experience with enterprise features | Northflank | Balance of powerful private networking, security controls, and ease of use without Kubernetes complexity |
| Multi-region and global applications | Google Cloud VPC or Northflank | Global networking model or multi-cloud capabilities for worldwide low-latency deployment |
| Cost optimization | Northflank | Pay-as-you-go with per-second billing offers significant savings over minimum monthly fees |
| Maximum control and customization | AWS VPC or Google Cloud VPC | Full control over network architecture, ideal for complex enterprise workloads with dedicated infrastructure teams |
| Simple private networking needs | Render or Railway | Prioritize developer experience and rapid deployment for cloud-native applications in single regions |

## Getting started with a Heroku Private Spaces alternative

Private networking solutions provide teams with better options for securing applications and data. Alternatives like Northflank now provide comparable or superior capabilities with more flexible pricing and additional features than Heroku Private Spaces.

<InfoBox className="BodyStyle">

Start with our [free sandbox tier](https://app.northflank.com/signup) to try Northflank. Check out our [getting started documentation](https://northflank.com/docs) for guides on deploying your first applications with private networking, or [book a demo](https://cal.com/team/northflank/northflank-intro) to discuss your specific requirements.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>7 best Upsun alternatives for flexible cloud deployment</title>
  <link>https://northflank.com/blog/upsun-alternatives</link>
  <pubDate>2026-01-08T16:30:00.000Z</pubDate>
  <description>
    <![CDATA[Compare top Upsun alternatives including Northflank, Render, Railway, and Fly.io for Kubernetes-native deployments and BYOC flexibility]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/upsun_alternatives_76b1fe67c3.png" alt="7 best Upsun alternatives for flexible cloud deployment" />Upsun offers Git-driven deployments with production-like preview environments across AWS, Azure, and GCP for teams managing complex web applications with multi-cloud requirements.

If you're looking for alternatives to Upsun, it might be because of cost scaling concerns, a need for Kubernetes-native architecture, a desire to run infrastructure in your own cloud account, or the need for GPU support for AI workloads.

We'll cover some of the top Upsun alternatives in this article. 

<InfoBox className="BodyStyle">

## TL;DR: Top 7 Upsun alternatives

For a quick overview of the 7 best Upsun alternatives, here's the list based on their architectural approach and deployment flexibility:

1. **Northflank** – Kubernetes-native platform with BYOC support (deploy in [Northflank's cloud](https://northflank.com/features/managed-cloud) or [bring your own](https://northflank.com/features/bring-your-own-cloud) infrastructure: [AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), [Oracle](https://northflank.com/cloud/oci), or bare-metal), managed databases (PostgreSQL, MySQL, Redis, MongoDB), GPU workloads, zero-downtime deployments, and autoscaling for teams needing infrastructure control without operational overhead
2. **Render** – Git-based deployments with managed Postgres, zero-downtime releases, and background workers for structured production workloads
3. **Railway** –  Usage-based platform with visual service canvas and fast iteration for prototyping and small teams
4. **Fly.io** – Global edge deployment with VM-level control and Anycast networking for latency-sensitive applications
5. **DigitalOcean App Platform** – Managed PaaS with predictable pricing and DigitalOcean ecosystem integration
6. **Heroku** – PaaS with extensive add-on marketplace and buildpack support
7. **Vercel** – Frontend-optimized platform with serverless functions and edge network for Jamstack applications

</InfoBox>

## What to look for in Upsun alternatives

When evaluating Upsun alternatives, look out for the following capabilities:

**Key evaluation criteria:**

- **Architecture flexibility** – Native container and Kubernetes support vs proprietary deployment models
- **Infrastructure control** – Ability to run in your own cloud account (BYOC) or bring your own Kubernetes clusters
- **Pricing transparency** – Usage-based models that scale predictably vs per-user licensing
- **Modern workload support** – GPU capabilities, microservices orchestration, and stateful applications
- **DevOps integration** – Native CI/CD, GitOps support, and external tool compatibility
- **Escape velocity** – Migration paths and avoidance of vendor lock-in through standard technologies

## 7 best Upsun alternatives

We're evaluating these platforms based on their architectural approach, deployment flexibility, pricing models, and support for modern cloud-native workflows.

### 1. Northflank

Northflank provides a Kubernetes abstraction layer that delivers PaaS simplicity while maintaining the power and portability of container orchestration.

You can either deploy to Northflank's managed Kubernetes cloud or connect your existing infrastructure (AWS, GCP, Azure, Civo, Oracle, or bare-metal).

You can run containerized applications, managed databases, GPU workloads, scheduled jobs, and background workers on a single unified platform without requiring Kubernetes expertise.

![northflank--platform.png](https://assets.northflank.com/northflank_platform_d625d79568.png)

**Key capabilities:**

- **Kubernetes-native architecture with no vendor lock-in** – Built on K8s from the ground up with standard APIs and Dockerfiles, giving you container portability across any cluster or cloud provider without vendor-specific configuration formats
- **Bring Your Own Cloud (BYOC)** – Deploy to your AWS, GCP, Azure, Civo, Oracle, or bare-metal infrastructure while maintaining the same developer experience, addressing compliance and cost control requirements
- **Managed databases and persistent storage** – Provision PostgreSQL, MySQL, MongoDB, Redis with automated backups, monitoring, and production-grade persistent storage
- **GPU support** – Run AI inference, training, and LLM workloads with fractional GPU allocation and spot instance orchestration
- **Zero-downtime deployments with autoscaling** – Deploy production releases with automatic health checks, rollback capabilities, and horizontal/vertical scaling based on metrics
- **MicroVM isolation** – Secure code execution using Kata Containers for multi-tenant environments or untrusted code
- **Unified workflow management** – Deploy applications, databases, scheduled jobs, background workers, and release pipelines through consistent UI, CLI, API, or GitOps interfaces with external CI/CD integration (GitHub Actions, GitLab CI)
- **Flexible networking** – Private and public networking configuration for services, databases, and add-ons with built-in service discovery
- **Infrastructure as Code and preview environments** – Template systems for standardizing deployments and Git branch deployments that create isolated environments with configurable data sharing
- **Usage-based pricing** – Pay by the second for CPU, memory, GPU, networking, and storage

**Best for:** Platform engineering teams building internal developer platforms, SaaS companies requiring multi-tenancy and GPU workloads, enterprises needing BYOC (Bring Your Own Cloud) for compliance or cost optimization, teams running microservices architectures, organizations with AI/ML workloads requiring GPU support, development teams wanting Kubernetes benefits without YAML complexity, and companies needing infrastructure control with data residency requirements.

<InfoBox className="BodyStyle">

[Start with Northflank's free sandbox](https://app.northflank.com/signup) to deploy your first application, or [book a demo](https://cal.com/team/northflank/northflank-intro) with our engineering team to discuss your specific infrastructure requirements and migration path. See [full pricing details](https://northflank.com/pricing)

</InfoBox>

### 2. Render

Render provides managed application hosting with Git-based deployments and integrated services. 

![render's home page.png](https://assets.northflank.com/render_s_home_page_2982a329f2.png)

**Key capabilities:**

- Automatic deployments from Git with preview environments for pull requests
- Managed PostgreSQL with point-in-time recovery and automatic failover
- Native support for background workers and scheduled tasks
- Zero-downtime deployments with health checks
- Private networking between services

**Best for:** Teams migrating from Heroku who need managed databases and background job processing without infrastructure management.

<InfoBox className="BodyStyle">

See more:

- [7 Best Render alternatives for simple app hosting](https://northflank.com/blog/render-alternatives)
- [Render vs Vercel: Which platform suits your app architecture better?](https://northflank.com/blog/render-vs-vercel)
- [Render vs Heroku: Which platform-as-a-service is right for you](https://northflank.com/blog/render-vs-heroku)

</InfoBox>

### 3. Railway

Railway offers project-based deployments with visual service management and integrated provisioning.

![railway.png](https://assets.northflank.com/railway_76e4c28512.png)

**Key capabilities:**

- Visual project canvas showing service relationships and connections
- Template marketplace for common application stacks
- Volume support for persistent data
- Database provisioning (Postgres, MySQL, Redis, MongoDB)
- Usage-based pricing with monitoring and spend controls

**Best for:** Solo developers and small teams prioritizing deployment speed over infrastructure customization.


<InfoBox className="BodyStyle">

See more:

- [Railway vs Render: Which cloud platform fits your workflow better](https://northflank.com/blog/railway-vs-render)
- [6 best Railway alternatives: Pricing, flexibility & BYOC](https://northflank.com/blog/railway-alternatives)

</InfoBox>

### 4. Fly.io

Fly.io runs applications on lightweight VMs distributed globally across edge locations. The platform provides VM-level control with Anycast networking for routing traffic to the nearest instance.

![fly.io.png](https://assets.northflank.com/fly_io_87f030b697.png)

**Key capabilities:**

- Global deployment across 30+ regions with automatic request routing
- Fly Machines (lightweight VMs) with customizable CPU and memory
- WireGuard-based private networking between instances
- Volume support for persistent storage
- Metrics-based autoscaling using fly-autoscaler application that polls metrics and scales via the Machines API

**Best for:** Applications requiring global edge deployment and low-latency access for distributed user bases.

<InfoBox className="BodyStyle">

See more:

- [Top 6 Fly.io alternatives](https://northflank.com/blog/flyio-alternatives)
- [Fly.io vs Render: How they handle jobs, scaling, and production workloads](https://northflank.com/blog/flyio-vs-render)

</InfoBox>

### 5. DigitalOcean App Platform

DigitalOcean App Platform provides managed deployments integrated with DigitalOcean's infrastructure services. The platform handles builds, deployments, and scaling while connecting to managed databases and other DigitalOcean products.

![Digitalocean app platform's home page.png](https://assets.northflank.com/Digitalocean_app_platform_s_home_page_0f9ea04b7b.png)

**Key capabilities:**

- Automatic deployments from GitHub, GitLab, or container registries
- Integration with DigitalOcean Managed Databases and Spaces
- Auto-scaling based on traffic patterns
- Built-in CDN for static assets
- Alerts and monitoring dashboards

**Best for:** Teams already using DigitalOcean infrastructure who want managed application deployments within the same ecosystem.

<InfoBox className="BodyStyle">

See more:

- [10 best DigitalOcean alternatives for developers and teams](https://northflank.com/blog/best-digitalocean-alternatives-2025)
- [DigitalOcean vs AWS: A guide for developers, startups, and AI companies](https://northflank.com/blog/digitalocean-vs-aws)

</InfoBox>

### 6. Heroku

Heroku provides buildpack-based deployments with an extensive add-on marketplace. The platform abstracts infrastructure management through dynos (containerized processes) and managed services.

![heroku.png](https://assets.northflank.com/heroku_092e1c7f09.png)

**Key capabilities:**

- Buildpack support for automatic language detection and dependency installation
- Add-on marketplace with 200+ integrations for databases, monitoring, and caching
- Heroku Postgres with automated backups and rollback capabilities
- Review apps for automated preview environments

**Best for:** Organizations with existing Heroku deployments seeking minimal migration friction.

<InfoBox className="BodyStyle">

See more:

- [Heroku Pricing Comparison & Reduction](https://northflank.com/heroku-pricing-comparison-and-reduction)
- [Top Heroku alternatives](https://northflank.com/blog/top-heroku-alternatives)
- [[Documentation] Migrate from Heroku](https://northflank.com/docs/v1/application/migrate-from-heroku)
- [Heroku vs AWS: which cloud platform should you choose](https://northflank.com/blog/heroku-vs-aws)
- [Heroku Enterprise: capabilities, limitations, and alternatives](https://northflank.com/blog/heroku-enterprise-capabilities-limitations-and-alternatives)
- [How to migrate from Heroku: A step-by-step guide](https://northflank.com/blog/how-to-migrate-from-heroku-a-step-by-step-guide)

</InfoBox>

### 7. Vercel

Vercel specializes in frontend frameworks with serverless function support and global edge network distribution. The platform integrates deeply with Next.js and other modern JavaScript frameworks.

![vercel-homepage.png](https://assets.northflank.com/vercel_homepage_7ecf227d81.png)

**Key capabilities:**

- Automatic preview deployments for every Git push
- Edge network with 100+ global locations
- Serverless Functions for API endpoints
- Image optimization and caching
- Built-in analytics and Web Vitals monitoring

**Best for:** Frontend applications and Next.js projects requiring global CDN distribution and serverless backend capabilities.

<InfoBox className="BodyStyle">

See more:

- [Can you use Vercel for backend? What works and when to use something else](https://northflank.com/blog/vercel-backend-limitations)
- [Best Vercel Alternatives for Scalable Deployments](https://northflank.com/blog/best-vercel-alternatives-for-scalable-deployments)
- [Vercel vs Netlify: Choosing the right one in 2026 (and what comes next)](https://northflank.com/blog/vercel-vs-netlify-choosing-the-deployment-platform-in-2025)
- [Vercel vs Heroku: Which platform fits your workflow best?](https://northflank.com/blog/vercel-vs-heroku)
- [Top Vercel Sandbox alternatives for secure AI code execution and sandbox environments](https://northflank.com/blog/top-vercel-sandbox-alternatives-for-secure-ai-code-execution-and-sandbox-environments)

</InfoBox>

## How to choose the right Upsun alternative

Match the platform architecture to your application requirements and team capabilities rather than defaulting to the most feature-rich option.

| **Your priority** | **Best fit** | **Why** |
| --- | --- | --- |
| Infrastructure control & compliance | Northflank | BYOC option (Bring your own cloud) lets you run in your own cloud account with full control over networking, security, and data residency |
| Kubernetes without complexity | Northflank | K8s-native architecture with abstraction layer removes YAML management while preserving portability |
| GPU and AI workloads | Northflank | Native GPU support with spot instances and fractional allocation for cost optimization |
| Managed databases & workers | Render, Northflank | Managed database provisioning with background workers and scheduled jobs, plus monitoring and automated backups |
| Internal tools and side projects | Railway | Visual service canvas with template-based deployment and monitoring |
| Global edge performance | Fly.io | Anycast routing and edge deployment minimize latency for distributed users |
| DigitalOcean ecosystem | DigitalOcean App Platform | Native integration with DO databases, storage, and services |
| Existing Heroku apps | Heroku | Minimal migration complexity if already using Heroku buildpacks and add-ons |
| Next.js & frontend focus | Vercel | Deep Next.js integration with global CDN and serverless functions |

## Getting started with the right Upsun alternative

Northflank addresses the core limitations teams encounter with traditional PaaS platforms through Kubernetes-native architecture, infrastructure control via BYOC (Bring Your Own Cloud), and modern workload support, including GPU capabilities. The platform maintains developer experience simplicity while providing the flexibility required for complex production environments.

<InfoBox className="BodyStyle">

[Start with Northflank's free sandbox](https://app.northflank.com/signup) to deploy your first application, or [book a demo](https://cal.com/team/northflank/northflank-intro) with our engineering team to discuss your specific infrastructure requirements and migration path.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>How Yavendio scaled AI-powered WhatsApp commerce across LatAm with Northflank</title>
  <link>https://northflank.com/blog/yavendio-scaled-ai-powered-whatsapp-commerce-across-latam-with-northflank</link>
  <pubDate>2026-01-08T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[Yavendio is an AI company building intelligent AI sales agents for WhatsApp, focused on e-commerce businesses across Latin America.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Group_1410119477_359d49c8c8.png" alt="How Yavendio scaled AI-powered WhatsApp commerce across LatAm with Northflank" /><InfoBox className='BodyStyle'>

# 📌 TL;DR

- **Company:** Yavendio, AI-powered WhatsApp sales agents for e-commerce in Latin America (7,000+ customers)
- **Team:** 15 engineers, no dedicated DevOps
- **Challenge:** Needed Kubernetes power without the complexity; wanted to use AWS/GCP startup credits
- **Solution:** Northflank BYOC: deploy to their own cloud accounts with full CI/CD automation
- **Stack:** Python, TypeScript, Golang, FastAPI, Hono, LangGraph
- **Key wins:** Zero-downtime deployments, auto-scaling for traffic spikes, private networking via Tailscale, multi-cloud (AWS + Azure)
- **Result:** Scaling across Latin America with enterprise-grade infrastructure, managed entirely by their engineering team

</InfoBox>

[Yavendio](https://www.yavendio.com/en/) is an AI company building intelligent AI sales agents for WhatsApp, focused on e-commerce businesses across Latin America. With over 7,000 customers and offices expanding from Peru into Brazil, the company has raised several million dollars to fuel its growth in one of the world's most active WhatsApp markets.

## Challenge

As Yavendio grew, their engineering team faced a common scaling problem: they needed the power of Kubernetes but lacked the specialized expertise to manage it.

"We had a really inexperienced team in the early stages," explains Terry Cruz Melo, Co-founder and CTO at Yavendio. "Kubernetes and all its implications are a big pain for us. Great DevOps engineers are not as easily accessible in the region."

The team was managing 60-70 repositories and needed a platform that could handle complex orchestration without requiring deep Kubernetes knowledge. 

They also wanted to leverage existing AWS and GCP credits from startup programs rather than paying for additional cloud resources.

## Finding Northflank

Terry discovered Northflank through an AI-powered search (very fitting for an AI company). 

> "I was looking for something similar to Vercel, but for backend-oriented workloads. We found Northflank and I was really inclined toward it because of the good UI and Developer Experience. It was more friendly than others tools I’ve tried.
> 
> The one-to-one support we experienced was definitely a selling point. It was super easy to talk with the team directly whenever we had questions."
> 

### Bring Your Own Cloud

Since they have a lot of AWS and GCP credits from startup programs, they wanted to drain those directly without paying for additional cloud resources. Northflank's Bring Your Own Cloud model lets them do exactly that.

## Solution

Today, Yavendio's entire engineering team of 15 developers relies on Northflank for their infrastructure. Using Northflank's Bring Your Own Cloud (BYOC) model, they deploy to their own AWS and Azure accounts, maximizing the value of startup credits while getting enterprise-grade orchestration.

The platform handles their complete CI/CD pipeline: when engineers push code to GitHub, Northflank automatically builds containers and deploys them to production on AWS and Azure.

> "Northflank simplifies everything. Developers don't have to worry about Kubernetes configuration, they only worry about moving their service to production as fast as possible. Someone wants to deploy a new service? They go to settings, configure environment variables, do a few clicks, and it's done."
> 

### Key Northflank capabilities Yavendio leverages

- **Multi-cloud deployment:** Running workloads across both AWS and Azure while draining startup credits

- **Bring Your Own Cloud (BYOC):** Deploying to their own AWS and Azure accounts, letting them fully utilize startup program credits 

- **Automated CI/CD:** GitHub integration that builds and deploys without custom recipes

- **Horizontal auto-scaling:** Critical during high-traffic periods like the week before Christmas when traffic spikes dramatically

- **Zero-downtime deployments:** "Previously we'd be down for five or six minutes during updates. Now we can push updates continuously without affecting availability. That used to be really painful, we'd lose clients."

- **Private networking with Tailscale:** Keeping services off the public internet for security

- **Open source deployments:** Running tools like Metabase, Open WebUI (as a ChatGPT alternative using their Anthropic and cloud credits), and Langfuse for AI observability

## Technical stack

Yavendio deploys a polyglot architecture on Northflank:

- **Languages:** Python, TypeScript, Golang (previously Rust and Elixir)
- **Frameworks:** FastAPI, Hono
- **AI tooling:** LangGraph agents, custom OLIVE framework for agent tool sharing

The team even built their own agent protocol called OLIVE (an HTTP-based alternative to MCP) that's deeply integrated with their Northflank-powered Kubernetes infrastructure.

## Results

When evaluating an enterprise AI orchestration platform, Yavendio realized they'd already built equivalent capabilities on Northflank. 

"We told them, 'We have exactly the same thing on Northflank.' They were really impressed because they'd never seen something so similar built so quickly.”

The platform has enabled Yavendio to:

- Scale infrastructure to match traffic demands during peak commerce periods
- Fully leverage cloud credits through BYOC instead of paying for managed infrastructure
- Deploy continuously without service interruptions
- Empower all 15 engineers to manage deployments without DevOps expertise
- Maximize value from cloud credits across multiple providers

## Looking ahead

Yavendio is proof that a 15-person engineering team can run sophisticated infrastructure across multiple clouds without a dedicated DevOps function. 

As the team expands from Peru into Brazil, their Northflank-powered infrastructure is ready to scale with them. The BYOC model means they can spin up resources in new regions without migrating platforms or renegotiating contracts.

Plus, their lean engineering team can stay focused on building rather than wrangling Kubernetes.]]>
  </content:encoded>
</item><item>
  <title>Webapp.io alternatives for fast Docker builds and preview environments</title>
  <link>https://northflank.com/blog/webapp-io-alternatives-for-fast-docker-builds-and-preview-environments</link>
  <pubDate>2026-01-07T19:20:00.000Z</pubDate>
  <description>
    <![CDATA[With Webapp.io shutting down, Northflank is the top alternative offering robust CI/CD, automated testing, instant preview environments, Docker caching, managed infrastructure, and BYOC flexibility.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Webapp_io_alternatives_0270ac3e7d.png" alt="Webapp.io alternatives for fast Docker builds and preview environments" />With the recent announcement that Webapp.io is shutting down, many engineering teams are now searching for robust, reliable alternatives to continue delivering high-quality software quickly and efficiently. Webapp.io was widely appreciated for its ease of use in continuous integration (CI) and testing with super fast builds. If you're looking for a powerful replacement that not only matches but also enhances your workflow, **Northflank** stands out as the leading alternative.

## Why Northflank?

Northflank provides an all-in-one solution for continuous integration, automated testing, seamless preview environments, and streamlined release management to production. Engineered to simplify complex deployment workflows, Northflank ensures your team can focus on building rather than managing infrastructure.

### Features tailored to your needs:

- **Continuous integration (CI)**: Automate your development pipeline with intuitive, scalable CI processes designed to integrate seamlessly with your existing workflows.
- **Automated testing**: Run automated tests effortlessly to ensure code reliability and accelerate your release cycles.
- **Instant preview environments**: Quickly spin up fully isolated preview environments per pull request or feature branch, enabling efficient collaboration and faster feedback loops.
- **Release management & deployment**: Effortlessly manage your deployments with Northflank’s advanced release management capabilities, whether to staging or directly to production.

## Enhanced flexibility and performance

Northflank supports **any workload with Dockerfiles or Buildpacks**, offering maximum flexibility for your deployment needs. It includes two powerful Docker build engines: **Kaniko and Buildkit**. Leveraging Buildkit with local volumes, Northflank achieves caching performance just as fast as Webapp.io's Layerfile, meaning teams no longer require Layerfiles—a simple Dockerfile now suffices for optimal build speed and efficiency.

## Managed infrastructure or Bring Your Own Cloud (BYOC)

Northflank provides flexibility to choose the infrastructure setup that suits your organization:

- **Managed infrastructure**: Run your workloads on Northflank’s optimized, fully managed cloud infrastructure. Save time, reduce overhead, and leverage high-performance resources managed directly by Northflank.

- **Bring your own cloud (BYOC)**: Run workloads seamlessly within your existing cloud accounts (AWS, GCP, Azure, and more). Northflank’s BYOC approach enhances security, ensures compliance with internal standards, and gives you complete control over data and resources, maximizing your existing cloud investment.

## Proven momentum and trusted by industry leaders

Northflank recently raised a **Series A funding round**, accelerating product innovation and commercial growth. Today, more than **30,000 developers** rely on Northflank for critical production workloads. Industry-leading companies like **Sentry, Writer, and Northfield** trust Northflank to power their development and deployment pipelines, further validating Northflank’s position as a leader in the CI/CD and cloud infrastructure space.

## Seamless transition for Webapp.io customers

Many existing Northflank customers were also using Webapp.io but had proactively transitioned their development and CI workloads to Northflank even before Webapp.io's shutdown announcement. These customers recognized Northflank’s ability to enhance their development workflows, simplify CI processes, and scale effectively, making Northflank the natural successor to Webapp.io.

## Making the switch easy

Transitioning from Webapp.io to Northflank is straightforward. With comprehensive documentation and dedicated support, Northflank ensures a seamless migration process. The intuitive, user-friendly interface helps your team quickly get up to speed, minimizing downtime and maximizing productivity. Northflank can set up a shared direct Slack channel with your engineering team as part of a white glove migration.

## Ready to make the move?

Join the growing community of teams leveraging Northflank to deliver superior software faster.

[**Try Northflank today**](https://app.northflank.com/signup) and experience the future of developer pipelines, preview environments and production release management.
]]>
  </content:encoded>
</item><item>
  <title>What is PaaS hosting? Benefits and how it works</title>
  <link>https://northflank.com/blog/what-is-paas-hosting</link>
  <pubDate>2026-01-07T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[PaaS hosting automates infrastructure management so developers can focus on code. Learn the benefits, use cases, and how Northflank simplifies PaaS]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/what_is_paas_hosting_1_bd93de8579.png" alt="What is PaaS hosting? Benefits and how it works" /><InfoBox className="BodyStyle">

*PaaS hosting is a cloud computing model that provides a complete platform for deploying and managing applications without managing the underlying infrastructure. Developers push code, and the platform handles servers, scaling, security, and operations automatically.*

</InfoBox>

For developers and engineering teams, the jump from managing servers to simply deploying code represents a fundamental shift in how applications get built and scaled. Platform-as-a-Service (PaaS) hosting sits at the center of this shift, abstracting away infrastructure complexity while giving teams the tools they need to ship faster.

This guide explains what PaaS hosting is, its benefits, how it works, and what to look for when choosing a platform.

## What is PaaS hosting?

PaaS hosting is a cloud computing model that provides a complete platform for building, deploying, and managing applications without dealing with the underlying infrastructure. Instead of configuring servers, managing operating systems, or patching security vulnerabilities, developers push code, and the platform handles everything else.

The hosting industry has progressed through several stages:

- **Shared hosting**: Multiple sites on one server, minimal control
- **VPS hosting**: Dedicated resources, full server management required
- **IaaS (Infrastructure as a Service)**: Virtual machines you configure and maintain
- **PaaS hosting**: Managed platform with built-in deployment, scaling, and operations

PaaS hosting includes the runtime environment, middleware, databases, development tools, and deployment pipelines. It abstracts away the infrastructure layer while still giving you control over your application code and configuration.

Is web hosting a PaaS? Not exactly. Traditional web hosting focuses on serving websites from configured servers. PaaS hosting provides a development and deployment platform for applications, with integrated tools for continuous delivery, monitoring, scaling, and database management.

## How PaaS hosting works

PaaS hosting shifts your deployment workflow from infrastructure management to application delivery. Here's what happens behind the scenes:

- **Deployment**: You connect your git repository or container registry to the platform. When you push code, the PaaS hosting provider automatically builds, tests, and deploys your application. Many platforms support multiple deployment methods, including git push, Docker containers, or CI/CD pipeline integrations.
- **Managed infrastructure**: The platform handles server provisioning, load balancing, network configuration, security patches, and system updates. You don't SSH into servers or configure firewalls; the platform manages the entire infrastructure layer.
- **Built-in services**: PaaS hosting includes integrated databases (PostgreSQL, MySQL, MongoDB), caching layers (Redis, Memcached), message queues, monitoring tools, and logging systems. These services are provisioned with a few clicks and automatically configured to work with your application.
- **Automatic scaling**: The platform monitors your application's resource usage and scales compute resources up or down based on demand. This happens without manual intervention, ensuring your application stays responsive during traffic spikes.

## PaaS vs other hosting models

Understanding how PaaS hosting compares to other options helps you make the right choice for your team and applications.

### 1. PaaS vs traditional hosting (shared/VPS)

Traditional hosting gives you more control but requires significantly more operational work.

With shared or VPS hosting, you configure web servers, install dependencies, manage security updates, and handle scaling manually.

PaaS hosting automates all of this, trading some low-level control for significantly faster deployment cycles and reduced operational overhead.

### 2. PaaS vs IaaS

Infrastructure as a Service platforms like AWS EC2 or Google Compute Engine provide virtual machines you configure yourself.

You have complete control but must manage everything: operating systems, networking, security, backups, monitoring, and scaling. PaaS hosting builds on top of IaaS, adding the application platform layer.

*If you're comparing multiple platforms, read our analysis of the [best PaaS providers](https://northflank.com/blog/best-paas-providers) to see how they stack up.*

### 3. PaaS vs serverless/FaaS

Function-as-a-Service platforms like AWS Lambda work best for event-driven, stateless workloads.

PaaS hosting better suits stateful applications, long-running processes, complex microservices architectures, and applications that need persistent database connections.

*For teams building full-stack applications with multiple services, [Kubernetes PaaS](https://northflank.com/blog/kubernetes-paas) platforms offer the orchestration capabilities needed for modern architectures.*

## Benefits of PaaS hosting

PaaS hosting delivers tangible benefits for development teams at every stage:

- **Faster deployment velocity**: Deploy your first application in minutes instead of spending weeks on infrastructure setup. Automated CI/CD pipelines mean every git push can go straight to production once tests pass. Teams report 10x faster deployment cycles after moving to PaaS hosting.
- **Reduced DevOps overhead**: Small teams can't always justify hiring dedicated DevOps engineers. PaaS hosting provides enterprise-grade infrastructure management without requiring additional headcount. Your developers stay focused on building features instead of debugging configurations or troubleshooting network issues.
- **Built-in scalability and reliability**: PaaS platforms include load balancing, automatic failover, and horizontal scaling. When traffic spikes, your application scales automatically. When a container fails, the platform restarts it instantly.
- **Cost predictability**: PaaS hosting providers offer transparent pricing models where you know exactly what you're paying for. Many platforms include free tiers for development and staging environments.
- **Team productivity gains**: Developers spend more time writing code and less time in operations. Teams often move from weekly deployments to multiple deployments per day after adopting PaaS hosting. For teams prioritizing developer productivity, read our guide to the [best developer experience PaaS](https://northflank.com/blog/best-developer-experience-paas-2025) platforms available today.

## How Northflank simplifies PaaS hosting

Northflank takes a developer-first approach to PaaS hosting, combining the simplicity of traditional platforms with the power and flexibility of Kubernetes.

![northflank--platform.png](https://assets.northflank.com/northflank_platform_d625d79568.png)

Here's what sets us apart:

- **Kubernetes-native architecture**: Unlike legacy PaaS providers, Northflank runs on Kubernetes, giving you the orchestration capabilities modern applications require. Deploy microservices, monoliths, databases, cron jobs, and background workers, all from one platform.
- **Flexible deployment options**: Connect GitHub, GitLab, or Bitbucket for automatic deployments. Use Docker containers for maximum portability. Northflank adapts to your workflow instead of forcing you into a rigid deployment model.
- **Bring your own cloud**: Deploy on Northflank's managed infrastructure or connect your own cloud account (AWS, GCP, Azure, Civo, Oracle, or bare-metal). This flexibility means you can use Northflank's PaaS experience while maintaining control over your infrastructure when compliance or data residency requirements demand it.
- **Transparent pricing**: Pay only for the resources you use, with per-second billing and no hidden costs. Development environments run on generous free tiers.
- **Support for modern architectures**: Build full-stack applications with frontend, backend, databases, and workers all managed from one platform. Northflank's built-in observability tools give you production-ready infrastructure from the start.

<InfoBox className="BodyStyle">

[Start deploying on Northflank](https://app.northflank.com/signup) in minutes with our free sandbox, or [book a demo](https://cal.com/team/northflank/northflank-intro) to discuss your team's specific requirements. Check out our [documentation](https://northflank.com/docs) and [pricing](https://northflank.com/pricing) to learn more.

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  <title>5 best Bitbucket Pipelines alternatives for scalable CI/CD</title>
  <link>https://northflank.com/blog/bitbucket-pipelines-alternatives</link>
  <pubDate>2026-01-07T15:00:00.000Z</pubDate>
  <description>
    <![CDATA[Bitbucket Pipelines alternatives: Compare Northflank, GitHub Actions, GitLab CI/CD, Jenkins &amp; CircleCI for scalable CI/CD]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/bitbucket_pipelines_alternatives_51cdee1ef0.png" alt="5 best Bitbucket Pipelines alternatives for scalable CI/CD" /><InfoBox className="BodyStyle">

## TL;DR: Top 5 Bitbucket Pipelines alternatives

Bitbucket Pipelines alternatives include Northflank, GitHub Actions, GitLab CI/CD, Jenkins, and CircleCI. Each platform takes a different approach to continuous integration and deployment. 

See a quick list below that covers their key features and use cases (we go into detail later in the article):

1. **Northflank**: Provides complete CI/CD with GitOps workflows, release pipelines, and ephemeral preview environments.
    
    > Deploy in [Northflank's cloud](https://northflank.com/features/managed-cloud) or [bring your own](https://northflank.com/features/bring-your-own-cloud) infrastructure ([AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), [Oracle](https://northflank.com/cloud/oci), or bare-metal). Build services with rapid CI, automatic deployments, and [per-second billing](https://northflank.com/pricing). Unlike traditional CI/CD tools, Northflank combines pipelines with a complete platform for apps, databases, GPU workloads, and [secure sandboxes with microVMs](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh).
    > 
2. **GitHub Actions**: Provides cloud-native CI/CD integrated directly into GitHub repositories with marketplace actions.
3. **GitLab CI/CD**: Provides end-to-end DevOps platform with built-in CI/CD using YAML configuration.
4. **Jenkins**: Provides self-hosted, open-source automation server with extensive plugin ecosystem.
5. **CircleCI**: Provides cloud-based CI/CD with fast builds and intelligent caching.

</InfoBox>

## What is Bitbucket Pipelines?

Bitbucket Pipelines is a cloud-based CI/CD solution integrated directly into Bitbucket Cloud repositories. It enables teams to build, test, and deploy code automatically using YAML configuration files stored alongside their source code.

Pipelines run in Docker containers and integrate seamlessly with other Atlassian products like Jira and Confluence. Teams can define workflows with parallel steps, use cloud runners or self-hosted runners, and manage deployments across multiple environments from within Bitbucket.

## What to look for when choosing Bitbucket Pipelines alternatives

When evaluating alternatives to Bitbucket Pipelines, look out for these key factors that address common limitations and requirements:

- **Pricing model**: Look beyond per-minute billing; teams running comprehensive test suites can quickly consume tens of thousands of build minutes monthly at $10 per 1,000 minutes. Consider platforms with per-second billing, flat-rate pricing, or self-hosted options that reduce metered costs.
- **Version control flexibility**: Ensure the platform supports your preferred VCS (GitHub, GitLab, Bitbucket, Azure DevOps) or works across multiple providers simultaneously to avoid vendor lock-in.
- **Advanced CI/CD features**: Look for platforms offering multi-stage release pipelines, blue-green deployments, canary releases, advanced caching strategies, and granular deployment controls that go beyond basic build-test-deploy workflows.
- **Beyond CI/CD capabilities**: Determine if you need just a build tool or a complete platform that includes production infrastructure, database hosting, job scheduling, and workload management to get rid of tool fragmentation.
- **Deployment model options**: Choose between managed SaaS, bring-your-own-cloud (BYOC) deployments, or self-hosted solutions based on your data residency, compliance, and cost control requirements.
- **Infrastructure management overhead**: Evaluate whether self-hosted runners require acceptable levels of manual provisioning, configuration, updates, and security patching, or if managed solutions better fit your team's capacity.
- **Pipeline customization**: Look for platforms that provide granular control over build environments, robust debugging tools, flexible workflow orchestration, and reusable templates across repositories for complex workflows.
- **Enterprise requirements**: Verify support for RBAC, audit logging, SSO, compliance features, and security capabilities like VM-level isolation for running untrusted code or multi-tenant workloads.
- **Preview and staging environments**: Check if the platform offers built-in ephemeral environments for testing pull requests with full-stack dependencies, or if this requires additional tooling and custom scripts.

## 5 top Bitbucket Pipelines alternatives for scalable CI/CD

We'll review the 5 top Bitbucket Pipelines alternatives based on their CI/CD capabilities, deployment flexibility, pricing models, and platform features to help you choose the best fit for your team.

### 1. Northflank

Northflank is a complete cloud platform that combines production-grade CI/CD with full infrastructure capabilities for deploying applications, databases, jobs, and GPU workloads in your cloud or ours.

![northflank--platform.png](https://assets.northflank.com/northflank_platform_d625d79568.png)

**Key features**

- **Rapid CI/CD with GitOps**: Automatically build from GitHub, GitLab, and Bitbucket on every commit with path rules.
- **Release pipelines**: Manage multi-stage releases across development, staging, and production with visual pipeline workflows, conditional steps, automated rollbacks, and one-click promotions
- **Ephemeral preview environments**: Automatically spin up full-stack [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) for every pull request, complete with databases, microservices, and jobs, then tear them down when done
- **Complete platform beyond CI/CD**: Not just builds, run your entire stack including apps, [databases](https://northflank.com/features/databases), [jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs), and [inference workloads](https://northflank.com/gpu) on the same platform
- **True bring-your-own-cloud**: Deploy in your [AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), [Oracle](https://northflank.com/cloud/oci), or bare-metal infrastructure with complete control over costs and data residency
- **Secure sandbox execution**: [VM-level isolation with Kata Containers and gVisor](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation) for running untrusted code, AI agents, and multi-tenant workloads
- **Infrastructure as code**: Create reusable [templates](https://northflank.com/docs/v1/application/infrastructure-as-code/infrastructure-as-code) for complex deployments with JSON configuration and bidirectional Git sync
- **Auto-scaling and observability**: [Real-time logging](https://northflank.com/docs/v1/application/observe/view-logs), [metrics](https://northflank.com/docs/v1/application/observe/view-metrics), and [automatic scaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments) based on CPU, memory, or custom metrics
- **Per-second billing**: Pay only for what you use with granular per-second billing instead of rounding up to minutes or hours

**Best for:** Teams needing both CI/CD pipelines and a complete platform to run production workloads, organizations requiring secure multi-tenant isolation for code execution, and companies wanting to avoid vendor lock-in by deploying across multiple clouds or in their own infrastructure.

<InfoBox className="BodyStyle">

Northflank solves the fragmentation between CI/CD tools and production infrastructure.

Instead of using Bitbucket Pipelines for builds plus separate services for hosting, databases, and scaling, teams get an integrated platform that handles everything from git push to production with built-in observability.

The platform executes over [2 million isolated workloads](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes) monthly in production and is trusted by companies building production applications at scale.

> [Try Northflank](https://app.northflank.com/signup) to experience integrated CI/CD and production infrastructure, or [review our documentation](https://northflank.com/docs) to learn how you can leverage Northflank for your deployment workflows. For specific deployment questions or to discuss your CI/CD requirements, [talk to one of our expert engineers](https://cal.com/team/northflank/northflank-intro).
> 

</InfoBox>

**See these helpful guides:**

- [Continuous integration and delivery on Northflank](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank)
- [Set up a pipeline](https://northflank.com/docs/v1/application/getting-started/set-up-a-pipeline)
- [What is continuous delivery? Tools, pipelines, and how modern teams are implementing it](https://northflank.com/blog/continuous-delivery)
- [14 best CI/CD tools for teams](https://northflank.com/blog/best-ci-cd-tools)
- [10 best continuous deployment tools (includes app & automation deployment tools)](https://northflank.com/blog/continuous-deployment-tools)
[](https://northflank.com/blog/best-ci-cd-tools)

### 2. GitHub Actions

GitHub Actions is a cloud-native CI/CD platform integrated directly into GitHub repositories with workflow automation using YAML configuration.

![GitHub Actions.png](https://assets.northflank.com/Git_Hub_Actions_9df410abd8.png)

**Key features**

- **Native GitHub integration**: Workflows trigger automatically on push, pull request, issue creation, and other GitHub events
- **Self-hosted runners**: Run workflows on your own infrastructure for sensitive workloads or custom hardware
- **Matrix builds**: Test across multiple versions, operating systems, and configurations simultaneously
- **Secrets management**: Store encrypted secrets and environment variables securely within GitHub
- **Artifact handling**: Upload and download build artifacts between workflow steps

**Best for:** Teams already using GitHub for version control and organizations wanting tight integration between code and CI/CD.

**Limitations:** Locked to GitHub repositories (doesn't work with GitLab or Bitbucket), limited to 2,000 CI/CD minutes per month on free private repos, and no built-in production infrastructure beyond CI/CD workflows.

<InfoBox className="BodyStyle">

See the following guides:

- [How to use GitHub Actions with Northflank](https://northflank.com/docs/v1/application/infrastructure-as-code/use-github-actions-with-northflank)
- [Best GitHub Actions alternatives for modern CI/CD](https://northflank.com/blog/github-actions-alternatives)
- [GitHub Actions vs Jenkins: Which CI/CD tool is right for you?](https://northflank.com/blog/github-actions-vs-jenkins)

</InfoBox>

### 3. GitLab CI/CD

GitLab CI/CD is an integrated CI/CD solution built into GitLab's complete DevOps platform with configuration-as-code using `.gitlab-ci.yml` files.

![new gitlab cicd home page.png](https://assets.northflank.com/new_gitlab_cicd_home_page_6db2ffa6b1.png)

**Key features**

- **Complete DevOps platform**: Combines version control, CI/CD, security scanning, container registry, and release management in one tool
- **Auto DevOps**: Automatically detects, builds, tests, and deploys applications with predefined templates
- **Kubernetes integration**: Native support for deploying to Kubernetes clusters with environment management
- **Parallel execution**: Run jobs concurrently to reduce pipeline execution time

**Best for:** Teams wanting an all-in-one DevOps platform and organizations already using GitLab for version control.

**Limitations:** Resource-heavy for self-hosted deployments at scale, best value when using the entire GitLab ecosystem rather than just CI/CD, and external integrations require more configuration compared to using GitLab's native tools.

*See [9 Best GitLab alternatives for CI/CD](https://northflank.com/blog/best-gitlab-alternatives)*

### 4. Jenkins

Jenkins is an open-source automation server that provides extensive customization through a massive plugin ecosystem with over 1,800 available plugins.

![jenkins x home page.png](https://assets.northflank.com/jenkins_x_home_page_ea832a2d5d.png)

**Key features**

- **Plugin ecosystem**: Over 1,800 plugins for integrating with many tools or technologies
- **Distributed builds**: Master-agent architecture for scaling across multiple machines
- **Groovy-based pipelines**: Write complex workflows using Groovy DSL for maximum control
- **Platform agnostic**: Works with all major version control systems, languages, and deployment targets

**Best for:** Large enterprises with dedicated DevOps teams, organizations requiring complete control over their CI/CD infrastructure, and companies with complex compliance requirements or air-gapped environments.

**Limitations:** Steep learning curve with outdated UI, requires significant manual setup and ongoing maintenance, plugin compatibility can create stability issues, and self-hosted infrastructure demands constant attention from dedicated resources.

<InfoBox className="BodyStyle">

See the following guides:

- [Jenkins alternatives in 2026: CI/CD tools that won’t frustrate DevOps engineers](https://northflank.com/blog/jenkins-alternatives-2025)
- [Travis CI vs Jenkins: which CI/CD tool should you choose?](https://northflank.com/blog/travis-ci-vs-jenkins)

</InfoBox>

### 5. CircleCI

CircleCI is a cloud-based CI/CD platform designed for speed and simplicity with intelligent caching and parallel execution.

![circleci home page.png](https://assets.northflank.com/circleci_home_page_5010422a55.png)

**Key features**

- **Fast builds**: Optimized for speed with intelligent caching and resource allocation
- **Docker layer caching**: Cache Docker layers between builds to reduce build times
- **Parallel testing**: Automatically split tests across multiple containers
- **Insights dashboard**: Analytics showing build performance, success rates, and bottlenecks
- **Orbs**: Reusable configuration packages for common workflows and integrations
- **ARM support**: Native support for ARM architectures alongside x86

**Best for:** Teams prioritizing build speed and developer experience, startups and scale-ups needing quick setup without infrastructure management, and organizations using Docker-based workflows.

**Limitations:** Limited free tier with only 6,000 build minutes per month, pricing scales based on parallelism and compute resources, and requires upgrading to higher-tier plans for advanced features like test splitting and insights.

<InfoBox className="BodyStyle">

See the following guides:

- [CircleCI vs Jenkins: Which one fits your workflow?](https://northflank.com/blog/circleci-vs-jenkins)
- [CircleCI vs GitHub Actions: Which CI/CD tool is right for your team?](https://northflank.com/blog/circleci-vs-github-actions)

</InfoBox>

## Comparison table: Choosing the right Bitbucket Pipelines alternative

Look at how these platforms compare across key decision factors to find the best Bitbucket Pipelines alternative for your team's needs.

| Consideration | What to look for |
| --- | --- |
| **Deployment model** | Decide between managed SaaS, bring-your-own-cloud, or self-hosted. Northflank offers both SaaS and BYOC across AWS/GCP/Azure/Oracle/Civo. GitHub Actions and CircleCI are primarily SaaS with self-hosted runner options. GitLab provides cloud and self-managed. Jenkins requires complete self-hosting. |
| **Beyond CI/CD needs** | If you need production infrastructure, databases, and workload hosting alongside CI/CD, Northflank provides a complete platform. Traditional CI/CD tools require separate hosting solutions. |
| **Version control systems** | GitHub Actions only works with GitHub. GitLab CI/CD works primarily with GitLab. Northflank, Jenkins, and CircleCI support multiple VCS providers including GitHub, GitLab, and Bitbucket. |
| **Setup complexity** | GitHub Actions and CircleCI offer quickest setup with minimal configuration. GitLab requires moderate setup. Northflank provides simple setup with powerful features underneath. Jenkins demands significant expertise and time for initial configuration. |
| **Ecosystem requirements** | Choose GitLab if already using GitLab ecosystem. Choose GitHub Actions if locked into GitHub. Choose Northflank, Jenkins, or CircleCI for platform flexibility. |
| **Enterprise features** | For compliance, audit logging, SSO, and RBAC, consider Northflank (includes [audit logging](https://northflank.com/docs/v1/application/observe/audit-logs), [RBAC](https://northflank.com/docs/v1/application/secure/use-role-based-access-control), [SSO](https://northflank.com/docs/v1/application/secure/single-sign-on-multi-factor-authentication)), GitLab Premium, GitHub Enterprise, or self-hosted Jenkins with appropriate plugins. |
| **Security needs** | For running untrusted code or multi-tenant workloads, Northflank provides VM-level isolation with microVMs. Standard CI/CD tools use shared-kernel containers which may not provide sufficient isolation for security-sensitive workloads. |
| **Preview environments** | Northflank provides built-in ephemeral preview environments for full-stack testing. Other platforms require additional tooling or custom scripts to spin up temporary environments per PR. |

## Making the right choice

Choosing the right Bitbucket Pipelines alternative depends on your infrastructure needs and how much control you want over deployments.

Northflank unifies CI/CD and production infrastructure into a single platform: build, deploy, and run applications, databases, and jobs with release pipelines and per-second billing in our cloud or yours (including on-premises).

<InfoBox className="BodyStyle">

[Try Northflank](https://app.northflank.com/signup) to experience integrated CI/CD, release pipelines, and production infrastructure in one platform, or [review our documentation](https://northflank.com/docs) to learn how Northflank simplifies your deployment workflow.

For specific deployment questions or to discuss your CI/CD requirements, [talk to one of our expert engineers](https://cal.com/team/northflank/northflank-intro). See [full pricing details](https://northflank.com/pricing).

</InfoBox>]]>
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</item><item>
  <title>Best open source speech-to-text (STT) model in 2026 (with benchmarks)</title>
  <link>https://northflank.com/blog/best-open-source-speech-to-text-stt-model-in-2026-benchmarks</link>
  <pubDate>2026-01-07T01:45:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the best open source speech-to-text (STT) models in 2026. Benchmarks for WER, latency, languages, and deployment tips for Canary, Granite, Whisper and more.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/stt_3af98a0e9a.png" alt="Best open source speech-to-text (STT) model in 2026 (with benchmarks)" /># Best open source speech-to-text (STT) model in 2026? (with benchmarks)

<InfoBox className='BodyStyle'>

## **💡TL;DR**

The best open source speech-to-text (STT) models in 2026 are:

- **Canary Qwen 2.5B** for maximum English accuracy
- **IBM Granite Speech 3.3 8B** for enterprise-grade English ASR and translation
- **Whisper Large V3** for multilingual STT in 99+ languages
- **Whisper Large V3 Turbo or Distil-Whisper** when you need much faster throughput
- **Parakeet TDT** for ultra low-latency streaming
- **Moonshine** for edge and mobile devices

This guide compares WER accuracy, real-time factor (RTF), latency, languages, and deployment requirements, and shows how to run these models in production on [Northflank](https://northflank.com/product/gpu-paas).

</InfoBox>

Open source speech-to-text (STT) models now deliver accurate transcription that matches commercial services while offering deployment flexibility and cost advantages. 

For engineers building voice applications, meeting transcription tools, or accessibility features, selecting the right STT model determines project viability.

This guide evaluates leading open source STT models based on benchmark performance, latency characteristics, and deployment requirements.

## How do you evaluate open source speech-to-text (STT) model performance?

For open source speech-to-text (STT) models, the most important performance metrics are word error rate (WER), real-time factor (RTF), end-to-end latency, supported languages, and model size / VRAM usage. In practice, WER and latency determine user experience, while model size and VRAM determine how you can deploy the model in production.

**Word Error Rate (WER)**: The primary accuracy metric. Lower percentages indicate better transcription accuracy. A 5% WER means the model makes 1 error per 20 words on average.

**Real-Time Factor (RTFx)**: Measures throughput as audio duration divided by processing time. RTFx of 100 processes 100 seconds of audio per second of compute time. Higher numbers indicate faster processing.

**Latency**: Time from audio input to transcription output. Critical for real-time applications like voice assistants or live captioning.

**Language support**: Number and quality of supported languages beyond English.

**Model size**: Parameter count affects memory requirements and inference speed. Smaller models enable edge deployment.

## Benchmark comparison of open source STT models

| Model | WER (%) | RTFx | Parameters | Languages | VRAM | License |
| --- | --- | --- | --- | --- | --- | --- |
| Canary Qwen 2.5B | 5.63 | 418 | 2.5B | English | depends on precision and batch size (no official figure) | CC-BY-4.0 |
| Granite Speech 3.3 8B | 5.85 | not publicly specified | ≈9B | English ASR, multi-lang AST | high (≈9B parameters, requires high-end GPU) | Apache 2.0 |
| Whisper Large V3 | 7.4 | varies by runtime; often an order of magnitude faster than real-time on modern GPUs | ≈1.55B | 99+ | ~10GB | MIT |
| Whisper Large V3 Turbo | 7.75 | 216 | 809M | 99+ | ~6GB | MIT |
| Distil-Whisper Large V3 | close to Whisper Large V3 | ~5–6× Whisper Large V3 (implementation-dependent) | 756M | English | ~5GB | MIT |
| Parakeet TDT 1.1B | ~8.0 | >2,000 (among the fastest models on Open ASR) | 1.1B | English | ~4GB | CC-BY-4.0 |

**Benchmark comparison of leading open source speech-to-text (STT) models (WER, RTF, parameters, languages, license)**

All WER and speed numbers in this guide are taken from public leaderboards and vendor benchmarks as of late 2026. These values change over time, so always check the latest model card or leaderboard snapshot for current results.

<InfoBox className='BodyStyle'>

🔥 Deploy any AI/ML model on [Northflank](https://northflank.com/). Competitive on-demand GPU pricing.

</InfoBox>

## What are the best performing open source STT models?

### Canary Qwen 2.5B: Leading accuracy

![canary-qwen-2.5b.png](https://assets.northflank.com/canary_qwen_2_5b_cc4f10f647.png)

NVIDIA's Canary Qwen 2.5B currently tops the Hugging Face Open ASR Leaderboard with 5.63% WER. The model introduces a Speech-Augmented Language Model (SALM) architecture combining ASR with LLM capabilities.

The hybrid design pairs a FastConformer encoder optimized for speech recognition with an unmodified Qwen3-1.7B LLM decoder. This enables dual operation: pure transcription mode and intelligent analysis mode supporting summarization and question answering.

**Benchmark performance**:

- Word Error Rate: 5.63% (Open ASR Leaderboard average), 1.6% (LibriSpeech Clean), 3.1% (LibriSpeech Other)
- Real-Time Factor: 418x
- Training Data: 234,000 hours of English speech
- Parameter Count: 2.5 billion
- Noise Tolerance: 2.41% WER at 10 dB SNR

Training on diverse datasets including YouTube-Commons, YODAS2, LibriLight, and conversational audio provides robust performance across acoustic conditions. The model handles punctuation and capitalization automatically.

**Deployment notes**: Requires NVIDIA NeMo toolkit. Currently English-only. For audio longer than 10 seconds, use chunked inference with 10-second segments to prevent quality degradation.

### IBM Granite Speech 3.3 8B: Enterprise accuracy

IBM’s Granite Speech 3.3 8B is one of the top-ranked models on Hugging Face’s Open ASR leaderboard, with an average WER of about 5.85% across the benchmark suite.

The model achieves exceptional accuracy through a multi-stage training process: modality alignment of the Granite 3.3 8B Instruct model, followed by LoRA fine-tuning on diverse speech datasets. Training includes synthetic noise injection and random audio clipping to improve real-world robustness.

**Performance metrics**:

- Word Error Rate: 5.85% (Open ASR Leaderboard), 8.18% (Ionio clean speech benchmark)
- Real-Time Factor: – (not publicly specified)
- Languages: English, French, German, Spanish, with English-to-Japanese and English-to-Mandarin translation
- Parameter Count: ≈9B
- License: Apache 2.0

Independent benchmarks from Ionio show Granite achieving the lowest WER on clean audio while maintaining strong noise resilience with only 7.54% performance degradation from clean to noisy conditions.

### Whisper Large V3: Multilingual leader

OpenAI's Whisper Large V3 remains the gold standard for multilingual speech recognition. With 1.55 billion parameters and support for 99+ languages, the model handles diverse acoustic environments and rare vocabulary effectively.

**Key Features**:

- Language Support: 99+ languages with zero-shot capability
- Architecture: Transformer encoder-decoder with 32 decoder layers
- Training Data: 680,000 hours of multilingual web audio
- Mel-Spectrogram: 128 bins (increased from 80 in V2)
- Memory Requirements: ~10GB VRAM

The model performs automatic language identification, generates phrase-level timestamps, and handles punctuation/capitalization across supported languages. Multiple size variants (tiny through large) enable accuracy-speed trade-offs.

**Benchmark Results**: 7.4% WER average on mixed benchmarks. Performance varies by language based on training data distribution. Strongest on English, Spanish, French, German, and other high-resource languages.

### Whisper Large V3 Turbo: Optimized speed

Whisper Large V3 Turbo delivers 6x faster inference than Large V3 by reducing decoder layers from 32 to 4. Parameter count drops to 809 million while maintaining accuracy within 1-2% of the full model.

**Performance Characteristics**:

- WER: 7.75% on mixed benchmarks (comparable to Large V2)
- Inference Speed: 216x real-time factor on Groq infrastructure
- Parameter Count: 809 million
- Memory: ~6GB VRAM
- Languages: 99+ (same as Large V3)

The model was fine-tuned for two additional epochs on transcription data only. Translation performance declined because translation data was excluded from fine-tuning, but transcription quality matches Large V2 across most languages.

**When to use**: Applications prioritizing speed over maximum accuracy, especially for multilingual transcription where the 6x speedup justifies minor accuracy trade-offs.

## High-performance and efficient STT variants

### Distil-Whisper: Efficiency through distillation

Distil-Whisper achieves 6x faster inference than Whisper Large V3 while performing within 1% WER on out-of-distribution audio. Knowledge distillation creates a compact 756 million parameter model from Large V3's 1.54 billion.

**Technical approach**:

- Copies entire encoder from Whisper Large V3 (frozen during training)
- Uses only 2 decoder layers initialized from first and last layers of Whisper
- Trained on diverse pseudo-labeled dataset with WER filtering

**Performance benchmarks**:

- WER: Within 1% of Whisper Large V3 on short-form, within 1% on sequential long-form
- Benchmarks in the Distil-Whisper release show similar or slightly better performance than Whisper Large V3 on long-form, chunked audio, with fewer repeated phrases and lower insertion rates, while running several times faster.
- Speed: 6.3x faster than Large V3, 1.1x faster than Distil-Large-V2
- Noise Robustness: 1.3x fewer repeated 5-gram duplicates, 2.1% lower insertion error rate

**Limitation**: Currently English-only. For multilingual, use Whisper Turbo which applies similar optimization principles.

### Parakeet TDT: Ultra-fast processing

NVIDIA's Parakeet TDT models prioritize inference speed for real-time applications. The 1.1B parameter variant achieves RTFx near >2,000 (among the fastest models on Open ASR), as reported on the Hugging Face Open ASR leaderboard as of late 2026, processing audio dramatically faster than Whisper variants.

The RNN-Transducer architecture enables streaming recognition with minimal latency. Training on 65,000 hours of diverse English audio provides robust performance across conversational speech, audiobooks, and telephony.

**Speed vs accuracy trade-off**: Ranks 23rd in accuracy on Open ASR Leaderboard but processes audio 6.5x faster than Canary Qwen. CTC-based architecture optimizes for throughput over contextual understanding.

**Use cases**: Live captioning, real-time transcription, phone tree systems where speed determines user experience and minor accuracy trade-offs are acceptable.

## Foundation and alternative STT approaches

### Wav2Vec 2.0: Self-Supervised Learning

Meta's Wav2Vec 2.0 pioneered self-supervised speech recognition, demonstrating that models can achieve strong performance with minimal labeled data. The approach learns representations from unlabeled audio before fine-tuning on transcribed speech.

**Key Innovation**: Achieves 4.8/8.2 WER on LibriSpeech test sets using only 10 minutes of labeled data plus pretraining on 53,000 hours of unlabeled data. With full LibriSpeech training data, achieves 1.8/3.3 WER (clean/other).

**Architecture**: Encoder module processes raw audio into speech representations, fed to Transformer that captures sequence-level context. Contrastive learning during pretraining masks portions of speech representations and predicts them correctly.

**XLSR Variant**: Cross-lingual training on 53 languages enables representations shared across related languages. Achieves 72% relative phoneme error rate reduction on CommonVoice, 16% WER improvement on BABEL compared to monolingual training.

**Current Status**: Ionio benchmarks show 37.04% WER on clean speech and 54.69% WER on noisy speech, indicating Wav2Vec2 struggles in production environments compared to newer models. Best suited for research, fine-tuning on domain-specific tasks, or low-resource language development.

### Moonshine: Edge Deployment

Useful Sensors' Moonshine targets mobile and embedded deployment with models as small as 27 million parameters. Despite compact size, achieves competitive accuracy on resource-constrained devices.

The architecture enables offline transcription on smartphones, IoT devices, and edge hardware where cloud connectivity or privacy concerns preclude API usage. Moonshine variants outperform Whisper Tiny and Small despite significantly smaller model sizes.

**Deployment Scenarios**: On-device voice assistants, industrial equipment with offline requirements, privacy-sensitive applications, bandwidth-constrained environments.

## How do you deploy open source speech-to-text models on Northflank?

![CleanShot 2025-11-21 at 13.36.22@2x.png](https://assets.northflank.com/Clean_Shot_2025_11_21_at_13_36_22_2x_13dc910e87.png)

[Northflank](https://northflank.com/) provides production-ready, self-serve infrastructure for deploying open source STT models at scale with [GPUs](https://northflank.com/pricing), auto-scaling, and managed operations.

**Infrastructure benefits**:

- GPU instances (A100, H100, H200, B200) for accelerated inference
- Automatic scaling based on request volume and latency targets
- Container-based deployment with Docker and Kubernetes
- Environment management for model configurations and API keys
- Integrated monitoring, logging, and alerting
- Persistent storage for model weights and audio processing

**Deployment workflow**:

1. Package STT model in Docker container with dependencies
2. Configure GPU requirements based on model size
3. Set up auto-scaling policies for request patterns
4. Deploy to Northflank with managed infrastructure
5. Monitor performance and optimize resource allocation

For detailed deployment guidance, see our [guide on deploying open source text-to-speech models](https://northflank.com/blog/best-open-source-text-to-speech-models-and-how-to-run-them#how-to-run-anopen-source-texttospeech-model) which covers similar infrastructure patterns.

## Which open source STT model should you choose?

The best open source speech-to-text model for you depends on four constraints: language coverage, accuracy target, latency budget, and hardware limits. For English-only workloads with strict accuracy requirements, Canary Qwen 2.5B or IBM Granite Speech 3.3 8B are strong choices. For multilingual workloads, Whisper Large V3 or Whisper Large V3 Turbo are better. For low-latency streaming, Parakeet TDT or Distil-Whisper are more suitable. For edge devices, Moonshine provides the smallest footprint.

**For maximum accuracy (English)**:

- Primary: Canary Qwen 2.5B (5.63% WER)
- Alternative: IBM Granite Speech 3.3 8B (5.85% WER)

**For multilingual applications**:

- Best Quality: Whisper Large V3 (99+ languages)
- Best Speed: Whisper Large V3 Turbo (6x faster, 99+ languages)

**For speed-critical applications**:

- Real-Time: Parakeet TDT (2,728x RTFx)
- Balanced: Distil-Whisper (6x faster than Whisper, English-only)

**For resource-constrained deployment**:

- Edge/Mobile: Moonshine (27M parameters)
- Balanced: Distil-Whisper (756M parameters, low VRAM)

**For low-resource languages**:

- Foundation: Wav2Vec 2.0 XLSR (fine-tune on target language)
- Multilingual: Whisper models (strong zero-shot capability)

## Production considerations for STT systems

**Resource Planning**: Model size determines GPU requirements. Whisper Large V3 needs ~10GB VRAM, Turbo variants ~6GB, Canary Qwen ~8GB, Granite 8B requires substantial resources. Plan infrastructure accordingly.

**Batch vs Streaming**: Batch processing maximizes throughput for offline transcription. Streaming reduces latency for real-time applications but requires careful buffer management and affects accuracy.

**Audio preprocessing**: Models expect 16kHz mono audio. Implement resampling and stereo-to-mono conversion before inference. Poor audio quality compounds transcription errors.

**Error patterns**: Models occasionally hallucinate repeated phrases or produce incorrect homophones. Implement post-processing validation, especially for critical applications like medical or legal transcription.

**Latency optimization**: Use model quantization (int8, int4) to reduce memory and increase speed. FastWhisper implementation achieves 4x speedup with minimal accuracy loss through CTranslate2 optimization.

**Cost management**: Smaller models reduce compute costs but may require accuracy trade-offs. Evaluate error cost versus infrastructure expense for your specific use case.

## Open source speech-to-text vs commercial APIs

While open source models dominate accuracy leaderboards, commercial API services offer managed infrastructure, advanced features, and enterprise support.

### Leading Commercial Services

**Deepgram Nova-3**:Independent AA-WER benchmarks report Nova-3 around 18% WER on mixed real-world datasets, with sub-300 ms latency. Pricing is roughly $4.30 per 1,000 minutes for basic transcription as of mid-2026, but check Deepgram’s pricing page for current tiers.

**AssemblyAI Universal-2**: Highest accuracy among streaming commercial models at 14.5% WER. Supports 99+ languages with integrated speech intelligence (sentiment analysis, PII detection, speaker diarization). AssemblyAI’s Universal / Universal-2 models are priced on a per-hour basis, currently around $0.15/hour according to their pricing page, with earlier announcements citing $0.27/hour. Check AssemblyAI’s site for the latest rates.

**Google Cloud Chirp**: Best batch transcription accuracy at 11.6% WER. Supports 125+ languages with deep Google Cloud integration. Suitable for recorded content where streaming is not required.

**OpenAI GPT-4o-Transcribe**: 100+ language support with 320ms latency. Multimodal LLM approach handles complex audio conditions and code-switching effectively.

### Commercial vs open-source trade-offs

**Open source advantages**:

- Lower cost at scale (no per-minute fees)
- Complete data privacy (on-premises deployment or through Northflank’s [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud))
- Model customization and fine-tuning
- Canary Qwen 2.5B matches or beats many commercial APIs on independent leaderboards, although Google’s Chirp 2 still holds the top AA-WER score.

**When to choose commercial**: Rapid prototyping, low-volume applications, need for advanced features without custom development, regulated industries requiring vendor SLAs.

**When to choose open source**: High-volume applications, data privacy requirements, customization needs, cost optimization at scale, specific accuracy requirements met by latest models.

[**Start deploying open-source speech-to-text (STT) models on Northflank today**](https://app.northflank.com/signup)

Or [talk to an engineer](https://cal.com/team/northflank/northflank-intro?overlayCalendar=true) if you need help.]]>
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  <title>5 best Portainer alternatives for enterprise Kubernetes and Docker management</title>
  <link>https://northflank.com/blog/portainer-alternatives</link>
  <pubDate>2026-01-06T16:19:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for Portainer alternatives? Find the best Portainer alternative for Kubernetes and Docker management. Compare Portainer vs. Rancher, KubeSphere, OpenShift, and more.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/portainer_alternatives_c5f4bbbae5.png" alt="5 best Portainer alternatives for enterprise Kubernetes and Docker management" />> *“A UI abstracts Kubernetes." So why are you still manually handling deployments and infrastructure?*
> 

[Portainer](https://www.portainer.io/)  gives you a dashboard to manage Kubernetes and Docker, simplifying cluster operations with GitOps automation, ingress management templates, and deployment controls. However, teams still need to configure Helm charts, manage networking, and fine-tune deployments for advanced workflows.

The CNCF Annual Survey 2023 found that [81% of organizations](https://www.cncf.io/reports/cncf-annual-survey-2023/) now run Kubernetes in production, and as usage grows, so do the challenges of scaling and automation.

A visual interface helps with cluster visibility but does not remove the complexity of managing production workloads, multiple clusters, or automated deployments.

Some Portainer alternatives provide more automation and flexibility for Kubernetes operations:

1. [Northflank](https://northflank.com/): A cloud-native alternative that integrates CI/CD, infrastructure, and scaling.
2. [Rancher](https://www.rancher.com/): Full Kubernetes cluster management platform with multi-cluster support.
3. [Red Hat OpenShift](https://www.redhat.com/en/technologies/cloud-computing/openshift): Enterprise Kubernetes solution with built-in CI/CD and security.
4. [Lens](https://k8slens.dev/): Open-source Kubernetes management tool for cluster visualization.
5. [KubeSphere](https://kubesphere.io/): Kubernetes DevOps platform with automation and multi-tenancy.
6. [Docker Enterprise](https://www.docker.com/products/business/): Enterprise-grade container management with security and governance.

Before comparing them, let’s discuss why Portainer might not be enough.

## Why look at alternatives to Portainer?

First, what is Portainer? Portainer is container management software that provides a Docker GUI and a UI-based management tool to simplify Kubernetes and Docker container operations.

What is Portainer used for? It provides a graphical interface for managing containers, networks, and volumes, helping teams handle Kubernetes and Docker environments without relying solely on command-line tools.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img
    src="https://assets.northflank.com/1_Portainer_UI_898afc52cd.png"
    alt="Portainer UI"
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{
    marginTop: "8px",
    fontSize: "14px",
    color: "#555",
    textDecoration: "none",
    display: "block"
  }}>
   Portainer UI
  </figcaption>
</figure>

So, Portainer provides a UI for managing Kubernetes and Docker, but container orchestration is only part of the equation. Deploying, scaling, and automating workloads require more than a graphical interface.

If your team manages production workloads, multi-cluster environments, and infrastructure provisioning, they often need a system that integrates these operations into a single workflow. This is one of the many reasons why teams start looking at Portainer alternatives.

Let’s look at some of the key reasons in more detail.

### Portainer as a Kubernetes UI vs. a full Developer platform

Portainer provides a UI that simplifies Kubernetes interactions and includes automation features like GitOps for declarative deployments and ingress management templates. However, teams still need to configure Helm charts, networking, and deployments manually for advanced workflows. If your team requires deeper CI/CD integration, infrastructure provisioning, or full workload automation, Portainer may not cover everything needed.

A [developer platform](https://northflank.com/use-cases/self-service-developer-experience-for-kubernetes) should remove operational overhead, not surface it through a UI. If you are still maintaining YAML manifests, configuring RBAC (Role-Based Access Control), and managing infrastructure through separate tools, then Portainer functions as a management layer rather than an integrated deployment platform.


### You need integrated CI/CD for more than container management

Deployments start with source code, pipelines, and release automation, not just container orchestration.

Portainer provides deployment controls and integrates with existing CI/CD pipelines rather than replacing them. It includes GitOps automation, enabling declarative deployments from Git repositories. However, teams looking for a Kubernetes platform with built-in CI/CD and release automation may consider alternatives like OpenShift or Northflank.

If your team needs automated deployments, Portainer lacks built-in CI/CD, which is why your team might look for Kubernetes deployment tools that integrate pipelines, rollbacks, and release management.

A platform that combines CI/CD with container management reduces operational overhead and speeds up application delivery.

See [Scaling 30,000 deployments with 100% uptime. How Clock uses Northflank to simplify infrastructure.](https://northflank.com/blog/scaling-30-000-deployments-with-100-uptime-how-clock-uses-northflank-to-simplify-infrastructure)

### Infrastructure provisioning depends on team preferences
Kubernetes is just one part of an application’s infrastructure. Databases, message queues, storage, and cloud networking are required alongside workloads.

Portainer is designed as a lightweight, self-hosted Kubernetes management tool. While Portainer does not provide full infrastructure provisioning, teams can integrate it with Terraform, Pulumi, or cloud-native tools to provision infrastructure alongside Kubernetes management.

### Multi-Cluster Management: Portainer vs. Rancher vs. OpenShift

Portainer includes fleet-wide multi-cluster management with centralized governance, security policies, and GitOps-based deployments. Organizations managing multiple clusters across environments can use Portainer to enforce RBAC policies, apply network rules, and deploy workloads at scale.

However, for highly automated fleet-wide operations, alternatives like Rancher or OpenShift offer deeper automation, workload-specific autoscaling, and policy-based governance. These platforms are designed for large-scale Kubernetes environments where teams need to automate application deployments, enforce compliance, and centrally manage networking across thousands of clusters.

Teams that need centralized visibility and deployment automation may find Portainer sufficient, while those needing fully automated fleet-wide governance might consider alternatives.

### Limited observability and monitoring

Basic logs and metrics are helpful, but full observability requires more than built-in logs.

Portainer includes monitoring and logging features, but teams may still integrate additional observability tools like Prometheus and Grafana for deeper insights.

See this guide on “[Application Performance Monitoring on Northflank with Autometrics](https://northflank.com/guides/application-performance-monitoring-on-northflank-with-autometrics)”

*Now that we’ve covered where Portainer has gaps, the next step is choosing a Portainer alternative that provides a more integrated and automated approach to Kubernetes management.*

## Top 5 Portainer alternatives

You know the challenges by now. Managing Kubernetes is more than having a UI. It requires deployments, automation, infrastructure, and scaling without unnecessary friction.

When looking at different Kubernetes management tools and container orchestration tools, teams often compare Portainer, Rancher, OpenShift, and other alternatives to find the best fit for their workflows.

*What are the best alternatives if Portainer doesn’t cover everything your team needs?*

Let’s break down the best Portainer alternatives, how they approach Kubernetes management, and what makes them work in production.

### 1. Rancher - Managing Kubernetes across multiple environments

If your team runs Kubernetes across multiple clusters, cloud providers, or on-premise environments, [Rancher](https://www.rancher.com/) provides a centralized way to manage them all.

When comparing Portainer vs Rancher, the key difference is that Portainer provides a UI for managing Kubernetes and Docker, while Rancher is a full Kubernetes management platform designed for multi-cluster orchestration.

Rancher is one of the most comprehensive Kubernetes cluster management tools with built-in security policies, RBAC, and multi-cluster support.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img 
    src="https://assets.northflank.com/rancher_b6d321ca6b.png" 
    alt="Rancher’s Cluster Dashboard UI – A Portainer alternative for Kubernetes cluster management" 
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
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    Rancher’s Cluster Dashboard UI – Portainer alternative (Source: Medium)
  </figcaption>
</figure>

Among Portainer alternatives, Rancher stands out for teams running multiple Kubernetes clusters. With built-in security, monitoring, and multi-cluster management, it provides a more integrated approach.

In the Rancher vs. Portainer debate, Rancher is the better choice for organizations needing centralized control over Kubernetes at scale

### 2. Red Hat OpenShift - Kubernetes with built-in DevOps automation

If your team runs Kubernetes in production, you need more than a control plane. You need a system that automates deployments, enforces security, and integrates with your DevOps workflows. [Openshift](https://www.redhat.com/en/technologies/cloud-computing/openshift) does exactly that.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img
    src="https://assets.northflank.com/openshift_6974e66877.png"
    alt="OpenShift Web Console (Developer Perspective) – A Portainer alternative with built-in CI/CD"
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{
    marginTop: "8px",
    fontSize: "14px",
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    textDecoration: "none",
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  }}>
    OpenShift Web Console (Developer Perspective) – Portainer alternative (Source: Red Hat)
  </figcaption>
</figure>

It has built-in CI/CD pipelines, a secure container registry, and policy-driven security. Rather than managing separate tools for each step, OpenShift provides a platform that covers the entire application lifecycle. It simplifies Kubernetes operations across hybrid and multi-cloud environments.

### 3. Lens –  A Kubernetes dashboard for developers

You need a faster way to inspect your clusters, check logs, and troubleshoot workloads without running endless CLI commands. [Lens](https://k8slens.dev/) gives you a real-time view of your Kubernetes environment, showing metrics, events, and resource usage in one place.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img
    src="https://assets.northflank.com/lens_330b27282f.png"
    alt="Lens – A Kubernetes dashboard that serves as a Portainer alternative"
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{
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    textDecoration: "none",
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  }}>
   Lens – Portainer alternative (Source: k8slens.dev)
  </figcaption>
</figure>

It runs as a standalone application, making managing multiple clusters easy without switching between dashboards. It works across different Kubernetes distributions and gives you live monitoring without extra setup, so you can focus on running applications instead of searching for the right commands.

### 4. KubeSphere – Kubernetes with built-in DevOps and automation

Teams that prefer a self-hosted approach may use multiple tools for CI/CD, observability, and multi-cluster operations, while managed alternatives integrate these features into a single platform. [KubeSphere](https://kubesphere.io/) brings everything into one platform, giving your team a simpler way to deploy, monitor, and manage applications.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img
    src="https://assets.northflank.com/kubesphere_e0a37ed8fd.png"
    alt="KubeSphere – A Portainer alternative with integrated DevOps and multi-cluster management"
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{
    marginTop: "8px",
    fontSize: "14px",
    color: "#555",
    textDecoration: "none",
    display: "block"
  }}>
    KubeSphere – A Portainer alternative (Source: KubeSphere)
  </figcaption>
</figure>

It comes with built-in pipeline management, service mesh capabilities, and multi-tenant support, making it easier for infrastructure teams and developers to work together. KubeSphere provides a unified approach that helps teams run Kubernetes with less overhead and complexity.

### 5. Docker Enterprise – Kubernetes and container security at scale

If your team runs containerized applications in production, security, and compliance are not optional. [Docker Enterprise](https://www.docker.com/products/business/) provides built-in governance, RBAC, and authentication integration, making enforcing security policies across your infrastructure easier.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img
    src="https://assets.northflank.com/Screenshot_2025_02_28_at_21_31_43_1e6c3abe60.png"
    alt="Docker Enterprise – A secure option among Portainer alternatives"
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{
    marginTop: "8px",
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    color: "#555",
    textDecoration: "none",
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  }}>
    Docker Enterprise – A secure option among Portainer alternatives
  </figcaption>
</figure>

As an enterprise-grade Docker container management solution, it provides security policies, RBAC, and compliance features for large-scale deployments.

It also integrates with Kubernetes and Swarm orchestration, giving you more control over how workloads are deployed and managed.

Rather than layering extra security tools, Docker Enterprise helps containers meet compliance standards without adding unnecessary complexity.

## Choosing a system that does more than container management

Running containers is one thing. Running applications in production is another. Deployments, rollbacks, infrastructure, and scaling all need to work together, yet they often end up as disconnected processes. You’re still managing Helm charts, writing Terraform scripts, and setting up CI/CD pipelines separately just to get the code live.

A system built for developers should remove that extra work. Tools like [Northflank](https://northflank.com/), a platform that [integrates CI/CD](https://northflank.com/docs/v1/application/release/manage-ci-cd), infrastructure provisioning, and [scaling](https://northflank.com/features/scale), bring these workflows together so you’re not switching between different tools just to [deploy and manage applications](https://northflank.com/product/deployments).

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img
    src="https://assets.northflank.com/northflank_s_dashboard_16337b89f6.png"
    alt="Northflank's Dashboard"
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{
    marginTop: "8px",
    fontSize: "14px",
    color: "#555",
    textDecoration: "none",
    display: "block"
  }}>
   Northflank's Dashboard
  </figcaption>
</figure>

Pushing code, [provisioning databases](https://northflank.com/features/databases) and services, and [monitoring](https://northflank.com/docs/v1/application/observe/monitor-containers) everything happen in one place without the overhead of maintaining custom scripts and integrations.

Reducing the number of moving parts in your deployment process means less time spent on setup and maintenance. Managing infrastructure piece by piece slows teams down, while platforms that support the full application lifecycle help teams focus on shipping and running applications without the operational burden.

## Making the right choice for your team

If you only need a dashboard to check on containers, Portainer or Lens can handle that. When the focus shifts to building, deploying, provisioning, and monitoring applications, a system that integrates these workflows makes operations more straightforward. Managing everything separately adds unnecessary complexity, while a platform that brings it together helps teams focus on delivery.

If you’re looking into Portainer alternatives, the right choice depends on how your team operates Kubernetes. Some alternatives to portainer provide better multi-cluster management, others focus on CI/CD integration, and some, like [Northflank](https://northflank.com/), bring deployments, infrastructure, and monitoring into one workflow. 

You can use tools like Northflank to automate deployments, manage infrastructure, and monitor applications in one place. Try it for [free](https://app.northflank.com/signup) and see how it fits into your workflow.]]>
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</item><item>
  <title>Heroku Enterprise: capabilities, limitations, and alternatives</title>
  <link>https://northflank.com/blog/heroku-enterprise-capabilities-limitations-and-alternatives</link>
  <pubDate>2026-01-06T11:00:00.000Z</pubDate>
  <description>
    <![CDATA[Heroku Enterprise streamlines deployment but falls short on cost, flexibility, and scalability. Businesses outgrow its limits and turn to alternatives like Kubernetes or Northflank for better automation, control, and cloud efficiency.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Heroku_Enterprice_Blog_post_1_fd4d7d50c3.png" alt="Heroku Enterprise: capabilities, limitations, and alternatives" />Heroku pioneered the dream of workload deployment without complexity. For simple workloads, they nailed it. However, as businesses grew and demands evolved, many turned to Heroku Enterprise for more power and flexibility.

It promised a shortcut to innovation: "*Deploy faster, manage less, build more.*" It sounded like a dream—developers could focus on building great software without getting bogged down by infrastructure.

However, as businesses scale and technology advances, cloud platforms need to keep up. And for many, Heroku Enterprise is no longer keeping pace.

Imagine building a high-performance car, only to realize it struggles at top speeds when you need it most. That’s what many organizations are experiencing—what once felt like a rocket booster now feels more like a handbrake on innovation.

In this article, we’ll explore why companies are moving beyond Heroku Enterprise, breaking down its capabilities, limitations, and the alternatives that offer a better path forward.

## What is Heroku Enterprise?

Heroku Enterprise is a cloud platform-as-a-service (PaaS) designed specifically for enterprise organizations. Building on Heroku's developer-friendly foundation, it eliminates infrastructure management headaches so development teams can focus exclusively on creating business-critical applications.

The platform handles deployment, scaling, and operational concerns automatically, significantly reducing the need for dedicated DevOps resources. For organizations already using Salesforce products, Heroku Enterprise provides native integration capabilities that create a unified experience across customer data and custom applications.

## Why do organizations consider Heroku Enterprise?

Organizations are drawn to Heroku Enterprise for several compelling reasons:

- **Simplified Deployment and Management** – Heroku’s fully managed platform eliminates the complexities of infrastructure management, allowing teams to focus on building and scaling applications effortlessly.
- **Developer Productivity and Familiar Workflows** – With built-in support for Git-based deployments, CI/CD, and popular programming languages, Heroku enables developers to work efficiently using the tools they already know.
- **Reduced Operational Overhead** – Unlike self-managed platforms, Heroku takes care of infrastructure provisioning, scaling, and maintenance, freeing teams from time-consuming operational tasks.
- **Seamless Salesforce Integration** – Organizations using Salesforce benefit from Heroku’s deep integration, making it easier to build and extend applications that interact with customer data in real time.
- **Support for Legacy Applications** – Many businesses have existing applications running on Heroku. Upgrading to Heroku Enterprise ensures continued reliability, enhanced security, and better scalability without requiring a full migration.

## What are Heroku Enterprise's core offerings?

Heroku Enterprise provides several powerful capabilities designed specifically for large organizations with complex application needs. These core offerings work together to create a comprehensive cloud platform that balances developer productivity with enterprise requirements.

1. **Heroku private spaces** create isolated environments where your applications run separately from other customers' workloads. These dedicated runtime environments offer enhanced security through network isolation while maintaining the simplicity of the Heroku developer experience. Heroku private spaces allow you to deploy applications in specific geographic regions to meet compliance requirements or optimize performance for your users.
2. **Heroku Connect** serves as a crucial integration layer between Heroku applications and Salesforce data. This two-way synchronization service enables seamless data flow between your custom applications and your Salesforce org, allowing developers to build experiences that leverage and extend your existing customer data. This tight integration with the Salesforce ecosystem represents one of Heroku Enterprise's most distinctive advantages for organizations already invested in Salesforce technologies.
3. **Heroku Postgres** delivers enterprise-grade database services optimized for applications running on the platform. This fully managed database service handles provisioning, backups, monitoring, and scaling automatically. Enterprise customers receive access to larger database instances, point-in-time recovery capabilities, and advanced data protection features that support mission-critical workloads.
4. **Team collaboration** features enable organizations to implement governance at scale through fine-grained access controls, permission management, and team structures that mirror your organization. Administrators can organize developers into teams, control deployment permissions, and establish approval workflows that align with your governance requirements.
5. **Enterprise support and SLAs** provide guaranteed response times for critical issues, ensuring your business-critical applications receive prompt attention when problems arise. Enterprise customers gain access to dedicated support channels, technical account managers, and service level agreements designed for production applications where downtime translates directly to business impact.
6. **Single sign-on and identity management** capabilities allow seamless integration with your existing identity providers such as Okta, Azure AD, or Google Workspace. This integration streamlines user onboarding and off-boarding while enhancing security through centralized authentication and authorization policies that apply across your entire application portfolio.

Additional enterprise features include comprehensive audit logs for compliance reporting, extended application metrics and monitoring capabilities, and specialized runtime environments that support particular language requirements or regulatory constraints. Together, these core offerings create a platform that enables large organizations to gain the productivity benefits of platform-as-a-service while maintaining the control, visibility, and security required in enterprise environments.

## What are the limitations of Heroku Enterprise?

Heroku Enterprise presents several limitations that organizations should carefully consider when evaluating it for mission-critical workloads. These constraints can impact performance, reliability, cost management, and operational flexibility in ways that may prove challenging for enterprise requirements.

- **High costs with unpredictable scaling** – Heroku Enterprise pricing can quickly become a financial burden, with Private Spaces starting at $1.389/hr and Shield Private Spaces starting $4.167/hr (pricing as of March 2025). Costs escalate unpredictably with usage, making budgeting difficult for growing organizations, especially when compared to more flexible alternatives that offer enterprise features at a fraction of the price.
- **Limited cloud flexibility and vendor lock-in** – Unlike platforms that allow [Bring Your Own Cloud (BYOC)](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) models, Heroku locks organizations into its proprietary infrastructure. This prevents businesses from leveraging existing AWS, Azure, or Google Cloud credits and restricts their ability to optimize cloud strategies for cost, performance, or compliance.
- **Geographic and multi-region constraints** – Heroku offers fewer regional deployment options than major cloud providers, which can hinder performance optimization and compliance with data residency requirements. The platform’s lack of robust multi-region support also complicates disaster recovery and high-availability strategies for mission-critical applications.
- **Networking and infrastructure limitations** – Organizations with complex networking needs may find Heroku’s restricted VPC configurations and networking controls insufficient. This limits the ability to implement advanced security, interconnectivity, or hybrid cloud strategies.
- **Limited support for modern DevOps practices** – While Heroku simplifies deployment, it lacks strong integration with GitOps, Infrastructure as Code (IaC), and other DevOps automation tools. Enterprises that rely on declarative infrastructure and version-controlled deployments may find Heroku’s approach misaligned with their operational best practices.
- **Insufficient support for specialized workloads** – Heroku does not support custom hardware configurations such as GPUs, making it unsuitable for AI, machine learning, and other computationally intensive applications. Additionally, its backup, disaster recovery, and high-availability options may not meet the stringent requirements of organizations running mission-critical workloads.

## What are the Heroku Enterprise alternatives?

As organizations outgrow Heroku Enterprise, they face a crucial decision: stick with a platform that no longer meets their needs or explore a more scalable, cost-effective solution. This isn’t just about switching platforms—it’s about rethinking cloud infrastructure to support long-term innovation.

### Serverless and Managed Platforms

Many teams initially turn to serverless platforms or managed Kubernetes services, hoping for the same ease of deployment Heroku once promised. Services like AWS Elastic Beanstalk, Azure App Service, and Google Cloud Run offer infrastructure abstraction and quick deployment, making them attractive alternatives.

But these platforms come with their own challenges. Limited deployment patterns, restrictive runtime environments, and unpredictable costs can quickly make them feel as rigid as Heroku. What starts as an effort to simplify infrastructure can lead to new operational headaches.

### Building your own platform

Some organizations take the ambitious route of building their own cloud platform using Kubernetes. The appeal is obvious: total control over infrastructure, security, and scaling.

However, the reality is far more complex. Running Kubernetes at scale requires a dedicated platform engineering team to handle:

- Designing deployment workflows
- Managing security and networking
- Keeping up with evolving Kubernetes technologies

Beyond the initial setup, maintaining a Kubernetes-based platform demands continuous investment in tooling, documentation, and expertise. What seems like a path to flexibility can quickly become a full-time operational burden. A homegrown platform is a product in itself that requires maintenance, operational support, iteration, documentation, and so forth. DIY = higher costs and more overhead. 

### Modern cloud Platforms

A new wave of cloud platforms is bridging the gap between simplicity and control. Platforms like [Northflank](https://northflank.com) take the best of Heroku’s developer-friendly experience while offering the flexibility enterprises need.

These modern platforms provide:

- Kubernetes-based infrastructure without the operational complexity
- Seamless deployment workflows similar to Heroku
- Multi-cloud hosting with predictable pricing
- Advanced scaling and automation without requiring DevOps expertise

The biggest difference? Unlike legacy platforms that force organizations to adapt to rigid limitations, these modern alternatives are designed to evolve alongside your business and consolidate multiple tools into one post-commit pipeline.

## How to Choose the Right Alternative

Selecting a Heroku replacement depends on business needs:

- **If you want full control and are willing to manage infrastructure and build and maintain a developer platform:** Kubernetes-based solutions (EKS, AKS, GKE) are a good fit.
- **If you prefer a hands-off approach with some flexibility:** serverless platforms like Cloud Run and Azure App Service can be a great fit. However, these tools are designed to handle stateless workloads efficiently, meaning you'll need to find alternative solutions for persistent storage, long-running processes, and complex networking requirements.
- **If you need Heroku's simplicity with enterprise flexibility:** Platforms like [Northflank](https://northflank.com) provide a balance of ease and power.

## How Northank can help

Moving beyond Heroku Enterprise isn’t just about finding another cloud platform—it’s about choosing one that evolves with your business. [Northflank](https://northflank.com) eliminates the constraints of traditional PaaS by combining the ease of Heroku with the flexibility of Kubernetes, all while supporting modern DevOps workflows like GitOps and Infrastructure as Code (IaC).

With [Northflank](https://northflank.com), you can deploy directly within your own Virtual Private Cloud, leverage existing cloud credits, and scale workloads seamlessly across multiple regions. Whether you're running complex machine learning models, fine-tuning CI/CD pipelines, or optimizing infrastructure costs, Northflank provides the control and transparency Heroku lacks—without the operational burden of managing everything yourself.

Your cloud platform should empower innovation, not restrict it. If Heroku Enterprise is holding you back, it’s time for a solution built for the future. [**Get started with Northflank today and take control of your cloud strategy**](https://northflank.com).]]>
  </content:encoded>
</item><item>
  <title>Best AI deployment platforms in 2026</title>
  <link>https://northflank.com/blog/ai-deployment-platforms</link>
  <pubDate>2026-01-05T17:00:00.000Z</pubDate>
  <description>
    <![CDATA[AI deployment platforms compared: Northflank's full-stack approach vs Vertex AI, SageMaker, Hugging Face. GPU support, pricing, deployment workflows]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/ai_deployment_platforms_cb63b74693.png" alt="Best AI deployment platforms in 2026" />AI deployment platforms bridge the gap between trained models and production applications, handling infrastructure, scaling, and model serving so teams like yours can focus on building AI features.

This guide covers the technical features, GPU support, pricing models, and deployment workflows of 7 platforms to help you choose based on your workload requirements and team structure.

<InfoBox type="info">

## TL;DR: Best AI deployment platforms compared

See this quick list that compares the 7 AI deployment platforms this article covers:

1. **Northflank** – Full-stack AI deployment platform for production and enterprise use. Deploy both AI workloads (LLMs, models, agents, inference APIs) and non-AI workloads (databases, caching, job queues, APIs) together with Git-to-production workflows and transparent pricing.
    
    > You can deploy AI workloads (GPUs) on [Northflank's managed cloud](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-on-northflank-cloud) or in [your own cloud](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-in-your-own-cloud) (AWS, Azure, GCP, Oracle, Civo, CoreWeave, bare-metal) while keeping the same workflow. Northflank offers built-in [GPU support](https://northflank.com/gpu) for production-grade reliability without DevOps overhead.
    > 
    > 
    > [*Get started with the free sandbox tier](https://app.northflank.com/signup) or*  [request access to high-performance GPU clusters](https://northflank.com/request/gpu) *for AI workloads.*
    > 
2. **Google Vertex AI** – ML platform with AutoML and custom training. Best for teams already using GCP extensively. Complex pricing structure, GCP lock-in considerations.
3. **AWS SageMaker** – End-to-end ML platform with established tooling. Suitable for large AWS deployments. Steep learning curve, costs scale quickly with usage.
4. **Azure Machine Learning** – Enterprise-focused with Microsoft integration. Best for organizations with existing Azure infrastructure.
5. **Hugging Face Inference** – Pre-built models with simple API access. Ideal for prototyping and inference-only workloads. Limited customization for production requirements.
6. **Replicate** – One-line deployment for community models. Suitable for experimentation and production use cases with official models that avoid cold starts.
7. **Railway** – Developer-friendly platform for straightforward deployments. Limited GPU support and scaling capabilities make it unsuitable for demanding AI workloads.

</InfoBox>

## What is an AI deployment platform?

An AI deployment platform handles the infrastructure required to serve machine learning models in production, including model serving, scaling, monitoring, and API management.

Training a model in a Jupyter notebook is one thing. Serving it reliably at scale is another. Deployment platforms bridge this gap by providing inference optimization, load balancing, auto-scaling, version management, and monitoring capabilities that development environments don't include.

Our guide on [AI infrastructure and how to build your stack](https://northflank.com/blog/ai-infrastructure) covers how deployment infrastructure fits into the broader AI stack.

## What features should AI deployment platforms include?

Once you understand what AI deployment platforms do, the next question is what capabilities separate production-ready platforms from basic hosting solutions.

### GPU orchestration

Look for platforms that handle GPU scheduling and resource allocation automatically. Modern AI models (transformers, computer vision, generative models) require GPUs like A100s or H100s, and you don't want to manage Kubernetes clusters or GPU drivers yourself. The right platform, like Northflank, abstracts this complexity while giving you access to the compute you need.

### Auto-scaling infrastructure

Your traffic won't be constant, so you need both horizontal scaling (adding more instances) and vertical scaling (increasing instance size) based on actual demand. Platforms should scale automatically based on CPU/memory utilization or custom metrics like request queue depth, preventing both downtime during spikes and wasted spend during low traffic.

### CI/CD integration

Deployment friction kills velocity. Look for Git-push deployments with automatic Docker builds and instant rollbacks. This means you can ship model improvements quickly, and if something breaks, you can revert to the previous version immediately without complicated procedures.

### Observability and monitoring

You need visibility into what's happening with your models in production. Real-time metrics (latency percentiles, throughput, error rates), structured logs, and distributed tracing let you debug issues fast and understand how your system performs under real-world conditions.

### Multi-service orchestration

AI applications aren't just model endpoints. You need vector databases for RAG systems, Redis for caching, PostgreSQL for application data, and job queues for async processing. Platforms that let you deploy all these services together with private networking eliminate integration headaches and reduce operational complexity.

<InfoBox type="info">

Platforms like Northflank provide these capabilities out of the box: GPU orchestration without Kubernetes complexity, Git-push deployments with automatic rollbacks, and the ability to deploy models alongside vector databases, caching, and APIs on a unified platform.

[Request GPU access](https://northflank.com/request/gpu) for high-performance clusters or see our [comparison of GPU hosting platforms](https://northflank.com/blog/top-gpu-hosting-platforms-for-ai) for infrastructure considerations.

</InfoBox>

## What are the best AI deployment platforms in 2026?

These seven platforms represent different approaches to AI deployment, from full-stack solutions to specialized inference services.

### 1. Northflank - **Full-stack AI deployment platform**

Northflank is a full-stack AI deployment platform for production and enterprise environments. Deploy AI workloads (LLMs, models, agents, inference APIs) and non-AI workloads (databases, caching, job queues, APIs) together on one unified platform with built-in GPU support, without managing Kubernetes or multiple platforms.

![northflank-main-homepage.png](https://assets.northflank.com/northflank_main_homepage_47784b77df.png)

**What Northflank offers:**

- **Native GPU support**: Access high-performance GPUs, including B200, H200, H100, A100, L40S, A10, V100, and other [NVIDIA accelerators](https://northflank.com/gpu) for both training jobs and persistent model serving. Transparent per-hour pricing with no unexpected costs, so you know exactly what you're paying for compute.
- **Multi-cloud flexibility:** Deploy on Northflank-managed infrastructure or your own cloud accounts (AWS, GCP, Azure, Oracle, Civo, CoreWeave, bare-metal). Same platform and workflows regardless of where your infrastructure runs. ([Deploy GPUs on Northflank's managed cloud](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-on-northflank-cloud) or [deploy GPUs in your own cloud](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-in-your-own-cloud))
- **One-click AI stack templates:** Deploy complete AI applications instantly with pre-configured stacks including LLMs (Qwen, DeepSeek, Ollama), AI tools (Open WebUI, Langflow, n8n), and infrastructure (vector databases, observability). [Browse AI stack templates](https://northflank.com/stacks?category=ai).
- **Transparent pricing:** Per-resource costs with clear pricing for compute, memory, storage, and networking. No hidden fees. Track spending per service and environment. (See the [pricing calculator](https://northflank.com/pricing) to estimate costs upfront).
- **Enterprise-ready infrastructure:** Deploy on your own cloud accounts with full control over data residency and compliance requirements, or use Northflank's managed infrastructure with transparent SLAs.
- **Git-to-production workflow:** Push to your repository, and Northflank handles the build and deployment. Works with Dockerfiles or detects your stack automatically. Most deployments go live in under 10 minutes.
- **Instant rollback capability:** Every deployment is versioned. Roll back to any previous release with one click to revert your pipeline stage to its earlier state. Zero downtime. (See [**Roll back a release**](https://northflank.com/docs/v1/application/release/run-and-manage-releases#roll-back-a-release))
- **Auto-scaling:** Scales horizontally by adding instances automatically based on CPU, memory, RPS, or custom metrics. Scales vertically when you upgrade compute plans for more CPU and memory per instance. (See [Scale on Northflank](https://northflank.com/docs/v1/application/scale/scale-on-northflank))
- **Multi-service orchestration:** Deploy your model alongside databases, caching layers (Redis), job queues, and APIs. Services can communicate over [private networking](https://northflank.com/docs/v1/application/network/networking-on-northflank).
- **Infrastructure as code:** Template-based infrastructure management with GitOps support. Define your entire stack (integrations, resources, deployments) in templates that can be version-controlled and reproduced across environments via UI or API. (See [Infrastructure as code on Northflank](https://northflank.com/docs/v1/application/infrastructure-as-code/infrastructure-as-code))
- **Built-in observability:** Real-time logs and metrics for all deployments, health monitoring, audit logs, and alerting (Slack, Discord, Teams, webhooks). Integrate with external log aggregators when needed. (See [Observability on Northflank](https://northflank.com/docs/v1/application/observe/observability-on-northflank))
- **Preview environments:** Automatically create isolated environments for each pull request or branch. Test changes before production without affecting your live system.

<InfoBox type="info">

Learn more in our [GPU documentation](https://northflank.com/docs/v1/application/gpu-workloads/gpus-on-northflank) or [request access to high-performance GPU clusters](https://northflank.com/request/gpu).

</InfoBox>

<InfoBox type="warning">

**Pricing**

**Sandbox tier**

- Free resources to test workloads
- 2 free services, 2 free databases, 2 free cron jobs
- Always-on compute with no sleeping

**Pay-as-you-go**

- Per-second billing for compute (CPU and GPU), memory, and storage
- No seat-based pricing or commitments
- Deploy on Northflank's managed cloud (6+ regions) or bring your own cloud (600+ BYOC regions across AWS, GCP, Azure, Civo)
- GPU pricing: NVIDIA A100 40GB at $1.42/hour, A100 80GB at $1.76/hour, H100 at $2.74/hour, H200 at $3.14/hour, B200 at $5.87/hour
- Bulk discounts available for larger commitments

**Enterprise**

- Custom requirements with SLAs and dedicated support
- Invoice-based billing with volume discounts
- Hybrid cloud deployment across AWS, GCP, Azure
- Run in your own VPC with managed control plane
- Secure runtime and on-prem deployments
- Audit logs, Global back-ups and HA/DR
- 24/7 support and FDE onboarding

Use the [Northflank pricing calculator](https://northflank.com/pricing) for exact cost estimates based on your specific requirements, and see the pricing page for more details

</InfoBox>

**Best suited for:**
Teams deploying production AI applications requiring more than model serving, enterprises needing compliant infrastructure without sacrificing deployment speed, and organizations pursuing multi-cloud strategies.

**Deployment types supported:**
Real-time inference APIs, batch processing jobs, background workers, scheduled tasks, e.t.c. For workload-specific guidance, see our breakdown of [5 types of AI workloads and how to deploy them](https://northflank.com/blog/ai-workloads).

<InfoBox type="info">

**Related resources:**

If you're getting started with AI deployments on Northflank, these resources can help:

**Stack templates** (one-click deployments):

- [Deploy Qwen3 models with vLLM](https://northflank.com/stacks/deploy-qwen3-30b-instruct-32k)
- [Deploy DeepSeek R1-70B](https://northflank.com/stacks/deploy-deepseek-r1-70b-aws)
- [Deploy Ollama for local LLMs](https://northflank.com/stacks/deploy-ollama)
- [Deploy Open WebUI for LLM interfaces](https://northflank.com/stacks/deploy-openwebui)
- [Deploy Langflow for visual AI workflows](https://northflank.com/stacks/deploy-langflow)
- [Browse all AI stack templates](https://northflank.com/stacks?category=ai)

**Guides and documentation:**

- [Best GPUs for AI](https://northflank.com/blog/best-gpu-for-ai)
- [Cheapest cloud GPU providers](https://northflank.com/blog/cheapest-cloud-gpu-providers)
- [Running AI on cloud GPUs](https://northflank.com/blog/running-ai-on-cloud-gpus)
- [Top GPU hosting platforms for AI](https://northflank.com/blog/top-gpu-hosting-platforms-for-ai)
- [AI infrastructure guide](https://northflank.com/blog/ai-infrastructure)
- [AI workloads deployment guide](https://northflank.com/blog/ai-workloads)

</InfoBox>

### 2. Google Vertex AI

Google Vertex AI provides an integrated ML platform for teams operating within the GCP ecosystem, handling model training, deployment, and monitoring through GCP-native services.



**Capabilities of Vertex AI:**

- **AutoML:** Automated model training for classification, regression, and forecasting tasks
- **Vertex AI Workbench:** Development environment integrated with GCP services
- **Feature Store:** Centralized feature management and serving at scale
- **Online prediction endpoints:** Auto-scaling inference endpoints with managed infrastructure
- **GCP integration:** Native connections to BigQuery, Cloud Storage, Dataflow, and other GCP services

**Considerations:**
Works best for teams already invested in GCP with existing data in BigQuery or Cloud Storage. Pricing model includes compute, storage, API calls, and predictions. Vertex AI-specific tooling and concepts require time to learn effectively.

**Best suited for:**
Teams with significant GCP investment, organizations needing managed AutoML capabilities, projects already using GCP data services.

### 3. AWS SageMaker

AWS SageMaker offers end-to-end ML platform capabilities for organizations operating within AWS infrastructure, from experimentation to production deployment.

**Capabilities of SageMaker:**

- **SageMaker Studio:** Integrated development environment for ML workflows with team collaboration
- **Built-in algorithms:** Pre-configured algorithms and pre-trained model zoo
- **Model registry:** Versioning and lineage tracking for deployed models
- **Real-time endpoints:** Auto-scaling inference with managed hosting
- **AWS integration:** Deep connections to S3, Lambda, Step Functions, and EventBridge

**Considerations:**
Platform includes many sub-services that take time to understand and configure properly. Cost structure across instance hours, data transfer, and endpoint hosting requires careful planning. Works best for organizations already operating within AWS.

**Best suited for:**
Large enterprises invested in AWS infrastructure, teams with dedicated ML platform engineers, organizations requiring deep AWS service integration.

### 4. Azure Machine Learning

Azure Machine Learning provides ML capabilities for organizations operating within Microsoft and Azure ecosystems.

**Capabilities of Azure ML:**

- **Azure ML Studio:** Browser-based environment for model development and deployment
- **Automated ML:** Automated model selection and hyperparameter tuning
- **MLOps features:** Pipelines, model registry, and monitoring with Azure DevOps integration
- **Real-time endpoints:** Managed inference endpoints with auto-scaling
- **Microsoft integration:** Native connections to Power BI, Azure Synapse, and other Microsoft tools

**Considerations:**
Works best when your data and infrastructure already exist within Azure. Platform includes many Azure-specific concepts and abstractions. Pricing spans compute, storage, and inference costs.

**Best suited for:**
Organizations already using Azure infrastructure, teams requiring Microsoft tool integration, and enterprises with existing Azure investments.

### 5. Hugging Face Inference

Hugging Face Inference specializes in deploying transformer models and other pre-trained architectures, focusing specifically on NLP and generative AI workloads.

**Capabilities of Hugging Face:**

- **Model library:** Access to thousands of pre-trained models, including transformers and diffusion models
- **Inference API:** Single-line deployment for supported models from the Hugging Face Hub
- **Serverless inference:** Automatic scaling based on request volume
- **Custom models:** Support for deploying proprietary models in Hugging Face format
- **GPU acceleration:** Access to GPUs for large model inference

**Considerations:**
Focused specifically on model inference without infrastructure for building complete applications. Teams need separate solutions for APIs, databases, caching, and business logic. Custom models require conversion to Hugging Face format.

**Best suited for:**
LLM prototyping, inference-only requirements, teams already using Hugging Face models and workflows.

### 6. Replicate

Replicate focuses on making community-contributed models accessible through simple APIs, prioritizing ease of use for experimentation.

**Capabilities of Replicate:**

- **Community models:** Deploy any public model from Replicate's library with minimal configuration
- **API access:** Simple REST API for running predictions
- **Automatic scaling:** Transparent GPU allocation and scaling
- **Custom deployment:** Package and deploy your own models following Replicate's format

**Considerations:**
Suitable for experimentation and production use cases with official models that avoid cold starts. Limited control over infrastructure and performance optimization for community models.

**Best suited for:**
Prototyping, demonstrations, exploratory projects, and evaluating different models before committing to deployment infrastructure.

### 7. Railway

Railway provides straightforward deployment for web applications, with AI model serving as one of many supported workload types rather than the primary focus.

**Capabilities of Railway:**

- **Git deployment:** Simple workflow directly from Git repositories with automatic builds
- **Multi-framework:** Support for multiple languages and frameworks
- **Basic scaling:** Auto-scaling for web services based on traffic
- **Workload types:** Web services, background workers, and scheduled jobs
- **Managed databases:** PostgreSQL, MySQL, Redis, and MongoDB hosting

**Considerations:**
Platform doesn't include native GPU support, which limits capabilities for modern AI workloads. Designed primarily for web applications rather than ML-specific infrastructure.

**Best suited for:**
Simple applications with minimal AI requirements, side projects, and applications where AI features are supplementary to core functionality.

## How do you choose the right AI deployment platform?

Selecting a platform requires matching its capabilities to your workload requirements, team structure, and budget constraints.

| Platform | Best for workload | GPU support | Full-stack deployment | Best for team type | Deployment speed | Pricing model | Key advantage |
| --- | --- | --- | --- | --- | --- | --- | --- |
| **Northflank** | Both AI and non-AI workloads - production AI applications (LLM serving, RAG systems, inference APIs) plus databases, caching, and job queues | Native support (B200, H200, H100, A100, L40S, A10, V100, and [more](https://northflank.com/gpu)) | Yes - models, APIs, databases, vector DBs, caching, queues | Startups to enterprises, platform teams, ML engineers needing full infrastructure | Fast (Git-push to production) | Transparent per-resource pricing, no hidden fees | Deploy complete AI stack (both AI and non-AI workloads) on one platform, multi-cloud flexibility |
| **Google Vertex AI** | Teams with data in BigQuery, GCP-native ML workflows | Yes (GCP GPUs) | Limited - focused on ML lifecycle | Large teams with GCP expertise | Moderate (requires GCP setup) | Complex (compute + storage + API calls + predictions) | Deep GCP integration, AutoML |
| **AWS SageMaker** | Large-scale ML with AWS integration | Yes (AWS GPUs) | Limited - focused on ML lifecycle | Enterprise teams with AWS infrastructure | Moderate (many sub-services to configure) | Complex (instance hours + data transfer + endpoints) | Comprehensive AWS integration |
| **Azure Machine Learning** | Microsoft-heavy organizations | Yes (Azure GPUs) | Limited - focused on ML lifecycle | Enterprise teams using Microsoft tools | Moderate (Azure-specific concepts) | Complex (compute + storage + inference) | Microsoft ecosystem integration |
| **Hugging Face Inference** | Pre-trained model deployment, LLM prototyping | Yes (managed GPUs) | No - inference only | Individual developers, researchers, small teams | Very fast (one-line deployment) | Pay-per-inference or subscription | Massive model library, simple API |
| **Replicate** | Experimentation, prototyping, community models | Yes (managed GPUs) | No - model inference only | Developers, researchers, prototyping teams | Very fast (community models) | Pay-per-prediction | Easy access to community models |
| **Railway** | Simple web apps with minimal AI | No native GPU support | Yes - general web services | Small teams, side projects | Fast (Git deployment) | Simple per-resource pricing | Easy to use for web apps |

**Quick selection guide:**

- **Need GPUs + full application stack?** → Northflank
- **Already deep in GCP?** → Vertex AI
- **Already deep in AWS?** → SageMaker
- **Already deep in Azure?** → Azure ML
- **Just need to deploy a Hugging Face model?** → Hugging Face Inference
- **Prototyping with community models?** → Replicate
- **Simple web app without AI compute?** → Railway

Different workload types have distinct infrastructure needs. Our guide on [AI workloads and deployment strategies](https://northflank.com/blog/ai-workloads) covers the technical requirements for each category.

## How does Northflank simplify AI deployment?

Production AI applications need more than just model serving. Northflank lets you deploy your complete stack: models, inference APIs, vector databases, caching, and job queues on one platform with Git-push workflows.

You can access GPUs (B200, H200, H100, A100, and [more](https://northflank.com/gpu)) without managing Kubernetes or drivers. Scale automatically based on traffic, roll back instantly when needed, and track spending per service with transparent pricing.

Start on Northflank's managed cloud or deploy to your own cloud accounts (AWS, GCP, Azure, Oracle, Civo, CoreWeave, bare-metal) while keeping the same workflows.

<InfoBox type="info">

[Get started with the free sandbox tier](https://app.northflank.com/signup) or [request GPU access](https://northflank.com/request/gpu) for production workloads or [book a demo](https://cal.com/team/northflank/northflank-intro) to discuss enterprise requirements with an engineer

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>PostgreSQL vector search guide: Everything you need to know about pgvector</title>
  <link>https://northflank.com/blog/postgresql-vector-search-guide-with-pgvector</link>
  <pubDate>2026-01-05T16:39:00.000Z</pubDate>
  <description>
    <![CDATA[pgvector enables vector search in PostgreSQL, allowing semantic search, AI recommendations, and similarity queries without extra infrastructure. Learn how to deploy and use it on Northflank to enhance your database capabilities.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_pgvector_2_b6982f42ca.png" alt="PostgreSQL vector search guide: Everything you need to know about pgvector" />> *Just want to get pgvector running? Follow our quick guide to [deploy pgvector in 1 minute.](https://northflank.com/blog/how-to-deploy-pgvector)*
> 

You're probably already using Postgres as your database for everything from user profiles to transaction histories. But what if that same database could understand the meaning behind your data, not just store it?

Imagine searching your database by meaning and similarity, not just exact matches or keywords. This is the promise of vector search technology, and pgvector brings this capability directly to your existing PostgreSQL database. In today's world of exponentially growing unstructured data, traditional search methods fall short. Vector search lets you find semantically similar content, power recommendation engines, and build AI-enhanced applications without overhauling your infrastructure.

In this article, you will learn everything you need to know about pgvector, how it compares to popular vector databases, how to deploy PostgreSQL with pgvector as an addon on Northflank in less than 5 minutes, and how to test your vector database.

## What is pgvector?

[pgvector](https://github.com/pgvector/pgvector) is an extension for PostgreSQL that adds vector similarity search capabilities to this widely-used relational database. It allows you to store embedding vectors (numerical representations of data) alongside your traditional data and perform efficient similarity searches. These vectors can represent virtually anything—text documents, images, audio, user behavior patterns, or any other data that can be meaningfully embedded into vector space.

The beauty of pgvector lies in its seamless integration with your existing PostgreSQL infrastructure. Rather than introducing a completely new database system, pgvector extends what you already have, allowing you to leverage PostgreSQL's robust features like transactions, backups, and security while gaining powerful vector search capabilities.

## The importance of Vector Databases

Vector databases bridge the gap between traditional data storage and how humans naturally think about information. While conventional databases excel at exact matches ("find customer #12345"), they struggle with meaning-based queries ("find articles similar to this one").

Vector databases solve this by converting data into mathematical vectors where similar concepts exist close together in multi-dimensional space. This enables powerful "nearest neighbor" searches based on semantic similarity rather than exact matches.

This capability transforms multiple industries - from e-commerce product recommendations and content discovery systems to intelligent customer support and advanced anomaly detection in security. By understanding the meaning behind data, vector databases enable applications to find relevant information even when there's no exact keyword match.

## **How do Vector Databases work?**

Think of vector space like a cosmic map where words or concepts are stars. Similar concepts (like "happy" and "joyful") appear close together, while unrelated ones ("happy" and "taxation") are far apart. When searching, we're essentially asking "what stars are closest to this one?" rather than looking for exact matches.

Vector databases operate through a straightforward four-step process:

1. **Embedding generation**: An AI model converts raw data (text, images, etc.) into numerical vectors - essentially transforming content into points in mathematical space where similar items cluster together.
2. **Vector storage**: These numerical representations are stored in specialized formats optimized for rapid similarity searches rather than exact matches.
3. **Similarity calculation**: When searching, your query gets converted to a vector too. The database finds matches by measuring distances between vectors using methods like cosine similarity (angle between vectors) or Euclidean distance (straight-line distance).
4. **Optimized search algorithms**: To handle millions of vectors efficiently, pgvector uses approximate nearest neighbor (ANN) algorithms like HNSW and IVF, letting you balance between search speed and precision for your specific needs.

## **Are managed Vector Databases overhyped?**

The vector database space has seen an explosion of funding, with companies like Pinecone, Weaviate, and Chroma raising **hundreds of millions of dollars** to build dedicated vector search engines. But do you really need a separate database just for vector search?

If you’re already using PostgreSQL, **pgvector brings powerful vector search directly into your existing database**—without extra infrastructure, proprietary lock-in, or vendor pricing models. Many "AI-native" vector databases market themselves as groundbreaking, but under the hood, they’re often just **specialized indexes with a sleek API**.

Before you adopt a vector database, it’s worth understanding the pros and cons of the various options so you can pick the tool best suited to your particular use case. If you plan to step out of your existing PostgreSQL implementation, you want to be sure that updating your architecture warrants the benefits.

In the next section, we’ll compare pgvector with these managed alternatives and see whether the hype is justified.

## Comparing pgvector to other Vector Databases

### 1. pgvector vs Weaviate

Weaviate functions as a complete knowledge graph with vector capabilities built from the ground up, offering a different approach than pgvector's extension model.

**Weaviate advantages:**

- Purpose-built vector search engine with specialized optimizations for vector operations. For example, Weaviate can search through 10 million product embeddings in milliseconds, while pgvector might take seconds for the same operation.
- GraphQL API that simplifies complex vector-related queries
- Built-in classification and data enrichment capabilities

**pgvector advantages:**

- Leverages your existing PostgreSQL infrastructure rather than introducing a new technology
- Familiar SQL interface for teams with PostgreSQL experience
- Benefits from PostgreSQL's mature ecosystem and decades of development

### 2. pgvector vs Pinecone

As a fully managed vector database service, Pinecone focuses exclusively on vector operations.

**Pinecone advantages:**

- Optimized specifically for massive-scale vector workloads
- Managed service reduces operational complexity and maintenance
- Specialized performance for high query-per-second (QPS) requirements

**pgvector advantages:**

- Keeps vector data alongside traditional data, eliminating synchronization challenges
- More cost-effective for many use cases compared to specialized service pricing
- Provides full relational database capabilities in addition to vector operations

### 3. pgvector vs Chroma

This open-source embedding database targets AI application development with a streamlined approach.

**Chroma advantages:**

- Simplified API designed specifically for AI/ML workflows
- Strong focus on document retrieval use cases
- Lightweight implementation for certain applications

**pgvector advantages:**

- Battle-tested PostgreSQL foundation provides enterprise-grade reliability
- Richer query capabilities through full SQL integration
- Larger community and support ecosystem

## When to choose pgvector

Still wondering if pgvector is right for your needs? Here's when it makes the most sense as your vector database solution:

1. **When your data is already in PostgreSQL**: If your application already relies on PostgreSQL, introducing pgvector is a natural extension rather than adopting an entirely new database technology.
2. **For hybrid search needs**: When you need both traditional queries and vector similarity search in the same application, pgvector allows you to combine these naturally in SQL.
3. **When operational simplicity matters**: Managing a single database system rather than multiple specialized systems reduces operational complexity and costs.
4. **For moderate-scale vector operations**: For applications with thousands to millions of vectors, pgvector offers excellent performance without the need for specialized infrastructure.
5. **When SQL integration is valuable**: If your application benefits from combining vector searches with complex SQL queries, joins, and PostgreSQL's rich feature set.

## When not to choose pgvector

While pgvector offers many advantages, it isn't the ideal solution for every use case. Consider alternatives when:

1. **You're starting from scratch**: Without existing PostgreSQL infrastructure or expertise, the advantages of integrating with your current database diminish, potentially making specialized vector databases more attractive.
2. **You need a highly scalable vector database**: For applications requiring billions of vectors or thousands of queries per second, dedicated vector databases like Pinecone or Weaviate may deliver superior performance at scale.
3. **Vector search is your primary workload**: When vector similarity is your application's core functionality rather than an additional feature, purpose-built vector databases offer specialized optimizations that may deliver better results.
4. **Real-time performance is critical**: For applications where consistent millisecond-level response times are essential, dedicated vector databases with hardware-optimized indexing might be necessary.

## How to create a PostgreSQL database on Northflank

To create a PostgreSQL database on [Northflank](https://northflank.com/), go to your dashboard, create a new project with any name of your choice, select a region of your choice, and click **Create project**.

 ![](https://assets.northflank.com/pawelzmarlak_2025_02_25_T05_10_41_562_Z_eb34f06b42.png) 

After successfully creating your project, go to the **Addons** tab and click **Create Addon**. Select **PostgreSQL**, then enter the required information, such as the name and version, based on your needs. Finally, click the **Create Addon** button.

 ![](https://assets.northflank.com/pawelzmarlak_2025_02_25_T05_09_02_071_Z_8fe115e90d.png) 

*Note: Instead of making your database publicly accessible, we recommend using the [Northflank CLI's forward command](https://northflank.com/docs/v1/application/databases-and-persistence/access-a-database#access-a-database-locally) to securely access your database locally. Publicly exposing your database increases security risks. If you must enable internet access to the database (not recommended), ensure that TLS is enabled.*

## How to connect to your Postgres database locally

You can forward your Postgres database for local access using the [Northflank CLI](https://northflank.com/docs/v1/api/use-the-cli).

- To forward a specific database:`sudo northflank forward addon --projectId [project-name] --addonId [addon-name]`
- To forward all ports in a project:`sudo northflank forward all --projectId [project-name]`

## How to install pgvector in Postgres

Once you've created your PostgreSQL database on [Northflank](https://northflank.com/) and you have made it accessible locally, you're ready to enable [pgvector](https://github.com/pgvector/pgvector) and start working with vector embeddings. First, connect to your database using your database connection string:

```bash
psql "$DATABASE_URL"

```

Next, you'll need to enable the vector extension. This only needs to be done once per database:

```bash
CREATE EXTENSION vector;
```

## How to use pgvector

Now that pgvector is enabled, let's create a simple table with a vector column. This example uses 3-dimensional vectors, but in real applications, you might use vectors with hundreds or thousands of dimensions to represent complex data:

```bash
CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3));
```

Let's insert some sample vector data. Notice how vectors are represented as simple arrays:

```bash
INSERT INTO items (embedding) VALUES ('[1,2,3]'), ('[4,5,6]');
```

Get the nearest neighbors by L2 distance

Finally, let's perform a basic vector similarity search using the L2 distance operator (`<->`). This query finds the vectors closest to `[3,1,2]`:

```bash
SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 5;
```

This simple example demonstrates the fundamental operation of vector similarity search - finding items most similar to your query vector. In real applications, these vectors would represent embeddings of text, images, or other data generated by machine learning models, enabling semantic search across your content.

The beauty of pgvector is that you can easily combine these vector searches with traditional SQL queries, joining vector similarity results with other tables in your database to create rich, context-aware search experiences.

## Conclusion

You've now seen how pgvector turns your existing PostgreSQL database into a powerful vector search engine. This extension lets you keep your traditional data and vector embeddings in one place, combining familiar SQL capabilities with modern semantic search.

Deploying PostgreSQL with pgvector on [Northflank](https://northflank.com/) takes just minutes, giving you a production-ready vector database without the complexity of managing separate systems. Whether building recommendation engines, semantic search, or AI-powered applications, pgvector offers a practical solution that leverages your existing PostgreSQL expertise.

As the boundary between structured and unstructured data continues to blur, solutions like pgvector represent the future of database technology – where meaning and context become first-class citizens alongside traditional data types.

Why not give it a try? Your current database might be just an extension away from powering your next AI innovation.]]>
  </content:encoded>
</item><item>
  <title>Docker Build and Buildx best practices for optimized builds</title>
  <link>https://northflank.com/blog/docker-build-and-buildx-best-practices-for-optimized-builds</link>
  <pubDate>2026-01-04T18:38:00.000Z</pubDate>
  <description>
    <![CDATA[Learn Docker Build and Buildx best practices, including multi-stage builds, caching, and optimizing Dockerfiles for better performance.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_orleans_guide_720f0bbc48.png" alt="Docker Build and Buildx best practices for optimized builds" />> There was a time someone asked me why their Docker builds took so long and why the images were so large. It’s a question I still hear often, especially from developers starting with containers.

Docker build is the foundation of creating [container](https://northflank.com/deploy/run-persistent-and-ephemeral-docker-containers) images and bundling your application with everything it needs to run. But there’s more to bundling than just running commands. With tools like [Buildx](https://docs.docker.com/reference/cli/docker/buildx/) and [BuildKit](https://docs.docker.com/build/buildkit/), you can speed up the process, reduce image sizes, and make builds much more predictable. Let’s talk about how you can get the most out of your builds.

<InfoBox className='BodyStyle'>

You can build directly from a custom `Dockerfile` on [Northflank](https://northflank.com/product/deployments), with:
- BuildKit caching enabled by default  
- Option to deploy from Git or a container registry  
- Access to logs, build history, and environment variables  
- Configuration for CPU, memory, and number of instances  
- Support for both managed and BYOC infrastructure  

Try it out by [starting for free](https://app.northflank.com/signup) and go from Dockerfile to deployment in minutes.

</InfoBox>

## What is Docker Build and how it works
I remember someone once saying, “I run `docker build`, but I don’t really know what happens under the hood.” That comes up a lot in conversations about containers.

At its core, the `docker build` takes a set of instructions from a Dockerfile and turns them into an image. That image is what your container runs. Every time you run `docker build`, the **docker build command** reads the Dockerfile, processes each step, and creates a **docker build image** that includes everything your application needs. This includes your code, dependencies, and system libraries.

This image is not just a single block of data. It is built in layers, with each step in the Dockerfile adding a new layer. These layers help with caching, which speeds up future builds by reusing unchanged parts instead of starting from scratch. Writing a good Dockerfile matters because it affects how fast your builds run and how much space your images take up.

Now that we know what happens when you run `docker build`, let’s talk about what happens when you need something faster, more flexible, or built for multiple platforms.

## Why Docker Buildx and BuildKit make builds faster and more flexible

At some point, most developers run into the limits of the standard `docker build` command. Maybe you need to build images for different architectures, or you want faster builds with better caching. **Docker** **buildx** and **BuildKit** make that possible by extending the build process with more advanced features.

The biggest difference between **docker build** and **docker buildx** is flexibility. Buildx allows you to create multi-platform images, run parallel builds, and take advantage of BuildKit’s improved caching. If you have ever waited too long for an image to build or struggled with caching, BuildKit changes that by making builds more predictable and reusable.

BuildKit processes layers more intelligently, and caching works better across builds. Instead of repeating the same steps every time, BuildKit skips what has not changed. This means faster builds and smaller images, which is a big deal when working with containers at scale.

Since **docker buildx** and **BuildKit** add more flexibility to the build process, let’s look at how to make builds faster, more reliable, and easier to manage.

## How to write better Dockerfiles and build images the right way

A slow build or a large image is usually a sign that something can be done differently. The way a Dockerfile is written affects how fast the image builds, how much caching is used, and how much space the final image takes up. A few small changes can change how builds run. Let’s see how:

### Use layering to speed up builds

Each command in a Dockerfile creates a new layer. The order of these layers matters because Docker caches them. If nothing changes in a layer, Docker reuses the cached version instead of rebuilding it. This is why commands that change often, like adding application code, should be placed later in the file. The base layers, which include dependencies and system packages, should come first so they do not get rebuilt every time.

### Use multi-stage builds to keep images small

A **docker multi-stage build** helps remove unnecessary files by splitting the build process into multiple steps. The first stage includes everything needed to compile the application, while the final image only keeps the file required to run it. This reduces the image size without affecting how the application works.

Here is an example for a Go application:

```docker
# First stage: Build the application
FROM golang:1.19 AS builder
WORKDIR /app
COPY . .
RUN go build -o myapp

# Second stage: Create a lightweight runtime image
FROM alpine:latest
WORKDIR /root/
COPY --from=builder /app/myapp .
CMD ["./myapp"]
```

The first stage compiles the application, but the final image only includes the binary, making it much smaller.

### Avoid unnecessary layers and caching issues

Using **docker build no cache** can force a full rebuild, but in most cases, caching should be used instead. Installing dependencies before copying application code helps keep the cache useful since package installations do not need to be repeated if nothing has changed.

A better way to structure a Node.js Dockerfile looks like this:

```docker
FROM node:20
WORKDIR /app

# Install dependencies first
COPY package.json package-lock.json ./
RUN npm install

# Copy the rest of the application
COPY . .
CMD ["node", "server.js"]
```

Since dependencies are copied first, they will not be reinstalled unless **package.json** changes. This saves time and keeps builds predictable.

Writing a Dockerfile with docker build best practices helps keep images smaller and builds faster. There are still a few more ways to make Dockerfiles smaller, cleaner, and easier to work with. Let’s go over some practical ways to optimize them.

## Smaller, faster, and cleaner Dockerfiles that actually work

Some Docker images take longer to build than they should, and some are larger than necessary. A few adjustments in a Dockerfile can fix that while keeping everything easier to maintain. Let’s look at some ways to make builds more predictable and images as small as possible.

### Start with a smaller base image

The base image affects both build speed and image size. A full OS image like **ubuntu** comes with extra tools that are rarely needed, while a smaller alternative like **alpine** keeps things minimal.

Using a smaller base image reduces unnecessary files and makes images easier to work with. For example, instead of using the full Node.js image, switching to an Alpine-based version cuts down on extra system utilities:

```docker
# Instead of this
FROM node:20

# Use a smaller base image
FROM node:20-alpine
```
For other languages, many official images have `-slim` or `-alpine` versions that remove files that are not needed for running applications.

### Reduce the number of layers

Each command in a Dockerfile creates a new layer in the final image. Too many layers slow down builds and take up more space. Instead of writing separate `RUN` commands for each step, combining them into one keeps the image cleaner.

For example, when installing dependencies, running separate commands creates extra layers:

```docker
# Each RUN creates a new layer
RUN apt-get update
RUN apt-get install -y curl
RUN apt-get clean
```

Combining them into a single command avoids unnecessary layers and removes temporary files that do not need to be stored in the final image:

```docker
RUN apt-get update && apt-get install -y curl && rm -rf /var/lib/apt/lists/*
```

### Copy only what is needed

Copying an entire project directory into an image is easy, but it often brings in files that are not required. A **.dockerignore** file prevents this by blocking files that do not need to be in the build.

A typical **.dockerignore** file might look like this:

```file
**node_modules
.git
.env**
```

## Dockerizing a small microservice

A microservice is a self-contained service that handles a specific function within a larger system. Instead of one big application handling everything, microservices break tasks into smaller services that communicate over a network.

In this example, we’ll containerize a User Service, a simple microservice that provides user data through an API. A real-world system might have multiple services, like an Auth Service for logins, a Payments Service for processing transactions, and more.

This microservice will:

- Provide user data through an API.
- Run inside a container.
- Communicate with other services using a Docker network.

### Before you begin
To follow along, make sure you have:

- **Docker installed**: Download it from [Docker’s official site](https://docs.docker.com/get-started/get-docker/).
- **Node.js installed**: Get it from [Node.js official site](https://nodejs.org/en/download).
- **A terminal or command prompt**: Any shell that supports Docker commands will work.

### Setting up the microservice
Let’s start by creating the User Service. This service will expose an API that returns a list of users.
First, create a new project directory and initialize a Node.js application:

```bash
mkdir user-service && cd user-service
npm init -y
```
Next, install Express to handle API requests:

```bash
npm install express
```

Now, create an index.js file for the microservice:

```javascript
const express = require('express');
const app = express();

const users = [
    { id: 1, name: "Alice" },
    { id: 2, name: "Bob" }
];

app.get('/users', (req, res) => {
    res.json(users);
});

const PORT = process.env.PORT || 3001;
app.listen(PORT, () => {
    console.log(`User service running on port ${PORT}`);
});
```
This service listens on port 3001 and responds with a list of users when accessed at **/users**.

### Writing the Dockerfile

Now that the microservice is ready, the next step is to containerize it by writing a Dockerfile. A Dockerfile is a plain text file (without an extension) that contains a set of instructions for building a Docker image. Each instruction adds a layer to the final image, making it possible to reuse parts of the build for faster rebuilds.

Create a new file named **Dockerfile** in the **user-service** directory (make sure there is no file extension).

```bash
touch Dockerfile
```
Now, open the file and add the following instructions:

```docker
FROM node:20-alpine

WORKDIR /app

COPY package.json package-lock.json ./
RUN npm install

COPY . .

EXPOSE 3001

CMD ["node", "index.js"]
```
Each line in the Dockerfile tells Docker how to build the image. Every command creates a layer, and layers that do not change can be cached to speed up future builds. Let’s go through each instruction step by step:

- `FROM node:20-alpine`: This sets the base image, which is the foundation for the container. Instead of using a full Node.js image, we use `node:20-alpine`, a smaller version based on Alpine Linux. This keeps the final image lightweight and removes unnecessary system files.
- `WORKDIR /app`: This sets the working directory inside the container. Any subsequent commands in the Dockerfile will run inside this director. Setting a working directory prevents issues with relative paths when running commands inside the container.
- `COPY package.json package-lock.json ./`: This copies only the **package.json** and **package-lock.json** files into the container before copying the rest of the code. Why does this matter? Because Docker caches layers. By copying these files first, Docker can reuse the cached version of `npm install` unless dependencies change, making builds much faster.
- `RUN npm install`: This installs all dependencies listed in **package.json**. Since we copied the package files first, Docker only runs this step if dependencies have changed.
- `COPY . .`: This copies all remaining files from the project directory into the container. Since dependencies are already installed, this step does not trigger a reinstall, keeping the cache intact.
- `EXPOSE 3001`: This documents that the container listens on port 3001. It does not actually publish the port; it just lets other containers know which port to use when communicating internally. To access this service from the host machine, we need to explicitly map the port when running the container.
- `CMD ["node", "index.js"]`: This sets the default command to run when the container starts. The service will run using Node.js and execute **index.js**. If another command is provided when running the container, it will override this default.


### Building and running the microservice

Once the Dockerfile is ready, we need to build a Docker image. A Docker image is an immutable snapshot of the application, including everything it needs to run. Each step in the Dockerfile becomes a layer in the image, allowing Docker to cache and reuse parts of it in future builds.

To build an image, run:

```bash
docker build -t user-service .
```

Let’s break it down:

- `docker build` → This tells Docker to create a new image.
- `t user-service` → This assigns the name user-service to the image so we can reference it easily.
- `.`→ This tells it to use the current directory (where Dockerfile is located) as the build context.

So, to show the command was successful, you should see something like this in your terminal:

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img 
    src="https://assets.northflank.com/docker_build_command_result_cf4e6bba0b.png" 
    alt="Showing the result of running the docker build command" 
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{ 
    marginTop: "8px", 
    fontSize: "14px", 
    color: "#555", 
    textDecoration: "none", 
    display: "block" 
  }}>
    Showing the result of running the docker build command
  </figcaption>
</figure>

Once the build is complete, we can run the container: 

```bash
docker run -p 3001:3001 user-service
```
**What happens here?**

- `*docker run**` → This starts a new container from the user-service image.
- `*-p 3001:3001**` → This maps port 3001 on the host machine to port 3001 inside the container, allowing external access.
- `*user-service**` → This is the name of the image we built earlier.

Now, the microservice is running inside a container and accessible at:

```http
<http://localhost:3001/users>
```
If you open this URL in a browser or use a tool like curl or Postman, you will see the JSON response from the microservice:

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img 
    src="https://assets.northflank.com/Showing_the_JSON_response_from_the_microservice_in_the_browser_08de930f7c.png" 
    alt="Showing the JSON response from the microservice in the browser" 
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{ 
    marginTop: "8px", 
    fontSize: "14px", 
    color: "#555", 
    textDecoration: "none", 
    display: "block" 
  }}>
    Showing the JSON response from the microservice in the browser
  </figcaption>
</figure>


## Understanding Docker images and layers

A Docker image is made up of multiple layers, each representing a step from the Dockerfile. These layers allow Docker to reuse unchanged parts of an image, making builds faster and reducing storage usage.

We can check all available images with:

```bash
docker images
```
And see which containers are running with:

```bash
docker ps
```
Once you run the command, you will see the following:

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img 
    src="https://assets.northflank.com/Showing_the_container_that_is_running_707c5dcddb.png" 
    alt="Showing the container that is running" 
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{ 
    marginTop: "8px", 
    fontSize: "14px", 
    color: "#555", 
    textDecoration: "none", 
    display: "block" 
  }}>
    Showing the container that is running
  </figcaption>
</figure>

If we need to stop a running container:

```bash
docker stop <container-id>
```
To remove a container that is no longer needed:

```bash
docker rm <container-id>
```

## Networking and communication between containers

In real-world microservices, services do not run in isolation. They communicate with other containers using **Docker networks** instead of **localhost**.

To allow services to talk to each other, create a network:

```bash
docker network create my-network
```
Now, run the User Service inside the network:

```bash
docker run -p 3001:3001 --network my-network --name user-service user-service
```
Since this container is now in **my-network**, any other service in the same network can communicate with it using the container’s name instead of **localhost**.

For example, if another microservice needs to request user data, it would send a request to:

```
<http://user-service:3001/users>
```
instead of

````
<http://localhost:3001/users>
````
This makes sure that even if the service is running inside a container, it remains accessible to other microservices.


## Using Docker Compose for multiple services

When running multiple microservices, managing them manually with `docker run` becomes repetitive. Docker Compose simplifies this by letting us define multiple services in a single file and start them with a single command.

Create a new file named **docker-compose.yml** in the **user-service** directory and add the following content:

```yml
version: '3'

services:
  user-service:
    build: .
    ports:
      - "3001:3001"
    networks:
      - my-network

networks:
  my-network:
````

Breaking down the Docker Compose file:

- `version: '3’`: → Specifies the Docker Compose file format version. Version 3 is widely supported.
- `services` → Defines the services (containers) that should be created.
- `user-service` → The name of the service. This will run as a container.
- `build: .` → Tells Docker Compose to build the image using the Dockerfile in the current directory.
- `ports` → Maps port 3001 on the host to 3001 inside the container, making it accessible from the outside.
- `networks` → Assigns the service to a Docker network so it can communicate with other services.
- `my-network` → Defines a custom network named **my-network**, which other services can join.

### Running the service with Docker Compose

Now start the service using:

```bash
docker compose up
```

This will:

- Build the `user-service` image if it doesn't exist.
- Create a container and run the service
- Connect it to the `my-network` Docker network.

To stop all running services, press `Ctrl + C` to run:

```bash
docker compose down
```
This shuts down all containers and removes the network, making cleanup easier.

## Cleaning up build caches

Docker caches layers to speed up builds, but over time, unused data takes up space. To remove unnecessary cached layers, use:

```bash
docker builder prune
```
This removes old build cache while keeping active containers and images intact.
To free up even more space by removing stopped containers, unused networks, and dangling images, use:

```bash
docker system prune
```

## Bringing everything together

You’ve seen how small changes in a Dockerfile, smarter caching, and tools like Buildx make builds faster and images smaller. If you’re working with microservices, running them in a Docker network and managing them with Docker Compose keeps everything simple and scalable.

If you don’t want to spend time managing builds and deployments yourself, platforms like [Northflank](https://northflank.com/about) handle the setup for you. That way, you don’t have to worry about infrastructure and can focus on writing code and shipping features while everything runs in the background. You can [get started for free](https://app.northflank.com/signup) and see how it fits into your workflow.

## More to read on building and deploying services

If you want to go deeper into microservices and containerized deployments, these articles might help:

- [How to build a scalable software architecture part 1: Monolith vs. Microservices](https://northflank.com/blog/how-to-build-a-scalable-software-architecture-part-1-monolith-vs-microservices) - A breakdown of monolithic and microservice architectures, how they compare, and when to use each approach.
- [ECS (Elastic Container Service): deep dive and alternatives](https://northflank.com/blog/aws-ecs-elastic-container-service-deep-dive-and-alternatives) - A closer look at ECS, how it works, and other options for running containers at scale.

These cover different ways to structure and run services, from working with Docker to managing production-ready microservices.


## Frequently Asked Questions (FAQ)
**How to build a Docker image?**

To build a Docker image, use:

```bash
docker build -t my-image .
```

This command reads the Dockerfile in the current directory (`.`) and creates an image tagged as `my-image`.

**How to build a Docker image from a Dockerfile?**

A Dockerfile is a script that defines how to build an image. Run:

```bash
docker build -t my-app .
```

Make sure your Dockerfile includes the necessary instructions, such as installing dependencies and copying application files.


**How to build a Docker image from scratch?**

To build a minimal image with no base OS, use `scratch`:

```docker
FROM scratch
COPY my-binary /my-binary
CMD ["/my-binary"]
```
This works best for compiled applications like Go. The image only includes the necessary executable.

**How to build a Docker image locally?**

To build an image without pushing it to a registry:

```bash
docker build -t my-local-image .
```

To verify the image:

```bash
docker images
```

You can then run it with:

```bash
docker run -p 8080:8080 my-local-image
```

**What is a Docker build?**

A Docker build is the process of creating a Docker image using a Dockerfile. The image includes all necessary dependencies, libraries, and code to run an application.

**What is the command for Docker build?**

The main command is:

```bash
docker build -t image-name .
```

This tells Docker to create an image using the Dockerfile in the current directory.

**What is the difference between docker run and docker build?**

- `docker build` → Creates a Docker image.
- `docker run` → Starts a container from an image.

Think of `docker build` as preparing a dish, and `docker run` as serving it.


**What is the difference between docker build and docker up?**

- `docker build` → Builds an image.
- `docker-compose up` → Starts containers based on a docker-compose.yml file, handling multiple services together.

If using Docker Compose, `docker-compose up` automatically builds images if they don’t exist.
























]]>
  </content:encoded>
</item><item>
  <title>Top Heroku alternatives in 2026</title>
  <link>https://northflank.com/blog/top-heroku-alternatives</link>
  <pubDate>2026-01-03T08:30:00.000Z</pubDate>
  <description>
    <![CDATA[Heroku's free tier is gone—discover top Heroku alternatives like Northflank, Render, and Fly.io with better pricing, scalability, and features. Find the best cloud platform for your apps in this guide!]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_heroku_alternatives_6_97865c2b7c.png" alt="Top Heroku alternatives in 2026" /><InfoBox className="BodyStyle">
## TL;DR: What are the top Heroku alternatives in 2026?

Heroku is a PaaS that made deployment simple with git-based workflows and free dynos. It works well for specific usecase, but falls short when you need BYOC, serious scaling, compliance, or a full production stack without surprise bills. Northflank is the strongest alternative built for it.

- [**Northflank**](top-heroku-alternatives) – The deployment platform for serious workloads. Run services, databases, jobs, AI inference, and sandboxes on Northflank's cloud or bring your own (AWS, GCP, Azure, bare metal). Full CI/CD, autoscaling, secrets management, private networking, and SOC 2 compliance, all in one platform, without the Kubernetes headache.
- **DigitalOcean App Platform** – Solid, no-frills PaaS with managed databases and CI/CD, good for simple Heroku migrations on a budget
- **Render** – Clean developer experience with zero-downtime deploys and PR preview environments, best for straightforward web app workloads
- **Fly.io** – Globally distributed platform that runs containers at the edge, strong for latency-sensitive applications
- **Vercel** – Frontend and serverless-first platform with a world-class edge network, ideal for Next.js and JAMstack teams
- **Netlify** – Git-based deployments for static sites and JAMstack with built-in serverless functions
- **Platform.sh** – Enterprise PaaS with multi-cloud and multi-region support for teams with strict compliance requirements
</InfoBox>

When Heroku discontinued its free tier in late 2022, it marked the end of an era in cloud development. For over a decade, countless developers and startups had launched their first applications on Heroku's free dynos, drawn to its legendary simplicity and git-based deployments. Now, with even simple hobby projects requiring paid plans, developers are discovering that modern cloud platforms offer not just more competitive pricing, but also the sophisticated capabilities their growing applications demand. In this article, we'll explore the landscape beyond Heroku, examining platforms that combine the simplicity developers love with the advanced features today's applications need.

## Why consider Heroku alternatives?

The landscape of cloud computing has evolved dramatically since Heroku's inception. Several factors are driving developers to explore other options:

- **Rising costs drive change**: After Heroku eliminated its free tier, many developers face significantly higher hosting expenses. While Heroku offers premium solutions through **Heroku Enterprise** and **Heroku Private Spaces**, these options start at several thousand dollars monthly and may be overkill for many teams. Organizations are discovering they can get similar enterprise features - like private networking, regional deployment, and advanced security - at more competitive prices through alternative platforms, making cost optimization a key factor in their migration decisions.
- **Customization limitations**: As applications grow more complex, developers find themselves constrained by Heroku's abstraction layer. Modern development teams need granular control over their infrastructure to implement specialized solutions and optimize performance, which Heroku's platform doesn't always accommodate. For example, developers can't directly manage container orchestration for custom scaling strategies, or modify low-level network configurations for specialized inter-service communication patterns.
- **Evolving performance needs**: Today's applications require sophisticated scaling capabilities and efficient resource management. While Heroku's traditional dyno-based system served well in the past, modern cloud platforms offer more advanced options for handling complex workloads and traffic patterns. Kubernetes has emerged as a powerful alternative, providing container orchestration that offers greater control over resource allocation, automated scaling, and self-healing capabilities that many growing applications demand.
- **Bring your own cloud (BYOC)**: Development teams now prefer platforms that allow them to leverage their existing cloud infrastructure investments; unlike Heroku's AWS-only approach, contemporary platforms like [Northflank](https://northflank.com/) enable developers to deploy applications across multiple cloud providers or their preferred infrastructure. This flexibility helps organizations maintain consistency with their existing cloud strategy while avoiding vendor lock-in.
- **The graduation problem:** As organizations grow, they find themselves needing to transition from platforms like Heroku to more robust managed cloud providers like AWS, GCP, and Azure. This "graduation" from Heroku to direct cloud provider services becomes necessary to gain deeper infrastructure control, cost optimization, and integration with cloud-native services. However, these transitions often prove challenging, requiring significant architectural changes and potential downtime, leading teams to seek more flexible alternatives that better align with their long-term cloud strategy.

## What are developers saying about Heroku?

Recent developer community discussions highlight Heroku's enduring strengths and growing challenges. Here's what developers are sharing about their experiences:

- **Aisik_oduro** shares a particularly negative experience with Heroku's billing practices.

 ![](https://assets.northflank.com/image_1_3dbf1cddea.png) 
- **r_s**, a developer, shares a nuanced perspective about Heroku's cost-benefit equation.

 ![](https://assets.northflank.com/image_2_f69cd55b90.png) 

- **Sleepyhead** raises serious concerns about Heroku's reliability and support, particularly in Europe.

 ![](https://assets.northflank.com/image_3_59b9a9e0c2.png) 

- **Sad-Bobcat-2103** posted a more detailed critique: " Goodbye, Heroku. We're Breaking Up.”

 ![](https://assets.northflank.com/image_4_c663ba45a4.png) 

These testimonials paint a picture of a platform that, while still valued for its ease of use, is facing significant challenges in meeting modern development needs and maintaining customer satisfaction.

## Top criteria for evaluating Heroku alternatives

When you're looking to move beyond Heroku, these eight essential factors will help you make the right choice for your team. Think of them as your checklist for finding a platform that matches Heroku's convenience and takes your development to the next level.

1. **Ease of deployment -** Your new platform should make deployment feel like a breeze, just as Heroku did. Look for modern CI/CD pipeline support and container-based deployments that let you push code with confidence. The best platforms strike that sweet spot between simplicity and flexibility – they should handle the heavy lifting while still giving you control when you need it.
2. **Transparent pricing** - Nobody likes surprise bills at the end of the month. The ideal platform offers crystal-clear pricing that grows sensibly with your needs. Watch out for platforms that hide costs in complicated usage metrics. You should be able to predict your monthly costs without needing a spreadsheet and a calculator.
3. **Developer experience** - A platform's success often comes down to how it feels to use it day-to-day. Great documentation that answers your questions before you ask them, a CLI that feels like an extension of your fingers, and workflows that fit naturally with your existing tools – these aren't just nice-to-haves, they're essential for keeping your team productive and happy.
4. **Language and framework support** - Your chosen platform should speak your language – literally. Whether you're running Node.js microservices or Python data processing jobs, make sure the platform not only supports your tech stack but understands its unique requirements. This includes having the right buildpacks, runtime environments, and framework-specific optimizations.
5. **Community and support** - When you hit a roadblock, having a strong community and responsive support team can make all the difference. Look for platforms with active forums, comprehensive knowledge bases, and support teams that understand developer needs. A vibrant community often means better resources, more solutions to common problems, and faster issue resolution.
6. **Performance and reliability** - Your applications need to run smoothly, consistently, and quickly. Evaluate each platform's track record for uptime, their global infrastructure coverage, and how they handle peak loads. The best platforms build on battle-tested infrastructure solutions like Kubernetes rather than reinventing the wheel, leveraging its proven reliability and robust container orchestration capabilities while adding their own value on top. Remember, the best performance metrics are the ones that match your specific use cases – a blog and an AI model have very different needs.
7. **Scalability** - Your platform should grow with you, handling everything from your first user to your millionth without breaking a sweat. Look for platforms that leverage industry-standard solutions like Kubernetes for container orchestration, providing proven scaling capabilities, self-healing features, and automated load balancing without requiring manual intervention. The platform should make scaling feel like turning up the volume, not rebuilding the speaker.
8. **Security and compliance** - Security isn't just a checkbox – it's a fundamental requirement. Your platform should offer built-in security features like SSL/TLS support, network isolation, and regular security updates. If you're handling sensitive data or operating in regulated industries, make sure the platform can meet your compliance requirements without excessive custom configuration.
9. **Bring your own cloud (BYOC)** - Modern platforms should play well with your existing cloud infrastructure investments. Rather than creating proprietary systems, the best platforms build on top of industry standards like Kubernetes, allowing you to deploy across multiple cloud providers while maintaining consistent workflows. This approach lets you leverage your existing infrastructure expertise and relationships while preventing vendor lock-in and optimizing costs across different providers based on your specific needs.
10. **Simplification of day 2 operations** - While initial deployment is important, the real test of a platform comes in its ability to streamline ongoing operations. Your chosen solution should simplify monitoring, logging, debugging, and maintenance tasks that occur after the initial deployment. The best platforms achieve this by building on top of battle-tested infrastructure like Kubernetes, adding value through integrated observability tools, automated backup solutions, and straightforward update processes that don't require extensive downtime or manual intervention. This approach turns the complexity of daily operations into manageable, automated workflows that let your team focus on building features rather than fighting fires.

## Top Heroku alternatives

Are you looking for a new home for your applications? Here are seven powerful alternatives that combine Heroku's simplicity with compelling modern features.

### 1. Northflank

[Northflank](https://northflank.com) is the most complete platform on this list. It provides a full infrastructure stack for developers to build, deploy, and scale applications, sandboxes, services, databases, CI/CD pipelines, GPU workloads, and jobs on Northflank's managed cloud or any cloud through a self-service approach. For DevOps and platform teams, Northflank provides a powerful abstraction layer over Kubernetes, enabling templated, standardized production releases with intelligent defaults while maintaining necessary configurability.

Northflank advances the legacy of pioneers like Heroku and Pivotal Cloud Foundry. While Heroku perfected the self-service developer experience, it didn't support complex workloads in enterprise cloud accounts. Cloud Foundry offered the right application abstraction to simplify complexity, but its underlying infrastructure proved costly and difficult to implement. Northflank delivers the best of both worlds: support for complex workloads, exceptional developer experience, and appropriate abstractions in your cloud environment—all within minutes and at a reasonable cost.

![image-74.png](https://assets.northflank.com/image_74_a50d717270.png)

**Key features**:

- GPU workloads — NVIDIA L4, A100, H100, H200 and others
- Secure microVM sandboxes for untrusted code and multi-tenant workloads
- End-to-end CI/CD with build pipelines, preview environments, and GitOps
- Managed databases (PostgreSQL, MySQL, MongoDB, Redis, RabbitMQ) with HA, backups, and forking
- Autoscaling, real-time logs, metrics, and log sink integrations
- Secrets management, network policies, service mesh with mTLS, and static egress IPs
- BYOC deployment into AWS, GCP, Azure, CoreWeave, Oracle, and bare metal
- SOC 2 Type 2 compliant
- Northflank operates at 99.99% historical uptime. For customers on enterprise agreements, this uptime is guaranteed under an SLA with service credits if not met.

**Pricing:**

[Northflank offers](https://northflank.com/pricing) a generous free tier that includes deployment of 2 services, 2 jobs, and 1 addon. Users can connect their existing cloud account, with limited resources and plans available. A Pay-as-you-go Pro plan provides additional capabilities.

**How does Northflank compare to Heroku?**

- **Market position**: While Northflank hasn't achieved Heroku's level of recognition, it has earned the trust of prominent companies like [Writer](https://writer.com/) and [Sentry](https://sentry.io/welcome/), serving over 35,000 developers.
- **Cost-effectiveness**: Northflank's generous free tier and transparent pay-as-you-go pricing model offer better value, particularly for developers working on smaller projects or just starting out.
- **Deployment flexibility**: Unlike Heroku's AWS-centric approach, Northflank supports a broader range of cloud service providers, including AWS, Google Cloud Platform, and Azure.

### 2. DigitalOcean app platform

[DigitalOcean app platform](https://www.digitalocean.com/products/app-platform) is a PaaS solution built on DigitalOcean's robust infrastructure. It strikes an optimal balance between simplicity and control for growing applications.

 ![](https://assets.northflank.com/image_6_022540644b.png) 

**Key features**:

- Integrated CI/CD pipelines
- Automatic vertical and horizontal scaling
- Built-in monitoring and alerting
- Seamless integration with DigitalOcean's managed databases
- Global CDN support

**Pricing:**

[DigitalOcean app platform](https://www.digitalocean.com/pricing/app-platform) includes a free tier supporting up to 3 static sites with 1GiB data transfer allowance per app. Paid plans begin at $5 per month with enhanced features.

**How does DigitalOcean app platform compare to Heroku?**

- While less widely recognized than Heroku, it maintains a growing user base
- Offers more competitive pricing
- Features a focused but expanding community

### 3. Render

[Render](https://render.com/) is a modern cloud platform that streamlines the hosting of web applications, static sites, APIs, and databases, providing automatic SSL certification and CDN integration.

 ![](https://assets.northflank.com/image_7_04cbeab21d.png) 

**Key features**:

- Zero-downtime deployments
- Automatic HTTPS and DDoS protection
- Native SSD storage
- Pull request preview environments
- Custom domain support

**Pricing**: 

[Render](https://render.com/pricing) provides a free tier for low-traffic applications, with paid plans starting at $19 per user monthly.

**How does Render compare to Heroku?**

- While Render's documentation isn't as extensive as Heroku's, it's well-maintained and growing
- Offers more competitive pricing across all tiers
- Features a smaller but carefully curated ecosystem of add-ons

### 4. [Fly.io](http://fly.io/)

[Fly.io](http://fly.io/) is a globally distributed application platform that positions your code closer to users, delivering exceptional performance without the complexity of traditional infrastructure management.

 ![](https://assets.northflank.com/image_8_535b905e4b.png) 

**Key features**:

- Intelligent global load balancing
- Integrated Postgres and Redis support
- Native IPv6 compatibility
- Docker-based deployment pipeline
- Extensive edge network coverage

**Pricing**: 
[Fly.io](http://fly.io/pricing) maintains a free tier for low-traffic applications and implements a pay-as-you-go model for its Professional plans.

**How does [Fly.io](http://fly.io/) compare to Heroku?**

- Emphasizes performance optimization through advanced latency reduction techniques
- Provides automatic scaling based on demand, simplifying infrastructure management
- Implements comprehensive security measures, including firewalls, intrusion detection, and DDoS protection

### 5. Vercel

[Vercel](https://vercel.com/) is a cloud platform optimized for frontend frameworks and static sites, delivering exceptional performance and developer experience.

 ![](https://assets.northflank.com/image_9_36a9aaf661.png) 

**Key features**:

- Advanced performance optimization
- Comprehensive serverless function support
- Integrated CI/CD pipeline
- Global edge network deployment
- Sophisticated analytics and monitoring

**Pricing**:
[Vercel's free](https://vercel.com/pricing) tier accommodates frontend applications with up to 1,000,000 monthly requests. Premium plans start at $20 monthly with advanced features.

**How does Vercel compare to Heroku?**

- Specializes in frontend development and modern web applications
- Implements usage-based pricing for better cost control
- Offers unique capabilities, including edge functions, serverless previews, and global edge network distribution

### 6. Netlify

[Netlify](https://www.netlify.com/) is an innovative platform for modern web projects that seamlessly integrates CI/CD, serverless functions, and edge computing.

 ![](https://assets.northflank.com/image_10_2520a29eea.png) 

**Key features**:

- Git-based continuous deployment
- Integrated CDN with asset optimization
- Comprehensive serverless function support
- Built-in form handling and authentication
- Advanced split testing capabilities

**Pricing**: 

[Netlify offers a free](https://www.netlify.com/pricing/) tier for sites consuming less than 100 GB of monthly bandwidth. Premium plans begin at $19 monthly, supporting up to 1 TB of bandwidth.

**How does Netlify compare to Heroku?**

- Focuses primarily on static and JAMstack applications
- Provides specialized features for modern web development workflows
- Maintains competitive pricing with transparent bandwidth-based tiers

### 7. [Platform.sh](http://platform.sh/)

[Platform.sh](http://platform.sh/) delivers an end-to-end solution for building, deploying, and scaling web applications, with particular emphasis on enterprise requirements and multi-cloud deployments.

 ![](https://assets.northflank.com/image_11_e94ff02dd9.png) 

**Key features**:

- Flexible multi-cloud and multi-region deployment options
- Sophisticated environment management
- Comprehensive database and service support
- Enterprise-grade security features
- Automated DevOps workflows

**Pricing**: 

While [Platform.sh](http://platform.sh/pricing) doesn't offer a free plan, it provides a 30-day trial period with full access to premium features. Detailed pricing information is available on their [website](https://platform.sh/pricing/).

**How does Netlify compare to Heroku?**

- More expensive than Heroku
- Advanced security features
- Built-in CI/CD pipeline

## Conclusion

Heroku’s free tier was a game-changer for developers, and its removal pushed many to look for alternatives. While it was a great platform for getting started, the cloud landscape has evolved, offering more flexible, cost-effective, and powerful options. Whether you need better pricing, easier scaling, or more control over your infrastructure, there are plenty of platforms that can match or even surpass what Heroku offers. The best choice comes down to what works best for your projects and workflow—so explore, experiment, and find the right fit for you.]]>
  </content:encoded>
</item><item>
  <title>Top Cloud Foundry alternatives in 2026</title>
  <link>https://northflank.com/blog/cloud-foundry-journey-and-alternatives-internal-developer-platform</link>
  <pubDate>2026-01-02T00:15:00.000Z</pubDate>
  <description>
    <![CDATA[Cloud Foundry, once a pioneering platform, now faces competition from Kubernetes and modern tools. Rising costs, complexity, and cloud-native evolution push organizations to explore top Cloud Foundry alternatives like Northflank.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_cloud_foundry_alternatives_3ee4a95a6b.png" alt="Top Cloud Foundry alternatives in 2026" />Imagine deploying software in 2010: developers would spend weeks crafting code, only to watch it stumble in production as operations teams grappled with incompatible environments and conflicting dependencies. This "*works on my machine*" problem wasn't just frustrating – it was costing organizations millions in delayed releases and lost productivity. Development and operations teams spoke different languages, used different tools, and often worked in complete isolation from each other.

Cloud Foundry emerged in 2011 with an ambitious vision to solve this disconnect. By introducing a standardized platform that could automatically handle deployment complexities, it promised to transform how organizations delivered software. Developers could focus on writing code while operations teams managed a consistent platform – a bridge across the traditional dev-ops divide.

But technology rarely stands still. As cloud computing matured and Kubernetes revolutionized container orchestration, organizations found themselves at a crossroads. Cloud Foundry, once the pioneer of modern application platforms, now shares the stage with an expanding ecosystem of cloud-native tools. Today's technical leaders face a critical question: should they continue investing in Cloud Foundry's mature but complex ecosystem, or is it time to explore modern alternatives that might better serve their cloud-native future?

In this article, we'll trace Cloud Foundry's journey from revolutionary platform to today's reality, examining why organizations are reevaluating their platform strategies in an increasingly Kubernetes-centric world.

## What is Cloud Foundry?

Cloud Foundry is an open-source Platform-as-a-Service (PaaS) that enables organizations to deploy and manage applications across multiple cloud environments. What made it truly revolutionary wasn't just its technology – it was its philosophy. Before Cloud Foundry, most DevOps tools were built primarily for operations teams, merely attempting to copy development patterns. Cloud Foundry broke this mold by creating the first truly shared platform for all teams involved in the software lifecycle.

The platform's success was driven by three strategic masterstrokes. First, it recognized that enterprise software was predominantly built in Java and .NET, not newer languages like Ruby or Python that most DevOps tools catered to. By making Spring Boot a first-class citizen and partnering with Netflix (a prominent Java shop), Cloud Foundry became the gateway for traditional enterprise developers to enter the modern cloud era. Second, its pricing model brilliantly aligned with how enterprises were already buying **WebSphere** and **WebLogic** licenses, making it easier for organizations to justify the switch. Finally, with a massive sales organization of 900 people and the recruitment of industry luminaries, Cloud Foundry didn't just build technology – it built a movement.

## The history of Cloud Foundry

The platform's story began in 2009 with three VMware engineers who saw a problem that needed solving. **Mark Lucovsky**, **Derek Collison**, and **Vadim Spivak** weren't just building another tool – they were reimagining how organizations could deploy software. By April 2011, their vision became reality with Cloud Foundry's launch as the industry's first open-source PaaS.

In 2012, VMware and EMC decided to give it room to grow by spinning it off into **Pivotal Software** – a strategic move that was far more than a mere corporate restructuring. Pivotal Software became a dedicated innovation hub that provided Cloud Foundry with the organizational independence and resources needed to mature as an open-source platform. This allowed Cloud Foundry to develop more flexibly, attract broader industry collaboration, and accelerate its technological innovation.

Then came Dell Technologies' acquisition of EMC in 2016, expanding Cloud Foundry's reach into the enterprise world. This acquisition further amplified Pivotal's significance, embedding Cloud Foundry more deeply into enterprise technology infrastructures.

The story took another turn in 2019 when Pivotal hit rough waters – experiencing almost 50% stock plunge after a tough quarter. VMware saw an opportunity to bring Cloud Foundry back home. The timing couldn't have been better: Cloud Foundry had just cracked the code on Kubernetes integration, and developers were flocking to the platform. Wall Street loved the move, sending Pivotal's stock soaring 70%.

## How Kubernetes affected Cloud Foundry

Cloud Foundry's relationship with Kubernetes marked a critical turning point in its history. When Kubernetes emerged as the de-facto standard for container orchestration, Cloud Foundry faced a decisive moment: adapt or risk becoming obsolete.

The challenge was both technical and cultural. Cloud Foundry had invested heavily in **Diego**, its own container orchestration system, and many in the organization believed it was technically superior to Kubernetes. While competitors like Red Hat's OpenShift quickly embraced Kubernetes, Cloud Foundry's response was more hesitant.

Eventually, the platform launched "**cf-for-k8s,**" allowing Cloud Foundry to run on Kubernetes infrastructure. But this transition came with significant costs:

- **Added complexity**: Organizations now needed expertise in both Cloud Foundry and Kubernetes
- **Cultural resistance**: Many teams struggled to shift from Cloud Foundry's opinions about how things should work to Kubernetes' more flexible approach
- **Integration challenges**: The marriage of two complex systems created new operational hurdles

This period highlighted a crucial lesson in enterprise software: sometimes being technically superior isn't enough. The ability to adapt to changing market dynamics and embrace industry standards can be more important than maintaining a perfectly crafted, but isolated, technical solution.

## Why organizations are looking for Cloud Foundry alternatives?

The cloud platforms landscape has evolved dramatically since Cloud Foundry's early days, presenting several significant challenges for teams:

1. **Cost management burden**: Organizations face escalating financial pressures that extend far beyond initial licensing fees. The platform demands substantial investments in infrastructure, specialized training programs, and continuous maintenance. Many companies have found themselves maintaining dedicated teams solely for Cloud Foundry operations.
2. **The platform engineering paradox**: What initially seemed like a strength – the rise of Platform Engineering teams – revealed a critical flaw. As these teams customized their deployments to meet specific organizational needs, they created technical debt that grew increasingly difficult to manage. Each upgrade became more complex, requiring careful navigation of custom configurations and integrations. When platform experts inevitably left for new opportunities, organizations struggled to maintain these highly customized deployments.
3. **Maintenance complexity**: Modern teams require specialists with expertise not only in Cloud Foundry's core systems but also in its interactions with cloud providers, container orchestration systems, and Kubernetes integration. This complexity often results in extended deployment timelines and heightened risks during updates.
4. **The DIY integration challenge**: Despite selecting a platform that promised to abstract away infrastructure complexity, teams frequently find themselves constructing and maintaining numerous custom integrations and workflows, requiring custom monitoring solutions, specialized deployment pipelines, and complex networking configurations.

## Case study: why GOV.UK PaaS was decommissioned

The [UK Government Digital Service (GDS)](https://gds.blog.gov.uk/2022/07/12/why-weve-decided-to-decommission-gov-uk-paas-platform-as-a-service/) provides a compelling case study of how the cloud-native landscape has evolved beyond Cloud Foundry. Their [GOV.UK](http://gov.uk/) PaaS, built on Cloud Foundry, initially served as a central platform for government digital services. After seven successful years of operation from 2015 to 2022, GDS made the strategic decision to sunset the platform – a decision that perfectly illustrates the changing dynamics in the platform space.

### The [GOV.UK](http://gov.uk/) PaaS Journey

During its lifetime, [GOV.UK](http://gov.uk/) PaaS demonstrated the immense value that Cloud Foundry could provide:

- Supported over 172 digital services across 60+ departments and agencies
- Maintained 99.95% uptime with only one major incident in 7 years
- Handled over 122 deployments per day across 3,200 applications
- Proved crucial during the COVID-19 pandemic, enabling rapid service deployment and scaling

However, several factors led to its decommissioning:

1. **Market evolution**: Major cloud providers like AWS, Azure, and GCP significantly improved their offerings and reduced barriers to entry for digital teams.
2. **Organizational maturity**: Government departments built stronger in-house cloud engineering capabilities and increasingly adopted Kubernetes-based architectures.
3. **Technology inflection point**: The platform reached a crossroads requiring either significant technical architecture investment or strategic redirection.
4. **Resource optimization**: As a central government service, GDS needed to focus resources on products with the highest impact and growth potential.

This transition highlights a broader industry pattern: organizations that adopted Cloud Foundry are now facing similar strategic decisions as their platforms age and the technology landscape evolves.

## What are the Cloud Foundry alternatives?

Organizations looking to transition from Cloud Foundry today have several paths forward, each with its own considerations and implications:

### Managed kubernetes services

Major cloud providers now offer enterprise-grade Kubernetes platforms that can serve as a foundation for your cloud-native journey. These services, such as GKE Enterprise, AKS, and OpenShift Cloud, provide several advantages.

The primary benefit is rapid deployment capability - what once took months to set up can now be accomplished in minutes. These platforms handle complex infrastructure management, including node provisioning, scaling, and security updates.

However, organizations should understand that while these services excel at container orchestration, they don't provide the same level of developer experience that Cloud Foundry offers out of the box. Technical teams will need to build additional layers on top of these services to achieve similar developer workflows.

This might include implementing continuous deployment pipelines, setting up monitoring and logging solutions, and creating developer-friendly interfaces. The total cost of ownership (TCO) calculation should factor in both the direct platform costs and the engineering effort required to build and maintain these additional layers.

### DIY platform engineering

Some organizations, particularly those with specific compliance requirements or unique technical needs, opt to build their own platforms on Kubernetes. This approach offers maximum flexibility but comes with significant responsibilities.

Creating a custom platform requires extensive expertise in Kubernetes, networking, security, and platform design. Organizations need to invest in a dedicated platform engineering team that can not only build but also maintain and evolve the platform over time.

This team will be responsible for creating developer tools, establishing deployment workflows, implementing security controls, and managing the entire platform lifecycle. The advantages include complete control over the platform's architecture and features, the ability to optimize for specific use cases, and independence from vendor-specific implementations.

However, organizations should be prepared for the ongoing commitment this approach requires - from keeping up with Kubernetes releases to maintaining custom tooling and documentation.

 ### Modern application platforms

A new generation of platforms, including solutions like [Northflank](https://northflank.com/), aims to provide the developer-friendly experience of Cloud Foundry while leveraging modern Kubernetes infrastructure. These platforms offer several key benefits.

They maintain the simplicity of the `cf push` experience while providing access to native Kubernetes capabilities. Development teams can continue using familiar workflows, while operations teams benefit from standard Kubernetes management tools and practices.

These platforms often include built-in solutions for common needs like automated SSL certificate management, application scaling, and logging integration. Organizations should evaluate these platforms based on their specific needs, considering factors like support for existing applications, integration capabilities with current tools, and the platform's roadmap alignment with their technical strategy.

### Hybrid approach

Many organizations opt for a gradual transition strategy that maintains existing Cloud Foundry deployments while incrementally moving workloads to new platforms. This approach offers several advantages.

Teams can learn and adapt to new technologies without the pressure of a complete platform switch. To support that learning curve, some organizations use a [learning content management system](https://360learning.com/blog/learning-content-management-system/) to organize training materials, migration guides, and internal knowledge in one place. Critical applications can continue running on the familiar Cloud Foundry infrastructure while new projects adopt modern platforms. This approach also allows organizations to validate their chosen migration path with lower-risk workloads before committing to full migration.

However, running multiple platforms introduces additional operational complexity. Organizations need to maintain expertise in both systems, manage separate deployment pipelines, and potentially deal with cross-platform service integration. Clear governance and migration criteria become essential to manage this complexity effectively.

## How Northflank carries on the Cloud Foundry vision with Kubernetes

Northflank emerges as a breath of fresh air for teams exhausted by traditional platform complexities. Born from the same vision that drove Cloud Foundry – making software deployment simple and developer-friendly – Northflank takes a radically different approach by embracing Kubernetes natively.

<FancyQuote
  body={
    <>
Cycle time is everything. With Northflank, I can make 100 commits and 100 deployments in a single day – it keeps up with my pace like nothing else. When it comes to debugging, the meantime to resolution is unbeatable. I can identify issues and deploy fixes faster than customers can even report them. I know about problems before they do and have them fixed before they call.
       </>
  }
   attribution={
    <TestimonialHeader
      name="Joshua McKenty"
      position="CEO @ Polyguard, Former Field CTO @ Cloud Foundry, OpenStack, NASA"
      avatar="https://northflank.com/images/landing/quotes/joshua.jpeg"
      linkedin="https://www.linkedin.com/in/joshuamckenty/"
      mb={0}
    />
  }
/>

Its architecture reflects deep lessons learned from Cloud Foundry's journey, particularly in its innovative separation of control plane and data plane. The control plane runs in the cloud, offering rapid evolution and easy updates, while the data plane can be deployed wherever organizations need it – in their own VPC or on-premise – providing the security and control enterprises demand.

 ![](https://assets.northflank.com/northflank_byoc_cloud_foundry_d2435ad6c2.png) 

Like Cloud Foundry, Northflank maintains crucial separations between code and configuration, build time and runtime, and development and production environments. However, it achieves this without requiring organizations to maintain large platform engineering teams. By providing a consistent experience across its web UI, CLI, and API, Northflank delivers on the original promise of a truly unified platform for modern software delivery.

For organizations feeling the weight of maintaining complex platform engineering teams, Northflank offers a compelling proposition: all the isolation and security of Cloud Foundry, but with Northflank serving as your platform engineering team. This approach dramatically reduces the operational burden while maintaining the developer-first philosophy that made Cloud Foundry revolutionary in the first place.

Think of it like this: Where Cloud Foundry required teams to become platform specialists, Northflank lets developers do what they do best – write code. By leveraging Kubernetes' powerful infrastructure, the platform dramatically simplifies what used to take months of configuration into a process that can be completed in just hours.

For technical leaders caught between legacy platforms and the promise of cloud-native technologies, Northflank represents more than just a tool. It's a strategic approach that bridges the gap between traditional deployment models and the future of software delivery. We'd love to show you how Northflank can help. [Schedule a live demo here](https://cal.com/team/northflank/northflank-demo?date=2025-01-13&month=2025-01), or [get started with Northflank here](https://app.northflank.com/signup).]]>
  </content:encoded>
</item><item>
  <title>ECS (Elastic Container Service): deep dive and alternatives</title>
  <link>https://northflank.com/blog/aws-ecs-elastic-container-service-deep-dive-and-alternatives</link>
  <pubDate>2026-01-01T10:00:00.000Z</pubDate>
  <description>
    <![CDATA[Deep dive into Amazon ECS: explore its benefits, limitations, and top container orchestration alternatives. Discover why teams switch to platforms like Northflank for scalable, flexible deployments.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/ecs_elastic_container_service_deep_dive_alternatives_e36d11622b.png" alt="ECS (Elastic Container Service): deep dive and alternatives" />When getting started with containerization, Elastic Container Service (ECS) often feels like the perfect starting point. It handles the basics well - getting your containers up and running, making sure they stay healthy, and connecting them with other services you're already using. The promise of managed container orchestration makes those first steps into the container world feel less daunting.

But here's the thing - as your team grows and your applications become more sophisticated, ECS begins to reveal its limitations. Those simple container deployments turn into complex juggling acts, involving multiple services, complex configurations, and time-consuming operational tasks. Many teams find themselves asking: *"Isn't there a better way to do this?"* Let's dive deeper into ECS and explore ECS alternatives that might better suit your needs.

## What is ECS?

Elastic Container Service (ECS) is a fully managed container orchestration service that helps teams deploy, manage, and scale containerized applications. Think of it as the command center that handles the complex coordination needed to keep your containerized applications running smoothly. ECS acts as a control plane that handles container scheduling, cluster management, and task coordination.

While ECS handles the orchestration layer, Fargate steps in as AWS's serverless compute engine that powers your containers, eliminating the need to manage servers and letting you focus purely on your applications. This pairing is powerful because it combines ECS's orchestration capabilities with Fargate's serverless infrastructure, meaning you can run containers without worrying about the underlying server management.

When you're working with ECS, you actually have two ways to run your containers. You can either manage your own cluster of EC2 instances (what we call the EC2 launch type) or let AWS handle everything through Fargate (the Fargate launch type). The choice between these two approaches comes down to how much control you need over your infrastructure versus how much operational overhead you're willing to handle.

ECS excels in running web applications and microservices, particularly for teams already using AWS. It has deep integration with services like application load balancers and IAM making it a natural choice for containerized applications that need automatic scaling, service discovery, and reliable task scheduling. 

## Where does ECS fit into the release/deployment process?

When it comes to deploying applications, ECS serves as your runtime environment—the place where your containers actually live and run. But here's what's interesting: ECS itself doesn't handle the entire release process. You can think of ECS as the final destination for your containers, but you'll need to figure out how to get them there.

In a typical deployment pipeline, your application code goes through several stages: building, testing, packaging into containers, and deployment. ECS handles that last mile—taking your container images and running them according to your specifications. You'll need to set up your own CI/CD pipeline using tools like Jenkins to automate the journey from code commit to container deployment on ECS.

## Why do teams like ECS?

ECS has earned its popularity for good reasons. For teams already invested in AWS, it feels like a natural extension of their existing infrastructure. The learning curve isn't too steep - if you understand basic container concepts, you can get started with ECS relatively quickly. The tight integration with other AWS services means you can easily set up load balancers, handle permissions through IAM, and monitor your applications using CloudWatch.

The pricing model is another attractive feature. You only pay for the compute resources you use, and when paired with Fargate, you can achieve true pay-per-use container execution. For many teams, especially those just starting with containers, this combination of simplicity and cost-effectiveness makes ECS an appealing choice.

## What are ECS limitations?

As your containerized applications grow more sophisticated, ECS's simplicity can become a double-edged sword. Let's dive into some key limitations that teams often encounter:

- **Day-to-Day operations:** ECS provides basic container orchestration, but it leaves many operational concerns up to you. There's no built-in solution for automated scaling based on custom metrics, and managing backups or ensuring zonal redundancy requires significant additional configuration. The platform doesn't offer native support for stateful services or scheduled tasks, which means you'll need to build these capabilities yourself or rely on external tools.
- **Developer experience:** While ECS handles container execution well, it doesn't provide a comprehensive developer experience out of the box. There's no integrated CI/CD solution, meaning you'll need to piece together your own deployment pipeline. Secret management isn't built in, so you'll need to figure out how to securely handle sensitive information like API keys and database credentials.
- **Observability challenges:** Monitoring and troubleshooting in ECS can be challenging. While CloudWatch provides basic metrics, getting deep insights into your application's behavior often requires additional tooling. The observability story isn't first-class - you'll find yourself stitching together various services to get a complete picture of your application's health and performance.
- **Stateful services:** ECS presents significant challenges for stateful applications. Teams must manually configure persistent storage using Amazon Elastic File System (EFS) or Amazon Elastic Block Store (EBS), each with unique complexities. EFS provides shared storage across tasks, while EBS volumes are tied to specific EC2 instances, requiring careful volume management and data durability strategies.
- **Scheduled tasks:** ECS lacks native support for scheduled tasks. Instead, teams must integrate Amazon EventBridge (formerly CloudWatch Events) and manually configure cron-like schedules for task execution. This means building custom solutions for recurring jobs like data processing, backups, or batch tasks, adding another layer of operational complexity to container management.

## Why do teams "graduate" from ECS?

As teams scale their containerized applications, they often find themselves bumping up against ECS's fundamental limitations. One of the most significant challenges stems from ECS's nature as a proprietary AWS service. Unlike open container orchestration platforms, ECS follows its own unique approach that doesn't align with broader industry standards.

This proprietary nature becomes particularly apparent when we look at Kubernetes integration. ECS can't run Kubernetes workloads because it's built as AWS's own container orchestration system, completely separate from the Kubernetes ecosystem. For teams that want to adopt cloud-native practices or leverage Kubernetes tools and patterns, this limitation often becomes a dealbreaker.

The operational challenges also compound as applications grow. Consider a team trying to implement a canary deployment - where you gradually roll out a new version to a small subset of users. In Kubernetes, this is a well documented pattern with built-in support. In ECS, you'll need to build this capability yourself, often through a complex combination of custom scripts and additional AWS services.

These limitations create a snowball effect. As your application scales, you'll find yourself spending more time building workarounds for capabilities that come standard on other platforms. Tasks that should be straightforward, like splitting traffic between different versions of your application or implementing sophisticated deployment strategies, require significant custom development effort in ECS.

When teams reach this point, they realize they're investing more time managing ECS's limitations than building and improving their applications. This realization usually marks the beginning of their search for more flexible and capable container orchestration solutions.

## What are the ECS alternatives?

When teams outgrow ECS's limitations, they typically explore a few different paths. Let's examine each approach and understand why teams often find themselves looking for something more comprehensive.

Many teams first consider serverless platforms like AWS App Runner, Azure App Service and Google Cloud Run. These platforms promise to eliminate infrastructure management entirely - an appealing proposition for teams feeling overwhelmed by ECS's operational demands. However, teams quickly discover new constraints: runtime environments are restricted, deployment patterns are limited, and scaling costs can become unpredictable.

Some organizations, particularly larger enterprises, opt to build their own platform on Kubernetes. This path seems attractive because it offers complete control over the container infrastructure. However, the reality of managing a Kubernetes platform is complex. Even with managed services like EKS, organizations need dedicated platform teams to handle infrastructure, security, and developer tooling. The initial investment is substantial, and the ongoing maintenance requires specialized expertise that's increasingly difficult to hire for.

At Northflank, we've carefully studied how teams evolve beyond ECS, and we've built our platform to address these challenges comprehensively. We understand that teams want advanced capabilities like sophisticated deployment strategies and cross-cloud flexibility, but they don't want to manage complex infrastructure or piece together multiple tools.

Our platform provides everything growing teams need: integrated CI/CD pipelines, robust observability tools, and native support for stateful services. You can implement canary deployments, split traffic between versions, and manage secrets across environments - all through an intuitive interface that maintains the simplicity teams initially loved about ECS.

What sets our approach apart is how we've maintained simplicity while delivering power. Teams can deploy across any cloud provider and implement advanced deployment strategies without becoming infrastructure experts. We've built the platform we wished existed when we were scaling our own applications - one that grows with your needs without growing in complexity.

For teams ready to move beyond ECS, the choice ultimately comes down to your priorities. If you're looking for a platform that combines the simplicity of serverless with the power of Kubernetes, without the operational overhead of building your own platform, we'd love to show you how Northflank can help. [Schedule a live demo here](https://cal.com/team/northflank/northflank-demo?date=2025-01-13&month=2025-01)*, or* [get started with Northflank here](https://app.northflank.com/signup)*.*]]>
  </content:encoded>
</item><item>
  <title>Best cloud hosting platforms for 2026</title>
  <link>https://northflank.com/blog/best-cloud-hosting-platforms</link>
  <pubDate>2025-12-29T17:45:00.000Z</pubDate>
  <description>
    <![CDATA[Cloud hosting platforms compared: AWS, Azure, GCP vs Northflank, Render, Railway. Northflank lets you deploy in your own cloud to avoid vendor lock-in]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/best_cloud_hosting_platforms_33b17e894e.png" alt="Best cloud hosting platforms for 2026" />Cloud hosting has moved beyond the AWS, Azure, and GCP decision. Teams now choose between infrastructure control and developer productivity, but the best platforms deliver both.

This guide compares traditional cloud providers with modern platforms that remove complexity while maintaining enterprise control.

<InfoBox type="info">

## TL;DR: Best cloud hosting platforms for 2026
1. **Northflank** – Deploy any workload (apps, databases, AI/ML with GPUs) in Northflank's cloud, your own cloud (AWS, Azure, GCP, Oracle, Civo, CoreWeave, bare-metal), or your customer's infrastructure. Transparent pricing with free sandbox, pay-as-you-go self-service, and enterprise tier for custom SLAs.
2. **Render** – Heroku alternative with automatic Git deployments, managed PostgreSQL and Redis, static sites with global CDN
3. **Railway** –  Template marketplace, Nixpacks or Dockerfile builds, automated service discovery with internal networking
4. **Fly.io** – Global edge deployment with microVMs
5. **AWS** – Comprehensive service catalog with 200+ services including EC2, S3, RDS, Lambda across 30+ regions
6. **Azure** – Microsoft ecosystem integration with Windows Server, .NET, Active Directory, hybrid cloud support
7. **GCP** – Vertex AI, BigQuery, GKE (Google Kubernetes Engine), multi-region deployment
8. **DigitalOcean** – Droplets (VMs), managed Kubernetes, managed databases (PostgreSQL, MySQL, Redis)
9. **Vultr** – Cloud compute, bare metal servers, block storage across 32 global locations

**Why Northflank bridges both categories:** Most cloud hosting forces you to choose between simplicity (Heroku, Render) and control (AWS, Azure). Northflank's [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) model lets you deploy in your own cloud accounts while getting a fully managed platform. You avoid vendor lock-in, use existing cloud credits, and maintain data sovereignty, without managing Kubernetes yourself.

</InfoBox>


## What is cloud hosting?

Cloud hosting runs applications on virtual servers distributed across multiple data centers. Two main categories exist:

- **Traditional cloud providers** (AWS, Azure, GCP) give you virtual machines, object storage, and managed databases. You configure everything.
- **Modern cloud platforms** (Northflank, Render, Railway) abstract infrastructure with built-in CI/CD, auto-scaling, and GitOps workflows. You deploy code, they handle infrastructure.

## Which modern cloud hosting platforms deliver both control and simplicity?

Modern platforms abstract infrastructure complexity while maintaining enterprise capabilities. Here are the leading options:

### 1. Northflank – Option to deploy in your cloud or on-premise, or use managed infrastructure

[Northflank](https://northflank.com/) solves the core problem with cloud hosting: you shouldn't have to choose between simplicity and control.

Traditional PaaS platforms (Heroku, Render) run on their own infrastructure, which means you can't deploy in your own cloud account. Traditional cloud (AWS, Azure) requires dedicated DevOps teams. Northflank's [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) architecture gives you both: managed platform experience while running in your own AWS, Azure, GCP, Oracle, Civo, CoreWeave, bare-metal, or on-premise infrastructure.

![northflank-paas-home-page.png](https://assets.northflank.com/northflank_paas_home_page_0cff0595d9.png)

**What Northflank offers:**

- Deploy to 6+ managed cloud regions and 600 BYOC regions for global reach and data residency
- Built-in CI/CD pipelines, GitOps workflows, and preview environments for every pull request
- Managed databases ([PostgreSQL](https://northflank.com/dbaas/managed-postgresql), [MySQL](https://northflank.com/dbaas/managed-mysql), [MongoDB](https://northflank.com/dbaas/mongodb-on-northflank), [Redis](https://northflank.com/dbaas/managed-redis)) with automated backups and scaling
- GPU orchestration for AI workloads including NVIDIA A100, H100, H200, B200, and more [GPU options](https://northflank.com/gpu)
- Kubernetes-powered without Kubernetes complexity for production-grade reliability (without requiring Kubernetes expertise)

<InfoBox type="warning">

**Pricing:**

Northflank offers transparent usage-based pricing with three tiers:

- **Free sandbox tier:** Get started at no cost with always-on compute, 2 free services, 2 free databases, and 2 free cron jobs for testing
- **Pay-as-you-go tier:** Self-service with minimal restrictions. Pay only for what you consume:
    - Compute: $0.01667 per vCPU/hour, $0.00833 per GB memory/hour
    - Storage: $0.15 per GB/month for NVMe disks
    - Networking: $0.06 per GB data transfer
    - Managed databases: PostgreSQL, MongoDB, Redis, and MySQL included
- **Enterprise tier:** Custom requirements, SLAs, white-label options, always-on support, and BYOC (Bring Your Own Cloud) deployment to AWS, GCP, Azure, Oracle, Civo, CoreWeave, on-premises, or bare-metal infrastructure

Use the [pricing calculator](https://northflank.com/pricing) to estimate costs. With BYOC, you pay a platform fee while consuming compute through your own cloud provider, leveraging existing credits and commitments.

</InfoBox>

**Best for:**

- Teams wanting managed platform experience without vendor lock-in
- Enterprises with data sovereignty and compliance requirements
- Organizations leveraging existing cloud credits (AWS, Azure, GCP)
- AI/ML workloads requiring GPU orchestration
- Companies migrating from Heroku or traditional PaaS
- DevOps teams needing Kubernetes benefits without operational overhead

**Why choose Northflank over alternatives:**

- **vs Heroku/Render:** You own the infrastructure, use existing cloud credits, no vendor lock-in
- **vs AWS/Azure/GCP:** Managed platform removes DevOps overhead while you keep control
- **vs other BYOC platforms:** Full-stack (apps, databases, CI/CD, monitoring), not just orchestration

<InfoBox type="info">

Learn more about [private cloud deployments](https://northflank.com/blog/7-best-private-cloud-hosting-platforms-in-2025) and [managed cloud hosting](https://northflank.com/blog/managed-cloud-hosting) options.

[Try Northflank free](https://app.northflank.com/signup), [book a demo](https://cal.com/team/northflank/northflank-intro), or [request GPU access](https://northflank.com/request/gpu) for AI workloads.

</InfoBox>

### 2. Render

Render provides automatic deployments from Git with native support for static sites, web services, and databases.

![render's home page.png](https://assets.northflank.com/render_s_home_page_2982a329f2.png)

**What Render offers:**

- Automatic builds and deployments from GitHub, GitLab
- Managed PostgreSQL and Redis with automated backups
- Static sites with global CDN distribution
- Zero-downtime deployments with health checks
- Preview environments for pull requests

**Best for:** Teams migrating from Heroku, full-stack applications requiring managed databases.

Compare [Render vs Heroku](https://northflank.com/blog/render-vs-heroku) or explore [Render alternatives](https://northflank.com/blog/render-alternatives).

### 3. Railway

Railway provides a template marketplace and automated service discovery for rapid deployment.

![railway-min.png](https://assets.northflank.com/railway_min_10957de907.png)

**What Railway offers:**

- Deploy from templates or connect GitHub repositories
- Railpack or Dockerfile-based builds
- Automated service discovery with private networking
- Scheduled jobs using crontab expressions
- Multi-region replica deployment

**Best for:** Side projects, early-stage startups, developers wanting rapid deployment from templates.

### 4. Fly.io

Fly.io runs containers globally using Firecracker microVMs optimized for low latency.

![fly.io-min.png](https://assets.northflank.com/fly_io_min_bfc65ba670.png)

**What Fly.io offers:**

- Global deployment across multiple regions
- Firecracker microVMs for fast cold starts
- Automatic HTTPS and custom domains
- WireGuard-based private networking
- Persistent volumes for stateful applications

**Best for:** Applications requiring global low latency, edge computing use cases, containerized workloads.

## **What are the best traditional cloud hosting providers?**

Traditional cloud providers offer infrastructure-level control with comprehensive service catalogs. They require dedicated DevOps expertise but provide maximum flexibility for custom architectures:

### 5. AWS

AWS provides the most extensive cloud service catalog with global infrastructure and deep service integration.

![aws-homepage.png](https://assets.northflank.com/aws_homepage_becac6f4be.png)

**What AWS offers:**

- EC2 compute instances with extensive configuration options
- RDS managed databases (PostgreSQL, MySQL, Oracle, SQL Server)
- Lambda serverless compute
- VPC networking with granular security controls
- 200+ services across compute, storage, databases, ML, analytics

**Best for:** Enterprises with dedicated DevOps teams, applications requiring AWS-specific services, organizations needing maximum service breadth.

**Trade-off:** Requires significant operational expertise, complex billing structure, steep learning curve.

### 6. Azure

Azure provides tight integration with Microsoft technologies and comprehensive enterprise tooling.

![azure-homepage.png](https://assets.northflank.com/azure_homepage_a747d6831d.png)

**What Azure offers:**

- Virtual Machines with Windows and Linux support
- Azure Active Directory for identity management
- Hybrid cloud with Azure Arc and Azure Stack
- .NET application hosting and development tools
- Blob Storage and Azure SQL Database

**Best for:** Microsoft-centric organizations, hybrid cloud deployments, enterprises requiring extensive compliance certifications.

**Trade-off:** Less flexible outside Microsoft ecosystem.

### 7. GCP

Google Cloud specializes in data analytics, machine learning, and Kubernetes orchestration.

![gcp-homepage.png](https://assets.northflank.com/gcp_homepage_c7eb40c44d.png)

**What GCP offers:**

- Vertex AI for machine learning model training and deployment
- [BigQuery for data analytics](https://blog.coupler.io/bigquery-for-data-analysts/)
- GKE with industry-leading Kubernetes management
- Compute Engine with sustained use discounts
- Cloud Storage with multiple storage classes
- Multi-cloud portability with Anthos

**Best for:** Data-intensive applications, AI/ML workloads, Kubernetes-native development, teams prioritizing data analytics.

**Trade-off:** Smaller service catalog compared to AWS.

### 8. DigitalOcean

DigitalOcean simplifies cloud infrastructure with straightforward configurations and predictable pricing.

![Digitalocean app platform's home page.png](https://assets.northflank.com/Digitalocean_app_platform_s_home_page_0f9ea04b7b.png)

**What DigitalOcean offers:**

- Droplets with fixed resource configurations
- Managed Kubernetes for container orchestration
- Managed databases with automated backups
- Spaces object storage compatible with S3 API
- App Platform for application deployment
- Load balancers and block storage

**Best for:** SMBs, developers learning cloud infrastructure, teams wanting simplified management.

See [DigitalOcean alternatives](https://northflank.com/blog/best-digitalocean-alternatives-2025) for comparisons.

### 9. Vultr

Vultr provides high-performance compute options with bare metal availability.

![vultr.png](https://assets.northflank.com/vultr_355dc4cdae.png)

**What Vultr offers:**

- Cloud Compute instances with hourly billing
- Bare Metal servers for dedicated hardware
- Block Storage for persistent volumes
- Object Storage compatible with S3
- DDoS protection included
- 32 data center locations globally

**Best for:** Performance-critical applications, teams requiring bare metal servers, global distribution needs.

## Which cloud hosting platform should you choose?

Different platforms suit different needs. Use this decision framework to match your requirements with the right solution:

| Choose this | If you need |
| --- | --- |
| **Traditional cloud (AWS/Azure/GCP)** | Dedicated DevOps engineers available, specific services only major providers offer, custom infrastructure architectures, enterprise cloud agreements already in place |
| **Modern platforms (Northflank/Render/Railway)** | Focus on code instead of infrastructure, minimal DevOps resources, developer productivity over granular control, standard web applications or APIs |
| **Northflank specifically** | Modern platform experience with enterprise control, BYOC ([Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud)) to avoid vendor lock-in, [GPU orchestration](https://northflank.com/request/gpu) for AI workloads, data sovereignty and compliance controls, leverage existing cloud credits while getting managed benefits |

## What are the specialized cloud hosting scenarios?

Beyond standard web applications, certain workloads require specific infrastructure capabilities. Here's how different platforms handle specialized deployment needs:

### Private cloud hosting

For organizations requiring complete infrastructure control and data residency, [private cloud hosting](https://northflank.com/blog/7-best-private-cloud-hosting-platforms-in-2025) offers dedicated resources. Northflank's BYOC model provides private cloud benefits with managed platform simplicity.

### Multi-cloud deployments

Running across multiple clouds prevents vendor lock-in and optimizes costs. Northflank's [multi-cloud platform](https://northflank.com/blog/best-multi-cloud-management-platforms) supports deployment to 6+ managed cloud regions and 600+ BYOC regions across AWS, Azure, GCP, Oracle, Civo, CoreWeave, on-premise, and bare-metal. Learn about [multi-cloud vs hybrid cloud](https://northflank.com/blog/multi-cloud-vs-hybrid-cloud) approaches.

### AI and ML workloads

GPU-intensive applications need specialized infrastructure. Northflank offers native [GPU support](https://northflank.com/gpu) (H100, B200, and more) with fractional GPU allocation. Traditional cloud providers require manual configuration of GPU instances and orchestration.

## Ready to choose your cloud hosting platform?

Cloud hosting divides into two distinct categories: traditional providers (AWS, Azure, GCP) offer maximum control but require DevOps expertise, while modern platforms (Render, Railway) prioritize simplicity but run only on their infrastructure.

Northflank bridges this gap. Deploy in your own cloud account (AWS, Azure, GCP, Oracle, Civo, CoreWeave, bare-metal, or on-premise) while getting a fully managed platform. You maintain infrastructure ownership, use existing cloud credits, and avoid vendor lock-in, without managing Kubernetes yourself.

For teams building modern applications, especially those with enterprise requirements or AI workloads, Northflank delivers both developer productivity and infrastructure control.

<InfoBox type="info">

[Try Northflank free](https://app.northflank.com/signup), [book a demo](https://cal.com/team/northflank/northflank-intro), or [request GPU access](https://northflank.com/request/gpu) for AI workloads.

</InfoBox>

## Related resources

These guides provide deeper insights into cloud hosting options, platform comparisons, and deployment strategies:

- [Best cloud application hosting platforms](https://northflank.com/blog/cloud-application-hosting-platforms)
- [7 best private cloud hosting platforms](https://northflank.com/blog/7-best-private-cloud-hosting-platforms-in-2025)
- [What is managed cloud hosting?](https://northflank.com/blog/managed-cloud-hosting)
- [Best PaaS that runs in your own cloud account](https://northflank.com/blog/best-paas-that-runs-in-my-own-cloud-account-bypc-self-hosted-paas)
- [Best multi-cloud management platforms](https://northflank.com/blog/best-multi-cloud-management-platforms)]]>
  </content:encoded>
</item><item>
  <title>Best e-commerce hosting platforms in 2026</title>
  <link>https://northflank.com/blog/best-ecommerce-hosting</link>
  <pubDate>2025-12-29T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[Best ecommerce hosting platforms: Northflank for scalable infrastructure, Shopify, BigCommerce, WooCommerce. Compare features for 2026.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/best_ecommerce_hosting_f1696696bd.png" alt="Best e-commerce hosting platforms in 2026" /><InfoBox type="info">

Quick summary: Best e-commerce hosting ranges from simple setup platforms to scalable infrastructure for custom applications.

For development teams from solo technical founders to enterprise engineering teams building unique online stores that need to scale, Northflank provides complete cloud hosting and infrastructure management, including databases, automated deployments, and traffic scaling with transparent usage-based pricing.

The article also covers e-commerce-specific platforms like Shopify, BigCommerce, and WooCommerce.

</InfoBox>

Choosing ecommerce hosting determines whether your online store loads in 2 seconds or 6, handles 100 orders per day or 10,000, and scales efficiently as you grow.

But "best ecommerce hosting" means different things at different stages. A merchant launching their first store with an all-in-one platform needs different infrastructure than a development team deploying headless commerce with Next.js and TypeScript.

This guide breaks down e-commerce hosting options from simple setup platforms to scalable infrastructure, covering when each approach makes sense.

## Which cloud hosting is best for e-commerce sites?

The answer depends on what you're building and your team's structure.

### Complete cloud hosting platform for scalable e-commerce

Northflank lets development teams from solo technical founders to enterprises build and scale custom ecommerce platforms that handle everything from 100 to 100,000+ orders per day.

You can deploy across Northflank's managed cloud or your own cloud with AWS, GCP, Azure, Oracle, CoreWeave, on-premises, or bare-metal infrastructure.

The platform manages databases, automated deployments, monitoring, and scaling while giving developers complete flexibility to build unique shopping experiences like custom storefronts, headless commerce systems, or white-label platforms that can serve multiple brands from one codebase.

### E-commerce-specific platforms

Shopify and BigCommerce are built specifically for online stores with product catalogs, checkout systems, and payment processing included as core features. These work well for standard retail workflows but use template-based designs and predetermined feature sets.

### WordPress-focused hosting

Managed WordPress hosts like WP Engine and Bluehost provide WordPress-optimized infrastructure for teams building WooCommerce stores. These are designed specifically for the WordPress ecosystem and WooCommerce plugin architecture.

<InfoBox type="warning">

The key differentiator is specialization versus flexibility.

E-commerce-specific platforms optimize for standard online retail workflows and work best when your store fits their template model.

Complete cloud hosting platforms like Northflank provide managed infrastructure with flexibility to build any e-commerce architecture, from simple storefronts to complex multi-tenant systems, while handling operational complexity like autoscaling, database management, and zero-downtime deployments.

</InfoBox>

## What ecommerce hosting features matter most?

Understanding key features helps you evaluate platforms and make informed decisions for your business.

- **Security:** SSL certificates encrypt customer data during transmission and come standard with modern hosting.
- **Performance:** Server response times affect conversions and search rankings. Look for CDN integration, database optimization, and the ability to scale resources during traffic spikes.
- **Deployments:** Managed platforms handle updates through their interface. Developer platforms like [Northflank](https://northflank.com/docs/v1/application/build/build-code-from-a-git-repository) use git-based workflows where code pushes trigger automatic deployments and enable preview environments for testing.
- **Database management:** Managed platforms include database hosting. Developer platforms provide managed database options (PostgreSQL, MongoDB, Redis, MySQL) with automatic backups and scaling.
- **Support:** Managed platforms offer 24/7 customer support. Developer platforms provide infrastructure support while you handle application decisions.
- **Integrations:** Managed platforms offer app marketplaces. Developer platforms provide API access for custom integrations.

## Best e-commerce hosting platforms in 2026

Modern e-commerce spans a spectrum from all-in-one platforms for beginners to sophisticated infrastructure for custom applications. Each approach serves different needs and team structures.

### 1. Northflank: best for teams building modern, scalable ecommerce platforms

Northflank is a complete cloud hosting platform for scalable ecommerce that lets technical teams deploy and scale containerized applications across multiple cloud providers without managing Kubernetes directly.

**Best for:** Development teams building custom e-commerce platforms, headless commerce implementations, teams migrating from Heroku, SaaS, or multi-tenant storefronts, and engineering teams where infrastructure control is a competitive advantage

![northflank-paas-home-page.png](https://assets.northflank.com/northflank_paas_home_page_0cff0595d9.png)

**Why businesses and teams choose Northflank for e-commerce hosting**

Northflank provides complete e-commerce hosting infrastructure that handles deployments, databases, and scaling without requiring dedicated DevOps engineers. Host custom storefronts built with Next.js, headless commerce platforms like Vendure or Medusa, or ecommerce applications using Node.js and Python with databases like PostgreSQL, MongoDB, and Redis.

**Hosting capabilities:**

- **Automated deployments** - Push code to GitHub, and Northflank automatically deploys your e-commerce store with zero downtime.
- **Database hosting** - Host PostgreSQL, MongoDB, Redis, or MySQL within your infrastructure. Store product data, transactions, and catalogs with automatic backups and scaling.
- **Preview environment** - Test new checkout flows and payment integrations in automatically created staging environments before going live.
- **Traffic-based scaling** - Your hosting automatically scales during flash sales and high-traffic periods based on actual demand.
- [**Host in your own cloud**](https://northflank.com/features/bring-your-own-cloud) - Deploy ecommerce hosting to your AWS, GCP, Azure, Civo, Oracle, CoreWeave, on-premises, or bare-metal infrastructure while keeping data in your VPC.

**Additional hosting features:**

- Host separate services for product catalogs, payments, and order fulfillment
- Schedule automated tasks for inventory updates and customer emails
- Built-in monitoring and alerting for your hosted applications
- [Template-based hosting](https://northflank.com/features/templates) for deploying complete e-commerce environments instantly

<InfoBox type="info">

**Pricing structure**

Northflank offers transparent usage-based pricing with three tiers:

**Free Sandbox tier:** Get started at no cost with always-on compute, 2 free services, 2 free databases, and 2 free cron jobs, perfect for testing and building trust with the platform.

**Pay-as-you-go tier:** Self-service with minimal restrictions and no salesperson needed. Pay only for what you consume:

- **Compute:** $0.01667 per vCPU per hour, $0.00833 per GB memory per hour
- **Storage:** $0.15 per GB per month for NVMe disks
- **Networking:** $0.06 per GB for data transfer
- **Databases:** Managed PostgreSQL, MongoDB, Redis, and MySQL included

**Enterprise tier:** Custom requirements, SLAs, white-label options, and always-on support with volume discounts, annual commitments, and the ability to run in your VPC across AWS, GCP, Azure, Oracle, Civo, CoreWeave, on-premises, or bare-metal infrastructure.

Pricing scales linearly with your resource usage. Use the [pricing calculator](https://northflank.com/pricing#calculator) to estimate costs based on your specific requirements.

With Bring Your Own Cloud (BYOC), you pay a platform fee and consume compute resources through your own cloud provider, leveraging existing credits or committed use agreements for additional cost savings.

</InfoBox>

<InfoBox type="success">

**When Northflank makes sense for e-commerce hosting**

Choose Northflank for e-commerce hosting if you're:

- Building or hosting custom e-commerce platforms with modern frameworks like Next.js, Vue, or headless commerce systems like Vendure and Medusa
- Outgrowing Heroku's pricing constraints and needing better economics at scale
- Running a multi-tenant ecommerce where each customer needs an isolated infrastructure
- Need preview environments for testing new features before production
- Want infrastructure control without hiring Kubernetes experts
- Require a scalable e-commerce infrastructure where your technical architecture is a competitive advantage

[Start hosting on Northflank](https://app.northflank.com/signup) with the free sandbox tier, see the [Vendure deployment guide](https://northflank.com/stacks/deploy-vendure) for deploying modern commerce frameworks on Northflank, or [talk to an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) to discuss your specific infrastructure requirements.

</InfoBox>

### 2. Shopify

Shopify is an all-in-one e-commerce platform that handles hosting, payment processing, inventory management, and storefront in a single system. The platform includes templates, checkout flows, and shipping integrations designed to get stores online quickly. Shopify works through a subscription model with different tiers based on business size and feature requirements.

**Typical users:** First-time merchants, physical product sellers, businesses without technical resources

**Key features:**

- Built-in payment processing
- Mobile app for management
- App marketplace for extensions
- Template-based storefront design
- Integrated shipping tools

### 3. BigCommerce

BigCommerce is an e-commerce platform that includes features like B2B tools and multi-channel selling as standard, reducing dependency on third-party apps. The platform provides product catalog management, order processing, and marketing tools within the base subscription. BigCommerce supports selling across multiple channels, including Amazon, eBay, and social media platforms, from a single dashboard.

**Typical users:** Growing businesses, B2B sellers, merchants needing native B2B and multi-channel capabilities

**Key features:**

- Multi-channel selling
- Native B2B functionality
- SEO features
- Product catalog management
- Integrated marketing tools

### 4. Bluehost WooCommerce hosting

Bluehost provides WordPress hosting with WooCommerce optimization for stores built on WordPress. The hosting includes server configurations optimized for WordPress performance and WooCommerce plugin compatibility. Bluehost handles the hosting infrastructure while you manage the WordPress site and WooCommerce configuration through the WordPress admin panel.

**Typical users:** WordPress users, content-focused stores, bloggers adding ecommerce to existing sites

**Key features:**

- One-click WooCommerce installation
- Domain included
- SSL certificate
- WordPress-optimized servers
- cPanel management interface

### 5. WP Engine

WP Engine provides managed WordPress hosting with infrastructure management included. The platform handles WordPress core updates, security patches, and server optimization specifically for WordPress sites. WP Engine includes staging environments for testing changes before deploying to production, and provides support from WordPress-focused technical teams.

**Typical users:** WooCommerce stores at scale, agencies with multiple clients, high-traffic WordPress sites

**Key features:**

- Automatic WordPress updates
- Daily backups
- Staging environments
- CDN
- WordPress security
- Managed infrastructure

## What hosting should I choose for headless commerce platforms?

Headless commerce separates your frontend (what customers see) from your backend (product management, orders, payments), requiring different infrastructure than traditional all-in-one platforms. Popular frameworks include Vendure, Medusa, Saleor, and Commerce.js.

These frameworks need container orchestration for running Node.js or Python backends, managed databases for product catalogs and orders, CI/CD pipelines for deploying frontend and backend independently, and preview environments for testing new features before production deployment.

[Northflank](https://northflank.com/) is built for containerized applications from the ground up. The [Vendure deployment guide](https://northflank.com/stacks/deploy-vendure) shows deploying a complete headless commerce stack with databases, monitoring, and automated deployments in a few minutes for running production headless commerce.

![deploy-vendure-on-northflank.png](https://assets.northflank.com/deploy_vendure_on_northflank_19f39948cf.png)

## Getting started with e-commerce hosting

The right ecommerce hosting, whether that’s [managed Magento hosting](https://www.mgt-commerce.com/magento-hosting/) or another approach, depends on what you’re building and who’s building it.

All-in-one platforms like Shopify and BigCommerce work for standard retail stores. WordPress hosting suits content-heavy sites.

For teams building custom e-commerce platforms with modern frameworks or headless commerce, [Northflank](https://northflank.com/) provides complete infrastructure with managed databases, automated deployments, and the flexibility to host in your own cloud.

<InfoBox type="success">

[Start hosting on Northflank](https://app.northflank.com/signup), browse the [documentation](https://northflank.com/docs/v1/application/overview), or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30) to discuss your infrastructure needs.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>GitHub self-hosted runners cost increase and alternatives (2026)</title>
  <link>https://northflank.com/blog/github-pricing-change-self-hosted-alternatives-github-actions</link>
  <pubDate>2025-12-17T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[GitHub is charging $0.002/minute for self-hosted runners starting March 2026. Here are the alternatives.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/github_self_hosted_runners_1_9c62e7934a.png" alt="GitHub self-hosted runners cost increase and alternatives (2026)" /><InfoBox className='BodyStyle'>

## 📌 TL;DR

GitHub is [charging](https://www.reddit.com/r/devops/comments/1po8hj5/github_actions_introducing_a_perminute_fee_for/) $0.002/minute for self-hosted runners starting March 2026. 

Teams now have three options: 

1. Stay on GitHub-hosted runners and pay per-minute
2. Keep self-hosted runners and absorb the new platform fee
3. Migrate to a platform like [Northflank](https://northflank.com/) that handles CI/CD, deployments, and databases together without per-minute fees.

</InfoBox>

GitHub Actions is the default CI/CD tool for many teams on GitHub. When GitHub-hosted runners become too slow or expensive, self-hosted runners seem like the obvious next step. But starting March 2026, self-hosted runners come with new per-minute fees, and they've always come with significant operational overhead.

This guide covers GitHub Actions pricing changes, what self-hosted runners require to operate, and alternatives that might save your team time and money.

![CleanShot 2025-12-17 at 08.48.17@2x.png](https://assets.northflank.com/Clean_Shot_2025_12_17_at_08_48_17_2x_25a4b561fc.png)

## What are GitHub self-hosted runners?

Self-hosted runners are machines you provision and maintain that execute GitHub Actions workflows. Your code runs on infrastructure you control (VMs in AWS, bare-metal servers, or Kubernetes clusters) instead of GitHub's virtual machines.

Teams move to self-hosted runners for:

- **Cost control** at scale
- **Performance** (faster CPUs, more memory, local caching)
- **Custom environments** (specific OS versions, pre-installed dependencies)
- **Security** (code stays in your network)
- **Access to internal resources** (databases, APIs behind firewalls)

## GitHub self-hosted runners pricing in March 2026

![CleanShot 2025-12-17 at 09.34.59@2x.png](https://assets.northflank.com/Clean_Shot_2025_12_17_at_09_34_59_2x_a3976c3335.png)

Self-hosted runners have been free to use, you only paid for your own infrastructure. That changes on March 1, 2026.

GitHub is introducing a **$0.002 per minute platform charge** for self-hosted runner usage in private repositories. This covers the Actions control plane: job orchestration, scheduling, and workflow automation.

| Monthly Minutes | Platform Cost |
| --- | --- |
| 10,000 | $20 |
| 50,000 | $100 |
| 100,000 | $200 |
| 500,000 | $1,000 |

This fee applies regardless of where your runners are hosted, AWS, GCP, your own data center. You're paying GitHub for orchestration.

Public repositories and GitHub Enterprise Server customers are exempt.

## Your options going forward

With the March 2026 Github self-hosted runners pricing changes, teams have three paths:

**1. Stay on GitHub-hosted runners**

Accept the per-minute costs for GitHub's managed runners. This works if your build volume is low enough that the convenience outweighs the cost. GitHub is reducing hosted runner prices by up to 39% starting January 2026, which helps. But at scale, minutes add up fast, and you're still limited by GitHub's runner specs and concurrency limits.

**2. Keep self-hosted runners and absorb the new costs**

If you've already invested in self-hosted infrastructure and your team has the capacity to maintain it, paying the $0.002/min platform fee might be acceptable. You keep your custom environments and performance optimizations. You also keep the operational burden: ARC management, runner updates, scaling logic, and debugging.

**3. Migrate to a platform that handles CI/CD and deployment together**

Instead of managing runners, use a platform where builds, deployments, databases, and infrastructure are integrated. You trade GitHub Actions workflows for a system designed to handle the full path from code to production.

For teams evaluating option 3, here's what's available.

## Alternatives to Github Actions

If the operational overhead doesn't fit your team, here are some [alternatives](https://northflank.com/blog/github-actions-alternatives) to Github Actions worth evaluating.

### Northflank - #1 Alternative to Github Actions

Northflank handles CI/CD alongside deployment, databases, and infrastructure in one platform. Instead of managing runners, you connect a repository and Northflank builds every commit.

**How it works:**

1. Connect your GitHub, GitLab, or Bitbucket repository
2. Northflank builds every commit using your Dockerfile or buildpack
3. Builds deploy automatically to preview, staging, or production environments
4. Logs, metrics, and health checks are included

**CI/CD features:**

- Automatic builds on push, PR, or branch rules
- Build caching for Docker layers and dependencies
- Parallel builds for monorepos
- Real-time build logs

**Beyond CI/CD:**

Northflank includes preview environments with isolated databases for every PR, managed databases (Postgres, MySQL, MongoDB, Redis), cron jobs, autoscaling (including scale-to-zero), custom domains with automatic TLS, secret management, and RBAC.

**Deployment options:**

- **Northflank Cloud**: Fully managed
- **BYOC**: Deploy to your AWS, GCP, or Azure account with Northflank managing Kubernetes in your VPC

**Pricing**: Compute resources (vCPU and memory) prorated to the second. No per-minute platform fee. Free Developer Sandbox tier available.

For teams not ready to fully move away from GitHub Actions, Northflank integrates with existing workflows, keep your CI processes while using Northflank for deployments and hosting.

### CircleCI

Established CI/CD platform with cloud and self-hosted runners. Highly customizable YAML-based workflows with Docker, Kubernetes, and VM support.

Best for larger teams with complex pipelines that need flexibility and integrations. No native hosting or service management, you'll need separate tools for deployments and databases.

### GitLab CI

CI/CD built into GitLab's DevOps platform. Works with cloud-hosted and self-hosted environments.

Best for teams already on GitLab who want integrated project management alongside CI/CD. Less flexible for multi-cloud setups.

### Buildkite

Hybrid model: pipelines run from the cloud, builds run on your own infrastructure via lightweight agents.

Best for teams with strict security or compliance requirements who need infrastructure control. You manage and scale your own build infrastructure.

### Faster GitHub Actions Runners

If you want to keep GitHub Actions workflows but need better performance, third-party runner providers offer faster, managed self-hosted runners:

- **Depot**: Faster builds with managed runners
- **WarpBuild**: High-performance runners, often 2x faster than GitHub-hosted
- **Namespace Labs**: Remote development environments with fast CI

These still use GitHub Actions, they replace the runner infrastructure, not the workflow system.

## Comparison

| Concern | GitHub Self-Hosted | Northflank | CircleCI | Buildkite |
| --- | --- | --- | --- | --- |
| Setup time | Days to weeks | Minutes | Hours | Hours + agent management |
| Autoscaling | ARC or custom | Built-in | Configurable | Agent-based |
| Platform fee (March 2026) | $0.002/min | None | Usage-based | Flat + infra |
| Kubernetes required | For ARC | No | No | No |
| Preview environments | Build yourself | Built-in | No | No |
| Databases | Separate service | Included | Separate | Separate |
| Observability | Add tooling | Included | Add tooling | Add tooling |

## When self-hosted runners make sense

Self-hosted runners fit specific scenarios:

- **Extreme customization** (specific hardware, exotic OS)
- **Existing Kubernetes expertise** and operational capacity
- **GitHub Enterprise Server** customers (no platform fee)
- **Public repositories** (no platform fee)

## When a platform (like Northflank) makes sense

For most teams, a platform approach is more practical:

- Teams without dedicated DevOps engineers
- Startups that need to ship without infrastructure work
- Growing companies hitting GitHub-hosted runner limits
- Teams wanting preview environments without building them
- Anyone who'd rather write application code than CI infrastructure, and that includes enterprises

## Getting started

1. **Audit current usage**: How many minutes? What's the operational load?
2. **Calculate total cost**: Infrastructure + platform fee + engineering time
3. **Try alternatives**: Northflank's free tier lets you deploy and build without configuration
4. **Compare**: Does a platform approach reduce complexity for your team?

Sign up at [northflank.com](https://northflank.com/) and deploy your first project in minutes.

## Summary

GitHub self-hosted runners solve cost, performance, and control problems. They also require infrastructure management, autoscaling complexity, and, starting March 2026, a per-minute platform fee.

Northflank provides builds, deployments, databases, and observability in one platform. No Kubernetes expertise required. No runner maintenance. No platform fees on compute.

Managing self-hosted runners is one option. Using a platform that handles CI/CD and deployment together is another. Choose based on your team's capacity and priorities.

## FAQ

### How much do GitHub self-hosted runners cost?

Starting March 1, 2026, GitHub charges $0.002 per minute for self-hosted runner usage in private repositories. At 50,000 minutes/month, that's $100. At 100,000 minutes, $200. This is on top of your own infrastructure costs. Public repositories and GitHub Enterprise Server customers are exempt.

### Are GitHub Actions self-hosted runners free?

Until March 2026, yes. After March 1, 2026, GitHub introduces a platform fee of $0.002/minute for private repositories. You still pay for your own infrastructure (VMs, Kubernetes clusters, etc.) separately.

### What is the best alternative to GitHub Actions?

Northflank is the best GitHub Actions alternative for teams that want CI/CD, deployments, databases, and infrastructure in one platform. Other options include CircleCI for complex pipelines, GitLab CI for teams already on GitLab, and Buildkite for teams needing infrastructure control.

### How do I set up GitHub Actions self-hosted runners?

You provision a machine (VM, container, or bare metal), install the GitHub runner application, configure networking for outbound HTTPS, and register the runner with your repository or organization. For autoscaling, you need Actions Runner Controller (ARC) on Kubernetes or a custom webhook-based solution.

### What is Actions Runner Controller (ARC)?

ARC is a Kubernetes operator that manages and scales self-hosted GitHub Actions runners. It requires a Kubernetes cluster, Helm, and cert-manager. GitHub is deprecating legacy ARC modes in favor of the new Scale Set architecture.

### Can I use GitHub Actions without self-hosted runners?

Yes. GitHub-hosted runners are fully managed and require no infrastructure setup. You pay per minute based on runner size and OS. GitHub is reducing hosted runner prices by up to 39% starting January 2026.

### Why are teams moving away from GitHub Actions?

Common reasons include build performance bottlenecks, scaling costs, limited infrastructure control, no built-in hosting or databases, unpredictable pricing at scale, and vendor lock-in concerns. Platforms like Northflank address these by combining CI/CD with deployment and infrastructure management.

### Does Northflank work with GitHub?

Yes. Northflank connects directly to GitHub repositories. Every commit triggers a build, and deployments happen automatically to preview, staging, or production environments. Teams can also integrate existing GitHub Actions workflows with Northflank for deployments and hosting.

### What's the difference between GitHub-hosted and self-hosted runners?

GitHub-hosted runners are managed VMs that GitHub provisions and maintains. Self-hosted runners are machines you provision and maintain yourself. Self-hosted gives you more control over specs, software, and network access, but requires operational overhead and (starting March 2026) a platform fee.

### How do I reduce GitHub Actions costs?

Options include optimizing workflow efficiency, using caching, reducing build minutes, or switching to a platform with different pricing. Northflank charges for compute resources with no per-minute platform fee. Third-party runner providers like Depot and WarpBuild offer faster builds that reduce total minutes.]]>
  </content:encoded>
</item><item>
  <title>The best Container Orchestration Platforms in 2026</title>
  <link>https://northflank.com/blog/container-orchestration-platforms-kubernetes</link>
  <pubDate>2025-12-14T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[Learn how Northflank delivers enterprise Kubernetes capabilities without operational complexity.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/container_orechstration_28f8de5266.png" alt="The best Container Orchestration Platforms in 2026" /><InfoBox type="success" title="TL;DR">

## 📌 Key takeaways

<br />

<p> Container orchestration platforms form the backbone of modern infrastructure, handling container scheduling, networking, storage management, and system resilience as code evolves. </p>

<br />

<p> [Northflank](https://northflank.com/) is a **Modern Enterprise Container Platform** that delivers enterprise-grade orchestration with simplified operations, focusing on software delivery and developer experience. </p>

<br />

<p> **Northflank stands out** by running on Kubernetes while exposing developer-friendly abstractions that eliminate complexity without sacrificing power. Teams get enterprise capabilities, multi-cloud deployment, BYOC, RBAC and other enterprise features, strong isolation, GitOps workflows, without the operational burden of traditional platforms. </p>

</InfoBox>

## What is container orchestration?

In practice, a container orchestration platform standardizes how teams deploy, scale, and operate applications on Kubernetes.

Container orchestration platforms handle the critical infrastructure work that keeps modern applications running:

- **Workload scheduling** across infrastructure nodes
- **Service discovery and networking** between components
- **Deployment management** including rollouts, restarts, and failure recovery
- **Dynamic scaling** based on demand
- **Configuration and secrets** distribution
- **Security boundaries** and resource isolation

While Kubernetes powers most modern platforms, the differentiation lies in how platforms abstract complexity, enforce safety, and deliver operational efficiency.

## What engineers need from these platforms

When evaluating a **Kubernetes orchestration platform** or **enterprise Kubernetes platform**, teams consistently look for the following.

**Operational simplicity**: Reducing Kubernetes complexity without losing capabilities

**Workload versatility**: Supporting diverse application types within unified systems

**Infrastructure control**: Running inside your VPC or on-premises environments

**Security enforcement**: Implementing strong isolation and compliance boundaries

**Developer productivity**: Providing intuitive interfaces that accelerate delivery

## Types of Container Orchestration Platforms on Kubernetes

### 1. Raw Kubernetes and DIY Platforms

Building directly on Kubernetes APIs gives flexibility but comes with significant tradeoffs.

**Strengths:**

- Complete control over Kubernetes primitives
- No vendor abstractions or limitations

**Limitations:**

- Substantial ongoing operational costs
- Multi-year development timelines for internal platforms
- Fragmented developer workflows across teams
- Low adoption rates due to complexity

### 2. Traditional Enterprise Kubernetes Platforms

Platforms like OpenShift, VMware Tanzu, AKS, and GKE package Kubernetes with governance layers and vendor-supported integrations for centralized operations.

**Core capabilities:**

- Certified Kubernetes distributions with vendor backing
- Comprehensive RBAC and policy enforcement
- Integrated networking and storage solutions
- Multi-cluster management capabilities

**Tradeoffs:**

- Complex installation and upgrade procedures
- Heavy operational requirements for day-two management
- Slower iteration cycles for product teams
- Focus on control over delivery speed

### 3. 💡 Modern Enterprise Container Platforms

Northflank represents a new category: enterprise-grade orchestration designed around software delivery rather than infrastructure control. It represents a modern **enterprise Kubernetes platform** focused on software delivery rather than cluster administration.

### The Northflank approach

Built on Kubernetes, Northflank abstracts complexity while preserving power. Instead of exposing raw Kubernetes resources, it provides higher-level primitives matching how teams actually deploy software.

### Unified workload model

Northflank supports all workload types through consistent primitives:

- **Long-running services** for APIs and web applications
- **Background workers** for asynchronous processing
- **Scheduled jobs** for cron-like operations
- **Batch jobs** for one-time processing
- **Databases and stateful services** with persistent storage
- **GPU workloads** for machine learning inference and training

All workloads share the same deployment model, networking configuration, and scaling behaviors. No separate systems to learn or manage.

### Build and deployment pipeline

Northflank handles the complete build-to-production workflow:

**Build capabilities:**

- Container builds from source code or prebuilt images
- Multi-stage builds with reproducible outputs
- Integrated build caching for faster iterations

**Deployment features:**

- Zero-downtime rollout strategies with health checks
- Git-based deployments for declarative infrastructure
- CLI and API support for automation
- Automatic rollback on deployment failures

<FancyQuote
            body={
              <>
                Northflank is way easier than gluing a bunch of tools together
                to spin up apps and databases. It’s the ideal platform to deploy
                containers in our cloud account, avoiding the brain damage of
                big cloud and Kubernetes. It’s more powerful and flexible than
                traditional PaaS – all within our VPC.{' '}
                <Text as="span" color="success" fontWeight={500}>
                  Northflank has become a go-to way to deploy workloads at
                  Sentry
                </Text>
                .
              </>
            }
            attribution={
              <TestimonialHeader
                name="David Cramer"
                position="Co-Founder and CPO @ Sentry"
                avatar="/images/landing/quotes/david-c.jpeg"
                linkedin="https://www.linkedin.com/in/dmcramer/"
                mb={0}
              />
            }
            height="100%"
            small
          />

### Infrastructure flexibility

Northflank runs across diverse infrastructure environments:

**Managed cloud regions:**

- Fully managed platform-as-a-service option
- No infrastructure management required

**Bring Your Own Cloud ([BYOC](https://northflank.com/features/bring-your-own-cloud)):**

- **Google Cloud Platform**: Google Kubernetes Engine (GKE). [Docs here](https://northflank.com/docs/v1/application/bring-your-own-cloud/gcp-on-northflank).
- **Amazon Web Services**: Elastic Kubernetes Service (EKS). [Docs here](https://northflank.com/docs/v1/application/bring-your-own-cloud/aws-on-northflank).
- **Microsoft Azure**: Azure Kubernetes Service (AKS). [Docs here](https://northflank.com/docs/v1/application/bring-your-own-cloud/azure-on-northflank).
- **Civo**: Civo Kubernetes. [Docs here](https://northflank.com/docs/v1/application/bring-your-own-cloud/civo-on-northflank).
- **Oracle Cloud Infrastructure**: Oracle Kubernetes Engine (OKE). [Docs here](https://northflank.com/docs/v1/application/bring-your-own-cloud/oci-on-northflank).
- **CoreWeave**: CoreWeave Kubernetes Service (CKS). [Docs here](https://northflank.com/docs/v1/application/bring-your-own-cloud/coreweave-on-northflank).
- **On-premises and bare-metal**

This flexibility allows enterprises to maintain data boundaries and compliance requirements while adopting modern platform capabilities.

![CleanShot 2025-12-14 at 15.29.22@2x.png](https://assets.northflank.com/Clean_Shot_2025_12_14_at_15_29_22_2x_00d5b49217.png)

**Environment management:**

- Multi-cluster deployments across regions
- Environment-level isolation for staging and production
- Separate environments for internal systems

### Production-grade networking

Northflank provides comprehensive networking without per-service YAML configuration:

- Automatic service discovery within clusters
- Internal networking between components
- Public ingress with automated TLS management
- Private networking across clusters
- Traffic routing and load balancing
- Support for high-throughput, low-latency requirements

### Built-in observability

Northflank includes operational tooling without requiring separate monitoring stacks:

- Centralized logging across all workloads
- Real-time metrics and performance monitoring
- Deployment history and runtime visibility
- Health check monitoring and failure alerts
- Complete audit trails for compliance

### Security and isolation

Modern workloads often require stronger isolation than standard containers provide.

**Northflank delivers:**

- Strong workload isolation for multi-tenant architectures
- MicroVM-based isolation for sensitive workloads
- Security boundaries for untrusted code execution
- Network segmentation and access controls

### A* [Developer Experience](https://northflank.com/use-cases/self-service-developer-experience-for-kubernetes)

Northflank provides multiple interaction patterns without forcing a single workflow:

**GitOps workflows:**

- Automatic deployments from Git repositories
- Branch-based environment strategies
- Pull request preview environments

**CLI tooling:**

- Complete CLI for local development
- Scriptable automation for CI/CD pipelines
- Shell integration for common tasks

**API-driven automation:**

- Full REST API for programmatic control
- Webhook integrations for event-driven workflows
- Infrastructure-as-code compatibility

**Web interface:**

- Intuitive dashboard for visual management
- Real-time logs and metrics
- Quick debugging and troubleshooting

These interfaces are bidirectional and equal. A service deployed via Git behaves identically to one deployed via CLI or API. There are no second-class citizens.

![CleanShot 2025-12-14 at 15.28.03@2x.png](https://assets.northflank.com/Clean_Shot_2025_12_14_at_15_28_03_2x_b54ce98116.png)

Unlike traditional PaaS platforms that hit scaling limits, Northflank sits directly on Kubernetes infrastructure. Teams avoid artificial constraints, runtime limitations, or forced platform migrations as systems mature.

## Platforms comparison

The following tools are commonly evaluated as **enterprise Kubernetes platforms** or **Kubernetes management platforms** alongside Northflank.

### VMware Tanzu

VMware Tanzu targets enterprises deeply invested in VMware ecosystems.

**Core features:**

- Kubernetes distribution with VMware integration
- Centralized operations and governance
- On-premises optimization
- Long-lived cluster management

**Considerations:**

- Heavy operational requirements
- Developer workflows mediated by platform teams
- Better suited for VMware standardization than delivery optimization

### Red Hat OpenShift

OpenShift provides comprehensive enterprise Kubernetes with strong security emphasis.

**Capabilities:**

- Opinionated Kubernetes distribution
- Integrated CI/CD pipelines
- Built-in networking and security
- Robust RBAC and policy enforcement

**Tradeoffs:**

- Complex installation procedures
- Significant upgrade overhead
- Slower iteration for product teams

### Rancher (SUSE)

Rancher focuses on Kubernetes cluster management rather than complete application orchestration.

**Strengths:**

- Centralized multi-cluster visibility
- Governance across Kubernetes fleets
- Lighter weight than OpenShift

**Limitations:**

- Limited application-level abstractions
- Requires additional delivery tooling
- More cluster manager than full platform

### Platform9

Platform9 delivers managed Kubernetes with operational simplicity.

**Offerings:**

- Managed Kubernetes control planes
- Enterprise support and SLAs
- Simplified cluster operations

**Considerations:**

- Limited application layer opinions
- Developers still interact with raw Kubernetes
- Primarily operations-focused

### Portainer

Portainer provides lightweight management for Docker and Kubernetes environments.

**Features:**

- Simple UI for container visibility
- Easy adoption for small teams
- Basic cluster management

**Limitations:**

- Limited orchestration abstractions
- Not designed for complex workloads
- Functions more as visibility tool

**Best for:** Small teams needing container management UI without full platform requirements.

### Porter

Porter offers open-source PaaS capabilities on Kubernetes.

**Capabilities:**

- Kubernetes-native abstractions
- Git-based deployment workflows
- Simple deployment model

**Constraints:**

- Limited enterprise features
- Self-hosting and maintenance required
- Scalability challenges at larger sizes

## Why teams choose Northflank

Container orchestration is essential at scale, but most platforms force difficult tradeoffs between power and usability. Northflank eliminates this choice.

### **Enterprise capabilities without enterprise overhead:**

Run across any infrastructure, managed regions, your own cloud accounts, or on-premises. Support any workload type through unified primitives. Enforce strong security boundaries. All without the operational burden of traditional platforms.

### **Developer productivity at scale**

Teams deploy through Git, CLI, API, or UI, whichever fits their workflow. Sensible defaults accelerate initial delivery while override capabilities support sophisticated requirements.

### **Real Kubernetes underneath**

Unlike PaaS platforms that hit scaling limits, Northflank runs on actual Kubernetes infrastructure. Avoid artificial constraints, runtime limitations, and forced platform migrations.

### **Progressive complexity**

Start simple and add sophistication as needs evolve. No need to rebuild workflows or switch platforms as systems mature.

Organizations choosing Northflank typically seek enterprise-grade orchestration without dedicating teams to Kubernetes operations, or need sophisticated delivery capabilities their current platform can't provide.

**Ready to see how Northflank handles your orchestration challenges?**

[Schedule a demo](https://cal.com/team/northflank/northflank-intro) to discuss your requirements and explore how Northflank fits your infrastructure.

## Related resources

- [**Documentation**](https://northflank.com/docs): Complete guides for getting started
- [**Case Studies**](https://northflank.com/blog/tag/case-study): How teams use Northflank in production]]>
  </content:encoded>
</item><item>
  <title>What’s the best PaaS that can run in my own cloud account?</title>
  <link>https://northflank.com/blog/best-paas-that-runs-in-my-own-cloud-account-bypc-self-hosted-paas</link>
  <pubDate>2025-12-14T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[Northflank is the leading PaaS that deploys in your AWS, GCP, or Azure account.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/paas_byoc_f61a80aa77.png" alt="What’s the best PaaS that can run in my own cloud account?" /><InfoBox type="success" title="TL;DR">

<p> [**Northflank**](https://northflank.com/) is the leading PaaS that runs in your own cloud account. It deploys to AWS, GCP, Azure (and other deployment targets including on-prem, Oracle, Coreweave, etc), and 600+ regions, managing Kubernetes infrastructure while keeping all your data in your VPC. </p>

<p> Unlike traditional PaaS platforms, Northflank gives you complete control over data residency, compliance, and costs while providing the developer experience of platforms like Heroku.</p>

</InfoBox>

A Platform as a Service (PaaS) that runs in your own cloud account is a deployment model that combines the convenience of managed platform services with the security, compliance, and control benefits of running infrastructure in your own AWS, GCP, or Azure account. 

[Northflank](https://northflank.com/) stands out as the best PaaS that can run in your own cloud account.

Unlike traditional SaaS PaaS offerings where your applications run on the vendor's infrastructure, a PaaS like Northflank solutions deploys directly into your cloud environment while still providing the developer experience and automation you'd expect from a modern platform.

## Northflank: The best PaaS that can run in your own cloud account

![CleanShot 2025-12-14 at 12.40.37@2x.png](https://assets.northflank.com/Clean_Shot_2025_12_14_at_12_40_37_2x_98e89606ab.png)

[**Northflank**](https://northflank.com/) stands out as the top choice for organizations seeking a PaaS that operates [within their own cloud infrastructure](https://northflank.com/features/bring-your-own-cloud). It's specifically designed to bridge the gap between developer productivity and enterprise requirements for data sovereignty, security, and compliance.

### **How Northflank works**

Northflank operates through a [Bring Your Own Cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) architecture that deploys into your existing cloud accounts. Here's the technical breakdown:

### **Control Plane and runtime architecture**

Northflank's architecture separates the control plane from the runtime. 

The control plane runs in Northflank's infrastructure and provides the management interface, while the runtime (all application workloads, databases, and sensitive data) runs exclusively in your cloud account. This separation ensures your data never leaves your infrastructure while you benefit from Northflank's unified management experience.

### **Kubernetes as the foundation**

Northflank leverages Kubernetes as an operating system to give you the best of cloud native capabilities without the operational overhead. When you connect your cloud account, Northflank provisions and manages Kubernetes clusters using your cloud provider's managed Kubernetes services:

- **Google Cloud Platform**: Google Kubernetes Engine (GKE). [Docs here](https://northflank.com/docs/v1/application/bring-your-own-cloud/gcp-on-northflank).
- **Amazon Web Services**: Elastic Kubernetes Service (EKS). [Docs here](https://northflank.com/docs/v1/application/bring-your-own-cloud/aws-on-northflank).
- **Microsoft Azure**: Azure Kubernetes Service (AKS). [Docs here](https://northflank.com/docs/v1/application/bring-your-own-cloud/azure-on-northflank).
- **Civo**: Civo Kubernetes. [Docs here](https://northflank.com/docs/v1/application/bring-your-own-cloud/civo-on-northflank).
- **Oracle Cloud Infrastructure**: Oracle Kubernetes Engine (OKE). [Docs here](https://northflank.com/docs/v1/application/bring-your-own-cloud/oci-on-northflank).
- **CoreWeave**: CoreWeave Kubernetes Service (CKS). [Docs here](https://northflank.com/docs/v1/application/bring-your-own-cloud/coreweave-on-northflank).
- You can run Northflank **on-prem** and **bare-metal infrastructure**

The platform handles cluster upgrades, scaling, and maintenance automatically, so you don't need Kubernetes expertise to run production workloads.

<FancyQuote
            body={
              <>
                Northflank is way easier than gluing a bunch of tools together
                to spin up apps and databases. It’s the ideal platform to deploy
                containers in our cloud account, avoiding the brain damage of
                big cloud and Kubernetes. It’s more powerful and flexible than
                traditional PaaS – all within our VPC.{' '}
                <Text as="span" color="success" fontWeight={500}>
                  Northflank has become a go-to way to deploy workloads at
                  Sentry
                </Text>
                .
              </>
            }
            attribution={
              <TestimonialHeader
                name="David Cramer"
                position="Co-Founder and CPO @ Sentry"
                avatar="/images/landing/quotes/david-c.jpeg"
                linkedin="https://www.linkedin.com/in/dmcramer/"
                mb={0}
              />
            }
            height="100%"
            small
          />


### **Multi-cloud and multi-region support**

Northflank provides true multi-cloud capability with over 600 BYOC regions across all major cloud providers. Deploy the same Northflank workloads and projects across any cloud provider without changing a single configuration detail. This gives you:

- Data residency control with 60+ regions across the Americas, Europe, Asia Pacific, and the Middle East
- Protection against vendor lock-in
- Ability to optimize costs by choosing the best region for each workload
- Compliance with data sovereignty laws

### **Infrastructure deployment process**

1. **Cloud integration**: Connect your AWS, GCP, Azure, or other cloud account by providing IAM credentials with appropriate permissions
2. **Cluster provisioning**: Northflank provisions a managed Kubernetes cluster in your specified region and VPC 
3. **Node pool configuration**: Define node pools with your desired compute types, including GPU-enabled nodes for AI workloads
4. **Network setup**: All resources deploy within your VPC with configurable security groups and network policies

### **Building and deploying applications:**

Northflank is completely language, framework, and architecture agnostic. You can build anything using:

- **Dockerfile**: Build with any Dockerfile
- **Buildpacks**: Automatic detection using heroku/builder-classic or other buildpack builders
- **Container Images**: Pull images from any container registry (Docker Hub, ECR, GCR, ACR)

### **GitOps Integration**

- Native integration with GitHub, GitLab, and Bitbucket (both cloud and self-hosted)
- Automatic deployments triggered by Git commits
- Bidirectional GitOps: Changes to templates in Northflank commit to your repository, and changes in Git automatically update Northflank
- Build and deploy every commit, or create rules for specific branches and pull requests

### **Networking and security**

- All resources deploy within your VPC with full control over networking
- Supports HTTP/TCP/UDP ports with custom domains and subdomains
- IP policies and basic authentication built-in
- Integration with your existing VPN or direct connect solutions
- Service mesh with mTLS for secure service-to-service communication
- Namespace isolation for multi-tenant deployments

### **Database and stateful workloads**

Northflank can provision:

- Managed databases from your cloud provider (RDS, Cloud SQL, Azure Database)
- Containerized databases with automated backups and point-in-time recovery
- Supported databases: PostgreSQL (with pgvector for AI applications), MySQL, MongoDB, Redis
- Persistent volumes using your cloud provider's native storage (EBS, Persistent Disks, Azure Disks)

### **Observability**

- Built-in real-time logging and metrics with 30 days retention (first 10 GB/month free)
- Support for forwarding to external monitoring stacks (Datadog, New Relic, Prometheus)
- Container logs accessible via UI, CLI, and API
- All observability data stays within your infrastructure

### **Secret management**

- Integrates with cloud-native secret managers (AWS Secrets Manager, GCP Secret Manager, Azure Key Vault)
- Encrypted secret storage within your cluster
- Environment variables and build arguments securely injected into containers

### **Infrastructure as Code (IaaC)**

Northflank's template system provides comprehensive IaC capabilities:

- Define entire projects, services, databases, and workflows as JSON templates
- Store templates in Git repositories for version control
- One-click deployment links for sharing infrastructure setups
- Dynamic templates with variables for deploying across multiple environments

### **CI/CD and Release Pipelines**

- Visual pipeline builder for multi-stage deployments
- Preview environments that automatically generate temporary instances for pull requests and branches
- Release flows with automated tasks: database backups, build triggering, image promotion
- Git or webhook triggers for automatic releases

### **Scaling**

- Vertical scaling from 0.1 vCPU to 32 vCPU and 256 MB to 256 GB memory
- CPU and memory-based autoscaling supported
- Horizontal scaling with automatic load balancing
- GPU workloads with NVIDIA A100, H100, H200, and B200 support

### **Production features**

- Health checks with automatic container restart
- Rollback capabilities
- Blue-green deployments
- Backup and restore for databases
- Job scheduling for cron jobs and one-time tasks
- Migrations that run before deployments

### **Cost structure**

You pay your cloud provider directly for all infrastructure costs (compute, storage, networking) at standard rates. Northflank charges separately for the platform management layer. This means:

- You can utilize existing cloud credits, commitments, and discounts
- Direct visibility into infrastructure costs
- Usage-based billing from Northflank (compute resources billed by the second)

### **Compliance and Data Residency**

Because all compute and data storage occurs in your cloud account, you maintain complete data sovereignty. Your runtime environment and runtime remain within your cloud boundary, making compliance simple for standards like HIPAA, SOC 2, and ISO 27001. Your data never transits through Northflank's infrastructure, only control plane metadata does.

<InfoBox title="ENTERPRISE">

For larger enterprise customers, they can forward deploy the control plane, managed by Northflank so the control plane AND runtime can be self hosted. 

</InfoBox>

### **Multi-tenancy support**

For companies building multi-tenant applications, Northflank provides:

- Sandboxed runtime environments
- Secure network policies
- Service mesh with mTLS
- Namespace isolation
- Secret injection per tenant
- Disaster recovery capabilities

All of these multi-tenancy features come out of the box when you deploy in your own cloud account.

## Northflank pricing

🔗 Detailed information on pricing [here](https://northflank.com/pricing).

Northflank's BYOC PaaS pricing model separates infrastructure costs from platform management costs.

With Northflank's Bring Your Own Cloud solution, you pay two separate bills:

1. **Your cloud provider** (AWS, GCP, or Azure) for infrastructure: compute, storage, and networking at standard rates
2. **Northflank** for platform management and tooling

### Northflank Platform Pricing

**Compute Costs:**

- CPU: $0.01389 per vCPU per hour
- Memory: $0.00139 per GB per hour
- Billing is usage-based, calculated per second for precise costs

**Fixed Platform Costs:**

- Control Plane Egress: $0.06 per GB
- Builds & Backups: $0.08 per GB per month
- Logs & Metrics: $0.20 per GB (first 10 GB per month free, 30 days retention)
- Cluster Management: $0.00 per cluster per hour (included)

![CleanShot 2025-12-14 at 12.37.38@2x.png](https://assets.northflank.com/Clean_Shot_2025_12_14_at_12_37_38_2x_4fd4d34fa3.png)

## Alternative self-hosted PaaS options

### Porter

Porter is an open-source PaaS that deploys into your AWS, GCP, or Azure account with a focus on simplicity. It provisions Kubernetes clusters and provides a Heroku-like experience through a web dashboard and CLI. Porter handles application deployments, add-ons (databases, caches), and preview environments. The main limitation compared to Northflank is less enterprise-grade features around team management, RBAC, and multi-cluster orchestration. Porter offers a self-hosted version where you run both the control plane and workloads entirely in your infrastructure, or a managed option where Porter hosts the control plane.

### Qovery

Qovery connects to your AWS, GCP, or Azure account and automates Kubernetes cluster creation and application deployment. It emphasizes developer self-service with environment cloning, preview environments for pull requests, and integrated CI/CD. Qovery generates Terraform configurations for infrastructure provisioning, giving you Infrastructure as Code benefits. The platform includes cost tracking per environment and application. While comprehensive, it's generally positioned more toward startups and mid-market companies rather than large enterprises with complex compliance requirements.

### SpectroCloud Palette

SpectroCloud Palette is a Kubernetes management platform that deploys into your AWS, GCP, Azure, or on-premises infrastructure. It uses "cluster profiles" to declaratively manage the entire Kubernetes stack, handling cluster provisioning, upgrades, and day-2 operations in your cloud accounts. Palette is designed for platform engineering teams managing Kubernetes at scale across multi-cloud environments, with built-in governance, policy enforcement, and cost visibility. While it provides deeper infrastructure control than traditional PaaS, it requires Kubernetes expertise and is less abstracted than developer-focused platforms like Northflank or Porter.

### Rafay

Rafay is a Kubernetes Operations Platform that provisions and manages Kubernetes clusters in your AWS, GCP, Azure accounts or on-premises data centers. It provides a centralized control plane for multi-cluster management with GitOps-based application delivery, zero-trust security, and policy-driven automation. Rafay is designed for enterprise platform teams building internal Kubernetes-as-a-Service offerings, providing features like namespaces-as-a-service, environment management, and compliance controls. It sits between raw Kubernetes management and fully abstracted PaaS, requiring Kubernetes knowledge but offering more control and customization than traditional PaaS platforms.

### Red Hat OpenShift

Red Hat OpenShift is an enterprise Kubernetes platform that can be deployed in your AWS, GCP, Azure accounts, on-premises data centers, or as a managed service. It provides a comprehensive application platform built on top of Kubernetes with integrated CI/CD pipelines, developer tooling, container registry, and enterprise security features. OpenShift can run in your infrastructure through self-managed installations or via cloud provider managed services (ROSA on AWS, ARO on Azure, OCP on GCP). While powerful and feature-rich with strong enterprise support, OpenShift has a steeper learning curve, higher operational overhead, and typically higher costs compared to modern BYOC PaaS platforms. It's best suited for large enterprises with existing Red Hat relationships and teams experienced with Kubernetes and container orchestration.

### Portainer

Portainer is a lightweight container management platform that provides a web-based UI for managing Docker and Kubernetes environments in your own infrastructure. It can be self-hosted in your cloud accounts, on-premises servers, or edge locations, giving you a visual interface to deploy containers, manage stacks, and configure networking without command-line expertise. Portainer is significantly simpler and more lightweight than enterprise Kubernetes platforms, focusing on ease of use for small to mid-sized teams. However, it lacks the advanced features of full PaaS solutions like automated GitOps workflows, multi-cloud orchestration, preview environments, and integrated CI/CD pipelines. It's best suited for teams wanting basic container management with minimal complexity rather than a complete application platform.

### Cloud Foundry (self-hosted)

Cloud Foundry is a mature, open-source PaaS platform that predates the Kubernetes era. You can deploy Cloud Foundry distributions like Tanzu Application Service into your own infrastructure. It uses buildpacks to detect and deploy applications with a simple `cf push` command. While powerful and battle-tested, Cloud Foundry has a steeper learning curve for operations teams and requires more infrastructure management compared to newer Kubernetes-native solutions. It remains relevant for enterprises with existing Cloud Foundry expertise or multi-cloud portability requirements.

## FAQs

### What is a PaaS that runs in my own cloud?

A PaaS that runs in your own cloud is a platform-as-a-service solution that deploys directly into your AWS, GCP, or Azure account. Northflank is the leading example, managing Kubernetes infrastructure in your cloud while you maintain complete control over data residency, security, and costs.

### What is the best self-hosted platform as a service?

Northflank is the best self-hosted platform as a service, offering deployment to 600+ regions across AWS, GCP, Azure, Oracle Cloud, Civo, and CoreWeave. It provides full Kubernetes management, GitOps integration, and enterprise-grade security while running entirely in your infrastructure.

### What is a BYOC PaaS solution?

A BYOC PaaS solution (Bring Your Own Cloud) is a platform that deploys into your existing cloud accounts instead of vendor-hosted infrastructure. Northflank's BYOC PaaS solution lets you maintain data sovereignty and use existing cloud credits while getting a managed developer platform.

### Can I run a PaaS in my AWS account?

Yes, Northflank lets you run a PaaS in your AWS account using Elastic Kubernetes Service (EKS). All workloads, databases, and data remain in your AWS VPC while Northflank's control plane manages deployments, scaling, and operations.

### How do I run a PaaS in my GCP account?

To run a PaaS in your GCP account, connect your Google Cloud Platform credentials to Northflank. The platform provisions Google Kubernetes Engine (GKE) clusters in your specified region, deploying all resources within your GCP project and VPC.

### Can I run a PaaS in my Azure account?

Yes, Northflank supports running a PaaS in your Azure account using Azure Kubernetes Service (AKS). Connect your Azure subscription, and Northflank manages Kubernetes infrastructure while all data stays within your Azure environment.

### What's the difference between traditional PaaS and self-hosted PaaS?

Traditional PaaS runs on vendor infrastructure (like Heroku), while self-hosted PaaS like Northflank runs in YOUR cloud account. Self-hosted platform as a service gives you data sovereignty, compliance control, and the ability to use existing cloud credits.

### What is Bring Your Own Cloud PaaS?

Bring Your Own Cloud PaaS (BYOC PaaS) is a deployment model where the platform manages your applications in your own AWS, GCP, or Azure account. Northflank pioneered this approach, separating the control plane from the runtime for maximum security and control.

### How does a PaaS in my own cloud account work?

A PaaS in your own cloud account connects to your AWS, GCP, or Azure via API credentials, provisions managed Kubernetes clusters in your VPC, and deploys applications while keeping all data in your infrastructure. Northflank's control plane manages operations without accessing your sensitive data.

### Why choose a self-hosted platform as a service over traditional PaaS?

A self-hosted platform as a service offers data sovereignty, regulatory compliance (HIPAA, SOC 2, ISO 27001), cost transparency, and the ability to use existing cloud credits. Companies choose Northflank's self-hosted PaaS to avoid vendor lock-in while maintaining developer productivity.

### What are the benefits of BYOC PaaS solutions?

BYOC PaaS solutions provide complete data residency control, compliance with data sovereignty laws, direct cloud provider billing, use of existing cloud commitments, and protection against vendor lock-in. Northflank's BYOC approach gives you enterprise control with startup agility.

### How much does a PaaS in my own cloud cost?

With a PaaS in your own cloud like Northflank, you pay your cloud provider directly for infrastructure (compute, storage, networking) and Northflank separately for platform management. This means you can leverage existing cloud credits and get transparent, usage-based billing. More information on pricing [here](https://northflank.com/pricing).]]>
  </content:encoded>
</item><item>
  <title>We reduced network pricing &amp; introduced Melbourne and Tokyo</title>
  <link>https://northflank.com/changelog/reduced-network-pricing-and-introduced-melbourne-and-tokyo</link>
  <pubDate>2025-12-11T06:45:00.000Z</pubDate>
  <description>
    <![CDATA[60% cheaper networking ($0.06/GB) and new PaaS regions, including Tokyo and Melbourne.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/pricing_and_regions_6d0d577ad0.png" alt="We reduced network pricing &amp; introduced Melbourne and Tokyo" />## TL;DR

Effective immediately:

- **Request pricing is removed entirely**
- **Network egress pricing drops from $0.15 → $0.06 per GB**
- **NVMe disk pricing drops from $0.30 → $0.15 per GB per month**

This is a **global price** applied across our **8 existing regions**, with **8 additional regions launching in January**.  

With these reductions, **high-bandwidth applications are now cheaper to operate on Northflank than on AWS and other public clouds**.

---

## Two new regions: Melbourne and Tokyo

Based on customer demand, we’ve launched:

- **Melbourne, Australia** 🇦🇺
- **Tokyo, Japan** 🇯🇵, with **GPU support available at launch**

If you need to deploy workloads into production in these geos, [come chat with us,](https://cal.com/team/northflank/northflank-intro) we can help you size, migrate, and optimise your workloads.

---

# What you can now run more cost-effectively on Northflank

The new disk and network pricing unlocks major savings for teams running bandwidth-heavy or storage-intensive systems. Workloads that now perform better (and cost less) on Northflank compared to AWS include:

### High-bandwidth and real-time applications
- AI inference endpoints with large model outputs  
- Video streaming, VOD processing, and real-time media pipelines  
- Multiplayer game backends and simulation servers  
- Websocket-heavy SaaS platforms 

### Storage-intensive
- Databases and caches (Postgres, MySQL, MongoDB)
- Search clusters (Elasticsearch, Meilisearch)  
- High-IOPS containerised systems benefiting from lower storage cost  

---

# Let’s talk

Enterprise and volume discounts are available for networking and storage. If you’re evaluating how these pricing changes impact your workloads, or planning to deploy in Melbourne or Tokyo, we’re here to help.

**Come [chat](https://cal.com/team/northflank/northflank-intro) with us and we’ll show you how to run faster and cheaper on Northflank.**
]]>
  </content:encoded>
</item><item>
  <title>Runpod GPU pricing: A complete breakdown and platform comparison</title>
  <link>https://northflank.com/blog/runpod-gpu-pricing</link>
  <pubDate>2025-12-08T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[Runpod GPU pricing breakdown and comparison. See how Northflank offers competitive GPU rates plus databases, APIs, CI/CD, and monitoring in one platform.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/runpod_gpu_pricing_1b68b01d45.png" alt="Runpod GPU pricing: A complete breakdown and platform comparison" />When evaluating Runpod GPU pricing, you're likely comparing costs across GPU cloud providers. Runpod focuses on providing GPU compute infrastructure.

When deploying production AI applications, you need more than GPU compute; you also need databases, APIs, CI/CD pipelines, and monitoring tools to make your deployment work. Total infrastructure costs extend beyond GPU hourly rates.

This guide covers Runpod's pricing structure and compares it with platform alternatives like Northflank to help you evaluate the full picture.

## TL;DR: Runpod GPU pricing & platform comparison at a glance

When comparing Runpod and Northflank, you're looking at two different approaches: GPU-only pricing versus platform pricing that bundles everything you need.

### Price comparison at a glance

| GPU model | Runpod community | Runpod secure | Northflank | What you're actually comparing |
| --- | --- | --- | --- | --- |
| H100 SXM 80GB | $2.69/hr | $2.69/hr | $2.74/hr | GPU only vs GPU + full platform |
| H200 | $3.59/hr | $3.59/hr | $3.14/hr | Northflank more affordable here |
| A100 SXM 80GB | $1.39/hr | $1.49/hr | $1.76/hr | Lower GPU rate vs bundled infrastructure |
| A100 40GB | $1.19/hr | $1.39/hr | $1.42/hr | Comparable across platforms |

**What this means for your total infrastructure costs:**

With Runpod at $2.69/hr for H100 SXM, you still need to add:

- Database hosting (PostgreSQL, Redis, MongoDB)
- API server hosting for inference endpoints
- CI/CD platform for deployments
- Monitoring and observability tools
- Integration and management time

With Northflank at $2.74/hr for H100 SXM, these services are included in your platform. You pay $0.05/hr more for the GPU but get databases, APIs, CI/CD, and monitoring bundled, often resulting in lower total costs and faster shipping.

<InfoBox className="BodyStyle">

**The key question:** Which approach fits your team? GPU-only pricing (Runpod) or complete platform (Northflank)?

→ [Request GPU access](https://northflank.com/request/gpu) to compare total costs with your workloads

</InfoBox>

## What is Runpod's GPU pricing structure?

Runpod offers three ways to access GPU compute, each suited for different workload patterns. Let's break down each option.

![runpod-homepage.png](https://assets.northflank.com/runpod_homepage_a696c3aa97.png)

### Community cloud GPU pricing

Community Cloud connects you to GPUs through a marketplace model. You'll find options across three tiers:

| GPU tier | GPU model | VRAM | Price per hour |
| --- | --- | --- | --- |
| **Enterprise** | H200 | 141GB | $3.59/hr |
|  | B200 | 180GB | $5.98/hr |
|  | H100 SXM | 80GB | $2.69/hr |
|  | H100 PCIe | 80GB | $1.99/hr |
|  | A100 SXM | 80GB | $1.39/hr |
|  | A100 PCIe | 80GB | $1.19/hr |
| **Mid-range** | L40S | 48GB | $0.79/hr |
|  | RTX 6000 Ada | 48GB | $0.74/hr |
| **Consumer** | RTX 4090 | 24GB | $0.34/hr |
|  | RTX 3090 | 24GB | $0.22/hr |

You're billed per second, which works well when you're running training experiments or short development sessions.

### Secure cloud GPU pricing

If your production workloads need enterprise features, Secure Cloud adds $0.10-$0.40/hr for SOC2 compliance and dedicated infrastructure:

| GPU Model | Community Cloud | Secure Cloud |
| --- | --- | --- |
| H100 PCIe | $1.99/hr | $2.39/hr |
| A100 PCIe | $1.19/hr | $1.39/hr |
| A100 SXM | $1.39/hr | $1.49/hr |

### How does Runpod serverless GPU pricing work?

If you need GPUs that scale automatically based on demand, serverless offers two pricing tiers:

| GPU Model | Flex Workers | Active Workers (30% off) |
| --- | --- | --- |
| H100 | $4.18/hr | $3.35/hr |
| A100 | $2.72/hr | $2.17/hr |
| 4090 | $1.10/hr | $0.77/hr |

You'll pay 2-3x more than pod pricing, but you get FlashBoot (sub-200ms cold starts) and automatic orchestration. This makes sense for inference APIs or workloads with variable traffic.

### What does Runpod charge for storage?

Runpod separates storage costs from GPU compute:

| Storage Type | Price |
| --- | --- |
| Pod volume (running) | $0.10/GB/month |
| Pod volume (idle) | $0.20/GB/month |
| Network volume (less than 1TB) | $0.07/GB/month |
| Network volume (greater than 1TB) | $0.05/GB/month |

You won't pay for data ingress or egress, which helps when moving large datasets.

## What else will you need beyond GPU compute?

When you deploy production AI applications, GPU compute is just the starting point. Your infrastructure stack will also require:

- **Database hosting** - PostgreSQL, Redis, or MongoDB for your application data
- **API servers** - Deploy and serve your model inference endpoints
- **Frontend applications** - User interfaces for your AI products
- **CI/CD pipelines** - Automated deployment and testing workflows
- **Monitoring and observability** - Track performance and debug issues
- **Background job processing** - Handle async tasks and data processing

Each of these means working with another vendor, managing separate billing, and building integrations. Your [GPU cluster](https://northflank.com/blog/what-is-a-gpu-cluster) needs to connect with all these components to build a complete system.

## How does Northflank GPU pricing compare?

Now that you've seen what Runpod offers and what else you'll need to build around it, let's look at how Northflank approaches this differently.

Northflank bundles GPU pricing with the complete development platform you need. Instead of paying for GPUs separately and then stitching together databases, APIs, and CI/CD from other vendors, you get [GPU as a service](https://northflank.com/blog/gpu-as-a-service) plus all those infrastructure tools in one place.

![northflank-ai-homepage-2.png](https://assets.northflank.com/northflank_ai_homepage_2_ea495c361e.png)

### Northflank GPU pricing breakdown

Here's what you'll pay for GPU and CPU compute on Northflank:

**GPU compute:**

| GPU Model | VRAM | Price per Hour |
| --- | --- | --- |
| A100 40GB | 40GB | $1.42/hr |
| A100 80GB | 80GB | $1.76/hr |
| H100 | 80GB | $2.74/hr |
| H200 | 141GB | $3.14/hr |
| B200 | 180GB | $5.87/hr |

**CPU compute:**

Your API servers, databases, and other services run on CPU instances priced at:

| Resource | Price |
| --- | --- |
| vCPU | $0.01667/vCPU/hour |
| Memory | $0.00833/GB/hour |

**Fixed pricing (included in all plans):**

These platform services are bundled into every deployment:

| Service | Price |
| --- | --- |
| Networking | $0.15/GB, $0.50/1M requests |
| Disk storage | $0.30/GB/month |
| Builds & backups | $0.08/GB/month |
| Logs & metrics | $0.20/GB |

You're billed per second with transparent pricing. No hidden fees or surprise charges for data transfer.

Want to see how this compares with your current infrastructure costs? [Request GPU access](https://northflank.com/request/gpu) to test your workloads, or [talk to an engineer](https://cal.com/team/northflank/northflank-demo) about your specific requirements.

### Runpod GPU pricing vs Northflank: Direct comparison

Before we compare GPU hourly rates, remember what we covered earlier: Runpod focuses on GPU compute, while Northflank bundles GPUs with your complete infrastructure stack (databases, APIs, CI/CD, monitoring).

So when you're looking at these numbers, you're comparing GPU-only pricing against platform pricing.

Here's how the GPU rates stack up:

| GPU model | Runpod Community | Runpod Secure | Northflank | What you're actually comparing |
| --- | --- | --- | --- | --- |
| H100 SXM 80GB | $2.69/hr | $2.69/hr | $2.74/hr | GPU only vs GPU + full platform |
| H200 | $3.59/hr | $3.59/hr | $3.14/hr | Northflank has competitive pricing here |
| B200 | $5.98/hr | $5.19/hr | $5.87/hr | Similar pricing, different scope |
| A100 SXM 80GB | $1.39/hr | $1.49/hr | $1.76/hr | Lower GPU rate vs bundled infrastructure |
| A100 40GB | $1.19/hr | $1.39/hr | $1.42/hr | Comparable across all platforms |

**Here's what this means for your infrastructure costs:**

If you go with Runpod at $2.69/hr for H100 SXM, you still need to add:

- Database hosting (PostgreSQL, Redis, MongoDB)
- API server hosting for inference endpoints
- CI/CD platform for deployments
- Monitoring and observability tools
- Integration and management time

With Northflank at $2.74/hr for H100 SXM, all of those services are included in your platform. You're paying $0.05/hr more for the GPU, but you get databases, APIs, CI/CD, and monitoring bundled together.

The question isn't just "which hourly rate is lower?" but "what's your total infrastructure cost?" For teams building production applications, having everything in one platform often costs less overall and ships faster.

## What platform features does Northflank include?

Beyond GPU compute, Northflank provides a full-stack developer platform designed for teams building and deploying AI applications at scale:

| Category | Features |
| --- | --- |
| **Complete application stack** | GPU workloads (training, inference, fine-tuning), managed databases (PostgreSQL, MySQL, Redis, MongoDB), frontend and backend services, background jobs and cron scheduling, static site hosting |
| **Developer workflow** | Native Git integration (GitHub, GitLab, Bitbucket), automated CI/CD pipelines, preview environments for every pull request, buildpacks and custom Dockerfiles, Infrastructure as Code support |
| **Production features** | Real-time logs and metrics, auto-scaling based on CPU, memory, or custom metrics, secret management and environment variables, team collaboration with RBAC, audit logs and compliance tracking |
| **Enterprise capabilities** | Deploy in your own cloud (GCP, AWS, Azure, Oracle, CoreWeave, Civo, bare-metal), secure runtime with microVM isolation (gVisor, Kata, Firecracker), 24/7 support and SLA guarantees |

This comprehensive platform approach means you're comparing a focused GPU provider against a complete development ecosystem. Both approaches have merit depending on your team's needs.

## When does a platform approach make sense?

The choice between focused GPU providers like Runpod and platform solutions depends on your infrastructure needs:

### Building production inference APIs

**With focused GPU providers:**

- GPU provider for training
- Separate API hosting service
- Different database service
- Another frontend service
- Additional monitoring tool
- Integration work between services

**With platform approach:** Deploy everything in one place. Auto-scaling, automated backups, unified logging. The [cloud GPU](https://northflank.com/blog/what-is-a-cloud-gpu) infrastructure integrates seamlessly with other components.

### Managing multiple AI projects

Teams running several AI workloads benefit from unified deployment, consistent monitoring, shared configuration, and preview environments for testing.

### Compliance and security requirements

Platform solutions can address this through BYOC capabilities, letting you deploy [GPUs in your own cloud](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-in-your-own-cloud) while maintaining platform benefits for data residency compliance and enterprise security integration.

### Long-term cost optimization

Total cost of ownership includes more than hourly GPU rates. Consolidating multiple vendors can reduce operational complexity, billing relationships, and infrastructure integration time.

## Which GPU cloud provider should you choose?

Your decision comes down to your infrastructure needs and team capabilities. Here's how to think about it:

| Runpod works well for teams that: | Northflank works well for teams that: |
| --- | --- |
| Need dedicated GPU access without additional services | Build production AI applications |
| Have experienced DevOps teams | Need databases, APIs, and GPU compute together |
| Already maintain separate infrastructure | Want to reduce infrastructure management |
| Run research projects or experiments | Have compliance or data residency requirements |
| Prioritize lowest per-hour GPU cost | Need to deploy in their own cloud |
| Want widest GPU hardware variety | Value total cost of ownership |

Your choice depends on your infrastructure needs and team structure.

## Getting started with GPU infrastructure

Understanding total infrastructure costs means looking beyond headline GPU pricing.

Runpod provides competitive compute rates for teams focused on GPU access. Northflank combines GPU pricing with databases, APIs, CI/CD, and deployment tools in one platform.

<InfoBox className="BodyStyle">

Need to see how GPU infrastructure integrates with databases, APIs, and deployment tools? [Request GPU access](https://northflank.com/request/gpu) to test the platform with your workloads, or [book a demo](https://cal.com/team/northflank/northflank-demo) if you have specific organizational requirements.

</InfoBox>

## Frequently asked questions about Runpod GPU pricing

**How does Runpod GPU pricing compare to AWS or GCP?**

Runpod typically runs 40-60% cheaper than AWS or GCP on-demand instances. Major clouds provide committed use discounts that narrow this gap. Platform solutions like Northflank bridge this by providing competitive GPU pricing while letting you deploy in your own cloud through [BYOC](https://northflank.com/features/bring-your-own-cloud).

**Does Runpod charge for data transfer?**

No, Runpod doesn't charge for ingress or egress data transfer, which helps when moving large datasets or model weights.

**What's the difference between Community Cloud and Secure Cloud?**

Community Cloud provides more GPU variety at lower prices through a marketplace model. Secure Cloud adds $0.10-$0.40/hr per GPU for SOC2 compliance, dedicated resources, and enhanced support.

**Can I use spot instances to reduce costs?**

Runpod operates on a marketplace model where prices reflect availability. Other platforms support [spot GPUs](https://northflank.com/blog/what-are-spot-gpus-guide) when deploying in your own cloud for 60-90% savings on interruptible workloads.

**Which GPU should I choose for deep learning?**

The [best GPU for AI](https://northflank.com/blog/best-gpu-for-ai) depends on your workload. H100 or H200 for large language models and transformer training. A100 or L40S for inference or smaller models. Both Runpod and Northflank provide access to these options.

**What's the difference between SXM and PCIe GPUs?**

SXM GPUs offer higher performance with faster interconnect speeds (NVLink) and better thermal design, making them ideal for large-scale training. PCIe GPUs cost less but have lower bandwidth. Runpod offers both variants with clear pricing distinctions. When comparing providers, check which form factor is included in the quoted price.]]>
  </content:encoded>
</item><item>
  <title>Top 7 Fluidstack alternatives in 2026</title>
  <link>https://northflank.com/blog/fluidstack-alternatives</link>
  <pubDate>2025-12-04T17:00:00.000Z</pubDate>
  <description>
    <![CDATA[Fluidstack alternatives: Compare Northflank, RunPod, Lambda Labs, Vast.ai &amp; more for GPU cloud in 2026. Find the right platform for your AI workloads]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/fluidstack_alternatives_25842146f5.png" alt="Top 7 Fluidstack alternatives in 2026" />Fluidstack offers enterprise-grade GPU infrastructure for large-scale AI workloads, but you might need an alternative that provides more transparent pricing, developer-friendly workflows, or full-stack application support.

This guide helps you find the right GPU cloud platform based on your team's specific requirements, from self-service access and infrastructure control to a complete development environment beyond raw compute.

<InfoBox className="BodyStyle">

## TL;DR: Best 7 Fluidstack alternatives at a glance

If you're evaluating Fluidstack alternatives, here's what you need to know:

1. **Northflank** is a unified cloud platform that supports both GPU and CPU workloads, providing access to H100, H200, B200, A100, L4, and [more](https://northflank.com/gpu) alongside Git-based CI/CD, databases, APIs, BYOC (Bring Your Own Cloud) deployment, and more modern DevOps features.
    
    > You can deploy in your own cloud (AWS/GCP/Azure/Oracle/Civo/bare-metal) or use Northflank's managed infrastructure with transparent per-second billing from $2.74/hour for H100s. You can [request GPU clusters](https://northflank.com/request/gpu) directly or start with the [free sandbox tier](https://app.northflank.com/signup). Best for teams building complete AI applications who need a platform that handles their entire stack.
    > 
2. **RunPod** provides on-demand GPU access with serverless capabilities across Community Cloud and Secure Cloud tiers.
3. **Lambda Labs** offers GPU infrastructure with pre-configured ML stacks and 1-Click Clusters.
4. **Vast.ai** operates a marketplace model connecting you with distributed GPU providers.
5. **Together AI** specializes in serving open-source models through managed inference endpoints.
6. **TensorDock** focuses on marketplace-based GPU access with VM control.
7. **Modal** provides serverless compute for Python-based ML workflows.

</InfoBox>

## What should you look for in Fluidstack alternatives?

When evaluating GPU cloud platforms, the right choice depends on how you actually build and deploy AI applications, not just access to hardware. Consider these criteria:

- **GPU availability and variety** - Access to current GPU models including H100, H200, B200, A100, and L4 cards with availability that matches your timeline. Your team shouldn't wait months for hardware access when you're ready to scale.
- **Pricing transparency** - Hidden fees for data transfer, storage, or support can multiply your actual costs well beyond advertised GPU rates. Platforms with per-second billing and bundled resources give you predictable expenses.
- **Infrastructure control** - Can you deploy in your own cloud account? Do you have access to your VPC, networking, and security configurations? Teams working with sensitive data or strict compliance requirements need this level of control.
- **Development workflow integration** - Git-based deployments, automated CI/CD pipelines, preview environments, and rollback capabilities should feel native to the platform, not bolted on as afterthoughts.
- **Full-stack capabilities** - For teams building production applications, you need more than GPU compute. Look for platforms that support databases, APIs, background jobs, and observability tools alongside your GPU workloads.
- **Scalability options** - From one GPU for prototyping to hundreds for production training, the platform should accommodate teams at any stage without forcing you into massive cluster commitments.
- **Support and compliance** - Production AI workloads require responsive support, security certifications (SOC 2, ISO 27001), and compliance capabilities. Evaluate SLAs and whether you get direct access to technical experts.

## What are the best Fluidstack alternatives?

We've evaluated the following alternatives based on deployment flexibility, developer experience, and scalability to help you find the best fit for your requirements.

### 1. Northflank

Northflank is a unified cloud platform combining GPU compute with complete infrastructure management and multi-cloud flexibility. Built for teams needing more than raw GPU access, Northflank lets you deploy your entire stack, including GPU workloads, databases, applications, APIs, background jobs, and CI/CD pipelines, across multiple clouds from a single platform.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

**Key features**

- **Multi-cloud GPU deployment** - Deploy GPU workloads on AWS, GCP, Azure, Oracle Cloud, Civo, or bare-metal from a unified platform. Choose from 6+ cloud regions or 600 BYOC regions without vendor lock-in. Run on Northflank's managed cloud or bring your own cloud account (BYOC) to maintain existing cloud relationships and billing.
- **Transparent, predictable pricing** - Simple usage-based pricing with per-second billing for CPU, GPU, memory, and storage. No hidden fees for networking, monitoring, or data transfer. Compare costs across providers in real-time and optimize spending with built-in cost analytics.
- **Unified infrastructure platform** - Deploy GPU compute alongside managed databases (PostgreSQL, MySQL, MongoDB, Redis), applications, APIs, background jobs, and CI/CD pipelines on the same platform. Create complete environments with GPUs and supporting infrastructure together.
- **Developer-first workflows** - Git-based deployments with automatic builds on every commit. Preview environments for pull requests to test changes safely. Connect locally using Northflank CLI without exposing infrastructure publicly. Support for custom Docker containers and popular ML frameworks.
- **Built-in observability** - Real-time log tailing with filtering and search. Performance metrics for GPU utilization, memory, network, and storage displayed in intuitive dashboards. Configure alerts via Slack, email, or webhooks.
- **Enterprise-ready security** - Private networking between services without complex VPC configurations. TLS/SSL encryption enabled by default. Fine-grained role-based access controls. Deploy in your own Kubernetes clusters (EKS, GKE, AKS) for maximum control. 24/7 enterprise support.
- **Flexible GPU options** - Access NVIDIA A100, H100, H200, B200, L4, L40S, and other [GPU types](https://northflank.com/gpu) across multiple cloud providers. Scale from single GPUs for development to multi-GPU instances for training.

<InfoBox className="BodyStyle">

**Pricing**

**Sandbox tier**

- Free resources to test workloads
- 2 free services, 2 free databases, 2 free cron jobs
- Always-on compute with no sleeping

**Pay-as-you-go**

- Per-second billing for compute (CPU and GPU), memory, and storage
- No seat-based pricing or commitments
- Deploy on Northflank's managed cloud (6+ regions) or bring your own cloud (600 BYOC regions across AWS, GCP, Azure, Civo)
- GPU pricing: NVIDIA A100 40GB at $1.42/hour, A100 80GB at $1.76/hour, H100 at $2.74/hour, H200 at $3.14/hour, B200 at $5.87/hour
- Bulk discounts available for larger commitments

**Enterprise**

- Custom requirements with SLAs and dedicated support
- Invoice-based billing with volume discounts
- Hybrid cloud deployment across AWS, GCP, Azure
- Run in your own VPC with managed control plane
- Secure runtime and on-prem deployments
- Audit logs, Global back-ups and HA/DR
- 24/7 support and FDE onboarding

Use the [Northflank pricing calculator](https://northflank.com/pricing) for exact cost estimates based on your specific requirements, and see the pricing page for more details

</InfoBox>

**Why choose Northflank**

Northflank addresses common GPU cloud challenges:

- **Multi-cloud freedom** - Deploy GPU workloads anywhere without infrastructure lock-in. Switch providers or go multi-cloud without infrastructure rewrites.
- **Unified platform advantage** - Manage GPU compute with databases, applications, and CI/CD in one place instead of piecing together separate GPU cloud and infrastructure providers.
- **Transparent costs** - Predictable per-second billing with real-time cost visibility. No surprises from networking or egress fees.
- **Developer velocity** - Git-based workflows, preview environments, and integrated CI/CD reduce time from code to GPU-powered production. No separate orchestration tools required.
- **Enterprise flexibility** - BYOC (Bring Your Own Cloud) deployment on your own AWS, GCP, Azure, Civo, Oracle Cloud, or bare-metal infrastructure maintains cloud commitments while gaining unified infrastructure control.
- **Flexible scaling** - Start with one GPU and scale to hundreds without massive cluster minimums or enterprise contracts.

<InfoBox className="BodyStyle">

Learn more: [GPU Workloads on Northflank](https://northflank.com/gpu) | [GPU instances on Northflank](https://northflank.com/cloud/gpus) | [Documentation](https://northflank.com/docs) | [Request your GPU cluster](https://northflank.com/request/gpu)

</InfoBox>

### 2. RunPod

RunPod provides GPU cloud with deployment across multiple regions. Offering GPU instances across 30+ regions, RunPod serves developers and teams needing access to GPUs.

![runpod-homepage.png](https://assets.northflank.com/runpod_homepage_a696c3aa97.png)

**Key features**

- GPU deployment across 30+ regions
- Secure Cloud and Community Cloud options
- Serverless GPU with automatic scaling
- Support for custom Docker containers and pre-built templates
- CLI and API for automation and CI/CD integration
- Spot instances for interruptible workloads

**Best for**

Individual developers, ML teams, prototyping, and inference serving.

### 3. Lambda Labs

Lambda Labs offers GPU cloud infrastructure with emphasis on ML workloads. Known for 1-Click Clusters that provision interconnected GPUs, Lambda serves research teams and AI startups.

![lambda-homepage.png](https://assets.northflank.com/lambda_homepage_21b6ec7a15.png)

**Key features**

- On-demand NVIDIA HGX B200, H100, A100, and GH200 instances
- 1-Click Clusters with pre-configured networking
- Pre-installed ML stack with PyTorch, TensorFlow, CUDA, and Jupyter
- Lambda Private Cloud for dedicated GPU clusters
- NVIDIA Quantum-2 InfiniBand networking for distributed training
- Used by research institutions

**Best for**

Academic researchers, AI startups, teams prototyping models, and organizations wanting GPU access without complex cloud configurations.

### 4. Vast.ai

Vast.ai operates a marketplace model connecting users with GPU providers globally. The platform aggregates spare GPU capacity 
from data centers and individual providers.

![vastai's homepage.png](https://assets.northflank.com/vastai_s_homepage_194c175a50.png)

**Key features**

- Marketplace with bid-based pricing
- Access to NVIDIA GPUs including H100, A100, and consumer cards
- Docker container deployment
- SSH access to instances
- Search and filter by GPU specs, bandwidth, and storage

**Best for**

Experimentation, research projects, and workloads that can tolerate interruptions.

### 5. Together AI

Together AI specializes in serving open-source models through managed inference endpoints. The platform focuses on deploying pre-trained models rather than training infrastructure.

![togetherai-homepage.png](https://assets.northflank.com/togetherai_homepage_d0e3c7e279.png)

**Key features**

- Managed endpoints for open-source models
- Support for LLaMA, Mistral, Mixtral, and other popular models
- API-based access with OpenAI-compatible endpoints
- Automatic scaling based on demand
- Integration with popular ML frameworks

**Best for**

Teams deploying pre-trained models, inference serving, and applications needing model APIs without infrastructure management.

### 6. TensorDock

TensorDock provides marketplace-based GPU access with full VM control. The platform offers both on-demand and reserved instances.

![tensordock-homepage.png](https://assets.northflank.com/tensordock_homepage_febf532ad3.png)

**Key features**

- Marketplace model for GPU access
- Full VM control with Windows and Linux support
- NVIDIA GPUs including H100, A100, and RTX series
- KVM virtualization for isolation
- SSH and RDP access

**Best for**

Teams wanting VM-level control, specific OS configurations, or security isolation beyond containers.

### 7. Modal

Modal provides serverless compute for Python-based ML workflows. The platform handles infrastructure automatically while you define functions and dependencies.

![modal-homepage.png](https://assets.northflank.com/modal_homepage_a7380e6d35.png)

**Key features**

- Serverless execution model
- Python-native API
- Automatic scaling from zero
- GPU support including A100 and H100
- Container-based isolation
- Integration with popular ML libraries

**Best for**

Python developers, batch processing, serverless inference, and teams wanting infrastructure abstraction.

## How do these Fluidstack alternatives compare?

Use this comparison to identify which alternative aligns with your technical requirements and deployment needs.

| Alternative | Best for | Key advantages | GPU options | Pricing model |
| --- | --- | --- | --- | --- |
| **Northflank** | Startups to enterprises needing multi-cloud flexibility and unified infrastructure (both CPU and/or GPU workloads) | Multi-cloud deployment across AWS, GCP, Azure, Oracle Cloud, Civo, and bare-metal; unified platform with databases and CI/CD; BYOC option; Git-based workflows | B200, H200, H100, A100, L4, L40S, GH200, and [more](https://northflank.com/gpu) | Per-second billing; H100 at $2.74/hr, H200 at $3.14/hr, B200 at $5.87/hr |
| **RunPod** | Individual developers and ML teams | Community and Secure Cloud options; serverless capabilities | H100, A100, RTX 4090, and more | Varies by cloud tier and GPU type |
| **Lambda Labs** | Researchers and AI startups | 1-Click Clusters; pre-installed ML stack | B200, H100, A100, GH200 | Varies by instance type |
| **Vast.ai** | Budget-conscious experimentation | Marketplace with bid-based pricing | H100, A100, consumer GPUs | Pay-by-the-second marketplace rates |
| **Together AI** | Inference serving for pre-trained models | Managed model endpoints; OpenAI-compatible APIs | Managed infrastructure | Per-token usage-based |
| **TensorDock** | Teams needing VM control | Full VM access with KVM isolation | H100, A100, RTX series | Hourly and monthly rates |
| **Modal** | Python-based batch processing | Serverless execution; automatic scaling | A100, H100 | Pay-per-execution |

## Which Fluidstack alternative is right for your team?

For teams evaluating alternatives to Fluidstack's infrastructure, several options provide different approaches to GPU cloud computing.

Northflank stands out as a unified cloud platform (both CPU and GPU workloads), not just a GPU provider. You get multi-cloud flexibility to deploy on AWS, GCP, Azure, Oracle Cloud, Civo, or bare-metal from a single interface.

Unlike specialized GPU clouds locked to their own infrastructure, Northflank lets you run your entire stack in one place: GPU workloads alongside databases, applications, APIs, background jobs, and CI/CD pipelines. This removes the need to manage separate tools for GPU compute and infrastructure, while transparent per-second billing ensures cost predictability across providers.

<InfoBox className="BodyStyle">

From GPUs for training models to databases for your application, everything is managed from one platform with Git-based workflows and preview environments.

- [Start with a free account](https://app.northflank.com/signup) or go straight to [request your GPU cluster](https://northflank.com/request/gpu)
- Test workloads and infrastructure
- [Book a demo](https://cal.com/team/northflank/northflank-intro) with an expert engineer
- Calculate savings with the [pricing calculator](https://northflank.com/pricing)
- Learn more: [GPU Workloads on Northflank](https://northflank.com/gpu) | [GPU instances on Northflank](https://northflank.com/cloud/gpus) | [Documentation](https://northflank.com/docs)

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Top 5 SaladCloud alternatives for production GPU workloads in 2026</title>
  <link>https://northflank.com/blog/saladcloud-alternatives</link>
  <pubDate>2025-12-03T20:00:00.000Z</pubDate>
  <description>
    <![CDATA[SaladCloud alternatives: Compare Northflank, Vast.ai, RunPod, Lambda Labs &amp; more for production GPU workloads with stable infrastructure in 2026]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/saladcloud_alternatives_336f69a9ca.png" alt="Top 5 SaladCloud alternatives for production GPU workloads in 2026" />SaladCloud alternatives provide different approaches to deploying GPU workloads, from distributed networks to enterprise-grade infrastructure platforms.

If you need production reliability with stable infrastructure, full-stack platform capabilities beyond GPU access, or specific deployment control, understanding your options helps you choose the infrastructure that matches your technical and operational needs.

<InfoBox className="BodyStyle">

## TL;DR: See this quick summary of the top 5 SaladCloud alternatives

1. **Northflank** - Deploy GPU workloads alongside your entire application stack (databases, APIs, jobs, CI/CD) across AWS, GCP, Azure, Oracle Cloud, Civo, or bare-metal from one platform.
    
    > Northflank provides stable infrastructure with production-grade reliability and a full-stack platform where GPU services work alongside databases, CI/CD, and application hosting. Access both enterprise datacenter [GPUs](https://northflank.com/gpu) (H100, H200, B200, A100, L40S, etc.) and professional RTX GPUs with unified workflows across your entire application.
    > 
2. **Vast.ai** - Marketplace model connecting to consumer and datacenter GPUs with flexible rental options
3. **RunPod** - Community and Secure Cloud options with serverless capabilities across 30+ global regions
4. **Lambda Labs** - 1-Click Clusters built for academic researchers and AI teams with ML-ready environments
5. **AWS/GCP/Azure** - Traditional hyperscalers with GPU instances integrated into broader cloud ecosystems

</InfoBox>

## What should you consider when evaluating SaladCloud alternatives?

Before examining specific platforms, understanding evaluation criteria helps you match alternatives to your requirements.

- **Infrastructure reliability and node stability:**
Does your workload require stable, production-grade infrastructure or can it handle interruptions? Some GPU platforms use distributed node architectures where instances can be interrupted without warning as nodes go offline. Other platforms provide infrastructure where GPU instances remain stable and available. Consider whether your application needs consistent uptime or can architect for fault tolerance with interruption-prone environments.
- **Platform scope and integrated capabilities:**
Do you need just GPU compute, or are you deploying complete applications? Some platforms focus on containerized GPU workloads, while others provide unified infrastructure for GPUs, databases, APIs, and CI/CD. The scope affects how many vendors you'll need to coordinate and how integrated your development workflow can be.
- **Infrastructure control and deployment flexibility:**
Can you deploy in your own cloud accounts? Some platforms operate on their own infrastructure, while others support BYOC (Bring Your Own Cloud) capabilities. This matters for teams with existing cloud commitments, compliance requirements, or data residency needs.
- **GPU type requirements:**
Do you need specific GPU models or access to the latest datacenter hardware? Platforms vary in their GPU offerings, from consumer GPUs (RTX series) to enterprise datacenter GPUs (H100, A100, L40S) to professional workstation GPUs. Teams may need consistent access to specific GPU types depending on workload requirements.
- **Developer workflow integration:**
Does the platform support your development process? Git integration, automated builds, preview environments, and comprehensive observability affect how quickly teams ship changes. Some platforms provide container orchestration with API access, while others offer broader DevOps capabilities integrated into the workflow.

## Which SaladCloud alternatives should you consider for GPU workloads?

We'll review the top SaladCloud alternatives based on reliability characteristics, platform scope, deployment flexibility, and integrated capabilities to help your team make an informed decision.

### 1. Northflank

Northflank approaches GPU deployment differently than distributed GPU networks by treating GPU workloads as components within your complete application architecture rather than isolated compute resources.

The platform lets you deploy GPU services alongside managed databases (PostgreSQL with pgvector, MySQL, MongoDB, Redis), web applications, APIs, background jobs, and scheduled tasks. When building an AI application, for example, you deploy your frontend, backend API, database, and GPU-powered inference service within the same project using Git-based workflows.

Northflank delivers a stable datacenter infrastructure with production-grade reliability. Your GPU workloads run in environments designed for consistent availability, while maintaining the flexibility to deploy on Northflank's managed cloud or in your own cloud accounts.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

**Key capabilities of Northflank for teams building AI applications**

- **Multi-cloud deployment:** Works across AWS, GCP, Azure, Oracle Cloud, Civo, and bare-metal without vendor lock-in. You can deploy on Northflank's [managed cloud](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-on-northflank-cloud) or connect your [own cloud accounts](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-in-your-own-cloud) (BYOC) to maintain existing relationships and billing structures. The platform provides 6+ managed cloud [regions](https://northflank.com/cloud/northflank/regions) and access to 600 BYOC regions through this multi-cloud approach.
- **Git-based deployments:** Connect to GitHub, GitLab, or Bitbucket repositories. Each commit triggers automated builds and deployments. [Preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) automatically spin up for pull requests, giving you isolated testing environments before merging changes. This workflow applies to your entire stack, including [GPU workloads](https://northflank.com/gpu).v
- **Built-in observability:** Includes real-time log tailing with filtering and search, performance metrics for GPU utilization, memory usage, network traffic, and storage. Cost analytics show spending across different providers. Alerts integrate with Slack, email, or webhooks. These capabilities work without configuring separate monitoring tools.
- **Security features:** Include private networking between services, VPC support, role-based access controls, audit logs, and SAML SSO. You can deploy in your own Kubernetes clusters (EKS, GKE, AKS) for maximum infrastructure control.
- **GPU options on Northflank:** The platform supports NVIDIA B200, H200, H100, A100, L4, L40S, RTX Pro 6000 Blackwell Server Edition, and other [GPU types](https://northflank.com/gpu) across multiple cloud providers. GPU time-slicing and NVIDIA MIG let you run multiple independent workloads on provisioned GPUs to optimize resource utilization.

<InfoBox className="BodyStyle">

**Northflank's pricing structure**

**Sandbox tier**

- Free resources to test workloads
- 2 free services, 2 free databases, 2 free cron jobs
- Always-on compute with no sleeping

**Pay-as-you-go**

- Per-second billing for compute (CPU and GPU), memory, and storage
- No seat-based pricing or commitments
- Deploy on Northflank's managed cloud (6+ regions) or bring your own cloud (600 BYOC regions across AWS, GCP, Azure, Civo)
- GPU pricing: NVIDIA A100 40GB at $1.42/hour, A100 80GB at $1.76/hour, H100 at $2.74/hour, H200 at $3.14/hour, B200 at $5.87/hour
- Bulk discounts available for larger commitments

**Enterprise**

- Custom requirements with SLAs and dedicated support
- Invoice-based billing with volume discounts
- Hybrid cloud deployment across AWS, GCP, Azure
- Run in your own VPC with managed control plane
- Secure runtime and on-prem deployments
- Audit logs, Global back-ups and HA/DR
- 24/7 support and FDE onboarding

Use the [Northflank pricing calculator](https://northflank.com/pricing) for exact cost estimates based on your specific requirements, and see the pricing page for more details

</InfoBox>

**When does Northflank make sense as a SaladCloud alternative?**

Northflank fits teams that need:

- **Production reliability:** Customer-facing applications requiring stable infrastructure and consistent availability
- **Full-stack platform:** Unified interface for GPU compute, databases, APIs, and CI/CD pipelines (both GPU and CPU workloads)
- **Developer workflows:** Git-based deployments, preview environments, and integrated observability
- **Infrastructure control:** BYOC deployment in your own AWS, GCP, Azure, Oracle Cloud, Civo, or bare-metal infrastructure for compliance or data residency

<InfoBox className="BodyStyle">

[Start by creating a Northflank account](https://app.northflank.com/signup) to test GPU workloads alongside your application infrastructure, or [request your GPU cluster](https://northflank.com/request/gpu) to discuss specific requirements. Learn more about [GPU workloads on Northflank](https://northflank.com/gpu), explore [available GPU instances](https://northflank.com/cloud/gpus), or review the [documentation](https://northflank.com/docs) for implementation details.

</InfoBox>

### 2. Vast.ai

Vast.ai operates as a marketplace connecting users to GPU resources from both consumer hardware providers and datacenter operators, creating a decentralized compute network.

![vastai's homepage.png](https://assets.northflank.com/vastai_s_homepage_194c175a50.png)

**Key features**

- **Marketplace model:** Browse available GPUs with pricing set by individual providers, creating competition that can drive costs down for various GPU types.
- **GPU variety:** Access ranges from consumer RTX GPUs to datacenter A100s and H100s depending on marketplace availability at any given time.
- **Rental flexibility:** Choose between on-demand instances or interruptible compute for workloads that can handle interruptions.

**Best for:** Teams with fault-tolerant workloads who want marketplace pricing dynamics and can manage potential interruptions, though reliability varies across different providers in the marketplace.

### 3. RunPod

RunPod offers GPU deployment across 30+ geographic regions through Secure Cloud (tier-3/tier-4 data centers) and Community Cloud (individual GPU providers).

![runpod's homepage.png](https://assets.northflank.com/runpod_s_homepage_14648d1a93.png)

**Key features**

- **Serverless GPU:** Automatic scaling and idle shutdown for variable workloads with pay-per-use pricing.
- **Custom containers:** Docker containers for exact runtime environments or pre-built templates for common frameworks.
- **Automation tools:** CLI and API for integration with CI/CD pipelines and programmatic deployment.

**Best for:** Teams needing distributed deployment options with serverless capabilities, though Community Cloud may have lower uptime guarantees and the platform focuses on GPU compute without integrated databases or comprehensive development workflows.

### 4. Lambda Labs

Lambda Labs targets academic researchers and AI teams through pre-configured GPU access built for machine learning workflows.

![lambda-homepage.png](https://assets.northflank.com/lambda_homepage_21b6ec7a15.png)

**Key features**

- **1-Click Clusters:** Interconnected GPUs with pre-installed PyTorch, TensorFlow, CUDA, and Jupyter.
- **GPU options:** NVIDIA HGX B200, H100, A100, and GH200 instances with Lambda Private Cloud available.
- **InfiniBand networking:** NVIDIA Quantum-2 for distributed training across multiple GPU nodes.

**Best for:** University research groups and academic projects who need ML-ready environments without infrastructure setup complexity, though teams need separate solutions for databases and production infrastructure management.

### 5. AWS/GCP/Azure

Traditional cloud providers offer GPU instances as part of comprehensive cloud platforms with deep integration across storage, networking, security, and managed services.

- **AWS capabilities:** P5 instances with H100 GPUs, SageMaker, and integration with S3, Lambda, and RDS.
- **GCP capabilities:** A2 and A3 instances with A100/H100 GPUs, TPU alternatives, Vertex AI, and BigQuery integration.
- **Azure capabilities:** NC-series with NVIDIA GPUs, Azure ML integration, and Microsoft ecosystem connectivity.

**Best for:** Enterprises with existing cloud investments who need GPU capabilities within current infrastructure and established compliance frameworks, though GPU availability can be constrained during high-demand periods.

## How do SaladCloud alternatives compare for GPU workloads?

Use the comparison table to match your deployment needs, reliability requirements, and application architecture with the platform that addresses your specific requirements.

| Alternative | Best for | Key advantages | GPU options | Infrastructure model |
| --- | --- | --- | --- | --- |
| Northflank | Production AI applications needing unified platform capabilities | Multi-cloud deployment with GPUs, databases, apps, jobs, and CI/CD; BYOC support; Git-based workflows; stable infrastructure | B200, H200, H100, A100, L40S, RTX Pro 6000, L4, GH200 and [more](https://northflank.com/gpu) | Stable datacenter infrastructure (managed cloud or BYOC) |
| Vast.ai | Cost-sensitive teams comfortable with marketplace dynamics | Marketplace pricing competition; GPU variety; flexible rental options | RTX series to H100s (varies by marketplace) | Marketplace of consumer and datacenter providers |
| RunPod | Distributed deployment with serverless needs | Serverless autoscaling; global regions; Community and Secure Cloud options | H100, A100, RTX 4090, 3090 and more | Secure Cloud data centers and Community Cloud |
| Lambda Labs | Academic research and ML experimentation | Pre-configured ML environments; InfiniBand networking; 1-Click Clusters | B200, H100, A100, GH200 | Lambda data centers and Private Cloud |
| AWS/GCP/Azure | Existing cloud ecosystem integration | Comprehensive cloud services; managed AI platforms; compliance frameworks | H100, A100, V100, L4 and more | Hyperscaler data centers |

## What should you know about choosing a SaladCloud alternative?

GPU platforms take different approaches based on workload requirements. Some focus on distributed networks for cost optimization, others provide marketplace pricing flexibility, while comprehensive platforms offer complete application deployment with integrated infrastructure.

Northflank treats GPU workloads as integrated components within your full application stack.

With Northflank, you can deploy your databases, APIs, background jobs, and GPU services within the same project using unified workflows across AWS, GCP, Azure, Oracle Cloud, Civo, or bare-metal. The platform provides stable datacenter infrastructure with support for both enterprise datacenter GPUs (H100, H200, B200, A100, L40S, etc.) and professional RTX GPUs.

<InfoBox className="BodyStyle">

[Start by creating a Northflank account](https://app.northflank.com/signup) to test GPU workloads alongside your application infrastructure, or [request your GPU cluster](https://northflank.com/request/gpu) to discuss specific requirements. Learn more about [GPU workloads on Northflank](https://northflank.com/gpu), explore [available GPU instances](https://northflank.com/cloud/gpus), or review the [documentation](https://northflank.com/docs) for implementation details.

</InfoBox>

### Related guides to help you choose the right GPU platform

These resources provide additional context on GPU infrastructure options, pricing models, and deployment strategies to inform your platform decision.

- [**What is a cloud GPU? A guide for AI companies using the cloud**](https://northflank.com/blog/what-is-a-cloud-gpu) - Learn how cloud GPUs work, when to use them versus local hardware, and how platforms like Northflank let you attach GPUs to any workload.
- [**What are spot GPUs? Complete guide to cost-effective AI infrastructure**](https://northflank.com/blog/what-are-spot-gpus-guide) - Understand spot GPU instances, interruption handling, and how to optimize costs while maintaining reliability for AI workloads.
- [**7 cheapest cloud GPU providers**](https://northflank.com/blog/cheapest-cloud-gpu-providers) - Compare pricing across GPU providers including cost optimization strategies and when to prioritize reliability over price.
- [**The best serverless GPU providers**](https://northflank.com/blog/the-best-serverless-gpu-cloud-providers) - Review serverless GPU platforms for on-demand workloads, including orchestration capabilities and production-grade features.]]>
  </content:encoded>
</item><item>
  <title>Top 7 Zeabur alternatives for deployment in 2026</title>
  <link>https://northflank.com/blog/zeabur-alternatives</link>
  <pubDate>2025-12-02T16:45:00.000Z</pubDate>
  <description>
    <![CDATA[Zeabur alternatives: Northflank, Railway, Render, and Fly.io compared for production infrastructure, Kubernetes deployment, and multi-cloud BYOC.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/zeabur_alternatives_561344df5d.png" alt="Top 7 Zeabur alternatives for deployment in 2026" />Zeabur alternatives offer more infrastructure control and production-grade features for teams with specific requirements.

Zeabur provides quick, zero-config deployments, while alternatives like Northflank offer Kubernetes-powered infrastructure, multi-cloud flexibility with BYOC ([Bring Your Own Cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment)), enterprise compliance features, and support for both CPU and GPU workloads.

This guide compares platforms based on infrastructure control, observability, deployment flexibility, and production readiness.

<InfoBox className="BodyStyle">

## TL;DR: quick comparison of Zeabur alternatives

1. **Northflank** – Production-grade Kubernetes platform with BYOC (Bring Your Own Cloud) support, built-in CI/CD, preview environments, GPU and CPU workload support, and enterprise infrastructure resilience.
    
    > Northflank gives you infrastructure control when needed while abstracting complexity through Kubernetes. Deploy to your own AWS, GCP, Azure, Civo, Oracle Cloud, neoclouds like CoreWeave, or bare-metal infrastructure, across 6+ cloud regions and 600 BYOC regions.
    > 
2. **Railway** – Platform with automatic builds (Railpack) and visual service management
3. **Render** – Managed platform with buildpack deployments and persistent infrastructure
4. **Fly.io** – Distributed VM platform with global edge deployment and Anycast routing
5. **Vercel** – Serverless platform for frontend applications with edge functions
6. **DigitalOcean App Platform** – Managed container platform integrated with DigitalOcean infrastructure
7. **Heroku** – Buildpack-based platform with dyno containers and add-on integrations

</InfoBox>

## What features should you look for in Zeabur alternatives?

When evaluating deployment platforms, consider these key capabilities:

- **Infrastructure control and flexibility** - Look for platforms that let you deploy to your own cloud accounts (BYOC) or offer multi-cloud options. This matters when you need data residency control, want to avoid vendor lock-in, or have existing cloud commitments.
- **Production-grade observability** - Real-time logs, metrics dashboards, health checks, and alerting help you understand application behavior and catch issues before they impact users. Built-in observability reduces the need for additional monitoring tools.
- **CI/CD and preview environments** - Automated build and deployment pipelines integrated with Git repositories streamline development workflows. Preview environments for pull requests let teams test changes in isolated environments before production.
- **Workload diversity support** - If you're running or planning to run GPU workloads alongside standard services, choose platforms that support both CPU and GPU compute from a unified interface. This avoids operational complexity from managing separate platforms.
- **Backup and recovery capabilities** - Automated backups, point-in-time recovery, and disaster recovery features protect your data and reduce downtime when issues occur. These features become critical as your application scales.
- **Team collaboration features** - Role-based access controls, team management, and audit logs help larger teams work together while maintaining security. Fine-grained permissions control who can deploy, modify, or view resources.
- **Infrastructure as Code support** - The ability to define infrastructure in code, stored in Git alongside applications, enables reproducible deployments and version-controlled infrastructure changes.

## What are the top Zeabur alternatives in 2026?

We've evaluated the following platforms based on infrastructure control, production readiness, observability, deployment flexibility, and support for diverse workloads to help you find the right fit for your team's requirements.

### 1. Northflank

For teams needing production-grade infrastructure with Kubernetes power but without operational complexity, Northflank provides infrastructure control when you need it while abstracting the complexity away. The platform handles workloads from single containers to thousands of services across enterprise and high-scale AI infrastructure.

The key difference from Zeabur: Northflank doesn't hide infrastructure; it abstracts complexity while giving you control for production workloads. The platform supports both CPU and GPU workloads from a single interface, offers BYOC (Bring Your Own Cloud) deployment to your own cloud accounts, and runs on battle-tested Kubernetes foundations without requiring you to manage clusters directly.

![northflank--platform.png](https://assets.northflank.com/northflank_platform_d625d79568.png)

**Key feature**

- Multi-cloud deployment across AWS, GCP, Azure, Civo, Oracle Cloud, or bare-metal with BYOC (Bring Your Own Cloud) support
- CPU and GPU workload support from a unified platform for web services, AI inference, and ML training
- Managed databases (PostgreSQL, MySQL, Redis, MongoDB, MinIO, RabbitMQ) with automated operations
- Built-in CI/CD pipelines with Git-based deployments and automated preview environments for pull requests
- Private networking, health checks, and multi-tenancy support
- Horizontal and vertical autoscaling with scheduled jobs, background workers, and cron tasks
- Real-time observability with logs, metrics dashboards, and configurable alerting
- Automated backups, point-in-time recovery, and disaster recovery capabilities
- Infrastructure as Code with JSON templates and comprehensive API
- Role-based access controls, team management, and audit logs for enterprise compliance
- Deploy in your own VPC with SOC 2 compliance, SSO, and 24/7 support options

<InfoBox className="BodyStyle">

**Pricing**

**Sandbox tier**

- Free resources to test workloads
- 2 free services, 2 free databases, 2 free cron jobs
- Always-on compute with no sleeping

**Pay-as-you-go**

- Per-second billing for compute (CPU and GPU), memory, and storage
- No seat-based pricing or commitments
- Deploy on Northflank's managed cloud (6+ regions) or bring your own cloud (600 BYOC regions across AWS, GCP, Azure, Civo)
- GPU pricing: NVIDIA A100 40GB at $1.42/hour, A100 80GB at $1.76/hour, H100 at $2.74/hour, H200 at $3.14/hour, B200 at $5.87/hour
- Bulk discounts available for larger commitments

**Enterprise**

- Custom requirements with SLAs and dedicated support
- Invoice-based billing with volume discounts
- Hybrid cloud deployment across AWS, GCP, Azure
- Run in your own VPC with managed control plane
- Secure runtime and on-prem deployments
- Audit logs, Global back-ups and HA/DR
- 24/7 support and FDE onboarding

Use the [Northflank pricing calculator](https://northflank.com/pricing) for exact cost estimates based on your specific requirements, and see the pricing page for more details

</InfoBox>

### 2. Railway

For teams wanting quick deployment with visual project management, Railway uses Railpack to automatically analyze source code and generate Docker images across multiple languages without Dockerfiles.

![railway-min.png](https://assets.northflank.com/railway_min_10957de907.png)

**Key features:**

- Visual project canvas showing services and relationships
- Automatic image generation from source code (GitHub, Docker images, or direct uploads)
- One-click database provisioning (PostgreSQL, MySQL, Redis, MongoDB) with automatic backups
- Built-in private networking between services
- Environment management with SSL certificates and public URLs
- Logs, metrics, and deployment history

### 3. Render

For teams needing managed cloud infrastructure, Render supports web services, static sites, background workers, cron jobs, and databases through unified management.

![render's home page.png](https://assets.northflank.com/render_s_home_page_2982a329f2.png)

**Key features:**

- Git-based deployments with automatic builds
- Managed PostgreSQL with daily backups, point-in-time recovery, and encryption
- Private networking for secure service communication
- Persistent disks for stateful applications
- Health checks with automatic instance restarts
- Metrics dashboards and log aggregation

### 4. Fly.io

For applications requiring global edge deployment, Fly.io runs lightweight VMs across worldwide data centers with traffic routing to the nearest location.

![fly.io-min.png](https://assets.northflank.com/fly_io_min_bfc65ba670.png)

**Key features:**

- Edge deployment across multiple global regions
- CLI-centric workflows with fly.toml configuration files
- Persistent volumes for local storage across deployments
- Anycast networking for automatic traffic routing to closest machines
- Support for background tasks, scheduled jobs, and worker processes
- Machine autoscaling based on traffic patterns

### 5. Vercel

For frontend applications and serverless deployments, Vercel focuses on Next.js projects with deployment to a global edge network.

![vercel-homepage.png](https://assets.northflank.com/vercel_homepage_7ecf227d81.png)

**Key features:**

- Framework-defined infrastructure deriving components from application code
- Automatic preview deployments for pull requests with unique URLs
- Serverless functions with on-demand execution and automatic scaling
- Edge Functions running at global edge locations for minimal latency
- Intelligent caching, incremental builds, and Incremental Static Regeneration for Next.js
- Environment variable management across development, preview, and production

### 6. DigitalOcean App Platform

For teams within the DigitalOcean ecosystem, App Platform provides managed hosting with GitHub/GitLab integration.

![Digitalocean app platform's home page.png](https://assets.northflank.com/Digitalocean_app_platform_s_home_page_a7b876bb7f.png)

**Key features:**

- Automatic language and framework detection
- Multiple service types (web services, workers, static sites, scheduled jobs)
- Managed databases with operations and backups handled by DigitalOcean
- Manual scaling configuration with automatic load balancing
- Private networking between services
- Basic metrics and log collection with alerting

### 7. Heroku

For teams needing managed hosting through buildpacks, Heroku provides containerized dyno environments for web processes, workers, and scheduled tasks.

![heroku.png](https://assets.northflank.com/heroku_092e1c7f09.png)

**Key features:**

- Buildpack system for automatic language and framework detection
- Extensive add-on ecosystem for databases, caching, monitoring, and other services
- Scaling through dashboard or CLI (horizontal and vertical)
- Multi-stage deployment pipelines with review apps for pull requests
- Git-based deployments with slug compilation
- Integration through environment variables

## How to choose the right Zeabur alternative for your team

Use this comparison to identify which platform aligns with your infrastructure requirements, workload types, and deployment model preferences.

| Platform | Best for | Multi-cloud/BYOC | GPU support | Key strength |
| --- | --- | --- | --- | --- |
| Northflank | Production workloads needing infrastructure control, teams running CPU and/or GPU workloads, enterprises requiring compliance | Yes (AWS, GCP, Azure, Civo, Oracle, bare-metal) | Yes | Kubernetes-powered infrastructure with full control, unified CPU/GPU platform |
| Railway | Quick prototyping with visual management | No | No | Visual project canvas |
| Render | Teams needing managed PostgreSQL and persistent storage | No | No | Managed PostgreSQL with point-in-time recovery |
| Fly.io | Global applications requiring edge deployment | No | No | Multi-region VM deployment with Anycast |
| Vercel | Frontend teams using Next.js | No | No | Serverless functions and edge optimization |
| DigitalOcean | Teams already using DigitalOcean services | No | No | DigitalOcean ecosystem integration |
| Heroku | Teams wanting extensive add-on marketplace | No | No | Mature platform with buildpack system |

## Getting started with the right alternative

When evaluating platforms, consider your infrastructure control needs, workload types (CPU vs GPU), production requirements (backups, observability, disaster recovery), and deployment model preferences (managed vs BYOC).

Teams moving from prototypes to production typically need platforms with operational maturity. If you require Kubernetes-level capabilities, multi-cloud deployment, or unified CPU/GPU management, platforms like Northflank provide that infrastructure control without requiring you to manage clusters directly.

Test representative workloads on shortlisted platforms to evaluate documentation, CI/CD integration, and deployment workflows before committing.

<InfoBox className="BodyStyle">

[Start with Northflank's free Developer Sandbox](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to discuss your infrastructure requirements.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Top 7 Hyperbolic AI alternatives for GPU workloads in 2026</title>
  <link>https://northflank.com/blog/hyperbolic-ai-alternatives</link>
  <pubDate>2025-12-01T17:30:00.000Z</pubDate>
  <description>
    <![CDATA[Hyperbolic AI alternatives like Northflank, Together AI, Fireworks AI, and CoreWeave offer different approaches to deploying GPU workloads in 2026.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/hyperbolic_ai_alternatives_e777b65b0e.png" alt="Top 7 Hyperbolic AI alternatives for GPU workloads in 2026" />Hyperbolic AI alternatives provide different approaches to deploying GPU workloads, from specialized inference services to comprehensive infrastructure platforms. 

If you need multi-cloud deployment, complete application stack management, or specific compliance requirements, understanding your options helps you choose the platform that matches your technical and operational needs.

<InfoBox className="BodyStyle">

## TL;DR: Hyperbolic AI alternatives at a glance

See this quick summary of the top 7 Hyperbolic AI alternatives

1. **Northflank** - Deploy GPU workloads alongside your entire application stack (databases, APIs, jobs, CI/CD) across AWS, GCP, Azure, Oracle Cloud, Civo, or bare-metal from one platform.
    
    > Northflank stands out as a **unified cloud platform** where you deploy [GPU workloads](https://northflank.com/gpu) alongside your databases, APIs, and CI/CD pipelines, all from one interface with Git-based workflows.
    
    With true BYOC (Bring Your Own Cloud) support across AWS, GCP, Azure, Oracle Cloud, Civo, and bare-metal, you can run GPU capabilities as part of your complete application stack rather than managing separate platforms for compute, hosting, and deployment.
    > 
2. **Together AI** - Access 200+ open-source models through serverless inference APIs with fine-tuning capabilities
3. **Fireworks AI** - Specialized inference engine with low-latency serving and multi-modal model support
4. **CoreWeave** - Kubernetes-native GPU infrastructure for training and inference at scale
5. **Lambda Labs** - 1-Click Clusters designed for academic researchers and AI teams
6. **RunPod** - Community and Secure Cloud options across 30+ global regions
7. **Replicate** - Container-based model deployment for prototyping and production
8. **AWS/GCP/Azure** - Traditional hyperscalers with GPU instances integrated into broader cloud ecosystems

</InfoBox>

## What criteria matter when evaluating Hyperbolic AI alternatives?

Before examining specific platforms, understanding evaluation criteria helps you match alternatives to your requirements.

- **Infrastructure control and deployment flexibility**:
    
    Can you deploy in your own cloud accounts? Some platforms lock you into their infrastructure, while others support deploying in your AWS, GCP, or Azure environments. BYOC ([Bring Your Own Cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment)) capabilities matter for teams with existing cloud commitments, compliance requirements, or cost management needs.
    
- **Complete stack deployment versus specialized GPU access**:
    
    Do you need just GPU compute, or are you deploying complete applications? Platforms differ in scope; some focus exclusively on GPU provisioning and model serving, while others integrate GPU workloads with databases, application hosting, and deployment automation.
    
- **Developer workflow integration**:
    
    Does the platform support your development process? Git integration, automated builds, preview environments, and CI/CD capabilities affect how quickly teams ship changes. Some platforms require external tools for these workflows, while others provide them natively.
    
- **Observability and cost transparency**:
    
    Can you monitor GPU utilization, track costs across providers, and debug performance issues? Built-in logging, metrics, and cost analytics reduce the need for separate monitoring tools and help identify optimization opportunities.
    
- **Security and compliance capabilities**:
    
    Does your use case require specific security features? Private networking, VPC deployment, audit logs, RBAC, and compliance certifications (SOC 2, HIPAA) become essential for regulated industries or enterprise environments.
    

## What are the top Hyperbolic AI alternatives?

We'll review the top Hyperbolic AI alternatives based on infrastructure control, stack integration, developer workflows, and deployment flexibility to help your team make an informed decision.

### 1. Northflank - GPU workloads alongside full application stacks

Northflank approaches GPU deployment differently than specialized providers by treating [GPU workloads](https://northflank.com/gpu) as components within your complete application architecture rather than isolated resources.

The platform lets you deploy GPU services alongside managed databases (PostgreSQL with pgvector, MySQL, MongoDB, Redis), web applications, APIs, background jobs, and scheduled tasks.

When building a RAG application, for example, you deploy your Next.js frontend, FastAPI backend, PostgreSQL database with vector extensions, and GPU-powered inference service from the same Git repository using the same workflow.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

**Key capabilities of Northflank for teams building AI applications**

- **Multi-cloud deployment:** Works across AWS, GCP, Azure, Oracle Cloud, Civo, and bare-metal without vendor lock-in. You can deploy on [Northflank's managed cloud](https://northflank.com/cloud/northflank) or [connect your own cloud accounts](https://northflank.com/features/bring-your-own-cloud) (BYOC) to maintain existing relationships and billing structures. The platform provides access to 600 regions through this multi-cloud approach.
- **Git-based deployments:** Connect to GitHub, GitLab, or Bitbucket repositories. Each commit triggers automated builds and deployments. Preview environments automatically spin up for pull requests, giving you isolated testing environments before merging changes. This workflow applies to your entire stack, including GPU workloads.
- **Built-in observability:** Includes real-time log tailing with filtering and search, performance metrics for GPU utilization, memory usage, network traffic, and storage. Cost analytics show spending across different providers. Alerts integrate with Slack, email, or webhooks. These capabilities work without configuring separate monitoring tools.
- **Security features:** Include private networking between services, VPC support, role-based access controls, audit logs, and SAML SSO. You can deploy in your own Kubernetes clusters (EKS, GKE, AKS) for maximum control.
- **GPU options on Northflank:** The platform supports NVIDIA B200, H200, H100, A100, L4, L40S, and other GPU types across multiple cloud providers. GPU time-slicing and NVIDIA MIG let you run multiple independent workloads on provisioned GPUs to optimize resource utilization

**Northflank’s pricing structure**

**Sandbox tier**

- Free resources to test workloads
- 2 free services, 2 free databases, 2 free cron jobs
- Always-on compute with no sleeping

**Pay-as-you-go**

- Per-second billing for compute (CPU and GPU), memory, and storage
- No seat-based pricing or commitments
- Deploy on Northflank's managed cloud (6+ regions) or bring your own cloud (600 BYOC regions across AWS, GCP, Azure, Civo)
- GPU pricing: NVIDIA A100 40GB at $1.42/hour, A100 80GB at $1.76/hour, H100 at $2.74/hour, H200 at $3.14/hour, B200 at $5.87/hour
- Bulk discounts available for larger commitments

**Enterprise**

- Custom requirements with SLAs and dedicated support
- Invoice-based billing with volume discounts
- Hybrid cloud deployment across AWS, GCP, Azure
- Run in your own VPC with managed control plane
- Secure runtime and on-prem deployments
- Audit logs, Global back-ups and HA/DR
- 24/7 support and FDE onboarding

Use the [Northflank pricing calculator](https://northflank.com/pricing) for exact cost estimates based on your specific requirements, and see the pricing page for more details

<InfoBox className="BodyStyle">

**When to choose Northflank over Hyperbolic AI alternatives**

Northflank fits teams building complete AI products rather than just calling inference APIs. If your application includes a frontend, backend, database, and GPU-powered features, managing these components on separate platforms creates coordination overhead.

The platform works for teams with existing cloud commitments who need to use their AWS, GCP, or Azure accounts while gaining better GPU management and unified infrastructure control. Companies requiring specific data residency, compliance certifications, or VPC deployment also benefit from BYOC capabilities.

Development teams wanting Git-to-production workflows, preview environments for every PR, and integrated CI/CD find Northflank reduces context switching between tools. The unified dashboard covers deployment, monitoring, and cost management across your entire stack.

> [Try Northflank free](https://app.northflank.com/signup) | [Request your GPU cluster](https://northflank.com/request/gpu) | [View documentation](https://northflank.com/docs) | [Explore GPU workloads](https://northflank.com/gpu)
> 

</InfoBox>

### 2. Together AI

Together AI provides serverless access to over 200 open-source language, vision, and embedding models through API endpoints. The platform handles infrastructure scaling automatically, letting developers focus on building applications rather than managing GPU clusters.

![togetherai-homepage.png](https://assets.northflank.com/togetherai_homepage_d0e3c7e279.png)

**Key features**

- **Model library and fine-tuning:** Includes LLaMA, Mistral, BLOOM, Stable Diffusion with fine-tuning and Weights & Biases integration.
- **OpenAI-compatible endpoints:** Switch from proprietary APIs to open-source models by changing a few lines of code.
- **GPU infrastructure:** 10K+ GPUs with InfiniBand networking for distributed training and automatic job scheduling.

**Best for:** Teams focused on model experimentation who need access to hundreds of pre-configured models without managing infrastructure, and applications requiring serverless autoscaling for variable traffic.

### 3. Fireworks AI

Fireworks AI specializes in serving open-source models through optimized inference infrastructure with proprietary FireAttention CUDA kernels.

![fireworks-ai-homepage.png](https://assets.northflank.com/fireworks_ai_homepage_05b247670c.png)

**Key features**

- **Multi-LoRA serving:** Deploy multiple fine-tuned variants of a model without separate hosting.
- **Multi-modal support:** Text, image, and audio models with FireLLaVA for processing text and visual inputs.
- **Compliance and security:** HIPAA and SOC2 certifications with VPC and VPN connectivity for private networking.

**Best for:** Teams needing optimized model serving with low latency, though BYOC deployment requires enterprise contracts and the platform doesn't support complete application deployment.

### 4. CoreWeave

CoreWeave provides GPU infrastructure designed around Kubernetes orchestration for AI training and inference workloads, operating data centers with NVIDIA H100, H200, GB200 NVL72, and enterprise GPUs.

![coreweave.png](https://assets.northflank.com/coreweave_5a85a78642.png)

**Key features**

- **Bare-metal Kubernetes:** Performance without virtualization overhead with Mission Control software for automated operations.
- **InfiniBand networking:** NVIDIA Quantum-2 for high-bandwidth, low-latency connections between GPU nodes.
- **Reserved capacity:** Guaranteed GPU availability for production workloads with extended training or continuous inference.

**Best for:** AI labs and research organizations training foundation models with Kubernetes expertise who need large-scale GPU clusters with specialized networking.

### 5. Lambda Labs

Lambda Labs targets academic researchers and AI teams through pre-configured GPU access designed for machine learning workflows.

![lambda-ai-homepage.png](https://assets.northflank.com/lambda_ai_homepage_d987e1a760.png)

**Key features**

- **1-Click Clusters:** Interconnected GPUs with pre-installed PyTorch, TensorFlow, CUDA, and Jupyter.
- **GPU options:** NVIDIA HGX B200, H100, A100, and GH200 instances with Lambda Private Cloud available.
- **InfiniBand networking:** NVIDIA Quantum-2 for distributed training across multiple GPU nodes.

**Best for:** University research groups and academic projects who need ML-ready environments without infrastructure setup complexity, though teams need separate solutions for databases and production infrastructure management.

### 6. RunPod

RunPod offers GPU deployment across 30+ geographic regions through Secure Cloud (tier-3/tier-4 data centers) and Community Cloud (individual GPU providers).

![runpod-homepage.png](https://assets.northflank.com/runpod_homepage_a696c3aa97.png)

**Key features**

- **Serverless GPU:** Automatic scaling and idle shutdown for variable workloads with pay-per-use pricing.
- **Custom containers:** Docker containers for exact runtime environments or pre-built templates for common frameworks.
- **Automation tools:** CLI and API for integration with CI/CD pipelines and programmatic deployment.

**Best for:** Teams needing distributed deployment options, though Community Cloud may have lower uptime guarantees and the platform focuses on GPU compute without integrated databases or comprehensive development workflows.

### 7. Replicate

Replicate enables model deployment through containerized infrastructure, letting developers package models with dependencies and serve them as HTTP APIs.

![replicate-homepage.png](https://assets.northflank.com/replicate_homepage_38062bccda.png)

**Key features**

- **Container workflow:** Push code and weights for automatic building, GPU allocation, and endpoint exposure.
- **Public model library:** Pre-deployed models for immediate use covering image generation, language processing, and speech recognition.
- **Automatic scaling:** Resource adjustment based on request volume and scales to zero when not in use.

**Best for:** Solo developers and small teams prototyping and experimenting without dedicated DevOps resources, though building production applications requires additional platforms for databases and application logic.

### 8. AWS, GCP, Azure

Traditional cloud providers offer GPU instances as part of comprehensive cloud platforms with deep integration across storage, networking, security, and managed services.

**AWS capabilities:** P5 instances with H100 GPUs, SageMaker, and integration with S3, Lambda, and RDS.

**GCP capabilities:** A2 and A3 instances with A100/H100 GPUs, TPU alternatives, Vertex AI, and BigQuery integration.

**Azure capabilities:** NC-series with NVIDIA GPUs, Azure ML integration, and Microsoft ecosystem connectivity.

**Best for:** Enterprises with existing cloud investments who need GPU capabilities within current infrastructure and established compliance frameworks, though GPU availability can be constrained during high-demand periods and pricing structures include multiple fees.

## How do these Hyperbolic AI alternatives compare?

Use the comparison table above to match your deployment needs, existing infrastructure, and application architecture with the platform that addresses your specific requirements.

| Alternative | Best for | Key advantages | GPU options | Best for use case |
| --- | --- | --- | --- | --- |
| **Northflank** | Teams building complete AI applications across multiple clouds | Multi-cloud deployment with unified platform for GPUs, databases, apps, jobs, and CI/CD; BYOC (Bring Your Own Cloud) support; Git-based workflows | B200, H200, H100, A100, L4, L40S, GH200 and more [GPU types](https://northflank.com/gpu) | Products with frontend, backend, database, and AI features needing unified deployment |
| **Together AI** | Serverless inference and model fine-tuning | 200+ models; fine-tuning capabilities; OpenAI-compatible endpoints | Access through managed service | Model experimentation, testing, and prototyping without infrastructure management |
| **Fireworks AI** | Low-latency inference with multi-modal support | Optimized inference engine; multi-LoRA serving; HIPAA/SOC2 certified | Access through managed service | Optimized model serving with compliance requirements |
| **CoreWeave** | Kubernetes-native training and inference at scale | Bare-metal Kubernetes; InfiniBand networking; reserved capacity | H100, H200, GB200 NVL72, RTX PRO 6000 | Organizations needing fine-grained workload management and container orchestration control |
| **Lambda Labs** | Academic researchers and ML teams | Pre-configured ML environments; 1-Click Clusters; Jupyter notebooks | HGX B200, H100, A100, GH200 | Research teams and academic projects focused on experimentation over production |
| **RunPod** | Distributed deployment across many regions | 30+ regions; Community and Secure Cloud; serverless option | Various NVIDIA GPUs | Teams needing geographic distribution with flexible deployment options |
| **Replicate** | Container-based model deployment | Public model library; automatic scaling; developer-friendly API | Access through managed service | Solo developers and small teams prototyping without DevOps resources |
| **AWS/GCP/Azure** | Organizations with existing cloud infrastructure | Deep service integration; global regions; compliance certifications | H100, A100, various NVIDIA GPUs | Enterprises with existing cloud investments and established compliance requirements |

## What's next for your GPU deployment?

Hyperbolic AI alternatives provide different approaches to GPU workloads based on your specific requirements. Specialized inference platforms focus on model serving, Kubernetes-native solutions offer container orchestration control, and comprehensive platforms address complete application deployment.

Northflank stands out by treating GPU workloads as components within your full application stack rather than isolated resources. Deploy your databases, APIs, background jobs, and GPU services from the same Git repository using unified workflows across AWS, GCP, Azure, Oracle Cloud, Civo, or bare-metal.

<InfoBox className="BodyStyle">

[Start by creating a Northflank account](https://app.northflank.com/signup) to test GPU workloads alongside your application infrastructure, or [request your GPU cluster](https://northflank.com/request/gpu) to discuss specific requirements. Learn more about [GPU workloads on Northflank](https://northflank.com/gpu), explore [available GPU instances](https://northflank.com/cloud/gpus), or review the [documentation](https://northflank.com/docs) for implementation details.

</InfoBox>]]>
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</item><item>
  <title>Top 7 Crusoe alternatives in 2026</title>
  <link>https://northflank.com/blog/crusoe-alternatives</link>
  <pubDate>2025-11-28T16:15:00.000Z</pubDate>
  <description>
    <![CDATA[Crusoe alternatives: Northflank, CoreWeave, Lambda Labs, and RunPod offer flexible GPU cloud options with better pricing and multi-cloud support.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/crusoe_alternatives_a5eeb9622e.png" alt="Top 7 Crusoe alternatives in 2026" />Crusoe alternatives offer diverse options for teams looking for different approaches to GPU cloud infrastructure.

Crusoe Cloud is a sustainable AI infrastructure provider with competitive pricing, though some teams need multi-cloud deployment options or different geographic coverage for their specific requirements.

If you're evaluating GPU cloud providers or considering options beyond a single vendor, this guide compares the top alternatives to help you make an informed decision.

<InfoBox className="BodyStyle">

## TL;DR: Top 7 Crusoe alternatives at a glance

See a quick list of the top 7 Crusoe alternatives we'll review in this guide:

1. **Northflank** - Best for startups and enterprises looking for multi-cloud GPU deployment with unified infrastructure management and no vendor lock-in.
    
    > Northflank is a unified cloud platform where you can deploy both CPU and GPU workloads alongside your databases, applications, APIs, background jobs, and CI/CD pipelines on AWS, GCP, Azure, Oracle Cloud, Civo, or bare-metal, all from a **single interface** with Git-based workflows and preview environments.
    > 
2. **CoreWeave** - Best for AI training and inference requiring Kubernetes-native infrastructure
3. **Lambda Labs** - Best for researchers and AI teams needing on-demand GPU clusters
4. **RunPod** - Best for developers needing GPU deployment
5. **Nebius** - Best for teams needing GPU cloud with technical capabilities
6. **Hyperstack** - Best for teams requiring specific data residency and managed services
7. **Traditional hyperscalers (AWS, GCP, Azure)** - Best for organizations already invested in a specific cloud ecosystem

</InfoBox>

## What should you look for in a Crusoe alternative?

When evaluating GPU cloud providers, consider these criteria to find the best fit for your team's requirements.

- **Cost transparency** - Look for clear, predictable pricing without hidden fees for networking, storage, or data transfer. Compare hourly rates across GPU types, and evaluate spot vs. reserved instance options. Consider the total cost of ownership, including support, monitoring, and orchestration tools.
- **GPU availability and variety** - Assess the range of GPU options from consumer cards (RTX series) to enterprise GPUs (A100, H100, H200, GB200). Check availability across regions and the ability to scale from single GPUs to multi-node clusters. Spot instance availability and pricing volatility matter for cost optimization.
- **Developer experience** - The best alternatives provide intuitive interfaces, robust APIs, CLI tools, and integration with popular ML frameworks. Look for features like Jupyter notebook support, custom Docker containers, Git-based workflows, and orchestration options (Kubernetes, Slurm).
- **Performance and reliability** - Evaluate uptime SLAs, networking bandwidth (InfiniBand, RDMA), storage performance (NVMe, parallel file systems), and fault tolerance mechanisms. Check independent benchmarks like ClusterMAX ratings for objective performance comparisons.
- **Scalability and orchestration** - Consider both vertical scaling (single GPU to multi-GPU instances) and horizontal scaling (multi-node clusters). Managed orchestration services, auto-scaling capabilities, and support for distributed training frameworks reduce operational overhead.
- **Support and compliance** - Production AI workloads require responsive support, security certifications (SOC 2, ISO 27001), and compliance capabilities. Evaluate SLAs, support response times, and whether you get direct access to technical experts.

## What are the top Crusoe alternatives?

We've evaluated the following alternatives based on cost-effectiveness, deployment flexibility, developer experience, performance, and scalability to help you find the best fit for your requirements.

### 1. Northflank

Northflank is a unified cloud platform combining GPU compute with complete infrastructure management and multi-cloud flexibility. Built for teams evaluating Crusoe alternatives without vendor lock-in, Northflank lets you deploy your entire stack, including [GPU workloads](https://northflank.com/gpu), databases, applications, APIs, background jobs, and CI/CD pipelines, across multiple clouds from a single platform.

![northflank--platform.png](https://assets.northflank.com/northflank_platform_d625d79568.png)

**Key features**

- **Multi-cloud GPU deployment** - Deploy GPU workloads on AWS, GCP, Azure, Oracle Cloud, Civo, or bare-metal from a unified platform. Choose from 600 regions without vendor lock-in. Run on [Northflank's managed cloud](https://northflank.com/features/managed-cloud) or [bring your own cloud account](https://northflank.com/features/bring-your-own-cloud) (BYOC) to maintain existing cloud relationships and billing.
- **Transparent, predictable pricing** - Simple usage-based pricing with per-second billing for CPU, GPU, memory, and storage. No hidden fees for networking, monitoring, or data transfer. Compare costs across providers in real-time and optimize spending with built-in cost analytics. Try out the [pricing calculator](https://northflank.com/pricing).
- **Unified infrastructure platform** - Deploy GPU compute alongside managed databases ([PostgreSQL](https://northflank.com/dbaas/managed-postgresql), [MySQL](https://northflank.com/dbaas/managed-mysql), [MongoDB](https://northflank.com/dbaas/mongodb-on-northflank), [Redis](https://northflank.com/dbaas/managed-redis)), applications, APIs, [background jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs), and [CI/CD pipelines](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) on the same platform. Create complete environments with GPUs and supporting infrastructure together.
- **Developer-first workflows** - Git-based deployments with automatic builds on every commit. [Preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) for pull requests to test changes safely. Connect locally using [Northflank CLI](https://northflank.com/docs/v1/api/use-the-cli) without exposing infrastructure publicly. Support for custom Docker containers and popular ML frameworks.
- **Built-in observability** - Real-time log tailing with filtering and search. Performance metrics for GPU utilization, memory, network, and storage displayed in intuitive dashboards. Configure alerts via Slack, email, or webhooks. No separate monitoring tools needed.
- **Enterprise-ready security** - Private networking between services without complex VPC configurations. TLS/SSL encryption enabled by default. Fine-grained role-based access controls. Deploy in your own Kubernetes clusters (EKS, GKE, AKS) for maximum control. 24/7 enterprise support.
- **Flexible GPU options** - Access NVIDIA A100, H100, H200, B200, L4, L40S, and other [GPU types](https://northflank.com/gpu) across multiple cloud providers. Scale from single GPUs for development to multi-GPU instances for training. Right-size instances without overpaying for unused resources.

<InfoBox className="BodyStyle">

**Pricing**

**Sandbox tier**

- Free resources to test workloads
- 2 free services, 2 free databases, 2 free cron jobs
- Always-on compute with no sleeping

**Pay-as-you-go**

- Per-second billing for compute (CPU and GPU), memory, and storage
- No seat-based pricing or commitments
- Deploy on Northflank's managed cloud (6+ regions) or bring your own cloud (600 BYOC regions across AWS, GCP, Azure, Civo)
- GPU pricing: NVIDIA A100 40GB at $1.42/hour, A100 80GB at $1.76/hour, H100 at $2.74/hour, H200 at $3.14/hour, B200 at $5.87/hour
- Bulk discounts available for larger commitments

**Enterprise**

- Custom requirements with SLAs and dedicated support
- Invoice-based billing with volume discounts
- Hybrid cloud deployment across AWS, GCP, Azure
- Run in your own VPC with managed control plane
- Secure runtime and on-prem deployments
- Audit logs, Global back-ups and HA/DR
- 24/7 support and FDE onboarding

Use the [Northflank pricing calculator](https://northflank.com/pricing) for exact cost estimates based on your specific requirements, and see the pricing page for more details

</InfoBox>

**Why choose Northflank**

Northflank addresses major Crusoe pain points:

- **Multi-cloud freedom** - Deploy GPU workloads anywhere without Crusoe's specific data center limitations. Switch providers or go multi-cloud without infrastructure rewrites.
- **Unified platform advantage** - Manage GPU compute with databases, applications, and CI/CD in one place instead of piecing together separate GPU cloud and infrastructure providers.
- **Transparent costs** - Predictable per-second billing with real-time cost visibility vs. complex GPU cloud pricing structures. No surprises from networking or egress fees.
- **Developer velocity** - Git-based workflows, preview environments, and integrated CI/CD reduce time from code to GPU-powered production. No separate orchestration tools required.
- **Enterprise flexibility** - BYOC (Bring Your Own Cloud) deployment on your own AWS, GCP, Azure, Civo, Oracle Cloud, or bare-metal infrastructure maintains cloud commitments while gaining superior GPU management and unified infrastructure control.

Learn more: [GPU Workloads on Northflank](https://northflank.com/gpu) | [GPU instances on Northflank](https://northflank.com/cloud/gpus) | [Documentation](https://northflank.com/docs) | [Request your GPU cluster](https://northflank.com/request/gpu)

### 2. CoreWeave

CoreWeave provides Kubernetes-native GPU cloud infrastructure for AI training and inference. CoreWeave operates infrastructure at scale for AI labs and organizations across multiple data centers.

![coreweave.png](https://assets.northflank.com/coreweave_5a85a78642.png)

**Key features**

- Kubernetes-native architecture with bare-metal performance
- GPUs including NVIDIA H100, H200, GB200 NVL72, and RTX PRO 6000 Blackwell
- Capability to operate GPU clusters with multiple nodes
- Mission Control software for automated health checks and lifecycle management
- InfiniBand networking with NVIDIA Quantum-2

**Best for**

AI labs, model training operations, and organizations needing Kubernetes-native infrastructure with capacity guarantees.

**Considerations**

Focused on reserved capacity arrangements. Kubernetes expertise required to leverage platform capabilities. Less suited for teams needing short-term or on-demand access.

### 3. Lambda Labs

Lambda Labs offers GPU cloud infrastructure with emphasis on simplicity. Known for 1-Click Clusters that provision interconnected GPUs, Lambda serves research teams and AI startups needing compute access.

![lambda-ai-homepage.png](https://assets.northflank.com/lambda_ai_homepage_d987e1a760.png)

**Key features**

- On-demand NVIDIA HGX B200, H100, A100, and GH200 instances
- 1-Click Clusters with pre-configured networking
- Pre-installed ML stack with PyTorch, TensorFlow, CUDA, and Jupyter
- Lambda Private Cloud for dedicated GPU clusters
- NVIDIA Quantum-2 InfiniBand networking for distributed training
- Used by research universities

**Best for**

Academic researchers, AI startups, teams prototyping models, and organizations wanting GPU access without complex cloud configurations.

**Considerations**

Limited to Lambda's own infrastructure with no multi-cloud options. Smaller geographic footprint compared to hyperscalers. Fewer features like custom VPC networking or integration with broader cloud services. Suited for compute-focused workloads rather than complex multi-service architectures.

### 4. RunPod

RunPod provides GPU cloud with deployment across multiple regions. Offering GPU instances across 31 regions, RunPod serves developers and small teams needing access to GPUs.

![runpod's homepage.png](https://assets.northflank.com/runpod_s_homepage_14648d1a93.png)

**Key features**

- GPU deployment across 30+ regions
- Secure Cloud and Community Cloud options
- Serverless GPU with automatic scaling and idle shutdown
- Support for custom Docker containers and pre-built templates
- CLI and API for automation and CI/CD integration
- Spot instances for interruptible workloads

**Best for**

Individual developers, small ML teams, rapid prototyping, and inference serving.

**Considerations**

Community Cloud providers may have lower uptime than data centers. Limited support for multi-node clusters. Documentation and tooling less mature than established providers. Suited for smaller workloads rather than training large models.

### 5. Nebius

Nebius offers GPU cloud infrastructure with technical capabilities. Using ODM hardware and lightweight virtualization, Nebius maintains Gold-tier performance ratings in independent benchmarks.

![nebius.png](https://assets.northflank.com/nebius_f04bd58e0b.png)

**Key features**

- Provisioning of GPU clusters
- Lightweight VMs with bare-metal class performance using kubevirt
- GPU inventory availability
- Data center presence in Europe for regional compliance
- ODM hardware strategy

**Best for**

Teams needing GPU cloud with technical capabilities and organizations requiring access to GPUs.

**Considerations**

Newer entrant with smaller ecosystem than established providers. Less brand recognition may concern procurement teams. Limited geographic presence outside Europe. Smaller customer base means fewer community resources and examples.

### 6. Hyperstack

Hyperstack provides GPU cloud infrastructure from data centers in Europe. Offering NVIDIA H100, A100, and RTX-series GPUs, Hyperstack serves teams requiring data residency in Europe with managed services.

![hyperstack.png](https://assets.northflank.com/hyperstack_be84074004.png)

**Key features**

- Data centers in Europe for GDPR compliance and data residency
- NVIDIA H100, A100, and RTX GPUs
- VPC networking and security controls
- Managed Kubernetes and orchestration services
- Support for custom Docker containers and ML frameworks
- Sales support for accounts

**Best for**

Organizations requiring data residency in Europe, teams with GDPR compliance needs, and companies wanting regional GPU providers with managed services.

**Considerations**

Limited to geographic presence in Europe. Smaller GPU inventory compared to global providers. Less mature ecosystem and tooling than established alternatives.

### 7. Traditional hyperscalers (AWS, GCP, Azure)

AWS, Google Cloud, and Microsoft Azure offer GPU instances as part of comprehensive cloud platforms. While not specialized for AI workloads, hyperscalers provide integration with broader cloud services, global infrastructure, and compliance certifications.

**Key features**

- **AWS** - P5 instances with H100 GPUs, SageMaker for managed ML, service integration
- **GCP** - A2 and A3 instances with A100/H100 GPUs, TPU alternatives, Vertex AI platform
- **Azure** - NC-series with NVIDIA GPUs, Azure ML integration, Microsoft ecosystem integration
- Data center presence across multiple regions
- Compliance certifications (SOC 2, ISO 27001, HIPAA, PCI-DSS)
- Integration with storage, networking, security, and database services
- Committed to using discounts and spot instances

**Best for**

Organizations already using AWS/GCP/Azure for infrastructure, enterprises requiring specific compliance certifications, and teams needing integration with other cloud services.

**Considerations**

Higher costs compared to specialized GPU cloud providers. Complex pricing structures with multiple fees for networking, storage, and data transfer. GPU availability can be limited during high-demand periods. Suited for organizations prioritizing ecosystem integration.

## How do Crusoe alternatives compare?

Use this comparison to identify which alternative aligns with your technical requirements and deployment needs.

| Alternative | Best for | Key advantages | GPU options | Pricing model |
| --- | --- | --- | --- | --- |
| **Northflank** | Startups to enterprises needing multi-cloud flexibility and unified infrastructure | Multi-cloud deployment across AWS, GCP, Azure, Oracle Cloud, Civo, and bare-metal; unified platform with databases and CI/CD; BYOC option; Git-based workflows | B200, H200, A100, H100, L4, L40S, GH200, and more [GPU types](https://northflank.com/gpu) across multiple clouds | A100 40GB from $1.42/hr, A100 80GB from $1.76/hr, H100 from $2.74/hr, H200 from $3.14/hr, B200 from $5.87/hr |
| **CoreWeave** | AI labs needing Kubernetes-native infrastructure | Kubernetes-native architecture | H100, H200, GB200 NVL72, RTX PRO 6000 | Reserved capacity arrangements |
| **Lambda Labs** | Research teams and academic institutions | 1-Click Clusters, pre-installed ML stack | HGX B200, H100, A100, GH200 | On-demand and reserved options |
| **RunPod** | Developers needing deployment flexibility | Deployment across 30+ regions, serverless options, Community and Secure Cloud | H100, A100, RTX 4090, RTX 3090 | Per-second billing model |
| **Nebius** | Teams needing technical capabilities | kubevirt architecture | A100, H100, NVIDIA GPUs | Various pricing options |
| **Hyperstack** | Organizations with European data residency requirements | Data centers in Europe, GDPR compliance, managed services | H100, A100, RTX-series | Hourly and monthly options |
| **Hyperscalers** | Organizations invested in cloud ecosystems | Service integration, compliance certifications, global infrastructure | Various NVIDIA GPUs, TPUs (GCP) | Varies by provider |

## Finding the right GPU cloud provider for your needs

For teams evaluating alternatives to Crusoe's infrastructure, several options provide different approaches to GPU cloud computing.

Northflank stands out as a unified cloud platform (both CPU and GPU workloads), not just a GPU provider. You get multi-cloud flexibility to deploy on AWS, GCP, Azure, Oracle Cloud, Civo, or bare-metal from a single interface.

Unlike specialized GPU clouds locked to their own infrastructure, Northflank lets you run your entire stack in one place: GPU workloads alongside databases, applications, APIs, background jobs, and CI/CD pipelines. This removes the need to manage separate tools for GPU compute and infrastructure, while transparent per-second billing ensures cost predictability across providers.

From GPUs for training models to databases for your application, everything is managed from one platform with Git-based workflows and preview environments.

<InfoBox className="BodyStyle">

### Get started with Northflank

- [Start with a free account](https://app.northflank.com/signup) or go straight to [request your GPU cluster](https://northflank.com/request/gpu) - Test workloads and infrastructure
- [Book a demo](https://cal.com/team/northflank/northflank-intro) with an expert engineer
- Calculate savings with the [pricing calculator](https://northflank.com/pricing)
- Learn more: [GPU Workloads on Northflank](https://northflank.com/gpu) | [GPU instances on Northflank](https://northflank.com/cloud/gpus) | [Documentation](https://northflank.com/docs)

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>November 2025 | Product releases</title>
  <link>https://northflank.com/changelog/platform-november-2025-release</link>
  <pubDate>2025-11-27T16:30:00.000Z</pubDate>
  <description>
    <![CDATA[Unified jobs, pipeline-free workflows, dynamic storage, new template features, faster Granite Rapids compute + B200 GPUs, smarter autoscaling, cleaner UI, and a batch of new stack templates.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Group_1410119482_cc53f26083.png" alt="November 2025 | Product releases" />Here's what we shipped last month.

## **Clusters & Infrastructure**

- **CoreWeave is now a supported Bring Your Own Cloud provider.** You can connect your CoreWeave organisation via API key and immediately create and orchestrate your CoreWeave GPU capacity. 

![cluster-nodepool.png](https://assets.northflank.com/cluster_nodepool_4f75760a11.png)
- **New projects on PaaS in all regions are now deployed on Intel Xeon 6 Granite Rapids** leading to a performance improvement of 30-70% based on the workload. All machines have an all-core turbo frequency of 3.9 GHz and a max turbo frequency of 4.2 GHz, DDR5 memory, local NVME. Northflank can support plans and custom deployments of up to 288vCPU, 2.2TB of memory, 200Gbps of network bandwidth per dedicated node.
- **B200 GPUs in Asia, Europe regions:** Added B200 GPUs in Asia and Europe regions.
- **CoreDNS deployment customisation:** Implemented CoreDNS deployment customisation for nameservers and plugin overrides.
- **Build submission validation for self-hosted clusters:** Added new validation to self-hosted cluster build submissions based on node pool configuration.
- **Configurable Istio ingress gateway name:** The Istio ingress gateway name is now configurable.
- **Improved BYOC cluster error display:** Improved the display of BYOC cluster errors.
- **Improved BYO Registry cluster view:** Improved the BYO Registry cluster detail view.
- **Fixed BYOK cluster spot instances:** Fixed the spot instance behavior for BYOK clusters.

## **Jobs & Workflows**

- **Unified job types**: Northflank cron jobs and manual jobs have been unified into a single Job resource type. Simply enable a schedule, define a crontab, and configure the concurrency policy, time limit, and retry limit. Useful for running migrations, background jobs and training tasks such as Pytorch. These can now be created using the same API endpoint.

![image (34).png](https://assets.northflank.com/image_34_cff521dc4f.png)
- **Volumes can now be linked to Jobs:** Volumes can now be attached to job resources.

![image (35).png](https://assets.northflank.com/image_35_e00f1c01cd.png)

## **Storage & Volumes**

- **Dynamic storage class system:** New storage class system to support dynamic storage classes for BYOC and PaaS clusters.
- **Faster and more dynamic volumes for PaaS:** New PaaS workloads are now deployed with high-performance network-attached NVME with low latency, and a higher level of default IOPS and throughput. Better than 99.999% durability. In the near future, you will be able to configure independent control IOPS and throughput.
- **Performance:**
    - 3,000 minimum IOPS to a maximum of 160,000 IOPS
    - 140MiB/s minimum throughput with a maximum of 2,400 MiB/s
    - 6GB minimum capacity with a maximum of 64TB
- **CI caching:** Northflank combined and build services with Buildkit now support local volume caching on PaaS and BYOC.

![image (36).png](https://assets.northflank.com/image_36_77ad1ba07e.png)

- **Storage scaling for custom addon types:** Support added for scaling storage for persistent volume claims for custom addon types.
- **Auto-attach volumes via Service:** Volumes created on the service are correctly attached to the service on creation.

## **Templates**

- **Template array and object manipulation:** Added a number of template functions to support array and object manipulation.
- **Approval node:** Added a new template node, Approval, which pauses the template and waits for a specified number of users to click approve before continuing.
- **OpenTofu logs:** Adds OpenTofu logs into Template nodes for running or finished runs.
- **OpenTofu plan approval:** Adds option to prompt for OpenTofu plan approval before proceeding with a template run.
- **Creatable values and references in Addon/Volume nodes:** Addon and Volume nodes in templates now allow creatable values and references.
- **Build reuse considers build arguments:** The option for a workflow to reuse a build now takes into account the build arguments that were used for that build.
- **Template run triggers from PR tags:** Added support for triggering templates based on PR tags.
- **Improved template performance:** Improved the performance of template runs and the template UI.
- **Fixed template scaling behavior:** Updating a resource in a template no longer incorrectly scales it when autoscaling is enabled.
- **Template meta bar:** Added a new meta bar component with GitOps info for new template types.
- **Fixed template date display:** Fixed an incorrect date display in template runs.
- **Fixed template editor loading:** The template editor will no longer load at the wrong size under certain conditions.

## **Addons & Databases**


![image (37).png](https://assets.northflank.com/image_37_ac3e8d6312.png)

- **MySQL High Availability direct routing:** Added support to MySQL High Availability for direct routing via K8s services without router deployment.
- **Download helm chart bundles:** Added the option to download an archive file of a helm chart bundle.
- **PostgreSQL readiness probe improvements:** Improved the handling of PostgreSQL readiness probes.
- **Redis probe configuration improvements:** Improved Redis Addon probe configuration to avoid unnecessary probe failures.
- **Fixed PostgreSQL forking edge cases:** Fixed an issue with forking PostgreSQL addons in certain edge cases.
- **Fixed custom addon scaling issues:** Fixes some edge cases where some custom addons were getting stuck in a scaling state.
- **Fixed MySQL HA on large plans:** Fixed some issues with MySQL High Availability addons running on large plans.


## **Domains & Certificates**

- **Certificate expiry validation:** Added validation for certificates that are soon to expire.
- **TLS mode consistency validation:** Fixed an issue with inconsistent TLS modes on subdomains under a wildcard-enabled domain.
- **Wildcard DCV pre-flight check:** Added a pre-flight check for conflicting TXT records on wildcard DCV.
- **Improved domain validation messages:** Improved validation error messages for domains.

## **Builds & Registry**

- **Optimised Docker image checks:** Optimised Docker image checks.
- **Registry enhancements:** Phase one improvements for Docker build upload and pull performance have been released
- **Configurable build container resources:** Added configurable resources for build containers.
- **Advanced buildpack options:** Added the includeGitFolder and fullGitClone options to the buildpack advanced build options.
- **Fixed build stage dependency crash:** Fixed an issue with builds crashing when a stage has multiple identical dependencies.

## **Deployment & Scaling**

- **Custom deployment strategies:** Added custom deployment strategy support allowing users to set their own maxSurge and maxUnavailable values instead of using predefined options.
- **Improved custom metric autoscaling handling:** Improve handling when modifying custom metric autoscaling to prevent issues with stale configuration.

## **Logging & monitoring**

- **Fixed log sink project deletion issue:** Fixed an issue with log sinks getting soft locked when a project they contain is deleted.
- **Improved observability stats loading:** Improved the loading display on observability stats.

## **Security & access control**

- **Project access restrictions for API/RBAC roles:** API and RBAC roles can now be set to be restricted to all projects except the listed ones.
- **Security policy ref support:** Added ref support for security policies.
- **Fixed Directory Sync user updates:** Fixed an issue with Directory Sync users not updating correctly.

## **Integrations**

- **AWS integration JSON view:** Added Verify button for AWS integration when switching to JSON view.
- **Backblaze B2 provider support:** Added Backblaze B2 provider support.
- **Improved GCP permissions UI:** Improved the GCP permissions UI.

## **UI/UX improvements**

- **Addon size value improvements:** Improved the UX of minimum and maximum addon size values.
- **Service creation example values:** Added example values to the service creation UI.

**Improved addon HA selection:** Improved the UI for selecting addon high availability.

- **Command menu favorites:** The command menu now prioritises favourite teams and projects.
- **Keyboard shortcuts for modals:** Commit message and template draft creation modals can be submitted with CMD+Enter / Ctrl+Enter.
- **Resource meta bar improvements:** Improved the resource meta bar.
- **Fixed command menu search persistence:** The command menu no longer has query search strings that incorrectly persist.
- **Fixed modal editor closing**: The preview code view modal no longer closes itself.
- **Fixed addon container status overflow:** Fixed an overflowing addon container status component.
- **Improved deployment page performance:** Improved the performance of the deployment page.

## **Bug fixes**

- **Fixed metrics labels:** Fixed some missing metrics labels.
- **Fixed pricing display:** Fixed the pricing display on Volumes and Addons for invalid configs and fixed the pricing display for vCPU.
- **Fixed undefined metrics query:** Fixed an issue with an undefined metrics query result.
- **Fixed Networking and Resources page data:** Fixed data failing to be displayed on the Networking and Resources pages.
- **Fixed resource spec view duplicates:** Fixed some duplicate fields in resource spec view.
- **Fixed secret file argument substitution:** Fixed some arguments in secret files not correctly being substituted.
- **Fixed global secrets validation:** Fixed a validation error not being displayed for global secrets.
- **Fixed node metrics display:** Fixed the display of node metrics timestamps and undefined metric sets.
- **Fixed command bar error:** Fixed an undefined function error in the command bar menu.
- **Fixed password reset flow:** Fixed some issues with the password reset flow.
- **Fixed team email change prompt:** Fixed an issue where trying to change the email for a team was opening an incorrect settings prompt.
- **Fixed secret group descriptions:** Fixed secret group descriptions not showing on the list view.
- **Fixed environment editor stale values:** Fixed the environment editor saving stale values in certain circumstances.
- **Fixed template JSON cleanup:** Added logic to remove unused keys when inputting JSON to a template.
- **Unified project tabs in templates:** Unified the new and existing project tabs in template project nodes.

## **Stack templates**

- **New stack templates** Added stack templates for [Cal.com](https://northflank.com/stacks/deploy-calcom), [Umami](https://northflank.com/stacks/deploy-umami), [Listmonk](https://northflank.com/stacks/deploy-listmonk), [Directus](https://northflank.com/stacks/deploy-directus), [Infisical](https://northflank.com/stacks/deploy-infisical), [AnythingLLM](https://northflank.com/stacks/deploy-anythingllm), [Langflow](https://northflank.com/stacks/deploy-langflow), [FlowiseAI](https://northflank.com/stacks/deploy-flowiseai), [Dialoqbase](https://northflank.com/stacks/deploy-dialoqbase), [Langtrace](https://northflank.com/stacks/deploy-langtrace), [Meilisearch](https://northflank.com/stacks/deploy-meilisearch), [n8n](https://northflank.com/stacks/deploy-n8n) (updated).]]>
  </content:encoded>
</item><item>
  <title>Managed Redis: what it is and how to choose a provider</title>
  <link>https://northflank.com/blog/managed-redis-guide-for-developers</link>
  <pubDate>2025-11-25T17:45:00.000Z</pubDate>
  <description>
    <![CDATA[Managed Redis handles operations, scaling, and backups for your Redis database. Learn what to look for in a provider and how to get started.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/managed_redis_guide_for_developers_8589bc7811.png" alt="Managed Redis: what it is and how to choose a provider" /><InfoBox className="BodyStyle">

Managed Redis is a fully managed database service where a provider handles operational tasks like provisioning, backups, scaling, and monitoring. Key options include:

- **Northflank**: Kubernetes-native managed Redis starting at $2.70/month. Deploy in Northflank's cloud or your own infrastructure (AWS, Azure, GCP, Civo, Oracle, bare-metal) with developer-friendly CLI & API. Includes pause/resume for dev databases to save costs
- **Cloud platforms**: Azure Managed Redis, AWS ElastiCache, and Redis Cloud offer tightly integrated solutions within their ecosystems

Choose based on your priority: deployment flexibility and transparent pricing, or native cloud integration.

</InfoBox>

Redis powers demanding applications, but running it in production means managing replication, failover, backups, and scaling, operational tasks that distract from building features.

Managed Redis handles these operations for you. This guide covers what it offers, how to choose a provider, and how to get started.

## What is managed Redis?

Redis is an open-source, in-memory data structure store used as a database, cache, message broker, and streaming engine. It stores data in RAM for sub-millisecond response times and supports data types including strings, hashes, lists, sets, sorted sets, JSON documents, and vector embeddings.

Managed Redis means a provider handles all operational tasks, like provisioning, replication, backups, monitoring, and scaling. You deploy through a web interface or API, connect your application, and the provider manages the rest.

## Why do developers choose managed over self-hosted Redis?

Self-hosting Redis requires configuring high availability, implementing backups, monitoring memory usage, handling security updates, and planning for scaling, which creates operational overhead that can become a bottleneck for small teams and high-growth applications.

| **Feature** | **Self-hosted Redis** | **Managed Redis** |
| --- | --- | --- |
| **Backups** | Manual configuration and scheduling | Automatic backups with point-in-time recovery |
| **High availability** | Configure Redis Sentinel or Cluster manually | Built-in failover with minimal downtime |
| **Scaling** | Plan capacity, provision servers, reconfigure | One-click vertical and horizontal scaling |
| **Security** | Configure TLS, networks, and access controls | TLS encryption, network isolation by default |
| **Monitoring** | Set up metrics, logs, and alerting | Built-in monitoring and proactive alerts |
| **Patching** | Manual security updates and version upgrades | Automated patching with zero downtime |

## What are the common use cases for managed Redis?

Redis is ideal for workloads requiring fast data access and real-time operations:

- **Caching**: Store frequently accessed data to reduce database load and latency by up to 80%
- **Session management**: Keep user sessions accessible across any server without sticky sessions
- **Real-time analytics**: Track page views, events, and metrics with sub-millisecond speed for live dashboards
- **Message queues and pub/sub**: Build job queues and real-time messaging for chat, notifications, or microservices
- **Leaderboards and rankings**: Maintain ordered data efficiently for gaming leaderboards and social feeds
- **Rate limiting**: Prevent API abuse with fast counters and automatic expiration
- **AI and vector workloads**: Enable semantic caching for LLMs and memory for AI agents with vector search

## What key features should you look for in a managed Redis provider?

Evaluate providers based on these critical features and questions:

- **High availability and replication**: What's the uptime SLA? How long does failover take? Can you deploy across availability zones? Look for primary-replica replication with automatic failover via Redis Sentinel, and multi-region support for global applications.
- **Backup and disaster recovery**: How frequently are backups taken? Can you restore to any point in time? Where are backups stored? Ensure automated backups, long-term retention, easy restoration, and data import capabilities.
- **Security and compliance**: Is TLS enabled by default? Can you deploy in private networks? What compliance certifications exist? Expect TLS encryption, authentication, network isolation, and standards like SOC 2, HIPAA, PCI DSS, or GDPR.
- **Scaling capabilities**: Can you scale without downtime? How many replicas can you add? Is Redis Cluster supported? Look for simple vertical and horizontal scaling, plus Redis Cluster for sharding large datasets.
- **Monitoring and observability**: What metrics are available? Can you export to your monitoring stack? Are there pre-built dashboards? Quality providers offer memory usage, latency, hit rates, log streaming, and slow command analysis.
- **Developer experience**: Is there a CLI for local access? How complete is the API? Can you automate via infrastructure-as-code? Prioritize intuitive interfaces, local forwarding, comprehensive APIs, and clear documentation.
- **Pricing and cost predictability**: What's included in the base price? Are there minimum commitments? Can you estimate costs upfront? Compare instance-based vs serverless pricing and watch for hidden costs like data transfer.

## What managed Redis providers are available?

Several providers offer managed Redis, each with different strengths depending on your infrastructure and priorities.

### 1. Northflank

Northflank provides Kubernetes-native managed Redis with flexible deployment options. You can run Redis on Northflank's cloud or deploy to your own infrastructure (AWS, Azure, GCP, Civo, Oracle, bare-metal) while Northflank handles the management layer.

![northflank-for-redis.png](https://assets.northflank.com/northflank_for_redis_3eed8c0484.png)

Key features:

- Starting at $2.70/month with transparent, serverless pricing
- Ready for scale with horizontal and vertical scaling
- Backups and restores with automated scheduling
- Real-time monitoring with log tailing and performance metrics
- Pause/resume for dev databases to save costs
- Advanced networking with secure private networks and CLI proxy
- TLS-secured connections by default
- Developer-friendly CLI tools and local forwarding for debugging
- Enterprise support available

Best for teams wanting deployment flexibility, predictable pricing, and developer productivity without vendor lock-in, from startups to enterprises.

### 2. Azure Managed Redis

Microsoft's Redis offering built on Redis Enterprise technology. It integrates deeply with Azure services and requires commitment to the Azure ecosystem.

![azure-managed-redis.png](https://assets.northflank.com/azure_managed_redis_ec10e7823c.png)

Key features:

- Multiple performance tiers (memory optimized, balanced, compute optimized, flash optimized)
- Native integration with Azure services
- Microsoft Entra ID authentication
- Zone redundancy and high availability

Best for teams already heavily invested in Azure infrastructure. Pricing can be complex with additional costs for networking, storage, and cross-region traffic.

### 3. AWS ElastiCache for Redis

Amazon's managed Redis service integrated with AWS services. Offers both serverless and node-based options, though node-based clusters can be complex to configure and scale. You're locked into AWS infrastructure.

![aws-elasticache-for-redis.png](https://assets.northflank.com/aws_elasticache_for_redis_c4d745cb38.png)

Key features:

- Compatible with Redis OSS and Valkey (open-source Redis fork)
- VPC isolation for security
- Native integration with AWS services (CloudWatch, CloudTrail, IAM)
- Advanced configuration options for node-based clusters
- Serverless option for simplified deployment

Best for teams committed to AWS infrastructure. Note that ElastiCache does not support Redis Enterprise features like Redis Query Engine or active-active geo-distribution.

### 4. Google Cloud Memorystore

Google's managed Redis service focused on simplicity within the GCP ecosystem. Limited to Google Cloud infrastructure with fewer deployment options.

![google-cloud-memorystore.png](https://assets.northflank.com/google_cloud_memorystore_7f4fee5bd9.png)

Key features:

- Basic and standard tiers with automatic failover
- Integration with GCP services
- Supports Redis 7.0+
- Simple deployment workflow

Best for teams using Google Cloud Platform. Regional availability is more limited compared to other providers.

### 5. Redis Cloud

The managed service from Redis Ltd. offering the latest Redis features. Pricing starts higher than alternatives, and you're dependent on Redis Ltd.'s infrastructure choices.

![redis-cloud.png](https://assets.northflank.com/redis_cloud_f685c0d903.png)

Key features:

- Latest Redis versions (including Redis 8)
- Active-active geo-replication
- Redis Stack modules (vector search, JSON, time series data)
- Available on all major clouds

Best for teams needing **the latest** Redis features who can justify premium pricing for advanced capabilities.

<InfoBox className="BodyStyle">

**Tips on choosing the right provider**

Cloud platform providers (Azure, AWS, GCP) work best when you're already committed to that ecosystem, though you'll face vendor lock-in and complex pricing.

Redis Cloud offers the latest Redis innovations but at premium pricing.

> Northflank stands out for teams prioritizing deployment flexibility, transparent pricing starting at $2.70/month, and developer productivity for startups and enterprises alike. You get Kubernetes-native tooling, the ability to deploy anywhere (including your own infrastructure), and features like pause/resume that directly reduce costs, all without vendor lock-in.
> 

</InfoBox>

## How to get started with managed Redis?

Deploying managed Redis is straightforward across most providers. The key is choosing the right configuration for your needs before deployment.

### What are the general deployment considerations?

Before deploying, decide on these key configurations:

- **Redis version**: Use the latest stable version (Redis 8.2.2 or 7.2.11) for new projects
- **Replication**: Enable at least one replica for production high availability
- **Memory size**: Start with your estimated dataset size plus 20% buffer for Redis overhead
- **TLS**: Enable encryption by default for secure connections
- **Backups**: Configure automatic daily backups with appropriate retention
- **Network access**: Use private networks for production, public access only for development

### How to deploy Redis on Northflank

Northflank makes Redis deployment simple through its interface, CLI, or API. Here's the quick process:

![northflank-redis-test.png](https://assets.northflank.com/northflank_redis_test_397f167695.png)

1. **Create an addon**: Navigate to your project and click "Create New" → "Addon"
2. **Select Redis**: Choose from versions
3. **Enable TLS**: Secure connections are enabled by default
4. **Configure advanced options**:
    - **Redis Sentinel**: Add automated failover monitoring for high availability
    - **Maxmemory policy**: Choose eviction strategy (noeviction, allkeys-lru, volatile-lru, etc.)
    - **Zonal redundancy**: Deploy replicas across multiple availability zones
    - **Backup schedules**: Set automated backup frequency and retention
5. **Select resources**: Choose compute plan based on your workload requirements
6. **Link to secret groups**: Automatically inject connection details into your services

Your Redis instance provisions in minutes with automatic backups, real-time monitoring, and TLS-secured connections. Northflank also automatically generates and injects connection details into your applications as environment variables.

<InfoBox className="BodyStyle">

For detailed deployment instructions, see the [Northflank Redis deployment guide](https://northflank.com/docs/v1/application/databases-and-persistence/deploy-databases-on-northflank/deploy-redis-on-northflank) and learn how to [migrate your existing Redis deployment](https://northflank.com/docs/v1/application/databases-and-persistence/migrate-data-to-northflank/migrate-your-redis-deployment-to-northflank) to Northflank.

[Sign up for a free sandbox](https://app.northflank.com/signup) to get started, or [book a demo](https://cal.com/team/northflank/northflank-intro) to discuss your specific needs with our expert engineers.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Top 7 AWS RDS alternatives in 2026</title>
  <link>https://northflank.com/blog/aws-rds-alternatives</link>
  <pubDate>2025-11-24T16:30:00.000Z</pubDate>
  <description>
    <![CDATA[AWS RDS alternatives 2025: Northflank, Google Cloud SQL, Neon, Azure &amp; more. Compare managed databases with multi-cloud flexibility &amp; pricing.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/aws_rds_alternatives_8d2340b58c.png" alt="Top 7 AWS RDS alternatives in 2026" />AWS RDS alternatives are gaining momentum as development teams search for more cost-effective, flexible, and developer-friendly database solutions.

While Amazon RDS has been a reliable managed database service since 2009, escalating costs, complex pricing structures, AWS vendor lock-in, and limited deployment flexibility are pushing organizations to evaluate alternatives.

If you're paying $80-$1,000+ monthly for AWS RDS or struggling with unpredictable bills and AWS-only deployment, this guide compares the top alternatives to help you make an informed decision.

<InfoBox className="BodyStyle">

## TL;DR: Quick list of the best 7 AWS RDS alternatives

See a quick list of the top 7 AWS RDS alternatives we'll review in this guide:

1. **Northflank** - Best for startups to enterprise teams wanting managed databases with multi-cloud flexibility, transparent pricing, auto-scaling, and modern DevOps features. Deploy PostgreSQL and MySQL on AWS, GCP, Azure, Oracle Cloud, Civo, or bare-metal from a single platform with built-in CI/CD, preview environments, and significant cost savings compared to RDS.
2. **Google Cloud SQL** - Best for teams using the Google Cloud Platform needing native GCP service integration
3. **Neon** - Best for serverless PostgreSQL with database branching capabilities
4. **Supabase** - Best for full-stack developers needing database, auth, and real-time APIs
5. **Azure Database for PostgreSQL** - Best for organizations standardized on Microsoft Azure infrastructure
6. **DigitalOcean Managed Databases** - Best for small businesses wanting predictable costs and simple management
7. **Railway** - Best for side projects and rapid prototyping with simple setup

</InfoBox>

## What to look for in AWS RDS alternatives

When evaluating AWS RDS alternatives, focus on these key criteria:

- **Cost transparency** - Look for clear, usage-based pricing without hidden fees for backups, monitoring, or networking. Pricing calculators should accurately predict costs, and you should be able to pause development databases to save money.
- **Deployment flexibility** - Modern teams need multi-cloud options (AWS, GCP, Azure) from a single platform. [Bring Your Own Cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) (BYOC) lets you maintain existing cloud relationships while gaining managed convenience.
- **Developer experience** - The best alternatives provide intuitive interfaces, CLI/API support, Git-based workflows, preview databases for pull requests, and unified application and database deployment rather than separate tools.
- **Performance and scaling** - Evaluate both vertical and horizontal scaling capabilities, auto-scaling features, connection pooling for high-connection workloads, and whether serverless or scale-to-zero options fit your needs.
- **Security and compliance** - Production databases require automated backups with point-in-time recovery, high availability with automatic failover, encryption, network isolation, fine-grained access controls, and relevant compliance certifications (SOC 2, ISO 27001, HIPAA, PCI-DSS).

## What are the best AWS RDS alternatives?

We've evaluated the following alternatives based on cost-effectiveness, deployment flexibility, developer experience, performance, and scalability to help you find the best fit for your requirements.

### 1. Northflank

Northflank delivers a modern cloud platform combining managed database simplicity with multi-cloud flexibility and transparent pricing. Built for teams in need of AWS RDS alternatives without vendor lock-in, Northflank lets you deploy managed databases, including PostgreSQL, MySQL, MongoDB, Redis, MinIO, and RabbitMQ, across multiple clouds from a unified platform.

![northflank-databases.png](https://assets.northflank.com/northflank_databases_1c87718302.png)

**Key features:**

- **Multi-cloud deployment** - Deploy databases on AWS, GCP, Azure, Oracle Cloud, Civo, or bare-metal from a single platform. Choose from 30+ global regions without vendor lock-in. Run on Northflank's managed cloud or bring your own cloud account (BYOC) to maintain existing cloud relationships and billing.
- **Transparent, predictable pricing** - Reduce costs compared to AWS RDS with simple usage-based pricing. Pay per-second for only CPU, memory, and storage you consume with no hidden fees. Managed databases including PostgreSQL, MySQL, MongoDB, and Redis start from as low as $3.91/month. Pause development databases to save costs during inactive periods.
- **Flexible scaling** - Scale horizontally with read replicas and vertically starting from 0.1 vCPU and 256 MB memory. Adjust resources without downtime. Support for PostgreSQL extensions (pgvector, PostGIS, timescaledb) and MySQL configurations.
- **Developer-first workflows** - Git-based deployments with automatic builds on every commit. Preview databases for pull requests to test schema changes safely. Fork databases for testing. Connect locally using Northflank CLI proxy without exposing databases publicly.
- **Unified platform** - Deploy databases alongside applications, APIs, background jobs, and CI/CD pipelines on the same platform. Create ephemeral environments for every pull request with databases and applications together.
- **Built-in observability** - Real-time log tailing with filtering and search. Performance metrics for CPU, memory, network, IOPS displayed in intuitive dashboards. Configure alerts via Slack, email, or webhooks. No separate monitoring tools needed.
- **Automated backups and recovery** - Daily automated backups with configurable retention. Create manual backups before major changes. Restore to any point in time or from specific backups. Import backups from URLs, files, or connection strings.
- **Enterprise-ready security** - Private networking between services removes the need for complex VPC configurations. TLS/SSL encryption enabled by default. Fine-grained role-based access controls. Deploy in your own Kubernetes clusters (EKS, GKE, AKS) for maximum control. 24/7 enterprise support with SLAs available.

**Pricing:**

- **Free sandbox** - 2 free databases, 2 free services, 2 free cron jobs with always-on compute
- **Pay-as-you-go** - Self-service with minimal restrictions (Only pay for consumption, infinitely scalable, 6+ cloud regions, 600 BYOC regions, Deploy with CPU & GPU)
- **Enterprise plan** - SSO, audit logs, on-premises deployment, global backups, HA/DR, dedicated support with SLAs, hybrid cloud across AWS, GCP, Azure, and more.

> Use the [pricing calculator](https://northflank.com/pricing) for exact cost estimates.
> 

**Why choose Northflank:**

Northflank addresses major AWS RDS pain points:

- **Cost predictability** - Transparent usage-based pricing with per-second billing for CPU, memory, and storage consumed
- **Multi-cloud freedom** - Deploy anywhere without AWS lock-in while maintaining managed convenience
- **Simplified operations** - Deploy production-ready databases quickly vs RDS's complex VPC and security group setup
- **Unified developer experience** - Manage databases with applications using Git workflows and preview environments instead of piecing together multiple AWS services
- **Enterprise flexibility** - BYOC (Bring Your Own Cloud) deployment on your AWS, GCP, Azure, Oracle Cloud, Civo, or bare-metal infrastructure maintains your cloud commitments while gaining superior management

Learn more: [Managed PostgreSQL](https://northflank.com/dbaas/managed-postgresql) | [Managed MySQL](https://northflank.com/dbaas/managed-mysql) | [Managed Redis](https://northflank.com/dbaas/managed-redis) | [MongoDB Hosting](https://northflank.com/dbaas/mongodb-on-northflank) | [Deployment guides](https://northflank.com/docs/v1/application/databases-and-persistence/deploy-a-database)

### 2. Google Cloud SQL

Google Cloud SQL provides fully managed PostgreSQL, MySQL, and SQL Server on GCP infrastructure with native service integration.

![google-cloud-sql-home-page.png](https://assets.northflank.com/google_cloud_sql_home_page_473a171747.png)

**Key features:**

- 99.95% SLA with automatic failover
- Native integration with Cloud Run and BigQuery
- Automatic storage scaling
- Point-in-time recovery
- Cloud SQL Insights for performance monitoring

**Best for:** Teams building on Google Cloud Platform needing GCP service integration and leveraging Google's global network.

**Considerations:** Pricing increases significantly for larger instances. GCP-only deployment creates vendor lock-in. Less extensive PostgreSQL extension support.

### 3. Neon

Neon offers serverless PostgreSQL with separated storage and compute architecture enabling database branching and scale-to-zero.

![neon-homepage.png](https://assets.northflank.com/neon_homepage_5047827c87.png)

**Key features:**

- Auto-scale to zero when inactive with sub-second cold starts
- Database branching for testing
- Built-in connection pooling
- Automatic storage scaling
- Time travel queries

**Best for:** Development teams needing ephemeral databases, applications with sporadic traffic, and teams wanting database branches in Git workflows.

**Considerations:** Relatively new platform with smaller ecosystem. Cold starts may not suit latency-sensitive applications. PostgreSQL only.

### 4. Supabase

Supabase provides PostgreSQL as the foundation with integrated backend services including auth, storage, and real-time functionality.

![supabase-homepage.png](https://assets.northflank.com/supabase_homepage_9cecadd065.png)

**Key features:**

- Real-time database subscriptions
- Auto-generated REST and GraphQL APIs
- Integrated authentication system
- Built-in file storage
- Full PostgreSQL access

**Best for:** Full-stack developers building web/mobile apps wanting integrated backend platform and Firebase-like functionality with PostgreSQL.

**Considerations:** Coupled architecture may not suit teams preferring separation of concerns. Self-hosting requires significant DevOps expertise.

### 5. Azure Database for PostgreSQL

Microsoft Azure's managed PostgreSQL with enterprise features and deep Microsoft ecosystem integration.

![azure-database-for-postgresql-homepage.png](https://assets.northflank.com/azure_database_for_postgresql_homepage_882ac8b44e.png)

**Key features:**

- Zone-redundant high availability
- Hyperscale with Citus for horizontal scaling
- Advanced threat protection
- Hybrid capability with Azure Arc
- Microsoft ecosystem integration

**Best for:** Organizations on Azure infrastructure, enterprises needing Microsoft integrations, and teams with hybrid cloud architectures.

**Considerations:** Azure-only deployment creates vendor lock-in. Complex pricing with multiple components. Some features limited to specific tiers.

### 6. DigitalOcean Managed Databases

DigitalOcean provides managed databases for PostgreSQL, MySQL, MongoDB, and Redis.

![digitalocean-managed-postgreSQL.png](https://assets.northflank.com/digitalocean_managed_postgre_SQL_5b3fc81d48.png)

**Key features:**

- Daily automated backups with point-in-time recovery
- High availability with standby nodes
- Vertical scaling through interface
- SSD-backed storage

**Best for:** Small to medium businesses wanting predictable costs and management without enterprise complexity.

**Considerations:** Limited to DigitalOcean datacenters. Fewer advanced features than major cloud providers. Less suitable for enterprises.

### 7. Railway

Railway offers developer-focused database deployment with emphasis on rapid setup.

![railway-min.png](https://assets.northflank.com/railway_min_10957de907.png)

**Key features:**

- Database deployment through web interface or CLI
- Git-based deployment with automatic builds
- Built-in metrics dashboard
- Database forking for testing
- CLI and web interface

**Best for:** Side projects, personal applications, and rapid prototyping where setup speed matters most.

**Considerations:** Railway-only infrastructure with no multi-cloud. Pricing can increase for production workloads. Smaller community and ecosystem.

## How to choose the right AWS RDS alternative for your needs

Use this comparison to identify which alternative aligns best with your specific requirements, budget, and technical constraints.

| Alternative | Best for | Key advantages | Deployment options |
| --- | --- | --- | --- |
| **Northflank** | Teams wanting multi-cloud flexibility, cost savings vs RDS, avoiding vendor lock-in, unified app+database platform, Git-based workflows, preview environments, enterprise BYOC deployment | Multi-cloud deployment, unified platform with CI/CD, transparent per-second billing, BYOC option, no vendor lock-in, database forking, automated backups | AWS, GCP, Azure, Oracle Cloud, Civo, bare-metal |
| **Google Cloud SQL** | GCP-focused teams | Native GCP integration, 99.95% SLA, automatic storage scaling | GCP only |
| **Neon** | Variable workloads, development environments | Serverless auto-scaling, database branching, scale-to-zero | Neon cloud |
| **Supabase** | Full-stack developers | Integrated backend with auth, real-time, and auto-generated APIs | Supabase cloud, self-hosted |
| **Azure Database** | Microsoft ecosystem | Azure integration, hybrid cloud support, Hyperscale with Citus | Azure only |
| **DigitalOcean** | Small businesses | Predictable fixed pricing, vertical scaling | DigitalOcean datacenters |
| **Railway** | Side projects, prototyping | Git-based deployment, quick setup | Railway cloud |

## Making the switch from AWS RDS

For teams facing escalating AWS RDS costs, vendor lock-in, or wanting modern DevOps capabilities, evaluating alternatives can deliver significant benefits.

Northflank provides the smoothest path forward, offering managed database simplicity with multi-cloud flexibility, transparent pricing, and comprehensive infrastructure management. Teams typically achieve significant cost savings while gaining better developer experience and deployment options.

**Get started with Northflank:**

- [Start with a free sandbox](https://app.northflank.com/signup) - 2 free databases, always-on compute
- [Book a demo](https://cal.com/team/northflank/northflank-intro) with an expert engineer
- Calculate savings with the [pricing calculator](https://northflank.com/pricing)
- Learn more: [Managed PostgreSQL](https://northflank.com/dbaas/managed-postgresql) | [Managed MySQL](https://northflank.com/dbaas/managed-mysql) | [Managed Redis](https://northflank.com/dbaas/managed-redis) | [MongoDB Hosting](https://northflank.com/dbaas/mongodb-on-northflank) | [Deployment guides](https://northflank.com/docs/v1/application/databases-and-persistence/deploy-a-database)]]>
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</item><item>
  <title>What is database hosting? Best tools and guides for 2026</title>
  <link>https://northflank.com/blog/database-hosting</link>
  <pubDate>2025-11-21T15:15:00.000Z</pubDate>
  <description>
    <![CDATA[Database hosting lets you run databases without managing servers. Compare the best tools, pricing, and features for MySQL, PostgreSQL, and more.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/database_hosting_51fe4120c3.png" alt="What is database hosting? Best tools and guides for 2026" /><InfoBox className="BodyStyle">

Database hosting is a service where third-party providers manage the infrastructure and operational lifecycle required to run your databases. Instead of setting up and maintaining your own servers, you deploy databases on managed platforms that handle backups, security, updates, and scaling, so you can focus on building applications rather than managing infrastructure.

</InfoBox>

Database hosting enables teams to build applications without managing their own server infrastructure. If you're building a SaaS product, running a high-traffic website, or developing internal tools, you need reliable database infrastructure without the overhead of maintaining it yourself.

In this guide, we'll cover:

- What database hosting is and why teams use it
- What types of databases you can host
- Typical pricing models and cost considerations
- What features to look for in a database hosting provider
- What the best database hosting tools are in 2026
- How to choose the right provider for your use case
- How to get started with database hosting

## What is database hosting?

Database hosting is the practice of running databases on infrastructure managed by a third-party provider, rather than maintaining your own physical servers or self-managed virtual machines.

Such providers handle server provisioning, operating system and database software updates, security patches, backup management, and ongoing maintenance. These responsibilities align with the common definition of Database-as-a-Service (DBaaS).

"Database hosting" services today often go beyond infrastructure rental. Most modern providers deliver DBaaS features such as:

- automated backups,
- performance monitoring,
- scaling capabilities,
- high availability/replication, and
- snapshot or point-in-time recovery.

This enables production-grade databases without requiring your own infrastructure or full database management expertise.

## Why use database hosting instead of self-hosting?

Self-hosting databases (on your own servers or unmanaged cloud VMs) generally requires significant engineering and operational overhead:

- **Infrastructure expertise**: Configuring servers, networking, OS, database software, security, and backup systems correctly
- **Ongoing maintenance**: Applying OS and database security patches and software updates, monitoring performance and availability
- **Disaster recovery**: Implementing, storing, and regularly testing backups and restoration procedures
- **Scaling complexity**: Anticipating growth, building redundancy, load balancing, and capacity planning
- **Cost overhead**: Hardware/VM costs, storage, backup systems, monitoring tools, and time of engineering staff to manage the system

<InfoBox className="BodyStyle">

Managed (hosted) database services outsource these burdens. You can typically deploy new instances quickly, get automated backups, built-in monitoring and metrics, and rely on the provider’s operational support rather than implementing everything yourself.

> Platforms like Northflank let you [provision](https://northflank.com/docs/v1/application/databases-and-persistence/deploy-a-database) managed database addons (PostgreSQL, MySQL, MongoDB, Redis, etc.) fairly easily using the UI, API, or CLI.
> 

For most teams, the cost of managed hosting is lower than the engineering time required for self-hosting when you account for setup, maintenance, and incident response.

</InfoBox>

## What types of databases can you host?

Database hosting providers support a variety of database engines, broadly categorized into **SQL (relational)** and **NoSQL (non-relational)** options.

### SQL databases (relational)

- **PostgreSQL**: Open-source, highly extensible, supports complex queries, strong SQL compliance and advanced features
- **MySQL**: Widely-used relational database known for reliability and broad ecosystem support
- **MariaDB**: MySQL-compatible fork with enhanced performance features

### NoSQL databases (non-relational)

- **MongoDB**: Document-based database storing data in flexible JSON-like structures
- **Redis**: In-memory data store used for caching, session management, and real-time features
- **Cassandra**: Distributed database designed for massive scale across multiple data centers

Choosing a provider that supports multiple database engines can reduce operational complexity if your application stack uses a mix (e.g. PostgreSQL for transactions, Redis for caching).

## How much does database hosting cost?

Database hosting pricing varies based on resources, features, and provider models. Most providers follow one of three pricing approaches:

| Pricing model | Description / When used |
| --- | --- |
| **Resource-based (usage-based)** | You pay for CPU, memory, storage, and other resource consumption, typically pro-rated to usage. This can be cost-effective for variable workloads. |
| **Fixed-tier / Plan-based** | Pre-defined packages (e.g. “small”, “medium”, “large”) with fixed monthly pricing that bundle compute, storage, and features. |
| **Serverless / On-demand** | You may pay for compute or active usage rather than reserved capacity; scaling and resource allocation is managed transparently by the provider.  |

### Example: Northflank pricing model

Northflank uses usage-based billing (“you only pay for the resources your services consume”). The pricing page includes a **Pricing calculator** to help estimate monthly spend based on your resource needs.

<InfoBox className="BodyStyle">

Northflank's base compute plan starts at $2.70/month for 0.1 shared vCPU and 256 MB of memory. A complete MySQL database with that compute plus 4GB storage costs around $3.91/month total, while more powerful configurations scale proportionally based on your exact resource needs.

</InfoBox>

You can estimate your costs using [Northflank's pricing calculator](https://northflank.com/pricing).

![northflank-database-pricing-calculator.png](https://assets.northflank.com/northflank_database_pricing_calculator_7b47dce798.png)

Northflank pricing calculator for MySQL database with basic resources

When evaluating costs, consider the total cost of ownership, including backup storage, data transfer, and any add-on features, rather than just base database pricing.

## What features should you look for in database hosting?

These are some of the key features to evaluate when choosing a database hosting provider:

- **Automated backups**: Look for daily backups with point-in-time recovery so you can restore to any moment
- **High availability**: Choose providers offering replication and failover to minimize downtime
- **Performance storage**: SSD or NVMe drives deliver significantly better read/write speeds
- **Security and encryption**: Ensure data is encrypted both at rest and in transit with TLS/SSL
- **Network isolation**: Private networking keeps your database accessible only to authorized services
- **Access controls**: Role-based permissions let you manage team access appropriately
- **Built-in monitoring**: Real-time logs and metrics help you track performance and troubleshoot issues
- **Easy scaling**: Add CPU, RAM, or storage without downtime as your application grows

## What are the best database hosting tools?

The right database hosting solution depends on if you need only a database or an entire application infrastructure platform.

### Full-stack / platform + managed DB (end-to-end)

[Northflank](https://northflank.com/features/databases) provides database hosting within a unified cloud platform for deploying entire application stacks.

Rather than managing databases separately, you can deploy PostgreSQL, MySQL, MongoDB, Redis, MinIO, and RabbitMQ alongside your applications, background jobs, and CI/CD pipelines, all through a unified interface.

![northflank-databases.png](https://assets.northflank.com/northflank_databases_1c87718302.png)

**Key capabilities include**:

- **Integrated infrastructure**: Deploy databases alongside apps and jobs in one platform
- **GitOps workflows**: Automatic deployments on every Git push with [preview environments](https://northflank.com/use-cases/preview-environments-backend-for-kubernetes)
- **Bring Your Own Cloud**: Deploy on your [AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), [Oracle](https://northflank.com/cloud/oci), [Civo](https://northflank.com/cloud/civo) accounts, or bare-metal infrastructure, or use Northflank's [managed cloud](https://northflank.com/features/managed-cloud)
- **Kubernetes-powered**: Cloud-native benefits without Kubernetes complexity
- **Developer-friendly**: [CLI](https://northflank.com/docs/v1/api/use-the-cli), [API](https://northflank.com/docs/v1/api/use-the-api), and web UI with instant preview environments

<InfoBox className="BodyStyle">

Database features include [automated backups](https://northflank.com/docs/v1/application/databases-and-persistence/backup-restore-and-import-data), [real-time metrics](https://northflank.com/docs/v1/application/databases-and-persistence/database-observability-and-monitoring), [horizontal](https://northflank.com/docs/v1/application/scale/autoscale-deployments) and vertical scaling, [network isolation](https://northflank.com/docs/v1/application/network/networking-on-northflank), and point-in-time recovery. The transparent resource-based pricing means you pay only for what you consume with no hidden fees.

Northflank works particularly well for startups and scale-ups building production applications, teams migrating from Heroku, AI/ML workloads requiring GPUs and databases together, and enterprises needing compliant infrastructure without building from scratch.

**When to choose Northflank or similar platforms**:

- You want your applications, storage, and infrastructure managed in one place.
- Your team wants to avoid managing separate services for compute, storage, databases, etc.
- You desire Git-driven workflows and reusability with infrastructure-as-code / templates.

**Get started**:

- [Deploy your first database](https://northflank.com/docs/v1/application/databases-and-persistence/deploy-a-database) - Step-by-step guide
- [Try the free sandbox](https://app.northflank.com/signup)
- [View all managed database options](https://northflank.com/features/databases) - PostgreSQL, MySQL, MongoDB, Redis, and more

</InfoBox>

### **Specialized / database-only providers**

Some providers focus exclusively on particular database technologies:

- **Supabase**: PostgreSQL with built-in authentication and storage
- **Neon**: Serverless PostgreSQL with database branching
- **MongoDB Atlas**: Official managed MongoDB service
- **PlanetScale**: MySQL hosting with schema branching features

These providers offer deep specialization for their specific database types. However, if your application needs databases alongside other infrastructure like web services, background jobs, or CI/CD pipelines, you'll need to coordinate multiple providers or choose a platform that handles everything together.

### **Major cloud providers / ecosystem-first**

AWS RDS, Google Cloud SQL, and Azure Database services offer fully managed databases with deep cloud ecosystem integration. They work well when you're already invested in their platforms or need specific compliance certifications.

However, they require meaningful DevOps knowledge to configure networking, security groups, IAM policies, and high availability setups properly. While these providers handle database patching and maintenance, most development teams find the infrastructure complexity requires dedicated expertise to navigate effectively.

## How do you choose the right database hosting?

Look at these factors when selecting a provider:

| Factor | Database-only / specialized | Full-stack platforms (e.g. Northflank) | Major cloud provider DB services |
| --- | --- | --- | --- |
| Scope (just DB vs full app stack) | Focused on database engine & features | Full infrastructure + CI/CD + database in one place | Full cloud ecosystem with DB + services + integrations |
| Ease of use / setup | Low to moderate complexity for DB tasks | Very simple for whole stack; minimal infrastructure burden | Requires familiarity with cloud infrastructure, IAM, network, etc. |
| Scaling / flexibility | Typically excellent for the dedicated engine | Excellent for both apps and stateful components | Very high flexibility & range; pay-as-you-grow models |
| Pricing & transparency | Plan/feature based, varies by provider | Consumption / usage-based, transparent with tools like calculators | Pricing tiers, reserved/spot models, potential cost variability |
| Best for teams | Small-to-mid teams who need turnkey database specialization | Startups, SMEs, Enterprises and teams needing unified full-stack management | Large organizations, or teams with dedicated DevOps / infra expertise |

**Use the full-stack model when**:

- Your application has multiple components (web APIs, queues, scheduled jobs, databases).
- You want unified observability, environment management, and consistent workflows across services.
- Your team is small or wants to avoid maintaining separate systems for DB, compute, networks.

**Choose database-only hosting when**:

- You don’t need other platform features (just database access).
- You need specialized features unique to a particular engine or database architecture.
- You want to fine-tune performance or have cloud-native needs tied to a specific provider.

**Stick with major cloud provider DB services when**:

- Your application is deeply integrated with other cloud services (analytics, IAM, storage, ML).
- You need high-end SLAs, global scale, compliance certifications, or advanced configuration you can't get elsewhere.
- Your devs/infra team can handle more complexity and you benefit from deep cloud integrations long-term.

## How do you get started with database hosting?

Starting with database hosting is straightforward:

1. **Choose a provider** based on your needs (database-only or full-stack platform)
2. **Sign up** and create a new project or organization
3. **Provision a database** by selecting your database type (PostgreSQL, MySQL, etc.) and resource allocation
4. **Connect your application** using the provided connection string and credentials
5. **Configure backups and monitoring** according to your reliability requirements

For production deployments, use environment variables for database connection strings rather than hardcoding them. Enable automated backups immediately and test restoration procedures before you need them.

Full-stack platforms like Northflank simplify this further by letting you deploy databases alongside your applications with automatic networking configuration. Connect your Git repository, and your entire stack deploys together with preview environments for every pull request.

<InfoBox className="BodyStyle">

Deploy managed databases alongside your entire application stack. [Try Northflank's free sandbox](https://app.northflank.com/signup) to experience databases, apps, and CI/CD in one platform with production-grade infrastructure deployed in minutes.

Have specific requirements or enterprise needs? [Book a demo](https://cal.com/team/northflank/northflank-intro) to discuss how Northflank can support your organization.

</InfoBox>

### Related resources

**Getting started:**

- [Deploy a database on Northflank](https://northflank.com/docs/v1/application/databases-and-persistence/deploy-a-database) - Step-by-step deployment guide
- [Deploy a PostgreSQL database guide](https://northflank.com/guides/deploy-postgres-database-on-northflank) - Quick PostgreSQL setup

**Database options:**

- [Managed PostgreSQL](https://northflank.com/dbaas/managed-postgresql) - Full PostgreSQL features
- [Managed MySQL](https://northflank.com/dbaas/managed-mysql) - Reliable MySQL hosting
- [Managed Redis](https://northflank.com/dbaas/managed-redis) - High-performance caching
- [MongoDB Hosting](https://northflank.com/dbaas/mongodb-on-northflank) - Flexible document storage

**Learn more:**

- [Best PostgreSQL hosting providers](https://northflank.com/blog/best-postgresql-hosting-providers) - Provider comparison 2026
- [Stateful workloads on Northflank](https://northflank.com/docs/v1/application/databases-and-persistence/stateful-workloads-on-northflank) - Advanced database concepts]]>
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</item><item>
  <title>Managed Postgres: when to use it, how to choose, and what to expect</title>
  <link>https://northflank.com/blog/managed-postgres-guide</link>
  <pubDate>2025-11-20T17:00:00.000Z</pubDate>
  <description>
    <![CDATA[Managed Postgres simplifies database operations with automated backups, scaling, and maintenance. Learn when to switch and how to choose the right service.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/managed_postgres_guide_67c0c31c5c.png" alt="Managed Postgres: when to use it, how to choose, and what to expect" /><InfoBox className="BodyStyle">

Managed Postgres is a fully-hosted PostgreSQL database service where providers handle infrastructure, backups, updates, and scaling. Teams should consider switching when database maintenance is consuming significant engineering time that could be spent on product development, or when they need high availability and disaster recovery without dedicated database administrators.

</InfoBox>

If you've spent another evening troubleshooting a backup failure or rushing to apply a security patch to your PostgreSQL database, you understand the time sink. Running production Postgres requires constant attention to backups, security, performance tuning, and scaling.

A managed Postgres database removes these operational burdens by offloading infrastructure management to specialized providers. This guide explains when it's right for your team, what to look for, and how to make the transition smoothly.

## What is managed Postgres and why is it important?

Managed Postgres is a cloud-hosted PostgreSQL service where your provider handles every infrastructure task: server provisioning, OS updates, PostgreSQL upgrades, automated backups, replication, and monitoring.

You get a production-ready database endpoint without configuring servers or implementing high availability yourself. Your provider manages the complexity while you focus on application logic and data models.

The difference from self-hosted comes down to who's responsible when things break at “2 AM”. With self-hosted, your team owns everything. With a managed Postgres database, your provider handles infrastructure while you manage database design and queries.

## When should you switch to managed Postgres?

The decision to move to a managed Postgres database depends on your team size, growth stage, and the value of your engineering time.

### Early-stage startups

Start with managed Postgres from day one. The time you save on database operations goes directly into building product features. Even cheap managed Postgres options cost less than a week of engineering time spent on infrastructure issues.

### Growing teams

You've hit the tipping point when database maintenance becomes a bottleneck instead of a background task. Your engineers are context-switching between application development and database operations, which slows down feature velocity and creates single points of failure when only one person understands your database setup.

Watch for these warning signs:

- Backup management is taking up significant team time
- Scaling decisions require multi-day planning
- Performance issues pull engineers away from feature work
- You lack dedicated database administrators

### Scaling companies

You need managed Postgres when downtime costs exceed management fees. High availability, automated failover, and point-in-time recovery become essential, and building these yourself is expensive.

Setting up proper replication, monitoring, and failover automation can take weeks of engineering time. Maintaining these systems requires ongoing attention that pulls resources from product development. Most teams find that managed services cost less than the engineering time required to build and maintain equivalent infrastructure in-house.

## What should you look for in a managed Postgres service?

Not all managed Postgres providers offer the same capabilities, so your choice depends on what matters most to your application. See the key factors to evaluate:

### Performance and reliability

Look for SLA guarantees, automated failover, and read replica support. Connection pooling should be built-in or easy to add. Query performance insights help you spot slow queries before they become problems.

### Pricing transparency

Watch for hidden costs beyond the base price:

- Backup storage beyond included limits
- Data transfer fees (egress charges)
- IOPS charges for disk operations
- Cross-region replication costs

Calculate the total cost for your workload, not just the base tier. Pricing models vary significantly between providers like DigitalOcean, Render, and others.

Compare these factors across providers:

| Cost factor | What to check | Why it's important |
| --- | --- | --- |
| Compute | Per-core or bundled tiers | Affects scaling flexibility |
| Storage | Per-GB pricing + IOPS | Can exceed compute costs |
| Backups | Retention period included | Daily backups add up |
| Bandwidth | Ingress/egress charges | High for data-heavy apps |

### Scaling options

You need both vertical scaling (more CPU/RAM) and horizontal scaling (read replicas). Check whether you can scale up without downtime during business hours.

Some providers require scheduled maintenance windows for upgrades, while others handle scaling transparently. The ability to add read replicas quickly becomes important as your traffic grows and read-heavy queries start impacting write performance.

### Backup and recovery

Automated backups should run at least daily with configurable retention. Point-in-time recovery lets you restore to any point in time, which is critical for recovering from accidental deletions.

Test the restoration process before you need it in production.

### Developer experience

Easy connection strings, CLI tools, and infrastructure-as-code support reduce friction. Look for:

- Environment variable injection for connection strings
- One-command database creation
- Simple migration tools
- Clear documentation with examples

### Security basics

SSL/TLS encryption should be the default. VPC isolation, IP whitelisting, and role-based access control protect your data. If you need compliance certifications (SOC 2, HIPAA, GDPR), verify your provider has them.

## What pitfalls should you avoid?

Even managed services require planning, and certain issues catch teams off guard during migration or early operation.

### Connection limits

Most managed Postgres services cap concurrent connections based on instance size. If your application opens many short-lived connections, you'll hit limits fast. Plan for connection pooling from day one.

### Regional latency

Database location impacts performance. Every millisecond of latency multiplies across queries. Choose a provider with data centers close to your application servers.

### Over-provisioning

Start small and grow as needed. Most managed services make scaling up straightforward. Downgrading is harder, so avoid locking yourself into oversized commitments.

### Monitoring gaps

Managed doesn't mean "set and forget." Set up alerts for connection pool exhaustion, replication lag, and storage limits. Catching problems early prevents outages.

## How can managed Postgres on Northflank help your team?

Northflank takes a developer-first approach to managed Postgres that solves common frustrations with traditional providers.

Your managed Postgres database lives on the same platform as your applications. Connection strings inject automatically into your environment. Scale both your application and database as needed through a unified platform. When you need a staging database, clone production in one click.

![northflank-managed-postgresql.png](https://assets.northflank.com/northflank_managed_postgresql_dcb428fdb6.png)

**Key features that save time:**

- **Automated backups with point-in-time recovery** - Roll back to any moment without manual snapshot management. Configure retention periods that match your compliance requirements, and restore databases to specific timestamps when issues occur.
- **Automated failover and read replicas** - Deploy multiple replicas with automatic promotion when the primary fails. Scale read-heavy workloads with dedicated read replicas that stay in sync with your primary database.
- **Built-in connection pooling** - Handle high-concurrency applications without hitting connection limits. Configure primary and read connection poolers separately based on whether your workload is read or write intensive.
- **One-click scaling with zero downtime** - Adjust CPU, memory, and storage through sliders in the dashboard. Deploy across multiple availability zones for redundancy without maintenance windows or manual configuration changes.
- **Built-in monitoring and observability** - Track query performance, connection usage, and resource metrics without configuring third-party tools. Get alerts before problems impact your users, with detailed logs for troubleshooting.
- **Preview environments with database forking** - Each feature branch gets its own isolated database. Fork production databases to create exact copies for testing migrations and schema changes safely.
- **Private networking and TLS by default** - Secure communication between your database and applications with Let's Encrypt TLS certificates. Use private networking to keep databases isolated or forward them locally with the Northflank CLI for development.
- **Straightforward pricing with no surprises** - A basic PostgreSQL database starts at just $3.91/month (0.1 shared vCPU, 256 MB memory, 4 GB storage, 1 replica) with backups, SSL certificates, and monitoring included. Scale up as you grow with transparent, usage-based pricing (no hidden charges for IOPS, bandwidth, or standard features). See [full pricing details](https://northflank.com/pricing) and calculate your costs with our pricing calculator.

![northflank-postgres-pricing.png](https://assets.northflank.com/northflank_postgres_pricing_d0bf34a0bf.png)

<InfoBox className="BodyStyle">

[Deploy a Postgres database on Northflank](https://northflank.com/guides/deploy-postgres-database-on-northflank) in minutes using the web UI, CLI, or infrastructure-as-code. Start with our [free developer sandbox](https://app.northflank.com/signup) to test it risk-free.

Need help planning your migration or have specific requirements? [Book a demo](https://cal.com/team/northflank/northflank-intro) to speak with an expert engineer about your team's needs.

Check out [Northflank's managed PostgreSQL](https://northflank.com/dbaas/managed-postgresql) to see how it compares. For a deeper comparison across the market, read our guide on the [best PostgreSQL hosting providers](https://northflank.com/blog/best-postgresql-hosting-providers).

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Best PostgreSQL hosting providers for developers in 2026</title>
  <link>https://northflank.com/blog/best-postgresql-hosting-providers</link>
  <pubDate>2025-11-19T17:00:00.000Z</pubDate>
  <description>
    <![CDATA[Best PostgreSQL hosting providers compared: Northflank, Neon, Supabase, AWS RDS, Render, and DigitalOcean for developers in 2026]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/best_postgresql_hosting_providers_7416a262ca.png" alt="Best PostgreSQL hosting providers for developers in 2026" />Choosing the right PostgreSQL hosting provider can make or break your application's performance and your development workflow.

If you're building a side project, launching a startup, or managing production databases at scale, the hosting provider you choose affects everything from deployment speed to long-term costs.

This guide compares the leading PostgreSQL hosting options in 2026, focusing on what developers prioritize: pricing, performance, developer experience, and scalability.

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## TL;DR: Best PostgreSQL hosting providers

1. **Northflank** - Managed PostgreSQL that deploys alongside your applications, APIs, and background services on one platform. Offers fast deployments in minutes, preview environments for testing, BYOC (Bring Your Own Cloud) support, built-in observability, and team collaboration features. For production SaaS applications and development teams.
2. **Neon** - Serverless PostgreSQL with automatic scale-to-zero and database branching. For variable workloads and development environments.
3. **Supabase** - PostgreSQL bundled with authentication, real-time subscriptions, and auto-generated APIs. For MVPs and rapid prototyping.
4. **Render** - Managed PostgreSQL with simple setup. For developers wanting straightforward database hosting.
5. **DigitalOcean** - Managed databases with predictable monthly pricing. For teams already using DigitalOcean infrastructure.
6. **AWS RDS** - Enterprise-grade PostgreSQL with extensive configuration options. For complex requirements and AWS-native workloads.

</InfoBox>

## What to look for in PostgreSQL hosting providers

Before comparing specific providers, consider what's most important when evaluating PostgreSQL hosting:

- **Deployment speed**: How quickly can you get a production-ready database running? The best providers offer deployment in minutes, not hours.
- **Pricing transparency**: Look for clear costs without hidden fees. Watch out for separate charges for backups, I/O operations, and data transfer that can inflate your bill.
- **Free tier availability**: Essential for testing, development environments, and small projects. A good free tier lets you evaluate the platform without commitment.
- **Developer experience**: CLI tools, APIs, and integration capabilities matter. The easier it is to deploy and manage databases programmatically, the faster your team ships.
- **Scalability options**: You need both vertical scaling (more CPU/RAM) and horizontal scaling (read replicas). Consider how easy it is to scale up as your application grows.
- **High availability**: Look for uptime guarantees, automated failover, and multi-zone deployments. Production databases need redundancy.
- **Observability**: Built-in monitoring, logs, and metrics help you catch issues before they impact users. External monitoring tools add complexity and cost.
- **Backup and recovery**: Automated daily backups are standard, but point-in-time recovery (PITR) is crucial for production workloads.

## What are the best PostgreSQL hosting providers?

We'll review the top PostgreSQL hosting providers based on deployment speed, developer experience, pricing, and production-readiness. 

### 1. Northflank: Best for full-stack applications

[Northflank](https://northflank.com/dbaas/managed-postgresql) provides database hosting as part of a complete application platform where you can deploy your PostgreSQL database alongside your applications, background jobs, and Redis instances.

![northflank-managed-postgresql.png](https://assets.northflank.com/northflank_managed_postgresql_dcb428fdb6.png)

**Why Northflank for PostgreSQL:**

- **Lightning-fast deployment**: Deploy production-ready PostgreSQL in seconds to minutes. No lengthy configuration wizards or complex setup. [Deploy your first database](https://northflank.com/guides/deploy-postgres-database-on-northflank) and start building immediately.
- **Bring Your Own Cloud (BYOC)**: Unlike most managed providers, [Northflank](https://northflank.com/product/bring-your-own-cloud) lets you deploy PostgreSQL in your own AWS, GCP, Azure, Civo, or Oracle Cloud VPC, or on bare-metal infrastructure, while keeping the fully managed experience. This is critical for teams with compliance requirements (SOC 2, HIPAA, GDPR) or those who want infrastructure control without operational overhead.
- **Preview environments**: Automatically create isolated database instances for every pull request. Your team can test database migrations and schema changes in production-like environments before merging. This feature alone saves hours of QA time and prevents production incidents.
- **Full-stack platform**: [Deploy your entire stack](https://northflank.com/product/deployments): databases, APIs, web services, background workers, and Redis, on one platform. No need to stitch together separate services from multiple vendors. Your application and database live in the same network, reducing latency and simplifying networking.
- **Built-in observability**: Real-time logs, performance metrics, and resource monitoring without installing external tools. Track query performance, connection counts, and resource utilization from a single dashboard.
- **Team collaboration features**: Role-based access control (RBAC), audit logs, and cost attribution by project. See exactly how much each environment or team costs, making it easier to manage cloud spending.
- **Advanced networking**: Secure private networking between your services, optional public load balancing, and local database access via the Northflank CLI proxy for development.
- **Database management**: Automated backups, point-in-time recovery, and one-click database restores. Fork databases for testing, pause non-production databases to save costs, and scale compute resources without downtime.
- **Kubernetes without the complexity**: [Northflank runs on Kubernetes](https://northflank.com/product/app-platform), giving you the benefits of container orchestration without requiring Kubernetes expertise. You get scalability, reliability, and modern infrastructure patterns without writing YAML.
- **Multiple PostgreSQL versions supported**: Choose from PostgreSQL 12, 13, 14, 15, 16, or 17 depending on your application's requirements.

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**Pricing**:

Pay-as-you-go consumption-based model with a **free Sandbox tier** that includes 2 free services, 2 free databases, and 2 free cron jobs with always-on compute.

Production databases start at $2.70/month (0.1 vCPU, 256 MB) for the smallest plan, with popular plans like nf-compute-100-2 (1 vCPU, 2 GB) at $24/month.

You only pay for resources you actually use, billed per second (pause development databases when not needed to minimize costs).

([See full pricing details](https://northflank.com/pricing))

</InfoBox>

**Best for**: Development teams building SaaS applications, startups needing to scale from prototype to production, platform teams managing multiple environments, enterprises requiring BYOC (Bring Your Own Cloud) for compliance, and organizations that need SSO, audit logs, and SLA guarantees while maintaining developer velocity.

**Real-world use case**: A typical SaaS team uses Northflank to run PostgreSQL for their main application database, Redis for caching and queues, and multiple API services; all deployed together. Preview environments automatically spin up for each feature branch, complete with isolated databases, enabling thorough testing before production deployment.

### 2. Neon

Neon offers serverless PostgreSQL with an architecture that separates storage and compute. The database scales to zero when idle, reducing costs for development and staging environments.

![neon-homepage.png](https://assets.northflank.com/neon_homepage_5047827c87.png)

**Key features:**

- Serverless with automatic scale-to-zero
- Database branching for development workflows
- Point-in-time recovery
- Supports multiple PostgreSQL versions
- Cold start time of 1-2 seconds when waking from idle

**Best for**: Side projects, variable workloads, and teams needing database branching capabilities.

### 3. Supabase

Supabase combines PostgreSQL with authentication, real-time subscriptions, and auto-generated APIs as a backend-as-a-service platform.

![supabase-postgres-homepage.png](https://assets.northflank.com/supabase_postgres_homepage_a86b1620ac.png)

**Key features:**

- PostgreSQL with authentication and real-time subscriptions
- Auto-generated REST and GraphQL APIs
- PostgreSQL extensions including pgvector
- Database management dashboard
- Opinionated architecture optimized for the full Supabase stack

**Best for**: MVPs, indie developers, and applications requiring authentication and real-time features.

### 4. Render

Render provides managed PostgreSQL hosting with straightforward setup and configuration.

![render's home page.png](https://assets.northflank.com/render_s_home_page_2982a329f2.png)

**Key features:**

- Automatic daily backups with point-in-time recovery (7-day retention on paid plans)
- Internal network connectivity for Render-hosted applications in the same region
- SSL/TLS encryption for all connections
- Multiple PostgreSQL versions supported (PostgreSQL 17 default)
- Hosted entirely within Render's cloud infrastructure

**Best for**: Developers wanting PostgreSQL without complex configuration, especially those already running applications on Render.

### 5. DigitalOcean managed databases

DigitalOcean's managed PostgreSQL provides straightforward setup with predictable monthly costs.

![digitalocean-managed-postgreSQL.png](https://assets.northflank.com/digitalocean_managed_postgre_SQL_5b3fc81d48.png)

**Key features:**

- Monthly pricing includes storage and bandwidth
- Automated daily backups with point-in-time recovery
- Optional standby nodes for high availability
- 1 TB outbound bandwidth included
- Multiple data center locations

**Best for**: Small to medium businesses wanting managed PostgreSQL.

### 6. AWS RDS for PostgreSQL

AWS RDS provides managed PostgreSQL with extensive configuration options and AWS ecosystem integration.

![aws-rds-for-postgresql-database-homepage.png](https://assets.northflank.com/aws_rds_for_postgresql_database_homepage_e4ac835fe3.png)

**Key features:**

- Multiple instance types and configurations
- Multi-AZ deployments for high availability
- Read replicas for horizontal scaling
- Integration with AWS services (IAM, CloudWatch, VPC)
- Complex pricing model with variable costs

**Best for**: Enterprises with complex requirements, teams using AWS infrastructure, and applications requiring extensive customization.

## How to choose the right PostgreSQL hosting provider

Different PostgreSQL hosting providers excel at different use cases. This table maps common scenarios to the providers best suited for each situation.

| Your situation | Recommended provider | Why |
| --- | --- | --- |
| Building a SaaS product with multiple services | Northflank | Platform supports databases, applications, background jobs, and Redis with preview environments and BYOC |
| Need serverless PostgreSQL | Neon | Automatic scale-to-zero for idle databases |
| Building an MVP quickly | Supabase | Includes authentication, real-time subscriptions, and auto-generated APIs |
| Want simplest possible setup | Render | Straightforward setup and configuration |
| Already on DigitalOcean | DigitalOcean | Integration with existing DigitalOcean infrastructure |
| Enterprise with complex requirements | AWS RDS | Extensive configuration options and AWS service integration |
| Migrating from Heroku | Northflank | Platform supports similar workflows with improved flexibility |
| Compliance requirements | Northflank | BYOC deployment in your own VPC with audit logs and RBAC |
| Building full-stack applications | Northflank | Deploy databases alongside applications, APIs, and services on one platform |
| Need team collaboration features | Northflank | Built-in RBAC, audit logs, and cost attribution by project |

## Deploy PostgreSQL hosting that scales with your application

The "best" PostgreSQL hosting provider depends entirely on your specific needs.

For teams building production SaaS applications, Northflank's full-stack platform approach, BYOC capabilities, and preview environments provide the flexibility and developer experience modern teams need.

Choosing the right provider from the start saves time and prevents costly migrations down the road.

Start deploying PostgreSQL with [Get started with Northflank](https://northflank.com/docs/v1/application/databases-and-persistence/deploy-databases-on-northflank/deploy-postgresql-on-northflank) and have your first database running in under few minutes.]]>
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  <title>Top 8 Heroku Postgres alternatives in 2026</title>
  <link>https://northflank.com/blog/heroku-postgres-alternatives</link>
  <pubDate>2025-11-18T17:45:00.000Z</pubDate>
  <description>
    <![CDATA[Heroku Postgres alternatives: Compare Northflank, Neon, Supabase, AWS RDS &amp; more. Find cost-effective managed PostgreSQL with better flexibility.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/heroku_postgres_alternatives_a3a2b195bf.png" alt="Top 8 Heroku Postgres alternatives in 2026" />Heroku Postgres alternatives are gaining attention as development teams look for more cost-effective, flexible, and scalable database solutions.

While Heroku Postgres has been a reliable managed database service for over 12 years, Heroku pricing concerns, limited deployment options, and vendor lock-in are prompting organizations to evaluate alternatives.

If you're paying $50-$500+ monthly for Heroku Postgres or need better multi-cloud flexibility, this guide compares the top alternatives to help you make an informed decision.

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## TL;DR - **Best Heroku Postgres alternatives**

See a quick list of the top 5 Heroku Postgres alternatives we’ll review in this guide:

1. **Northflank** - Best for startups to enterprise teams wanting Heroku-like simplicity with multi-cloud flexibility, transparent pricing, auto-scaling, and modern DevOps features. You can deploy PostgreSQL on AWS, GCP, Azure, Oracle Cloud, Civo, or bare-metal from a single platform with built-in CI/CD, preview environments, and significant cost savings.
2. **Neon** - Best for serverless PostgreSQL with database branching capabilities
4. **AWS RDS** - Best for AWS-native teams needing the widest extension support and tight AWS service integration
5. **Railway** - Best for side projects and prototypes with simple setup

**Keep these considerations in mind:** Cost transparency, deployment flexibility, developer experience, performance requirements, and compliance needs.

</InfoBox>

## What to look out for when evaluating Heroku Postgres alternatives

Understanding why teams migrate away from Heroku Postgres helps identify what to prioritize in an alternative. You can use the following evaluation criteria:

### 1. Cost and pricing transparency

Heroku's pricing can become expensive as you scale. Essential tier starts at $5/month but jumps to $50/month for Standard-0 (without high availability), then $200+ for production-ready plans. Look for alternatives with clear, usage-based pricing that scales predictably with your application.

### 2. Deployment flexibility and multi-cloud support

Heroku Postgres Essential and Standard tiers are limited to US East and EU West regions. Additional regions require Enterprise-level Private Spaces. If you need specific geographic deployments for compliance or low latency, these limitations can be constraining. Modern alternatives provide deployment across multiple cloud providers and regions at all pricing tiers.

### 3. Performance and scalability

While Heroku Postgres is reliable, some alternatives now deliver better query throughput, lower latency, and more flexible scaling options. Evaluate your performance requirements, including connection pooling, read replicas, and auto-scaling capabilities.

### 4. Developer experience

The best alternatives maintain Heroku's developer-friendly approach while adding modern features like Git-based workflows, preview environments, database branching, and comprehensive observability (logs, metrics, traces). Look for intuitive interfaces, good documentation, and comprehensive CLI/API support.

### 5. Backup, recovery, and high availability

Automated backups, point-in-time recovery, and high availability are non-negotiable for production workloads. Heroku provides these features, but often at premium pricing tiers. Evaluate how alternatives handle disaster recovery and what their SLAs guarantee.

### 6. Security and compliance

For regulated industries, compliance certifications (PCI, HIPAA, SOC 2, ISO) and security features like encryption at rest and in transit, network isolation, and access controls are essential. Review your specific compliance requirements when evaluating options.

### 7. Vendor lock-in and portability

Being tightly coupled to the Heroku ecosystem can make migration difficult. Alternatives offering standard PostgreSQL with infrastructure independence provide easier exit paths and integration flexibility.

## Top Heroku Postgres alternatives

We've evaluated the following alternatives based on cost-effectiveness, deployment flexibility, developer experience, performance, and scalability to help you find the best fit for your specific requirements.

### 1. Northflank

Northflank delivers a modern cloud platform that combines the simplicity developers love about Heroku with the flexibility and cost-efficiency that growing teams need. Built for the multi-cloud era, Northflank lets you deploy and manage PostgreSQL databases on AWS, GCP, Azure, Oracle Cloud, Civo, or bare-metal from a single, unified platform.

![northflank-managed-postgresql.png](https://assets.northflank.com/northflank_managed_postgresql_de4db7bf0c.png)

**Key features:**

- **Multi-cloud deployment flexibility:** Choose AWS, GCP, Azure, Oracle Cloud, Civo, or bare-metal for each database based on your needs. Deploy in 30+ regions globally for low-latency access without vendor lock-in or platform changes.
- **Cost-effective and transparent pricing:** Reduce database expenses by 40-60% compared to equivalent Heroku plans with predictable, usage-based pricing. Start small and scale efficiently as you grow, with the ability to pause development databases to save costs.
- **Smooth Heroku migration path:** Maintain the git-based, developer-friendly workflow you're familiar with while gaining multi-cloud flexibility. Comprehensive [migration guide](https://northflank.com/blog/how-to-migrate-from-heroku-a-step-by-step-guide) and step-by-step [documentation](https://northflank.com/docs/v1/application/migrate-from-heroku) make the transition straightforward.
- **Serverless PostgreSQL with flexible scaling:** Scale horizontally with read replicas and vertically with increased compute capacity. Scale resources up or down based on demand without downtime.
- **Developer-first workflows:** Git-based deployments with automatic builds on every commit. Preview databases for pull requests to test schema changes safely. Connect locally to remote databases using the Northflank CLI proxy.
- **Comprehensive observability:** Real-time log tailing with filtering and search. Performance metrics including CPU, memory, network, and database-specific metrics. Configure alerts to notify your team before issues impact users.
- **Built-in backups and point-in-time recovery:** Automated daily backups with configurable retention. Create manual backups before changes. Restore your database to any point in time, protecting against accidental data loss.
- **Advanced networking and security:** Private networking between services. TLS/SSL encryption for all connections. Fine-grained access controls. Option to deploy on your own Kubernetes cluster for maximum control.
- **Enterprise-ready database management:** Support for PostgreSQL versions 11.x through 14.x with automated patching. Enable PostgreSQL extensions as needed. Import from existing databases. Fork databases for testing.

<InfoBox className="BodyStyle">

**Pricing:**

Northflank offers transparent, usage-based pricing that scales with your actual resource consumption. Unlike Heroku's rigid tier structure, you pay only for the compute, memory, and storage you use:

- **Free sandbox environment:** Test Northflank with a free sandbox to evaluate the platform before committing
- **Pay-as-you-go:** No monthly minimums or commitments - scale up and down as needed
- Enterprise: For high growth startups and Enterprises with thousands of developers (SSO, SAML/OIDC, Audit logs, Secure runtime and on-prem deployments, Global back-ups and HA/DR and more)
- **Significant cost savings:** Teams typically see 40-60% cost reduction compared to equivalent Heroku Postgres plans
- **Transparent pricing calculator:** Know exactly what you'll pay before deploying
- **No hidden fees:** What you see is what you pay (no surprise charges for backups, networking, or other essentials)

For detailed cost comparisons and savings calculations, visit our [Heroku pricing comparison tool](https://northflank.com/heroku-pricing-comparison-and-reduction). Also see the full [pricing details](https://northflank.com/pricing).

</InfoBox>

**Why choose Northflank:**

Northflank works well for startups to enterprise teams that need Heroku's simplicity but want more control, better economics, and modern capabilities. You get production-grade managed PostgreSQL with the flexibility to deploy across multiple cloud providers, comprehensive DevOps tooling built-in, and pricing that makes sense at every stage of growth.

The platform is particularly suited for teams running multiple environments (development, staging, production) who need consistent infrastructure management, preview environments for testing, and the ability to scale infrastructure without scaling costs proportionally.

Learn more about [managed PostgreSQL on Northflank](https://northflank.com/dbaas/managed-postgresql) or check out our guides on [top Heroku alternatives](https://northflank.com/blog/top-heroku-alternatives) and [enterprise capabilities](https://northflank.com/blog/heroku-enterprise-capabilities-limitations-and-alternatives).

### 2. Neon

Neon offers a serverless PostgreSQL platform with an architecture that separates storage from compute.

![neon-homepage.png](https://assets.northflank.com/neon_homepage_5047827c87.png)

**Key features:**

- **Serverless architecture:** Auto-scales to zero when not in use, reducing costs
- **Instant branching:** Create database branches for testing and development
- **Sub-second cold starts:** Fast wake-up times from idle state
- **Built-in connection pooling:** Handles a number of connections efficiently

**Best for:**
Development teams needing ephemeral databases, applications with sporadic traffic, and teams wanting database branches for testing.

### 3. Supabase

Supabase positions itself as an open-source Firebase alternative with PostgreSQL at its core.

![supabase-postgres-homepage.png](https://assets.northflank.com/supabase_postgres_homepage_a86b1620ac.png)

**Key features:**

- **Real-time subscriptions:** Built-in real-time functionality for database changes
- **Auto-generated APIs:** REST and GraphQL APIs generated from your schema
- **Authentication:** Integrated auth system with row-level security
- **Storage:** File storage alongside your database

**Best for:**
Full-stack developers building web and mobile applications who want an all-in-one backend solution with PostgreSQL as the foundation.

### 4. Amazon RDS for PostgreSQL

AWS RDS is a managed PostgreSQL service from a major cloud provider.

![aws-rds-for-postgresql-database-homepage.png](https://assets.northflank.com/aws_rds_for_postgresql_database_homepage_e4ac835fe3.png)

**Key features:**

- **Extensive features:** Multi-AZ deployments, read replicas, automated backups
- **92+ extensions:** Extensive extension support among managed services
- **Flexible scaling:** Vertical and horizontal scaling options
- **Integration:** Integration with other AWS services

**Best for:**
Organizations already using AWS infrastructure, enterprise applications requiring extensive features and compliance certifications.

### 5. Google Cloud SQL for PostgreSQL

Google Cloud SQL provides a fully managed PostgreSQL service on GCP infrastructure.

![google-cloud-sql-for-postgresql-homepage.png](https://assets.northflank.com/google_cloud_sql_for_postgresql_homepage_7bfc801d9b.png)

**Key features:**

- **High availability:** 99.95% SLA with automatic failover
- **Integration:** Native integration with Google Cloud services
- **Automatic storage increase:** Storage scales as needed
- **Point-in-time recovery:** Restore to any point within retention period
- **Performance:** Consistent throughput and latency characteristics

**Best for:**
Teams using Google Cloud Platform, applications requiring integration with GCP services like BigQuery or Cloud Run.

### 6. Azure Database for PostgreSQL

Microsoft Azure's managed PostgreSQL offering provides enterprise-grade features and reliability.

![azure-database-for-postgresql-homepage.png](https://assets.northflank.com/azure_database_for_postgresql_homepage_882ac8b44e.png)

**Key features:**

- **Flexible server:** Single and flexible server deployment options
- **High availability:** Zone-redundant high availability options
- **Advanced threat protection:** Built-in security intelligence
- **Hybrid capability:** Integration with on-premises infrastructure
- **Hyperscale option:** Citus extension for horizontal scaling

**Best for:**
Organizations with existing Azure infrastructure, enterprises requiring Microsoft ecosystem integration.

### 7. Railway

Railway offers a simple, developer-focused platform for deploying databases and applications.

![railway-min.png](https://assets.northflank.com/railway_min_10957de907.png)

**Key features:**

- **Simple setup:** Deploy PostgreSQL in seconds
- **Git-based deployment:** Automatic deployments from Git
- **Built-in metrics:** CPU, memory, and network monitoring
- **CLI and dashboard:** Manage databases via command line or web interface
- **Database forking:** Create database copies for testing and development

**Best for:**
Side projects and prototypes with simple setup requirements.

### 8. DigitalOcean Managed Databases

DigitalOcean provides managed PostgreSQL for teams needing operational database hosting.

![digitalocean-managed-postgreSQL.png](https://assets.northflank.com/digitalocean_managed_postgre_SQL_5b3fc81d48.png)

**Key features:**

- **Automated backups:** Daily automated backups with point-in-time recovery
- **High availability:** Standby nodes for automatic failover
- **Vertical scaling:** Scale through the interface as needed
- **SSD-backed storage:** Solid-state storage for consistent throughput
- **Monitoring dashboard:** Track database performance and health

**Best for:**
Small to medium businesses wanting predictable costs and straightforward management.

<InfoBox className="BodyStyle">

**Need help migrating from Heroku?**

Moving from Heroku Postgres doesn't have to be complicated. Check out our comprehensive resources:

- [Step-by-step migration guide](https://northflank.com/blog/how-to-migrate-from-heroku-a-step-by-step-guide) - Detailed walkthrough of the migration process
- [Migration documentation](https://northflank.com/docs/v1/application/migrate-from-heroku) - Technical documentation for developers

</InfoBox>

## Which Heroku Postgres alternative is right for you

The best Heroku Postgres alternative depends on your team's specific requirements, technical constraints, and growth trajectory. Use this comparison table to identify which solution aligns with your priorities:

| Choose this alternative | If you need |
| --- | --- |
| **Northflank** | Managed PostgreSQL with Heroku-like experience, multi-cloud deployment (AWS, GCP, Azure, Oracle Cloud, Civo, bare-metal), auto-scaling, built-in CI/CD, preview environments, and 40-60% cost reduction vs Heroku. Git-based workflows with database forking. For teams migrating from Heroku, startups to enterprise needing deployment flexibility without vendor lock-in. |
| **Neon** | Serverless auto-scaling with database branching capabilities for variable workloads. Suitable for development environments and applications with unpredictable traffic patterns. |
| **Supabase** | Includes database, auth, storage, real-time subscriptions, and auto-generated APIs. Suitable for full-stack developers building web or mobile applications needing an integrated backend platform. |
| **AWS RDS** | Established managed PostgreSQL service with 92+ extensions, extensive AWS integration, and enterprise-grade features. Suitable if you're already invested in AWS infrastructure or need comprehensive feature support. |
| **Google Cloud SQL** | Native integration with Google Cloud services and 99.95% SLA. Suitable if you're building on GCP or need specific Google Cloud integrations like BigQuery. |
| **Azure Database** | PostgreSQL with Microsoft ecosystem integration, hybrid cloud capabilities, and zone-redundant high availability. Suitable for organizations standardized on Azure or requiring Microsoft integrations. |
| **Railway** | Database deployment with Git-based workflows and CLI tooling. Suitable for side projects, prototypes, and development environments needing quick setup. |
| **DigitalOcean** | Managed PostgreSQL with fixed-tier plans and vertical scaling. Suitable for small to medium businesses needing operational database hosting. |

## Start your migration today

For teams currently on Heroku facing escalating costs, limited deployment options, or in search of modern DevOps capabilities, now is the time to evaluate your options.

Northflank provides the smoothest path forward for Heroku users, delivering the developer experience you're familiar with while adding multi-cloud flexibility, transparent pricing, and modern infrastructure management. Teams typically achieve 40-60% cost savings while gaining better performance, more deployment options, and comprehensive observability.

**Your next steps:**

1. **Evaluate your current costs and requirements** - Document what you're paying for Heroku Postgres and identify your pain points
2. **Test alternatives hands-on** - Most platforms offer free tiers or trial periods for evaluation
3. **Calculate total cost of ownership** - Use our [pricing comparison tool](https://northflank.com/heroku-pricing-comparison-and-reduction) to understand potential savings
4. **Plan your migration** - Review our [migration guide](https://northflank.com/blog/how-to-migrate-from-heroku-a-step-by-step-guide) and [documentation](https://northflank.com/docs/v1/application/migrate-from-heroku)
5. **Start small** - Begin with development environments before moving production workloads

<InfoBox className="BodyStyle">

**Get started with Northflank:**

- [Start with a free sandbox](https://app.northflank.com/signup)
- [Book a demo](https://cal.com/team/northflank/northflank-intro) to speak with an expert engineer about your specific requirements
- Learn more about [managed PostgreSQL on Northflank](https://northflank.com/dbaas/managed-postgresql)

Making the move away from Heroku Postgres can seem daunting, but with the right alternative and proper planning, most teams complete their migration in days, not months. The investment in evaluating alternatives now can save significant costs and provide better infrastructure for years to come.

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  <title>What is managed cloud hosting? Choosing the right platform</title>
  <link>https://northflank.com/blog/managed-cloud-hosting</link>
  <pubDate>2025-11-17T14:15:00.000Z</pubDate>
  <description>
    <![CDATA[Managed cloud hosting gives you cloud power without DevOps overhead. Learn what it is, how to choose the right platform, and key features to look for]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/managed_cloud_hosting_10221daf1d.png" alt="What is managed cloud hosting? Choosing the right platform" /><InfoBox className="BodyStyle">

**Managed cloud hosting** is a service where a provider handles your infrastructure, server management, and operational tasks while you focus on building and deploying code. Unlike traditional hosting, it runs on cloud infrastructure (AWS, GCP, Azure) while abstracting away the complexity, giving you cloud power without a dedicated DevOps team.

</InfoBox>

Managing cloud infrastructure shouldn't slow down shipping code. Yet many development teams find themselves caught between two extremes: traditional shared hosting that's too limiting, or raw cloud providers that demand too much operational overhead.

Managed cloud hosting bridges this gap, but the market has moved far beyond simple "managed WordPress hosting" packages. Microservices, containerized workloads, and API-driven platforms now need a different approach entirely.

This guide breaks down what managed cloud hosting means in 2026, what to look for, and how to choose the right solution for your application.

## What is managed cloud hosting?

Managed cloud hosting is a service where a provider handles the infrastructure, server management, and operational tasks of running your application in the cloud, while you focus on building and deploying your code.

Unlike traditional hosting, where you're locked into specific server configurations, managed cloud hosting runs on cloud infrastructure (AWS, GCP, Azure) but abstracts away the complexity. The provider handles:

- Server provisioning and configuration
- Security patches and updates
- Monitoring and alerting
- Backup and disaster recovery
- Scaling infrastructure as needed
- Network configuration and load balancing

You get cloud infrastructure power without needing a dedicated DevOps team to manage it.

## How is managed cloud hosting different from other hosting types?

Choosing between hosting options can be confusing. Managed cloud hosting gives you cloud-level resources with the convenience of managed services, striking a balance between control and operational simplicity.

| Hosting type | What it offers | Who manages what | Best for |
| --- | --- | --- | --- |
| **Shared hosting** | Server space shared with hundreds of other sites | Provider handles everything, but resources are extremely limited | Simple websites and blogs with predictable traffic |
| **VPS (Virtual Private Server)** | Your own virtual server slice with dedicated resources | You manage OS, security patches, updates, configurations | Users who want control and have technical expertise |
| **Managed cloud hosting** | Cloud infrastructure (AWS, GCP, Azure) with managed operations | Provider manages infrastructure; you handle code and deployment | Applications that need to scale without a dedicated DevOps team |
| **PaaS (Platform-as-a-Service)** | Fully abstracted platform where you just push code | Provider manages everything from infrastructure to scaling | Developers who want zero infrastructure management |

## What should you look for in a managed cloud hosting provider?

Not all managed cloud hosting is created equal. When evaluating providers, ask these questions:

- **Can you deploy to multiple clouds?** Being locked to one vendor (AWS, GCP, or Azure only) limits your flexibility and negotiating power.
- **Does it auto-scale based on traffic?** Automatic scaling is essential for handling traffic spikes without manual intervention.
- **Can you deploy globally?** Multi-region deployment improves performance and provides redundancy for your users worldwide.
- **What security is included?** Look for automated SSL/TLS, DDoS protection, security patching, and compliance certifications.
- **How do you deploy code?** Git-based deployments with CI/CD integration and preview environments make shipping faster and safer.
- **How quickly can you deploy?** The difference between minutes and hours compounds over time and affects your ability to respond to issues.
- **Can you easily rollback?** When something breaks, one-click rollbacks to previous versions can save you from extended downtime.
- **How much control do you need?** Providers range from full management (they handle everything) to infrastructure-level management (you handle application details). Choose based on your team's expertise.
- **Is pricing transparent?** Understand whether you're paying for provisioned resources or actual usage, and watch for hidden fees on data transfer, API calls, or additional environments.
- **Does it support your specific workload?** If you're running containers, microservices, database-heavy applications, stateful workloads, or batch jobs, verify the provider has native support rather than workarounds.

## When is managed cloud hosting the right fit?

Managed cloud hosting works best when your application needs have outgrown simple hosting, but you don't want the overhead of managing raw cloud infrastructure.

| Choose managed cloud hosting if: | Look at alternatives if: |
| --- | --- |
| You're working with containers, microservices, or applications that need to scale dynamically | You're running a simple content site or blog with predictable traffic (traditional hosting is fine) |
| Your team is developer-heavy without dedicated DevOps engineers | You have a skilled DevOps team that wants full infrastructure ownership (raw cloud is better) |
| Deployment speed matters and you want to ship features fast | You have complex, unique infrastructure requirements that demand granular control |
| You're scaling a startup or SaaS product with growing, unpredictable traffic | Your requirements are very basic and don't justify the additional cost |
| Your application has outgrown shared hosting but you don't want to become a cloud expert | You need very specific infrastructure configurations not offered by managed providers |

## How Northflank approaches managed cloud hosting

Northflank takes a developer-first approach to managed cloud hosting, designed specifically for modern application architectures.

Instead of limiting you to proprietary infrastructure, Northflank runs on your cloud accounts ([AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), [Oracle](https://northflank.com/cloud/oci), or bare-metal) or on Northflank's [managed cloud](https://northflank.com/features/managed-cloud), giving you flexibility without vendor lock-in. You get the benefits of major cloud providers with the operational simplicity of a fully managed platform.

![northflank-managed-cloud.png](https://assets.northflank.com/northflank_managed_cloud_a047f024ec.png)

**What makes Northflank different:**

- **GitOps-native workflow**: Connect your GitHub, GitLab, or Bitbucket repository, and Northflank automatically builds and deploys on every commit. No manual steps, no complex CI/CD configuration.
- **Built for containers and microservices**: Native support for Docker containers, Kubernetes under the hood, and first-class support for complex microservices architectures. Deploy multiple services, databases, and cron jobs as a unified application.
- **Instant preview environments**: Every pull request can spin up an isolated environment for testing before merging to production. No more "works on my machine" issues. ([See how](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment))
- **Bring your own cloud or use ours**: Bring your own cloud or use ours: Deploy to AWS, GCP, Azure, Civo, Oracle, or bare-metal for complete control and billing transparency. Or use Northflank's managed cloud infrastructure.
- **Developer-friendly pricing**: Pay for what you use with transparent, resource-based pricing. ([See full pricing details](https://northflank.com/pricing))
- **Full-stack platform**: Beyond compute, Northflank includes managed [PostgreSQL](https://northflank.com/dbaas/managed-postgresql), [MySQL](https://northflank.com/dbaas/managed-mysql), [MongoDB](https://northflank.com/dbaas/mongodb-on-northflank), [Redis](https://northflank.com/dbaas/managed-redis), and other addons. Everything you need to run production applications without juggling multiple services.

<InfoBox className="BodyStyle">

Northflank is built for developers who want to focus on code, not infrastructure. If you're building a SaaS product, API platform, or containerized application, Northflank handles the operational complexity so you can ship faster.

[Learn more about Northflank's managed cloud approach](https://northflank.com/features/managed-cloud) or [see how Northflank's cloud infrastructure works](https://northflank.com/cloud/northflank).

To simplify your infrastructure, start building on Northflank's managed cloud platform and deploy your first application in minutes, not hours. [Read more about what managed cloud means](https://northflank.com/blog/what-is-managed-cloud), [book a demo with an expert engineer](https://cal.com/team/northflank/northflank-intro) to discuss your specific requirements, or [get started today with the free sandbox](https://app.northflank.com/signup).

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Best developer experience PaaS 2026</title>
  <link>https://northflank.com/blog/best-developer-experience-paas-2026</link>
  <pubDate>2025-11-06T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[Northflank is the best developer experience PaaS 2026. It delivers Kubernetes power with PaaS simplicity through real-time UI, Git-push deployments, and true multi-cloud flexibility.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Best_developer_experience_Paa_S_2025_c2e6c9227f.png" alt="Best developer experience PaaS 2026" />[**Northflank**](https://northflank.com/) is the best developer experience PaaS 2026. Trusted by 50,000+ developers and companies like Sentry and Writer, Northflank delivers Kubernetes power with PaaS simplicity through real-time UI, Git-push deployments, and true multi-cloud flexibility.

**Key advantages:**

- Native Kubernetes orchestration without ops overhead
- Any language, any framework, any container
- Built-in CI/CD optimized for microservices
- Managed databases and services included
- Transparent, predictable pricing
- Multi-cloud and hybrid deployment support

![11.png](https://assets.northflank.com/11_784f7ec4d0.png)

## What defines the best developer experience PaaS 2026

The best developer experience PaaS 2026 eliminates the traditional tradeoff between simplicity and control. Modern development teams need platforms that abstract infrastructure complexity without creating new bottlenecks or vendor lock-in.

**Core requirements:**

- Instant deployment workflows
- Real-time observability without configuration overhead
- Multi-cloud portability to avoid infrastructure coupling
- Full language and framework freedom
- Transparent, usage-based pricing that scales economically

Traditional PaaS platforms like Heroku prioritize simplicity but sacrifice power. 

DIY Kubernetes setups provide control but demand specialized expertise. 

The best developer experience PaaS 2026 delivers both: Kubernetes capabilities through intuitive abstractions that accelerate velocity rather than adding friction.

## Why Northflank has the best developer experience PaaS 2026

### Real-time interface

**Instant feedback:**

- Instant UI updates
- Choice of UI, CLI, APIs & GitOps
- Live log streaming
- Real-time metrics

Every action reflects immediately. No waiting. No refresh delays.

### Git-push deployment

**How it works:**

- Connect GitHub, GitLab, or Bitbucket
- Push code
- Northflank builds and deploys automatically
- No CI/CD configuration needed

<InfoBox className='BodyStyle'>

💡 Learn more about Render alternatives [here](https://northflank.com/blog/render-alternatives) and DigitalOcean [here](https://northflank.com/blog/best-digitalocean-alternatives-2025). 

</InfoBox>

### Any language, any framework

**Deploy anything:**

- Node.js, Python, Go, Java, Rust, .NET, Ruby, PHP, Elixir
- React, Next.js, Vue, Svelte
- Dockerfiles or Buildpacks

Completely language and framework agnostic. No buildpack limits.

### Kubernetes without complexity

**What you get:**

- Full container orchestration
- Autoscaling and load balancing
- No YAML files or cluster management
- Deploy in your VPC in less than 30 minutes (Bring Your Own Cloud)

Kubernetes power with PaaS simplicity. Accessible even for teams without DevOps expertise.

<InfoBox className='BodyStyle'>

💡Learn more about Azure App Service alternatives [here](https://northflank.com/blog/azure-alternatives).

</InfoBox>

### Everything integrated

**One platform includes:**

- CI/CD pipelines
- Container builds
- Managed databases (PostgreSQL, MySQL, MongoDB, Redis)
- Ephemeral preview environments
- Cron jobs and scheduled tasks
- Logs, metrics, secret management

No tool stitching. Everything works together.

![image.png](https://assets.northflank.com/image_7da8ac2fce.png)

### Deploy anywhere

**Multi-cloud support:**

- Northflank's managed cloud (US, Europe, Asia)
- AWS, GCP, Azure, Oracle Cloud, Civo or neo clouds like CoreWeave
- Your own Kubernetes clusters (GKE, EKS, AKS)
- On-premise infrastructure

100+ regions. 300+ availability zones. Same developer experience everywhere.

### Transparent pricing

**Cost structure:**

- $0/month starting tier
- Pay only for compute resources used
- Billed by the second
- No per-service or per-container fees
- Free tier: 2 services, 2 jobs, 1 database

## Best developer experience PaaS 2026: Quick comparison

| Feature | Northflank | Heroku | Render | Vercel |
| --- | --- | --- | --- | --- |
| **Kubernetes-Native** | ✅ Yes | ❌ No | ❌ No | ❌ No |
| **Real-time UI** | ✅ Yes | ❌ No | ❌ No | ❌ No |
| **BYOC (Bring Your Own Cloud)** | ✅ AWS, GCP, Azure, Oracle, Civo, and more | ❌ No | ❌ No | ❌ No |
| **Managed PostgreSQL** | ✅ Yes | ✅ Yes | ✅ Yes | ❌ No |
| **Managed Redis** | ✅ Yes | ✅ Via add-ons | ✅ Yes | ❌ No |
| **Background jobs** | ✅ Built-in | ✅ Via workers | ✅ Yes | ❌ No |
| **Preview environments** | ✅ Ephemeral | ✅ Yes | ✅ Yes | ✅ Yes |
| **Starting price** | $0/month | $5/month (Eco dyno) | $0/month (Free tier) | $0/month (Hobby) |
| **Container support** | ✅ Full Docker | ✅ Limited | ✅ Yes | ❌ Serverless only |
| **Git integration** | GitHub, GitLab, Bitbucket | GitHub, Git | GitHub, GitLab | GitHub, GitLab, Bitbucket |

<InfoBox className='BodyStyle'>

💡 Learn how to migrate from Heroku [here](https://northflank.com/blog/how-to-migrate-from-heroku-a-step-by-step-guide).

</InfoBox>

## When to choose Northflank

**Best for:**

- Teams needing Kubernetes without complexity
- Multi-cloud or BYOC requirements
- Full-stack microservices architectures
- Production workloads requiring scale
- Cost efficiency (pay for resources, not services)

**Choose alternatives when:**

- Heroku: Extreme simplicity on tiny projects
- Render: Don't need Kubernetes or multi-cloud
- Railway: Early prototyping only
- Vercel: Frontend-only Next.js projects

The best developer experience PaaS 2026 adapts to your architecture rather than forcing architectural compromises. 

Northflank provides this flexibility while maintaining the simplicity developers expect from modern platforms.

<InfoBox className='BodyStyle'>

💡 Learn more about Google Cloud Run alternatives [here](https://northflank.com/blog/best-google-cloud-run-alternatives-in-2026).

</InfoBox>

## Summary

Northflank delivers the best developer experience PaaS 2026 by combining Kubernetes power with PaaS simplicity. Deploy in seconds with Git-push workflows, run any workload on any cloud, and scale without infrastructure complexity. Trusted by 50,000+ developers for production workloads.

[**Get started** today by signing up.](https://app.northflank.com/signup)]]>
  </content:encoded>
</item><item>
  <title>Best PaaS for full-stack microservices in 2026</title>
  <link>https://northflank.com/blog/best-paas-for-full-stack-microservices</link>
  <pubDate>2025-11-06T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[Northflank is the best PaaS for full-stack microservices because it combines native Kubernetes power with developer-friendly workflows, integrated CI/CD, and true multi-cloud flexibility.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Best_Paa_S_for_full_stack_microservices_6443a47cc7.png" alt="Best PaaS for full-stack microservices in 2026" />## TL;DR

[Northflank](https://northflank.com/) is the best PaaS for full-stack microservices because it combines native Kubernetes power with developer-friendly workflows, integrated CI/CD, and true multi-cloud flexibility, without the complexity of enterprise platforms or the limitations of simplified PaaS solutions. Allows support for any language and framework.

**Key advantages:**

- Native Kubernetes orchestration without ops overhead
- Any language, any framework, any container
- Built-in CI/CD optimized for microservices
- Managed databases and services included
- Transparent, predictable pricing
- Multi-cloud and hybrid deployment support

![11.png](https://assets.northflank.com/11_784f7ec4d0.png)

## Why Northflank is the best PaaS for full-stack microservices

### Kubernetes power with none of the complexity

Northflank runs on Kubernetes but removes the operational burden.

**What you get:**

- Container orchestration with autoscaling
- Native service discovery and networking
- No YAML file management or cluster configuration

Traditional platforms like Heroku oversimplify and limit control. Enterprise Kubernetes platforms like OpenShift require dedicated DevOps teams. Northflank abstracts Kubernetes complexity through an intuitive interface while giving you full container orchestration capabilities.

### Language and framework freedom

Northflank is completely language and framework agnostic, if it runs in a container, it runs on Northflank.

**Deploy any stack:**

- Node.js, Python, Go, Java, Rust
- .NET, Ruby, PHP, Elixir
- React, Next.js, Vue, Svelte frontends
- Build with Dockerfiles or Buildpacks

No buildpack limitations. No deprecated runtime versions forcing upgrades. Frontend and backend services deploy using identical workflows.

![image.png](https://assets.northflank.com/image_1725776185.png)

### Integrated CI/CD built for microservices

Connect GitHub, GitLab, or Bitbucket and deploy every commit automatically. Northflank builds repositories using Dockerfiles or Buildpacks, then deploys services and jobs from those builds or from external Docker registries.

**Key pipeline capabilities:**

- Build and deploy on every commit or configure rules for specific branches
- [Preview environments for pull requests](https://northflank.com/use-cases/preview-environments-backend-for-kubernetes)
- Release pipelines for multi-environment workflows
- Templates for deploying complex infrastructure as code

Unlike AWS Elastic Beanstalk or Azure App Service where you stitch together separate CI/CD tools, Northflank integrates everything. Push code, and Northflank handles builds, tests, and deployments across environments.

![image.png](https://assets.northflank.com/image_7da8ac2fce.png)

### Databases and stateful services included

Run microservices, cron jobs, and stateful addons from one platform.

**Managed services:**

- PostgreSQL with automatic backups
- MySQL databases with replicas
- MongoDB clusters
- Redis for caching and queues
- Custom services (RabbitMQ, Kafka, etc.)

These databases deploy alongside your services with private networking and sub-millisecond latency. Unlike Google Cloud Run or DigitalOcean App Platform where databases are separate products with separate billing, Northflank treats data stores as integrated parts of your architecture.

### A* developer experience and advanced control

Northflank prioritizes developer experience with Git-push deployment, automatic HTTPS certificates, and real-time logging. Devs can use it from the UI, CLI, API. 

**Advanced features available when needed:**

- Highly configurable networking with public/private HTTP, TCP, UDP ports
- Custom domains, subdomains, IP policies, and basic auth
- Secure environment variables and build args
- Horizontal and vertical scaling with optional autoscaling
- Cron jobs and scheduled tasks
- Real-time metrics and monitoring

You start simple but can access powerful features as your architecture grows. No rigid patterns or upfront infrastructure decisions.

### Deploy anywhere with BYOC

Avoid vendor lock-in with Northflank's Bring Your Own Cloud model.

**Deployment options:**

- Northflank's managed cloud
- Your own GCP, AWS, Azure, or neo-clouds like CoreWeave account
- Your Kubernetes clusters (GKE, EKS, AKS)
- On-premise infrastructure
- Hybrid cloud architectures

Run services close to users, keep regulated data in specific regions, and maximize existing cloud credits. Platforms like Heroku and Render lock you to their infrastructure. Northflank gives you true portability.

### Transparent, usage-based pricing

Northflank charges for actual compute resources by the second, not per service or container.

**Pricing advantages:**

- Pay for compute and storage usage
- No per-service or per-container fees
- No hidden data transfer charges
- Granular billing broken down per resource
- Predictable monthly costs

Traditional PaaS platforms charge per dyno or instance, with 15 microservices, costs explode. Northflank's model means architectural complexity doesn't multiply your bill.

### Built for team collaboration

Multiple teams work together with fine-grained access control.

**Team features:**

- Role-based access control (RBAC)
- Granular permissions per user and project
- Project scoping for team organization
- Shared environments and resources
- API for infrastructure as code

Your frontend, backend, and platform teams coordinate seamlessly with appropriate access levels. No permission bottlenecks or access issues.

### When to choose Northflank over alternatives as the best PaaS for full-stack microservices

**Choose Northflank instead of Heroku when:**
You need container flexibility, Kubernetes features, or multi-cloud deployment. Heroku works for simple apps but breaks down with complex microservice architectures.

<InfoBox className='BodyStyle'>

💡Learn how to migrate from Heroku [here](https://northflank.com/blog/how-to-migrate-from-heroku-a-step-by-step-guide).

</InfoBox>

**Choose Northflank instead of Google Cloud Run when:**
You need stateful services, persistent storage, or complex networking between services. Cloud Run excels at stateless functions but struggles with traditional microservice patterns.

<InfoBox className='BodyStyle'>

💡Learn more about Google Cloud Run alternatives [here](https://northflank.com/blog/best-google-cloud-run-alternatives-in-2025).

</InfoBox>

**Choose Northflank instead of Azure App Service when:**
You want Kubernetes portability without Azure lock-in. App Service ties you to Azure-specific services and pricing models.

<InfoBox className='BodyStyle'>
💡 Learn more about Azure App Service alternatives [here](https://northflank.com/blog/azure-alternatives).

</InfoBox>

**Choose Northflank instead of Red Hat OpenShift when:**
You want Kubernetes power without enterprise complexity. OpenShift requires dedicated platform teams. Northflank gives you Kubernetes benefits with PaaS simplicity.

<InfoBox className='BodyStyle'>
💡 Learn more about OpenShift alternatives [here](https://northflank.com/blog/best-open-shift-alternatives-finding-the-right-kubernetes-platform).

</InfoBox>

**Choose Northflank instead of Render or DigitalOcean when:**
You need advanced orchestration, multi-cloud flexibility, or enterprise-grade features. These platforms work for smaller deployments but lack sophistication for complex architectures.

<InfoBox className='BodyStyle'>
💡 Learn more about Render alternatives [here](https://northflank.com/blog/render-alternatives) and DigitalOcean [here](https://northflank.com/blog/best-digitalocean-alternatives-2025). 

</InfoBox>

## Summary: Why Northflank is the best PaaS for full-stack microservices

Northflank solves the core challenge of microservice deployment: balancing power with simplicity. You get native Kubernetes orchestration, complete technology freedom, integrated CI/CD, managed data services, and multi-cloud flexibility, all through a developer-friendly interface that doesn't require platform expertise.

Traditional PaaS solutions either oversimplify (losing critical features) or overcomplicate (requiring dedicated ops teams). 

Northflank delivers the best PaaS for full-stack microservices by giving developers the control they need without the operational burden they don't want.

Whether you're building a new microservice architecture or migrating from another platform, Northflank accelerates development, reduces operational complexity, and scales seamlessly from prototype to enterprise production.

**Ready to deploy your full-stack microservices?** 

[Start building on Northflank today.](https://app.northflank.com/signup)]]>
  </content:encoded>
</item><item>
  <title>4 best GitHub Codespaces alternatives for secure sandboxing</title>
  <link>https://northflank.com/blog/github-codespaces-alternatives</link>
  <pubDate>2025-11-04T15:45:00.000Z</pubDate>
  <description>
    <![CDATA[GitHub Codespaces alternatives: Northflank, Gitpod, Coder &amp; more for secure sandboxing, self-hosting &amp; cost savings in 2026.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/github_codespaces_alternatives_4de05b9f9c.png" alt="4 best GitHub Codespaces alternatives for secure sandboxing" /><InfoBox className="BodyStyle">

## TL;DR: Top GitHub Codespaces alternatives

GitHub Codespaces alternatives include Northflank, Gitpod, Coder, and DevPod. Each platform takes a different approach to cloud development environments. See a quick list below that covers their key features and use cases (we go into detail later in the article):

1. **Northflank:** Provides microVM isolation with Kata Containers and gVisor for [secure code execution](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh), plus a complete cloud platform for apps, databases, and GPU workloads.
    
    > Deploy in [Northflank's cloud](https://northflank.com/features/managed-cloud) or [bring your own](https://northflank.com/features/bring-your-own-cloud) infrastructure ([AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), [Oracle](https://northflank.com/cloud/oci), or bare-metal). Includes [RBAC](https://northflank.com/docs/v1/application/secure/use-role-based-access-control), [audit logging](https://northflank.com/docs/v1/application/observe/audit-logs), [SSO](https://northflank.com/docs/v1/application/secure/single-sign-on-multi-factor-authentication), and [per-second billing](https://northflank.com/pricing).
    > 
    > 
    > Trusted by companies like cto.new who use Northflank's microVMs to [scale secure sandboxes](https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes) without sacrificing speed or cost.
    > 
2. **Gitpod (now Ona):** Provides ephemeral development environments using Dev Containers. 
3. **Coder:** Provides self-hosted development environments defined as Terraform code.
4. **DevPod:** Provides client-only development environments without server-side setup.

</InfoBox>

## What are GitHub Codespaces?

GitHub Codespaces are cloud-hosted development environments that spin up from your repository with a pre-configured setup based on Dev Containers.

They run on virtual machines in GitHub's cloud, providing instant access to a full development environment through VS Code in the browser or your local IDE. Codespaces handle compute and storage in the cloud, reducing the need for local setup.

## Why look for GitHub Codespaces alternatives?

Organizations and developers search for alternatives to GitHub Codespaces for several strategic and technical reasons that better align with their specific requirements.

1. **Need VM-level isolation for untrusted code execution?** If you're building AI agents, code interpreters, or platforms that execute user-generated code, you need VM-level isolation with microVMs or gVisor rather than shared-kernel containers. This prevents container escapes and protects your infrastructure from malicious code.
2. **Want self-hosted or bring-your-own-cloud deployment?** Many enterprises require complete control over where their code and data reside for compliance, security, or data sovereignty reasons. Self-hosted solutions let you run development environments in your own VPC or on-premises infrastructure rather than GitHub's cloud.
3. **Looking for more cost-effective options?** GitHub Codespaces pricing can escalate quickly for teams with multiple active developers or resource-intensive workloads. Alternative solutions often provide better pricing models, automatic shutdown of idle resources, or the flexibility to use [spot instances](https://northflank.com/blog/spot-instances) and [cheaper cloud providers](https://northflank.com/blog/cheapest-cloud-gpu-providers).
4. **Require platform-agnostic version control integration?** Teams using GitLab, Bitbucket, Azure DevOps, or multiple version control systems simultaneously need solutions that aren't locked to GitHub's ecosystem and can integrate with their existing workflows.
5. **Need deeper customization and infrastructure control?** Some development scenarios require specific networking configurations, custom hardware access, GPU support, or integration with proprietary internal tools that GitHub Codespaces doesn't support or makes difficult to implement.

## 4 top GitHub Codespaces alternatives for secure sandboxing and flexible deployment

### 1. Northflank

Northflank is a complete cloud platform that uniquely combines production-grade microVM isolation for secure code execution with full infrastructure capabilities for deploying applications, databases, jobs, and GPU workloads.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

**Key features:**

- Multiple isolation technologies: Kata Containers with Cloud Hypervisor and gVisor for VM-level security
- True bring-your-own-cloud: Deploy in your [AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), [Oracle](https://northflank.com/cloud/oci), or bare-metal infrastructure with full control
- Avoid vendor lock-in: Run workloads across multiple clouds or your own infrastructure without being tied to a single provider
- Complete platform: Not just sandboxes, you can run your entire stack including [CI/CD](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank), [databases](https://northflank.com/features/databases), and [inference workloads](https://northflank.com/gpu)
- Secure multi-tenancy: Isolated workloads with project-level separation for SaaS platforms and multi-tenant deployments
- Ephemeral preview environments: Spin up ephemeral, full-stack [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), including [databases](https://northflank.com/features/databases), [microservices](https://northflank.com/docs/v1/application/run/run-containers-and-micro-services), and [jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs), on every pull request for testing and collaboration
- GitOps and IaC: Infrastructure as code with [templates](https://northflank.com/docs/v1/application/infrastructure-as-code/infrastructure-as-code) for repeatable deployments across GitHub, GitLab, and Bitbucket
- Auto-scaling and observability: [Real-time logging](https://northflank.com/docs/v1/application/observe/view-logs), [metrics](https://northflank.com/docs/v1/application/observe/view-metrics), and [automatic scaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments) based on resource usage
- Production-proven scale: Executes over [2 million isolated workloads](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes) monthly, in production.
- Unlimited session duration: Sandboxes persist until you terminate them, unlike time-limited alternatives
- Any OCI container image: Use existing containers from any registry directly

**Best for:** Teams building and deploying CPU or GPU workloads (or BOTH) that require secure execution, enterprises needing secure [multi-tenant](https://northflank.com/blog/what-is-multitenancy#how-northflank-helps-you-manage-multitenant-workloads) isolation, and organizations in need of a **unified platform** for both development environments and production infrastructure with flexible deployment options.

<InfoBox className="BodyStyle">

**See these very helpful guides (a must-read!)**:

- [How to spin up a secure code sandbox & microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
- [Your containers aren’t isolated. Here’s why that’s a problem. microVMs, VMMs and container isolation](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation)
- [How Cedana uses Northflank to deploy workloads onto Kubernetes with microVMs and secure runtimes](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes)
- [Secure runtime for codegen tools: microVMs, sandboxing, and execution at scale](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale)
- [How Northflank helps you manage multitenant workloads](https://northflank.com/blog/what-is-multitenancy#how-northflank-helps-you-manage-multitenant-workloads)

</InfoBox>

> [Try Northflank's free sandbox](https://app.northflank.com/signup) to experience secure microVM deployments, or [review our documentation](https://northflank.com/docs) to learn how you can leverage Northflank for secure multi-tenant deployments. For specific deployment questions or to discuss your secure sandboxing requirements, [talk to one of our expert engineers](https://cal.com/team/northflank/northflank-intro). See [full pricing details](https://northflank.com/pricing).
> 

### 2. Gitpod (Ona)

Gitpod (now Ona) is a cloud development environment platform that provides ephemeral workspaces with AI coding assistant integration.

![gitpod-ona-homepage.png](https://assets.northflank.com/gitpod_ona_homepage_4452801194.png)

**Key features:**

- Ephemeral environments using Dev Container standard
- Self-hosted deployment in your VPC with vendor management
- AI agent integration for code generation and PR workflows
- Prebuilds that prepare environments before workspace creation
- Works with GitHub, GitLab, Bitbucket, and Azure DevOps

**Best for:** Teams using AI-assisted development workflows, organizations needing self-hosted CDEs with vendor management, and developers requiring pre-built environment provisioning.

### 3. Coder

Coder is a self-hosted cloud development environment platform that uses Terraform infrastructure-as-code to define workspaces.

![coder-homepage.png](https://assets.northflank.com/coder_homepage_57ec076708.png)

**Key features:**

- Open-source with enterprise options available
- Terraform-based templates for defining workspaces as code
- Support for human developers and AI agents with granular permissions
- Deploy on Kubernetes, Docker, or VMs on any cloud or air-gapped on-premises
- RBAC, audit logging, SSO, and template management

**Best for:** Organizations requiring infrastructure control and self-management, platform engineering teams using Terraform, and enterprises with air-gapped environments or compliance requirements.

### 4. DevPod

DevPod is a client-only, open-source tool that creates reproducible development environments without server-side setup.

![devpod-homepage.png](https://assets.northflank.com/devpod_homepage_52a6d32384.png)

**Key features:**

- Client-only architecture with no server backend to deploy
- Works with local Docker, cloud providers, Kubernetes, or remote machines
- Automatic shutdown of idle environments
- Supports VS Code, JetBrains suite, or SSH-compatible editors
- Uses DevContainer standard

**Best for:** Individual developers and small teams, organizations running development environments on their own infrastructure, and teams using multiple cloud providers.

## How to choose the most suitable alternative to GitHub Codespaces

| Consideration | What to look for |
| --- | --- |
| **Security needs** | For untrusted code execution or multi-tenant applications, prioritize solutions with microVM isolation (Kata, gVisor, Firecracker) over standard containers. Northflank and Coder offer the highest isolation options. |
| **Deployment model** | Decide between managed SaaS, vendor-managed self-hosted, or fully self-managed options. GitPod (Ona) offers SaaS and vendor-managed self-hosting, Coder and DevPod are self-managed, while Northflank provides **both SaaS and BYOC ([Bring your own cloud](https://northflank.com/features/bring-your-own-cloud)) deployments** for flexible infrastructure control. |
| **Cost optimization** | Decide between managed SaaS, vendor-managed self-hosted, or fully self-managed options. Ona offers SaaS and vendor-managed self-hosting, Coder and DevPod are self-managed, while Northflank stands out by supporting both SaaS and BYOC (Bring your own cloud) deployments for maximum flexibility. |
| **Infrastructure control** | If you need to run in your VPC, on-premises, or across multiple clouds, choose solutions with true BYOC support like Northflank, Coder, or DevPod rather than cloud-only platforms. |
| **Team size and scale** | Larger teams benefit from enterprise features like RBAC, audit logging, and template management. Coder and Northflank provide comprehensive governance for enterprise scale. |
| **Beyond development** | If you need to run production workloads, databases, GPU inference, or CI/CD alongside development environments, choose a complete platform like Northflank rather than development-only tools. |

## Start building with secure sandboxing today

Choosing the right GitHub Codespaces alternative depends on your security requirements, infrastructure preferences, and whether you need development environments alone or a complete platform.

Northflank stands out by combining production-grade microVM isolation with full cloud infrastructure capabilities, letting you build everything from secure AI code execution to GPU-powered inference on your infrastructure.

<InfoBox className="BodyStyle">

[Try Northflank's free sandbox](https://app.northflank.com/signup) to experience secure microVM deployments, or [review our documentation](https://northflank.com/docs) to learn how you can leverage Northflank for secure multi-tenant deployments.

For specific deployment questions or to discuss your secure sandboxing requirements, [talk to one of our expert engineers](https://cal.com/team/northflank/northflank-intro).

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Top 13 Pulumi alternatives in 2026</title>
  <link>https://northflank.com/blog/pulumi-alternatives</link>
  <pubDate>2025-11-03T17:30:00.000Z</pubDate>
  <description>
    <![CDATA[Find Pulumi alternatives like Northflank, Terraform, OpenTofu, and AWS CDK. Compare IaC tools to find the best fit for your team.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/pulumi_alternatives_7f701a70a6.png" alt="Top 13 Pulumi alternatives in 2026" /><InfoBox className="BodyStyle">

## TL;DR: Pulumi alternatives at a glance

Pulumi alternatives range from traditional Infrastructure as Code (IaC) tools to complete platform solutions that handle infrastructure, deployment, and operations together. Here are the top options:

1. [**Northflank**](https://northflank.com/) - Kubernetes-native platform with [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) support (deploy in your own AWS, Azure, GCP, Civo, Oracle, or bare-metal/on-premise), integrated CI/CD, databases, and zero vendor lock-in. Complete deployment solution beyond infrastructure provisioning.
2. **Terraform** - IaC tool using HCL language with large ecosystem, though now under Business Source License.
3. **OpenTofu** - Open-source Terraform fork under Linux Foundation with full Terraform compatibility and community governance.
4. **AWS CDK** - Code infrastructure for AWS using TypeScript, Python, or other languages, but AWS-only.
5. **Crossplane** - Kubernetes-native IaC using CRDs and GitOps workflows for teams already running Kubernetes.
6. **Ansible** - Agentless automation using YAML playbooks, better for configuration management than complex infrastructure.
7. **Azure Bicep** - Modern DSL for Azure resources, cleaner than ARM templates but Azure-specific.
8. **CDK for Terraform** - Write Terraform infrastructure in programming languages instead of HCL.
9. **AWS CloudFormation** - AWS-native templates in YAML/JSON with deep service integration.
10. **Azure ARM Templates** - Microsoft's JSON-based IaC for Azure (Bicep is recommended modern alternative).
11. **Chef** - Ruby-based configuration management for traditional infrastructure.
12. **Puppet** - Declarative configuration management with agent-based architecture.
13. **Vagrant** - Tool for building and managing virtualized development environments with simple configuration.

</InfoBox>

## What to look out for in Pulumi alternatives?

When evaluating alternatives to Pulumi, look out for these factors based on common pain points teams experience:

- **Do I need more than just infrastructure provisioning?** If you're looking for a complete platform that handles deployment, CI/CD, databases, and operations alongside infrastructure management, look out for platforms rather than point tools.
- **How important is keeping data within my security boundary?** For regulated industries requiring strict data residency and compliance controls, look for solutions with built-in [Bring Your Own Cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) (BYOC) capabilities rather than SaaS-only state management.
- **What's my team's technical background?** Developer-heavy teams may prefer programming languages, while operations-focused teams often work better with declarative DSLs or managed platforms.
- **Can I avoid vendor lock-in?** Check whether the tool uses open standards like Kubernetes and Docker, or proprietary SDKs and languages that make migration difficult.
- **What are the total costs at scale?** Look beyond free tiers and understand pricing models based on resources, users, or compute usage to avoid surprises as you grow.
- **Will this tool grow with my team?** Check if the solution supports multi-cloud deployments, handles increasing complexity, and provides the right level of abstraction for your current and future needs

## 13 best Pulumi alternatives for cloud infrastructure management in 2026

These alternatives fall into three categories: complete platforms that handle more than just provisioning, multi-cloud IaC tools, and cloud-specific solutions.

### 1. Northflank

***Best for:** Teams needing a **complete deployment platform** with features to help meet compliance standards ([Bring Your Own Cloud](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) (BYOC)), CI/CD pipelines, managed databases, multi-cloud support, and zero vendor lock-in, all without infrastructure complexity*

Northflank takes a different approach than traditional IaC tools by providing a complete platform for deploying and operating applications, beyond provisioning infrastructure. Built on standard Kubernetes, it abstracts complexity while maintaining portability.

The platform's [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) model addresses compliance requirements by keeping all runtime, data, and logs within your cloud boundary across AWS, GCP, Azure, Civo, Oracle, or on-premise/bare-metal.

Because Northflank runs on standard Kubernetes and Docker, you avoid vendor lock-in. Your applications remain portable and can be exported to run on any Kubernetes cluster. This contrasts with tools that use proprietary SDKs or domain-specific languages, which require code rewrites for migration.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

**Key capabilities:**

- Infrastructure as code using [templates](https://northflank.com/docs/v1/application/infrastructure-as-code/write-a-template) with bidirectional [GitOps](https://northflank.com/docs/v1/application/infrastructure-as-code/gitops-on-northflank)
- Built-in [CI/CD](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) pipelines and [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment)
- Managed databases ([PostgreSQL](https://northflank.com/dbaas/managed-postgresql), [MongoDB](https://northflank.com/dbaas/mongodb-on-northflank), [MySQL](https://northflank.com/dbaas/managed-mysql), [Redis](https://northflank.com/dbaas/managed-redis))
- Real-time [logs](https://northflank.com/docs/v1/application/observe/view-logs) and [metrics](https://northflank.com/docs/v1/application/observe/view-metrics)
- [GPU workload](https://northflank.com/gpu) support for AI/ML applications
- [Multi-cloud](https://northflank.com/features/bring-your-own-cloud) deployment with unified interface
- Usage-based pricing per compute resources, not per resource count

<InfoBox className="BodyStyle">

Teams like Weights have [scaled to millions](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s) of users running 10,000+ AI training jobs daily with minimal infrastructure overhead using this approach. 

Northflank works well for developer teams wanting to ship applications quickly, organizations with strict compliance requirements, and companies running workloads or AI/ML applications (both CPU & GPU) across multiple clouds.

</InfoBox>

**Pricing:** Free developer tier, usage-based pricing for production workloads

**([See full pricing details](https://northflank.com/pricing))**

### 2. Terraform

**Best for:** Organizations using HCL-based infrastructure automation

Terraform uses HashiCorp Configuration Language (HCL) to define infrastructure across multiple cloud providers. The 2023 license change to Business Source License means it's no longer fully open-source, creating restrictions for some commercial use cases.

![terraform-by-hashicorp.png](https://assets.northflank.com/terraform_by_hashicorp_03e70a66a2.png)

**Key features:**

- Declarative HCL syntax
- Execution planning (terraform plan)
- Multi-cloud provider support
- Module registry with community contributions
- State management with remote backends
- Infrastructure drift detection

**License:** Business Source License v1.1

### 3. OpenTofu

**Best for:** Teams wanting fully open-source Terraform compatibility

OpenTofu is a community fork of Terraform governed by the Linux Foundation, maintaining full compatibility with Terraform configurations while remaining open-source. Built-in state encryption adds security without external tools.

![opentofu.png](https://assets.northflank.com/opentofu_42e3339091.png)

**Key features:**

- Full Terraform compatibility
- Community governance under Linux Foundation
- Built-in state encryption
- Enhanced security features
- Access to 3,900+ providers and 23,600+ modules
- Advanced workflow capabilities

**License:** Mozilla Public License v2.0

### 4. AWS CDK

**Best for:** AWS-native applications using programming languages

AWS Cloud Development Kit lets developers define AWS infrastructure using TypeScript, Python, Java, C#, or Go. Code synthesizes into CloudFormation templates for deployment, limited to AWS only.

![aws-cdk.png](https://assets.northflank.com/aws_cdk_e8a37ed242.png)

**Key features:**

- Multiple language support (TypeScript, Python, Java, C#, Go)
- Construct library (L1-L3 abstractions)
- Automatic synthesis to CloudFormation
- Native AWS service integration
- Built-in testing support
- IDE integration with autocomplete and type checking

**License:** Apache License 2.0

### 5. Crossplane

**Best for:** Platform teams managing infrastructure through Kubernetes

Crossplane extends Kubernetes to manage cloud infrastructure using Custom Resource Definitions. Platform engineers can define custom infrastructure APIs for developers to consume.

![crossplane-homepage.png](https://assets.northflank.com/crossplane_homepage_e1a8306946.png)

**Key features:**

- Kubernetes-native design (infrastructure as CRDs)
- GitOps compatible
- Custom infrastructure APIs through compositions
- Multi-cloud support (AWS, Azure, GCP, Alibaba Cloud)
- Declarative configuration
- Separation of concerns between platform and developers

**License:** Apache License 2.0

### 6. Ansible

**Best for:** Agentless automation and configuration tasks

Ansible uses YAML playbooks for automation without requiring agent installation on managed nodes. Procedural execution and less sophisticated state management make it less suitable for complex infrastructure compared to tools purpose-built for IaC.

![ansible's website.png](https://assets.northflank.com/ansible_s_website_7950302603.png)

**Key features:**

- Agentless architecture (SSH/WinRM)
- YAML-based playbooks
- Wide ecosystem of modules
- Procedural execution model
- Simple learning curve
- Orchestration capabilities

**License:** GNU GPL v3.0

### 7. Azure Bicep

**Best for:** Azure deployments needing cleaner syntax than ARM templates

Microsoft developed Bicep to address ARM template verbosity, transpiling to ARM JSON at runtime. Limited to Azure infrastructure only.

![azure-bicep.png](https://assets.northflank.com/azure_bicep_5108fa7210.png)

**Key features:**

- Concise, human-readable syntax
- Full ARM template compatibility
- Native Azure support with same-day feature access
- Built-in IDE tooling and validation
- Modular and reusable design
- Type safety and IntelliSense support

**License:** MIT License

### 8. CDK for Terraform

**Best for:** Developers wanting Terraform providers with programming languages

CDKTF combines Terraform's provider ecosystem with code-based infrastructure definitions in TypeScript, Python, Java, C#, or Go. Code compiles into standard Terraform JSON, maintaining compatibility with Terraform workflows.

![cdk-for-teraform.png](https://assets.northflank.com/cdk_for_teraform_c52a9d7ea6.png)

**Key features:**

- Multiple language support (TypeScript, Python, Java, C#, Go)
- Terraform compatibility and provider ecosystem
- Type safety and IDE integration
- Programming language constructs (loops, conditionals, functions)
- Compiles to standard Terraform JSON
- Access to Terraform modules

**License:** Mozilla Public License v2.0

### 9. AWS CloudFormation

**Best for:** AWS-only infrastructure with native service integration

CloudFormation is AWS's native IaC using YAML or JSON templates with first-party support for AWS services. Templates can be verbose and lack programming constructs.

![aws-cloudformation.png](https://assets.northflank.com/aws_cloudformation_bb6af5a493.png)

**Key features:**

- AWS-native with first-party support
- Declarative templates in YAML/JSON
- Automatic dependency resolution
- Change sets for preview
- Drift detection
- Stack management and rollback capabilities

**License:** Proprietary (free to use)

### 10. Azure ARM Templates

**Best for:** Legacy Azure deployments (Bicep now recommended)

ARM Templates define Azure infrastructure using JSON with native platform integration. Microsoft now recommends Bicep for new deployments due to ARM's verbose syntax.

![azurea-arm-templates.png](https://assets.northflank.com/azurea_arm_templates_27477d40ba.png)

**Key features:**

- Native Azure integration
- JSON-based declarative syntax
- Idempotent deployments
- Integration with Azure policies and RBAC
- Template validation and what-if analysis
- Secure parameter handling

**License:** Proprietary

### 11. Chef

**Best for:** Traditional infrastructure configuration

Chef automates infrastructure setup using Ruby-based DSL with procedural and imperative approaches. Requires Ruby knowledge and uses agent-based architecture.

![chef-homepage.png](https://assets.northflank.com/chef_homepage_05bc66277f.png)

**Key features:**

- Ruby-based DSL
- Procedural and imperative approach
- Agent-based architecture
- Configuration enforcement
- Test-driven infrastructure development
- Cookbook sharing and reuse

**License:** Apache License 2.0

### 12. Puppet

**Best for:** Large-scale server configuration

Puppet enforces system configuration using declarative language and agent-master architecture. Designed for consistent configuration across server fleets rather than cloud provisioning.

![puppet.png](https://assets.northflank.com/puppet_ed9af6d77b.png)

**Key features:**

- Declarative configuration model
- Agent-master architecture
- State enforcement and drift correction
- Module ecosystem (Puppet Forge)
- Reporting and compliance tracking
- Multi-platform support

**License:** Apache License 2.0

### 13. Vagrant

**Best for:** Creating consistent development environments

Vagrant simplifies creating and managing virtualized development environments using simple configuration files. Works with providers like VirtualBox, VMware, and Docker.

![vagrant-homepage.png](https://assets.northflank.com/vagrant_homepage_2a459dfcf1.png)

**Key features:**

- Simple configuration files (Vagrantfile)
- Multiple provider support (VirtualBox, VMware, Docker)
- Environment consistency across teams
- Plugin ecosystem
- Provisioning integration with Ansible, Chef, Puppet
- Box sharing and distribution

**License:** MIT License

## How to choose the right Pulumi alternative for your team

This comparison helps you quickly identify which tools match your specific requirements and team structure.

| If you need... | Consider... | Because... |
| --- | --- | --- |
| Deployment + operations + infrastructure in one platform | Northflank | Handles complete application lifecycle beyond provisioning |
| Strict compliance with data residency requirements | Northflank (BYOC), self-hosted OpenTofu | Keeps all data within your security boundary |
| Mature ecosystem with extensive community modules | Terraform, OpenTofu | Largest collection of providers and pre-built modules |
| Zero vendor lock-in with open standards | Northflank, OpenTofu, Crossplane | Built on Kubernetes, Docker, or open-source foundations |
| AWS-specific infrastructure with programming languages | AWS CDK | Native AWS integration with TypeScript, Python, Java support |
| Multi-cloud without proprietary lock-in | OpenTofu, Northflank | Works across clouds with portable configurations |
| GitOps-native Kubernetes infrastructure | Crossplane | Treats infrastructure as Kubernetes resources |
| Simple configuration management | Ansible | Agentless with straightforward YAML syntax |
| Azure-native with modern syntax | Bicep | Cleaner than ARM templates with same-day feature support |
| Transparent usage-based pricing | Northflank | Pay for compute resources used, not resource counts |

## Start with the right foundation

The best alternative depends on your team's needs, technical background, and infrastructure requirements. Traditional IaC tools like Terraform and OpenTofu work well for infrastructure provisioning when you have dedicated DevOps resources. Cloud-specific tools like AWS CDK or Azure Bicep make sense for single-cloud deployments.

<InfoBox className="BodyStyle">

If you need more than just infrastructure provisioning, such as deployment pipelines, runtime operations, compliance controls, and developer self-service, platforms like [Northflank](https://northflank.com/) provide integrated solutions without forcing you into proprietary lock-in.

[Try Northflank's free developer sandbox](https://app.northflank.com/signup) to see how it compares with your current setup, or [book a demo with an engineer](https://cal.com/team/northflank/northflank-intro) to discuss your specific requirements.

</InfoBox>]]>
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  <title>What is the best global PaaS with multi‑region deployments?</title>
  <link>https://northflank.com/blog/global-paas-with-multi-region-deployments</link>
  <pubDate>2025-11-03T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[Global PaaS platforms with multi-region deployments = a cloud platform that runs applications across multiple geographic locations simultaneously. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/global_paas_b411a19b7b.png" alt="What is the best global PaaS with multi‑region deployments?" /><InfoBox className='BodyStyle'>

# TL;DR

- The best global PaaS with multi-region deployments is [**Northflank**](https://northflank.com/).
- Global PaaS platforms with multi-region deployments = a cloud platform that runs applications across multiple geographic locations simultaneously.
- Alternatives include AWS Elastic Beanstalk, Google Cloud Run, Heroku, Railway, and Render. Each offers different trade-offs in flexibility, pricing, and ease of use.
- [Free Developer Sandbox](https://northflank.com/pricing) plan and paid plans starting at $2.7/month.
- Northflank has 6 [regions available](https://northflank.com/cloud/northflank/regions): **Americas:** US - Central, US - East, US - West, **Europe (EMEA):** Europe West - London, Europe West - Amsterdam, **Asia Pacific:** Asia - Singapore.
- Northflank has 600+ regions available through [BYOC](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes), via AWS, GCP, Azure, OCI, Civo, and bare-metal/on-prem.

</InfoBox>

## What is a global PaaS with multi-region deployments?

A global PaaS with multi-region deployments is a cloud platform that runs applications across multiple geographic locations simultaneously. 

In a global PaaS with multi-region deployments, you workloads, databases, and services in data centers across different continents, enabling applications to serve users from the nearest location while maintaining redundancy. 

Multi-region deployments reduce latency, ensure high availability during regional outages, and help organizations comply with data residency regulations.

<InfoBox className='BodyStyle'> 
💡 Multi-region architecture is a fundamental infrastructure pattern used across all deployment models, from bare metal servers to virtual machines, containers, and serverless functions. While this article focuses specifically on Platform-as-a-Service (PaaS) solutions for multi-region deployments, the underlying principles of geographic distribution, failover, and latency reduction apply regardless of your infrastructure approach. 
</InfoBox>

## Multi-region deployment patterns

**Active-Active**: All regions handle live traffic simultaneously. Best for maximum uptime and global performance. Higher cost and complexity.

**Active-Passive**: Primary region handles traffic, secondary regions on standby for failover. Simpler and cheaper but longer recovery times.

**What to deploy**: Full stack (microservices + databases) in each region, or hybrid approach with stateless services everywhere and replicated databases. Choice impacts latency and consistency.

## **Northflank is the best global PaaS with multi-region deployments**

Northflank provides the most comprehensive and flexible global PaaS solution for teams that need production-grade multi-region capabilities without Kubernetes complexity.

Here’s why that is:

### **1. Full-stack regional deployments**

Deploy microservices, databases (PostgreSQL, MongoDB, Redis), jobs, and queues within each region for both active-active and active-passive architectures.

### 2. Deploy anywhere: Managed cloud or your own infrastructure

Northflank offers deployment across 6+ managed cloud regions plus 600+ regions through Bring Your Own Cloud (BYOC). 

Connect your AWS, GCP, or Azure account to deploy in any region those providers offer while maintaining the same developer experience. This flexibility means you control data residency, deployment regions, security policies, and cloud expenses.

### 3. Built on Kubernetes, minus the complexity

Northflank leverages Kubernetes as an operating system to deliver cloud-native benefits without operational overhead. Deploy to Northflank's managed cloud for maximum simplicity or connect your GKE, EKS, AKS, or bare-metal clusters to get a managed platform experience in minutes. You get the power of Kubernetes without managing it directly.

### 4. True pay-per-use pricing

Northflank charges only for resources consumed, pro-rated by the second. 

No seat-based pricing, no added costs for running in your VPC, and no hidden fees. Scale compute from 0.1 vCPU to 32 vCPU and memory from 256 MB to 256 GB based on actual needs. Teams only pay for what they use across all regions.

### 5. Complete platform with all the bells and whistles

Northflank provides everything needed for production deployments: managed databases, build pipelines, preview environments, release flows, secrets management, real-time monitoring, autoscaling, backups, and logs. 

All features work consistently across every region whether using managed cloud or BYOC.

Plus, you can deploy any GPU workload, too.

### 6. Enterprise-grade security and compliance

Environment variables are encrypted at rest and securely injected at runtime.

SSO with Google, GitHub, GitLab, and Bitbucket is standard, with SAML and OIDC support for enterprise requirements. 

Fine-grained API tokens follow granular permission models for secure programmatic access across regions.

## Key capabilities for multi-region deployments

**Project-based region control**: Create projects in specific regions to run services and jobs closer to customers, development teams, or external services for better connectivity. Each project deploys all its resources (services, databases, jobs) in the chosen region.

**Cross-region domain management**: Move domains between services across regions seamlessly, enabling flexible traffic routing and gradual migration strategies.

**Global preview environments**: Generate dynamic subdomains across multiple regions for preview environments, supporting distributed development workflows.

**Real-time collaboration**: The platform interface updates in real-time to reflect changes made by any team member, supporting globally distributed teams working across time zones.

**Comprehensive monitoring**: Built-in logs, metrics, autoscaling, and backups across all regions.

## How to get started with Northflank

First and foremost, [sign up here](https://app.northflank.com/signup). 

**Step 1: Access the create menu**
From the Northflank dashboard, click the blue "Create new" button in the top navigation. A dropdown appears with options including Project, Template, Stack, Domain, API Token, and various service types.

![CleanShot 2025-11-03 at 16.21.06@2x.png](https://assets.northflank.com/Clean_Shot_2025_11_03_at_16_21_06_2x_d5664307f6.png)


**Step 2: Create a Project and choose your region**
The project creation form has three key sections:

- **Basic information**: Enter your project name and select a color for easy identification
- **Deployment target**: Choose between "Northflank Cloud" (managed PaaS regions) or "Bring Your Own Cloud" (deploy into clusters in your own cloud accounts)
- **Which region do you want to deploy in?**: This is where multi-region deployment starts. Select from available regions organized by geography:
    - **Americas**: US - Central, US - East, US - West
    - **EMEA**: Europe - West (with specific countries like Netherlands shown)
    - **Asia Pacific**: Asia - Southeast

![CleanShot 2025-11-03 at 16.22.20@2x.png](https://assets.northflank.com/Clean_Shot_2025_11_03_at_16_22_20_2x_ef45855868.png)

**Step 3: Configure advanced options (Optional)**
Under "Advanced options" you can configure:

- **Multi-project networking**: Select "Ingress projects" to allow network access from other projects in your organization. This enables services in different regional projects to communicate privately.
- **Custom DNS hosts**: Enable if you need custom DNS configuration
- **Project Tailscale settings**: Integrate with Tailscale for secure connectivity

Click "Create project" to complete setup. Your new project opens with a welcome screen.

![CleanShot 2025-11-03 at 16.23.27@2x.png](https://assets.northflank.com/Clean_Shot_2025_11_03_at_16_23_27_2x_0c3bb72f39.png)

**Step 4: Deploy your first service**
From the new project dashboard, click "Add new service". Configure your deployment:

- **Build options**: Choose Dockerfile (requires a Dockerfile in your repo) or Buildpack (automatically detects and builds your application)
- **Resources**: Select compute plan starting from 0.5 shared vCPU with 1,024 MB memory. The interface shows real-time pricing.
- **Instances**: Set the number of instances (replicas) for your service
- **Autoscaling**: Enable horizontal autoscaling based on CPU or memory thresholds
- **Networking**: Add ports to expose your service publicly or privately

![CleanShot 2025-11-03 at 16.24.17@2x.png](https://assets.northflank.com/Clean_Shot_2025_11_03_at_16_24_17_2x_07589dee3e.png)

**Step 5: Expand to additional regions for global deployment**
To create a true multi-region deployment:

1. Return to the main Projects view from the top navigation
2. Click "Create project" again
3. Select a different region (for example, if your first project was in Europe - West, create another in US - East or Asia - Southeast)
4. Deploy the same service configuration to the new regional project
5. Use the Domains section to configure traffic routing between regions
6. Configure multi-project networking in Advanced options to enable private communication between regional deployments

Each project operates independently in its chosen region with all services, databases, and jobs running locally in that geography. 

## Alternative solutions to global PaaS with multi-region deployments

**[Heroku](https://northflank.com/blog/top-heroku-alternatives)**: Traditional PaaS with limited region options and higher costs at scale. No BYOC option or Kubernetes foundation.

**[Railway](http://northflank.com/blog/railway-alternatives)**: Simple PaaS focused on developer experience but with fewer enterprise features and limited multi-region capabilities.

**[Render](https://northflank.com/blog/render-alternatives)**: Modern PaaS with good developer experience but limited to their managed infrastructure without BYOC flexibility.

**[AWS Elastic Beanstalk](https://northflank.com/blog/elastic-beanstalk-alternatives)**: AWS-native PaaS with multi-region support but requires AWS expertise and lacks the developer experience of modern platforms.

**[Google Cloud Run](https://northflank.com/blog/best-google-cloud-run-alternatives-in-2025)**: Serverless container platform with global deployment but limited to Google Cloud infrastructure.


## FAQs

**How do you deploy to multiple regions with zero downtime?**

Deploy to multiple regions with zero downtime by creating separate projects in each region, using health checks and load balancers for traffic routing, and implementing rolling deployments. On Northflank, create a project in your first region, deploy your service, then create additional projects in other regions with the same configuration. Use multi-project networking to enable cross-region communication and configure domains to route traffic based on geographic proximity.

**How do you rank global container deployment platforms?**

Global container deployment platforms rank based on region coverage, deployment flexibility, pricing model, and developer experience. Top-ranked platforms include: 1) Northflank (600+ regions via BYOC, Kubernetes-based, pay-per-second), 2) AWS ECS/EKS (24+ regions, AWS ecosystem, complex setup), 3) Google Cloud Run (38+ regions, serverless, GCP-only), 4) Fly.io (30+ regions, edge-focused, simpler apps), 5) Railway (limited regions, simple DX, growing).

**Which platform is best for multi-region Kubernetes?**

Northflank is the best platform for multi-region Kubernetes because it manages Kubernetes complexity while letting you deploy to any cloud provider. Unlike raw Kubernetes or AWS EKS, Northflank provides a unified interface for deploying across GKE, EKS, AKS, and bare-metal clusters in 600+ regions. You get Kubernetes power with PaaS simplicity, plus features like automated deployments, secrets management, and monitoring across all clusters.

**How do global deployment platforms compare?**

Global deployment platforms compare across region coverage, pricing, flexibility, and features. Northflank: 600+ regions (BYOC), pay-per-second, full platform; AWS: 33 regions, complex pricing, most features; GCP: 40 regions, per-second billing, GCP-only; Fly.io: 30+ regions, edge-focused, simpler apps; Heroku: 2 regions, dyno-based pricing, limited scale; Railway: 2 regions, simple pricing, growing; Render: 3 regions, straightforward, less flexibility. Northflank uniquely combines broad coverage with BYOC flexibility and complete platform features.]]>
  </content:encoded>
</item><item>
  <title>8 best GitOps tools for platform engineers in 2026</title>
  <link>https://northflank.com/blog/gitops-tools</link>
  <pubDate>2025-10-31T15:30:00.000Z</pubDate>
  <description>
    <![CDATA[GitOps tools compared: Northflank, Argo CD, Flux, and more. Find the right GitOps tool for Kubernetes, CI/CD, and infrastructure automation]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/gitops_tools_139715f8f1.png" alt="8 best GitOps tools for platform engineers in 2026" /><InfoBox className="BodyStyle">

## TL;DR

GitOps tools automate infrastructure and application deployments by treating Git repositories as the single source of truth. See the top GitOps tools for 2026:

1. [**Northflank**](https://northflank.com/) - Full-stack platform with bidirectional GitOps, built-in CI/CD, IaC templates, and monitoring. Language and framework agnostic. Runs anywhere: Northflank's cloud, your own VPC, or bring your own cloud (AWS, Azure, GCP, Civo, Oracle, bare-metal). Manages apps, databases, jobs, and infrastructure with multi-tenancy support.
2. **Argo CD** - Kubernetes GitOps continuous delivery with web UI and multi-tenancy support
3. **Flux CD** - Lightweight toolkit for Kubernetes continuous delivery
4. **Codefresh** - Argo-powered platform with monitoring and management features
5. **GitHub Actions** - Native GitHub automation for CI/CD workflows
6. **GitLab** - Integrated platform for source control, CI/CD, and Kubernetes deployments via GitLab Agent
7. **Pulumi** - Infrastructure as Code using programming languages like TypeScript, Python, and Go
8. **Terraform/OpenTofu** - Standard IaC tools that work with GitOps workflows

> If you're looking for a platform that combines GitOps with complete deployment automation and runs in any environment, [Northflank](https://northflank.com/) extends beyond traditional Kubernetes-only tools to manage your entire application stack.
> 

</InfoBox>

## What is a GitOps tool?

A GitOps tool automates software delivery by using Git repositories as the source of truth for declarative infrastructure and applications. Instead of manually running deployment commands, you commit configuration changes to Git, and the tool automatically syncs those changes to your environments.

Most GitOps tools use an agent-based pull model. An agent runs in your production environment, periodically checking Git repositories for changes and automatically applying them. This is more secure than traditional push-based CI/CD pipelines that need direct access to your infrastructure.

The core workflow is straightforward: developers commit changes to Git, the GitOps tool detects the change, compares it with the live state, and reconciles any differences. This creates an audit trail, enables easy rollbacks, and prevents configuration drift.

## What is GitOps vs DevOps?

DevOps and GitOps are related but distinct concepts. See how they compare:

| Aspect | DevOps | GitOps |
| --- | --- | --- |
| **Scope** | Broad cultural and technical movement | Specific implementation pattern within DevOps |
| **Focus** | Collaboration between development and operations teams | Git as single source of truth for all changes |
| **Practices** | CI/CD, infrastructure automation, monitoring, testing | Declarative config in Git with automated reconciliation |
| **Relationship** | Overall philosophy and culture | Way to implement DevOps principles |
| **Goal** | Faster delivery and collaboration | Auditability, consistency, and automated deployment |

GitOps doesn't replace DevOps. It's a way to implement DevOps principles with built-in guarantees around auditability, consistency, and automation. You still need monitoring, testing, and collaboration practices, but GitOps provides the framework for how changes reach production.

## What to look for when choosing a GitOps tool

Not all GitOps tools are built the same. Keep these factors in mind when choosing a GitOps tool:

- **Deployment flexibility**: Can it run in your cloud, their cloud, or on-premises? Some tools lock you into specific environments.
- **Scope of automation**: Does it only handle Kubernetes manifests, or can it manage your entire stack, including apps, databases, and infrastructure?
- **Developer experience**: Teams need intuitive interfaces (UI, CLI, API) that don't require deep GitOps expertise.
- **Drift detection**: Continuous monitoring that catches when live state diverges from Git, with automatic remediation.
- **Preview environments**: Ability to test changes in ephemeral environments before production.
- **Integration depth**: Native support for your Git provider (GitHub, GitLab, Bitbucket) and existing CI/CD tools.
- **Security model**: Secrets management, RBAC, and audit logging that meet your compliance requirements.
- **Infrastructure-as-Code support:** Can it work with your IaC tools (Terraform, Pulumi), or does it require proprietary templates?

## 8 best GitOps tools for platform engineers in 2026

Each tool below offers different strengths depending on your infrastructure needs, team size, and deployment requirements.

### 1. Northflank

[Northflank](https://northflank.com/) is a full-stack deployment platform that takes GitOps beyond Kubernetes manifests to manage your entire infrastructure. Unlike tools that only sync K8s configs, Northflank handles applications, databases, jobs, AI workloads, and infrastructure across any cloud provider or on-premises environment.

The platform's bidirectional GitOps sync is particularly useful. Changes to templates in your Git repository automatically update Northflank, and changes made in Northflank's UI are committed back to your repository. This enforces Git as the source of truth while allowing teams to work however they prefer.

Northflank uses a template-based approach to infrastructure as code. Templates are JSON files that define your entire stack, with support for dynamic arguments, functions, and references. This lets you create reusable infrastructure patterns without copy-paste configuration.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

**Key features:**

- Bidirectional GitOps sync with automatic template updates
- Runs on Northflank's managed cloud or your own VPC (AWS, GCP, Azure, bare-metal)
- Manages complete stack: apps, databases, scheduled jobs, and GPU workloads
- Built-in CI/CD pipelines with automatic builds from Git
- Preview environments from pull requests
- Release flows for multi-step deployments
- Multi-tenancy support with team management and RBAC
- Kubernetes under the hood without requiring K8s expertise
- Template system with argument overrides for secrets and environment-specific config

<InfoBox className="BodyStyle">

**What makes it different:**

Most GitOps tools focus exclusively on Kubernetes resource synchronization. Northflank abstracts away Kubernetes complexity while giving you a platform that handles everything from Git commit to production deployment. You get GitOps principles applied to your entire stack beyond container orchestration.

The ability to run [Northflank](https://northflank.com/) in your own cloud environment addresses data residency and security requirements that pure SaaS tools can't meet, while maintaining the same developer experience.

</InfoBox>

**Best for:** Teams wanting comprehensive deployment automation with GitOps principles, multi-cloud flexibility, and the ability to manage more than just Kubernetes manifests.

### 2. Argo CD

Argo CD is a Kubernetes GitOps continuous delivery tool. It monitors Git repositories containing Kubernetes manifests, Helm charts, or Kustomize templates and automatically syncs them to your clusters.

![argocd home page.png](https://assets.northflank.com/argocd_home_page_e0d0be5da4.png)

**Key features:**

- Declarative GitOps for Kubernetes
- Supports Helm, Kustomize, and plain manifests
- Multi-cluster and multi-tenancy support
- Web interface with resource visualization
- Automated sync and health assessment
- SSO integration and RBAC

**Best for:** Teams running Kubernetes workloads.

See [Argo CD alternatives that don’t give you brain damage and simplify DX for GitOps, clusters & deployments](https://northflank.com/blog/argo-cd-alternatives-northflank-developer-platform-git-ops-self-service)

### 3. Flux CD

Flux CD provides a toolkit approach to GitOps with composable components. The architecture is fully declarative, with Flux itself configured via Kubernetes resources.

![fluxcd-home-page.png](https://assets.northflank.com/fluxcd_home_page_dafb03db26.png)

**Key features:**

- Lightweight, composable toolkit
- Fully declarative configuration stored in Git
- Multi-cluster management from single Flux instance
- Source controller supports Git, Helm, and S3-compatible storage
- Native Kustomize support

**Best for:** Platform teams that want flexibility in assembling components.

See [7 best Flux CD alternatives](https://northflank.com/blog/flux-cd-alternatives)

### 4. Codefresh

Codefresh is a GitOps platform built on Argo CD with additional management layers and monitoring dashboards for larger organizations.

![codefresh home page.png](https://assets.northflank.com/codefresh_home_page_4f83c49bd0.png)

**Key features:**

- Argo CD-powered
- Built-in monitoring and GitOps analytics
- Reusable templates
- Hosted, on-premises, and hybrid deployment

**Best for:** Organizations wanting managed Argo CD.

See [7 best Codefresh alternatives](https://northflank.com/blog/codefresh-alternatives)

### 5. GitHub Actions

GitHub Actions automates deployments from Git repositories using a push-based model. Workflows run in GitHub's infrastructure and deploy changes to your environments.

![GitHub Actions.png](https://assets.northflank.com/Git_Hub_Actions_9df410abd8.png)

**Key features:**

- Native GitHub integration
- Flexible YAML-based workflows
- Large marketplace of reusable actions
- Secrets management built-in
- Self-hosted runners available

**Best for:** Teams on GitHub wanting CI/CD automation.

See [the best GitHub Actions alternatives for modern CI/CD](https://northflank.com/blog/github-actions-alternatives) 

### 6. GitLab (with Flux integration)

GitLab provides source control, CI/CD, and GitOps through its Flux integration. The GitLab Agent connects Kubernetes clusters for Flux-based continuous delivery.

![gitlab-homepage.png](https://assets.northflank.com/gitlab_homepage_55ffb99398.png)

**Key features:**

- Unified platform for code, CI/CD, and GitOps
- GitLab Agent connects clusters
- Flux-powered Kubernetes deployments
- Centralized access management

**Best for:** Organizations standardized on GitLab.

See [9 Best GitLab alternatives for CI/CD](https://northflank.com/blog/best-gitlab-alternatives)

### 7. Pulumi

Pulumi defines infrastructure using programming languages like TypeScript, Python, and Go. Changes to Pulumi programs in Git can trigger automated infrastructure updates.

![ Pulumi website.png](https://assets.northflank.com/Pulumi_website_c8b5d0ebdc.png)

**Key features:**

- Infrastructure as Code in TypeScript, Python, Go, C#
- Multi-cloud support
- State management with drift detection
- Works with GitOps patterns via automation API

**Best for:** Teams preferring programming languages for infrastructure.

### 8. Terraform/OpenTofu (with GitOps workflows)

Terraform and OpenTofu are infrastructure provisioning tools that work within GitOps workflows through tools like Atlantis or Flux's Terraform Controller.

![terraform-by-hashicorp.png](https://assets.northflank.com/terraform_by_hashicorp_03e70a66a2.png)

**Key features:**

- Declarative infrastructure definition
- Extensive provider ecosystem (AWS, GCP, Azure, etc.)
- State management and drift detection
- Requires additional tooling for GitOps

**Best for:** Infrastructure teams needing broad provider support.

See [top 10 Terraform alternatives to optimize your infrastructure](https://northflank.com/blog/terraform-alternatives)

## How to choose the right GitOps tool for your team

Different teams have different priorities when selecting a GitOps tool. Use this comparison to identify which tool aligns with your requirements:

| Tool | Best for | Deployment options | Scope | Learning curve |
| --- | --- | --- | --- | --- |
| **Northflank** | Full-stack automation across any cloud | Managed cloud, your VPC, BYOC (AWS, GCP, Azure, Oracle, Civo, bare-metal) | Apps, databases, jobs, infrastructure, AI workloads | Low - abstracted complexity |
| **Argo CD** | Kubernetes-focused teams | Self-hosted in your cluster | Kubernetes manifests only | Medium - K8s knowledge required |
| **Flux CD** | Platform teams wanting flexibility | Self-hosted in your cluster | Kubernetes manifests only | Medium-High - assembly required |
| **Codefresh** | Enterprises needing managed Argo | SaaS, on-premises, hybrid | Kubernetes manifests only | Low-Medium - managed service |
| **GitHub Actions** | GitHub-centric workflows | GitHub-hosted or self-hosted runners | Any deployment target | Low - familiar YAML syntax |
| **GitLab** | GitLab-standardized organizations | GitLab SaaS or self-hosted | Kubernetes via GitLab Agent | Low-Medium - integrated UI |
| **Pulumi** | Infrastructure teams preferring code | Any environment | Infrastructure provisioning | Medium - programming required |
| **Terraform/OpenTofu** | Multi-cloud infrastructure provisioning | Any environment with added tooling | Infrastructure provisioning | Medium - HCL and tooling setup |

## Frequently asked questions about GitOps tools

1. **What is a GitOps tool?**
    
    A GitOps tool automates deployments by treating Git repositories as the source of truth for infrastructure and application configuration. It continuously monitors Git for changes and automatically syncs those changes to your environments, ensuring what's in Git matches what's running in production.
    
2. **What is GitOps vs DevOps?**
    
    DevOps is a broad cultural and technical philosophy focused on collaboration and automation. GitOps is a specific implementation pattern within DevOps that uses Git as the single source of truth with automated reconciliation. GitOps is a way to practice DevOps principles, not a replacement for them.
    
3. **Can you name 5 DevOps tools?**
    
    Five essential DevOps tools include: Northflank (deployment platform), Argo CD (GitOps continuous delivery), GitHub Actions (CI/CD automation), Terraform (infrastructure as code), and Datadog (monitoring and observability). These cover the spectrum from code to production monitoring.
    
4. **Is GitHub Actions a GitOps tool?**
    
    GitHub Actions enables GitOps workflows but uses a push-based model rather than the pull-based agent model of pure GitOps tools. It can achieve similar outcomes by automatically deploying changes from Git, but lacks continuous reconciliation and drift detection that characterize true GitOps tooling.
    

## Choosing the right GitOps tool

Choosing the right GitOps tool depends on your specific infrastructure, team size, and operational requirements.

If your teams want a platform approach that goes beyond Kubernetes manifests to manage complete application stacks across any cloud environment, Northflank is worth checking out. The bidirectional GitOps sync, template system, and flexibility to run anywhere make it suited for teams that need comprehensive automation **without vendor lock-in**.

GitOps is a journey rather than a destination. Start with the tool that solves your immediate pain points and adapt as your requirements change.

<InfoBox className="BodyStyle">

[Try Northflank for free](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to discuss your specific deployment needs with our engineering team.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>7 best Crossplane alternatives for infrastructure as code</title>
  <link>https://northflank.com/blog/crossplane-alternatives</link>
  <pubDate>2025-10-30T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[Crossplane alternatives for simpler IaC: Northflank, Terraform, Pulumi &amp; more. Compare tools for infrastructure automation without complexity]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/crossplane_alternatives_0b43191e3b.png" alt="7 best Crossplane alternatives for infrastructure as code" /><InfoBox className="BodyStyle">

## TL;DR

Crossplane offers Kubernetes-native infrastructure management through control planes, but its steep learning curve, operational complexity, and ecosystem changes have teams searching for alternatives. Here are the top options:

- **Northflank** - Developer platform that abstracts infrastructure complexity with templates, GitOps, and multi-cloud deployment (Northflank's [managed cloud](https://northflank.com/features/managed-cloud) or your own [AWS](https://northflank.com/cloud/aws)/[GCP](https://northflank.com/cloud/gcp)/[Azure](https://northflank.com/cloud/azure) accounts). Handles full-stack apps to [AI workloads](https://northflank.com/blog/ai-workloads#how-does-northflank-handle-each-type-of-ai-workload) with built-in observability, backups, and compliance. Delivers self-service infrastructure without Kubernetes expertise.
- **Terraform/OpenTofu** -  IaC tools with extensive ecosystems and provider support. OpenTofu is the open-source fork maintaining OSS licensing.
- **Pulumi** - Code-first IaC using TypeScript, Python, Go, or C#. Treat infrastructure like application code with familiar programming languages.
- **AWS CDK / CloudFormation** - AWS-native IaC with CloudFormation templates or CDK's programming language support for AWS infrastructure.
- **Google Config Connector** - Kubernetes-native GCP resource management for teams on Google Cloud. Optimized for GKE with deep integration.
- **Ansible** - Configuration management and provisioning tool with agentless architecture and straightforward YAML playbooks for multi-cloud automation.

</InfoBox>

## What to look out for when evaluating Crossplane alternatives

Not all infrastructure tools are built the same. Try answering these questions and using the following as a criteria checklist:

1. **Do you need Kubernetes-native infrastructure management?** If your team isn't heavily invested in Kubernetes, look out for tools that don't require running a cluster just for IaC.
2. **What's your team's level of expertise?** Teams without deep Kubernetes knowledge benefit from platforms with simpler abstractions or traditional IaC tools with gentler learning curves.
3. **How important is true open source?** After recent licensing changes, verify whether tools maintain genuine open-source licenses without commercial restrictions on critical components.
4. **What's the provider ecosystem coverage?** Ensure the alternative supports all cloud resources and services your infrastructure requires with mature, well-maintained providers.
5. **What operational overhead can you accept?** Decide if you want to manage infrastructure state, run control planes, or prefer managed solutions that handle operations for you.
6. **Do you prefer declarative or imperative approaches?** Some teams work better with YAML and declarative configs, while others prefer writing infrastructure as code in programming languages.

## Top 7 Crossplane alternatives for infrastructure as code

Let's review seven Crossplane alternatives based on their approach to infrastructure management, operational complexity, and more, to help you find the most suitable solution for your organization or team's needs.

### 1. Northflank

Northflank is a developer platform that solves the same problems as Crossplane (self-service infrastructure and application deployment) but takes a different approach: it abstracts away complexity instead of exposing it.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

**What sets Northflank apart:**

- **Infrastructure abstraction** - No Kubernetes expertise required. Northflank handles the complexity while you focus on deploying applications and infrastructure.
- **Template-based IaC** - JSON-based [templates](https://northflank.com/docs/v1/application/infrastructure-as-code/create-a-template) with visual editor and bidirectional [GitOps](https://northflank.com/docs/v1/application/infrastructure-as-code/gitops-on-northflank). Define infrastructure workflows without complex YAML or CRDs.
- **Bring Your Own Cloud (BYOC)** - Deploy to your [AWS](https://northflank.com/cloud/aws), [GCP,](https://northflank.com/cloud/gcp) [Azure](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), or [Oracle](https://northflank.com/cloud/oci) accounts while using Northflank's [managed platform](https://northflank.com/features/managed-cloud) layer. Control data residency and cloud costs without operational overhead.
- **Unified platform** - Handle [microservices](https://northflank.com/docs/v1/application/run/run-containers-and-micro-services), [APIs](https://northflank.com/docs/v1/api/use-the-api), [databases](https://northflank.com/features/databases), [jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs), and [GPU workloads](https://northflank.com/gpu) from a single interface with built-in [observability](https://northflank.com/docs/v1/application/observe/observability-on-northflank), [backups](https://northflank.com/docs/v1/application/databases-and-persistence/backup-restore-and-import-data), and [rollbacks](https://northflank.com/docs/v1/application/release/run-and-manage-releases#roll-back-a-release).
- **Multi-cloud flexibility** - Deploy to Northflank's [managed cloud](https://northflank.com/features/managed-cloud) or [your own infrastructure](https://northflank.com/features/bring-your-own-cloud) seamlessly. No vendor lock-in.
- **Production-ready features** - Built-in [health checks](https://northflank.com/docs/v1/application/observe/configure-health-checks), automated [backups](https://northflank.com/docs/v1/application/databases-and-persistence/backup-restore-and-import-data), [disaster recovery](https://northflank.com/use-cases/disaster-recovery-for-kubernetes), and [RBAC](https://northflank.com/docs/v1/application/secure/use-role-based-access-control).

**Best for:** Teams wanting self-service infrastructure and GitOps workflows without Kubernetes complexity. Organizations needing enterprise compliance, multi-cloud deployment, or those frustrated with Crossplane's operational overhead.

**Pricing:**

- Developer Sandbox: Free tier for testing & always-on compute (no sleeping)
- Pay as you go: only pay for consumption, infinitely scalable, 6+ cloud regions, 600 BYOC regions, and deploy with CPU & GPU
- Enterprise: Custom requirements, SLAs, white-label, and always-on support (ability to run in your VPC, 24/7 support & SLA, FDE onboarding, & 100+ Enterprise features)

(See full [pricing details](https://northflank.com/pricing))

[Try Northflank's free Developer Sandbox](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to speak with an engineer.

### 2. Terraform / OpenTofu

Terraform is the standard for infrastructure as code, using HashiCorp Configuration Language (HCL) to define infrastructure declaratively. OpenTofu emerged as the open-source fork after HashiCorp's license change, maintaining OSS licensing.

![terraform-by-hashicorp.png](https://assets.northflank.com/terraform_by_hashicorp_03e70a66a2.png)

**Key characteristics:**

- Declarative HCL syntax with wide adoption and extensive documentation
- Extensive provider ecosystem covering cloud platforms and services
- State management tracks infrastructure across teams
- Plan/apply workflow shows changes before execution
- Community support and module registry

**Considerations:**

- Requires learning HCL domain-specific language
- State management adds operational complexity
- No built-in continuous reconciliation like Kubernetes-native tools
- Can become verbose for complex infrastructure setups

**Best for:** Teams needing IaC with extensive provider coverage. Organizations wanting traditional infrastructure provisioning without Kubernetes dependencies.

### 3. Pulumi

Pulumi brings infrastructure as code into familiar programming languages (TypeScript, Python, Go, C#, and Java) allowing teams to use software engineering practices for infrastructure.

![ Pulumi website.png](https://assets.northflank.com/Pulumi_website_c8b5d0ebdc.png)

**Key characteristics:**

- Write infrastructure in programming languages with IDE support
- Use standard testing frameworks and debugging tools
- Leverage packages, functions, and classes for abstraction
- State management handled by Pulumi service or self-hosted backends
- Import existing Terraform providers through compatibility layer

**Considerations:**

- Less declarative than pure IaC tools (more imperative code)
- Smaller ecosystem than Terraform though growing rapidly
- Requires development skills rather than pure operations knowledge
- Team must align on programming language choice

**Best for:** Development teams preferring to treat infrastructure as software. Organizations with strong programming expertise wanting familiar tooling and testing practices.

### 4. AWS CDK / CloudFormation

AWS Cloud Development Kit (CDK) and CloudFormation provide native infrastructure as code for AWS resources, with CDK offering programming language abstractions over CloudFormation's JSON/YAML templates.

![aws-cdk.png](https://assets.northflank.com/aws_cdk_e8a37ed242.png)

**Key characteristics:**

- Native AWS integration with comprehensive service coverage
- CDK supports TypeScript, Python, Java, C#, and Go
- CloudFormation handles state management automatically
- Built-in rollback and change set previews
- No additional tooling required for AWS infrastructure

**Considerations:**

- AWS-only (no multi-cloud support)
- CloudFormation templates can be verbose and complex
- Limited to AWS's update cadence for new services
- CDK abstractions add another layer to understand

**Best for:** AWS-centric organizations not requiring multi-cloud. Teams wanting official AWS tooling with guaranteed service coverage.

### 5. Google Config Connector

Config Connector is Google's Kubernetes-native solution for managing GCP resources, similar to Crossplane but specific to Google Cloud Platform.

![google-config-connector.png](https://assets.northflank.com/google_config_connector_98ae78cbff.png)

**Key characteristics:**

- Kubernetes-native GCP resource management through CRDs
- Integrated with GKE and Google Cloud's IAM
- Declarative YAML-based resource definitions
- Continuous reconciliation like Crossplane
- Official support from Google Cloud

**Considerations:**

- GCP-only (not multi-cloud like Crossplane)
- Requires Kubernetes cluster and Kubernetes expertise
- Smaller resource coverage compared to Terraform
- Limited community compared to broader IaC tools

**Best for:** Teams heavily invested in both Kubernetes and Google Cloud. Organizations wanting Kubernetes-native infrastructure management without multi-cloud complexity.

### 6. Ansible

Ansible is an agentless automation tool that handles configuration management and infrastructure provisioning through straightforward YAML playbooks, without requiring agent software on managed nodes.

![ansible's website.png](https://assets.northflank.com/ansible_s_website_7950302603.png)

**Key characteristics:**

- Agentless architecture using SSH connections
- Straightforward YAML playbooks readable by operations and development teams
- Idempotent operations ensure consistent results
- Extensive module library for cloud provisioning and configuration
- Can complement other IaC tools for post-provisioning configuration

**Considerations:**

- More imperative than purely declarative IaC tools
- No built-in state management like Terraform
- Best for configuration management; often paired with other tools for provisioning
- Can become complex for large-scale infrastructure orchestration

**Best for:** Teams needing configuration management alongside infrastructure provisioning. Organizations wanting agentless automation with minimal setup overhead.

## Choosing the right Crossplane alternative

Selecting a Crossplane alternative depends on your team's expertise, infrastructure requirements, and operational preferences:

| Platform | Choose if you... |
| --- | --- |
| **Northflank** | Want self-service infrastructure and GitOps without Kubernetes complexity. Your team needs enterprise features, multi-cloud flexibility, or you're building production applications requiring compliance and observability. |
| **Terraform/OpenTofu** | Need IaC with extensive provider coverage. Your team prefers traditional IaC tooling without Kubernetes dependencies and wants extensive community support. |
| **Pulumi** | Want to write infrastructure in familiar programming languages. You value IDE support, testing frameworks, and treating infrastructure like application code. |
| **AWS CDK/CloudFormation** | Are building exclusively on AWS and want native tooling. Your team prefers AWS-guaranteed service coverage and built-in state management. |
| **Config Connector** | Are heavily invested in both GKE and Google Cloud. You want Kubernetes-native resource management without multi-cloud complexity. |
| **Ansible** | Need agentless configuration management and provisioning. Your team wants straightforward YAML playbooks without complex state management. |

## Moving beyond Crossplane's complexity

Crossplane pioneered Kubernetes-native infrastructure management, but ecosystem changes and inherent complexity have led teams to reconsider their approach.

If you're frustrated with provider restrictions, operational overhead, or steep learning curves, alternatives exist across the spectrum, from traditional IaC tools to modern developer platforms.

For teams wanting Crossplane's self-service approach without its complexity, Northflank delivers infrastructure abstraction with enterprise-grade features. You get GitOps workflows, multi-cloud deployment, and production observability without requiring Kubernetes expertise.

<InfoBox className="BodyStyle">

To simplify your infrastructure management, [try Northflank's free Developer Sandbox](https://app.northflank.com/signup) or [schedule a demo](https://cal.com/team/northflank/northflank-intro) to see how it compares to Crossplane for your specific needs.

</InfoBox>]]>
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  <title>What is a hybrid cloud deployment? Models &amp; best practices</title>
  <link>https://northflank.com/blog/what-is-hybrid-cloud-deployment</link>
  <pubDate>2025-10-29T17:15:00.000Z</pubDate>
  <description>
    <![CDATA[Learn what hybrid cloud deployment is, compare deployment models, and discover when to use hybrid cloud architecture.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/what_is_hybrid_cloud_deployment_18577e0e86.png" alt="What is a hybrid cloud deployment? Models &amp; best practices" /><InfoBox className="BodyStyle">

**Hybrid cloud deployment** combines public cloud services with private cloud or on-premises infrastructure into a unified, interconnected environment. Read on to learn what hybrid cloud deployment is, how it works, the key benefits and challenges, and when to choose hybrid cloud over other deployment models.

</InfoBox>

## What is a hybrid cloud deployment model?

Hybrid cloud deployment is an infrastructure approach that integrates two or more distinct computing environments: typically a public cloud platform (like AWS, Google Cloud, or Azure) with either a private cloud or on-premises data center.

So, rather than simply running separate clouds side by side, a true [hybrid cloud](https://northflank.com/blog/what-is-hybrid-cloud-complete-infrastructure-guide) creates tight interconnection between these environments. This allows data and applications to move smoothly across them as a unified system.

![hybrid-cloud-deployment-architecture.png](https://assets.northflank.com/hybrid_cloud_deployment_architecture_435f9c0c3f.png)

Take a financial services application as an example. You keep customer financial data and transaction processing in your private data center where you have complete control and can meet strict regulatory requirements. At the same time, you run your customer-facing web application and marketing analytics in the public cloud where you can scale quickly during peak times. Then you could plug in a [white label lending platform](https://hesfintech.com/blog/white-label-lending-platform-guide/) to offer lending services under your brand while leaving sensitive data and compliance-heavy parts under your own control.

This approach lets you balance security requirements, compliance needs, existing infrastructure investments, and the benefits of cloud capabilities without having to choose one over the other.

## What are the four types of cloud deployment?

You need to understand how hybrid cloud fits within the broader cloud deployment options to know when it's the right choice for your needs. Let's look at the four primary cloud deployment models:

### 1. Public cloud

Public cloud services like AWS, Google Cloud, and Microsoft Azure are shared computing platforms operated by third-party providers who offer resources over the internet. You share infrastructure with other customers but get instant scalability with pay-as-you-go pricing and no upfront costs.

### 2. Private cloud

A private cloud is dedicated infrastructure used exclusively by your organization, either on-premises or hosted by a provider. You get complete control and customization, but handle all the management and costs.

### 3. Hybrid cloud

Hybrid cloud combines public and private environments into one integrated platform. You keep sensitive workloads private while using public cloud for scalability.

### 4. Multi-cloud

Multi-cloud means using multiple public cloud providers simultaneously, like running some workloads on AWS and others on Google Cloud. [Multi-cloud differs from hybrid cloud](https://northflank.com/blog/multi-cloud-vs-hybrid-cloud) because it doesn't necessarily involve private infrastructure.

## How does hybrid cloud deployment work?

Hybrid cloud deployment relies on three key components: network connectivity, data synchronization, and unified management.

**Network connectivity** is the foundation. Your environments connect through:

- Virtual Private Networks (VPNs) for secure, encrypted communication
- Dedicated connections like AWS Direct Connect, Azure ExpressRoute, or Google Cloud Interconnect for better performance
- APIs and service meshes for application-level integration

![hybrid-cloud-deployment.png](https://assets.northflank.com/hybrid_cloud_deployment_aa58898e11.png)

Once your environments are connected, **data synchronization** keeps everything consistent across them. Data might be replicated between storage locations, cached strategically, or kept in one place with access through APIs.

Finally, **unified management** brings separate environments together into one cohesive hybrid cloud. Modern [cloud infrastructure management platforms](https://northflank.com/blog/cloud-infrastructure-management-services) give you a single control plane to deploy and manage workloads anywhere. Container technologies like Kubernetes let developers package applications once and deploy them across any infrastructure.

<InfoBox className="BodyStyle">

Platforms like Northflank abstract this complexity through its [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) capabilities, letting you deploy across [AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Civo](https://northflank.com/cloud/civo), [Oracle](https://northflank.com/cloud/oci), [Azure](https://northflank.com/cloud/azure), and on-premises infrastructure from one unified interface.

</InfoBox>

## What are the benefits of using a hybrid cloud deployment architecture?

There are several advantages that come with hybrid cloud architecture for organizations, balancing control, cost, and flexibility.

### Flexibility and gradual migration
Hybrid cloud enables you to [migrate to the cloud from on-premise at your own pace](https://northflank.com/blog/on-premise-to-cloud-migration). You can move workloads incrementally instead of a risky "big bang" migration, maintaining critical systems on-premises while testing new ones in the cloud.

### Data sovereignty and compliance
If your organization operates in a regulated industry such as finance, healthcare, or government, hybrid cloud addresses a critical challenge. You can keep sensitive data in your private environment where you control security and ensure compliance with regulations like HIPAA or GDPR, while using public cloud for non-sensitive applications.

### Cost optimization
Hybrid cloud lets you [match workloads to the most economical environment](https://northflank.com/blog/cloud-cost-optimization). You can leverage existing on-premise investments instead of abandoning them, use public cloud for variable or peak demand (cloud bursting), and take advantage of [cloud credits](https://northflank.com/blog/how-to-get-free-aws-credits-for-your-startup) or committed-use discounts.

### Improved resilience and disaster recovery
Running workloads across multiple environments creates natural redundancy. If one environment has issues, critical applications can fail over to the other, strengthening business continuity and reducing the risk of complete outages.

### Access to cloud capabilities
You don't have to choose between control and modern services. You can experiment with machine learning, big data analytics, or serverless computing for appropriate workloads while maintaining traditional applications on familiar infrastructure.

## What are the challenges of using a hybrid cloud deployment architecture?

While hybrid cloud offers significant benefits, watch out for some of these challenges:

### Increased complexity
Managing multiple environments with different APIs, tools, and processes creates operational complexity. Your team needs expertise across various platforms, and ensuring consistent security policies and configurations across environments requires careful orchestration.

### Security considerations
Hybrid cloud expands your attack surface. Data moving between environments needs encryption, and you must maintain consistent security postures across both private and public infrastructure. However, with proper architecture and zero-trust security principles, hybrid cloud can be very secure.

### Network dependencies
Your hybrid cloud is only as reliable as the network connecting your environments. Latency between environments can impact application performance, especially for workloads requiring frequent communication across clouds. Network bandwidth, reliability, and associated costs become critical factors in your architecture design.

### Potential for higher costs
While hybrid cloud can optimize costs in many scenarios, it can also increase overall spending if not managed carefully. You're maintaining infrastructure in multiple locations, potentially duplicating data storage, and paying for network connectivity and specialized management tools.

<InfoBox className="BodyStyle">

**How do you avoid these challenges?**

That's where platforms like Northflank come in handy. With methods like Northflank's [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) model, you can deploy and manage workloads across [AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Civo](https://northflank.com/cloud/civo), [Oracle](https://northflank.com/cloud/oci), [Azure](https://northflank.com/cloud/azure), and on-premise infrastructure from a single control plane.

This means you don't need to switch between multiple vendor consoles and APIs, as everything is managed through a single unified interface.

</InfoBox>

## When to use a hybrid cloud vs other deployment models

Now that you understand the benefits and challenges, let's look at when a hybrid cloud is the right choice compared to other options.

| **Deployment model** | **Best used when** | **Avoid when** |
| --- | --- | --- |
| **Hybrid cloud** | You need data sovereignty or compliance; [gradual cloud migration](https://northflank.com/blog/what-is-cloud-migration-strategy) is preferred; workloads have different security needs | You're a startup with no existing infrastructure; all workloads are similar; network latency would impact critical applications |
| **Public cloud** | You're building new applications; you need maximum scalability; you want minimal infrastructure management | You have strict data residency requirements; you must maintain complete control; compliance mandates private infrastructure |
| **Private cloud** | You have extremely sensitive data; regulatory requirements prohibit public cloud; you need complete control over the stack | You need rapid scaling; you want to minimize capital expenditures; you lack infrastructure expertise |
| **Multi-cloud** | You want to avoid vendor lock-in; you need geographic distribution; you want best-of-breed services from multiple vendors | You have limited DevOps resources; you want to minimize complexity; you're just starting cloud adoption |

## How can Northflank simplify your hybrid cloud deployment?

You've seen the benefits and challenges of hybrid cloud deployment.

Northflank addresses the complexity challenge by providing a unified platform that works seamlessly across any infrastructure.

Through our [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) capabilities, you can deploy to [AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Civo](https://northflank.com/cloud/civo), [Oracle](https://northflank.com/cloud/oci), [Azure](https://northflank.com/cloud/azure), or on-premise Kubernetes clusters.

You can interface with the platform either through the UI, CLI, or API, so your team can focus on shipping features instead of managing infrastructure complexity.

<InfoBox className="BodyStyle">

[Start with Northflank's free tier](https://app.northflank.com/signup) or [book a demo with our engineering team](https://cal.com/team/northflank/northflank-intro) to discuss your specific hybrid cloud requirements.

</InfoBox>]]>
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  <title>cto.new uses Northflank’s microVMs to scale secure sandboxes without sacrificing speed or cost</title>
  <link>https://northflank.com/blog/cto-new-uses-northflank-for-secure-sandboxes</link>
  <pubDate>2025-10-28T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[They switched from custom Firecracker microVMs on AWS and used Northflank's microVM platform with per-second billing and API-driven provisioning.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/ctonew_1_1e05d8650a.png" alt="cto.new uses Northflank’s microVMs to scale secure sandboxes without sacrificing speed or cost" /><InfoBox className='BodyStyle'>

## **TL;DR**

- [cto.new](https://cto.new/) is a free AI coding platform that offers frontier models from Claude and OpenAI to over 30,000 developers.
- They needed a way to scale secure code execution environments without the huge cost jumps of provisioning entire EC2 metal instances.
- They switched from custom Firecracker microVMs on AWS and used [Northflank's](https://northflank.com/) [microVM](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh) platform with per-second billing and API-driven provisioning.
- **Results**: Only a couple of days to migrate. thousands of daily container deployments, flawless launch week performance, and linear scaling costs that made their free service economically viable.

</InfoBox>

## **Who is cto.new?**

[cto.new](https://cto.new/) is democratizing AI coding the way Gmail democratized email.

They offer frontier models completely free to anyone.

Their bet is simple: AI won't replace developers, it'll make them superhuman. Instead of writing repetitive syntax, developers orchestrate background agents that handle the grunt work while they focus on the big picture.

"We wanted to offer a best in class AI code agent that anyone can get access to completely for free. We think this is the way the industry is heading," says the team.

The response has been overwhelming, over 30,000 developers signed up in the first week, forcing the team to implement an invite system.

# **Problem**

cto.new's platform runs untrusted AI-generated code for thousands of developers.

Every coding task requires spinning up secure, isolated environments to analyze repositories and execute potentially dangerous code safely.

When you're giving away this compute-intensive service for free, you need infrastructure that can rapidly provision secure sandboxes at scale without breaking your economics.

They needed a solution that could handle unpredictable traffic spikes while maintaining the isolation and startup speeds that make their platform viable.

## **What they were running prior to Northflank**

cto.new's platform works by spinning up isolated environments for each coding task. When you connect a repo or ask for code generation, they create secure sandboxes to analyze your codebase and run AI-generated code.

Their initial setup: Firecracker microVMs running on big EC2 metal instances. For early testing with limited users, this worked fine. Firecracker gave them fast startup times and good isolation.

But load testing before launch revealed the problems:

- **Scaling meant huge jumps.** Adding capacity required full metal instances costing thousands per month, used or not
- **Slow, unreliable provisioning.** Metal nodes took too long to spin up and were sometimes out of capacity
- **No granular scaling:** You couldn't scale smoothly with demand
- **Operational overhead:** Managing metal instances, networking, security, all stuff they didn't want to spend time on

> "We had Firecracker VMs running on a really big EC2 instance," says Sudhir Balaji, their technical lead. "That worked well for quite a long time. Then prior to launch, we did load testing, which revealed it might not work so well with a huge spike in traffic."
> 

(Great hindsight given their successful launch!)

## **Why they needed something different**

The team had tried other platforms before but found them unreliable. One previous solution they'd used simply wasn't consistently online when needed. They needed something that could handle their specific requirements:

- Docker-in-Docker support
- API-driven provisioning
- Pay-per-use pricing instead of big capacity jumps
- Minimal operational overhead

> "We're a small team," explains cto Paul Groves. "What we really want to do is focus on shipping features to our users. There's no dedicated DevOps or sysadmin on our team. We are polyglot engineers who want to build **product**."
> 

# **Solution**

## **Switching to Northflank**

The migration happened faster than expected. Sudhir thought he'd need extensive support calls and custom work. Instead, he got their system working in a couple of days just using our docs.

> "Northflank’s docs were very straightforward and basically entirely sufficient for me to replicate what we had done on Firecracker over months in like a couple of days."
> 

**Their current setup: For every repo connection, they spin up two Northflank VMs, one for environment setup and configuration, another for code analysis and memory. They're now running thousands of projects and services per day.**

# **Results**

## **Launch week performance**

cto.new's launch exceeded all expectations. They had to implement an invite system within 24 hours to control demand. Throughout the chaos of handling 30,000+ signups, user feedback, and various product issues, their infrastructure performed without problems.

**"Northflank has offered us essentially an out-of-the-box solution that has been basically flawless,"** says Groves.

> "We've been launching for a week. We've been doing a lot of firefighting, chasing down errors and user behaviors on our platform. And **Northflank hasn't skipped a beat for us.**"
> 

The scaling economics made sense too. Instead of paying for entire metal instances regardless of usage, they now pay for resource consumption with per-second billing.

## **What this enabled**

With infrastructure handled, the team can focus on their product differentiation.

cto.new is building orchestration systems that understand project context and suggest what to do next, moving beyond simple prompt-to-code conversion.

The platform flexibility matters for their roadmap too. As they add more integrations and expand capabilities, they need infrastructure that can adapt without forcing architectural changes.

> "The fact that the future might be that we want some dedicated hardware, we want to bring our own cloud for this specific thing, but we're essentially integrating with the same company, the same APIs, is incredibly good in terms of technical vendor partnering and future proofing," notes Groves.> 

<InfoBox className='BodyStyle'>

**💡 Beyond secure sandboxes***

Northflank is built for whatever you're building. Bring any language, any framework, any workload, and any cloud. Deploy simple apps or complex workloads, databases, jobs, inference, or training.*

CI/CD pipelines, preview environments for every pull request, observability dashboards. If you have specific infrastructure needs, bring your own cloud and Northflank handles the operational stuff on top.

Want to deploy across AWS, GCP, AND Azure? Same interface, different clouds. Whether you're a three-person startup or an enterprise, Northflank takes care of the infrastructure headaches.

</InfoBox>

## **cto.new 🤝 Northflank**

For cto.new, the switch eliminated infrastructure as a concern. They replaced metal instance provisioning, boot times, and operational overhead with a solution that scales automatically.

Now: thousands of daily container deployments, smooth performance during traffic spikes, and an engineering team focused on building AI tools instead of managing infrastructure.

Try [cto.new](https://cto.new/) if you want a best-in-class AI coding agent that runs the latest Anthropic and OpenAI models, completely free.

<InfoBox className='BodyStyle'>

How to spin up a secure code sandbox & microVM in seconds with Northflank [here](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh). 

Or talk to one of Northflank's engineers [here](https://cal.com/team/northflank/northflank-intro). 

</InfoBox>]]>
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  <title>Top 10 cloud infrastructure management services in 2026</title>
  <link>https://northflank.com/blog/cloud-infrastructure-management-services</link>
  <pubDate>2025-10-27T19:00:00.000Z</pubDate>
  <description>
    <![CDATA[Cloud infrastructure management services compared: Northflank, AWS, Terraform, Kubernetes &amp; more. See how Northflank removes YAML &amp; vendor lock-in]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/cloud_infrastructure_management_services_f2a8f29a09.png" alt="Top 10 cloud infrastructure management services in 2026" /><InfoBox className="BodyStyle">

*Cloud infrastructure management services help organizations deploy, monitor, optimize, and secure their cloud resources without the operational overhead of managing complex systems like Kubernetes. This guide covers the leading solutions, key capabilities to evaluate, and how to choose the right approach for your team.*

</InfoBox>

## Key points on cloud infrastructure management services

- **What they are:** Cloud infrastructure management services provide the tools, platforms, and strategies to control cloud resources, from compute and storage to networking and security, without manual configuration of complex orchestration systems.
- **Why they’re important:** Engineering teams spend significant time managing infrastructure instead of shipping features. The right cloud infrastructure management solution reduces this overhead while maintaining control, security, and scalability.
- **Three main approaches:**
    - **Developer platforms**: Solutions like [Northflank](https://northflank.com/) that abstract cloud infrastructure complexity while preserving flexibility, giving teams centralized control over deployments, monitoring, and scaling without operational burden
    - **DIY cloud-native tools**: Full control using Kubernetes, Terraform, and native cloud tools, but requires significant engineering expertise and time investment
    - **Managed services** (outsourced): MSPs handle infrastructure for you, but require expensive annual contracts and slower response times for changes
- **Top solutions covered:**
    - **Northflank** – Developer platform for cloud infrastructure management with Kubernetes abstraction, [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) support, no vendor lock-in, and 5-minute deployment
    - **AWS Control Tower & CloudFormation** – Native AWS management and IaC provisioning
    - **Terraform** – Open-source infrastructure as code for multi-cloud provisioning
    - **Datadog** – Observability and monitoring across cloud infrastructure
    - **Kubernetes (self-managed)** – Industry-standard container orchestration for maximum control

*Read on to understand the core components of cloud infrastructure, challenges organizations face, key capabilities to evaluate, and how to choose between developer platforms, DIY approaches, or managed services. Modern teams are increasingly choosing platforms that deliver both simplicity and control for efficient cloud infrastructure management.*

## What are cloud infrastructure management services?

Cloud infrastructure management services encompass the practices, tools, and platforms used to deploy, monitor, optimize, and secure cloud-based infrastructure.

It includes everything from provisioning compute resources and configuring networking to implementing security policies and controlling costs across public, private, on-prem, [hybrid](https://northflank.com/blog/what-is-hybrid-cloud-complete-infrastructure-guide), or [multi-cloud](https://northflank.com/blog/multi-cloud-vs-hybrid-cloud) environments.

Your applications most likely need robust, scalable infrastructure to run reliably, but the challenge lies in manually managing your infrastructure. Doing it manually could eat up your engineering resources and add layers of complexity.

The goal of cloud infrastructure management is to maintain full control over your cloud resources while reducing the operational burden on your development teams.

### Why do you need cloud infrastructure management services?

Adopting cloud infrastructure management solutions can help address several key problems:

1. **Operational efficiency**: Manual infrastructure management doesn't scale. You need automation for provisioning, scaling, and maintaining resources across environments.
2. **Cost control**: Without proper management, your cloud spend spirals due to over-provisioned resources, forgotten instances, and poor resource allocation, all of which are common challenges in [cloud asset management](https://www.goworkwize.com/blog/cloud-asset-managementChallenges).
3. **Security and compliance**: Your infrastructure must meet security standards and regulatory requirements. Management solutions enforce consistent policies across all your cloud resources.
4. **Developer productivity**: Your engineers should build features, not write YAML or troubleshoot Kubernetes clusters. The right tools free your developers from infrastructure concerns.
5. **Multi-cloud flexibility**: If you're using multiple cloud providers, management solutions provide consistent workflows across AWS, GCP, Azure, on-prem, and other platforms.

## What are the four cloud infrastructure services?

When people talk about cloud infrastructure, they're really talking about four building blocks that work together:

1. **Compute** - gives you the processing power to run your applications, from virtual machines and containers to serverless functions and GPUs for AI workloads.
2. **Storage** - is where your data lives, from databases and file systems to object storage for backups and assets.
3. **Networking** - connects everything together through load balancers, firewalls, VPNs, and CDNs, ensuring your services communicate securely and perform well.
4. **Virtualization** - is the abstraction layer that lets multiple virtual resources share physical infrastructure (VMs, containers, and software-defined networks).

These four components get delivered through three main service models: **IaaS** (raw infrastructure you manage yourself, like AWS EC2), **PaaS** (managed platforms where you just deploy code, like Heroku), and **SaaS** (fully managed applications).

<InfoBox className="BodyStyle">

You don't have to choose between PaaS simplicity and IaaS control anymore. Platforms like Northflank give you the developer experience of PaaS with the flexibility of IaaS, especially through features like [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud), where you deploy in your own AWS, GCP, or Azure accounts while Northflank handles the complexity.

</InfoBox>

## **Three approaches to cloud infrastructure management**

Organizations typically choose between three main approaches:

### Approach A: Developer platform solutions (free from vendor lock-in)

Platforms like Northflank abstract infrastructure complexity while preserving control.

You deploy in minutes, scale from 1 to 1,000+ services without hitting limits, and have the option to run in your own cloud accounts via [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) or use the [managed infrastructure](https://northflank.com/features/managed-cloud).

It comes with [built-in CI/CD](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank), [monitoring](https://northflank.com/docs/v1/application/observe/observability-on-northflank), and [multi-cloud support](https://northflank.com/blog/multi-cloud-container-orchestration#how-does-northflank-solve-these-multicloud-container-orchestration-challenges) without vendor lock-in. You can also [deploy to Kubernetes without writing YAML](https://northflank.com/blog/deploy-to-kubernetes-without-writing-yaml).

<InfoBox className="BodyStyle">

Why choose this approach? Unlike traditional PaaS (Heroku), there's no graduation problem. Unlike cloud-native tools (AWS, GCP), you're not locked in. Unlike consulting firms, you maintain full control. Unlike DIY Kubernetes, no YAML hell. Best for teams (5-500 developers) who want to ship features, not manage infrastructure.

</InfoBox>

### Approach B: Outsourced managed services (creates vendor-dependency)

Third-party MSPs handle all infrastructure operations on your behalf. Completely hands-off but expensive with significant annual contracts, slow to implement changes, and creates vendor dependency. Suitable for organizations that prefer to completely outsource infrastructure management.

### Approach C: DIY cloud-native infrastructure (requires ongoing maintenance)

DIY cloud-native tools (Kubernetes, Terraform, CloudFormation) give maximum control but require expert platform teams. Engineers spend significant time on infrastructure instead of features. Steep learning curve, ongoing maintenance burden, and YAML complexity. [Learn about container orchestration](https://northflank.com/blog/container-orchestration) challenges first.

**See a quick overview to help you decide:**

| **Approach** | **Time to deploy** | **Control** | **Ops burden** | **Works well for** |
| --- | --- | --- | --- | --- |
| **Developer platforms (like Northflank)** | Minutes | High | Low | Teams (Startups & Enterprises) of any size wanting to ship fast |
| **Managed services** | Weeks | Low | None (outsourced) | Organizations preferring full outsourcing |
| **DIY cloud-native** | Weeks | Maximum | Very high | Teams with dedicated platform engineering resources |

## Top cloud infrastructure management tools & platforms

Now that you understand the different approaches and what to look for, let's review the top cloud infrastructure management tools and platforms across different categories to help you find the right fit:

### 1. Northflank

**Category:** Developer platform

**Best for:** Teams wanting production-grade cloud infrastructure without the operational complexity

A production-ready platform that simplifies cloud infrastructure management by abstracting Kubernetes complexity while preserving developer flexibility.

![northflank--platform.png](https://assets.northflank.com/northflank_platform_d625d79568.png)

**What it offers:**

- Deploy your first workload in under 5 minutes ([See how](https://northflank.com/docs/v1/application/run/run-containers-and-micro-services))
- [Built-in CI/CD](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) with GitOps workflows
- [Auto-scaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments) and [load balancing](https://northflank.com/docs/v1/application/network/load-balancing)
- Real-time [monitoring](https://northflank.com/docs/v1/application/observe/monitor-containers) and [logs](https://northflank.com/docs/v1/application/observe/view-logs)
- [GPU workloads](https://northflank.com/gpu) and [AI infrastructure](https://northflank.com/blog/ai-infrastructure#northflank-as-a-fullstack-ai-infrastructure-platform) support
- No YAML required (manage through UI, [CLI](https://northflank.com/docs/v1/api/use-the-cli), or [API](https://northflank.com/docs/v1/api/use-the-api))
- Multi-cloud management across [AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), [Oracle](https://northflank.com/cloud/oci), and bare-metal
- [Hybrid cloud](https://northflank.com/blog/what-is-hybrid-cloud-complete-infrastructure-guide) and [on-premise](https://northflank.com/blog/on-premise-to-cloud-migration) deployment options

**Pros:** Self-service platform with full control, no vendor lock-in, no graduation problem, massively simplified operations

**Cons:** Platform abstraction (though you still get Kubernetes primitives when needed)

<InfoBox className="BodyStyle">

**Why it stands out**: Northflank delivers production-grade cloud infrastructure management without operational burden. Unlike consulting firms, you maintain full control. Unlike Oracle/AWS, you're not locked to one cloud. Unlike traditional PaaS, there's no graduation problem. Unlike DIY Kubernetes, there's no YAML hell.

See [pricing details](https://northflank.com/pricing) & try the [free developer sandbox](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) with an expert engineer to see how Northflank can simplify your cloud infrastructure.

</InfoBox>

### 2. AWS CloudFormation & Control Tower

**Category:** Native cloud management

**Best for:** AWS-centric organizations

Infrastructure as code tool for AWS resources with governance and account management for multi-account environments. AWS Control Tower is the orchestrator, and AWS CloudFormation is an underlying tool.

![aws-cloudformation.png](https://assets.northflank.com/aws_cloudformation_bb6af5a493.png)

**What it offers:**

- Template-based infrastructure provisioning
- Multi-account governance
- Native AWS service integration
- Automated resource management

**Pros:** Deep AWS integration, no additional cost, powerful automation

**Cons:** AWS-only (vendor lock-in), steep learning curve, limited multi-cloud support

### 3. Terraform by HashiCorp

**Category:** Infrastructure as code

**Best for:** Multi-cloud infrastructure provisioning

Open-source IaC tool used to define and provision infrastructure across cloud providers.

![terraform-by-hashicorp.png](https://assets.northflank.com/terraform_by_hashicorp_03e70a66a2.png)

**What it offers:**

- Multi-cloud infrastructure provisioning
- Declarative configuration language
- State management
- Large provider ecosystem

**Pros:** Multi-cloud support, extensive ecosystem, mature tooling

**Cons:** Requires expertise, manual orchestration needed, steep learning curve

### 4. Datadog

**Category:** Observability & monitoring

**Best for:** Full-stack monitoring and analytics

SaaS-based monitoring solution providing metrics, traces, and logs across infrastructure and applications.

![datadog-homepage.png](https://assets.northflank.com/datadog_homepage_d756f0a987.png)

**What it offers:**

- Full-stack observability
- 200+ integrations
- Real-time metrics and alerting
- Application performance monitoring

**Pros:** Comprehensive observability, intuitive UI, powerful analytics

**Cons:** Can be expensive at scale

### 5. Kubernetes (self-managed)

**Category:** Container orchestration

**Best for:** Teams with dedicated platform engineering expertise

Industry-standard container orchestration system providing maximum flexibility and control.

![kubernetes-homepage.png](https://assets.northflank.com/kubernetes_homepage_8f618a65d0.png)

**What it offers:**

- Container scheduling and orchestration
- Auto-scaling and self-healing
- Service discovery and load balancing
- Extensive ecosystem

**Pros:** Powerful, extensive ecosystem, industry standard

**Cons:** Complex, requires constant maintenance, steep learning curve. Consider [Kubernetes management tools](https://northflank.com/blog/tools-for-managing-kubernetes-clusters) or [container management platforms](https://northflank.com/blog/container-management-tools) to reduce operational burden.

### 6. Azure Resource Manager

**Category:** Native cloud management

**Best for:** Azure-centric organizations

Azure's native infrastructure management platform with templates for deploying and managing resources.

![azure-resource-manager.png](https://assets.northflank.com/azure_resource_manager_162bc35780.png)

**What it offers:**

- Template-based deployments
- Role-based permissions
- Azure service integration
- Resource grouping and tagging

**Pros:** Deep Azure integration, no additional cost

**Cons:** Azure-only (vendor lock-in), limited multi-cloud capabilities

### 7. Rancher

**Category:** Kubernetes management

**Best for:** Multi-cluster Kubernetes environments

Open-source platform for managing multiple Kubernetes clusters across hybrid and multi-cloud environments.

![rancher-homepage.png](https://assets.northflank.com/rancher_homepage_274dfd6470.png)

**What it offers:**

- Multi-cluster management
- Centralized authentication
- Workload management UI
- Policy enforcement

**Pros:** Multi-cluster management, good UI, open-source

**Cons:** Still requires Kubernetes expertise, adds complexity layer

### 8. Flexera

**Category:** Cloud cost management and IT asset management

**Best for:** Enterprises managing multi-cloud costs and IT assets

Platform for cloud cost optimization, budget control, and IT asset management across cloud and on-premises environments.

![flexera-homepage.png](https://assets.northflank.com/flexera_homepage_7a4cd64924.png)

**What it offers:**

- Cloud cost analytics and reporting
- Budget alerts and forecasting
- Resource optimization recommendations
- Multi-cloud and hybrid-IT cost tracking

**Pros:** Cost analytics, multi-cloud support, bridges on-premises and cloud spending

**Cons:** Expensive with complex pricing, steep learning curve, advisory role rather than direct infrastructure provisioning

### 9. OpenShift by Red Hat

**Category:** Enterprise Kubernetes platform

**Best for:** Enterprises with extensive compliance requirements

Enterprise Kubernetes distribution with built-in security, developer tools, and operational features.

![redhat-openshift-honepage.png](https://assets.northflank.com/redhat_openshift_honepage_1de6159f57.png)

**What it offers:**

- Enterprise Kubernetes distribution
- Built-in security and compliance
- Developer console and CI/CD
- Multi-cloud deployment

**Pros:** Enterprise-ready, comprehensive security, strong support

**Cons:** Expensive, complex licensing, requires dedicated expertise

### 10. CloudBolt

**Category:** Hybrid cloud management

**Best for:** Hybrid cloud orchestration

Unified platform for managing on-premises and public cloud resources with workflow automation.

![cloudbolt-homepage.png](https://assets.northflank.com/cloudbolt_homepage_e5adefae83.png)

**What it offers:**

- Hybrid cloud orchestration
- Self-service portal
- Cost management
- Workflow automation

**Pros:** Hybrid cloud support, unified interface

**Cons:** Additional complexity layer, requires integration effort

## Start managing your cloud infrastructure without the complexity

Cloud infrastructure management doesn't have to mean choosing between rigid platforms that limit growth or spending months building DIY Kubernetes clusters. Today's teams need solutions that deliver both simplicity and control.

Northflank provides production-grade infrastructure built on Kubernetes without the operational burden. Deploy your first container in under 5 minutes, scale to production workloads serving millions of users, and run in your own cloud accounts (AWS, GCP, Azure, Civo, Oracle) or on Northflank's managed infrastructure.

If you're [migrating from Heroku](https://northflank.com/blog/how-to-migrate-from-heroku-a-step-by-step-guide), outgrowing a traditional PaaS, [moving to the cloud from on-premise](https://northflank.com/blog/on-premise-to-cloud-migration), or tired of managing raw Kubernetes, Northflank gives you the tools to ship faster without sacrificing flexibility.

<InfoBox className="BodyStyle">

[**Start deploying on Northflank today**](https://northflank.com/) with the free tier available.

See how companies like [Cedana use Northflank](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes) to deploy complex Kubernetes workloads with microVMs and secure runtimes in production, or learn about [container deployment best practices](https://northflank.com/blog/container-deployment). [Try the free developer sandbox](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) with an expert engineer to see how Northflank can simplify your cloud infrastructure.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>10 best container management tools to simplify deployment in 2026</title>
  <link>https://northflank.com/blog/container-management-tools</link>
  <pubDate>2025-10-23T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the 10 best container management tools for 2026. From Northflank to Kubernetes and find the right platform for your team's needs.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/container_management_tools_99eec65948.png" alt="10 best container management tools to simplify deployment in 2026" />It's one thing to run a few containers in development, but managing them at scale without overwhelming your team is another challenge entirely.

Container management software goes beyond basic container runtimes (like Docker) to handle the complexity of running containers in production at scale. It's the layer between "I can run a container on my laptop" and "I can reliably run 1,000 containers serving production traffic."

This guide covers the top 10 platforms for 2026, helping you find the right fit for your team's needs and expertise level.

<InfoBox className="BodyStyle">

## See the top 10 container management platforms at a glance

Container management software orchestrates the deployment, scaling, and monitoring of containerized applications across infrastructure. You need it when manual container management becomes unsustainable, typically when managing more than a handful of containers or when automated scaling and high availability are required.

**The top 10 container management platforms for 2026:**

1. [Northflank](https://northflank.com/) - Multi-cloud container management platform that abstracts DevOps complexity
2. Kubernetes - Open-source container orchestration
3. Rancher - Multi-cluster Kubernetes management
4. Portainer - Platform supporting Kubernetes, Docker, Swarm, Podman, and Azure ACI
5. Google Kubernetes Engine (GKE) - Managed Kubernetes on Google Cloud
6. Amazon EKS - Managed Kubernetes on AWS and on-premises
7. Azure Container Apps - Serverless containers on Microsoft Azure
8. Red Hat OpenShift Container Platform - Enterprise Kubernetes with built-in developer tools
9. Platform9 - Managed Kubernetes with SaaS, self-hosted, or air-gapped options
10. Mirantis Kubernetes Engine - Enterprise Kubernetes platform with optional Swarm support

**Which platform is right for you?**

- Want Kubernetes power without the complexity → Northflank or Rancher
- Have Kubernetes expertise in-house → GKE, Amazon EKS, or self-managed Kubernetes
- Need Docker and multi-orchestrator support → Northflank, Portainer
- Looking for cloud-native serverless → Azure Container Apps
- Require on-premise or air-gapped deployments → Amazon EKS, OpenShift, Platform9, or Mirantis

</InfoBox>

## Why you need container management software

Running production workloads at scale introduces challenges that quickly overwhelm manual processes.

### Deployment complexity spirals out of control

Each container carries unique dependencies, environment variables, and networking requirements. Coordinating these manually becomes error-prone and time-consuming. Container management platforms standardize deployment processes and provide visibility into what's running where.

### Resource allocation becomes inefficient

Without orchestration, containers compete for resources unpredictably. Some servers sit idle while others max out, degrading performance. Management platforms distribute containers based on available capacity and automatically optimize resource utilization.

### Scaling can't keep pace with demand

Traffic spikes during campaigns or unexpected growth. Manual scaling introduces delays during critical periods. Automated scaling responds in real-time, adding capacity within seconds and removing it when traffic subsides.

### Security vulnerabilities multiply

Maintaining security policies, managing secrets, and scanning vulnerabilities across hundreds of containers becomes impossible without automation. Container management platforms enforce security policies automatically and provide audit trails for compliance.

### Monitoring and debugging grow overwhelming

Identifying problems, accessing logs, and coordinating fixes across distributed systems are challenging for teams. Platforms provide unified dashboards, aggregate logs centrally, and surface issues proactively.

## How to choose the right container management platform

Your team's expertise, application complexity, and budget all play a role in the decision.

1. **Assess your team's Kubernetes knowledge**: If your team lacks Kubernetes experience, platforms like Northflank abstract this complexity. Teams with platform engineering capacity can leverage Kubernetes directly.
2. **Think about your scale**: Small teams benefit from managed platforms with intuitive interfaces. Enterprises require advanced networking, multi-tenancy, and compliance features.
3. **Review cloud strategy**: Cloud provider services offer tight integration but create friction during migration. Cloud-agnostic platforms provide flexibility across providers.
4. **Balance control and convenience**: Self-managed Kubernetes offers maximum customization but requires operational expertise. Managed platforms handle complexity but may limit some configuration options.
5. **Account for total cost**: Open-source tools are free but require engineering time for maintenance. Calculate platform fees, infrastructure costs, and engineering time spent on operations.

## 10 best container management platforms for 2026

Each platform addresses different needs, from reducing operational complexity to providing maximum control over infrastructure.

### 1. Northflank

Northflank is a multi-cloud container management platform that simplifies deployments without sacrificing power or flexibility, providing an intuitive interface for deploying applications across multiple clouds while abstracting infrastructure complexity.

![northflank--platform.png](https://assets.northflank.com/northflank_platform_d625d79568.png)

**Key features:**

1. **Developer experience**: Northflank minimizes the gap between writing code and running it in production. Developers deploy applications through an intuitive UI, API, or CLI without needing deep infrastructure knowledge.
2. **Multi-cloud deployment**: Deploy to AWS, GCP, Civo, Oracle, bare metal, or Azure using a single interface. Northflank manages the underlying Kubernetes infrastructure across cloud providers. ([See how](https://northflank.com/docs/v1/application/bring-your-own-cloud/use-other-cloud-providers-with-northflank))
3. **Built-in CI/CD**: Connect GitHub, GitLab, or Bitbucket repositories for automatic builds and deployments on every commit. ([See how](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank)) 
4. **Environment management**: Create preview environments for branches and manage staging and production from a single dashboard. ([See how](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment))
5. **Cost transparency**: Clear visibility into resource consumption and costs across environments. ([See pricing details & pricing calculator](https://northflank.com/pricing))

**Best for:**

- Teams without dedicated DevOps resources
- Organizations wanting Kubernetes benefits without operational complexity
- Companies requiring multi-cloud flexibility
- Startups and enterprises needing to move fast without sacrificing scalability

**Considerations:**

Teams requiring extremely granular Kubernetes configuration control may occasionally want deeper access, though the abstractions improve productivity for most use cases.

<InfoBox className="BodyStyle">

See how Weights scaled to over 3 million users with a 2-person engineering team, running 10,000 AI training jobs and half a million inference runs daily across 9 clusters on AWS, GCP, and Azure with 40+ microservices and 250+ concurrent GPUs. [Read the full case study](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)

Also, see [how to use other cloud providers with Northflank](https://northflank.com/docs/v1/application/bring-your-own-cloud/use-other-cloud-providers-with-northflank)

</InfoBox>

### 2. Kubernetes

Kubernetes is the open-source container orchestration standard, powering infrastructure for organizations from startups to enterprises.

![kubernetes-homepage.png](https://assets.northflank.com/kubernetes_homepage_8f618a65d0.png)

**Key features:**

1. **Configuration options**: Customize networking, storage, scheduling, and security to specific requirements.
2. **Cloud-agnostic deployment**: Run on any cloud provider, on-premise data centers, or edge locations using identical APIs.
3. **Tool ecosystem**: Access thousands of tools and integrations for monitoring, security, CI/CD, and service mesh.

**Best for:**

- Organizations with dedicated platform engineering teams
- Companies requiring infrastructure customization
- Large-scale deployments with specific requirements

**Considerations:**

Kubernetes requires operational overhead. Teams must handle installation, upgrades, security, monitoring, and troubleshooting without managed service support.

*See [how to deploy to Kubernetes without writing YAML](https://northflank.com/blog/deploy-to-kubernetes-without-writing-yaml)*

### 3. Rancher

Rancher provides a centralized platform for deploying and operating multiple Kubernetes clusters across any infrastructure.

![rancher-homepage.png](https://assets.northflank.com/rancher_homepage_274dfd6470.png)

**Key features:**

1. **Multi-cluster management**: Operate clusters from a single dashboard across different clouds and data centers.
2. **Cluster automation**: Automate provisioning, upgrades, and backup processes for Kubernetes clusters.
3. **Application catalog**: Deploy applications using Helm charts through a built-in catalog.

**Best for:**

- Organizations managing multiple Kubernetes clusters
- Teams wanting centralized Kubernetes tooling
- Multi-cloud or hybrid deployments

**Considerations:**

Rancher assumes basic Kubernetes knowledge. Initial setup requires planning.

*See [alternatives to Rancher](https://northflank.com/blog/rancher-alternatives) and [how it compares to OpenShift](https://northflank.com/blog/rancher-vs-openshift)*

### 4. Portainer

Portainer is a container management platform that supports Kubernetes, Docker, Docker Swarm, Podman, and Azure ACI.

![portainer-homepage.png](https://assets.northflank.com/portainer_homepage_2a6b6b9e25.png)

**Key features:**

1. **Multiple orchestrator support**: Works with Kubernetes, Docker, Docker Swarm, Podman, and Azure ACI environments.
2. **Container operations**: Deploy and manage containers without requiring CLI or YAML configuration.
3. **Edge deployment**: Deploy to disconnected, low-resource, or air-gapped environments.

**Best for:**

- Small teams managing Docker or Kubernetes environments
- Industrial and IoT deployments at the edge
- Organizations needing centralized container management

**Considerations:**

May require additional configuration for complex enterprise use cases. Best suited for Docker-focused environments, though Kubernetes support is available.

*See [5 best Portainer alternatives for enterprise Kubernetes and Docker management](https://northflank.com/blog/portainer-alternatives)*

### 5. Google Kubernetes Engine (GKE)

Google Kubernetes Engine is Google Cloud's managed Kubernetes service with integration across GCP services.

![gke-homepage1.png](https://assets.northflank.com/gke_homepage1_21b4accee8.png)

**Key features:**

1. **Managed operations**: Automatic cluster upgrades, security patches, and node auto-repair for failed infrastructure.
2. **GCP integration**: Connect to Google Cloud services, including Storage, SQL, Functions, and BigQuery.
3. **Autopilot mode**: Optional mode for automated node provisioning and configuration management.

**Best for:**

- Organizations using Google Cloud Platform
- Teams wanting reduced cluster management overhead
- Applications requiring GCP service integration

**Considerations:**

Creates dependency on Google Cloud Platform. Costs can accumulate for larger deployments.

*See the [best managed Kubernetes platforms in 2026: What to choose and why It matters.](https://northflank.com/blog/best-managed-kubernetes-platforms)*

### 6. Amazon Elastic Kubernetes Service (Amazon EKS)

Amazon EKS is a managed Kubernetes service that runs in AWS Cloud and on-premises data centers, with AWS handling the control plane infrastructure.

![amazon-eks-homepage.png](https://assets.northflank.com/amazon_eks_homepage_ad361b3de6.png)

**Key features:**

1. **AWS integration**: Connect to AWS services including RDS, S3, DynamoDB, Lambda, CloudWatch, and ECR.
2. **Managed control plane**: AWS handles control plane availability, upgrades, and scaling across availability zones.
3. **Deployment options**: Run nodes on EC2 instances, use Fargate for serverless compute, deploy to Outposts, or use EKS Anywhere for on-premises and air-gapped environments.

**Best for:**

- Organizations standardized on AWS
- Teams leveraging multiple AWS services
- Companies requiring AWS support contracts
- On-premises or hybrid cloud deployments

**Considerations:**

Creates AWS dependency. Costs include per-cluster pricing, compute resources (EC2/Fargate), and other AWS services used. 

[*Deliver apps, databases and cron jobs to production with Elastic Kubernetes Service (EKS) and your complete application platform, on AWS, now.*](https://northflank.com/cloud/aws)

### 7. Azure Container Apps

Azure Container Apps provides serverless container platform on Microsoft Azure, abstracting infrastructure management.

![azure container apps.png](https://assets.northflank.com/azure_container_apps_ef61416f2a.png)

**Key features:**

1. **Serverless model**: No infrastructure management required; Azure handles scaling, networking, and provisioning.
2. **Scale-to-zero**: Applications scale down to zero instances when idle, reducing costs for unused resources.
3. **Traffic splitting**: Deploy multiple revisions and split traffic for canary deployments or A/B testing.

**Best for:**

- Teams wanting straightforward container deployment
- Applications with variable traffic patterns
- Organizations using Microsoft Azure

**Considerations:**

Abstracts control that some teams require. Works best for stateless applications and HTTP-based services.

*See how to [Integrate your Microsoft Azure account to create and manage clusters using Northflank.](https://northflank.com/docs/v1/application/bring-your-own-cloud/azure-on-northflank)*

### 8. Red Hat OpenShift Container Platform

Red Hat OpenShift is an enterprise Kubernetes distribution with additional developer tools, security features, and operational capabilities.

![openshift-container-platform.png](https://assets.northflank.com/openshift_container_platform_ba6f05f9e7.png)

**Key features:**

1. **Security tooling**: Built-in security scanning, SELinux integration, and automatic certificate management.
2. **Source-to-image builds**: Build applications directly from source code without Dockerfile expertise.
3. **Integrated operations**: Monitoring, logging, alerting, and cluster management tools included.
4. **Deployment flexibility**: Deploy on-premise, in major clouds, or across hybrid infrastructures.

**Best for:**

- Enterprises with compliance requirements
- Organizations needing on-premise or hybrid deployments
- Companies wanting integrated developer and operations tooling

**Considerations:**

More complex than vanilla Kubernetes. Licensing costs can be significant for large deployments.

*See [OpenShift alternatives](https://northflank.com/blog/best-open-shift-alternatives-finding-the-right-kubernetes-platform), and [how it compares to Kubernetes](https://northflank.com/blog/openshift-vs-kubernetes)*

### 9. Platform9

Platform9 provides managed Kubernetes across any infrastructure. It's available as SaaS-managed (with control plane hosted by Platform9), self-managed on-premises (Private Cloud Director), or air-gapped deployments.

![platform9-homepage.png](https://assets.northflank.com/platform9_homepage_7b73874f21.png)

**Key features:**

1. **Deployment options**: Available as SaaS-managed, self-hosted on-premises, or air-gapped for secure environments.
2. **Infrastructure flexibility**: Deploy on any public cloud, private cloud, bare metal, or edge infrastructure.
3. **Managed operations**: Handles cluster provisioning, automated upgrades, security patching, monitoring, and troubleshooting.
4. **VM and container unification**: Manage both virtual machines and Kubernetes containers through a single platform.

**Best for:**

- Enterprises with heterogeneous infrastructure
- Organizations needing on-premise or edge Kubernetes deployments
- Companies requiring managed operations without giving up infrastructure control

**Considerations:**

SaaS option has control plane managed by Platform9. Self-hosted and air-gapped options available for organizations with data sovereignty requirements.

*See [Top 5 Platform9 alternatives: Finding the right private cloud solution](https://northflank.com/blog/platform9-alternatives)*

### 10. Mirantis Kubernetes Engine

Mirantis Kubernetes Engine is an enterprise container platform for Kubernetes and Swarm container orchestration.

![mirantis-kubernetes-engine.png](https://assets.northflank.com/mirantis_kubernetes_engine_6232f73cd0.png)

**Key features:**

1. **Orchestration support**: Kubernetes container orchestration with optional Swarm support (MKE 3.8.x maintains Swarm compatibility).
2. **Enterprise security**: Integrated RBAC, identity management, image scanning through Mirantis Secure Registry, DISA STIG and FIPS 140-2 encryption.
3. **Air-gapped deployment**: Fully supported offline installation and upgrades without internet connectivity.
4. **Composable architecture**: Deploy with default hardened open source components or swap in alternatives for specific requirements.

**Best for:**

- Organizations with Docker Enterprise investments
- Enterprises requiring air-gapped or secure environments
- Companies with strict compliance and security requirements
- On-premise Kubernetes deployments

**Considerations:**

Commercial platform with licensing costs. MKE 4.x focuses on Kubernetes with k0s at its core, while MKE 3.8.x maintains Swarm/Kubernetes dual support.

*See [Top 10 tools for managing Kubernetes clusters in 2026](https://northflank.com/blog/tools-for-managing-kubernetes-clusters)*

## Making the decision: which platform is right for you?

| Your situation | Recommended platforms | Why |
| --- | --- | --- |
| Small team, limited DevOps resources | Northflank, Portainer, Azure Container Apps | Abstract infrastructure complexity, enable fast deployment without specialists |
| Kubernetes expertise in-house | Kubernetes, GKE, Amazon EKS | Leverage existing skills for maximum control and customization |
| Enterprise compliance needs | Northflank, OpenShift, Platform9, Mirantis | Provide security controls, audit trails, and support guarantees |
| Multi-cloud strategy | Northflank, Rancher, Platform9 | Work consistently across cloud providers |
| Committed to specific cloud | GKE, Amazon EKS, Azure Container Apps | Offer deepest integration with provider ecosystem |

Most organizations benefit more from reducing operational complexity than from unlimited configuration options. Start with platforms that match your team's current capabilities and scale as requirements grow.

## Why developers choose Northflank for container management

Northflank bridges the gap between simplicity and production-grade power. Teams deploy containers confidently without becoming infrastructure experts.

The platform handles Kubernetes, cloud providers, networking, and scaling automatically while providing the visibility and control needed for production systems. Developers maintain velocity without compromising reliability or security.

Northflank's multi-cloud flexibility prevents vendor lock-in while providing consistent operations across AWS, GCP, Civo, Oracle, bare-metal, and Azure. You choose where workloads run without relearning infrastructure for each provider.

Built-in CI/CD, environment management, and cost visibility reduce the need for multiple tools. Teams access everything needed for modern container deployments through a single platform.

<InfoBox className="BodyStyle">

To begin managing and simplifying your container infrastructure:

[Start your free trial](https://app.northflank.com/signup) and deploy your first application in minutes

Or [book a demo](https://cal.com/team/northflank/northflank-intro) with an expert engineer to see how Northflank accelerates deployments for your team

</InfoBox>

## Frequently asked questions

1. **What's the difference between Docker and container management software?**
    
    Docker packages and runs individual containers. Container management software orchestrates multiple containers at scale, handling deployment, scaling, networking, and monitoring across infrastructure.
    
2. **Do I need Kubernetes to use container management software?**
    
    No. Platforms like Northflank abstract Kubernetes entirely, providing container orchestration without requiring Kubernetes expertise.
    
3. **How much does container management software cost?**
    
    Open-source tools are free but require engineering time for operations. Managed platforms range from free tiers to thousands monthly for enterprise features. Calculate total cost including platform fees, infrastructure, and engineering time.
    
4. **Can I migrate between container management platforms?**
    
    Standard Docker containers and Kubernetes manifests are portable. Platform-specific services, networking, or storage create migration friction. Platforms emphasizing standards make migrations easier.
    
5. **Should startups use container management software?**
    
    Yes. Container management platforms help startups deploy faster and scale efficiently. Platforms like Northflank provide enterprise capabilities at startup-friendly prices with free tiers for experimentation.]]>
  </content:encoded>
</item><item>
  <title>Kubernetes PaaS: should you build your own or use a platform?</title>
  <link>https://northflank.com/blog/kubernetes-paas</link>
  <pubDate>2025-10-22T16:45:00.000Z</pubDate>
  <description>
    <![CDATA[Kubernetes PaaS platforms abstract K8s complexity for developers. Compare building your own vs using platforms like Northflank to deploy faster.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/kubernetes_paas_f06e920e21.png" alt="Kubernetes PaaS: should you build your own or use a platform?" />Kubernetes was supposed to make container orchestration easier. Yet thousands of companies are now building another abstraction layer on top of it: a Kubernetes PaaS.

> A Kubernetes PaaS is a platform layer that sits above Kubernetes, abstracting away its complexity and providing developers with a simpler deployment experience. Rather than writing YAML files and managing pods directly, developers can focus on shipping code.
> 

This raises an important question: *should you build your own PaaS layer on Kubernetes, or use an existing platform?*

Building your own sounds great on paper because you get complete control, customization for your exact needs, and no vendor dependencies. However, the reality means committing 3 to 8+ engineers to maintaining infrastructure instead of building your product. It means chasing quarterly Kubernetes updates and debugging abstraction layers at 2 AM.

Meanwhile, platforms like Northflank offer production-grade alternatives without the operational burden. You get the same Kubernetes power and reliability, without the maintenance overhead.

This guide covers what a Kubernetes PaaS does, why teams need one, and how to choose the right approach for your organization.

## What is a Kubernetes PaaS? (and do you need one?)

A Kubernetes PaaS provides a developer-friendly abstraction layer on top of Kubernetes infrastructure. It handles the complexity of container orchestration while exposing simple interfaces for deployment, scaling, and management.

The key problems it solves include:

- Resource management (automatically handling CPU and memory limits)
- Service exposure (managing networking, load balancing, and ingress)
- Configuration (simplifying secrets, ConfigMaps, and environment variables)
- Self-service (letting developers deploy without waiting for ops teams)
- GitOps workflows (automating deployments from Git repositories).

### Is Kubernetes itself a PaaS?

No. Kubernetes is container orchestration infrastructure, closer to infrastructure-as-a-service (IaaS) that enables PaaS. While Kubernetes provides powerful primitives for running containers, it requires significant configuration and expertise to become developer-friendly.

Two types of Kubernetes PaaS exist. First, internal platforms are homegrown abstraction layers built by platform teams. Second, commercial platforms are third-party PaaS solutions built on Kubernetes infrastructure.

The question isn't about needing abstraction; most teams do. It's about building and maintaining it yourself versus using an existing solution.

<InfoBox className="BodyStyle">

## What you need to know before choosing a Kubernetes PaaS

If you're comparing Kubernetes PaaS options, you need to understand what makes them necessary and what approaches exist.

**The challenge**: Raw Kubernetes is too complex for most development teams. Managing YAML configs, resource limits, networking, and secrets requires specialized knowledge that slows down product development. Your developers want to ship features, not become Kubernetes experts.

**The temptation**: Building an internal PaaS seems like the natural answer. Large companies have tried this approach with massive engineering investments, only to discover the ongoing maintenance burden. Kubernetes releases quarterly, and every new feature means work for your platform team. Many of these companies have since pivoted toward different approaches that reduce this operational overhead.

> **The solution**: Most teams skip the build vs buy debate entirely. They use platforms like [Northflank](https://northflank.com/) that provide production-grade Kubernetes infrastructure with zero operational burden. You get simple Git-based deployments built on standard Kubernetes, which means simplicity without vendor lock-in. You get Kubernetes benefits (scalability, reliability, cloud-native architecture) without managing clusters or YAML files.
> 

Your other options include building your own platform (if you have 8+ dedicated engineers) or using managed Kubernetes with additional tooling (if you have 3 to 5 platform engineers and need deep customization).

The rest of this article breaks down each approach in detail so you can make an informed decision for your team.

</InfoBox>

## Why teams build PaaS layers on Kubernetes

Despite Kubernetes' complexity, teams continue building custom PaaS layers on top of it. Understanding why reveals what problems you're trying to solve.

### Kubernetes has a steep learning curve

Your developers shouldn't need to understand pods, namespaces, ingress controllers, or YAML syntax just to deploy applications.

When every deployment requires platform team assistance, velocity suffers.

A PaaS layer promises to hide this complexity behind simple interfaces: push to Git, deployment happens automatically, and logs appear in a dashboard.

### Resource management adds another layer of complexity

Setting CPU and memory limits sounds simple, but becomes complex in practice. Who owns creating these limits? Who sets them for production?

In the VM era, ops teams set fixed resources and occasionally threw more infrastructure at the problem. Kubernetes blurs the line between dev and ops, and wrong settings lead to either outages (limits too low) or cloud bill explosions (limits too high).

*Without a PaaS layer, every team might deploy differently. Some use Helm, others use raw manifests, and some cobble together scripts. A PaaS layer promises standardization: one way to deploy, one way to manage services, one interface for all teams.*

## What building your own PaaS will cost you

The math on building your own Kubernetes PaaS rarely works out. Even if you have the engineering talent, the opportunity cost is staggering.

### Kubernetes releases quarterly with new features and deprecations

Every change forces related changes in your PaaS layer. Your platform team faces an unending backlog of technical debt just to stay relevant.

Want to use the latest Kubernetes feature? You'll need to add it to your homegrown PaaS or wait months for your team to implement support.

While your competitors using platforms like Northflank get instant access to new capabilities, you're stuck maintaining infrastructure.

### Building a Kubernetes PaaS requires a dedicated platform engineering team

Requires typically 3 to 8+ engineers, depending on your organization's size. At an average fully-loaded cost of “$200K” per engineer, that's “$600K to $1.6M” annually just for salaries.

These engineers spend their time on continuous maintenance and updates, writing documentation and providing developer training, and debugging abstraction layers when issues arise.

### Then there's opportunity cost

What else could that team build for your business? Every hour spent maintaining your internal PaaS is an hour not spent on features that differentiate your product, optimizing your deployment pipeline, or improving observability.

For most teams, the economics are clear. A Kubernetes PaaS platform costs a fraction of maintaining your own, with none of the operational burden.

Unless you have multi-year Kubernetes expertise on staff, significant engineering resources to spare, and highly specialized requirements, building your own PaaS diverts focus from your core business.

## Your four options for PaaS on Kubernetes

Your choice depends on expertise, scale, and how much control you need. Think of these as a spectrum from maximum control (and maximum burden) to maximum velocity (with minimal overhead).

### 1. PaaS built on Kubernetes (the right choice for most teams)

Platforms provide Git-to-deploy workflows without requiring Kubernetes knowledge. Your developers never touch YAML or kubectl.

You get deployments with preview environments, automatic resource optimization, built-in CI/CD, observability, databases, and team collaboration.

This suits startups, scale-ups, and any team that wants to move fast without sacrificing reliability.

Platforms like [Northflank](https://northflank.com/) are built on standard Kubernetes, so your applications remain portable. You get enterprise-grade infrastructure without the enterprise-sized platform team.

### 2. DIY Kubernetes (no PaaS layer)

Raw Kubernetes management suits large enterprises with specialized requirements and deep K8s expertise already in place. You get complete control but need 24/7 operations and 10+ engineers dedicated to platform work. This makes sense for companies where Kubernetes expertise is part of your competitive advantage, not just infrastructure.

### 3. Managed Kubernetes + developer tools

Use managed Kubernetes (like EKS or GKE) and layer on tools like ArgoCD for GitOps workflows. You still handle developer training, CI/CD pipelines, observability stacks, and security configuration. This works for teams with 3 to 5+ platform engineers who need full Kubernetes control and have the capacity to maintain the tooling layer.

### 4. Open source PaaS on your infrastructure

Tools like Porter, Cozystack, or CapRover let you run PaaS-like experiences on your own Kubernetes clusters. You get control over infrastructure but still handle maintenance, updates, and support yourself. This can work if you have specific compliance requirements, but you're trading cost savings for operational burden.

## How Northflank handles Kubernetes complexity for you

Northflank provides production-grade Kubernetes infrastructure without the operational complexity of building your own PaaS.

Northflank runs on Kubernetes but **abstracts all complexity**. You get the benefits (scalability, reliability, cloud-native architecture) without managing clusters, YAML files, or infrastructure decisions. Your applications run on Kubernetes infrastructure and remain portable if your needs change.

Northflank **handles resource management** with intelligent scaling based on usage patterns, resource recommendations and optimization, and cost controls with budget alerts. You don't need to guess at CPU and memory settings or risk outages from misconfiguration.

Unlike managed Kubernetes where you piece together multiple tools, Northflank **provides everything in one platform**. You get Git-connected builds that trigger automatically, managed databases (Postgres, MySQL, MongoDB, Redis) that provision in seconds, centralized logging and metrics, and secrets management with role-based access control.

Coming from Heroku? You'll find a similar Git-based workflow with more control. Migrating from DIY Kubernetes? You can reduce your platform team's burden while maintaining flexibility. Moving from traditional hosting? You get cloud-native architecture without requiring your team to become Kubernetes experts.

## Deciding between building, buying, or using managed Kubernetes

The question isn't about needing a Kubernetes PaaS. Most teams do. It's about who maintains it and at what cost.

| Approach | Example | Best for | Team size | What you get | What you maintain |
| --- | --- | --- | --- | --- | --- |
| **PaaS platform** (recommended) | Northflank | Fast-moving teams prioritizing velocity | Any size (ideal for less than 100 engineers) | Git-to-deploy, everything built-in | Nothing (focus on product) |
| **Managed Kubernetes + tools** | EKS + ArgoCD | Teams needing deep Kubernetes customization | 3-5+ platform engineers required | Kubernetes flexibility with reduced ops | Tooling layer, CI/CD, observability |
| **Build your own** | Internal platform | Highly specialized, unique requirements | 10+ platform engineers required | Complete control and customization | Everything (platform, tools, updates) |

The data tells a clear story. A PaaS platform gives you the best return on investment regardless of team size.

The question is: do you want your engineers building your product or maintaining Kubernetes infrastructure? You get Kubernetes power without the operational burden, and you can always move to more control later if your needs change.

<InfoBox className="BodyStyle">

Platforms like [Northflank](https://northflank.com/) provide the fastest path to production for most teams. Your developers ship code faster, your platform team (if you have one) focuses on business priorities instead of infrastructure maintenance, and you maintain the flexibility to migrate if you eventually need it.

[Get started with a free sandbox](https://app.northflank.com/signup) to deploy your first project, or [book a demo](https://cal.com/team/northflank/northflank-intro) if you have specific requirements and want to speak with an expert engineer.

</InfoBox>

Start by thinking about what your team needs today, not what you might need at 10x scale. Most companies never reach the scale where building their own platform makes sense. Those that do have the resources to migrate when the time comes. Don't let theoretical future needs prevent you from moving fast today.]]>
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</item><item>
  <title>Travis CI vs CircleCI: which CI/CD platform should you choose in 2026?</title>
  <link>https://northflank.com/blog/travis-ci-versus-circleci</link>
  <pubDate>2025-10-20T16:30:00.000Z</pubDate>
  <description>
    <![CDATA[Compare Travis CI vs CircleCI for CI/CD. See how they differ and when a platform like Northflank provides more than just builds and tests.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/travis_ci_vs_circleci_d7bda31817.png" alt="Travis CI vs CircleCI: which CI/CD platform should you choose in 2026?" />Choosing between Travis CI and CircleCI means understanding what each platform does well and where they differ.

Both automate your build and test workflows, but they take different approaches to configuration, performance, and pricing.

This comparison examines what Travis CI and CircleCI offer, how they differ in practice, and what to consider beyond CI/CD when shipping production software.

<InfoBox className="BodyStyle">

## Quick summary of Travis CI vs CircleCI

**Travis CI:**

- Designed for multi-environment testing with straightforward YAML configuration
- Build matrix feature tests across multiple language versions and operating systems simultaneously
- Lower learning curve with language-specific defaults
- Works well for projects requiring compatibility verification

**CircleCI:**

- Built for Docker-native workflows with advanced caching
- Intelligent test parallelization distributes suites across containers
- More complex configuration enables sophisticated pipeline orchestration
- Suitable for teams with containerized applications and complex deployment requirements

**Beyond CI/CD:**
Both platforms handle building and testing code. Shipping software also requires:

- Deployment infrastructure for running applications
- Database management for staging and production
- Environment orchestration across dev, staging, and production
- Job scheduling for background tasks
- Preview environments for pull request reviews

> Teams typically combine their CI/CD tool with separate platforms for these needs. Platforms like [Northflank](https://northflank.com/) provide CI/CD alongside deployment infrastructure, databases, and orchestration in a **unified solution**.
> 

</InfoBox>

## What is Travis CI?

Travis CI is a cloud-based continuous integration platform that automates building and testing code changes.

When you connect Travis CI to GitHub, Bitbucket, or GitLab, it automatically triggers builds on every commit or pull request. You define your build steps in a `.travis.yml` file, and Travis CI executes them in a clean virtual environment.

**Key capabilities:**

- Multi-language support for 30+ programming languages including Python, Ruby, JavaScript, Java, and Go
- Build matrix for testing across multiple language versions, OS environments, and dependency combinations simultaneously
- Simple YAML configuration that's quick to set up for straightforward projects
- Automated deployments to Heroku, AWS, Google Cloud Platform, and GitHub Pages
- Caching to speed up builds by reusing dependencies

See [Top Travis CI alternatives for development workflows](https://northflank.com/blog/travis-ci-alternatives)

## What is CircleCI?

CircleCI is a continuous integration and delivery platform built for speed and developer experience.

You define pipelines in a `.circleci/config.yml` file with explicit jobs, steps, and workflows. While this requires more initial configuration than Travis CI, it provides more flexibility for complex deployment scenarios.

**Key capabilities:**

- Docker-first architecture with Docker Layer Caching for faster image builds
- Parallelization and test splitting that automatically distribute test suites across containers
- Workflows and orchestration for complex multi-stage pipelines with approval gates
- CircleCI Orbs - reusable configuration packages for common integrations
- Insights Dashboard showing pipeline performance, build duration trends, and bottlenecks
- SSH debugging into build environments for interactive troubleshooting
- Advanced caching including workspace persistence between jobs

## Travis CI vs CircleCI: side-by-side comparison

See how Travis CI and CircleCI compare across the features and capabilities that are important when choosing a CI/CD platform.

| Feature | Travis CI | CircleCI |
| --- | --- | --- |
| **Configuration** | `.travis.yml` with simple, language-specific defaults | `.circleci/config.yml` with explicit job and workflow definitions |
| **Best for** | Multi-environment testing, straightforward projects | Docker-heavy workflows, complex pipelines |
| **Learning curve** | Low - quick to set up | Moderate - more configuration required |
| **Docker support** | Supported via a service dependency | Docker-first architecture with Layer Caching |
| **Parallelization** | Build matrix runs jobs concurrently | Intelligent test splitting across containers |
| **Workflows** | Build stages for sequential execution | Advanced orchestration with dependencies and approval gates |
| **Caching** | Basic dependency caching | Advanced caching with workspace persistence |
| **Extensibility** | Deployment provider integrations, API access | CircleCI Orbs (reusable config packages) |
| **Analytics** | Basic build history and logs | Insights Dashboard with performance metrics |
| **Debugging** | Build logs | SSH into build environments |

### Travis CI vs CircleCI: configuration approach

Travis CI uses straightforward YAML with language-specific defaults. Simple projects need minimal configuration (having to specify the language and version).

CircleCI requires explicit definition of jobs and workflows, offering more control at the cost of a steeper learning curve. The platform provides configuration samples to help you get started.

### Travis CI vs CircleCI: performance and parallelization

Travis CI runs jobs in parallel when using the build matrix, with concurrency based on your plan limits.

CircleCI offers intelligent test splitting that distributes a single test suite across multiple containers using timing data from previous runs. This can reduce test execution time for large suites.

### Performance in practice

CircleCI's focus on parallelization, caching, and Docker optimization generally results in faster builds when configured effectively. Travis CI's simpler approach may take longer but requires less configuration effort.

## What happens after CI/CD?

Travis CI and CircleCI handle building and testing code. But shipping software requires more than a green build.

Once tests pass, you need:

- **Infrastructure to host your applications** - whether Heroku, AWS, or another provider
- **Database management** for staging and production with backups and connection credentials
- **Environment orchestration** across development, staging, and production
- **Job scheduling** for background workers and cron tasks
- **Preview environments** for reviewing pull requests in isolation
- **Observability** through logs, metrics, and alerting

Most teams combine their CI/CD platform with additional services: CircleCI for builds, Heroku for hosting, a separate database provider, and monitoring tools. This works but requires integrating multiple platforms with separate configurations and billing.

## Beyond CI/CD: what you need for the complete deployment lifecycle

If you're evaluating CI/CD tools, think about what you want to solve: just the build and test problem, or the entire deployment lifecycle.

Northflank takes a different approach by providing CI/CD alongside deployment infrastructure, database management, and environment orchestration in a single platform.

### How Northflank differs

While Travis CI and CircleCI focus on continuous integration and delivery, Northflank provides CI/CD as part of a broader platform that includes deployment infrastructure, databases, and orchestration

| Capability | Travis CI / CircleCI | Northflank |
| --- | --- | --- |
| **CI/CD** | Core focus | Included |
| **Application hosting** | Via separate platforms | Built-in |
| **Managed databases** | Requires separate provider | Built-in (PostgreSQL, MySQL, MongoDB, Redis) |
| **Preview environments** | Manual setup required | Automatic generation |
| **Job scheduling** | Limited or external | Built-in cron and background jobs |
| **Multi-cloud deployment** | Script-based deployment to external cloud providers using integrations and Orbs | Connect your cloud accounts (AWS, GCP, Azure, Civo, Oracle, bare-metal) and Northflank manages deployments to your Kubernetes clusters. |
| **GPU support** | Paid GPU-enabled environments for CI/CD tasks, but require separate platforms for deploying GPU applications in production. | Native GPU support (H100, B200, A100, and more) for both CI/CD and production workloads, with GPU sharing and optimization features.
 |

### What's included in Northflank

Connect your GitHub, GitLab, or Bitbucket repository, and Northflank handles both building and running your applications.

For simple workflows, combined services act as self-contained CI/CD pipelines, where you can link a repository and branch, and Northflank automatically builds and deploys the latest commits.

And for complex workflows, you can separate build and deployment services, use release pipelines for multi-stage deployments, and configure release flows that automate database backups, migrations, and environment promotions.

See how Northflank's capabilities extend beyond CI/CD:

- **Complete infrastructure** - Deploy services, databases, and scheduled jobs on managed infrastructure. Northflank handles compute resources, load balancing, and auto-scaling.
- **Managed databases** - PostgreSQL, MySQL, MongoDB, Redis, and others run directly within your projects with automatic backups and connection credential management.
- **Preview environments** - Automatically generated for every pull request, including the full application stack (frontend, backend, databases).
- **Release orchestration** - Visual pipelines for complex deployments with stages for development, staging, and production.
- **Multi-cloud deployment** - Run on Northflank's [managed cloud](https://northflank.com/features/managed-cloud) or bring your own [AWS,](https://northflank.com/cloud/aws) [GCP,](https://northflank.com/cloud/gcp) [Azure](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), [Oracle](https://northflank.com/cloud/oci), or bare-metal infrastructure. Deploy the same applications across different clouds without changing configurations.
- **GPU support** - NVIDIA H100, B200, A100 GPUs and [more](https://northflank.com/gpu) for AI/ML training, inference, and agent workloads.

### Who benefits from unified CI/CD and deployment

If you're using separate tools for CI/CD and hosting, or piecing together multiple services to ship software, consolidating can simplify your workflow.

- **Teams using multiple platforms** - If you're running CircleCI for CI/CD and Heroku for hosting, consolidating to a unified platform reduces services to manage and integrations to maintain.
- **Startups and growing companies** - Deploy full-stack applications from day one without piecing together multiple services or building internal platform teams.
- **AI and ML teams** - GPU support, model serving, and the ability to run training and inference alongside traditional web services.
- **Teams avoiding vendor lock-in** - [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) means your applications run in your infrastructure while Northflank manages deployment and orchestration.

## Making your decision

Here's a quick breakdown to help you choose the right platform for your needs:

| Choose this | If you need |
| --- | --- |
| **Northflank** | CI/CD as part of a complete deployment platform. Rather than choosing between CI/CD tools and then managing separate hosting, databases, and environments, Northflank provides the full application lifecycle in one place. |
| **Travis CI** | Straightforward multi-environment testing with minimal configuration. The build matrix makes testing across language versions and operating systems simple. |
| **CircleCI** | Extensive Docker support and sophisticated workflow orchestration. The parallelization and caching features provide advantages for complex pipelines. |

The question isn't just which platform builds and tests your code better. It's about what you want: specialized CI/CD tooling combined with separate services, or a platform where CI/CD is integrated with deployment, infrastructure, and operations.

For teams shipping production software, the choice extends beyond continuous integration to how you want to manage the entire path from code to customer.

<InfoBox className="BodyStyle">

See how Northflank manages your complete application lifecycle with [our quickstart guide](https://northflank.com/docs/v1/application/getting-started), [try the platform with our free sandbox](https://app.northflank.com/signup), or [book a demo](https://cal.com/team/northflank/northflank-intro) to discuss your specific requirements.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Today's AWS outage is another reminder that you need a multi-cloud strategy</title>
  <link>https://northflank.com/blog/aws-outage-today-october-2025-multi-cloud-strategy</link>
  <pubDate>2025-10-19T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[AWS outage today brings down half the Internet]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/aws_outage_a2f5438b36.png" alt="Today's AWS outage is another reminder that you need a multi-cloud strategy" />## AWS outage today brings down half the Internet

This morning, millions of businesses and users worldwide experienced a harsh reminder of the fragility of single-cloud dependency. A major Amazon Web Services (AWS) outage beginning at approximately 3:11 AM ET has taken down dozens of popular applications, websites, and services including Snapchat, Roblox, Fortnite, Duolingo, Ring, Reddit, Coinbase, and even Amazon's own services.

The outage originated from problems with AWS's DynamoDB database service and DNS issues in the critical US-East-1 region hosted in northern Virginia. While AWS has reported "significant signs of recovery," lingering network issues continue to affect services that support thousands of apps and customer systems.

## The scale of today's AWS outage

Experts estimate the total financial impact of this AWS service disruption will be in the billions of dollars. The outage affected:

- **Major streaming platforms**: Amazon Prime Video, Disney+, and other entertainment services
- **Financial services**: Coinbase cryptocurrency exchange, various banking platforms
- **Airlines**: United Airlines and Delta Airlines reported system disruptions affecting apps and websites
- **Gaming platforms**: Fortnite, Roblox, and other cloud-based games went offline
- **Enterprise tools**: Canva, Perplexity AI
- **Government services**: UK government websites including Gov.uk and HM Revenue and Customs

This AWS outage today represents a global ripple effect, as the US-EAST-1 (N. Virginia) region is AWS's oldest and largest data center hub, housing control planes for many global AWS services including Amazon Identity and Access Management (IAM) and Amazon CloudFront.

## Why single-cloud strategies are failing businesses

Today's AWS outage perfectly illustrates the critical vulnerability of relying on a single cloud provider. 

As one expert [noted](https://edition.cnn.com/2025/10/20/tech/aws-why-internet-outages-keep-happening#:~:text=%E2%80%9CThe%20internet%20was%20originally%20designed,impact%20is%20immediate%20and%20widespread.%E2%80%9D), "The internet was originally designed to be decentralized and resilient, yet today so much of our online ecosystem is concentrated in a small number of cloud regions. When one of those regions experiences a fault, the impact is immediate and widespread."

### The risks of AWS-only infrastructure

**Single point of failure**: AWS has experienced multiple significant outages in recent years, including disruptions in November 2014, September 2013, December 2012, and the major December 2021 outages that affected Netflix, Spotify, Venmo, and Tinder.

**Vendor lock-In**: Businesses become entirely dependent on one provider's infrastructure, pricing, and service availability.

## Multi-cloud strategy is your shield against provider outages

A [multi-cloud](https://northflank.com/blog/multi-cloud-container-orchestration) approach distributes your infrastructure across multiple cloud providers, ensuring business continuity when any single provider experiences issues. 

**This is where [Northflank](https://northflank.com/) comes in.**

### Key benefits of multi-cloud architecture

**1. Reliability and uptime**
Organizations using multi-cloud strategies from the start cite reliability as the most important benefit. Combining resources from multiple clouds means that if one provider has an outage, your entire backend won't fail, you simply compensate by shifting resources from one provider to the next.

**2. Disaster Recovery** 
Take Fidelity Investments… they [moved](https://www.cncf.io/blog/2021/03/04/case-study-how-fidelity-investments-built-its-multi-cloud-strategy-with-cloud-native-technologies/) thousands of critical applications across multiple cloud providers and saw major improvements in agility and resilience. Or look at what happened during Google Cloud's outage in June 2025: while competitors crashed, e-commerce giant Mercado Libre stayed online with zero downtime because they had workloads running across multiple clouds. They actually gained market share while everyone else was scrambling to get back online.

**3. Cost Optimization** 
Multi-cloud mitigates financial risk, opening up negotiation opportunities with future contracts. It's harder for a provider to raise rates or change terms when you have other options.

## What is Northflank?

[Northflank](https://northflank.com/) is a comprehensive multi-cloud management platform designed to eliminate the complexity and risk of single-cloud dependency. Built for businesses that can't afford downtime, Northflank provides a unified solution for deploying, managing, and scaling applications across multiple cloud providers including AWS, Microsoft Azure, Google Cloud, and specialized cloud services.

Unlike traditional cloud management tools that focus on single-provider optimization, Northflank takes a cloud-agnostic approach. The platform acts as an intelligent orchestration layer that automatically distributes your workloads across multiple cloud environments, ensuring maximum uptime, cost efficiency, and performance.

### Core features:

- **Multi-Cloud Orchestration**: Deploy applications across AWS, Azure, Google Cloud, and other providers from a single interface
- **Automated Failover**: Intelligent traffic routing that switches providers instantly during outages
- **Cost Optimization**: Real-time analysis to run workloads on the most cost-effective cloud at any given moment
- **Unified Monitoring**: Single dashboard visibility across all your cloud environments
- **Global Disaster Recovery**: Comprehensive offsite backup solutions with failover regions and global backup capabilities
- **Multi-AZ Support**: High availability across multiple availability zones for all data services
- **Multi-Provider Integration**: Seamless, consistent integration with 5+ major cloud providers including AWS, Azure, Google Cloud, and Oracle Cloud

## How Northflank enables multi-cloud management

Managing multiple cloud providers can be complex, but Northflank simplifies the process with:

### Unified multi-cloud orchestration

- Single places for managing workloads across AWS, Azure, Google Cloud, and other providers
- Automated failover capabilities that switch traffic seamlessly during outages
- Real-time monitoring and alerting across all cloud environments

### Intelligent workload distribution

- Automatic routing of applications to the most cost-effective and performant cloud provider
- Dynamic resource allocation based on demand and provider availability
- Geographic optimization to reduce latency for global users

### Simplified migration and deployment

- One-click deployment across multiple clouds
- Standardized infrastructure-as-code templates
- Very low downtime migration between cloud providers

## Multi-cloud best practices for 2025

**Start with critical workloads**
Begin by implementing cross-cloud DNS failover and pre-staging cold workloads in alternate environments. Keep "warm standby" or "cold standby" infrastructure pre-configured on another cloud and automate scaling when needed.

**Implement smart load balancing**
Global load balancing solutions using DNS-based or anycast approaches help route traffic efficiently across cloud environments. This not only improves performance but also enables seamless failover during outages.

**Design for independence**
Minimize hard dependencies on cloud-specific APIs for core business logic. These APIs can become bottlenecks during outages.

## You shouldn’t wait for the next outage

Around 85% of enterprises now use a multi-cloud strategy, and today's AWS outage demonstrates exactly why. Every minute of downtime costs your business money, customers, and reputation.

**Real-world impact**: The March 2017 AWS outage affected roughly 150,000 websites, including major brands like Airbnb, Business Insider, and Expedia. Companies with multi-cloud architectures maintained operations while single-cloud competitors went dark.

## Multi-cloud with Northflank

Don't let the next cloud outage catch your business unprepared. 

Northflank’s multi-cloud platform provides:

- **99.99% uptime guarantee** through intelligent failover
- **30% average cost savings** through optimized provider selection
- **Zero-downtime deployment** across multiple cloud providers
- **24/7 expert support** for seamless cloud management

[**Talk to a Northflank engineer.**](https://cal.com/team/northflank/northflank-intro)]]>
  </content:encoded>
</item><item>
  <title>Top Travis CI alternatives for development workflows in 2026</title>
  <link>https://northflank.com/blog/travis-ci-alternatives</link>
  <pubDate>2025-10-16T15:55:00.000Z</pubDate>
  <description>
    <![CDATA[Compare top Travis CI alternatives for 2026. Northflank, GitLab, CircleCI, Jenkins, GitHub Actions, and Harness for modern CI/CD workflows.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/travis_ci_alternatives_7e260c2e87.png" alt="Top Travis CI alternatives for development workflows in 2026" />You're looking for Travis CI alternatives for likely reasons:

1. "We want integrated hosting and infrastructure management alongside CI/CD to reduce tool sprawl"
2. "We need native preview environments so our team can review changes in full application stacks"
3. "We're looking for visual release pipeline management with advanced workflows and rollback capabilities"
4. "We want the flexibility to bring our own cloud infrastructure for compliance and cost control"
5. "Our team needs a platform built container-native and Kubernetes-native from the ground up"

That's exactly what we'll help you with. We'll compare the top Travis CI alternatives for 2026, looking at their architecture, deployment capabilities, and how they fit modern development workflows.

<InfoBox className="BodyStyle">

## A quick look at the top Travis CI alternatives

This quick list summarizes the Travis CI alternatives we'll compare - we'll go into more detail later in the article:

1. [**Northflank**](https://northflank.com/) – Full-stack cloud platform combining CI/CD, hosting, databases, and release management. Built for container-native workflows with native Kubernetes support, true preview environments, and multi-cloud flexibility. Best for teams wanting unified build-deploy-host capabilities without operational overhead.
2. **GitLab CI/CD** – Integrated CI/CD within the GitLab DevOps platform, ideal for teams already using GitLab for version control.
3. **CircleCI** – CI/CD platform with caching, parallelism, and extensive integrations for complex pipeline requirements.
4. **Jenkins** – Open-source automation server with plugin ecosystem and infrastructure control.
5. **GitHub Actions** – Built into GitHub with workflow automation and marketplace actions for GitHub-centric development.
6. **Harness** – Platform with AI-powered deployment analysis, canary deployments, and governance features.

</InfoBox>

## What to look for in a Travis CI alternative

When evaluating CI/CD platforms, consider these key factors:

1. **Architecture and infrastructure**: Native support for your technology stack, particularly containers and Kubernetes. Platforms like Northflank are built container-native from day one, while others add support through plugins or configuration.
2. **Build performance**: Fast build times, efficient caching, and the ability to scale parallel jobs without hitting hard limits or experiencing long queue times.
3. **Developer experience**: Clean interfaces, comprehensive documentation, intuitive configuration, and tools that match your workflow, including UI, CLI, and API access.
4. **Deployment capabilities**: Some platforms only handle CI, requiring separate solutions for hosting and infrastructure. Northflank provides integrated deployment, hosting, and database management alongside CI/CD.
5. **Environment management**: Native support for preview environments, staging, and production environments with easy promotion between stages. True ephemeral environments that include full application stacks differentiate modern platforms.
6. **Release management**: Visual pipeline tools, release flows, rollback capabilities, and deployment strategies beyond basic automation.
7. **Integration ecosystem**: Compatibility with your existing tools, version control platforms, notification systems, and cloud providers.
8. **Pricing transparency**: Clear, predictable pricing models that scale fairly with your usage without hidden costs or surprise charges.
9. **Multi-cloud flexibility**: The ability to deploy across different cloud providers or bring your own infrastructure. Northflank supports deployment on its managed cloud or your own GKE, EKS, AKS, or bare-metal Kubernetes clusters.

## Top Travis CI alternatives compared

Let's examine these platforms and compare them based on architecture, deployment capabilities, developer experience, and infrastructure flexibility:

### 1. Northflank

Northflank is a full-stack cloud platform built for modern, cloud-native development workflows. Rather than just handling CI/CD, Northflank provides an integrated solution for building, deploying, hosting, and managing your entire application stack, including databases and services.

The platform is designed for teams working with containers and Kubernetes, but removes the operational complexity typically associated with these technologies. You get the power and flexibility of Kubernetes without needing dedicated DevOps expertise.

<InfoBox className="BodyStyle">

**What makes Northflank stand out**

Northflank is container-native from the ground up, removing the tool sprawl that comes from combining separate CI/CD, hosting, and infrastructure management solutions.

The platform provides true preview environments that automatically spin up complete ephemeral stacks, including applications, databases, and services for every pull request.

With visual release pipelines, multi-cloud support, and native GPU capabilities for AI/ML workloads, Northflank handles the full application lifecycle from git push to production.

</InfoBox>

**Core capabilities of Northflank**

- **Rapid CI/CD pipelines**: Container-native builds with automatic triggers from GitHub, GitLab, and Bitbucket
- **Deployment and hosting**: Deploy and host applications, managed databases, and scheduled jobs without separate platforms
- **True preview environments**: Automatic ephemeral environments with full application stacks for every PR
- **Visual release pipelines**: Multi-stage workflows with conditional logic, approvals, and one-click rollbacks
- **Kubernetes without complexity**: Native Kubernetes support with automatic scaling and orchestration
- **Multi-cloud and BYOC (Bring Your Own Cloud)**: Deploy on Northflank's cloud or bring your own AWS, GCP, Azure, or bare-metal infrastructure
- **GPU workload support**: Native support for NVIDIA H100, B200, and other GPU configurations for AI/ML
- **Infrastructure as code**: Reusable templates for consistent deployments across environments
- **Comprehensive observability**: Built-in logs, metrics, and alerts with integration to monitoring tools
- **Developer-first tooling**: Modern UI, CLI, and API for workflow automation

**Best for**

Teams building cloud-native applications with microservices architectures, organizations wanting integrated CI/CD and hosting without tool sprawl, developers prioritizing preview environments as core workflow, teams needing multi-cloud or bring-your-own-cloud flexibility, and those working with GPU-accelerated workloads for AI and ML.

*Learn more about [continuous integration and delivery on Northflank](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) and see how to [set up a pipeline](https://northflank.com/docs/v1/application/getting-started/set-up-a-pipeline).*

### 2. GitLab CI/CD

GitLab CI/CD integrates directly into the GitLab platform, providing CI/CD alongside version control, project management, and security scanning. The platform uses configuration files to define pipelines with both cloud-hosted and self-hosted deployment options.

**Key features**

- **Docker and Kubernetes support** – Native container orchestration and deployment capabilities
- **Integrated security scanning and compliance** – Built-in vulnerability scanning, dependency checking, and compliance reporting
- **Container registry and artifact storage** – Host Docker images and build artifacts within GitLab
- **Cloud and self-hosted runners** – Run builds on GitLab's infrastructure or your own servers
- **Pipeline visualization and debugging** – Visual pipeline editor with detailed logs and debugging tools

**Best for**

Teams already using GitLab for version control.

*Compare [GitLab and other alternatives](https://northflank.com/blog/best-gitlab-alternatives) for platform selection.*

### 3. CircleCI

CircleCI offers customizable workflows with caching, parallelism, and extensive integrations. The platform supports Docker, Kubernetes, and VM-based workflows with both cloud and self-hosted options.

**Key features**

- **Advanced caching and parallelism** – Speed up builds with intelligent dependency caching and parallel job execution
- **Docker, Kubernetes, and VM support** – Run builds in containers, Kubernetes clusters, or traditional VMs
- **Customizable YAML workflows** – Define complex multi-stage pipelines with conditional logic
- **Cloud and self-hosted runners** – Use CircleCI's cloud infrastructure or run on your own hardware
- **Resource class controls** – Select CPU and memory configurations for different build requirements

**Best for**

Teams with complex pipeline requirements needing extensive customization.

*See [top CircleCI alternatives](https://northflank.com/blog/top-circleci-alternatives) for additional comparisons.*

### 4. Jenkins

Jenkins is an open-source automation server with thousands of plugins for customization. The platform requires self-hosting and manual configuration, providing complete control over infrastructure.

**Key features**

- **Extensive plugin ecosystem** – Access thousands of community plugins for integrations and functionality
- **Infrastructure control** – Full control over build servers, configurations, and security settings
- **Customizable pipeline scripts** – Write pipelines as code using Groovy-based DSL
- **Support for any workflow** – Build, test, and deploy any technology stack or deployment target
- **Active open-source community** – Large user base with extensive documentation and support

**Best for**

Teams wanting complete infrastructure control with dedicated DevOps resources.

*Review [Jenkins alternatives in 2026](https://northflank.com/blog/jenkins-alternatives-2025) for modern options.*

### 5. GitHub Actions

GitHub Actions integrates directly into GitHub repositories with workflow automation and marketplace actions. The platform automatically triggers on repository events with managed or self-hosted runners.

**Key features**

- **Native GitHub integration** – Workflows trigger automatically on commits, PRs, and other repository events
- **Marketplace of reusable actions** – Thousands of pre-built actions for common tasks and integrations
- **Matrix builds across environments** – Test code across multiple OS versions, languages, and configurations
- **Managed and self-hosted runners** – Use GitHub's infrastructure or run workflows on your own servers
- **Secrets management within GitHub** – Store and access credentials securely within repository settings

**Best for**

Teams with workflows centered on GitHub repositories.

*Compare [GitHub Actions alternatives](https://northflank.com/blog/github-actions-alternatives) for broader evaluation.*

### 6. Harness

Harness provides continuous delivery with AI-powered deployment analysis, canary deployments, and advanced governance features.

**Key features**

- **AI deployment analysis** – Machine learning models analyze deployments to detect anomalies and failures
- **Canary and blue-green deployments** – Progressive rollout strategies with automated traffic shifting
- **Integrated feature flags** – Control feature releases independently from deployments
- **Audit logging and compliance** – Complete deployment history with approval workflows and governance policies
- **Multi-cloud infrastructure support** – Deploy across AWS, GCP, Azure, and other cloud providers

**Best for**

Large enterprises with complex deployment and governance requirements.

*Check out [top Harness alternatives](https://northflank.com/blog/top-harness-alternatives) for enterprise comparisons.*

## Platform comparison by use case

When choosing between Travis CI alternatives, your decision depends on your specific workflow requirements and infrastructure needs. This table breaks down which platform fits different use cases: 

> **Note**: Northflank integrates with GitLab, GitHub, and Bitbucket, supports multi-cloud flexibility, and includes enterprise governance features. This table highlights where each platform is the primary/natural fit based on their core design
> 

| Use case | Best platform | Why |
| --- | --- | --- |
| Integrated CI/CD and hosting | Northflank | Unified platform for build, deploy, and host without separate tools |
| Existing GitLab users | GitLab CI/CD | Native integration removes context switching |
| Maximum flexibility | Jenkins | Complete control through extensive plugin ecosystem |
| GitHub-centric workflows | GitHub Actions | Seamless repository integration |
| Enterprise governance | Harness | Advanced deployment strategies and compliance features |
| Container-native development | Northflank | Built for Docker and Kubernetes from the ground up |
| Preview environments | Northflank | Full ephemeral stacks with applications and databases |
| Multi-cloud deployment | Northflank | Deploy anywhere with bring-your-own-cloud support |

## Making the transition

This starts with understanding your workflows and choosing projects to migrate incrementally. Follow these steps:

1. **Document your current setup** – Map your existing configurations, deployment targets, and integrations before beginning the transition.
2. **Start with a pilot project** – Choose a smaller, non-critical project to learn the new platform. Most platforms provide migration guides and configuration converters. Northflank offers [comprehensive documentation on managing CI/CD](https://northflank.com/docs/v1/application/release/manage-ci-cd) and [infrastructure as code](https://northflank.com/docs/v1/application/infrastructure-as-code/infrastructure-as-code) to support migration.
3. **Run parallel systems** – Set up parallel runs where both systems process the same commits, allowing validation before full cutover.
4. **Migrate incrementally** – Move projects gradually rather than attempting wholesale replacement.
5. **Update documentation** – Update your team documentation with new processes and configurations.

<InfoBox className="BodyStyle">

**Getting started with Northflank**

Northflank provides integrated CI/CD, deployment, and hosting in a single platform. Connect your GitHub, GitLab, or Bitbucket repositories to start building.

Deploy services, databases, and jobs using guided setup. Configure [continuous integration and delivery](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) with automatic builds, set up [ephemeral preview environments](https://northflank.com/docs/v1/application/getting-started/set-up-a-pipeline) for pull requests, and build visual workflows for complex release processes.

[Start building on Northflank's free developer sandbox](https://app.northflank.com/signup) or [book a demo with our engineering team](https://cal.com/team/northflank/northflank-intro) to discuss specific requirements.

</InfoBox>

### Related resources

Understanding CI/CD strategies helps optimize your development workflow:

- [Continuous deployment](https://northflank.com/blog/continuous-deployment) – Automated deployment strategies for production
- [Continuous delivery](https://northflank.com/blog/continuous-delivery) – Keeping code deployable with manual approval gates
- [Continuous deployment tools](https://northflank.com/blog/continuous-deployment-tools) – Comprehensive tool comparisons
- [Using GitHub Actions with Northflank](https://northflank.com/docs/v1/application/infrastructure-as-code/use-github-actions-with-northflank) – Integrate existing workflows

Access the full [Northflank documentation](https://northflank.com/docs) for platform guides and best practices.]]>
  </content:encoded>
</item><item>
  <title>Top 5 Skaffold alternatives for Kubernetes development and deployment in 2026</title>
  <link>https://northflank.com/blog/skaffold-alternatives</link>
  <pubDate>2025-10-15T15:15:00.000Z</pubDate>
  <description>
    <![CDATA[Compare Skaffold alternatives: Northflank for production, Tilt for local dev, and more. Find the right Kubernetes tool for your team.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/skaffold_alternatives_6612951efd.png" alt="Top 5 Skaffold alternatives for Kubernetes development and deployment in 2026" />Skaffold is Google's open-source command-line tool that automates the build, push, and deploy workflow for Kubernetes applications, making continuous development faster with features like file synchronization and integrated CI/CD support.

This guide covers alternatives to Skaffold, comparing their approaches to Kubernetes development workflows, production deployment capabilities, and use cases to help you find the right solution for your team.

<InfoBox className="BodyStyle">

### Quick look at the top 5 Skaffold alternatives

1. [**Northflank**](https://northflank.com/) (production deployment platform) – Complete platform that deploys and scales any containerized workload in production, from microservices to ML models.
    
    > While Skaffold optimizes inner-loop development and provides building blocks for CI/CD pipelines, Northflank offers complete production infrastructure with integrated CI/CD, release workflows, preview environments, and autoscaling through an interface that abstracts Kubernetes complexity. Deploy to Northflank's [managed cloud](https://northflank.com/features/managed-cloud) or bring your own [GKE](https://northflank.com/cloud/gcp), [EKS](https://northflank.com/cloud/aws), [AKS](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), [OKE](https://northflank.com/cloud/oci), or bare-metal clusters in minutes.
    > 
2. **Tilt** – Local Kubernetes development tool with a web UI for real-time feedback and live updates across microservices.
3. **DevSpace** – CLI tool with dev containers, file synchronization, and pipeline automation for Kubernetes development.
4. **Garden** – Development automation platform with graph-based dependency management for complex microservice architectures.
5. **Telepresence** – CNCF tool that connects local development environments to remote Kubernetes clusters without containerization.

</InfoBox>

## What to consider when evaluating Skaffold alternatives

Not all Kubernetes development tools solve the same problems. Before evaluating alternatives, clarify what you actually need.

1. **Development stage focus:**
    
    Does the tool optimize local development iteration, production deployment, or both? Understand where your bottleneck actually is – inner loop development or post-commit workflows. Tools like Skaffold and Tilt focus on fast local iteration, while platforms like Northflank handle deployment, scaling, and operations after code is committed.
    
2. **Cluster requirements:**
    
    Where will applications run during development? Consider whether your team needs local clusters like minikube, remote development environments, or production-grade infrastructure. Some teams prefer local control, while others want to avoid local cluster management entirely.
    
3. **Configuration complexity:**
    
    How much Kubernetes knowledge does your team have? Skaffold requires understanding of Dockerfiles and Kubernetes manifests. Some alternatives require similar expertise, while others like Northflank abstract complexity entirely, allowing developers to deploy without writing YAML.
    
4. **Live update capabilities:**
    
    Can the tool update running containers without full rebuilds? This is critical for fast iteration. Check if live updates support your stack, especially for compiled languages.
    
5. **CI/CD integration:**
    
    Does the tool provide building blocks for existing pipelines, or complete CI/CD automation? Skaffold offers commands you can integrate into Jenkins or GitHub Actions. Other solutions like Northflank include built-in CI/CD with automated builds, deployments, and release management.
    
6. **Multi-environment support:**
    
    Can you use the same tool and configuration for development, staging, and production? Skaffold supports profiles for different environments, but requires separate production deployment solutions. Platforms like Northflank provide unified workflows across all environments.
    
7. **Production readiness:**
    
    Does the solution support production workloads with high availability, monitoring, autoscaling, and rollbacks? Development-focused tools like Skaffold optimize local iteration and provide CI/CD building blocks, but require additional tooling for production operations. Production platforms like Northflank deliver both fast deployments and production reliability at scale.
    
8. **Cost structure:**
    
    What's the total cost of ownership? Open-source tools like Skaffold are free but require infrastructure management and operational overhead. Managed platforms charge for services but reduce the burden on engineering teams.
    

The right alternative depends on whether you're replacing local development capabilities, looking for production deployment infrastructure, or need a solution that handles both.

## Top 5 Skaffold alternatives for Kubernetes development and deployment

See a detailed comparison of each alternative below, including their key capabilities, use cases, and how they differ from Skaffold.

### 1. Northflank

[Northflank](https://northflank.com/) takes a fundamentally different approach from Skaffold. While Skaffold optimizes the inner loop for local Kubernetes development, Northflank is a production deployment platform that runs your applications at scale.

<InfoBox className="BodyStyle">

**How Northflank differs from Skaffold**

Skaffold is a development tool for the inner loop. You use Skaffold to iterate rapidly on your local machine or development cluster, seeing code changes reflected quickly through automated builds and file sync. Skaffold helps you write code, build containers, and test locally before committing. It's designed to make local microservice development fast and efficient.

> Northflank handles everything that happens after you commit code. Deploy applications to production, scale workloads automatically, manage release pipelines, provision preview environments from pull requests, monitor services, handle secrets, and orchestrate databases – all from a unified platform.
> 

While Skaffold requires you to set up and manage your own Kubernetes cluster (whether local or remote) and create Dockerfiles and Kubernetes manifests, Northflank provides production-grade Kubernetes infrastructure out of the box. Deploy to Northflank's [managed cloud](https://northflank.com/features/managed-cloud) or bring your own [GKE](https://northflank.com/cloud/gcp), [EKS](https://northflank.com/cloud/aws), [AKS](https://northflank.com/cloud/azure), or bare-metal clusters through [BYOC](https://northflank.com/features/bring-your-own-cloud) (Bring Your Own Cloud).

</InfoBox>

**Key capabilities of Northflank**

1. **Production deployment platform** – Complete infrastructure for running applications at scale with [CI/CD automation](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank), [release pipelines](https://northflank.com/docs/v1/application/release/create-a-pipeline-and-release-flow) with promotion workflows, [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) from pull requests, [secrets management](https://northflank.com/docs/v1/application/secure/manage-secret-groups), [monitoring](https://northflank.com/docs/v1/application/observe/monitor-containers) and [logging](https://northflank.com/docs/v1/application/observe/view-logs), [autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments), [health checks](https://northflank.com/docs/v1/application/observe/configure-health-checks), [rollbacks](https://northflank.com/docs/v1/application/release/run-and-manage-releases), and [RBAC](https://northflank.com/docs/v1/application/secure/use-role-based-access-control).
2. **Multi-cloud Kubernetes** – Deploy to Northflank's [managed cloud](https://northflank.com/features/managed-cloud) across global regions, or connect your own [GKE](https://northflank.com/cloud/gcp), [EKS](https://northflank.com/cloud/aws), [AKS](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), [OKE](https://northflank.com/cloud/oci), or bare-metal clusters. BYOC ([Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud)) provides a fully managed platform experience inside your VPC with complete control over data residency and security.
3. **Unified workload management** – Run any containerized workload: microservices, APIs, databases (PostgreSQL, MySQL, MongoDB, Redis), background jobs, scheduled tasks, and GPU-accelerated applications. Deploy from Docker images or build automatically from Git repositories without writing Dockerfiles or Kubernetes manifests.
4. **GitOps CI/CD** – Automatic builds and deployments triggered by commits, with support for GitHub, GitLab, and Bitbucket. No configuration files required.
5. **Developer experience** – No YAML required. Configure everything through an intuitive UI, API, or CLI. Build and deploy in minutes, not days. Access real-time logs, metrics, and container state without kubectl.

<InfoBox className="BodyStyle">

**Why teams use Northflank alongside or instead of Skaffold**

Teams using Skaffold for local development often need robust production infrastructure for running applications at scale. Northflank addresses deployment, scaling, and operational management in a single platform.

You can use both Skaffold and Northflank together:

- Use Skaffold for rapid inner loop development on your local machine
- Push to Git when ready
- Let Northflank handle CI/CD, preview environments, staging, and production deployment automatically

This workflow gives you the best of both worlds: fast local iteration with Skaffold, and production-ready deployment with Northflank.

Alternatively, teams often choose Northflank instead of Skaffold when they want to:

- Remove the need for local Kubernetes cluster management entirely
- Deploy preview environments automatically from pull requests
- Run development, staging, and production on the same platform
- Focus on shipping features rather than managing development infrastructure

Companies like Weights & Biases scaled to [serve millions of users](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s) using Northflank, running over 10,000 AI training jobs and half a million inference runs daily without managing Kubernetes directly. Teams across industries use Northflank to deploy everything from microservices and APIs to ML models and databases, with automatic scaling and GPU support when needed.

[Try Northflank's free developer sandbox](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to speak with an engineer. See [pricing details](https://northflank.com/pricing).

</InfoBox>

**Best for:** Teams needing production deployment infrastructure, organizations wanting to avoid local Kubernetes management, companies requiring preview environments and release pipelines, and teams wanting unified infrastructure for development through production.

### 2. Tilt

Tilt is an open-source local Kubernetes development tool with a web-based UI and real-time updates for local development.

**Key capabilities**

1. **Live updates without rebuilds** – Updates running containers in real-time without full rebuilds or redeployments.
2. **Web-based UI** – Unlike Skaffold's CLI-only approach, Tilt provides a browser interface showing build status, runtime logs, and service health.
3. **Resource dependencies** – Understands relationships between services and orchestrates startup sequences, ensuring dependencies are ready before dependent services start.
4. **Configuration** – Uses Starlark (a Python-like language) for configuration, providing more programming flexibility than YAML-based tools.
5. **Integration support** – Works with Docker, Kubernetes YAML, Helm charts, and custom scripts.

**Best for:** Teams managing complex microservice architectures and developers who prefer visual feedback during development.

### 3. DevSpace

DevSpace is an open-source CLI tool for developing and deploying applications on Kubernetes with dev containers, file synchronization, and pipeline automation.

**Key capabilities**

1. **Dev containers** – Creates development containers that mirror production environments while enabling hot reloading.
2. **File synchronization** – Changes to local files sync automatically to containers running in Kubernetes.
3. **Port forwarding and streaming** – Automatic port forwarding to services and real-time log streaming from multiple containers.
4. **Pipeline automation** – Define custom pipelines for build, deploy, and test workflows with hooks and custom commands.
5. **Namespace isolation** – Multi-tenancy support allows multiple developers to work in isolated namespaces on shared clusters.

**Best for:** Teams wanting development and production environment parity and developers comfortable with CLI-based workflows.

### 4. Garden

Garden takes a project-level approach to Kubernetes development, building a dependency graph of your entire application stack.

**Key capabilities**

1. **Graph-based dependencies** – Analyzes relationships between services at build, deploy, and test phases, understanding dependencies and optimizing the workflow accordingly.
2. **Multi-environment testing** – Built-in support for running automated tests in Kubernetes environments, including unit tests and integration tests.
3. **Service-level caching** – Rebuilds and redeploys only services that have changed or depend on changed services.
4. **Stack-wide operations** – Perform operations across your entire stack while Garden handles dependencies automatically.
5. **Remote Kubernetes support** – Deploy to cloud-based development clusters, reducing local resource requirements.

**Best for:** Teams managing complex microservice architectures with intricate dependencies and teams wanting project-level development tools.

### 5. Telepresence

Telepresence is a CNCF tool that connects your local development environment to a remote Kubernetes cluster, allowing you to run services locally while accessing cluster resources.

**Key capabilities**

1. **Local-to-cluster networking** – Creates a network tunnel between your laptop and a Kubernetes cluster, making cluster services accessible as if running locally.
2. **Service interception** – Intercepts traffic destined for a service in the cluster and redirects it to your local machine for testing local code changes against real dependencies.
3. **No containerization required** – Run code directly on your laptop using your IDE, debugger, and development tools without building containers for every change.
4. **Fast feedback loops** – Changes to local code are immediately testable against remote dependencies.
5. **Traffic filtering** – Create personal intercepts that redirect specific traffic to your local machine, avoiding disruption to other developers.

**Best for:** Teams working with remote Kubernetes clusters and developers wanting to reduce build times during development.

## Choosing the right Skaffold alternative

Selecting the right tool depends on what stage of the development lifecycle you're optimizing and whether you need just development tools or complete production infrastructure.

| Scenario | Tool type | When to use |
| --- | --- | --- |
| **Local development tools** (Skaffold, Tilt, DevSpace, Garden) | Inner loop focused | Fast local iteration with control over your development environment, offline capability, quick feedback before committing, and preference for local cluster management. |
| **Remote development tools** (Telepresence) | Connect local to remote | Reduce build times by running code locally while accessing remote cluster services, debug with local tools against production-like dependencies, and lower laptop resource requirements. |
| **Production platforms** (Northflank) | Post-commit workflows | Production-grade infrastructure with high availability, automated CI/CD and release workflows, preview environments from PRs, unified platform for all environments (dev/staging/prod), multi-cloud support, and ability to run any workload including microservices, databases, jobs, and GPUs. |
| **Use both together** | Optimal workflow | Local development tool (Skaffold, Tilt) for rapid iteration, production platform (Northflank) for everything after git push, giving you fast feedback loops plus production reliability. |

## Getting started with the right tool for your workflow

Many teams start with local development tools and later adopt production platforms as they scale. Some teams skip local Kubernetes entirely and use remote development environments or preview environments on production platforms.

[Northflank](https://northflank.com/) bridges the gap between development and production by providing a complete platform that handles both preview environments for development and production workloads at scale. You get fast iteration through preview environments combined with production-grade infrastructure, taking out the complexity of managing multiple tools.

<InfoBox className="BodyStyle">

Start with a solution that matches your primary use case. [Try Northflank's free developer sandbox](https://app.northflank.com/signup) or [schedule a demo](https://cal.com/team/northflank/northflank-intro) with an engineer to see how a unified deployment platform handles the full lifecycle from development to production.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Top 5 Tilt alternatives for Kubernetes development and deployment in 2026</title>
  <link>https://northflank.com/blog/tilt-alternatives</link>
  <pubDate>2025-10-14T15:30:00.000Z</pubDate>
  <description>
    <![CDATA[Find your Tilt alternative. Northflank for production deployment, or open-source tools like Skaffold, DevSpace, and Garden for Kubernetes.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/tilt_alternatives_fdabe18ed3.png" alt="Top 5 Tilt alternatives for Kubernetes development and deployment in 2026" />Tilt is an open-source toolkit for Kubernetes development that provides fast inner-loop iteration with live updates and a web UI for microservice development.

This guide covers alternatives to Tilt, comparing their approaches to Kubernetes development, production deployment capabilities, and use cases to help you find the right solution for your workflow.

<InfoBox className="BodyStyle">

## Quick look at the top Tilt alternatives

1. [**Northflank**](https://northflank.com/) (production deployment platform) – Complete platform that deploys and scales ANY containerized workload in production, from ML models to microservices.
    
    > Unlike Tilt's local development focus, Northflank provides production-grade infrastructure with CI/CD pipelines, release workflows, preview environments, and autoscaling. Deploy to Northflank's managed cloud or bring your own EKS, GKE, AKS clusters in minutes.
    > 
2. **Skaffold** – Google's command-line tool for continuous development on Kubernetes that automates the build, push, and deploy workflow. 
3. **DevSpace** – CLI tool for developing and deploying applications on Kubernetes with file sync, port forwarding, and development containers.
4. **Garden** – Development automation tool with a graph-based approach to managing complex dependencies across microservices.
5. **Okteto** – Cloud development environments that sync local code changes to remote Kubernetes clusters.

</InfoBox>

## What to look out for when searching for Tilt alternatives

Not all Kubernetes development tools solve the same problems. Before evaluating alternatives, clarify what you actually need.

1. **Development stage focus**:
    
    Does the tool optimize local development iteration, production deployment, or both? Understand where your bottleneck actually is – inner loop development or post-commit workflows. Platforms like Northflank handle deployment, scaling, and operations after code is committed.
    
2. **Cluster requirements**:
    
    Where will applications run during development? Consider whether your team needs local clusters, remote development environments, or production-grade infrastructure.
    
3. **Configuration complexity**:
    
    How much Kubernetes knowledge does your team have? Some tools require an understanding of Kubernetes concepts and YAML manifests, while others, like Northflank, abstract complexity entirely.
    
4. **Live update capabilities**:
    
    Can the tool update running containers without full rebuilds? This is critical for fast iteration. Check if it supports your stack, especially for compiled languages. Tools like Northflank support this.
    
5. **CI/CD integration**:
    
    Does the tool provide building blocks for existing pipelines, or complete CI/CD automation? Ensure compatibility with your current systems. Some tools offer commands you integrate into Jenkins or GitHub Actions, while platforms like Northflank include built-in CI/CD with automated builds and deployments.
    
6. **Multi-environment support**:
    
    Can you use the same tool and configuration for development, staging, and production? Using different tools for each environment creates inconsistencies. Platforms like Northflank provide unified workflows across all environments, while local-focused tools typically require separate production deployment solutions.
    
7. **Production readiness**:
    
    Does the solution support production workloads with high availability, monitoring, autoscaling, and rollbacks? Development-only tools prioritize local iteration speed, while production platforms like Northflank deliver both fast deployments and production reliability at scale.
    
8. **Cost structure**:
    
    What's the total cost of ownership? Open-source tools are free but require infrastructure management. Managed platforms charge for services but reduce operational overhead.
    

The right alternative depends on whether you're replacing local development capabilities, looking for production deployment infrastructure, or need a solution that handles both.

## Top 5 Tilt alternatives for Kubernetes development and deployment

See a detailed comparison of each alternative below, including their key capabilities, use cases, and how they differ from Tilt.

### 1. Northflank

[Northflank](https://northflank.com/) takes a fundamentally different approach from Tilt. While Tilt optimizes local Kubernetes development and the inner loop, Northflank is a production deployment platform that runs your applications at scale.

<InfoBox className="BodyStyle">

**How Northflank differs from Tilt**

Tilt is a development tool for the inner loop. You use Tilt to iterate rapidly on your local machine or development cluster, seeing code changes reflected in seconds through live updates. Tilt helps you write code, build containers, and test locally before committing. It's designed to make local microservice development fast and painless.

Northflank handles everything that happens *after* you commit code. Deploy applications to production, scale workloads automatically, manage release pipelines, provision preview environments from pull requests, monitor services, handle secrets, and orchestrate databases – all from a unified platform.

While Tilt requires you to set up and manage your own Kubernetes cluster (local or remote), Northflank provides production-grade Kubernetes infrastructure out of the box. Deploy to Northflank's [managed cloud](https://northflank.com/features/managed-cloud) or bring your own [GKE](https://northflank.com/cloud/gcp), [EKS](https://northflank.com/cloud/aws), [AKS](https://northflank.com/cloud/azure), or bare-metal clusters through [BYOC](https://northflank.com/features/bring-your-own-cloud) (Bring Your Own Cloud).

</InfoBox>

**Key capabilities of Northflank**

- **Production deployment platform** – Complete infrastructure for running applications at scale with [CI/CD automation](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank), [release pipelines](https://northflank.com/docs/v1/application/release/create-a-pipeline-and-release-flow) with promotion workflows, [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) from pull requests, [secrets management](https://northflank.com/docs/v1/application/secure/manage-secret-groups), [monitoring](https://northflank.com/docs/v1/application/observe/monitor-containers) and [logging](https://northflank.com/docs/v1/application/observe/view-logs), [autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments), [health checks](https://northflank.com/docs/v1/application/observe/configure-health-checks), [rollbacks](https://northflank.com/docs/v1/application/release/run-and-manage-releases), and [RBAC](https://northflank.com/docs/v1/application/secure/use-role-based-access-control).
- **Multi-cloud Kubernetes** – Deploy to Northflank's [managed cloud](https://northflank.com/features/managed-cloud) across global regions, or connect your own [GKE](https://northflank.com/cloud/gcp), [EKS](https://northflank.com/cloud/aws), [AKS](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), [OKE](https://northflank.com/cloud/oci), or bare-metal clusters. BYOC ([Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud)) provides a fully managed platform experience inside your VPC with complete control over data residency and security.
- **Unified workload management** – Run any containerized workload: microservices, APIs, databases (PostgreSQL, MySQL, MongoDB, Redis), background jobs, scheduled tasks, and GPU-accelerated applications. Deploy from Docker images or build automatically from Git repositories.
- **GitOps CI/CD** – Automatic builds and deployments triggered by commits, with support for GitHub, GitLab, and Bitbucket.
- **Developer experience** – No YAML required. Configure everything through an intuitive UI, powerful API, or CLI. Build and deploy in minutes, not days. Access real-time logs, metrics, and container state without kubectl.

<InfoBox className="BodyStyle">

**Why teams use Northflank alongside or instead of Tilt**

Teams using Tilt for local development often need robust production infrastructure for running applications at scale. Northflank addresses deployment, scaling, and operational management in a single platform.

You can use **both** Tilt and Northflank together:

- Use Tilt for rapid inner loop development on your local machine
- Push to Git when ready
- Let Northflank handle CI/CD, preview environments, staging, and production deployment automatically

This workflow gives you the best of both worlds: fast local iteration with Tilt, and production-ready deployment with Northflank.

Alternatively, teams often choose Northflank instead of Tilt when they want to:

- Remove the need for local Kubernetes cluster management entirely
- Deploy preview environments automatically from pull requests
- Run development, staging, and production on the same platform
- Focus on shipping features rather than managing development infrastructure

Companies like Weights & Biases scaled to [serve millions of users](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s) using Northflank, running over 10,000 AI training jobs and half a million inference runs daily without managing Kubernetes directly. AI companies deploy models trained anywhere, serving inference endpoints with automatic scaling and GPU provisioning.

[Try Northflank's free developer sandbox](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to speak with an engineer. See [pricing details](https://northflank.com/pricing).

</InfoBox>

**Best for:** Teams needing production deployment infrastructure, organizations wanting to avoid local Kubernetes management, companies requiring preview environments and release pipelines, and teams wanting unified infrastructure for development through production.

### 2. Skaffold

Skaffold is Google's open-source CLI tool for continuous development on Kubernetes, designed to handle the full workflow from code to deployment.

**Key capabilities**

- Pluggable build system with support for Dockerfile, Jib, Buildpacks, Bazel, and custom scripts
- Automated deployment to local or remote Kubernetes clusters using kubectl, Helm, or Kustomize
- File synchronization for fast updates without rebuilding containers
- Built-in debugging support for Java, Node.js, Python, and Go applications
- CI/CD integration for both inner and outer loop workflows
- Multi-environment configuration with profiles for dev, staging, and prod

**Best for:** Teams comfortable with CLI tools, organizations using Google Cloud, and developers wanting flexibility in build and deployment strategies.

### 3. DevSpace

DevSpace is an open-source CLI tool for developing and deploying applications on Kubernetes with file sync, port forwarding, and development containers.

**Key capabilities**

- Dev containers that mirror production environments while enabling hot reloading
- File synchronization between local machine and containers running in Kubernetes
- Port forwarding and log streaming for debugging
- Pipeline automation for build, deploy, and test workflows
- Namespace isolation for multi-tenancy and team development
- Hooks and custom commands for extending functionality

**Best for:** Teams wanting an open-source alternative with customization options, organizations needing development and production parity.

### 4. Garden

Garden takes a project-level approach to Kubernetes development, building a dependency graph of your entire stack.

**Key capabilities**

- Graph-based dependencies that understand relationships between services at build, deploy, and test phases
- Multi-environment testing with automated test execution in Kubernetes
- Service-level caching for faster builds and deployments
- Stack-wide operations that understand service dependencies
- Remote Kubernetes support for cloud-based development clusters
- Plugin system for extending capabilities

**Best for:** Teams managing complex microservice architectures with intricate dependencies, organizations needing advanced testing workflows.

### 5. Okteto

Okteto provides cloud development environments that sync local code changes to remote Kubernetes clusters in real-time.

**Key capabilities**

- Remote development environments that reduce the need for local Kubernetes
- File synchronization between local IDE and remote containers
- SSH server support for direct container access
- Automatic environment cleanup for temporary development spaces
- Namespace per developer for complete isolation
- CLI and cloud platform for both open-source and managed options

**Best for:** Teams preferring remote development over local clusters, organizations wanting to reduce laptop resource requirements.

## How to choose the right solution

Selecting the right tool depends on what stage of the development lifecycle you're optimizing.

| **Scenario** | **Tool Type** | **When to Use** |
| --- | --- | --- |
| **Local development tools** (Tilt, Skaffold, DevSpace) | Inner loop focused | Fast local iteration with control over your development environment, offline capability, and quick feedback before committing. |
| **Production platforms** (Northflank) | Post-commit workflows | Production-grade infrastructure with high availability, automated CI/CD and release workflows, preview environments from PRs, unified platform for all environments (dev/staging/prod), multi-cloud support, and runs any workload including apps, databases, jobs, and GPUs |
| **Use both together** | Optimal workflow | Local tool (Tilt, Skaffold) for rapid development iteration, production platform (Northflank) for everything after git push |

## Start optimizing your Kubernetes workflow

Many teams start with local development tools and later adopt production platforms as they scale. Some teams skip local Kubernetes entirely and use remote development environments or preview environments on production platforms.

[Northflank](https://northflank.com/) bridges the gap between development and production by providing a complete platform that handles both preview environments for development and production workloads at scale. You get fast iteration through preview environments combined with production-grade infrastructure, taking out the complexity of managing multiple tools.

<InfoBox className="BodyStyle">

Start with a solution that matches your primary use case. [Try Northflank's Developer free sandbox](https://app.northflank.com/signup) or [schedule a demo](https://cal.com/team/northflank/northflank-intro) with an Engineer to see how a unified deployment platform handles the full lifecycle from development to production.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Top 5 Lightning AI alternatives for ML teams in 2026</title>
  <link>https://northflank.com/blog/lightning-ai-alternatives</link>
  <pubDate>2025-10-13T15:15:00.000Z</pubDate>
  <description>
    <![CDATA[Compare Lightning AI alternatives: Northflank for deployment, Modal, Replicate, Runpod, and SageMaker. Find the right ML platform for 2026]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/lightning_ai_alternatives_bf0b756af6.png" alt="Top 5 Lightning AI alternatives for ML teams in 2026" />Lightning AI has become a popular platform for teams building and deploying machine learning models, offering specialized studios and GPU infrastructure for ML development.

This guide covers five alternatives to Lightning AI, comparing their technical approaches, deployment capabilities, and use cases to help you find the right solution for your ML workloads.

<InfoBox className="BodyStyle">

## Top 5 Lightning AI alternatives for ML teams

1. **[Northflank](https://northflank.com/)** (all-in-one deployment platform) – Complete unified platform that deploys and runs ANY containerized workload, including ML models, APIs, and GPU-accelerated applications.
    
    > Unlike Lightning AI's specialized ML studio approach, Northflank provides production-grade infrastructure for deploying trained models, serving inference endpoints, and running AI applications alongside your other services. Deploy ML workloads, databases, web apps, and GPU jobs through a unified interface in minutes.
    > 
2. **Modal** – Serverless platform for ML and AI applications with GPU support. Python-first developer experience for training models, running inference, and deploying ML workloads.
3. **Replicate** – Platform for deploying and running machine learning models with API access.
4. **Runpod** – GPU cloud platform for training models and running inference with serverless and dedicated compute options.
5. **Amazon SageMaker** – Comprehensive enterprise ML platform covering the full lifecycle from notebooks to production deployment.

</InfoBox>

## What is Lightning AI?

Lightning AI is a specialized platform for AI development that offers Lightning AI Studios, cloud-based IDEs designed specifically for machine learning workflows, including model training, fine-tuning, and experimentation.

Key features include:

- **AI development studios** – Cloud IDEs with pre-configured ML environments
- **Multi-cloud GPU marketplace** – Access to GPUs from AWS, GCP, Lambda, Nebius, and other providers
- **Zero-setup environment** – Pre-built templates for common ML tasks and frameworks
- **Collaborative coding** – Real-time collaboration features for ML teams
- **Automated scaling** – Infrastructure that scales with training workloads

Lightning AI focuses on the development and training phases of machine learning, providing data scientists with powerful tools for experimentation and model development.

## Understanding the difference: ML development platforms vs deployment platforms

Understanding this distinction is essential when evaluating Lightning AI alternatives.

**ML development platforms** like Lightning AI and Modal provide environments for building and training models. They offer notebooks, IDEs, and GPU access for experimentation. These platforms help data scientists write code, run training jobs, and iterate on models. While they may offer deployment features, their primary focus and differentiation is the development and training workflow.

**Deployment platforms** like [Northflank](https://northflank.com/) provide infrastructure for running trained models in production. They handle containerized ML applications, serve inference endpoints, manage scaling under load, and integrate with your existing infrastructure. These platforms focus on reliability, performance, and operational requirements for production AI workloads.

<InfoBox className="BodyStyle">

**All-in-one deployment platforms** like Northflank go further by running your entire application stack - not just ML models, but also APIs, databases, web applications, and background jobs.

This means you can deploy your ML inference endpoints alongside the web applications that use them, the databases that store results, and the APIs that orchestrate everything, all in one unified platform.

</InfoBox>

Some platforms attempt both development and deployment, while others specialize in one area. When choosing a Lightning AI alternative, determine if you need development tools, production deployment infrastructure, or both.

## What to consider when choosing a Lightning AI alternative

Understanding your specific requirements helps narrow down the right solution.

- **Primary use case** – Are you training models, deploying them to production, or both? Development platforms optimize for experimentation while deployment platforms prioritize reliability and scale.
- **GPU requirements** – What GPU types and quantities do you need? Training large models requires different compute than serving inference requests.
- **Production readiness** – Does the solution support production deployments with high availability, monitoring, and security? Development notebooks and production infrastructure have different requirements.
- **Cost structure** – How are you charged for compute? Options include pay-per-use, subscriptions, reserved instances, and spot pricing. Training costs differ significantly from inference serving.
- **Integration requirements** – Does the solution work with your existing MLOps tools and CI/CD pipelines? Deployment platforms need to integrate with your infrastructure, while development platforms focus on data science tools.
- **Team expertise** – What skills does your team have? Some solutions require Kubernetes knowledge, while others abstract infrastructure complexity.
- **Deployment velocity** – How quickly can you move from trained model to production? Some platforms handle this end-to-end while others require manual steps.

The following solutions represent different approaches to ML infrastructure, from complete deployment platforms to specialized development environments.

## Top 5 Lightning AI alternatives for ML teams

See a detailed comparison of each alternative below, including their key capabilities, use cases, and how they differ from Lightning AI.

### 1. Northflank (all-in-one deployment platform)

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

[Northflank](https://northflank.com/) provides a fundamentally different approach from Lightning AI. Lightning AI helps you train and develop machine learning models in specialized studios. Northflank focuses on deploying and running those models in production alongside your entire application infrastructure.

<InfoBox className="BodyStyle">

**How Northflank differs from Lightning AI**

Lightning AI is a platform for ML development and training. It provides cloud IDEs, notebooks, and GPU access for data scientists building models. You train models in Lightning AI Studios, experiment with different approaches, and iterate on your ML code. While Lightning AI offers deployment capabilities, its primary strength is the development and training workflow.

Northflank is a complete deployment platform that runs your ML models in production. After training your model anywhere (Lightning AI, locally, or other platforms), deploy it to Northflank as a containerized application.

Northflank handles deployment automation, GPU provisioning, scaling, monitoring, health checks, and all operational requirements. It runs ML inference APIs, model serving endpoints, and AI applications alongside your databases, web services, and other infrastructure in one unified platform.

</InfoBox>

**Key capabilities of Northflank**

- **Multi-cloud GPU deployment** – Deploy GPU workloads to Northflank's [managed cloud](https://northflank.com/features/managed-cloud) or connect your own [GKE](https://northflank.com/cloud/gcp), [EKS](https://northflank.com/cloud/aws), [AKS](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), [OKE](https://northflank.com/cloud/oci), or bare-metal Kubernetes clusters. [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) provides a fully managed platform experience inside your VPC, maintaining complete control over data residency and security.
- **Complete production infrastructure** – All essential capabilities built-in: [CI/CD automation](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank), [release pipelines](https://northflank.com/docs/v1/application/release/create-a-pipeline-and-release-flow) with promotion workflows, [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), [secrets management](https://northflank.com/docs/v1/application/secure/manage-secret-groups), [monitoring](https://northflank.com/docs/v1/application/observe/monitor-containers) and [logging](https://northflank.com/docs/v1/application/observe/view-logs), [autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments), [health checks](https://northflank.com/docs/v1/application/observe/configure-health-checks), [rollbacks](https://northflank.com/docs/v1/application/release/run-and-manage-releases), and [RBAC](https://northflank.com/docs/v1/application/secure/use-role-based-access-control). Access everything through real-time UI, [API](https://northflank.com/docs/v1/api/use-the-api), [CLI](https://northflank.com/docs/v1/api/use-the-cli), or [JavaScript client](https://northflank.com/docs/v1/api/use-the-javascript-client).
- **Unified infrastructure management** – Run ML models, APIs, databases, web applications, and background jobs in one platform. Deploy containerized workloads from any registry or build from Git repositories automatically.

<InfoBox className="BodyStyle">

**Why teams choose Northflank for ML deployment**

Teams training models in Lightning AI or other platforms often need robust production infrastructure for serving those models. Northflank addresses deployment, scaling, and operational management in one platform.

Companies like Weights scaled to serve [millions of users](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s) using Northflank, running over 10,000 AI training jobs and half a million inference runs daily without managing autoscaling or spot instance orchestration.

AI companies use Northflank to deploy models trained anywhere, serving inference endpoints with automatic scaling, GPU provisioning, and zero infrastructure overhead.

[Try Northflank's free developer sandbox](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) (optional) to speak with an engineer. See [pricing details](https://northflank.com/pricing)

</InfoBox>

**Best for:** Teams needing production deployment for trained ML models, organizations wanting to run AI applications with GPU requirements, companies in search of a unified infrastructure for ML workloads and traditional applications, and teams wanting Kubernetes power without operational complexity.

### 2. Modal

![modal-homepage.png](https://assets.northflank.com/modal_homepage_a7380e6d35.png)

Modal provides a serverless platform for ML and AI applications with a Python-first developer experience for training and deploying ML workloads.

**Key capabilities**

- Serverless GPU compute with automatic scaling for training and inference
- Python-native API for defining compute, storage, and dependencies
- Container-based execution with custom environments
- Scheduled jobs and cron workflows for ML pipelines
- Built-in secrets management and volume storage

**Best for:** Teams using Python-based ML workflows and organizations needing serverless compute for both training and inference workloads.

*See [6 best Modal alternatives for ML, LLMs, and AI app deployment](https://northflank.com/blog/6-best-modal-alternatives)*

### 3. Replicate

![replicate-homepage.png](https://assets.northflank.com/replicate_homepage_38062bccda.png)

Replicate provides a platform for deploying and running machine learning models through API calls.

**Key capabilities**

- Deploy any machine learning model with a simple API interface
- Automatic scaling from zero to handle traffic spikes
- Support for custom models and popular open-source models
- Docker-based deployment with custom dependencies

**Best for:** Teams needing model deployment via API, organizations running inference workloads with variable traffic.

*See [6 best Replicate alternatives for ML, LLMs, and AI app deployment](https://northflank.com/blog/6-best-replicate-alternatives)*

### 4. Runpod

![runpod-homepage.png](https://assets.northflank.com/runpod_homepage_a696c3aa97.png)

Runpod offers GPU cloud infrastructure optimized for AI workloads with both serverless and dedicated compute options.

**Key capabilities**

- Serverless GPU compute with automatic scaling
- Dedicated GPU pods for consistent performance
- Pre-built templates for common ML frameworks
- Community marketplace for GPU capacity
- Container-based deployments with Docker support

**Best for:** Teams running inference workloads at scale, and companies deploying containerized ML applications with varying compute demands.

*See [RunPod alternatives for AI/ML deployment beyond just a container](https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment)*

### 5. Amazon SageMaker

![aws-sagemaker-homepage.png](https://assets.northflank.com/aws_sagemaker_homepage_ab47bb19cc.png)

Amazon SageMaker provides a comprehensive enterprise ML platform covering the full machine learning lifecycle from development to production deployment.

**Key capabilities**

- Managed Jupyter notebooks with scalable compute
- Automated model training with hyperparameter tuning
- Model deployment with managed endpoints and monitoring
- MLOps capabilities including pipelines, model registry, and experiment tracking
- Integration with AWS services for data, security, and governance

**Best for:** Organizations using AWS infrastructure, teams with governance and compliance requirements, and companies with existing AWS data services.

*See [AWS SageMaker alternatives: Top 6 platforms for MLOps in 2026](https://northflank.com/blog/aws-sagemaker-alternatives-top-6-platforms-for-ml-ops)*

## Making the right choice for your ML workloads

Choosing the right Lightning AI alternative depends on understanding what stage of the ML lifecycle you're optimizing.

Development-focused solutions like Modal and Lightning AI itself provide environments for training models and experimenting with approaches.

Deployment-focused platforms provide production infrastructure for running trained models and serving inference endpoints.

<InfoBox className="BodyStyle">

[**Northflank**](https://northflank.com/) stands out as an all-in-one deployment platform that runs your entire application stack - ML models alongside APIs, databases, and web applications in a unified infrastructure.

Start with a solution that matches your primary use case. [Try Northflank's free sandbox](https://app.northflank.com/signup) or [schedule a demo](https://cal.com/team/northflank/northflank-intro) to see how a unified deployment platform handles ML production workloads alongside your entire application infrastructure.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Top 5 Backstage alternatives for platform engineering teams in 2026</title>
  <link>https://northflank.com/blog/backstage-alternatives</link>
  <pubDate>2025-10-10T15:50:00.000Z</pubDate>
  <description>
    <![CDATA[Compare 5 Backstage alternatives for platform engineering teams. Technical analysis of Northflank, Cycloid, Port, Cortex, and OpsLevel for 2026.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/backstage_alternatives_05a56e2d2f.png" alt="Top 5 Backstage alternatives for platform engineering teams in 2026" />Backstage has become the go-to framework for teams building internal developer portals, but its open-source nature comes with significant implementation and maintenance demands.

This guide covers five alternatives to Backstage, comparing their technical approaches, implementation requirements, and use cases to help you find the right solution for your team.

<InfoBox className="BodyStyle">

## TL;DR: Quick overview of Backstage alternatives

1. [**Northflank**](https://northflank.com/) – Complete unified platform that builds, deploys, and runs your infrastructure while providing centralized visibility.
    
    > Unlike Backstage's approach, Northflank handles workload execution with CI/CD, deployment automation, and infrastructure management included. Deploy apps, databases, jobs, and GPU workloads through a unified interface in minutes.
    > 
2. **Cycloid** – Portal and platform combining GitOps infrastructure automation with FinOps and GreenOps monitoring. Deploys infrastructure with governance controls.
3. **Port** – No-code internal developer portal with customizable blueprints.
4. **Cortex** – Commercial developer portal for service ownership and standards enforcement with scorecards and engineering metrics.
5. **OpsLevel** – Internal developer portal with automated catalog maintenance using AI-powered updates.

</InfoBox>

## What's the difference between developer portals and deployment platforms?

Understanding this distinction is essential when evaluating Backstage alternatives.

Developer portals like Backstage, Port, Cortex, and OpsLevel provide centralized views of your services. They display service catalogs, documentation, and metadata about your infrastructure.

Portals help teams discover services, find owners, access documentation, and understand dependencies. They catalog and display information but don't directly build code, deploy applications, or run infrastructure. Some can trigger actions in other tools through webhooks or APIs, but the actual execution happens in separate systems.

Deployment platforms like Northflank also provide centralized views, but go further by running your workloads. They build code from repositories, deploy containers, provision databases, manage networking, and handle scaling. These platforms both display and execute infrastructure operations, giving you a unified interface for managing and operating your services.

When choosing a Backstage alternative, first determine if you need a portal for visibility, a platform for deployment and operations, or a solution offering both.

## What to look out for when evaluating Backstage alternatives

Understanding your specific requirements helps narrow down the right solution.

1. **Implementation timeline** – How quickly can your team deploy and start using the platform? Options range from immediate deployment to several months of configuration.
2. **Maintenance requirements** – Who maintains the platform long-term? Open-source frameworks require dedicated engineering teams while commercial solutions provide ongoing maintenance.
3. **Actual capabilities** – Does the solution just display information or does it execute infrastructure operations? This fundamental distinction determines what problems it solves.
4. **Developer adoption factors** – Will your developers use this platform? Interface quality, ease of use, and value delivered directly impact adoption rates.
5. **Integration approach** – Does the solution integrate with existing tools or replace them? Determine if it orchestrates your current infrastructure or requires migration.
6. **Team expertise** – What technical skills does implementation require? Some solutions need React expertise, others work with your existing knowledge base.
7. **Cost structure** – What's the total cost, including engineering time? Account for licensing, implementation effort, and ongoing maintenance when comparing options.

## Top Backstage alternatives in 2026

The following solutions represent different approaches to solving developer platform challenges, from complete execution platforms to specialized portal frameworks.

### 1. Northflank: Complete platform for building, deploying, and running infrastructure

Northflank provides a fundamentally different approach from Backstage. Backstage helps you organize, discover, and document your services. Northflank does that too, but also runs your actual infrastructure.

<InfoBox className="BodyStyle">

**How Northflank differs from Backstage**

Backstage is a framework for building portals that show you what services exist, who owns them, and where to find documentation. It provides visibility into your infrastructure. You still need separate tools to build code, deploy applications, manage databases, and handle scaling.

Northflank is a complete platform that **provides visibility and executes your workloads**. Connect your repository and Northflank automatically builds containers, deploys to production, provisions databases, configures networking, and handles scaling (all while giving you real-time monitoring, logging, and observability). It both displays and manages your infrastructure with full self-service capabilities for developers.

</InfoBox>

**Key capabilities**

- **Multi-cloud deployment** – Deploy to Northflank's [managed cloud](https://northflank.com/features/managed-cloud) or connect your own [GKE](https://northflank.com/cloud/gcp), [EKS](https://northflank.com/cloud/aws), [AKS](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), [OKE](https://northflank.com/cloud/oci), or bare-metal Kubernetes clusters. [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) provides a fully managed platform experience inside your VPC, maintaining complete control over data residency and security.
- **Infrastructure management included** – All essential platform capabilities come built-in: [CI/CD automation](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank), [release pipelines](https://northflank.com/docs/v1/application/release/create-a-pipeline-and-release-flow) with promotion workflows, [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), [secrets management](https://northflank.com/docs/v1/application/secure/manage-secret-groups), [monitoring](https://northflank.com/docs/v1/application/observe/monitor-containers) and [logging](https://northflank.com/docs/v1/application/observe/view-logs), [autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments), [health checks](https://northflank.com/docs/v1/application/observe/configure-health-checks), [rollbacks](https://northflank.com/docs/v1/application/release/run-and-manage-releases), and [RBAC](https://northflank.com/docs/v1/application/secure/use-role-based-access-control). Access everything through real-time UI, [API](https://northflank.com/docs/v1/api/use-the-api), [CLI](https://northflank.com/docs/v1/api/use-the-cli), or [JavaScript client](https://northflank.com/docs/v1/api/use-the-javascript-client).

<InfoBox className="BodyStyle">

**Why teams choose Northflank over portal-only solutions**

Teams using platforms like Backstage sometimes find they've built visibility into infrastructure they still manage through separate tools. Northflank addresses both visibility and execution in one platform.

Companies like Weights scaled to serve [millions of users](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s) using Northflank, running over 10,000 AI training jobs and half a million inference runs daily without managing autoscaling or spot instance orchestration.

Other companies use Northflank as their deployment platform, making it easier than connecting multiple tools while providing more capabilities than traditional PaaS solutions.

[Try Northflank's free developer sandbox](https://app.northflank.com/signup) | [Book a demo](https://cal.com/team/northflank/northflank-intro) | See [pricing details](https://northflank.com/pricing)

</InfoBox>

**Best for:** Teams needing infrastructure execution alongside visibility, organizations wanting to deploy production workloads immediately, companies running containerized applications and AI/ML workloads with GPU requirements, and teams in search of Kubernetes power without maintenance overhead.

### 2. Cycloid

Cycloid provides an internal developer portal with GitOps-first infrastructure automation capabilities.

**Key capabilities**

- GitOps-first infrastructure automation with self-service portal interface
- Service catalog with StackForms abstracting infrastructure configuration through intuitive forms
- Infrastructure deployment and application management with governance controls
- FinOps and GreenOps modules for cloud cost and carbon footprint monitoring
- Plugin system supporting official, community, and custom integrations

**Best for:** Teams committed to GitOps workflows, organizations prioritizing FinOps and environmental impact monitoring, and platform teams needing infrastructure-as-code automation with governance controls.

### 3. Port

Port delivers a no-code approach to building internal developer portals with a focus on flexible catalog modeling and self-service capabilities.

**Key capabilities**

- Customizable blueprints for modeling data structures without coding
- No-code service catalog creation adapting to organization-specific needs
- Self-service actions using webhook backends to trigger workflows
- Scorecard systems enforcing standards across services

**Best for:** Teams prioritizing no-code customization and companies needing flexible catalog design without deep Kubernetes visibility requirements.

### 4. Cortex

Cortex provides a commercial internal developer portal platform emphasizing service ownership and standards enforcement.

**Key capabilities**

- Service catalog integrating with numerous tools to centralize service information
- Production readiness scorecards tracking compliance with organizational standards
- Initiative tracking and campaigns driving improvement across teams
- Engineering intelligence metrics providing visibility into velocity, incidents, and deployment patterns

**Best for:** Organizations prioritizing standards enforcement, companies with a budget for commercial solutions.

### 5. OpsLevel

OpsLevel provides an internal developer portal with focus on reducing manual overhead through automated catalog maintenance.

**Key capabilities**

- Automated service catalog maintenance powered by AI
- Customizable templates and built-in best practices
- Scoped scorecards for different service maturity levels
- TechDocs support for documentation hosting

**Best for:** Teams prioritizing automation to reduce operational overhead, organizations needing fast deployment timelines, and companies wanting catalog maintenance without manual YAML updates.

## Time to make your decision

Choosing the right Backstage alternative depends on understanding what problem you're solving.

Portal-focused solutions like Port, Cortex, or OpsLevel provide service catalogs, scorecards, and self-service actions for visibility and standards enforcement.

**Platforms like Northflank provide both visibility and complete infrastructure execution** - handling deployment, operations, and management alongside service discovery in one unified solution.

**Next steps:**

1. Define if you need service visibility, infrastructure execution, or both
2. Assess your team's capacity for building and maintaining frameworks
3. Test solutions with production workloads
4. Measure potential developer adoption through user feedback
5. Calculate the total cost, including engineering time investment

<InfoBox className="BodyStyle">

Start with a solution that offers both visibility and execution and also matches your technical requirements. [Try Northflank's free sandbox](https://app.northflank.com/signup) or [schedule a demo](https://cal.com/team/northflank/northflank-intro) to see how a unified platform compares to portal-only solutions.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Top 6 Humanitec alternatives for platform engineering teams in 2026</title>
  <link>https://northflank.com/blog/humanitec-alternatives</link>
  <pubDate>2025-10-09T15:56:00.000Z</pubDate>
  <description>
    <![CDATA[Compare 6 Humanitec alternatives for platform engineering teams. Technical analysis of Northflank, Backstage, Port, Cortex, Crossplane, and meshStack.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/humanitec_alternatives_d3c6577e2e.png" alt="Top 6 Humanitec alternatives for platform engineering teams in 2026" />Platform engineering teams building Internal Developer Platforms face a critical decision: *which platform orchestrator or IDP solution will best serve their organization's needs?*

While Humanitec offers platform orchestration capabilities, understanding the available alternatives helps you make an informed choice.

This guide covers six alternatives to Humanitec, comparing their technical approaches, deployment models, and ideal use cases to help you find the right fit for your team.

<InfoBox className="BodyStyle">

## TL;DR: Quick overview of Humanitec alternatives

1. [**Northflank**](https://northflank.com/) –  Complete unified platform that delivers production-ready infrastructure immediately.
    
    > Unlike Humanitec's orchestrator-only approach, Northflank provides the full stack from build to deployment to observability, with zero YAML maintenance. Works across any cloud with a true [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) feature (deploying in your own VPC). Deploy apps, databases, jobs, and GPU workloads through a single interface in minutes.
    > 
2. **Backstage** – Open-source developer portal framework for service catalogs and documentation. Requires dedicated engineers to build and maintain.
3. **Port** – No-code internal developer portal with customizable blueprints.
4. **Cortex** – Commercial developer portal for service ownership and standards enforcement with scorecards and metrics.
5. **Crossplane + ArgoCD** – Open-source GitOps stack managing infrastructure as Kubernetes resources. Requires deep Kubernetes expertise.
6. **meshStack** – Enterprise cloud foundation platform for multi-cloud governance, compliance, and account management.

</InfoBox>

## What's the difference between platform orchestrators and developer portals?

Platform orchestrators and developer portals solve different problems in your Internal Developer Platform architecture.

A **platform orchestrator** is the backend engine that coordinates infrastructure provisioning and configuration management. It generates application and infrastructure configurations dynamically with every deployment, enforcing standards across teams and workflows.

An **internal developer portal** is the frontend interface where developers discover services, access documentation, and trigger self-service actions.

<InfoBox className="BodyStyle">

Top-performing engineering organizations often combine both: a platform orchestrator handling backend automation with a developer portal providing the user interface.

However, modern platforms like [Northflank](https://northflank.com/) integrate both concerns into a unified solution, removing the need to connect multiple tools.

</InfoBox>

## What to look out for when evaluating Humanitec alternatives

When assessing alternatives to Humanitec, look out for these technical and organizational factors:

1. **Architectural approach** – Does the solution provide complete platform capabilities or just orchestration? Humanitec requires you to build the developer interface and integrate existing tools. Some alternatives, like Northflank, offer unified platforms with everything included.
2. **Time to production** – How quickly can your team deploy production workloads? Setup complexity varies significantly, from 30 minutes to six months, depending on the solution.
3. **Workload support** – What types of workloads does the platform handle natively? This includes applications, databases, scheduled jobs, GPU workloads, and other compute types your team needs.
4. **Cloud strategy alignment** – Does the platform support your preferred cloud providers and deployment models? Assess multi-cloud capabilities and determine if you need [Bring Your Own Cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) (BYOC) functionality.
5. **Developer experience** – Will your developers adopt this platform? The best technical platform is useless if it sits unused. Consider interface quality, learning curve, and workflow integration.
6. **Maintenance requirements** – Who maintains the platform? Open-source frameworks require dedicated engineering teams. Commercial platforms provide ongoing maintenance but vary in required customization effort.
7. **Configuration management** – How do developers define workload requirements? Some platforms require custom specifications, others work with standard Dockerfiles, or remove YAML entirely.
8. **Integration ecosystem** – Does the platform work with your existing CI/CD, IaC tools, and infrastructure? Determine if it orchestrates existing tools or replaces them entirely.

## Top Humanitec alternatives in 2025

The following platforms represent the leading alternatives for teams building Internal Developer Platforms, each offering distinct approaches to solving platform engineering challenges.

### 1. Northflank: Complete platform for immediate production deployment

[Northflank](https://northflank.com/) provides a unified developer platform that delivers production-ready infrastructure immediately, without the complexity of assembling multiple tools.

Unlike Humanitec's orchestrator-only approach that requires building additional components, Northflank offers the complete stack from build to deployment to observability.

<InfoBox className="BodyStyle">

Northflank abstracts Kubernetes complexity through a single platform. You can deploy applications, databases, jobs, and GPU workloads without managing YAML configurations.

The platform handles the entire post-commit pipeline automatically. Connect your repository, and Northflank automatically builds, packages, deploys, configures TLS, and monitors, removing weeks of setup time for orchestration engines and tool integration.

</InfoBox>

**Some key features Northflank offers**

1. **Multi-cloud and BYOC implementation**:
    
    Deploy to Northflank's managed cloud or connect your own GKE, EKS, AKS, or bare-metal Kubernetes clusters through true [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud).
    
    Unlike solutions that simply connect to your infrastructure, Northflank delivers a fully managed platform experience inside your VPC, giving you complete control over data residency and security while maintaining developer simplicity.
    
2. **Zero configuration overhead**:
    
    Northflank takes out YAML maintenance entirely. While Humanitec requires Score workload specifications plus resource definitions and driver configurations, Northflank works directly with Dockerfiles or buildpacks.
    
    Visual workflows, pipelines, and templates replace manual configuration management.
    
3. **Production-grade features included**:
    
    All essential platform capabilities come built-in: CI/CD automation, release pipelines with promotion workflows, preview environments, secrets management, monitoring and logging, autoscaling, health checks, rollbacks, and RBAC.
    
    Access everything through a real-time UI, comprehensive API, or CLI.
    
<InfoBox className="BodyStyle">

**When to choose Northflank**

Northflank fits teams that need production infrastructure immediately rather than in months.

The platform works particularly well for organizations running containerized applications and AI/ML workloads requiring GPU orchestration, teams wanting Kubernetes power without Kubernetes complexity, and companies needing multi-cloud flexibility or [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) deployment while maintaining developer simplicity.

Companies like Weights scaled to [serve millions of users](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s) using Northflank, running over 10,000 AI training jobs and half a million inference runs daily without managing autoscaling or spot instance orchestration.

Other companies also use Northflank as their go-to deployment platform, making it easier than connecting multiple tools while providing more capabilities than traditional PaaS solutions.

[Try Northflank's free developer sandbox](https://app.northflank.com/signup) or [Book a demo](https://cal.com/team/northflank/northflank-intro)

</InfoBox>

### 2. Backstage: Open-source developer portal framework

Backstage provides an open-source framework for building internal developer portals, serving as the frontend interface for service discovery, documentation, and developer workflows.

**Key capabilities**

- Software catalog that tracks services, components, and ownership across your organization
- Template system for scaffolding new services using organization-defined patterns
- Extensive plugin marketplace for integrating with popular engineering tools
- TechDocs for hosting documentation directly in the portal

**Best for:** Organizations with dedicated engineers for platform development who value maximum customization over maintenance burden and plan to use separate tools for orchestration.

### 3. Port: No-code developer portal platform

Port delivers a no-code approach to building internal developer portals with comprehensive software catalogs and self-service capabilities.

**Key capabilities**

- Customizable blueprints for modeling data structures without coding
- No-code service catalog creation that adapts to organization-specific needs
- Self-service actions using webhook backends to trigger workflows
- Scorecard systems to enforce standards across services

**Best for:** Teams prioritizing no-code customization who are willing to invest time in implementation and need basic self-service workflows where flexibility in portal design is more important than immediate production readiness.

### 4. Cortex: Standards-focused developer portal

Cortex provides a commercial internal developer portal platform emphasizing service ownership and operational excellence through standards enforcement.

**Key capabilities**

- Service catalog integrating with numerous tools to centralize service information
- Production readiness scorecards that track compliance with organizational standards
- Initiative tracking and campaigns to drive improvement across teams
- Engineering intelligence metrics providing visibility into velocity, incidents, and deployment patterns

**Best for:** Organizations prioritizing standards enforcement with a budget for commercial solutions, and organizations running primarily Kubernetes-based workloads

### 5. Crossplane + ArgoCD: Open-source GitOps infrastructure stack

Combining Crossplane with ArgoCD creates a GitOps-native infrastructure management solution for organizations deeply invested in Kubernetes.

**Key capabilities**

- Crossplane extends Kubernetes to manage infrastructure resources across cloud providers as native objects
- ArgoCD implements continuous delivery using GitOps principles
- Bi-directional reconciliation loop where Git becomes the single source of truth
- Composition framework for building abstractions and golden paths

**Best for:** Teams with deep Kubernetes expertise where open-source control is essential, companies willing to invest in building abstractions, and teams where GitOps is central to development philosophy.

### 6. meshStack: Enterprise cloud foundation platform

meshStack delivers a comprehensive cloud foundation platform for large enterprises managing complex, multi-cloud environments.

**Key capabilities**

- Enterprise-grade cloud account management with automatic provisioning across AWS, Azure, and other platforms
- Advanced compliance and governance controls with modular landing zones
- Multi-cloud cost management and chargeback capabilities
- Developer Portal bundling GitOps pipelines with Infrastructure as Code support

**Best for:** Large enterprises with complex multi-cloud governance requirements where compliance and regulatory controls are critical, and teams managing large-scale infrastructure complexity.

## Time to make your decision

Choosing the right Humanitec alternative depends on your specific technical requirements, team capabilities, and organizational priorities.

Determine if you need a complete unified platform or prefer assembling separate components, assess your team's capacity for building and maintaining infrastructure, and understand how quickly you need production deployment capabilities.

The most successful platform engineering initiatives start with clear requirements and an honest assessment of internal capabilities.

**Next steps:**

1. Define your specific platform requirements and pain points
2. Assess your team's available engineering resources
3. Test solutions with production workloads
4. Measure potential developer adoption through user feedback
5. Calculate total cost including engineering time investment

<InfoBox className="BodyStyle">

Start building your Internal Developer Platform with the solution that matches your needs. [Try Northflank's free sandbox](https://app.northflank.com/signup) or [schedule a demo](https://cal.com/team/northflank/northflank-intro) to see how a unified platform compares to assembling separate orchestration and portal tools.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Top 5 Okteto alternatives for Kubernetes development environments in 2026</title>
  <link>https://northflank.com/blog/okteto-alternatives</link>
  <pubDate>2025-10-08T16:15:00.000Z</pubDate>
  <description>
    <![CDATA[Compare 5 Okteto alternatives for Kubernetes development. Technical analysis of Northflank, Tilt, Skaffold, Telepresence, and Bunnyshell platforms.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/okteto_alternatives_30d37b6acc.png" alt="Top 5 Okteto alternatives for Kubernetes development environments in 2026" />You're building on Kubernetes, and you need development environments that don't slow your team down with lengthy build cycles or complex setup processes.

Okteto has been a reliable option for teams needing automated development environments with hot reload capabilities, but the platform ecosystem now includes multiple specialized solutions.

This is a technical comparison of five alternatives that solve similar problems, each with different deployment models, integration capabilities, and architectural approaches.

<InfoBox className="BodyStyle">

## Quick list of the top Okteto alternatives

If you're short on time, this is an overview of the platforms covered in this comparison:

1. [**Northflank**](https://northflank.com/) – Production-ready platform that handles the full application lifecycle from development through production. Built-in [features](https://northflank.com/features) including GitOps, CI/CD, ephemeral preview environments, observability, auto-scaling, and disaster recovery. Deployment flexibility across [managed cloud](https://northflank.com/cloud/northflank) or [bring-your-own-cloud](https://northflank.com/features/bring-your-own-cloud) (GKE, EKS, AKS, Civo, OKE, bare-metal). Real-time UI with sub-second updates.
2. **Tilt** –  Open-source toolkit that automates microservice development workflows. Live updates deploy code to running containers in seconds. Visual UI shows all services, logs, and errors in one view.
3. **Skaffold** – Command-line tool that automates the workflow for building, pushing, and deploying Kubernetes applications. Handles continuous development with optimized local workflows and policy-based image tagging. Works with your existing tools through pluggable architecture.
4. **Telepresence** – Establishes a two-way tunnel between your local machine and remote Kubernetes cluster. Intercepts remote traffic and routes it to your local service for development and testing. Access remote cluster resources as if running locally while using your preferred local tools.
5. **Bunnyshell** – Ephemeral environments platform that creates isolated, production-like environments for every pull request. Integrates with GitHub, GitLab, Bitbucket, Kubernetes, and major cloud providers. Auto-shuts down idle environments to optimize cloud costs.

</InfoBox>

## Understanding the Kubernetes development challenge

Before we go into the alternatives, it's worth understanding what problem these tools solve.

Traditional Kubernetes development workflows often force teams into one of these unsatisfactory approaches:

- **Local development** with tools like Docker Desktop or Minikube, which can't replicate the complexity of production environments
- **Build-push-deploy cycles** that add 5-10 minutes of wait time for every code change
- **Direct testing in shared staging environments**, creating bottlenecks and conflicts between developers

Modern Kubernetes development platforms address these challenges by providing ephemeral, production-like environments where developers can test changes in real-time without the overhead of traditional deployment cycles.

## What Okteto does well

Okteto provides automated Kubernetes development environments with real-time code synchronization, reducing typical build cycles from 5-10 minutes to under 3 seconds.

Key Okteto features include:

- **Development containers** that mirror production configurations
- **Hot reload capabilities** for instant feedback on code changes
- **Preview environments** automatically created for pull requests
- Integration with existing Helm charts and Docker Compose files

## What to consider when choosing an Okteto alternative

When evaluating platforms for Kubernetes development environments, focus on these key factors:

1. **Deployment model**
    
    Does your team need a managed cloud solution, or deployment in your own infrastructure? Platforms like [Northflank](https://northflank.com/) offer both options through [bring-your-own-cloud](https://northflank.com/features/bring-your-own-cloud), while others operate solely as managed services or require self-hosting.
    
2. **Scope of lifecycle management**
    
    Determine if you need a platform that handles only development environments or one that manages the complete application lifecycle from development through production. Some alternatives focus exclusively on the development phase, while others provide end-to-end deployment and monitoring capabilities.
    
3. **Integration requirements**
    
    Assess how the platform integrates with your existing toolchain. Check compatibility with your CI/CD pipelines, version control systems, monitoring tools, and cloud providers. Open-source tools typically offer more flexibility but may require additional configuration.
    
4. **Team expertise**
    
    Assess your team's Kubernetes knowledge. Some platforms abstract away Kubernetes complexity entirely, while others assume familiarity with Kubernetes concepts and provide more direct cluster access.
    
5. **Preview environment capabilities**
    
    If preview environments for pull requests are critical to your workflow, examine how each platform handles environment provisioning, teardown automation, database handling, and secrets management.
    
6. **Cost structure**
    
    Look at both platform costs and underlying infrastructure expenses. Review pricing models (per-user, per-environment, compute-based) and features like automatic resource scaling and idle environment shutdown that can reduce cloud costs.
    
7. **Scale and performance**
    
    Determine if the platform can handle your team size and deployment frequency. Look at factors like build speed, environment startup time, and support for concurrent environments.
    

## Top Okteto alternatives

Let’s see the five platforms in more detail that address similar Kubernetes development challenges, each with distinct approaches to deployment, developer experience, and infrastructure management.

### 1. Northflank: Production-ready platform for the full application lifecycle

[Northflank](https://northflank.com/) is a comprehensive developer platform that extends beyond development environments to handle your entire application lifecycle from development through production.

**What sets Northflank apart:**

- **Full lifecycle management**: Unlike Okteto's focus on development and preview environments, Northflank manages production deployments, [auto-scaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments), [observability](https://northflank.com/docs/v1/application/observe/observability-on-northflank), and [disaster recovery](https://northflank.com/use-cases/disaster-recovery-for-kubernetes). This means one platform for your entire workflow rather than piecing together multiple tools.
- **Deployment flexibility**: Deploy to Northflank's [managed cloud](https://northflank.com/cloud/northflank) for instant setup, or connect [your own infrastructure](https://northflank.com/features/bring-your-own-cloud) ([GKE](https://northflank.com/cloud/gcp), [EKS](https://northflank.com/cloud/aws), [AKS](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), [OKE](https://northflank.com/cloud/oci), bare-metal) while maintaining the same platform experience and features.
- **Built-in [features](https://northflank.com/features)**: [GitOps workflows](https://northflank.com/docs/v1/application/infrastructure-as-code/gitops-on-northflank), [CI/CD pipelines](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank), [ephemeral preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) with automated teardown, [secrets management](https://northflank.com/docs/v1/application/secure/manage-secret-groups), and [real-time observability](https://northflank.com/features/observe) come standard without additional integrations.
- **Real-time interface**: Sub-second UI updates provide instant feedback on deployments, builds, and system state across all environments.

**Best for:** Teams needing a production-ready platform that scales from development to production without switching tools. Particularly suited for organizations requiring [multi-cloud](https://northflank.com/blog/multi-cloud-vs-hybrid-cloud#what-is-multicloud) flexibility, enterprise security controls, or teams that want to move beyond development-only tooling.

<InfoBox className="BodyStyle">

Try [Northflank's free developer sandbox](https://app.northflank.com/signup), review [pricing plans](https://northflank.com/pricing), or [book a demo with an engineer](https://cal.com/team/northflank/northflank-intro) to discuss your team's specific requirements.

</InfoBox>

### 2. Tilt: Fast iteration for multi-service applications

Tilt is an open-source toolkit that automates microservice development workflows by watching files, building container images, and updating your environment automatically.

**Key capabilities:**

- **Live updates**: Deploys code to running containers in seconds, even for compiled languages or dependency changes, removing the build-push-deploy cycle during development.
- **Visual UI**: Shows all services, logs, broken builds, and runtime errors in a single view, giving you visibility across your entire microservices architecture.
- **Pluggable architecture**: Works with your existing tools and workflows. Integrates seamlessly with Helm, Docker, and Kubernetes without requiring changes to your deployment setup.

**Best for:** Teams working with complex microservices architectures who need fast feedback loops and visibility into multiple interdependent services running simultaneously.

### 3. Skaffold: Lightweight automation for Kubernetes

Skaffold is a command-line tool that automates the workflow for building, pushing, and deploying Kubernetes applications with optimized local workflows and policy-based image tagging.

**Key capabilities:**

- **Pluggable architecture**: Supports multiple build systems (Docker, Jib, Bazel, Buildpacks) and deployment tools (kubectl, Helm, kustomize).
- **Automated workflow**: Detects source code changes and handles the pipeline to build, push, test, and deploy automatically.
- **Client-side only**: No cluster-side component means zero overhead on your Kubernetes cluster. Fully open-source and maintained by Google.

**Best for:** Teams that want simple automation for their existing Kubernetes workflows without adopting a full platform.

### 4. Telepresence: Bridge between local and remote

Telepresence establishes a two-way tunnel between your local machine and remote Kubernetes cluster, routing remote traffic to your local service for development and testing.

**Key capabilities:**

- **Local development with remote access**: Run your service locally while accessing remote cluster resources as if your laptop is part of the cluster.
- **Traffic interception**: Routes specific cluster requests to your local machine for testing without deploying.
- **Use your local tools**: Work with your preferred IDE, debugger, and profiler while connected to the remote cluster.

**Best for:** Teams that want to preserve local development workflows while testing integration with remote services.

### 5. Bunnyshell: Environments-as-a-Service

Bunnyshell creates isolated, production-like environments for every pull request with AI-assisted setup and automated provisioning.

**Key capabilities:**

- **Ephemeral environments**: Automatically provisions isolated environments for each PR. Auto-shuts down idle environments to optimize cloud costs.
- **Multi-cloud integration**: Works with GitHub, GitLab, Bitbucket, Kubernetes, Docker, and all major cloud providers.
- **Reusable templates**: Create standardized environment configurations that can be shared across teams.

**Best for:** Teams needing multi-cloud flexibility and automated cost optimization through ephemeral infrastructure.

## Choosing the right platform for your team

Each platform addresses different aspects of the Kubernetes development workflow, from full lifecycle management to specialized tooling for specific use cases.

| Platform | Choose if you need |
| --- | --- |
| **Northflank** | • Production-ready platform managing the full application lifecycle (dev → staging → production) • Deployment flexibility with managed cloud or bring-your-own-cloud options • Built-in CI/CD, preview environments, observability, and disaster recovery without piecing together multiple tools • Enterprise security and compliance requirements |
| **Tilt** | • Fast feedback loops for complex microservices architectures • Visual dashboard showing all services, logs, and errors in one view • Willing to write and maintain Tiltfile configurations (Starlark-based) for customized workflows |
| **Skaffold** | • Lightweight, open-source automation without platform dependencies • Existing Kubernetes expertise on your team • Integration with current workflows and tooling rather than adopting a new platform |
| **Telepresence** | • Local development workflows while accessing remote cluster resources • Gradual transition from local development to Kubernetes • Testing service integration without full environment replication |
| **Bunnyshell** | • Multi-cloud flexibility and automated cost optimization • Ephemeral environments for every PR with automatic teardown • Reusable environment templates across multiple teams |

## Making the decision

The Kubernetes development tooling ecosystem offers multiple approaches to solving developer productivity challenges, from lightweight open-source CLIs to comprehensive platforms managing full application lifecycles.

Your choice depends on your team's specific needs, expertise level, and infrastructure strategy.

Teams needing only development environments may find focused tools sufficient, while those requiring end-to-end lifecycle management from development through production benefit from platforms like [Northflank](https://northflank.com/) that consolidate multiple tools into one solution.

<InfoBox className="BodyStyle">

Try [Northflank's free developer sandbox](https://app.northflank.com/signup), review [pricing plans](https://northflank.com/pricing), or [book a demo with an engineer](https://cal.com/team/northflank/northflank-intro) to discuss your team's specific requirements.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Top 10 AWS Amplify alternatives for development teams in 2026</title>
  <link>https://northflank.com/blog/aws-amplify-alternatives</link>
  <pubDate>2025-10-07T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[Compare AWS Amplify alternatives for 2026. Review Northflank, Vercel, Firebase, Supabase, and more to find the best deployment platform.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/aws_amplify_alternatives_5d4955f705.png" alt="Top 10 AWS Amplify alternatives for development teams in 2026" /><InfoBox className="BodyStyle">

## Quick summary of the top 10 AWS Amplify alternatives

AWS Amplify works well for AWS-centric teams but lacks multi-cloud flexibility and can get expensive at scale. Here are the top alternatives:

- **Northflank** - Production-grade platform supporting deployment to [Northflank's managed cloud](https://northflank.com/cloud/northflank) or your own [AWS/GCP/Azure accounts](https://northflank.com/cloud). Handles everything from full-stack apps to AI workloads with built-in observability, backups, and compliance features. Best for teams needing enterprise capabilities with infrastructure flexibility.
- **Firebase/Supabase** - Mobile-first BaaS with real-time databases. Firebase for Google Cloud integration, Supabase for open-source PostgreSQL-based alternative with self-hosting option.
- **Vercel/Netlify** - Frontend-optimized platforms. Vercel dominates Next.js deployment, Netlify offers broader framework support with extensive build plugins. Limited backend capabilities.
- **Render** - Heroku alternative with managed databases, background workers, and straightforward pricing. Good for traditional full-stack apps without serverless complexity.
- **Railway** - Simple deployment with transparent usage-based billing. Best for side projects and small teams wanting minimal configuration.
- **Fly.io** - Global edge deployment with low-latency access. Deploy Docker containers close to users worldwide. Requires more infrastructure knowledge.

</InfoBox>

## What is AWS Amplify?

AWS Amplify is Amazon's full-stack development platform designed to help frontend developers build web and mobile applications quickly.

It combines backend-as-a-service capabilities with Git-based frontend hosting, offering authentication, GraphQL APIs, storage, serverless functions, and CI/CD workflows in a unified package.

Amplify speeds up initial development through its TypeScript-first approach and visual Studio tool, making it well-suited for teams building React, Next.js, Vue, or mobile applications needing fast prototyping with AWS services

## What to look for when comparing AWS Amplify alternatives

When comparing platforms, keep these factors in mind:

1. **Deployment model** - Does your team need [managed cloud](https://northflank.com/blog/what-is-managed-cloud), [bring-your-own-cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) capability, or [hybrid deployment](https://northflank.com/blog/what-is-hybrid-cloud-complete-infrastructure-guide) options?
2. **Backend capabilities** - Evaluate authentication, database support, API management, file storage, and serverless function offerings against your requirements.
3. **Pricing structure** - Compare usage-based versus predictable pricing, considering your traffic patterns and growth projections.
4. **Developer experience** - Assess Git integration quality, [preview environments](https://northflank.com/blog/the-what-and-why-of-ephemeral-preview-environments-on-kubernetes-sandbox-testing), deployment speed, and how well the platform fits your team's workflow.
5. **Scaling and performance** - Review autoscaling capabilities, global distribution options, and performance optimization features.
6. **Team collaboration** - Consider access control, environment management, and how well the platform supports your team size and structure.

## Top 10 AWS Amplify alternatives (Detailed comparison)

Now that you know what to look out for, let's see how each platform compares across these criteria.

### 1. Northflank - Complete platform for production workloads

Northflank provides a comprehensive developer platform designed for teams that need both simplicity and production-grade capabilities. Unlike traditional PaaS solutions, Northflank offers enterprise features without sacrificing developer experience.

**What sets Northflank apart:**

- **Bring Your Own Cloud (BYOC)** - Deploy to your AWS, GCP, Azure, Civo, or Oracle accounts while enjoying Northflank's managed platform layer. Maintain control over data residency, compliance, and cloud costs without infrastructure overhead.
- **Full-stack deployment support** - Handle everything from microservices and APIs to databases, background workers, cron jobs, and GPU-powered AI workloads from a unified interface.
- **Production-ready by default** - Built-in observability, automated backups, health checks, rollbacks, and disaster recovery eliminate the need to piece together external tools.
- **Real-time collaboration** - Live interface updates, granular RBAC permissions, team roles, and SSO integration support enterprise security requirements.
- **No infrastructure YAML** - Deploy complex workflows through an intuitive UI, REST API, or CLI without writing Kubernetes configurations.

**Best for**: Teams building production applications that need enterprise compliance, multi-cloud flexibility, or want to avoid platform vendor lock-in while maintaining a seamless developer experience. Especially valuable for organizations requiring HIPAA, SOC 2, or ISO 27001 compliance.

<InfoBox className="BodyStyle">

**Northflank pricing:**

- **Developer Sandbox:** Free tier with generous limits for testing and small projects
- **Pay as you go:** Starting at $0/month with infrastructure usage billing, unlimited projects and collaborators
- **Enterprise:** Pricing based on features and deploy footprint

> See [full pricing details](https://northflank.com/pricing) or [book a demo](https://cal.com/team/northflank/northflank-intro) to speak with an engineer or [try out the platform](https://app.northflank.com/signup) via the free developer sandbox
> 

</InfoBox>

**Migration path from Amplify:** Northflank's Docker-based deployment accepts any containerized application. Teams can gradually migrate services while maintaining existing AWS resources, then optionally consolidate to Northflank's BYOC model for cost optimization.

### 2. Firebase - Google's mobile-first BaaS platform

Firebase provides Google's comprehensive backend-as-a-service solution with extensive mobile SDK support and real-time database capabilities.

**Key features:**

- Firestore NoSQL database with real-time synchronization
- Firebase Authentication with social provider integration
- Cloud Functions for serverless backend logic
- Cloud Storage for file uploads
- Hosting with global CDN via Google Cloud
- Built-in analytics and crash reporting
- Machine learning kit for mobile AI features

**Best for:** Mobile-first applications, real-time collaborative apps, prototypes needing rapid development, and teams already invested in Google Cloud ecosystem.

### 3. Vercel - Optimized for frontend frameworks

Vercel specializes in frontend deployment with support for React, Next.js, and modern JavaScript frameworks.

**Key features:**

- Zero-config Next.js deployment with ISR and SSR support
- Automatic preview deployments for every pull request
- Global edge network for fast content delivery
- Built-in analytics and performance monitoring
- Serverless functions for API routes
- Integration with headless CMS platforms

**Best for:** Frontend-focused teams building with Next.js, React, Vue, or Svelte. Ideal for marketing sites, e-commerce frontends, and documentation platforms prioritizing speed and developer experience.

### 4. Netlify - JAMstack-focused platform

Netlify pioneered the JAMstack approach with Git-based deployments and serverless architecture for static sites and SPAs.

**Key features:**

- Continuous deployment from Git repositories
- Built-in form handling and identity management
- Serverless functions via Netlify Functions
- Split testing and feature flags
- Large plugin ecosystem for build customization
- Edge functions for dynamic content at the edge

**Best for:** Static sites, SPAs, JAMstack applications, and teams wanting extensive build plugins. Strong choice for content-driven sites built with Gatsby, Hugo, or Eleventy.

### 5. Render - Modern Heroku alternative

Render provides a developer-friendly platform emphasizing simplicity while offering more flexibility than traditional PaaS solutions.

**Key features:**

- Support for web services, background workers, and cron jobs
- Managed PostgreSQL, Redis, and other databases
- Automatic SSL and custom domain support
- Private networking between services
- Git-based deployments with preview environments
- Docker and native buildpack support

**Best for:** Full-stack web applications, APIs with persistent databases, teams migrating from Heroku, and projects needing background job processing.

### 6. Railway - Simple deployment with usage-based pricing

Railway focuses on making deployment accessible while providing powerful features for growing applications.

**Key features:**

- One-click deployment for popular frameworks
- Built-in PostgreSQL, MySQL, MongoDB, and Redis
- Automatic preview environments from pull requests
- Simple usage-based billing model
- Template marketplace for common stacks
- Straightforward interface minimizing configuration

**Best for:** Side projects, startups, and small to medium teams wanting simple deployment without complex billing. Good for developers new to cloud deployment.

### 7. Supabase - Open-source Firebase alternative

Supabase provides an open-source backend-as-a-service built on PostgreSQL, offering more flexibility than proprietary alternatives.

**Key features:**

- PostgreSQL database with real-time subscriptions
- Built-in authentication with multiple providers
- Instant REST and GraphQL APIs from database schema
- File storage with image transformations
- Edge Functions using Deno runtime
- Self-hosting option for full control

**Best for:** Teams wanting open-source solutions, PostgreSQL enthusiasts, applications requiring relational database features, and organizations needing self-hosting capability.

### 8. Azure Static Web Apps - Microsoft's integrated solution

Azure Static Web Apps combines static site hosting with serverless API capabilities within the Azure ecosystem.

**Key features:**

- Automatic deployment from GitHub and Azure DevOps
- Integrated Azure Functions for API backend
- Global distribution via Azure CDN
- Custom domain and free SSL certificates
- Authentication integration with Azure AD
- Tight integration with Azure services

**Best for:** Organizations already using Azure, teams requiring Microsoft ecosystem integration, and enterprises with Azure Enterprise Agreements.

### 9. Heroku - Established PaaS platform

Despite recent challenges including pricing changes and outages, Heroku remains a popular choice for its simplicity and extensive add-on marketplace.

**Key features:**

- Simple Git-based deployment workflow
- Extensive add-on ecosystem (over 200 services)
- Support for multiple programming languages
- Automatic scaling with dyno management
- Review apps for pull request testing
- Pipeline workflow for deployment stages

**Best for:** Teams prioritizing deployment simplicity, applications benefiting from the add-on ecosystem, and prototypes requiring rapid iteration.

### 10. Fly.io - Global edge deployment platform

Fly.io enables running applications close to users worldwide through edge computing capabilities.

**Key features:**

- Deploy applications to data centers near users
- Low-latency access through geographic distribution
- Docker-native deployment model
- Private networking between regions
- Persistent volumes for stateful applications
- Flexible VM configurations

**Best for:** Globally distributed applications, real-time services requiring low latency, gaming backends, and applications with international user bases.

## Making the right choice for your team

Selecting an Amplify alternative depends on your specific requirements, team expertise, and long-term goals. Consider these scenarios:

| Platform | Best For |
| --- | --- |
| **Northflank** | Full-stack deployment with production-grade observability, [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) capability while maintaining platform benefits, Support for complex architectures including microservices, databases, and AI workloads, Team collaboration features with granular access control |
| **Firebase or Supabase** | Mobile-first development with SDKs, Real-time database synchronization, Rapid prototyping with comprehensive backend services, Authentication and storage without infrastructure management |
| **Vercel or Netlify** | Frontend-focused deployment, Minimal backend requirements, Framework-specific optimizations |
| **Render or Railway** | Traditional service-based architecture, Background workers and cron jobs, Straightforward pricing models, Heroku-like simplicity with better value |
| **Fly.io** | Global edge deployment, Low-latency international access, Full control over geographic distribution |

## Finding the right platform

AWS Amplify works well for AWS-centric teams, but as applications mature and requirements change, many need different capabilities around pricing predictability, infrastructure control, or multi-cloud support.

The alternatives in this guide each specialize in specific areas: Northflank for enterprise-grade features with Bring Your Own Cloud (BYOC) flexibility, Firebase and Supabase for mobile and real-time applications, Vercel and Netlify for frontend deployment, and Render and Railway for balanced full-stack solutions.

<InfoBox className="BodyStyle">

Want a platform that combines PaaS simplicity with enterprise-grade capabilities? Try [Northflank's free Developer Sandbox](https://app.northflank.com/signup).

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>September 2025 | Product releases</title>
  <link>https://northflank.com/changelog/platform-september-2025-release</link>
  <pubDate>2025-10-07T07:15:00.000Z</pubDate>
  <description>
    <![CDATA[Faster workloads, smarter templates, and new integrations across the platform.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_changelog_sept_bc86961ad6.png" alt="September 2025 | Product releases" />September brings platform improvements across infrastructure, templates, and developer experience. Faster workloads, more templates, and cleaner UI across the board.

Northflank now runs Kubernetes v1.33 on all major clouds, ships new AI model templates (DeepSeek, GPT OSS, Llama 4, Qwen 3, and more), and adds SolarWinds integration for better log management.

## **🏗 Infrastructure**

### **Enhancements**

- **Kubernetes Version Updates**: Bumped to v1.33 across all major cloud providers (AWS EKS, Azure AKS, Google GKE, Oracle OKE).
- **AWS Infrastructure**: Updated default AMI for AWS node pools for improved security and performance.
- **Signal Handling**: Improved signal forwarding to child processes, honoring Dockerfile STOPSIGNAL definitions for better container lifecycle management.
- **PostgreSQL Improvements**: Enhanced addon forking capabilities for better database management and backup workflows.
- **Addon Restart Behavior**: Improved predictability of addon restarts for better reliability.
- **Startup speed**: Reduced startup speed by 2 seconds for workload containers. 3-4 seconds without the sidecar service mesh. More next month!
- **Enhanced Database Support**: New Northflank-built MySQL versions with improved performance and compatibility.
- **MinIO Updates**: Added version 2025.9.7 for MinIO object storage addon.
- **Docker Registry Migration**: Moved all current deployments dependent on Bitnami Docker images to Northflank's platform registry.

### **Fixed**

- **Addon Management**: Fixed addon stuck deletion issues in certain situations, particularly for addons with TLS & external access disabled.
- **OCI Integration**: Resolved UI issues with Oracle Cloud Infrastructure (OCI) BYOC
- **Performance Optimization**: Northflank resources are applied to worker clusters at least a second faster than before
- **Database Lifecycle**: Deprecated old MongoDB versions as part of ongoing security and maintenance improvements.

## **🧩 Templates**

### **Added**

- **New AI/ML Stack Templates**
    - GPT OSS models
    - DeepSeek (including v3.1)
    - Llama 4
    - Qwen 3 4B (thinking and instruct variants)
    - Kimi models

- **New Guides**
    - https://northflank.com/guides/deploy-langflow-with-northflank
    - https://northflank.com/guides/deploy-anythingllm-with-northflank
    - https://northflank.com/guides/deploy-flowiseai-with-northflank
    - https://northflank.com/guides/deploy-listmonk-with-northflank
    - https://northflank.com/guides/deploy-calcom-with-northflank

- **OpenTofu Enhancements**: Support for idempotent template runs with empty OpenTofu nodes and improved resource deletion workflows with better UX and error handling.
- **Advanced Template Functions**: Expanded template function capabilities for more flexible infrastructure-as-code workflows.
- **Repository Integration**: Added VCS link ID option for repository clone nodes enabling more flexible Git integration.
- **Backup Scheduling**: Added compose reference schema support for addon backup schedules.

### **Enhancements**

- **Visual Editor Improvements**: Enhanced support for passing entire environment variable objects as arguments in visual editor, similar to other template UI components.
- **Template Organization**: Custom addon types now organized in separate collapsible sections, defaulting to expanded when there are 6 or fewer custom types or if the currently selected type is within the section.

### **Fixed**

- **Template Management**: Fixed Organization API templates not saving directory groups on creation and resolved intermittent template decryption issues.

## **👩‍💻 Developer experience**

### **Added**

- **SolarWinds Integration**: Added SolarWinds as a new log sink destination for improved log management.
- **Deploy Safety**: Added confirmation step when clicking "Deploy" button in Build/Commit lists to prevent accidental deployments.
- **BYOC Docker Registries**: Replaced BYOC docker registries with custom docker credentials for improved flexibility.

### **Enhancements**

- **Build Experience**: Updated rebuild button text to say "Build" instead of "Rebuild" when there are no previous builds, and resolved branch names vanishing in branch selector.
- **User Interface Polish**: Updated permission toggle styling to make active/inactive states more obvious at a glance, improved service dashboard spacing with proper gaps between buttons and timestamps, and enhanced action button layouts with better column widths to avoid clipping.
- **Log Management**: Enhanced log sink handling with better multiple URL support (fixed mis-rendering of log lines with multiple http(s):// URLs), improved type filter labels with custom label support, and added pause failure messages to log-sink schema.
- **Template UI**: Hidden progress/errors and unsaved changes when template setup modal is visible for less distractions, and restored editable description fields in page headers.
- **Domain Management**: Improved wildcard subdomain support for top-level domains and enhanced domain search to include domains with no subdomains in search results.

## **See you next month!**]]>
  </content:encoded>
</item><item>
  <title>AWS vs Azure vs Google Cloud: comprehensive comparison for 2026</title>
  <link>https://northflank.com/blog/aws-vs-azure-vs-google-cloud</link>
  <pubDate>2025-10-06T16:40:00.000Z</pubDate>
  <description>
    <![CDATA[Compare AWS vs Azure vs Google Cloud on complexity, costs, vendor lock-in, and Kubernetes management. Technical comparison for engineering teams.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/aws_vs_azure_vs_google_cloud_f29794c401.png" alt="AWS vs Azure vs Google Cloud: comprehensive comparison for 2026" />Comparing AWS, Azure, and Google Cloud usually comes down to four main concerns:

1. ***"My team is drowning in complexity"*** - We need simpler infrastructure without losing control
2. ***"Our cloud bills keep surprising us"*** - We need predictable, transparent pricing
3. ***"We're worried about vendor lock-in"*** - We need flexibility to switch providers or go multi-cloud
4. ***"Kubernetes is consuming our engineering time"*** - We need container orchestration without the operational burden

You could be selecting your first cloud provider, migrating from an existing one, or building a [multi-cloud strategy](https://northflank.com/blog/multi-cloud-vs-hybrid-cloud#what-is-multicloud). This comparison provides the information your team needs to make an informed decision.

<InfoBox className="BodyStyle">

**One thing to consider upfront:** You don't have to manage these platforms directly. Solutions like [Northflank](https://northflank.com/) let you deploy across [AWS](https://northflank.com/cloud/aws), [Azure](https://northflank.com/cloud/azure), or [Google Cloud](https://northflank.com/cloud/gcp) while abstracting away infrastructure complexity. You keep control of your cloud account while getting a managed platform experience.

</InfoBox>

## AWS vs Azure vs Google Cloud: quick comparison

Before we go into details, let's quickly see how AWS, Azure, and Google Cloud compare based on complexity, cost predictability, vendor lock-in risk, Kubernetes management, and GPU/AI workload support:

| What you care about | AWS | Azure | Google Cloud | Northflank (platform layer) |
| --- | --- | --- | --- | --- |
| **Complexity** | Overwhelming (200+ services) | High (Microsoft ecosystem dependencies) | Moderate (steeper learning curve) | Low (abstracts infrastructure) |
| **Cost predictability** | Poor (complex discounts, surprise fees) | Moderate (Hybrid Benefit helps) | Better (automatic discounts) | Transparent ([pricing calculator](https://northflank.com/pricing)) |
| **Vendor lock-in risk** | High (Lambda, RDS, DynamoDB) | Very high (Microsoft integration) | High (BigQuery, AI tools) | None ([Bring Your Own Cloud (BYOC) model](https://northflank.com/features/bring-your-own-cloud)) |
| **Kubernetes management** | High (EKS needs expertise) | High (AKS still complex) | Moderate (GKE best, still work) | Zero (fully managed) |
| **GPU/AI workloads** | Good (P5 instances, Spot) | Good (Azure ML, OpenAI) | Excellent (TPUs, Vertex AI) | [GPU support](https://northflank.com/gpu) on [Northflank cloud](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-on-northflank-cloud) or [your own cloud](https://northflank.com/cloud/gpus) with [automated spot orchestration](https://northflank.com/blog/what-are-spot-gpus-guide#how-northflank-cuts-spot-gpu-costs-with-automated-orchestration) (up to 90% savings) |

## What services do AWS, Azure, and Google Cloud offer?

We'll break down the core services across AWS, Azure, and Google Cloud to help you understand which services are portable, which ones create lock-in, and how to plan your infrastructure decisions accordingly.

### 1. Compute options across AWS, Azure, and Google Cloud

**AWS** offers the most comprehensive selection: EC2 (virtual machines), Lambda (serverless functions), ECS/EKS (containers), [Fargate](https://northflank.com/blog/what-is-aws-fargate) (serverless containers), and Elastic Beanstalk (PaaS). The range is impressive, but it can also be overwhelming when you're just trying to run an application.

**Azure** provides Virtual Machines with native Windows Server support, along with Azure Functions, AKS (Kubernetes), App Service (which is a better PaaS option than AWS's Elastic Beanstalk), and Container Instances. The entire platform assumes you're already deep in the Microsoft ecosystem, which works well if you are, but creates challenges if you're not.

**Google Cloud** gives you Compute Engine (VMs), Cloud Functions, GKE (the most advanced managed Kubernetes service, Cloud Run (their developer-friendly serverless container platform), and App Engine. There are fewer options compared to AWS, but the experience feels cleaner. If you're deciding between App Engine and Cloud Run, [this comparison](https://northflank.com/blog/app-engine-vs-cloud-run) breaks down the tradeoffs.

### 2. Database services

All three offer similar database options, though the specifics differ:

- **Relational databases**: AWS RDS, Azure SQL Database, and Cloud SQL handle your MySQL, PostgreSQL, and SQL Server needs
- **NoSQL databases**: AWS DynamoDB, Azure Cosmos DB, and Google Firestore/Bigtable for flexible, schema-less data
- **Data warehouses**: AWS Redshift, Azure Synapse, and Google BigQuery (which leads the pack for analytics performance)
- **In-memory caching**: AWS ElastiCache, Azure Cache for Redis, and Google Memorystore for fast data access

### 3. Storage and AI/ML services

**Storage** is comparable across all three. AWS S3, Azure Blob Storage, and Google Cloud Storage handle object storage similarly, and the same applies to file and block storage. Pricing and data transfer costs vary between providers, but functionally, they work the same way.

**AI and machine learning** are where the differences show up.

**AWS** provides SageMaker for ML workflows, plus pre-built services like Rekognition (computer vision) and Comprehend (natural language processing).

Azure offers Azure Machine Learning, which includes tools for building and managing ML models and workflows. 

**Google Cloud** has Vertex AI, AutoML, and proprietary TPUs (Tensor Processing Units). If your core product is AI or data analytics, GCP's tooling is worth considering.

<InfoBox className="BodyStyle">

**For AI workloads across any cloud**: [Northflank supports GPU deployments](https://northflank.com/gpu) with automated spot GPU orchestration that reduces costs up to 90% while handling interruptions automatically. You can deploy on [AWS](https://northflank.com/cloud/aws), [Azure](https://northflank.com/cloud/azure), or [GCP](https://northflank.com/cloud/gcp) without needing to manage Kubernetes yourself. Learn more about [deploying GPUs in your own cloud](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-in-your-own-cloud).

</InfoBox>

## AWS vs Azure vs Google Cloud pricing comparison

*Pricing accurate as of October 2025. Cloud pricing changes frequently; always verify the current rates.*

All three providers use similar pricing models, though the details vary:

### 1. Pay-as-you-go (On-Demand)

This is the most expensive option but gives you maximum flexibility. AWS and Azure bill per-second with a 60-second minimum, while Google Cloud bills per-second with a 1-minute minimum. You can spin resources up and down whenever you need them.

### 2. Reserved/Committed capacity

Commit to using resources for 1-3 years and you'll get significant savings:

- **AWS**: Reserved Instances or Savings Plans (up to 72% off)
- **Azure**: Reservations or Savings Plans (up to 72% off)
- **Google Cloud**: Committed Use Discounts (up to 70% off)

This approach works well for predictable workloads, but you need to forecast your capacity accurately. Overprovisioning wastes money, while underprovisioning results in expensive on-demand pricing.

### 3. Spot/Preemptible instances

You can get up to a 90% discount in exchange for accepting interruption risk with [spot instances](https://northflank.com/blog/spot-instances#what-are-spot-instances). AWS gives you a 2-minute warning before interruption, Azure gives 30 seconds, and Google Cloud may change prices once every 30 days. This is ideal for fault-tolerant workloads like batch processing or CI/CD runners where interruptions don't break your workflow.

### 4. Automatic discounts

Google Cloud applies sustained-use discounts automatically (up to 30% off after 25% monthly usage). With AWS and Azure, you need to manually configure Savings Plans to get similar benefits.

### Sample pricing comparison (US East region, October 2025)

Here's what you'd pay for a general-purpose instance (2 vCPU, 4GB RAM):

| Pricing model | AWS (t3.medium) | Azure (B2s) | GCP (e2-medium) |
| --- | --- | --- | --- |
| **On-Demand** | ~$30/month | ~$30/month | ~$24/month |
| **Reserved (1-year)** | ~$18/month | ~$17/month | ~$15/month |
| **Spot/Preemptible** | ~$9/month | ~$3/month | ~$6/month |

> *Note: Prices vary by region, instance type, and operating system.*
> 

Those list prices don't include everything you'll pay, though.

### What are the hidden costs in AWS, Azure, and Google Cloud?

All three providers give you the first 100GB/month free for data transfer (egress), then charge $0.09-0.12 per GB depending on the provider and destination. Cross-region transfers cost extra. Even cross-availability-zone transfers within the same region add up, and these often get overlooked until the bill arrives.

**Infrastructure costs you might forget**

These vary by provider, but here's what to budget for:

- **Load balancers**: $18-25/month each (across all three providers)
- **NAT gateways**: $32-45/month plus data processing fees (AWS and Azure; GCP uses Cloud NAT with similar costs)
- **Static IP addresses**: $3-4/month when not attached to running instances (all providers)
- **Storage**: $0.08-0.23 per GB-month depending on performance tier and provider

These "small" costs compound quickly when you're running production infrastructure. So how do you manage all this without becoming a pricing expert?

### How to optimize AWS, Azure, and Google Cloud costs without becoming an expert

Rather than spending weeks learning pricing models, [Northflank's transparent pricing calculator](https://northflank.com/pricing) shows exactly what you'll pay upfront.

![northflank-pricing-calculator.png](https://assets.northflank.com/northflank_pricing_calculator_1879d5ccbc.png)

With Northflank's [Bring Your Own Cloud option](https://northflank.com/features/bring-your-own-cloud), you can deploy into your own AWS, Azure, or GCP account and see platform costs and infrastructure costs separately. No surprises.

![byoc-northflank-homepage.png](https://assets.northflank.com/byoc_northflank_homepage_54ec210644.png)

Northflank's [autoscaling](https://northflank.com/features/scale) ensures you only pay for resources when needed, and spot instance support can cut compute costs by 90% with automated interruption handling. Learn more about [cloud cost optimization](https://northflank.com/blog/cloud-cost-optimization) strategies.

## How AWS, Azure, and Google Cloud handle complexity, costs, and lock-in

Now that you understand what each provider offers and how pricing works, let's look at how they handle the concerns that brought you here.

### 1. Managing complexity

**AWS** has 200+ services, making it difficult to know where to start. Teams spend months figuring out which storage option to use, which compute service fits their needs, and how to configure IAM policies correctly.

**Azure** assumes you already know the Microsoft ecosystem. If you don't, you'll struggle with Resource Groups, Service Principals, and ARM templates. Frequent Portal redesigns don't help.

**Google Cloud** has fewer services, but their approach to networking and identity management differs enough from AWS and Azure to create a learning curve.

<InfoBox className="BodyStyle">

**With Northflank**: Connect your [AWS](https://northflank.com/docs/v1/application/bring-your-own-cloud/aws-on-northflank), [Azure](https://northflank.com/docs/v1/application/bring-your-own-cloud/azure-on-northflank), or [GCP account](https://northflank.com/docs/v1/application/bring-your-own-cloud/gcp-on-northflank), push your code, and deploy. [Ultralight moved from AWS ECS to EKS](https://northflank.com/blog/ultralight-ditched-aws-ecs-for-eks-with-northflank) without becoming Kubernetes experts.

</InfoBox>

### 2. Controlling costs

**AWS** pricing requires expertise. Reserved Instances, Savings Plans, and Spot Instances each have different considerations, and understanding when to use which option takes time. Data transfer costs catch teams by surprise. Most teams overspend 30-40% before they figure out optimization.

**Azure** works better if you have Microsoft licenses (Hybrid Benefit saves 40%), but pricing stays opaque. You need to understand SKUs before you can estimate costs.

**Google Cloud** automatically applies sustained-use discounts, making costs more predictable. Data egress and networking fees still surprise teams, though.

### 3. Avoiding vendor lock-in

All three providers lock you in through proprietary services. **AWS** uses Lambda, DynamoDB, and RDS. **Azure** ties you in with Cosmos DB, Azure Functions, and Active Directory. **Google Cloud** does the same with BigQuery, Cloud Spanner, and Vertex AI. Migration means significant rewrites.

<InfoBox className="BodyStyle">

**With Northflank's [Bring Your Own Cloud approach](https://northflank.com/features/bring-your-own-cloud)**: Your deployment configurations stay cloud-agnostic. Deploy to AWS today, migrate to Azure tomorrow, or run [multi-cloud from day one](https://northflank.com/blog/multi-cloud-container-orchestration#how-does-northflank-solve-these-multicloud-container-orchestration-challenges).

See migration guides for [AWS](https://northflank.com/blog/aws-cloud-migration-guide), [Azure](https://northflank.com/blog/azure-cloud-migration-strategy-migrate), and [GCP](https://northflank.com/blog/complete-guide-for-google-cloud-gcp-migration).

</InfoBox>

### 4. Handling Kubernetes management

**AWS EKS**, **Azure AKS**, and **Google GKE** all require Kubernetes expertise. You're provisioning control planes, configuring worker nodes, setting up networking, managing autoscaling, and handling upgrades. GKE is the most mature managed Kubernetes service (Google created Kubernetes, after all), but "managed" still means you're doing significant operational work.

<InfoBox className="BodyStyle">

**With Northflank**: Kubernetes runs in the background handling all the operational complexity. You don't write Helm charts or debug pod networking issues. [Autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments) adjusts resources based on demand without manual configuration, and [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) spin up automatically from pull requests so your team can test changes before merging.

</InfoBox>

## Migrating between AWS, Azure, and Google Cloud

Beyond choosing your initial provider, many teams eventually need to migrate between clouds for cost optimization, performance needs, compliance requirements, or access to specific features.

### Migration challenges

The technical work is significant: rewriting infrastructure-as-code for the new provider, rebuilding CI/CD pipelines, migrating databases with minimal downtime, reconfiguring networking and security, and retraining teams. This takes weeks or months of engineering time and creates service disruption risk.

### How Northflank simplifies migration

Northflank abstracts provider-specific details, so your deployment configurations remain the same regardless of the underlying cloud.

You can migrate from [on-premise to cloud](https://northflank.com/blog/on-premise-to-cloud-migration), move between clouds without application changes, test [multi-cloud strategies](https://northflank.com/blog/multi-cloud-vs-hybrid-cloud), or run [hybrid cloud setups](https://northflank.com/blog/what-is-hybrid-cloud-complete-infrastructure-guide) seamlessly.

Your team can connect a new cloud account, redeploy, and you're migrated.

<InfoBox className="BodyStyle">

If you need step-by-step guidance, see specific migration guides for [AWS](https://northflank.com/blog/aws-cloud-migration-guide), [Azure](https://northflank.com/blog/azure-cloud-migration-strategy-migrate), and [GCP](https://northflank.com/blog/complete-guide-for-google-cloud-gcp-migration).

</InfoBox>

## Choosing between AWS vs Azure vs Google Cloud for your needs

Now that you understand what each provider offers and how they handle your concerns, here's a framework to help you decide:

| Provider | Choose if | Don't choose if |
| --- | --- | --- |
| **AWS** | You need the widest service selection, you're building infrastructure-as-code from scratch with time to invest, you have dedicated AWS expertise, or global reach with the most regions is critical | You want transparent pricing, need to avoid vendor lock-in, or don't want to manage complexity yourself |
| **Azure** | You're deeply invested in Microsoft licenses (Office 365, Windows Server, SQL Server), hybrid cloud with on-premise Active Directory is required, you have Azure expertise in-house, or compliance requires specific Azure certifications | You're not in the Microsoft ecosystem, you want a developer-friendly experience, or cost transparency matters |
| **Google Cloud** | AI/ML and data analytics are core to your product, you need the best Kubernetes experience, you value cleaner and more predictable pricing, or you're already using Google Workspace | You need enterprise relationship depth like AWS/Azure or require the widest service catalog |
| **Northflank** (platform layer) | You want to build products instead of managing infrastructure, you prefer flexibility over commitment to one cloud provider, you need transparent pricing without surprise bills, you want Kubernetes benefits without operational complexity, your team's time is better spent shipping features, you need to support both [traditional and AI workloads](https://northflank.com/blog/running-ai-on-cloud-gpus), you want to [reduce GPU costs with spot orchestration](https://northflank.com/blog/what-are-spot-gpus-guide), or you need to deploy across [multiple clouds](https://northflank.com/cloud) while maintaining portability | You want to manage infrastructure directly or are committed to learning one provider's ecosystem deeply |

## AWS vs Azure vs Google Cloud: making your decision

You've seen what each provider offers, how they handle complexity and costs, and the migration challenges involved. The hyperscalers provide the infrastructure.

Your decision comes down to this: *will you spend engineering time becoming infrastructure experts, or use a platform that handles operations while giving you portability?*

[Northflank](https://northflank.com/) works on top of AWS, Azure, or Google Cloud. You get hyperscaler infrastructure power without the operational burden. Your decision goes beyond selecting a cloud provider. It includes how you'll manage your infrastructure.

<InfoBox className="BodyStyle">

To see how this works with your infrastructure, [start with Northflank's free developer plan](https://app.northflank.com/signup) or [connect with an engineer](https://cal.com/team/northflank/northflank-intro) to discuss your specific setup.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Heroku vs AWS: which cloud platform should you choose in 2026?</title>
  <link>https://northflank.com/blog/heroku-vs-aws</link>
  <pubDate>2025-10-02T13:00:00.000Z</pubDate>
  <description>
    <![CDATA[Heroku vs AWS comparison: pricing, deployment, scaling, and GPU support. Learn which platform fits your needs and how Northflank combines both.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/heroku_vs_aws_6d0c5c4f26.png" alt="Heroku vs AWS: which cloud platform should you choose in 2026?" />When comparing Heroku vs AWS, you're asking: should I trade simplicity for control, or is there a better way?

If you're reading this, you likely have one of these concerns:

1. *"My Heroku bill jumped from $50 to $500/month and I need to know if AWS will save money or create new problems."*
2. *"My team has no DevOps engineers and Heroku's limitations hurt - we can't customize scaling, access infrastructure, or run GPU workloads."*
3. *"I need to recommend a platform to leadership but don't want to cause a budget explosion or migration disaster."*
4. *"We're leaving Heroku because of costs. Is direct AWS our only option, or is there something simpler?"*

This guide breaks down what each platform offers, where hidden costs live, and when each option fits.

We'll also show you how platforms like [Northflank](https://northflank.com/) solve the Heroku vs AWS dilemma by combining Heroku's simplicity with deployment in your own AWS, GCP, or Azure account.

<InfoBox className="BodyStyle">

## Quick comparison breakdown of Heroku and AWS

**At a glance:**

- **Heroku:** Git push deployments, minimal infrastructure management (Heroku handles OS updates, security patches, server provisioning), simple pricing that can reach $300-500/month for medium apps and $1,000-3,000+/month for high-traffic applications, managed AI services only (no direct GPU access for custom training)
- **AWS:** Extensive control over infrastructure configuration, can be 50-70% cheaper than Heroku at scale with Reserved Instances and optimization (requires expertise), doesn't require DevOps knowledge for basic usage but production-grade implementations need DevOps skills, full GPU access for custom workloads (H100, A100, V100) but complex to set up
- **Northflank (combines both approaches):** Git push deployments in your own cloud (AWS/GCP/Azure), transparent pricing, simple GPU access (H100, A100, B200) for custom training and inference, supports both AI and traditional workloads on a single platform

**Bottom line:** Choose Heroku for speed without DevOps staff, AWS for infrastructure control with technical expertise, or [Northflank](https://northflank.com/) for both simplicity and control.

</InfoBox>

## How Heroku and AWS differ in approach

We'll start by looking at how Heroku differs from AWS fundamentally.

### Firstly, what is Heroku?

Heroku is a Platform-as-a-Service (PaaS) owned by Salesforce that runs on AWS infrastructure.

It's designed for developers who want to deploy applications without thinking about servers or configuration.

You push your code via Git, and Heroku handles everything: provisioning servers (called "dynos"), managing scaling, applying security patches, and keeping your app running.

### Secondly, what is AWS?

AWS (Amazon Web Services), on the other hand, is an Infrastructure-as-a-Service (IaaS) provider with over 240 cloud services.

Unlike Heroku's "we handle everything" approach, AWS gives you raw infrastructure (servers, networking, storage, databases) and you decide how to use it.

You're responsible for choosing instance types, configuring security groups, setting up load balancers, managing updates, and orchestrating how services work together.

See the comparison table below that shows the key differences between Heroku and AWS based on their approach:

| **Heroku (PaaS)** | **AWS (IaaS)** |
| --- | --- |
| Deploy via Git push | Choose between EC2, ECS, EKS, Elastic Beanstalk, or Lambda |
| Runs apps in "dynos" (managed containers) | Configure and manage your own virtual servers or containers |
| Fully managed infrastructure | You manage OS updates, security patches, scaling policies |
| Built-in monitoring, logging, and add-ons marketplace | Assemble your own monitoring stack (CloudWatch, third-party tools) |
| AI capabilities: Managed Inference, MCP, pgvector for RAG | Full GPU support: P-series, G-series, H100, A100 instances |
| No GPU support for training AI models | Complete GPU access but requires Kubernetes/driver expertise |
| Limited customization by design | Unlimited customization and control |
| Gets expensive at scale | Cost-effective with optimization, but complex to manage |

## Heroku vs AWS: pricing comparison

Heroku is simple and predictable until it isn't. AWS is complex and variable but can be optimized heavily.

### Pricing at a glance

|  | **Heroku** | **AWS** |
| --- | --- | --- |
| **Pricing model** | Per dyno (container) | Pay-as-you-go per second |
| **Starting cost** | $5/month (Eco dyno) | $7.50/month (t3.micro) |
| **Small production app** | $135-150/month | $145-160/month (or $80-100 optimized) |
| **Medium app** | $300-1,000/month | $150-400/month |
| **Free tier** | None (removed 2022) | 12 months free for new accounts |
| **Cost optimization** | Limited | High (Reserved Instances save up to 72%, Spot up to 90%) |

<InfoBox className="BodyStyle">

Heroku's pricing includes convenience. AWS's lower costs require expertise. Platforms like [Northflank](https://northflank.com/) let you [deploy in your own cloud](https://northflank.com/features/bring-your-own-cloud) (AWS, GCP, or Azure) at provider rates while handling the complexity.

</InfoBox>

### Heroku pricing

Heroku charges per dyno (container). A typical small production app runs 2 Standard dynos ($50/month), Postgres Standard-0 ($50/month), Redis Mini ($15/month), and typical add-ons for monitoring and logging ($15-30/month) for around $130-160/month total.

**Dyno types:**

- Eco: $5/month for 1,000 dyno hours
- Basic: $7/month per dyno
- Standard: $25-50/month per dyno
- Performance: $250-500/month per dyno

**Databases:** Heroku Postgres starts at $5/month but production plans are $50-500+/month.

### AWS pricing

AWS uses pay-as-you-go billing with per-second charges. A small production app typically needs 2 t3.medium instances (around $60/month if running 24/7), RDS database (around $30/month), load balancer (around $20/month), ElastiCache (around $15/month), and storage/transfer (around $20/month) - roughly $145-160/month. With Reserved Instances and optimization, this can drop to $80-100/month.

<InfoBox className="BodyStyle">

**Note:** These are baseline estimates. Actual costs vary significantly based on data transfer volumes, storage requirements, backup retention, and usage patterns. High-traffic applications or those with heavy data transfer can see costs increase substantially.

</InfoBox>

**Common instances (On-Demand, estimated monthly cost if running 24/7):**

- t3.micro: around $7.50/month
- t3.medium: around $30/month
- t3.large: around $60/month

**Cost-saving options:**

- Reserved Instances: up to 72% savings
- Spot Instances: up to 90% savings
- Savings Plans: up to 66% savings

**Additional costs:** RDS databases ($15-200+/month), load balancers ($20/month), data transfer ($0.09/GB after 100GB free), storage ($0.10/GB/month), IPv4 addresses ($3.60/month per IP).

<InfoBox className="BodyStyle">

**Bottom line:** Looking at these numbers, you're likely thinking: "*I can't afford Heroku's premium at scale, but I don't have time to become an AWS expert either.*"

This is where platforms like [Northflank](https://northflank.com/) come in - you can deploy in your own cloud account to get provider pricing while the platform handles configuration and optimization.

</InfoBox>

## Feature comparison of Heroku vs AWS: deployment, scaling, and developer experience

Pricing isn't everything. How you deploy code, handle traffic spikes, and work with each platform day-to-day shapes your team's experience.

### 1. Deployment workflow

**Heroku:** Push your code via Git and you're live. Run `git push heroku main` and Heroku detects your language, installs dependencies, builds your app, and deploys it. Zero configuration for standard frameworks. You get automatic buildpack detection, Review Apps for every pull request, and built-in CI/CD with Heroku Pipelines.

**AWS:** Deployment requires choosing your approach. Elastic Beanstalk is closest to Heroku but still needs more configuration. ECS/Fargate requires building Docker images, pushing to ECR, and configuring task definitions. EKS means setting up Kubernetes clusters and writing YAML manifests. Most production setups involve managing your own CI/CD pipelines.

<InfoBox className="BodyStyle">

Heroku for simplicity. AWS requires significantly more setup.

Northflank gives you Git-based deployments like Heroku but deploys to Kubernetes in your own cloud account, avoiding vendor lock-in.

</InfoBox>

### 2. Scaling

**Heroku:** Move a slider or run `heroku ps:scale web=5` to add dynos instantly. Autoscaling is available on Performance dynos and above. Simple, but you scale entire dynos without granular resource control.

**AWS:** Define Auto Scaling Groups, configure CloudWatch metrics, and set target tracking policies. You can scale based on CPU, memory, request count, or custom application metrics across multiple dimensions. More powerful but requires configuration.

<InfoBox className="BodyStyle">

AWS for customization. Heroku for simplicity.

Northflank gives you both - simple scaling interface with the power to configure custom [autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments) policies in your own cloud.

</InfoBox>

### 3. Developer experience

**Heroku:** Clean dashboard showing app health at a glance. Intuitive CLI with commands like `heroku logs --tail`. Beginner-friendly documentation. Everything lives in one place.

**AWS:** Console with 240+ services can feel overwhelming. CLI requires learning AWS-specific syntax. Comprehensive documentation that assumes technical knowledge. You navigate between EC2, RDS, CloudWatch, IAM, and other services constantly.

<InfoBox className="BodyStyle">

Heroku for developer happiness. AWS for control.

Northflank combines Heroku's intuitive interface with AWS-level infrastructure access.

</InfoBox>

### 4. Language support

**Heroku** officially supports Node.js, Ruby, Python, Java, PHP, Go, Scala, and Clojure through buildpacks. Works best with standard web frameworks.

**AWS** supports anything that runs on Linux with full control over runtime environments.

<InfoBox className="BodyStyle">

Both cover what most teams need. AWS handles edge cases better.

Northflank supports any language or framework that runs on Linux, with the same ease as Heroku.

</InfoBox>

### 5. AI and GPU workloads

**Heroku:** No direct GPU access for custom training. Recently added AI features (Managed Inference, MCP) use external AI services - you're not running GPU workloads on Heroku infrastructure itself.

**AWS:** Full GPU support with H100, A100, V100, and T4 instances. Offers Capacity Blocks for ML to reserve GPU clusters and SageMaker for managed workflows. But setting up EKS/ECS with GPU support, managing drivers, and configuring orchestration requires significant expertise.

**Northflank:** Full [GPU access](https://northflank.com/cloud/gpus) (H100, H200, A100, B200) with simple deployment. You can deploy GPU workloads as easily as regular apps - no Kubernetes expertise required. Supports both AI workloads (training, inference, notebooks) and traditional apps on the same platform, in your own cloud account.

## Advantages and disadvantages of Heroku and AWS

Each platform has clear trade-offs that matter depending on your team size, expertise, and priorities. Look at the table below that sums up both the advantages and disadvantages of Heroku and AWS.

| **Aspect** | **Heroku** | **AWS** |
| --- | --- | --- |
| **Deployment speed** | Deploy in minutes with git push | hours, days or weeks to deploy what Heroku does in minutes |
| **Infrastructure management** | Minimal (Heroku handles OS updates, security patches, server provisioning) | You manage everything (updates, patches, monitoring, scaling policies) |
| **Developer experience** | Intuitive CLI, dashboard, and documentation | Steep learning curve (EC2, VPCs, IAM, security groups) |
| **Add-ons/services** | One-click add-ons for databases, caching, monitoring | 240+ services (compute, storage, ML, analytics, IoT) but requires configuration |
| **Review/testing** | Review Apps for every pull request | Build your own preview environments |
| **Cost at scale** | $500-3,000/month for high-traffic apps (3-5x more than AWS) | 50-70% cheaper at scale with Reserved/Spot Instances |
| **Customization** | Limited (can't customize infrastructure or access underlying systems) | Complete control over infrastructure and networking |
| **Vendor lock-in** | High (migration requires rearchitecting) | Lower (standard infrastructure patterns) |
| **GPU support** | None (can't train custom AI models) | Full GPU support (H100, A100, V100, T4 for AI/ML) |
| **Performance** | Abstraction layer adds overhead | Direct infrastructure access, better performance |
| **Cold starts** | Eco/Basic tiers sleep after 30 minutes of inactivity | No cold starts on standard instances |
| **Expertise required** | None (designed for developers without DevOps knowledge) | Requires DevOps expertise to set up and maintain |
| **Pricing complexity** | Simple and predictable | Confusing (surprise bills from data transfer, idle resources) |
| **Global reach** | Limited to Heroku's regions | 38 regions, 120+ availability zones globally |

<InfoBox className="BodyStyle">

**The middle ground:** [Northflank](https://northflank.com/) combines Heroku's deployment simplicity with AWS's infrastructure control. You get Git-based deployments, GPU access, and the ability to run in your own cloud account without needing DevOps expertise.

</InfoBox>

## When to choose Heroku vs AWS

Here's which platform fits your situation based on team size, expertise, and requirements.

| **Choose Heroku if you...** | **Choose AWS if you...** | **Choose Northflank if you...** |
| --- | --- | --- |
| Are a startup/small team without DevOps expertise | Have DevOps expertise or can hire it | Want both simplicity and infrastructure control |
| Need to ship fast (live in minutes, not weeks) | Need custom networking, specialized instances, or security configs | Need GPU access without Kubernetes complexity |
| Built a standard web app, API, or background workers | Are building microservices, multi-region, or hybrid cloud systems | Want to run both AI and traditional workloads on one platform |
| Are pre-revenue and budget isn't the primary constraint yet | Spend $1,000+/month on hosting (cost optimization matters) | Are outgrowing Heroku's costs or limitations |
| Value developer happiness over cost savings | Need GPU compute for AI/ML training | Want to avoid the AWS learning curve while getting AWS-level infrastructure |
| Want minimal operational overhead | Plan to scale to enterprise level eventually | Want Git-based deployments in your own cloud account (AWS, GCP, Azure) or need enterprise compliance and support |

## Migrating from Heroku to AWS (or alternatives)

If you've decided to leave Heroku, you have two main paths.

Migrating directly to AWS means rebuilding your container hosting (EC2/ECS/EKS), databases (RDS), load balancing, CI/CD pipelines, monitoring, and secrets management, a process that takes 1-2 weeks for simple apps and up to 3 months for complex ones.

The alternative is using a platform like [Northflank](https://northflank.com/) that keeps Heroku's Git-based workflow while deploying to Kubernetes in your own cloud account (AWS, GCP, or Azure), reducing migration time from weeks to days without the AWS learning curve.

**Helpful migration resources:**

- [How to migrate from Heroku: step-by-step guide](https://northflank.com/blog/how-to-migrate-from-heroku-a-step-by-step-guide)
- [Heroku migration documentation](https://northflank.com/docs/v1/application/migrate-from-heroku)
- [Heroku pricing comparison and reduction guide](https://northflank.com/heroku-pricing-comparison-and-reduction)

## Which platform should you choose?

The Heroku vs AWS decision comes down to matching requirements with your needs, but there's a solution that resolves this entirely.

Heroku works for speed and simplicity when you lack DevOps expertise. Good for early-stage startups and prototypes where getting live fast is the priority.

AWS works when you need infrastructure control and have the expertise to manage it. Good for complex applications with dedicated DevOps resources.

<InfoBox className="BodyStyle">

[**Northflank**](https://northflank.com/) removes the choice between simplicity and control by giving you both.

You get Git-based deployments like Heroku, but [running in your own cloud account](https://northflank.com/features/bring-your-own-cloud) ([AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), or [Azure](https://northflank.com/cloud/azure)).

[GPU access](https://northflank.com/cloud/gpus) for AI workloads without Kubernetes complexity.

A single platform for both AI and traditional applications.

No vendor lock-in - everything runs on Kubernetes infrastructure you control.

</InfoBox>

> If you're caught between Heroku's limitations and AWS's complexity, or building AI applications that need GPU access, [Northflank](https://northflank.com/) is built for this. Try the [free developer sandbox](https://app.northflank.com/signup) to see how it works, or if you're an enterprise with specific requirements, [book a demo](https://cal.com/team/northflank/northflank-intro) to discuss your setup.
> 

### Related resources

- [Top Heroku alternatives in 2026](https://northflank.com/blog/top-heroku-alternatives)
- [Heroku vs Render comparison](https://northflank.com/blog/render-vs-heroku)
- [Heroku pricing comparison and reduction guide](https://northflank.com/heroku-pricing-comparison-and-reduction)
- [Northflank on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-fjpivca45koho)
- [DigitalOcean vs AWS comparison](https://northflank.com/blog/digitalocean-vs-aws)
- [What is AWS Fargate?](https://northflank.com/blog/what-is-aws-fargate)]]>
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</item><item>
  <title>Fireworks AI vs Together AI: Which platform fits your stack?</title>
  <link>https://northflank.com/blog/fireworks-ai-vs-together-ai</link>
  <pubDate>2025-09-30T16:10:00.000Z</pubDate>
  <description>
    <![CDATA[Compare Fireworks AI, Together AI, and Northflank for AI deployment. Learn which platform fits your stack for inference and production apps.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/fireworks_ai_vs_together_ai_36f0e8f59f.png" alt="Fireworks AI vs Together AI: Which platform fits your stack?" />You've deployed your first LLM endpoint. Inference is fast, costs are manageable, and your demo impressed the stakeholders.

However, now you need to ship the actual product, including frontend, backend APIs, databases, background workers, CI/CD pipelines, and staging environments. Suddenly, your inference-only platform feels limiting.

This comparison reviews **Fireworks AI vs Together AI** and examines **Northflank** as a production-ready alternative that handles GPU workloads alongside your entire application stack

If you're evaluating these platforms, you'll understand which one matches your workflow and growth trajectory.

## Quick comparison: Fireworks AI vs Together AI vs Northflank

Before we go into detail, see this quick comparison below:

| Feature | Fireworks AI | Together AI | Northflank |
| --- | --- | --- | --- |
| **Primary focus** | Fast LLM inference | Open-source model hosting support | Full-stack apps & AI workloads |
| **Deployment model** | Serverless + on-demand | Serverless + dedicated | Containerized services |
| **GPU support** | H100, H200, A100, L40S | H100, H200, GB200, B200 | H100, H200, B200, A100 (40GB/80GB), L40S, A10. See [more supported GPUs here](https://northflank.com/gpu) |
| **Model catalog** | 100+ models | 200+ models | Bring your own ([1 click deploy templates available](https://northflank.com/stacks)) |
| **Fine-tuning** | LoRA, multi-LoRA serving | LoRA & full fine-tuning | Any containerized approach |
| [**BYOC (Bring Your Own Cloud)**](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) | Enterprise plans | Enterprise plans | Self-service (AWS, GCP, Azure, OCI and [many more](https://northflank.com/features/bring-your-own-cloud)) |
| **Full-stack support** | Inference only | Model hosting only  | Yes (APIs, databases, jobs, frontends) |
| **CI/CD** | External tools required | External tools required | Built-in Git-based CI/CD |
| **Pricing model** | Per-token / per-GPU-second | Per-token / per-minute | Per-second usage-based |
| **Best for** | Optimized inference APIs | Open-source model experimentation | Full-stack production AI applications |

## Overview: Fireworks AI and Together AI

Before going into how Northflank differs, let's examine what Fireworks AI and Together AI offer and where their strengths and limitations become apparent in production environments.

### Fireworks AI: Speed-optimized inference

Fireworks AI specializes in serving open-source LLMs with industry-leading performance. The platform's custom FireAttention CUDA kernels deliver inference speeds faster than standard implementations like vLLM, which matters for latency-sensitive applications.

**Key strengths:**

- Fast inference with optimized serving stack
- Multi-LoRA serving supports deploying multiple fine-tuned model variants without separate hosting fees
- Serverless deployment with per-token pricing
- Model optimization for high-throughput production workloads

**Where it falls short:**

- No infrastructure control (deploying in your own cloud requires enterprise contracts)
- Limited to inference and fine-tuning (no APIs, databases, or job orchestration)
- No native CI/CD integration
- Thin observability and debugging capabilities

Fireworks focuses on serving models fast. But once your product needs background processing, database-backed workflows, or multi-service architecture, you'll need supplementary tools.

### Together AI: Open-source model platform

Together AI provides comprehensive access to 200+ open-source models with a focus on flexibility and model selection. The platform supports everything from LLaMA and Mistral to multimodal models and embeddings.

**Key strengths:**

- Extensive model catalog with instant access
- Both LoRA and full fine-tuning support
- GPU clusters (H100, H200, GB200) for training workloads
- OpenAI-compatible APIs for easy migration

**Where it falls short:**

- BYOC (Bring Your Own Cloud) and hybrid deployments locked behind enterprise plans
- No support for deploying non-AI services (frontends, APIs, databases)
- Basic observability and monitoring
- No built-in CI/CD or environment management

Together AI works well for teams focused on model experimentation and serving. But if you're building a product that includes AI features rather than being solely an AI API, the platform's scope becomes restrictive.

### Direct comparison: Fireworks AI vs Together AI

When comparing Fireworks AI vs Together AI directly, the fundamental difference is optimization focus.

**Choose Fireworks AI if:**

- Inference latency is your primary concern
- You need to serve multiple fine-tuned variants efficiently
- You want the absolute fastest serving stack available
- Your use case centers on optimized API endpoints

**Choose Together AI if:**

- You need access to a broad model catalog for experimentation
- You want flexibility in fine-tuning approaches (full training vs LoRA)
- You're migrating from OpenAI and need compatible APIs
- You value model selection over raw inference speed

Both platforms handle GPU access well and provide quality inference services. However, neither supports deploying complete applications. Both lack native CI/CD integration. And both require enterprise contracts for infrastructure control through BYOC.

## Why Northflank takes a different approach

When teams outgrow inference-only platforms, they typically need capabilities that go beyond model serving.

They need to deploy frontends, manage databases, orchestrate background jobs, and implement proper CI/CD workflows, all while maintaining their AI workloads.

[Northflank](https://northflank.com/) provides a unified platform for these requirements.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

Let’s see some of the features Northflank offers:

### 1. Container-native flexibility

Northflank runs on standard Docker containers, meaning you can deploy Python ML workloads, Node.js APIs, React frontends, PostgreSQL databases, and background workers from the same platform. If it runs in a container, it runs on Northflank. You're not constrained to framework-specific patterns or inference-only abstractions.

### 2. Full-stack deployment support

Real products consist of multiple components. Northflank supports deploying your inference API alongside authentication services, data processing pipelines, scheduled jobs, vector databases, and frontend applications. You don't need multiple platforms to ship a complete product.

### 3. Git-based CI/CD as a core feature

While Fireworks and Together require external CI/CD tools, Northflank includes Git integration natively. Connect your GitHub, GitLab, or Bitbucket repository, and each commit triggers automated builds, tests, and deployments. Preview environments for pull requests let your team test changes before production.

### 4. Self-service BYOC without enterprise pricing

Northflank supports Bring Your Own Cloud for AWS, GCP, Azure, Oracle Cloud, and Civo without requiring enterprise contracts or sales calls. Deploy workloads in your own infrastructure while keeping the managed platform experience. This provides cost transparency, data residency control, and integration with existing cloud relationships.

### 5. Affordable, transparent GPU pricing

As of September, Northflank offers competitive GPU pricing with per-second billing:

- H100: $2.74/hour
- H200: $3.14/hour
- B200: $5.87/hour
- A100 (40GB/80GB): $1.42-1.76/hour

All pricing includes CPU, memory, and storage bundled together—no hidden fees or surprise costs.

### 5. Production-grade infrastructure features

Northflank includes private networking, VPC support, RBAC, audit logs, SAML SSO, and secure runtime isolation for AI-generated code. These enterprise features come standard, not locked behind premium tiers.

## Choosing the right platform for your use case

The decision between these platforms depends on your current needs and growth trajectory.

**Fireworks AI works best for:**

- Teams needing the fastest possible inference
- Use cases where you're serving many fine-tuned model variants
- Projects where the entire product is an inference API

**Together AI works best for:**

- Teams experimenting with multiple open-source models
- Projects requiring diverse model types (text, vision, audio, embeddings)
- Use cases where model selection flexibility matters most

**Northflank works best for:**

- Teams building complete AI products, not just inference APIs
- Projects requiring both AI and non-AI infrastructure
- Organizations needing infrastructure control through self-service BYOC
- Teams wanting to consolidate vendors and reduce operational complexity
- Use cases where AI is a component of a larger application stack

If you're serving isolated model endpoints and nothing else, Fireworks or Together handle that well. But if you're building a product that includes inference alongside databases, APIs, frontends, and scheduled jobs, forcing these components across multiple platforms creates unnecessary complexity.

Northflank doesn't force you to choose between deployment speed and infrastructure control. You get both, along with the production-ready features your team needs to scale confidently.

## Getting started

Ready to deploy your AI workload on a platform built for complete applications? [Start with Northflank's free tier](https://app.northflank.com/signup) to experience full-stack flexibility with GPU orchestration, or [book a demo with an engineer](https://cal.com/team/northflank/northflank-demo) to see how Northflank supports your specific use case.

For teams comparing Fireworks AI vs Together AI and discovering they need more than inference-only platforms provide, Northflank offers the infrastructure to build, deploy, and scale AI products without platform constraints.]]>
  </content:encoded>
</item><item>
  <title>Modal vs Baseten: Which AI deployment platform fits your stack?</title>
  <link>https://northflank.com/blog/modal-vs-baseten-vs-northflank</link>
  <pubDate>2025-09-29T15:20:00.000Z</pubDate>
  <description>
    <![CDATA[Compare Modal vs Baseten for AI deployment. Learn key differences, limitations, and why Northflank offers more flexibility for production workloads.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/modal_vs_baseten_vs_northflank_ccc8d1ef3f.png" alt="Modal vs Baseten: Which AI deployment platform fits your stack?" /><InfoBox className="BodyStyle">

**Quick summary**

Modal is a serverless platform for running Python functions with GPU access. It's built for batch jobs, workflows, and async tasks.

Baseten focuses on optimized model inference APIs for production workloads.

Both platforms handle their specific use cases well, but have limitations: neither supports full-stack applications, both lack built-in CI/CD, and both use platform-specific abstractions.

> [Northflank](https://northflank.com/) takes a different approach. It's a container-based platform that supports everything from model serving to full applications. Northflank provides built-in Git-based CI/CD, Bring Your Own Cloud (BYOC) without enterprise pricing, GPU orchestration, and production-grade infrastructure.
> 

If you need flexibility beyond isolated functions or model serving, Northflank provides that without sacrificing deployment speed. [Try it out directly](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo) with an Engineer.

</InfoBox>

You've built something that works. Your model performs well in notebooks, your inference pipeline is reliable, and now you need to deploy it without spending weeks configuring infrastructure.

Modal and Baseten both promise to get you there fast, but they take different approaches. Modal gives you serverless Python functions. Baseten gives you optimized model inference.

Both work well for specific use cases, but as your product grows, you might need something more flexible.

This article breaks down the **modal vs baseten** comparison, examines where each platform performs best, and introduces [Northflank](https://northflank.com/) as a production-ready alternative that combines speed with full-stack flexibility.

If you're choosing between these platforms, you'll leave with a clear understanding of which one fits your workflow.

## Comparison table: Modal vs Baseten vs Northflank

Below is an overview of how the three platforms compare across key features. We'll go into more detail later in the article.

| Feature | Modal | Baseten | Northflank |
| --- | --- | --- | --- |
| **Primary focus** | Python functions & workflows | Model inference APIs | Full-stack apps & AI workloads |
| **Deployment model** | Serverless functions | Model-as-a-service | Containerized services |
| **GPU support** | H100, A100, L40S, A10, L4, T4 | Custom inference-optimized GPUs | H100, A100 80GB/40GB, L40S, A10, up to B200. See [more supported GPUs here](https://northflank.com/gpu) |
| **Cold start time** | Sub-second | Optimized for inference | Fast startup with warm containers |
| **CI/CD integration** | External tools needed | Limited native support | Native Git-based CI/CD with preview environments |
| **Full-stack support** | Functions only | Model serving + basic UI builder | Complete: frontend, backend, databases, workers |
| **Networking** | Basic (no VPC, limited control) | Managed, inference-focused | Private networking, VPC, custom domains, service mesh |
| [**BYOC (Bring Your Own Cloud)**](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) | No | Enterprise only (requires sales) | Yes, from day one (self-service) |
| **Container control** | Modal-specific runtime | Limited customization | Full Docker control, BYO images |
| **Best for** | Async tasks, batch jobs, ML workflows | Model inference at scale | Production AI products, full-stack apps |
| **Pricing model** | Usage-based (per second) | Usage-based (inference-focused) | Usage-based (transparent per-resource) |
| **Vendor lock-in** | High (Modal-specific decorators) | Moderate (model-centric abstractions) | Low (standard containers, [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) option) |

## Overview: Understanding Modal and Baseten

Before breaking down the comparison, let's look at what each platform does, who uses them, and where their strengths and limitations become apparent in production environments.

### Modal: Serverless Python for ML workflows

Modal is a serverless platform for running Python functions in the cloud. You write a function, add a decorator, and it runs with GPU access. It handles batch processing, scheduled jobs, LLM fine-tuning, and async inference tasks.

![modal-homepage.png](https://assets.northflank.com/modal_homepage_a7380e6d35.png)

The platform is Python-based and scales automatically with sub-second cold starts. Key features include:

- GPU support: H100, A100, L40S, A10, L4, and T4
- Built-in scheduling for cron jobs, background tasks, and retries
- Functions served as HTTPS endpoints
- Network volumes, key-value stores, and queues
- Real-time logs and monitoring

However, the function-centric design comes with trade-offs. You can't deploy full applications with frontends and backends. CI/CD integration requires external tools, and networking capabilities are more limited compared to container-based platforms.

If your project grows beyond isolated Python functions, you may need to supplement with other tools or consider a different approach. 

### Baseten: Optimized inference for production models

Baseten focuses specifically on model inference. The platform is built for teams that need to serve ML models as production APIs with enterprise-grade performance.

![baseten-homepage.png](https://assets.northflank.com/baseten_homepage_2c66e73096.png)

Baseten's inference stack includes custom kernels, advanced caching, and performance optimizations built into the platform. Key features include:

- Deploy open-source models, custom models, or fine-tuned variants
- Autoscaling, monitoring, and reliability built-in
- Dedicated deployments for high-scale workloads
- Support for various model types: LLMs, image generation, transcription, and embeddings

However, the platform's model-first design has limitations. You can't deploy full-stack applications beyond model serving. [Bring Your Own Cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) (BYOC) options exist but require enterprise pricing and sales discussions.

If you're building a product that includes background workers, complex APIs, or multiple interconnected services, the platform's scope may not be sufficient

### What are the key differences between Modal and Baseten?

When comparing Modal vs Baseten directly, the fundamental difference is workflow focus.

Modal handles general-purpose Python compute (batch jobs, workflows, training), while Baseten specializes in serving models as inference APIs

**Choose Modal if:**

- You're running Python workflows, batch processing, or scheduled ML tasks
- You want to prototype quickly without infrastructure setup
- Your workload centers on isolated functions that can scale independently
- You're comfortable with function-as-a-service abstractions

**Choose Baseten if:**

- You need optimized, production-grade model inference
- You're serving models as APIs at enterprise scale
- You want built-in performance optimizations for LLMs and custom models
- Your primary focus is serving, not training or general compute

Both platforms handle GPU access well, but neither supports deploying full applications. Both lack native CI/CD integration. And both require you to work within their specific abstractions, which can create challenges as your requirements change.

## A more flexible alternative: Why teams choose Northflank

When evaluating modal vs baseten, some teams find they need capabilities beyond what either platform offers. They want deployment simplicity alongside the flexibility to build full products without being constrained to a specific deployment pattern.

[Northflank](https://northflank.com/) takes a different approach.

Rather than specializing in functions or inference, it provides a developer platform, with support for **both AI and non-AI workloads** (like your frontend, backend APIs, databases, and background workers) that handles model serving, full-stack applications, and everything in between.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

Let’s look at the key differences:

### 1. Container-first flexibility

Northflank is built on standard Docker containers. This means you can deploy Python ML workloads, Node.js APIs, React frontends, background workers, and databases from the same platform. You're not limited to framework-specific patterns. If it runs in a container, it runs on Northflank.

When building a product, your inference API is often just one component. You also need a frontend, authentication, data processing pipelines, and scheduled jobs. Northflank supports all of these without requiring multiple platforms.

### 2. Git-native development workflows

While Modal and Baseten require external tools for CI/CD, Northflank includes Git integration as a core feature. Connect your GitHub ([see how](https://northflank.com/docs/v1/application/getting-started/link-your-git-account)), GitLab ([see how](https://northflank.com/docs/v1/application/getting-started/link-your-git-account#link-your-gitlab-account)), or Bitbucket repository ([see how](https://northflank.com/docs/v1/application/getting-started/link-your-git-account#link-your-bitbucket-account)), and each commit triggers automated builds, tests, and deployments.

There are also preview environments ([try it out](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment)) for pull requests that allow your team to test changes before merging them to production.

### 3. Production-grade infrastructure without DevOps overhead

Northflank includes [private networking](https://northflank.com/docs/v1/application/network/networking-on-northflank#private-networking), VPC support, [RBAC](https://northflank.com/docs/v1/application/secure/use-role-based-access-control), [audit logs](https://northflank.com/docs/v1/application/observe/audit-logs), SAML SSO, and more as standard features.

The platform also provides [secure runtime isolation](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale#northflank-secure-runtime-for-codegen-workloads) for running untrusted AI-generated code, which matters for teams building fine-tuning platforms or AI agents.

For GPU support, the platform offers NVIDIA [H100](https://northflank.com/cloud/gpus/H100), [A100](https://northflank.com/cloud/gpus/A100) (40GB and 80GB), [L40S](https://northflank.com/cloud/gpus/L40S), [B200](https://northflank.com/cloud/gpus/B200), and more.

Also, [autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments), lifecycle management, and [cost optimization](https://northflank.com/blog/cloud-cost-optimization#how-northflank-helps-optimize-your-cloud-costs) are included.

### 4. Bring your own cloud from day one

Northflank supports [Bring Your Own Cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) without requiring enterprise pricing or sales calls.

You can deploy workloads in your own AWS ([try it out](https://northflank.com/cloud/aws)), GCP ([try it out](https://northflank.com/cloud/gcp)), Azure ([try it out](https://northflank.com/cloud/azure)), Civo ([try it out](https://northflank.com/cloud/civo)), or Oracle ([try it out](https://northflank.com/cloud/oci)) accounts while keeping the [managed platform](https://northflank.com/features/managed-cloud) experience.

This provides cost transparency, data residency control, and the flexibility to optimize your cloud spending.

Modal doesn't offer BYOC. Baseten supports it through enterprise contracts. Northflank makes it self-service.

### 5. Transparent, predictable pricing

Northflank uses usage-based pricing: you pay only for the resources your services consume. No hidden fees. You can estimate costs before deploying and track usage in real-time.

[See full pricing details](https://northflank.com/pricing)

## Choosing the right platform for your needs

The modal vs baseten decision depends on what you're building today and where you're headed.

If you're running isolated Python tasks or need optimized model inference with minimal setup, either platform works well.

But if you're building a product that will grow beyond those use cases, the constraints will become apparent.

Northflank doesn't force you to choose between speed and control. You get both, along with the production-ready infrastructure your team needs to scale confidently.

If you're serving models, running training jobs, or deploying full applications, the platform adapts to your requirements instead of constraining them.

<InfoBox className="BodyStyle">

**Deploy your AI workload on a platform built for production.** [Start with Northflank's free tier](https://app.northflank.com/signup) and experience full-stack flexibility with GPU orchestration, or [book a demo](https://cal.com/team/northflank/northflank-demo) to see how Northflank supports your specific use case.

</InfoBox>]]>
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</item><item>
  <title>What is a staging environment? (and how to set one up)</title>
  <link>https://northflank.com/blog/what-is-a-staging-environment-how-to-set-one-up</link>
  <pubDate>2025-09-25T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Staging environments are production-like replicas where you test applications before going live. They catch integration bugs, validate performance, and enable stakeholder sign-off without affecting real users. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/staging_env_95ca6c42e7.png" alt="What is a staging environment? (and how to set one up)" /><InfoBox className='BodyStyle'>

# ⌛  TL;DR

Staging environments are production-like replicas where you test applications before going live. They catch integration bugs, validate performance, and enable stakeholder sign-off without affecting real users. 

Key benefits include end-to-end testing, deployment pipeline validation, and final bug verification. 

[Northflank](https://northflank.com/) automates staging setup through pipelines, templates, and Infrastructure as Code, eliminating manual configuration while maintaining production parity.

</InfoBox>

**A staging environment is a near-perfect replica of your production environment where you test applications before deploying them to live users.** It's the final checkpoint that catches bugs, performance issues, and integration problems before they reach production.

Think of staging as your dress rehearsal, everything should work exactly like it will in production, using the same configurations, databases, and integrations, but without affecting real users.

## What is a staging environment used for?

Staging environments serve several critical purposes in the development workflow:

1. **End-to-end testing** is the primary use case. You run complete user workflows to make sure everything works together, from login to checkout, API calls to database updates. This is where you catch integration issues that unit tests might miss.
2. **Performance testing** happens in staging because it mirrors production resources and data volumes. You can run load tests, check response times, and see how your application behaves under realistic traffic conditions.
3. **User Acceptance Testing (UAT)** gives stakeholders, product managers, and QA teams a chance to sign off on features before they go live. Non-technical team members can click through the actual application and verify it meets requirements.
4. **Deployment pipeline testing** ensures your release process works correctly. You test database migrations, configuration changes, and deployment scripts in an environment that matches production, reducing the risk of deployment failures.
5. **Final bug fixes** get validated in staging. When you fix a critical issue, staging lets you verify the fix works in a production-like environment before pushing to live users.

## Staging vs other environments

Understanding how staging fits into your overall development workflow helps clarify its role:

**Staging environments vs Development environments** are where engineers write and debug code. They're fast and lightweight but don't represent real-world conditions. Staging comes after development when you need production-like testing.

**Staging environments vs QA environments** focus on structured testing of individual features and components. Staging takes over for broader integration testing and user acceptance testing with production-like data and configurations.

**Staging environments vs Preview environments** are temporary setups for specific pull requests or features. Staging is more permanent and comprehensive, testing the complete application as it will appear in production.

**Staging environments vs Production** is where real users interact with your application. Staging should mirror production as closely as possible while remaining separate and safe for testing.

For complex applications, you might use [hybrid cloud architectures](https://claude.ai/blog/what-is-hybrid-cloud) where staging runs in public cloud for easy access while production stays in private infrastructure for security and compliance.

## How staging environments work

Staging environments require careful setup to provide accurate testing conditions. The goal is creating an environment that behaves as much like production as possible while remaining safe for testing.

Infrastructure parity means your staging environment should match production hardware, operating systems, network configurations, and resource allocation. If production runs on AWS with specific instance types, staging should use the same setup.

For data management, most teams use production data snapshots or realistic test data. You need enough volume and variety to test real scenarios without exposing sensitive information. Many teams use data masking or synthetic data generation to achieve this balance.

Configuration management ensures staging uses the same environment variables, secrets, and settings as production. The only differences should be endpoints (staging APIs vs production APIs) and necessary safety measures.

Network setup should replicate production networking, including load balancers, CDNs, and external service integrations. Your staging environment should connect to staging versions of third-party services when possible.

<InfoBox className='BodyStyle'>
💡 Modern platforms like **Northflank** simplify staging environment management by providing production parity out of the box. You get the same containerized deployments, networking, and configurations as production while maintaining complete isolation.

</InfoBox>

## Staging environment best practices

1. **Automate environment creation** so staging stays synchronized with production changes. Use Infrastructure as Code and container deployments to ensure consistency and reduce manual setup errors.
2. **Schedule regular refreshes** of staging data and configurations. Set up automated processes to sync staging with production changes, update test data, and refresh environment settings on a regular schedule.
3. **Implement proper security** even though staging isn't user-facing. Use realistic security configurations, secure data handling, and access controls that mirror production requirements.
4. **Monitor staging performance** with the same tools you use in production. Set up logging, metrics, and alerting so you can identify performance issues before they reach users.
5. **Plan for quick teardown and recreation** when staging gets corrupted or outdated. Ephemeral environments that can be rebuilt quickly reduce the risk of testing against stale configurations.

<InfoBox className='BodyStyle'>
💡 [Northflank](https://northflank.com/) makes this easier by automating environment provisioning, handling configuration management, and providing consistent deployment pipelines across all environments. You can create staging environments that mirror production with minimal manual work.

</InfoBox>

## Common staging environment challenges

- **Resource costs** can be significant since staging needs production-level infrastructure. Consider using smaller instances for most testing and scaling up only for performance testing, or implement automated shutdown schedules for non-business hours.
- **Environment drift** happens when staging configurations slowly diverge from production. Regular automated synchronization, Infrastructure as Code practices, and monitoring help catch drift before it causes problems.
- **Data synchronization** becomes complex with large production datasets. You need strategies for data masking, subsetting, or synthetic data generation that maintain testing accuracy while protecting sensitive information.
- **Test data management** requires careful planning to avoid conflicts between different testing activities. Multiple teams testing simultaneously can interfere with each other without proper data isolation.
- **Deployment complexity** increases when you need to coordinate updates across multiple environments while maintaining consistency. Automated pipelines and deployment tools help manage this complexity.

## Setting up staging environments with Northflank

Northflank's platform is specifically designed to simplify staging environment creation and management through automated pipelines, templates, and Infrastructure as Code. Here's how to set up production-parity staging environments that mirror your production setup without the complexity.

First, sign up to Northflank [here](https://app.northflank.com/signup).

### Start with pipeline architecture

Northflank uses pipelines to manage your project's resources for different environments, from development through to production. You can automate your release workflows for each pipeline stage, and define ephemeral environments to preview pull requests and branches.

**Create your pipeline structure:**

I. Navigate to your project dashboard and select “Pipelines” → "Create new pipeline"
    
![CleanShot 2025-09-26 at 12.12.50@2x.png](https://assets.northflank.com/Clean_Shot_2025_09_26_at_12_12_50_2x_5074aaddca.png)
    
II. Enter a name to identify and select the project resources to add to each stage. You can add resources to each stage of your pipeline in whatever configuration best represents your workflow, and add or remove resources from the pipeline after creation.
    
![CleanShot 2025-09-26 at 12.13.17@2x.png](https://assets.northflank.com/Clean_Shot_2025_09_26_at_12_13_17_2x_2d0136549d.png)
    
III. Set up three core stages: Development, Staging, and Production

IV. Add your deployment services, jobs, and addons to each pipeline stage. Removing a deployment service from a pipeline will not unlink its build service or external image, nor pause the deployment service.
    
![CleanShot 2025-09-26 at 12.11.51@2x.png](https://assets.northflank.com/Clean_Shot_2025_09_26_at_12_11_51_2x_7e72dbabae.png)    

### Configure staging with production parity

**Infrastructure matching:** With Northflank, creating staging environments that mirror production is seamless. You can quickly replicate configurations, run end-to-end tests, and tear down the environment when done. Use the same compute plans, database configurations, and networking setup as production, but scale down resources appropriately for cost efficiency.

**Database and addon setup:** Add production-equivalent addons (PostgreSQL, Redis, MongoDB, MinIO) to your staging pipeline stage. Northflank also offers staging databases and the ability to fork a database from a backup. This ensures your staging data structure matches production while maintaining data isolation.

**Environment configuration:** Northflank's secure secret management and shared resources let you safely inject environment variables and reuse database or storage configurations across environments. No more environment inconsistencies because your preview, staging, and production environments can share the same secrets and data sources without risk.

### Set up automated release flows

![CleanShot 2025-09-26 at 12.15.28@2x.png](https://assets.northflank.com/Clean_Shot_2025_09_26_at_12_15_28_2x_4c5f0051fd.png)

**Create staging release flows:** Click add release flow in the header for the development stage and select get started with visual editor. Click and drag a start build node into the sequential workflow.

**Configure promotion workflows:** You can configure a release flow to promote images deployed in the preceding stage to the stage that contains the release flow. You can promote any image deployed to a deployment service or job, whether they are built on Northflank or deployed from an external container registry.

Key release flow nodes for staging:

- **Start Build**: Builds your application from the specified branch
- **Deploy Build**: Deploys the built image to staging services
- **Promote Deployment**: Promotes tested images from staging to production
- **Backup Addon**: Creates database backups before deployments
- **Execute Command**: Runs database migrations or custom scripts

### Implement Infrastructure as Code

**Use Northflank templates:** Northflank templates give you the ability to codify your workflow to create and update resources on Northflank. Everything you can do in the Northflank UI or API can be achieved in repeatable, programmatic templates.

**Template structure for staging:**

![CleanShot 2025-09-26 at 12.16.49@2x.png](https://assets.northflank.com/Clean_Shot_2025_09_26_at_12_16_49_2x_5c9e0bbff2.png)

**Enable GitOps:** GitOps on Northflank allows you to manage infrastructure, run releases, update deployments, and automate complex tasks using templates in a Git repository. Bidirectional sync means that any changes to your template in your repository are automatically reflected on Northflank.

**Set up Git triggers:** You can automatically run a release flow using Git triggers, or using the webhook trigger. This allows you to run your releases on merge to your relevant Git branch. Configure staging deployments to trigger automatically when code is merged to your staging branch.

![CleanShot 2025-09-26 at 12.19.24@2x.png](https://assets.northflank.com/Clean_Shot_2025_09_26_at_12_19_24_2x_1a0bf8fa88.png)

### Monitor and maintain staging environments

**Observability setup:** Gain full control without compromising developer agility. Our flexible, reusable IaC templates let operations manage infrastructure effortlessly, while developers enjoy self-service deployment. Use Northflank's built-in logging, metrics, and alerting to monitor staging performance with the same tools as production.

### Cost optimization strategies

**Resource scaling:** Use smaller compute plans for staging while maintaining the same architecture. We recommend leveraging a combination of robust, large-scale staging environments that closely replicate production and lightweight, ephemeral dev environments.

**On-demand environments:** Configure staging environments to scale down during off-hours or shut down completely when not in use. Northflank's template system allows you to quickly recreate environments when needed.

**Preview environments:** Northflank's integrated preview environments automatically create isolated, full-stack deployments for each new branch or pull request. This eliminates manual environment configuration and speeds up reviews. Use these for feature testing before promoting to main staging.

## Conclusion

Staging environments are essential for catching issues before they reach production users. They provide a safe space to test integrations, validate performance, and get stakeholder approval while mirroring production conditions as closely as possible.

Success with staging environments comes down to maintaining production parity, automating management processes, and integrating staging into your deployment workflow. Modern platforms like **Northflank** eliminate much of the complexity by providing consistent environments, automated provisioning, and integrated deployment pipelines.

**Key takeaways for engineering teams:**

- **Mirror production** as closely as possible for accurate testing
- **Automate environment management** to prevent drift and reduce manual work
- **Plan for data management** with regular refreshes and proper security
- **Integrate with CI/CD** to make staging part of your automated deployment process
- **Monitor and maintain** staging environments like you would production

When implemented well, staging environments catch critical issues before they impact users, improve deployment confidence, and enable faster, safer software delivery.

[Try Northflank](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo) to see how we simplify environment management.]]>
  </content:encoded>
</item><item>
  <title>Top 5 Platform9 alternatives: Finding the right private cloud solution</title>
  <link>https://northflank.com/blog/platform9-alternatives</link>
  <pubDate>2025-09-25T15:15:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the best Platform9 alternatives including Northflank, OpenShift, and Rancher. Find the right private cloud solution for your needs.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/platform9_alternatives_7043c9af49.png" alt="Top 5 Platform9 alternatives: Finding the right private cloud solution" />Platform9 handles private cloud infrastructure well, but your organization might have specific needs that require considering other approaches.

You might need more granular control over your Kubernetes environments, prioritize integrated development workflows, or prefer self-managed solutions over SaaS models.

The private cloud space offers several alternatives, each designed to address different requirements around developer experience, operational control, enterprise features, or cost structures.

Understanding these options can help you find the approach that best aligns with your organization's current priorities and future goals.

<InfoBox className="BodyStyle">

## TL;DR: Your Platform9 alternative options

If you're considering additional options beyond Platform9, several alternatives address different organizational needs.

[**Northflank**](https://northflank.com/) – The most comprehensive alternative, combining:

- Managed Kubernetes with enterprise-grade features
- Built-in CI/CD pipelines and developer workflows
- GPU and AI workload support
- Flexible deployment: [Bring your own cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) or use Northflank’s managed cloud
- No vendor lock-in, with secure runtimes and multi-cluster support

Ideal for fast-moving teams and large enterprises alike - delivering cloud-native power without the operational burden.

**Other platforms:**

- **OpenShift** – Enterprise platform with compliance tools, but higher operational overhead
- **Rancher** – Open-source multi-cluster management; self-managed
- **VMware Tanzu** – Good for teams staying in the VMware ecosystem
- **KubeSphere** – Free, open-source Kubernetes platform; requires self-management

> If your team needs a developer-focused platform with enterprise capabilities and flexible deployment options, you can [book a demo](https://cal.com/team/northflank/northflank-intro) to speak with an engineer or [try out Northflank's platform](https://app.northflank.com/signup) for free to see how it fits your workflow.
> 

</InfoBox>

## Quick comparison of the top 5 Platform9 alternatives

Before we do a detailed comparison, see how these platforms compare across the key features you need for private cloud deployments.

| Feature | Northflank | Red Hat OpenShift | Rancher | VMware Tanzu | KubeSphere |
| --- | --- | --- | --- | --- | --- |
| **Deployment model** | Full-stack cloud-native platform ([managed](https://northflank.com/features/managed-cloud) or [bring your own cloud](https://northflank.com/features/bring-your-own-cloud)) | Enterprise Kubernetes platform | Multi-cluster management | Enterprise application platform | Open-source Kubernetes platform |
| **Management overhead** | Fully managed (with option to use your own cloud) | Medium (enterprise tools) | High (self-managed) | Medium (enterprise support) | High (self-managed) |
| **VM support** | Container-native | Virtual machine support | Container-native | VM and container support | Container-native |
| **Kubernetes integration** | Native Kubernetes platform with developer-focused abstractions | Kubernetes-based | Kubernetes management | Kubernetes included | Kubernetes-based |
| **Multi-cloud support** | AWS, GCP, Azure, Civo, Oracle, on-premises ([BYOC](https://northflank.com/features/bring-your-own-cloud)) | Multi-cloud & hybrid | Multi-cloud & on-premises | Multi-cloud with VMware | Any infrastructure |
| **Built-in CI/CD** | Comprehensive CI/CD pipelines, GitOps, preview environments | Integrated CI/CD tools | Basic automation | Enterprise DevOps tools | Built-in DevOps platform |
| **Pricing model** | Transparent usage-based model (with free tier) | Subscription per core | Open-source, enterprise support | Subscription-based | Free open-source |
| **Enterprise support** | Enterprise-grade support (security, RBAC, compliance, audit logging) | Enterprise-grade support | Community + paid support | VMware enterprise support | Community support |
| **Best for** | Developer teams & enterprises needing secure, flexible, cloud-native apps | Large enterprises with compliance needs | Teams needing multi-cluster control | Organizations using VMware stack | Budget-conscious Kubernetes adopters |

## What does Platform9 do?

Platform9’s Private Cloud Director turns your existing infrastructure into a managed private cloud. It provides a SaaS control plane to manage both virtual machines and Kubernetes clusters on your own hardware, with features similar to VMware’s vMotion and DRS.

It’s often used as a middle ground between traditional virtualization and cloud-native platforms, allowing teams to run VMs and containers side-by-side.

Platform9 also includes tools like vJailbreak to support migrations from VMware environments, which some teams have explored following recent pricing changes in the ecosystem.

## Why look for Platform9 alternatives?

Even with Platform9's strengths, several factors drive teams to consider other options:

1. **SaaS dependency concerns**: Some organizations aren't comfortable with external management of their private cloud control plane, especially in regulated industries or highly security-conscious environments.
2. **Limited customization**: Platform9's managed approach means less flexibility compared to self-managed solutions. If you need deep customization of your Kubernetes environment or want to integrate specific tools, the abstraction layer can feel restrictive.
3. **Developer experience gaps**: While Platform9 handles infrastructure well, teams building modern applications often need more integrated development workflows like built-in CI/CD, preview environments, and developer-friendly deployment processes.
4. **Pricing model mismatch**: The per-node subscription model might not align with your usage patterns, especially if you're looking for more predictable costs or usage-based pricing.

## What to look for when choosing a Platform9 alternative

Before going into specific options, consider what matters most for your environment:

1. **Management philosophy**: Do you want something fully managed like Platform9, or are you willing to take on more operational responsibility for greater control and customization?
2. **Workload focus**: Are you primarily managing VMs, containers, or a mix? Some alternatives are better at one but compromise on the other.
3. **Integration requirements**: How important is seamless integration with your existing tools, CI/CD systems, and development workflows?
4. **Deployment flexibility**: Do you need multi-cloud support, or are you comfortable with on-premises-only solutions?
5. **Team expertise**: How much Kubernetes and infrastructure management knowledge does your team have? Some alternatives require significant operational expertise.
6. **Growth trajectory**: Are you scaling up container adoption, or do you need to maintain significant VM workloads long-term?

## Top 5 Platform9 alternatives for your private cloud needs

Let’s look at some of the alternatives, each addressing different aspects of what Platform9 provides:

### 1. Northflank (Most recommended)

Northflank takes a different approach from Platform9’s infrastructure-first model by putting developer experience, flexibility, and modern application needs at the center.

It’s a full-stack platform built around managed Kubernetes, designed for speed, security, and scale - regardless of being an agile startup or an enterprise modernizing complex infrastructure.

![northflank-platform.png](https://assets.northflank.com/northflank_platform_1f875d6f8a.png)

> **What makes it different:** Northflank combines comprehensive developer tooling with enterprise-grade capabilities. You get [built-in CI/CD](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank), [preview environments](https://northflank.com/use-cases/preview-environments-backend-for-kubernetes), secure runtimes, and [observability](https://northflank.com/docs/v1/application/observe/observability-on-northflank) out of the box - all while retaining full control over your infrastructure through multi-cloud, [bring-your-own-cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC), and multi-cluster support.
> 

**Here’s what stands out about Northflank**:

- **Developer-first design:** Pipelines, Git-based deploys, preview environments, and GitOps workflows built-in
- **Enterprise readiness:** Role-based access control (RBAC), secure and isolated runtimes, audit logging, and compliance support
- **AI & GPU support:** Run AI workloads with GPU-enabled containers and scale compute based on usage
- **Flexible deployment:** Deploy to Northflank’s managed cloud or use your own AWS, GCP, Azure, Civo, or Oracle accounts ([BYOC](https://northflank.com/features/bring-your-own-cloud))
- **Multi-cluster management:** Centralized control and visibility across environments
- **Observability & monitoring:** Built-in logging, monitoring, and alerting with no setup required
- **Integrated platform:** Deploy services, jobs, cron tasks, and databases all from a unified interface
- **No vendor lock-in:** Full workload portability and open standards support
- **Transparent pricing:** Usage-based pricing with a generous free tier - no upfront commitments

<InfoBox className="BodyStyle">

**Northflank pricing:**

- **Developer Sandbox:** Free tier with generous limits for testing and small projects
- **Pay as you go:** Starting at $0/month with infrastructure usage billing, unlimited projects and collaborators
- **Enterprise:** Custom pricing based on your deployment footprint and requirements, including BYOC options with flat fees for clusters, vCPU, and memory on your infrastructure with no markup on cloud costs

> See [full pricing details](https://northflank.com/pricing) or [book a demo](https://cal.com/team/northflank/northflank-intro) to speak with an engineer or [try out the platform](https://app.northflank.com/signup) via the free developer sandbox
> 

</InfoBox>

**Best for:** Enterprises and fast-moving teams who want a secure, developer-friendly platform with modern infrastructure capabilities (without sacrificing control, flexibility, or observability).

### 2. Red Hat OpenShift

OpenShift is an enterprise-grade Kubernetes platform with additional security, development tools, and operational capabilities built on top of upstream Kubernetes.

![openshift-min.png](https://assets.northflank.com/openshift_min_2d87ef258a.png)

**Key strengths:**

- **Enterprise security:** Built-in image scanning, policy enforcement, and compliance tools
- **Comprehensive platform:** Includes everything from development tools to operational monitoring
- **Multi-cloud flexibility:** Runs consistently across different infrastructure environments

**Best for:** Large enterprises with strict security and compliance requirements, organizations wanting a comprehensive application platform, and teams with dedicated platform engineering resources.

*See [**Best OpenShift alternatives: finding the right Kubernetes platform**](https://northflank.com/blog/best-open-shift-alternatives-finding-the-right-kubernetes-platform)*

<InfoBox className="BodyStyle">

Note: If your team requires enterprise-grade capabilities without the operational overhead or complexity of managing OpenShift, Northflank provides a simplified alternative with built-in CI/CD, security features, and support for multi-cloud and BYOC (Bring your own cloud) deployments - all in a developer-friendly platform.

</InfoBox>

### 3. Rancher

Rancher focuses on multi-cluster Kubernetes management, giving you centralized control over multiple Kubernetes environments across different locations and cloud providers.

![rancher-homepage.png](https://assets.northflank.com/rancher_homepage_274dfd6470.png)

**Key strengths:**

- **Multi-cluster management:** Centralized governance and operations across multiple clusters
- **Open-source foundation:** No vendor lock-in with community support
- **Infrastructure agnostic:** Works with any Kubernetes-compatible infrastructure
- **Policy management:** Centralized security policies and RBAC across clusters

**Best for:** Organizations running multiple Kubernetes clusters, teams needing centralized cluster governance, and environments spanning multiple clouds or locations.

**Limitations:** Requires more operational expertise than Platform9, less focus on traditional VM workloads, and more complex initial setup.

See [**7 Best Rancher alternatives in 2026**](https://northflank.com/blog/rancher-alternatives)

<InfoBox className="BodyStyle">

While Rancher offers comprehensive cluster management, your team may prefer Northflank’s managed approach with multi-cluster support built in if you're looking for a more integrated development experience, including CI/CD pipelines, preview environments, and secure runtimes.

</InfoBox>

### 4. VMware Tanzu

If you're evaluating Platform9 because of VMware pricing changes but aren't ready to leave the VMware ecosystem entirely, Tanzu offers an enterprise application platform that integrates with existing VMware infrastructure.

![vmware-tanzu.png](https://assets.northflank.com/vmware_tanzu_57fe820ceb.png)

**Key strengths:**

- **VMware integration:** Deep integration with existing vSphere environments
- **Application focus:** Tools for modernizing applications and development workflows
- **Enterprise features:** Comprehensive security, networking, and operational tools
- **Support structure:** Enterprise-grade support from VMware/Broadcom

**Best for:** Organizations heavily invested in VMware infrastructure, enterprises undergoing application modernization, and teams needing comprehensive platform engineering solutions.

See [**7 Top VMware Tanzu alternatives for DevOps in 2026**](https://northflank.com/blog/vmware-tanzu-alternatives)

<InfoBox className="BodyStyle">

If your team is moving away from VMware or looking to modernize beyond the vSphere ecosystem, Northflank offers a lighter-weight, cloud-native platform with comprehensive enterprise features - without the lock-in or complexity of traditional virtualization stacks

</InfoBox>

### 5. KubeSphere

KubeSphere offers an open-source alternative that provides a comprehensive Kubernetes platform with built-in DevOps, monitoring, and multi-tenancy features.

![kubesphere-homepage.png](https://assets.northflank.com/kubesphere_homepage_40125737ac.png)

**Key strengths:**

- **Complete platform:** DevOps, monitoring, logging, and alerting built-in
- **Multi-tenancy:** Native support for isolated environments and teams
- **Cost-effective:** Open-source with no licensing fees
- **Modular architecture:** Install only the components you need

**Best for:** Budget-conscious organizations, teams with a high level of Kubernetes expertise, and environments where self-management is preferred over managed services.

<InfoBox className="BodyStyle">

KubeSphere is feature-rich but self-managed. If your team wants similar DevOps, observability, and multi-tenancy benefits but without the operational complexity, Northflank offers a managed, ready-to-use alternative that accelerates delivery while maintaining control.

</InfoBox>

## Choosing the right Platform9 alternative

Your choice depends on your team’s goals and expertise. OpenShift suits enterprises with strict compliance needs, Rancher helps with multi-cluster control, and Tanzu fits VMware-heavy environments. KubeSphere is suitable for budget-conscious teams, but it requires more management.

> If you're looking for a developer-focused platform that reduces overhead while supporting enterprise features, [Northflank](https://northflank.com/) is the most comprehensive alternative. [Book a demo](https://cal.com/team/northflank/northflank-intro) or [try the free tier](https://app.northflank.com/signup).
>]]>
  </content:encoded>
</item><item>
  <title>Multi-cloud container orchestration: How to get started</title>
  <link>https://northflank.com/blog/multi-cloud-container-orchestration</link>
  <pubDate>2025-09-24T16:40:00.000Z</pubDate>
  <description>
    <![CDATA[Learn how multi-cloud container orchestration helps avoid vendor lock-in and manage containers across AWS, GCP, and Azure. See how Northflank simplifies it.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/multi_cloud_container_orchestration_8bab8c9bf9.png" alt="Multi-cloud container orchestration: How to get started" />> *Multi-cloud container orchestration is the practice of managing containerized applications across multiple cloud providers simultaneously. It helps organizations avoid vendor lock-in while maintaining consistent deployment workflows.

Platforms like [Northflank](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) simplify this complexity by providing a unified control plane that works across public clouds, private infrastructure, and managed environments.*
> 

Have you been in any of these situations:

1. Compliance requirements force you to keep data in regions your current provider doesn't support well
2. Your team knows one cloud well, but you need flexibility to leverage better pricing elsewhere
3. You're dependent on a single cloud provider, and their pricing changes or an outage halts your entire operation
4. You've acquired a company on a different cloud, and now you're managing two separate infrastructures

*These scenarios aren't hypothetical; they're happening to engineering teams every day.*

The solution isn't to avoid the cloud; it's to avoid putting all your eggs in one cloud basket (if we put it like that, the metaphor becomes painfully obvious, doesn't it?).

This is where **multi-cloud container orchestration** becomes not only useful, but essential for your infrastructure strategy.

<InfoBox className="BodyStyle">

We will cover:

- What multi-cloud container orchestration means for your team
- Why enterprises are making the switch and what benefits you can expect
- The main challenges you'll face and how to address them
- Available tools and platforms, plus how Northflank compares
- Practical steps to get started without overwhelming your infrastructure team

</InfoBox>

## What is multi-cloud container orchestration?

Multi-cloud container orchestration is the automated management of containerized applications across multiple cloud providers simultaneously.

I know you might be thinking, *"Okay, so what does this mean for my team?"*

You can deploy and manage the same application across different cloud providers like AWS, Google Cloud, and Azure using one control plane. Your deployment workflows, monitoring, and scaling work consistently across all environments while the orchestration system handles coordination between them.

Traditional [container orchestration](https://northflank.com/blog/container-orchestration) manages containers within a single cloud environment. Multi-cloud orchestration spans across different providers while automating scheduling, failover, and load balancing between them.

The main components that make this work:

- Unified control plane that handles cloud-specific differences
- Consistent networking across environments
- Standardized deployments regardless of infrastructure
- Cross-cloud load balancing and service discovery
- Coordinated scaling between providers

## Why do you need multi-cloud container orchestration?

Let's address the elephant in the room: managing containers across multiple clouds sounds complicated.

So why would you want to add this complexity? Because the benefits far outweigh the operational complexity, especially with the right tools.

Let’s see why teams make the switch:

1. ***“What if my cloud provider raises prices or limits features?”***
    
    When you're locked into a single provider, price increases or service limitations can directly impact your operations. Multi-cloud orchestration gives you negotiating power and flexibility to move workloads where they make sense.
    
2. ***“How do I meet compliance requirements across different regions?”***
    
    Regulatory requirements often demand that data stay within specific geographic boundaries. Multi-cloud orchestration lets you place workloads precisely where they need to be, while maintaining unified management.
    
3. ***“Can I optimize costs across different cloud pricing models?”***
    
    Different providers specialize in different services. AWS might offer better spot pricing for batch jobs, while GCP provides cheaper storage, and Azure gives better enterprise licensing deals. You can run each workload on the most cost-effective platform.
    
4. ***“What happens when I acquire companies on different clouds?”***
    
    If you've acquired companies using different providers, you're managing separate infrastructures that create operational silos. Multi-cloud orchestration provides a path to consolidate these environments without disruptive migrations.
    

<InfoBox className="BodyStyle">

The challenge is that implementing these benefits often requires significant engineering effort and expertise across multiple cloud platforms.

This is where platforms like [Northflank](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) come in - providing the multi-cloud orchestration capabilities you need without requiring your team to become experts in every cloud provider's specific tools and quirks.

</InfoBox>

## What challenges come with multi-cloud container orchestration?

While the benefits are compelling, you need to understand what you're signing up for. Multi-cloud orchestration introduces complexity that you'll need to manage effectively.

Let’s see the main challenges:

- **Managing different cloud-native services** - Each provider does things differently (e.g., AWS Lambda vs Google Cloud Functions), so you need to standardize or build abstractions that handle these differences.
- **Consistent security and compliance** - You need unified identity management, consistent network policies, and coordinated compliance monitoring across different security models.
- **Network complexity and latency** - Cross-cloud communication introduces latency and complexity, requiring efficient architecture design and backup plans for connectivity disruptions.
- **Monitoring and observability** - Your logging and monitoring must work consistently across all environments, either through cloud-agnostic tools or integration layers.

<InfoBox className="BodyStyle">

The key is having tools that abstract away most of this complexity while giving you the control you need.

This is what platforms like [Northflank](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) address by providing Kubernetes-based orchestration that works consistently across various cloud providers like AWS, GCP, Oracle, Civo, and Azure.

</InfoBox>

## How does Northflank solve these multi-cloud container orchestration challenges?

Now that we've covered the complexity involved, you might be thinking:

> “h*ow can my team implement multi-cloud container orchestration without drowning in operational complexity?*”
> 

This is where [Northflank](https://northflank.com/)'s approach becomes valuable.

![byoc-northflank-homepage.png](https://assets.northflank.com/byoc_northflank_homepage_da16be61c0.png)

Instead of building your own multi-cloud orchestration stack from scratch, you get a managed platform that handles these challenges:

1. **Unified experience across clouds** - When you're deploying to AWS, GCP, Azure, or other providers like Oracle and Civo, you use the same interface, workflows, and deployment processes. No need to learn each cloud's specific tooling.
2. **Built-in abstractions** - Northflank handles the cloud-specific differences for you. Your team defines what you want to deploy, and the platform manages how each cloud provider implements it.
3. **Integrated monitoring and security** - Instead of integrating different monitoring tools and security policies across clouds, you get consistent observability and compliance management through one control plane.
4. **Simplified networking** - Cross-cloud communication and service discovery work out of the box, without you having to architect complex networking solutions.

<InfoBox className="BodyStyle">

For enterprise teams looking to implement multi-cloud strategies without the typical engineering complexity, [booking a demo](https://cal.com/team/northflank/northflank-intro) or [trying out the platform directly](https://app.northflank.com/signup), can show you how this translates to your specific infrastructure needs.

</InfoBox>

## What multi-cloud container orchestration tools and platforms are available?

Of course, there are several approaches to multi-cloud container orchestration. Let's see what options are available and how Northflank compares to each one:

| Platform | How Northflank compares |
| --- | --- |
| **DIY Kubernetes** | Much easier deployment; less maintenance; fewer ops tasks (no clusters, upgrades, config, YAML boilerplate to manage) |
| **Managed Kubernetes (EKS, GKE, AKS)** | Added developer experience via [built-in CI/CD](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank), [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), [templates](https://northflank.com/features/templates). Also flexible: [use your own cluster](https://northflank.com/docs/v1/application/bring-your-own-cloud/deploy-workloads-to-your-cluster) or [Northflank's managed infrastructure](https://northflank.com/features/managed-cloud) |
| **Docker Swarm** | Better scalability and ecosystem support. Swarm is simpler but limited for complex multi-cloud scenarios |
| **OpenShift/Rancher** | More modern UI/UX with lower entry barrier; targeted for teams that want less infrastructure overhead without enterprise complexity |
| **HashiCorp Nomad** | Kubernetes ecosystem compatibility with better developer tooling integration and managed experience |

<InfoBox className="BodyStyle">

**The reality is:** While these tools can handle multi-cloud orchestration, most require significant setup, maintenance, and expertise.

Northflank gives you the power of Kubernetes-based multi-cloud orchestration without the operational burden.

To see how this works in action, [try Northflank's free developer sandbox](https://app.northflank.com/signup) to compare it with your current setup.

</InfoBox>

## Making multi-cloud container orchestration work for your team

We've covered what multi-cloud container orchestration is, why your team might need it, and what tools are available to make it happen.

*Now it's time to decide which approach fits your infrastructure strategy and team capabilities.*

The choice comes down to building and managing your own multi-cloud solution (*I wouldn't recommend*) or adopting a platform that handles the complexity for you (*I highly recommend*).

Both paths can work, but one requires significantly less operational overhead and expertise.

Your containers don't need to be constrained by vendor lock-in, and your team doesn't need to become experts in every cloud provider's quirks.

<InfoBox className="BodyStyle">

For your next steps, if you need more guidance for your team or enterprise, you can [book a demo](https://cal.com/team/northflank/northflank-intro) to see how Northflank's multi-cloud orchestration works with your specific infrastructure needs, or [try the free developer sandbox](https://app.northflank.com/signup) to test the platform yourself.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Top 6 Internal Developer Platforms for 2026</title>
  <link>https://northflank.com/blog/top-six-internal-developer-platforms</link>
  <pubDate>2025-09-23T16:40:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the top Internal Developer Platforms for 2026. Review Northflank, Backstage, Harness &amp; more to boost developer productivity and simplify deployments]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/top_six_internal_developer_platforms_7f8f66df85.png" alt="Top 6 Internal Developer Platforms for 2026" />Your developers are losing hours every week switching between multiple tools and waiting for infrastructure requests.

Meanwhile, your ops team is buried in tickets, deployments are getting more complex, and shipping is slowing down instead of speeding up.

Internal Developer Platforms (IDPs) solve this by giving developers self-service access to infrastructure while maintaining security and governance.

This guide breaks down the top IDPs for 2026, comparing their strengths and helping you choose the right platform to boost your team's velocity without the operational complexity.

<InfoBox className="BodyStyle">

## TL;DR

Internal Developer Platforms simplify software development by providing self-service infrastructure, automated deployments, and standardized workflows.

[**Northflank**](https://northflank.com/) stands out as a comprehensive, cloud-native IDP that abstracts away Kubernetes complexity while providing enterprise-grade security and multi-cloud support.

Building your own platform can take 12+ months and cost millions. Modern IDPs like Northflank let you focus on shipping products, not maintaining infrastructure.

> [Book a demo with an engineer](https://cal.com/team/northflank/northflank-intro) or [try our free developer sandbox](https://app.northflank.com/signup) to see how quickly you can get started.
> 

</InfoBox>

## What are Internal Developer Platforms?

An Internal Developer Platform (IDP) is built by a platform team to create standardized workflows and enable developer self-service.

Instead of your developers waiting on ops teams for infrastructure requests, they can provision environments, deploy applications, and manage resources themselves through the IDP.

For example, when your developer needs a new staging environment, they can create one in minutes through a simple interface rather than filing a ticket and waiting days for approval.

The key difference between an IDP and traditional DevOps tooling is the focus on **developer experience**. Rather than requiring every developer to become a DevOps expert, IDPs provide pre-built workflows that enforce best practices by default while still giving developers the control they need. For example, adding an [in-browser doc preview](https://apryse.com/blog/build-a-javascript-pdf-viewer-v2) to an internal portal can reduce context switching around PDFs like runbooks or specs.

## What is the difference between an Internal Developer Platform and a Portal?

Teams often confuse Internal Developer Platforms and Internal Developer Portals, but they serve different purposes:

1. **Internal Developer Platform (IDP)**: The comprehensive backend system that actually provisions infrastructure, manages deployments, handles secrets, and orchestrates your entire software delivery lifecycle. This is the "engine" that does the actual work.
2. **Internal Developer Portal**: The user interface or dashboard that developers interact with, essentially the frontend to your platform. A portal without a robust platform underneath is like having a cockpit without an airplane. It looks good, but it can't actually do anything.

Many organizations start with portals for visibility but quickly realize they need the full platform capabilities for true developer self-service.

## Benefits of Internal Developer Platforms

IDPs solve the core problems that slow down engineering teams and create friction between development and operations.

### 1. Improved developer productivity

An IDP provides developers with a unified interface where they can access everything they need to build, test, and deploy applications. This removes context switching between multiple tools and reduces the cognitive load on your development teams.

### 2. Faster time-to-market

With standardized workflows and automation, IDPs reduce deployment times from days to minutes, becoming the cornerstone of modern software delivery.

### 3. Better security and compliance

IDPs enforce security policies and governance frameworks at the platform level, ensuring that every deployment follows your organization's standards without requiring developers to remember complex security procedures.

### 4. Cost optimization

By providing standardized environments and automated resource management, IDPs prevent infrastructure waste and optimize cloud spending across your organization.

### 5. Reduced operational burden

IDPs remove most support tickets through self-service capabilities, freeing up your ops teams from constant interruptions and ticket overload.

## What should you look for when choosing an Internal Developer Platform?

The right IDP should solve your current problems while adapting to your future needs.

### 1. Self-service capabilities that actually work

Look for platforms that provide true self-service provisioning, not just visibility. Your developers should be able to spin up environments, deploy applications, and manage resources without filing tickets.

### 2. Integration with existing tools

If you've spent years building Terraform workflows, you likely don't want to discard them. Ensure the IDP can layer on top of your current investments without a complete rebuild.

### 3. Multi-cloud and hybrid support

Avoid vendor lock-in with platforms that support multiple cloud providers and on-premises infrastructure. Your needs will evolve, and your platform should evolve with you.

### 4. Security and compliance by default

Security shouldn't be an afterthought. Choose platforms that implement security controls, secret management, and compliance monitoring as core features.

### 5. Scalability for your growth

Whether you're a startup or enterprise, your IDP should grow with you. Look for platforms that can handle both simple use cases and complex enterprise requirements.

### 6. Developer experience focus

The best technical platform is useless if developers won't adopt it. Prioritize platforms that developers actually enjoy using, with intuitive interfaces and clear documentation.

<InfoBox className="BodyStyle">

**Note:** When evaluating platforms, look for solutions that check all these boxes rather than forcing you to compromise. Platforms like [Northflank](https://northflank.com/) are built specifically to address each of these requirements without requiring you to piece together multiple tools or sacrifice functionality.

</InfoBox>

## Top 6 Internal Developer Platforms for 2026

Let's review the leading IDPs that are helping engineering teams ship faster while maintaining security and operational control.

### 1. Northflank (Most Recommended)

Northflank solves the core challenge you're facing: getting the power of Kubernetes without the complexity. Instead of spending months building your own IDP or managing YAML manifests, you get a production-ready platform that your developers can use immediately.

![idp-northflank.png](https://assets.northflank.com/idp_northflank_380aa32fd0.png)

**Some of the features that stand out:**

- **Quick cloud setup**: Get running in your cloud of choice in 30 minutes or less. Northflank seamlessly integrates with EKS, GKE, AKS, and other Kubernetes services.
- **Bring Your Own Cloud (BYOC)**: Deploy to [AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), [Oracle](https://northflank.com/cloud/oci), or [Civo](https://northflank.com/cloud/civo) while maintaining control over your data residency and security.
- **Built-in CI/CD and automation**: Git-based deployment workflows with automatic builds, testing, and deployments triggered by code changes.
- **AI and GPU workloads**: Native support for NVIDIA GPUs including A100, H100, and B200 for ML training, inference, and LLM deployments.
- **Enterprise security**: Built-in secrets management, RBAC, network policies, and compliance controls for regulated environments.
- **Secure runtimes**: Isolated container execution environments with advanced security for sensitive workloads.
- **Cost optimization**: Real-time cost monitoring, automatic scaling, and transparent pricing with no hidden fees or cloud markups.
- **Developer-focused experience**: Northflank gets rid of the learning curve of Kubernetes and YAML manifests, so you can focus on coding.

<InfoBox className="BodyStyle">

**Northflank pricing:**

- **Developer Sandbox:** Free tier with generous limits for testing and small projects
- **Pay as you go:** Starting at $0/month with infrastructure usage billing, unlimited projects and collaborators
- **Enterprise:** Custom pricing based on your deployment footprint and requirements, including BYOC options with flat fees for clusters, vCPU, and memory on your infrastructure with no markup on cloud costs

> See [full pricing details](https://northflank.com/pricing) or [book a demo](https://cal.com/team/northflank/northflank-intro) to speak with an engineer or [try our free developer sandbox](https://app.northflank.com/signup) to see how quickly you can get started.
> 

</InfoBox>

**Battle-tested at scale**: Northflank has grown to empower tens of thousands of developers to deploy production workloads across more than six cloud providers, deploying over a million containers monthly. Used by companies like Weights, Sentry, Writer, and Clock for production workloads.

**Best for:** Teams that need production-ready infrastructure now, not in 12 months. Especially valuable if you're dealing with Kubernetes complexity, managing multiple cloud providers, or need your platform team to deliver value quickly without months of custom development.

> [Learn more about Northflank's IDP capabilities](https://northflank.com/use-cases/internal-developer-platform-idp-for-kubernetes) and read [How to build an Internal Developer Platform (and why you might not want to)](https://northflank.com/blog/how-to-build-an-internal-developer-platform) or listen to our [discussion on IDPs and developer experience](https://northflank.com/blog/talking-idp-paas-and-developer-experience-dx-on-the-tech-lounge-podcast).
> 

### 2. Backstage

Originally created by Spotify and now a CNCF project, Backstage is an open-source framework for building developer portals. You'll get a centralized platform for managing software catalogs, documentation, and developer workflows, but you'll need to build and maintain it yourself.

![backstage-homepage.png](https://assets.northflank.com/backstage_homepage_48f927629e.png)

**Key features:**

- Extensive plugin ecosystem for customization
- Service catalog and documentation management
- Template scaffolding for new projects
- Community support and CNCF backing

**Best for:** Organizations with dedicated platform teams who want maximum customization and have the engineering resources to build and maintain their own solution.

**Considerations:** Requires significant upfront engineering investment and ongoing maintenance.

### 3. Harness

Harness provides a comprehensive software delivery platform that includes IDP capabilities alongside CI/CD, feature flags, and cloud cost management. Their focus is on automation and AI-powered delivery pipelines.

![harness.png](https://assets.northflank.com/harness_6ed883f12e.png)

**Key features:**

- AI-powered deployment automation
- Comprehensive CI/CD with advanced deployment strategies
- Built-in security and policy governance
- Cloud cost optimization and FinOps capabilities

**Best suited for:** Enterprise teams seeking an all-in-one software delivery platform with robust governance and compliance features.

*See “[Top alternatives to Harness for CI/CD and DevOps](https://northflank.com/blog/top-harness-alternatives)”*

### 4. Porter

Porter simplifies Kubernetes management by connecting directly to your cloud account and automating infrastructure provisioning. You get the power of Kubernetes without having to manage the underlying complexity.

![porter-homepage.png](https://assets.northflank.com/porter_homepage_4890b2aa7e.png)

**Key features:**

- One-click Kubernetes cluster setup
- Git-based deployment workflows
- Support for AWS, GCP, and Azure
- Template-based application deployment

**Best for:** Teams that want Kubernetes capabilities without the operational complexity, particularly those already committed to containerized workflows.

*See “[Best Porter alternatives for scalable deployments](https://northflank.com/blog/best-porter-alternatives-for-scalable-deployments)”*

### 5. Humanitec

Humanitec focuses on enabling self-service infrastructure through its Platform Orchestrator, which standardizes configurations and workflows across your development teams.

![humanitec-homepage.png](https://assets.northflank.com/humanitec_homepage_291557df48.png)

**Key features:**

- Platform Orchestrator for standardizing configurations
- Focus on reducing cognitive load for developers
- Integration with existing CI/CD pipelines
- Enterprise-grade security and compliance controls

**Best for:** Large enterprises with complex, distributed systems that need standardization across multiple teams and environments.

### 6. Port

Port provides a no-code approach to building internal developer portals with comprehensive software catalogs and self-service capabilities. The focus is on flexibility and quick setup.

![port-homepage.png](https://assets.northflank.com/port_homepage_1c1a005392.png)

**Key features:**

- No-code portal builder with drag-and-drop workflows
- Comprehensive software catalog with custom data models
- Real-time scorecards and maturity tracking
- Integration with existing automation and workflows

**Best for:** Teams that want flexibility in designing their developer experience with minimal engineering investment and quick time-to-value.

## Making the right choice for your organization

**Start with Northflank if** you want to move fast without sacrificing control. Our platform removes the complexity of building your own IDP while providing the flexibility to customize as you grow. You'll get production-ready infrastructure in days, not months.

The key is to start with your pain points. Are your developers spending too much time on infrastructure? Are security and compliance slowing down deployments? Is tool management creating inefficiencies?

Choose a platform that lets your team focus on building great products, not maintaining infrastructure.

<InfoBox className="BodyStyle">

**Start with [Northflank's free tier](https://app.northflank.com/signup)** and see how quickly you can improve your development experience or [book a demo](https://cal.com/team/northflank/northflank-intro) to speak with an engineer.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Top 7 enterprise application platforms for 2026</title>
  <link>https://northflank.com/blog/enterprise-application-platform</link>
  <pubDate>2025-09-22T16:30:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the top 7 enterprise application platforms in 2026. Learn what they are, key features to look for, and why Northflank leads for multi-cloud and AI workloads.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/enterprise_application_platform_1_eeb76393e5.png" alt="Top 7 enterprise application platforms for 2026" />*An enterprise application platform is a unified cloud-based development and deployment environment that abstracts infrastructure complexity while providing enterprise-grade security, compliance, and scalability features.*

Your engineering teams are spending too much time managing infrastructure instead of building features that drive business value.

Complex deployment pipelines, multiple disconnected tools, and constant server maintenance are slowing down your development cycles.

Your developers are frustrated with context switching between dozens of platforms just to ship a single feature.

You need a better way to deploy and manage applications at enterprise scale.

Platforms like [Northflank](https://northflank.com/) are addressing these challenges by providing unified development environments that handle infrastructure complexity while maintaining control - with multi-cloud solutions that avoid vendor lock-in.

<InfoBox className="BodyStyle">

## Quick overview

This guide covers the top 7 enterprise application platforms that can simplify and optimize your development workflow.

You'll learn what these platforms are, how they help your business, what features to prioritize, and detailed comparisons of top solutions, including Northflank's multi-cloud and AI workload capabilities.

**How Northflank can HELP your organization:**

- Your team can deploy across multiple clouds ([AWS, GCP, Azure, etc](https://northflank.com/features/bring-your-own-cloud)) from one platform
- Your applications can stay in your own cloud accounts if you prefer - avoiding vendor lock-in
- Your AI and ML workloads get native GPU support and specialized runtimes
- Your developers get enterprise security and compliance frameworks built-in from day one
- Your development cycles speed up while your platform team maintains full governance control
- Your infrastructure costs stay predictable with transparent pricing and no hidden markups

> **If you're researching platforms for your organization, you can [book a demo](https://cal.com/team/northflank/northflank-intro) with [Northflank](https://northflank.com/) to discuss your specific requirements.**
> 

</InfoBox>

## What is an enterprise application platform?

An enterprise application platform is a comprehensive cloud service that provides everything your development teams need to build, deploy, and scale applications without managing underlying infrastructure.

Let me give you an example so it's clear.

Say your team pushes code to deploy a new customer API with an enterprise application platform. The platform automatically:

- Builds the application
- Provisions a PostgreSQL database
- Sets up Redis caching
- Configures load balancing
- Enables SSL certificates
- Deploys across multiple servers

Meaning that your developers don't have to write deployment scripts or configure infrastructure.

The platform connects to your existing development workflow through your code repositories.

When your developers push code, it handles server provisioning, database setup, monitoring, security, and scaling automatically.

Rather than managing separate tools for CI/CD, databases, caching, logging, and deployment, everything works together in one unified environment.

Enterprise application platforms go beyond basic hosting. They include advanced security controls, audit trails, compliance frameworks, and role-based access management that large organizations require.

## How can enterprise application platforms help your business?

Now that you understand what these platforms are, let's look at the specific business benefits that are most relevant to your organization.

### 1. Your operational costs decrease significantly

Large organizations often need 10-25 dedicated engineers to build and maintain [internal developer platforms](https://northflank.com/blog/how-to-build-an-internal-developer-platform) - that's around $2-3 million annually in personnel costs alone, as an example.

When you use an enterprise application platform, you get these capabilities as a managed service, converting those fixed infrastructure costs into predictable operational expenses.

### 2. Your time to market improves

Your developers can deploy applications in minutes instead of weeks.

This improvement multiplies across your entire engineering organization, enabling faster feature delivery and competitive advantages.

### 3. Your developer productivity increases

Your developers spend more time writing code and less time on deployment configurations, server maintenance, and tool integration.

This leads to higher job satisfaction and better retention of technical talent.

### 4. Your compliance and security become built-in

The platform provides security controls, audit trails, and compliance frameworks out of the box. You avoid the months of work required to implement these features across multiple tools.

### 5. Your applications scale without added complexity

Your applications scale based on demand without manual intervention. Your infrastructure costs are optimized during low usage periods while maintaining performance during traffic spikes.

## Why do you need an enterprise application platform (build or buy)?

At this point, you might be asking:

> *"Should we build our own internal platform or adopt an existing solution?"*
> 

This is one of the most frequently asked questions engineering teams encounter when scaling their infrastructure.

### Should we build our own enterprise application platform?

Building requires [dedicated development team services](https://www.hirededicateddevelopmentteam.com/), ongoing maintenance, and continuous feature development.

Most organizations underestimate the true cost, not just the engineering resources, but the opportunity cost of those developers not working on features that drive revenue.

### What about buying an enterprise application platform?

Enterprise application platforms give you immediate access to capabilities that would take 12-18 months to build internally.

Your teams can focus on your core product instead of infrastructure tooling.

### What are the risks of not using an enterprise application platform?

Established platforms have already solved complex problems around security, compliance, scaling, and reliability.

You benefit from years of development and real-world testing rather than learning these lessons the hard way.

## What to look for in an enterprise application platform

Now that we've covered the build vs buy decision, let's look at what features and capabilities to prioritize when evaluating different platforms.

1. **Multi-cloud and deployment flexibility**:
    
    Your platform should support deployment to your preferred cloud providers without vendor lock-in. Look for solutions that work with AWS, Google Cloud, Azure, and other infrastructure providers.
    
2. **Enterprise security and compliance**:
    
    Audit logs, role-based access control, single sign-on integration, and compliance certifications (SOC 2, ISO 27001, HIPAA) should be included, not optional add-ons.
    
3. **Developer experience**:
    
    Simple deployment workflows, integrated CI/CD, comprehensive monitoring, and intuitive interfaces reduce the learning curve for your development teams.
    
4. **Scalability and performance**:
    
    Automatic scaling, load balancing, database optimization, and global deployment capabilities ensure your applications perform well under varying loads.
    
5. **Integration capabilities**:
    
    Your platform should connect with existing tools in your development workflow - version control systems, monitoring tools, databases, and third-party services.
    
6. **Support for diverse workloads**:
    
    Beyond web applications, look for platforms that handle background jobs, scheduled tasks, databases, and specialized workloads like AI/ML model deployment.
    

<InfoBox className="BodyStyle">

When evaluating platforms against these criteria, you'll find that solutions like [Northflank](https://northflank.com/) have built the entire platform around these requirements - from multi-cloud flexibility and enterprise security to AI workload support and intuitive developer experiences.

</InfoBox>

## Top 7 enterprise application platforms

You've learned what to look for in an enterprise application platform. Now, let's review the top solutions available, starting with the most comprehensive option for enterprise teams that deal with multi-cloud requirements and AI workloads.

### 1. Northflank (Most recommended)

Northflank provides an enterprise application platform that combines developer simplicity with advanced capabilities for AI workloads and multi-cloud deployment.

It is a comprehensive platform that abstracts Kubernetes complexity while providing enterprise-grade features.

You can deploy across multiple cloud providers using your own accounts, avoiding vendor lock-in completely.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

See some of the features Northflank offers:

1. **Enterprise features:**
    
    Your applications stay in your own cloud accounts with the [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) model. You get [audit logs](https://northflank.com/docs/v1/application/observe/audit-logs) and compliance frameworks, [role-based access control](https://northflank.com/docs/v1/application/secure/use-role-based-access-control), enterprise support with SLAs, and [advanced networking](https://northflank.com/docs/v1/application/network/networking-on-northflank) and [security controls](https://northflank.com/docs/v1/application/secure/security-on-northflank) built-in.
    
2. **AI workload capabilities:**
    
    Your AI and ML teams get native [GPU support](https://northflank.com/gpu), including NVIDIA [A100](https://northflank.com/cloud/gpus/A100), [H100](https://northflank.com/cloud/gpus/H100), and [B200](https://northflank.com/cloud/gpus/B200) instances, as well as specialized runtimes for AI model training and inference. This support also includes allocation of GPUs for cost optimization, along with support for popular AI frameworks.
    
3. **Multi-cloud flexibility:**
    
    You can deploy to [AWS](https://northflank.com/cloud/aws), [Google Cloud](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), or [Oracle Cloud](https://northflank.com/cloud/oci) using your own accounts while benefiting from unified management and monitoring across all environments.
    
4. **Developer productivity features:**
    
    [Built-in CI/CD pipelines](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank), [automatic scaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments) and [load balancing](https://northflank.com/docs/v1/application/network/load-balancing), [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) for testing, comprehensive [monitoring](https://northflank.com/docs/v1/application/observe/observability-on-northflank) and [logging](https://northflank.com/docs/v1/application/observe/view-logs), [authentication systems](https://northflank.com/docs/v1/application/secure/single-sign-on-multi-factor-authentication), and support for multiple programming languages and frameworks on both Linux and Windows containers.
  
    

<InfoBox className="BodyStyle">

**Northflank pricing:**

- **Developer Sandbox:** Free tier with generous limits for testing and small projects
- **Pay as you go:** Starting at $0/month with infrastructure usage billing, unlimited projects and collaborators
- **Enterprise:** Custom pricing based on your deployment footprint and requirements, including BYOC options with flat fees for clusters, vCPU, and memory on your infrastructure with no markup on cloud costs

> See [full pricing details](https://northflank.com/pricing) or [book a demo](https://cal.com/team/northflank/northflank-intro) to speak with an engineer
> 

</InfoBox>

**Best for:** Organizations requiring multi-cloud flexibility, AI/ML workloads, teams wanting enterprise capabilities without vendor lock-in, companies needing specialized GPU infrastructure, development teams that want Kubernetes power without complexity, enterprises with strict data residency requirements, and organizations looking to avoid cloud provider markup costs.

### 2. Google Cloud Run

Google Cloud Run provides fully managed serverless container hosting that automatically scales from zero to handle millions of requests while integrating with Google Cloud's comprehensive service portfolio.

It is a serverless platform that runs containerized applications without infrastructure management. Cloud Run automatically scales your containers up and down from zero, supporting frontend and backend services, batch jobs, LLM hosting, and queue processing workloads.

![google cloud run home page-min.png](https://assets.northflank.com/google_cloud_run_home_page_min_25317b598a.png)

Some of its features:

1. **Enterprise features:** Built-in security with DDoS protection, integration with Google Cloud IAM, VPC connectivity for hybrid scenarios, multi-region deployment capabilities, and enterprise-grade compliance standards.
2. **Kubernetes integration:** Cloud Run is built on Kubernetes foundations, providing enterprise teams with container orchestration capabilities while abstracting the complexity of cluster management.
3. **Recent updates:** Support for Node.js 24 runtime in preview, GPU support in multiple regions, enhanced multi-region service deployment, and improved enterprise governance features.

**Best for:** Organizations using Google Cloud services, applications requiring automatic scaling from zero, teams comfortable with containerized serverless architectures.

<InfoBox className="BodyStyle">

Google Cloud Run works well for serverless container applications, but organizations requiring multi-cloud deployment or dedicated infrastructure control might consider platforms like Northflank that offer greater deployment flexibility across cloud providers.

</InfoBox>

*See the [Best Google Cloud Run alternatives in 2026](https://northflank.com/blog/best-google-cloud-run-alternatives-in-2025)*

### 3. AWS App Runner

AWS App Runner simplifies containerized application deployment on AWS by automatically handling infrastructure provisioning, scaling, and management from source code or container images.

It is a service designed to simplify the entire application lifecycle, from development to deployment, providing automatic scaling, built-in security, and seamless integrations with other AWS services.

![aws app runner home page-min.png](https://assets.northflank.com/aws_app_runner_home_page_min_36fbadd0c2.png)

Some of its features:

1. **Enterprise features:** Part of AWS's comprehensive cloud-native application portfolio that offers flexible options for building applications with strong AI capabilities and deep integration across AWS services. Includes VPC connectivity, custom domains, automatic scaling, and integrated monitoring.
2. **Recent updates:** Enhanced support for Node.js 22, improved runtime management, and better integration with AWS AI/ML services for enterprise applications.

**Best for:** Organizations standardized on AWS infrastructure, teams wanting simple container deployment without Kubernetes complexity, applications requiring tight AWS service integration.

<InfoBox className="BodyStyle">

Unlike AWS-specific solutions like App Runner, platforms such as Northflank provide the same enterprise capabilities across multiple cloud providers, giving you more flexibility for future infrastructure decisions.

</InfoBox>

*See [9 best AWS App Runner alternatives for scalable container apps](https://northflank.com/blog/aws-app-runner-alternatives)*


### 4. VMware Tanzu Application Platform

VMware Tanzu Application Platform provides an enterprise-grade, AI-ready platform that combines application development, deployment, and data services for comprehensive cloud-native development.

It is a unified platform that delivers on the speed and simplicity that developers expect from cloud-based managed services, but with the governance and cost efficiency that enterprises need for their most critical applications.

![vmware-tanzu.png](https://assets.northflank.com/vmware_tanzu_1864d96323.png)

Some of its features:

1. **Enterprise features:** Enhanced operational control, bolstered security posture, AI model governance capabilities, rate limiting, and implementation of quotas for AI applications. Includes multicluster management, compliance frameworks, and enterprise-grade security controls.
2. **AI capabilities:** Tanzu Platform expands support for GenAI and agentic use cases, including integration with Anthropic's Claude model and support for the Model Context Protocol (MCP) for enterprise AI applications.
3. **Developer experience:** Built on Cloud Foundry heritage with Kubernetes flexibility, providing developers with simplified deployment while maintaining enterprise governance requirements.

**Best for:** VMware-centric organizations, enterprises requiring comprehensive AI application development platforms, teams needing robust governance for cloud-native applications.

<InfoBox className="BodyStyle">

While VMware Tanzu offers comprehensive enterprise features, organizations seeking to avoid vendor lock-in or requiring multi-cloud flexibility might find platforms like Northflank more suitable for diverse infrastructure requirements.

</InfoBox>

*See [7 Top VMware Tanzu alternatives for DevOps in 2026](https://northflank.com/blog/vmware-tanzu-alternatives)*

### 5. Red Hat OpenShift Container Platform

Red Hat OpenShift Container Platform provides enterprise Kubernetes with integrated developer tools, security, and operational capabilities for hybrid cloud deployments.

It is a consistent hybrid cloud foundation for building and scaling containerized applications, built on Kubernetes with enterprise-grade enhancements and Red Hat Enterprise Linux.

![openshift-min.png](https://assets.northflank.com/openshift_min_2d87ef258a.png)

Some of its features:

1. **Enterprise features:** Core security capabilities like access controls, networking, and enterprise registry with built-in scanner, plus comprehensive backup and recovery, CI/CD, and high availability features. Includes role-based access control, enterprise authentication, and compliance certifications.
2. **Hybrid cloud capabilities:** Supports application deployments from on-premise to cloud to edge in a flexible operating environment, with consistent platform experience across all deployments.
3. **Kubernetes foundation:** Part of the Cloud Native Computing Foundation (CNCF) Certified Kubernetes program, ensuring compatibility and interoperability between container workloads.

**Best for:** Red Hat-centric organizations, enterprises requiring comprehensive hybrid cloud capabilities, teams needing enterprise Kubernetes with extensive operational tooling.

<InfoBox className="BodyStyle">

Red Hat OpenShift provides robust enterprise Kubernetes capabilities, though organizations requiring simpler deployment experiences or multi-cloud flexibility without Kubernetes complexity might consider platforms like Northflank for streamlined operations.

</InfoBox>

See [**Best OpenShift alternatives: finding the right Kubernetes platform**](https://northflank.com/blog/best-open-shift-alternatives-finding-the-right-kubernetes-platform)

### 6. Heroku (Salesforce)

Heroku provides a developer-focused platform that prioritizes ease of use while offering enterprise-grade capabilities for larger organizations.

It is a cloud platform that handles infrastructure management while providing extensive add-on ecosystem and Salesforce integration. Heroku runs your applications in containers called dynos and provides managed services for databases, caching, and monitoring.

![heroku-salesforce.png](https://assets.northflank.com/heroku_salesforce_77a6b515fc.png)

Some of its features:

1. **Enterprise features:** You get Private Spaces for network isolation, compliance certifications including SOC, PCI, and HIPAA, enterprise support with SLAs, and single sign-on integration with major identity providers.
2. **AI capabilities:** Heroku AI provides managed inference for AI models, integration with Amazon Bedrock, and support for building AI applications with tools like RAG workflows.

**Best for:** Organizations using the Salesforce ecosystem, rapid prototyping and development, and teams wanting minimal infrastructure management.

*Also see: [Heroku Enterprise: capabilities, limitations, and alternatives](https://northflank.com/blog/heroku-enterprise-capabilities-limitations-and-alternatives)*

<InfoBox className="BodyStyle">

While Heroku performs well at rapid development and Salesforce integration, organizations needing multi-cloud flexibility or extensive AI workloads may find platforms like Northflank more suitable for avoiding vendor lock-in.

</InfoBox>


### 7. Azure App Service

Microsoft Azure App Service provides comprehensive application hosting with integration for Microsoft-centric organizations and hybrid cloud scenarios.

It is a platform for hosting web applications, APIs, and mobile backends with built-in CI/CD, authentication, and scaling capabilities. Your applications run on both Windows and Linux with support for multiple programming languages and frameworks.

![Azure App Service home page.png](https://assets.northflank.com/Azure_App_Service_home_page_290d7deda0.png)

Some of its features:

1. **Enterprise features:** You get integration with Azure Active Directory, virtual network connectivity for hybrid scenarios, App Service Environment for fully isolated deployments, and comprehensive compliance certifications.
2. **Recent updates:** Sidecar container support for extending applications, enhanced AI integration capabilities, and improved support for latest development frameworks like .NET 9.

**Best for:** Microsoft-centric organizations, hybrid cloud requirements, teams leveraging Azure AI and data services.

<InfoBox className="BodyStyle">

Azure App Service provides comprehensive Microsoft ecosystem integration, though organizations seeking to avoid single-cloud dependency or requiring specialized AI infrastructure might benefit from multi-cloud platforms like Northflank.

</InfoBox>

## Why Northflank stands out for enterprise teams

After comparing these platforms, you're likely asking what makes Northflank different from these other options.

Let's look at how Northflank specifically addresses the pain points that enterprise development teams face when scaling their infrastructure and AI initiatives.

1. **No vendor lock-in with Bring Your Own Cloud (BYOC)**:
    
    Your applications and data remain in your own cloud accounts. You maintain control over costs, compliance, and data residency while benefiting from a managed platform experience.
    
2. **Support for AI workloads**:
    
    Built-in support for GPU workloads, AI model deployment, and machine learning pipelines. For example, one organization runs 10,000 AI training jobs and half a million inference runs daily through Northflank's platform without worrying about autoscaling or spot instance orchestration. (See how [Weights uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s))
    
3. **Enterprise-grade from day one**:
    
    Audit logs, RBAC, compliance frameworks, and security controls are included, not add-on features. This reduces the time and effort required to meet enterprise requirements.
    
4. **Developer experience without compromise**:
    
    Your developers can deploy applications in under five minutes while your platform teams maintain the control and visibility they need for enterprise governance.
    
5. **Proven at scale**:
    
    The platform handles over 1.3 million deployments monthly and serves billions of requests, demonstrating reliability at enterprise scale.
    

> For enterprise teams researching platforms, Northflank offers a [developer sandbox](https://app.northflank.com/signup) to test capabilities, or you can [book a demo](https://cal.com/team/northflank/northflank-intro) to discuss specific requirements with the engineering team.
> 

## Choosing the right enterprise application platform

Your platform choice should align with your organization's existing technology stack, strategic priorities, and specific requirements. If these are your concerns:

**1. "My organization needs to avoid vendor lock-in"**
Platforms with Bring Your Own Cloud (BYOC) capabilities, like Northflank, provide maximum flexibility for future infrastructure decisions.

**2. "We're investing heavily in AI/ML"**
You'll benefit from platforms like Northflank, which offer dedicated GPU support and AI-optimized workflows.

**3. "We're already committed to a specific cloud provider"**
You might benefit from deeper native integrations, though platform-agnostic solutions offer more long-term flexibility.

**4. "We need simple deployment but enterprise control"**
Look for platforms like Northflank that strike a balance between developer experience and the governance and customization your platform teams require.

The right enterprise application platform can significantly impact your development velocity, operational costs, and ability to attract technical talent.

<InfoBox className="BodyStyle">

See how Northflank's multi-cloud approach and AI capabilities can work for your organization. [Book a demo](https://northflank.com/) to discuss your specific requirements with our engineering team.

</InfoBox>

## Frequently asked questions about enterprise application platforms

1. **What is an enterprise application system?**
    
    An enterprise application system refers to large-scale software applications designed to operate across an organization, handling critical business processes like ERP, CRM, or supply chain management. These systems require robust infrastructure and deployment platforms to operate reliably.
    
2. **What are enterprise platforms?**
    
    Enterprise platforms are comprehensive technology foundations that provide the tools, services, and infrastructure needed to build, deploy, and manage business-critical applications at organizational scale.
    
3. **What is an example of an enterprise application?**
    
    Examples include customer relationship management (CRM) systems like Salesforce, enterprise resource planning (ERP) software like SAP, or custom applications that handle core business processes like inventory management or financial reporting.
    
4. **How do enterprise app development platforms differ from regular hosting?**
    
    Enterprise app development platforms provide integrated development workflows, automated deployment pipelines, built-in security controls, compliance frameworks, and scalability features that basic hosting services don't include.]]>
  </content:encoded>
</item><item>
  <title>Multi-cloud vs hybrid cloud: What are their differences?</title>
  <link>https://northflank.com/blog/multi-cloud-vs-hybrid-cloud</link>
  <pubDate>2025-09-19T16:05:00.000Z</pubDate>
  <description>
    <![CDATA[Find out the key differences between multi-cloud and hybrid cloud strategies. Learn which approach suits your enterprise needs with Northflank.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/multi_cloud_vs_hybrid_cloud_eecba9ab37.png" alt="Multi-cloud vs hybrid cloud: What are their differences?" />Cloud computing has fundamentally changed how businesses operate. And this is why it's highly important to determine which cloud strategy works best for your organization.

You've likely heard terms like "multi-cloud" and "hybrid cloud" thrown around in boardroom discussions. These terms represent fundamentally different approaches to managing your IT infrastructure.

Your choice between these strategies will impact everything from security and compliance to operational efficiency and budget allocation.

Understanding which approach fits your specific needs requires more than surface-level definitions. You need practical insights that help you assess your unique business requirements.

We'll walk you through multi-cloud vs hybrid cloud strategies to help you make an informed decision that aligns with your business objectives.

<InfoBox className="BodyStyle">

## TL;DR: Multi-cloud vs hybrid cloud at a glance

**Multi-cloud** uses multiple public cloud providers (like AWS + Azure + Google Cloud) to avoid vendor lock-in and leverage each provider's best services.

**Hybrid cloud** combines your on-premises infrastructure with public cloud services for better control and compliance.

**When to choose each approach:**

- **Multi-cloud** - if you want flexibility across providers and have cloud-native applications
- **Hybrid cloud** - if you have regulatory requirements or significant on-premises investments

**The challenge?** Managing workloads across multiple clouds or integrating on-premises with cloud services traditionally requires complex tooling and deep expertise.

**The solution?** [Northflank](https://northflank.com/) simplifies this by providing a unified platform where you can [bring your own cloud](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) accounts and manage everything through a single interface for both multi-cloud and hybrid cloud strategies.

*[Book a demo](https://cal.com/team/northflank/northflank-intro) to speak with one of our engineers about your specific requirements, or [try out the platform](https://app.northflank.com/signup) to see how it works for your use case.*

</InfoBox>

## Let's start with the fundamentals: Public cloud vs private cloud

Before we compare multi-cloud and hybrid strategies, you need to understand the building blocks that make up these approaches.

### What is a public cloud solution?

Public cloud services are computing resources provided by third-party providers over the internet, such as AWS, Microsoft Azure, Google Cloud Platform, or IBM Cloud.

The word “public” doesn't mean the services are free; it means the cloud storage space and computing capacity aren't dedicated to you when you're not using them.

Public clouds provide shared infrastructure, pay-as-you-use pricing, rapid scalability, minimal upfront investment, and provider-managed maintenance and security updates.

### What is a private cloud solution?

Private cloud infrastructure provides dedicated computing resources exclusively for your organization.

This can be hosted on-premises in your data center or by a third-party provider who dedicates specific hardware to your company.

Private clouds provide complete control over security and compliance, customizable configurations, predictable costs for consistent workloads, data sovereignty, and higher upfront investment, but potentially lower long-term costs.

## What is multi-cloud?

Multi-cloud refers to using multiple public cloud providers simultaneously to meet your diverse business needs. Rather than putting all your eggs in one basket, you distribute workloads across different cloud platforms like AWS, Azure, and Google Cloud Platform.

For instance, Netflix originally used only Amazon Web Services (AWS) but switched to multi-cloud by adding Google Cloud services for disaster recovery and artificial intelligence capabilities.

**Some advantages of the multi-cloud approach:**

- Avoid vendor lock-in and reduce dependency on a single provider
- Improved resilience if one provider experiences outages
- Cost optimization through competitive pricing and regional variations
- Access to best-of-breed services from each provider

**Well, it comes with its limitations, some of which include:**

- Managing multiple platforms requires diverse skill sets and increases complexity
- Each provider has different APIs, security models, and billing systems
- Data transfer fees between clouds can lead to unexpected costs

<InfoBox className="BodyStyle">

**Here's the thing:** These multi-cloud challenges don't have to be roadblocks. Northflank simplifies these complexities by providing a unified interface that handles the different APIs, billing systems, and platform management for you. Your team can get all the benefits of multi-cloud without the operational burden.

</InfoBox>

### How do you develop a multi-cloud strategy?

First, you need to assess your specific requirements and ask yourself these key questions:

1. **What are your primary business drivers?** The answer determines if you prioritize cost optimization, avoiding vendor lock-in, or accessing specific services.
2. **Which workloads suit which providers?** You might use AWS for compute-intensive applications, Google Cloud for machine learning, and Azure for Microsoft-integrated services.
3. **Do you have the expertise to manage multiple platforms?** Your team needs skills across different cloud environments, APIs, and management tools.

## What is hybrid cloud?

Hybrid cloud combines your on-premises private infrastructure with public cloud services, creating a unified computing environment. This approach allows data and applications to move smoothly between your private and public cloud environments.

For example, a regional bank could process millions of customer records every night. They could keep sensitive data on-premises for compliance while using public cloud capacity during quarter-end peaks when processing demands spike.

**Major benefits of the hybrid cloud approach:**

- Maintain control over sensitive data while accessing cloud scalability
- Meet regulatory compliance requirements more easily with on-premises infrastructure
- Achieve cost efficiency by right-sizing workloads to appropriate environments
- Get a gradual migration path from traditional IT infrastructure

**However, it has its challenges, which include:**

- Establishing reliable connectivity between different platforms and legacy systems
- Network performance dependencies that can impact overall system reliability
- Higher skill requirements for managing both on-premises and cloud environments
- Ongoing costs for maintaining and upgrading physical infrastructure

### How do you develop a hybrid cloud strategy?

The key is determining the optimal placement for each workload based on your specific business needs:

1. **Which workloads need to stay on-premises?** Look at regulatory requirements, data sensitivity, and performance needs.
2. **What should move to public cloud?** Look for variable workloads, development environments, and applications that benefit from cloud scalability.
3. **How will you handle data integration?** Plan for secure, reliable connections between your infrastructure and cloud services.

## Multi-cloud vs hybrid cloud: What are the key differences?

Now that you understand what each approach entails, it's time to compare them directly to determine which strategy best aligns with your specific business needs.

<InfoBox className="BodyStyle">

**Note:** Both strategies solve genuine business problems, but the complexity of managing them shouldn't become a problem itself.

[Northflank](https://northflank.com/) simplifies this complexity by providing a unified platform for either approach, so you can focus on your business goals rather than infrastructure management.

[Book a demo](https://cal.com/team/northflank/northflank-intro) to discuss your specific requirements, or [try out the platform](https://app.northflank.com/signup) to see how it works for your use case.

</InfoBox>

| **Aspect** | **Multi-cloud** | **Hybrid cloud** |
| --- | --- | --- |
| **Infrastructure types** | Multiple public clouds only | Private infrastructure + public cloud(s) |
| **Primary goal** | Avoid vendor lock-in, optimize services | Balance control with cloud benefits |
| **Complexity focus** | Managing multiple vendor relationships | Integrating on-premises with cloud |
| **Architecture approach** | Distributing workloads across different public cloud providers based on each service's strengths, pricing, or geographic requirements | Integration between private infrastructure and selected public cloud services, with careful consideration of data flow and security boundaries |
| **Best for** | Variable workloads, global scale, cloud-native applications | Regulated industries, predictable workloads, mission-critical applications |
| **Cost model** | Variable based on usage patterns | Mix of fixed (on-premises) and variable costs |
| **Security approach** | Distributed across multiple providers | Centralized control with selective cloud use |
| **Choose when you** | Want to avoid single provider dependency, need global reach with local performance, have cloud-native expertise | Have regulatory requirements, significant on-premises investments, need strict data governance, are transitioning gradually |
| **Ideal if you have** | Expertise to manage multiple platforms, variable workload patterns, need for best-of-breed services | Compliance requirements, predictable performance needs, existing infrastructure investments |

## How to achieve multi-cloud and hybrid cloud with Northflank (a must-read!)

You've seen the benefits of both approaches, but you're likely asking: "*How do I implement and manage these complex strategies without overwhelming my team?*"

[Northflank](https://northflank.com/) simplifies this challenge by providing a unified platform that sits above your infrastructure choices.

**See how Northflank makes it work:**

- **Bring your own cloud accounts** – Connect your existing AWS, Google Cloud, Azure, or on-premises accounts to one platform. ([See for yourself](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes))
- **Single interface management** – Manage everything through one unified dashboard
- **Automated complexity handling** – The platform manages multi-cloud networking, deployment pipelines, and monitoring
- **Focus on building** – Your team can focus on building quality products rather than struggling with infrastructure management

This gives your enterprise flexibility to adapt infrastructure choices as business needs change, freedom from vendor lock-in constraints, reduced operational complexity, and faster time-to-market for applications.

<InfoBox className="BodyStyle">

If you're ready to see how this could work for your organization, [book a demo](https://cal.com/team/northflank/northflank-intro) to discuss your requirements with our team, or [try out the platform](https://app.northflank.com/signup) to test the capabilities firsthand.

Also see how [**Weights uses Northflank to scale to millions of users without a DevOps team**](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)

</InfoBox>

## How to choose the right cloud strategy for your business

Now that you understand both approaches, let's determine which strategy fits your specific situation.

**Start with these key questions:**

- **Regulatory requirements** - Do you operate in heavily regulated industries like healthcare, finance, or government? Hybrid cloud often works better for strict compliance needs.
- **Existing infrastructure** - Do you have substantial on-premises investments that still deliver value? Hybrid cloud lets you extend those investments rather than abandon them.
- **Workload patterns** - Are your workloads predictable and steady (hybrid works well) or variable and bursty (multi-cloud often better)?
- **Team expertise** - Multi-cloud requires skills across multiple platforms, while hybrid cloud needs deep networking and integration expertise.
- **Future growth plans** - Think about your 3-5 year expansion plans, regulatory changes, and how your applications might change.

<InfoBox className="BodyStyle">

**Remember:** The right choice should provide a foundation for future growth, not create limitations that require expensive redesigns later.

With [Northflank](https://northflank.com/), you don't have to choose between flexibility and simplicity - you can implement either strategy through a single platform that adapts as your needs change.

</InfoBox>

## Bringing it all together: Your path forward

Your choice between multi-cloud and hybrid cloud strategies ultimately depends on your unique business context, technical requirements, and organizational capabilities.

Multi-cloud works best for flexibility and avoiding vendor lock-in, while hybrid cloud suits regulatory requirements and existing infrastructure investments.

The key is making a decision based on your specific requirements rather than industry hype.

If you choose multi-cloud, hybrid cloud, or eventually implement elements of both, Northflank can simplify the complexity of managing these strategies through a unified platform.

> [Book a demo](https://cal.com/team/northflank/northflank-intro) to discuss your specific requirements, or [try out the platform](https://app.northflank.com/signup) to see how it can optimize your cloud journey.
> 

## **Frequently asked questions about multi-cloud vs hybrid cloud**

**1. What is the difference between hybrid cloud and multi-cloud?**
Multi-cloud uses multiple public cloud providers, while hybrid cloud combines on-premises infrastructure with public cloud services.

**2. Can you have both multi-cloud and hybrid cloud strategies?**
Yes, many enterprises implement hybrid multi-cloud strategies, combining on-premises infrastructure with multiple public cloud providers.

**3. Is multicloud or hybrid cloud more cost-effective?**
Hybrid cloud often works better for predictable workloads, while multi-cloud can optimize costs for variable workloads through competitive pricing.

**4. How does vendor lock-in risk compare between multi-cloud vs hybrid cloud?**
Multi-cloud specifically addresses vendor lock-in by distributing workloads across multiple providers, while hybrid cloud reduces but doesn't eliminate dependency risks.

**5. What skills do teams need for multi-cloud vs hybrid cloud?**
Multi-cloud requires expertise across multiple platforms, while hybrid cloud needs networking and integration skills. Both require DevOps and security expertise.

## More guides that may interest you

- [What is hybrid cloud? Your complete infrastructure guide](https://northflank.com/blog/what-is-hybrid-cloud-complete-infrastructure-guide)
- [What is managed cloud? Managed cloud services for your organization](https://northflank.com/blog/what-is-managed-cloud)
- [On premise to cloud migration. The 2026 guide.](https://northflank.com/blog/on-premise-to-cloud-migration)
- [What are the best multi cloud management platforms in 2026?](https://northflank.com/blog/best-multi-cloud-management-platforms)
- [Bring Your Own Cloud (BYOC): What is it and why it's the future of deployment](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment)]]>
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  <title>What is managed cloud? Managed cloud services for your organization</title>
  <link>https://northflank.com/blog/what-is-managed-cloud</link>
  <pubDate>2025-09-18T15:10:00.000Z</pubDate>
  <description>
    <![CDATA[Learn what managed cloud is, how it differs from hosted cloud, and how platforms like Northflank simplify infrastructure management for your organization.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/what_is_managed_cloud_ccfb256e36.png" alt="What is managed cloud? Managed cloud services for your organization" />## What is managed cloud?

**Managed cloud is a service model where a third-party provider takes complete or partial responsibility for managing and maintaining your cloud infrastructure, applications, and operations.**

Maybe on a regular Tuesday, your team would be rushing to fix a server outage or applying urgent security updates rather than working on your product roadmap.

Or your CTO would be interviewing specialized DevOps engineers just to keep your applications running reliably.

With managed cloud, you outsource these responsibilities to products that monitor, optimize, secure, and maintain your cloud environment around the clock.

Your engineering team no longer needs to spend hours configuring servers or troubleshooting infrastructure issues at 2 AM.

This means your developers can focus on writing code and shipping features while cloud specialists handle the operational complexity behind the scenes.


<InfoBox className="BodyStyle">

## **A quick summary**

Managed cloud services handle your infrastructure so you don't have to.

Providers manage everything from servers and security to monitoring and backups, letting you focus on building your business.

The main difference between managed cloud and hosted cloud is customization:

1. Managed cloud gives you tailored solutions
2. Hosted cloud provides standardized environments.

> Luckily for your team, platforms like Northflank offer [managed cloud services](https://northflank.com/features/managed-cloud) that combine the simplicity of hosted solutions with the power of Kubernetes.
> 

And this enables developers to deploy applications through Git integration without the complexity of DevOps. You can start with the [managed infrastructure](https://northflank.com/cloud/northflank) or [bring your own cloud account](https://northflank.com/cloud).

</InfoBox>

## What are managed cloud services?

Managed cloud services are the built-in capabilities a provider delivers on top of their cloud platform.

Rather than providing you with basic infrastructure and leaving you to figure it out, the platform includes features such as monitoring, scaling, security, and backups as part of the service.

This takes on work that you'd otherwise need to build and maintain for:

1. **Managing your infrastructure**:
    
    Your provider handles all server setup, configuration changes, and ongoing maintenance across different regions so your applications stay running wherever your users are located.
    
2. **Securing your systems and compliance**:
    
    They implement security measures, control who can access what, watch for threats, and make sure your setup meets the regulatory requirements for your industry.
    
3. **Monitoring your performance**:
    
    Your provider monitors your infrastructure and applications to make automatic adjustments that maintain performance and reduce downtime that could impact your users.
    
4. **Backing up and recovering your data**:
    
    All your important data gets backed up automatically, and if something goes wrong, your provider can quickly restore everything to get you back online.
    
5. **Updating and maintaining your systems**:
    
    Software updates and security fixes happen behind the scenes without interrupting your applications or requiring your team's time.
    
6. **Optimizing your costs**: Your provider continuously analyzes your usage patterns and adjusts resources so you're not paying for computing power you don't need.

<InfoBox className="BodyStyle">

**Important note**:
*The quality of these managed cloud services can vary between providers.*

The best managed cloud platforms automate most of these functions while giving you control over deployment and scaling decisions.

*That's why platforms like Northflank exist to handle the infrastructure complexity.*

You can monitor everything from a [unified dashboard](https://northflank.com/docs/v1/application/observe/monitor-containers) and scale between Northflank's [managed infrastructure](https://northflank.com/features/managed-cloud) or [your own cloud account](https://northflank.com/cloud) as your requirements change.

</InfoBox>

## What are the differences between managed cloud and hosted cloud?

Both managed cloud and hosted cloud services take infrastructure burdens off your team's plate.

However, they work differently and serve unique needs. Let's see how they differ so you can choose the right one for your situation.

### Hosted cloud: infrastructure provided, you manage

Hosted cloud is a service where the provider owns and manages the entire infrastructure (compute, storage, networking) and gives you access to pre-configured environments with limited customization options.

It's like renting a furnished apartment.

With hosted cloud, your team gets quick setup with minimal configuration required, pre-installed software and standardized environments, and generally lower costs with predictable pricing. You’re essentially renting resources (VMs, containers, storage) in their data centers.

This works well when your applications fit standard configurations, but Rresponsibility for configuration, scaling, monitoring, backups, and security largely falls on you.

### Managed cloud: infrastructure provided AND actively operated/maintained for you

Managed cloud operates more like having an architect design and maintain your custom home. You choose your platform, specify your requirements, and the provider builds and manages a tailored solution.

This gives your organization highly customizable environments tailored to your specific needs, full control over platform selection and configuration, and comprehensive management, including optimization and scaling.

You get hosting plus active management services on top. The provider handles ongoing operations: patching, scaling, monitoring, performance tuning, backups, disaster recovery, and compliance.

### How do they differ: Managed cloud vs Hosted cloud

The main differences come down to control, customization, and complexity. Let's look at when each approach works best for your team:

**Hosted cloud works best when your team:**

- Needs quick, simple deployment with minimal technical requirements
- Has standardized applications that work well in pre-configured environments
- Wants the lowest upfront costs and predictable pricing
- Doesn't require extensive customization or specialized configurations

**Managed cloud works best when your organization:**

- Needs customized infrastructure tailored to specific business requirements
- Requires advanced security, compliance, or performance capabilities
- Wants expert guidance on cloud architecture and optimization decisions
- Prefers to focus entirely on core business while experts handle infrastructure complexity

## How Northflank delivers managed cloud for your organization

Your engineering team wants the power of Kubernetes without spending weeks learning cluster management and DevOps complexity.

Northflank bridges this gap with a modern approach to managed cloud services designed specifically for organizations that need to move fast.

Let's see how this works.

### 1. Developer-first managed cloud experience

Northflank's [managed cloud](https://northflank.com/features/managed-cloud) removes the steep learning curve commonly associated with Kubernetes deployments.

![northflank-managed-cloud.png](https://assets.northflank.com/northflank_managed_cloud_ad1699209e.png)

Your developers can deploy any project with a straightforward interface, regardless of their level of DevOps expertise.

You can connect your version control account and start deploying services ([See how](https://northflank.com/docs/v1/application/getting-started/link-your-git-account)).

This means less time learning complex cloud tooling and more time building your applications.

Your team gets fast [continuous integration (CI) builds](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) and [automated deployment pipelines](https://northflank.com/docs/v1/application/getting-started/set-up-a-pipeline) without the operational complexity.

### 2. Flexible deployment options for your requirements

Northflank handles all infrastructure complexity, including node management, load balancing, DNS configuration, disk storage, and auto-scaling.

Your organization gets two deployment options that can grow with your needs.

You can deploy directly to [Northflank's managed cloud](https://northflank.com/cloud/northflank) across multiple regions with pay-as-you-go pricing.

![northflank-managed-cloud-regions.png](https://assets.northflank.com/northflank_managed_cloud_regions_a23f51e91a.png)

Or use the platform while maintaining control by deploying into [your own cloud accounts](https://northflank.com/cloud) on [AWS](https://northflank.com/cloud/aws), [Google Cloud](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), [Civo](https://northflank.com/cloud/civo), or [Oracle](https://northflank.com/cloud/oci).

![deploy-in-your-own-cloud.png](https://assets.northflank.com/deploy_in_your_own_cloud_67828a5844.png)

### 3. Unified experience across your entire infrastructure

The platform combines multiple tools into a single, comprehensive solution, taking out the need to manage relationships with multiple cloud providers.

Your team gets 24/7 monitoring by experts to ensure high uptime regardless of the time.

<InfoBox className="BodyStyle">

You can [book a demo](https://cal.com/team/northflank/northflank-intro) to discuss enterprise-level SLAs and support options for your organization's specific requirements.

</InfoBox>

You can also easily upgrade from Northflank's managed cloud to bringing your own cloud at any time without changing your development workflow.

This flexibility means your infrastructure strategy can adapt as your organization scales.

If you need guidance on infrastructure transitions, read our guides on:

- [On-premise to cloud migration. The 2026 guide.](https://northflank.com/blog/on-premise-to-cloud-migration)
- [How to migrate from cloud to on-premise](https://northflank.com/blog/how-to-migrate-from-cloud-to-on-premise)

## What are the benefits of managed cloud services?

Managed cloud services deliver advantages that help businesses like yours scale while reducing operational complexity.

These benefits can change how your organization approaches infrastructure and resource allocation.

1. **Your team gains operational focus**:
    
    By outsourcing cloud management to specialists, your internal teams can concentrate on core business activities. This means less time spent on infrastructure maintenance and troubleshooting.
    
2. **You get predictable and optimized costs**:
    
    Managed cloud providers offer transparent pricing models with continuous resource optimization. This prevents over-provisioning and reduces unnecessary expenses.
    
3. **Your security posture improves**:
    
    Professional managed cloud providers implement security measures and maintain compliance certifications. These often exceed what internal teams can achieve with limited resources.
    
4. **Your infrastructure scales automatically**:
    
    Services can rapidly scale resources up or down based on actual demand. This ensures optimal performance during peak periods while reducing costs during low-usage times.
    

## How to choose the right managed cloud provider

When selecting a managed cloud provider, several factors determine if they'll meet your business needs and growth requirements.

| Factor | What to look for |
| --- | --- |
| **Experience and expertise** | Providers with proven experience in your specific industry and technology stack, plus relevant cloud certifications from major providers |
| **Security and compliance** | Security practices and compliance certifications that match your industry requirements, particularly important for regulated industries |
| **Service level agreements** | Uptime guarantees, fast support response times, and clear remediation procedures in their SLAs |
| **Scalability** | Ability to handle your current needs and future growth with automated scaling capabilities |
| **Pricing transparency** | Clear, predictable pricing models that align with your budget without hidden fees or surprise costs |

## Getting started with managed cloud

Managed cloud services provide an effective solution for organizations looking to leverage cloud computing benefits without the complexity of self-management.

Your success depends on finding a provider that aligns with your technical requirements, business goals, and growth plans.

For organizations ready to move beyond infrastructure management, platforms like [Northflank](https://northflank.com/features/managed-cloud) make it simple to deploy applications through Git integration.

You maintain the flexibility to scale between managed infrastructure or your own cloud accounts as requirements change.

<InfoBox className="BodyStyle">

**Next steps for your organization:**

- [Review Northflank's platform features](https://northflank.com/features) to see how it fits your technical stack
- Or [book a demo](https://cal.com/team/northflank/northflank-intro) to discuss your specific requirements with an expert
- [Try the platform](https://app.northflank.com/signup) with your development workflow

</InfoBox>]]>
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</item><item>
  <title>Alternatives to Depot's remote agent sandboxes</title>
  <link>https://northflank.com/blog/depot-remote-agent-sandboxes-alternatives</link>
  <pubDate>2025-09-17T16:35:00.000Z</pubDate>
  <description>
    <![CDATA[Compare Depot's remote agent sandboxes with secure runtime alternatives like Northflank, Modal, E2B, and Vercel.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/depot_remote_agent_sandboxes_alternatives_3488a2d507.png" alt="Alternatives to Depot's remote agent sandboxes" />When you're building applications with AI coding tools like Claude Code, you need reliable development environments that don’t start from scratch every time.

Depot recently launched remote agent sandboxes that provide persistent cloud environments with Git integration and session sharing.

These sandboxes move Depot closer to the secure runtime category, but they’re designed for productivity and persistence with Claude Code rather than hardened security for untrusted workloads.

This article covers the top alternatives to Depot's remote agent sandboxes, comparing them across security isolation, persistence capabilities, language support, and production readiness.

<InfoBox className="BodyStyle">

## TL;DR

Depot’s remote agent sandboxes add persistence and Git integration for Claude Code, but are limited to async-only sessions, containers (not microVMs), and single-agent support. Their focus is convenience, not airtight security.

**Top alternatives:**

- **Northflank**: Production-grade microVMs with unlimited persistence, any language support, and enterprise features (MFA, audit logs)
- **GitHub Codespaces**: Native GitHub integration with preconfigured containers, limited to GitHub ecosystem
- **Modal**: Python-optimized for ML workloads with GPU support, serverless model only
- **E2B.dev**: Purpose-built for AI agents with Firecracker isolation, short session limits
- **Vercel Sandbox**: Fast Firecracker microVMs with 45-minute execution limit

Depot is useful for Claude Code workflows where async-only execution is fine. For production applications requiring robust security, multi-language support, or persistent environments, Northflank offers the most comprehensive solution with proven scale (2M+ microVMs monthly in production).

</InfoBox>

## What are Depot's remote agent sandboxes? Why would you look for a Depot alternative?

Depot’s remote agent sandboxes are isolated container environments for running Claude Code agents in the cloud.

Each sandbox includes:

- 2 vCPUs and 4 GB RAM
- Startup times under 5 seconds
- Persistent filesystems
- Git integration for commits and pull requests

**Pros**

- Solves the “starting from scratch” problem in CI
- Fast startup and persistent state
- Git built-in

**Cons**

- Claude Code only
- Async sessions, no interactive dev
- Requires Depot UI for monitoring
- Isolated containers, suitable for Claude Code productivity but not designed as hardened runtimes for untrusted, multi-tenant workloads.

**Pricing**

- $0.01/minute, per-second billing
- Shuts down when agents exit
- Included on all Depot plans

In short: Depot improves developer productivity with Claude Code but isn’t built for secure, multi-tenant runtimes where untrusted code must be isolated.

## What are the use cases of sandbox environments?

Sandbox environments are crucial when:

1. Running AI-generated code from Claude Code, GitHub Copilot, or other assistants
2. Isolating user workloads in multi-tenant applications like online IDEs or bootcamps
3. Reviewing pull requests in clean, disposable environments
4. Executing untrusted code safely in coding challenges, educational tools, or SaaS platforms

The common thread: environments must stay isolated, persistent when needed, and production-ready for untrusted or AI-generated code.

## At-a-glance comparison: Depot sandbox alternatives

| Platform | Isolation method | Language support | Persistence | Starting price | Enterprise ready |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | Kata Containers, gVisor, Firecracker | Any language/runtime | Unlimited | $0.0038/hr | MFA, audit logs, RBAC |
| **Depot** | Isolated containers | Claude Code only | Session-based | $0.01/min | Limited enterprise features |
| **GitHub Codespaces** | Containers | Any language | Session-based | $0.18/hr | Enterprise GitHub integration |
| **Modal** | gVisor containers | Python only | Checkpointing | $0.192/core/hr | Limited enterprise features |
| **E2B.dev** | Firecracker microVMs | Python, JavaScript | 5-10 minutes | $0.10/hr | No enterprise features |
| **Vercel Sandbox** | Firecracker microVMs | Node.js, Python | 45 minutes max | $0.128/hr | No enterprise features |

## Top alternatives to Depot dev sandboxes

If you need different capabilities beyond Depot's Claude Code focus, here are the main alternatives to consider:

### 1. Northflank: Production-grade secure runtime platform

Northflank runs over 2 million microVMs monthly, in production. We contribute to Kata Containers, Cloud Hypervisor, QEMU, and more. Our platform supports [bring your own cloud](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) and runs securely in your VPC, with several companies using Northflank to run untrusted, multi-tenant workloads at scale.

**Key advantages over Depot:**

- **True production scale**: Unlike Depot’s Claude Code focus, Northflank supports [secure runtimes for any codegen tool](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale), offering microVM-backed containers that combine container-like performance with VM-grade security.
- **Multiple isolation technologies**: Firecracker, Kata, gVisor, Cloud Hypervisor - flexibility across AWS, GCP, Azure, or bare-metal.
- **Persistent and flexible**: Sandboxes remain alive until terminated, unlike time-limited environments.
- **Enterprise-ready**: [MFA](https://northflank.com/docs/v1/application/secure/single-sign-on-multi-factor-authentication), [audit logs](https://northflank.com/docs/v1/application/observe/audit-logs), RBAC, and compliance features built in.

**Northflank's secure runtime advantage**

Unlike standard containers that share the host kernel, Northflank utilizes microVM-backed [isolation](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation) (Kata Containers, Cloud Hypervisor) in production, running millions of workloads monthly within Kubernetes. This ensures VM-grade security with container-grade workflows.

<InfoBox className="BodyStyle">

**Northflank pricing**

- Free tier: Generous limits for testing and small projects
- CPU instances: Starting at $2.70/month ($0.0038/hr) for small workloads, scaling to production-grade dedicated instances
- GPU support: NVIDIA A100 40GB at $1.42/hr, A100 80GB at $1.76/hr, H100 at $2.74/hr, up to B200 at $5.87/hr
- Enterprise BYOC: Flat fees for clusters, vCPU, and memory on your infrastructure, no markup on your cloud costs
- Pricing calculator available to estimate costs before you start
- Fully self-serve platform, get started immediately without sales calls
- No hidden fees, egress charges, or surprise billing complexity

</InfoBox>

### 2. GitHub Codespaces: GitHub-native development

GitHub Codespaces provides hosted, container-based development environments tightly integrated with GitHub repositories and Dev Containers. It’s designed for consistent team environments and quick onboarding inside the GitHub ecosystem.

**Strengths**

- Native GitHub integration (pull requests, Actions, Codespaces devcontainers)
- Preconfigured container images and easy per-repo templates
- Smooth team onboarding and environment consistency

**Limitations**

- Tied to the GitHub ecosystem and a small set of regions
- Limited machine profiles; costs can add up at scale
- Container isolation rather than microVM-grade isolation, so not ideal for untrusted user code

### 3. Modal: Python-focused ML workloads

Modal offers serverless, gVisor-isolated containers optimised for Python functions and machine learning pipelines, with fast cold starts and GPU (Graphics Processing Unit) options. It’s built around a function/runtime model rather than long-lived services.

**Strengths**

- Python-first developer experience with simple function deployments
- Fast startup and checkpointing features for iterative ML work
- GPU options for training/inference workflows

**Limitations**

- Python-only for function definitions
- Serverless model (no always-on, persistent services)
- No [Bring Your Own Cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) (BYOC) / self-hosting; pricing can be opaque for complex pipelines
- gVisor containers rather than microVMs for isolation

*If you're evaluating Modal for your AI workloads, check out our* [detailed comparison of top Modal Sandboxes alternatives for secure AI code execution](https://northflank.com/blog/top-modal-sandboxes-alternatives-for-secure-ai-code-execution), *which provides a comprehensive analysis.*

### 4. E2B.dev: AI agent sandboxes

E2B.dev focuses on AI agent sandboxes with Firecracker microVM isolation and an SDK (Software Development Kit) geared toward agent workflows. It’s optimised for quick, programmatic environments rather than long-lived sessions.

**Strengths**

- Firecracker microVM isolation for agent execution
- Agent-friendly SDK and fast startup
- Purpose-built ergonomics for AI code execution

**Limitations**

- Short-lived sessions with limited persistence
- Less control over regions and underlying orchestration
- Not a full secure-runtime platform for multi-tenant production at scale

*For a comprehensive analysis of E2B and its alternatives, read our article on [the best alternatives to E2B.dev for running untrusted code in secure sandboxes](https://northflank.com/blog/best-alternatives-to-e2b-dev-for-running-untrusted-code-in-secure-sandboxes).*

### 5. Vercel Sandbox: Ephemeral microVMs

Vercel Sandbox uses Firecracker-backed microVMs to run isolated workloads with simple integration into the Vercel ecosystem. It emphasises speed and simplicity for short tasks.

**Strengths**

- Fast microVM provisioning and simple SDK
- Fits neatly into existing Vercel workflows
- Good for short, isolated executions

**Limitations**

- Session limit (up to ~45 minutes) makes it unsuitable for persistent agents
- Limited language/runtime breadth compared to general-purpose platforms
- No Bring Your Own Cloud (BYOC); tied to Vercel infrastructure

*If you're evaluating Vercel Sandbox, our comprehensive analysis of [top Vercel Sandbox alternatives for secure AI code execution and sandbox environments](https://northflank.com/blog/top-vercel-sandbox-alternatives-for-secure-ai-code-execution-and-sandbox-environments) covers the full range of options.*

## How Northflank compares to Depot and other alternatives

Depot’s new sandboxes bring persistence and Git integration to Claude Code agents, but remain container-based. That makes them useful for productivity, not secure multi-tenant workloads.

Northflank runs secure microVMs in production with enterprise features and persistent environments that maintain state across sessions, enabling AI agents and other workloads to run reliably.

- **Security and isolation**: Northflank provides VM-grade isolation with Kata Containers and Cloud Hypervisor. Depot uses containers.
- **Production readiness**: Northflank operates at proven scale with enterprise compliance.
- **Flexibility**: Northflank supports any language/runtime and multi-cloud deployment.
- **Cost**: Transparent pricing from $0.0038/hr, with GPU support and BYOC ([Bring your own cloud](https://northflank.com/features/bring-your-own-cloud)) options.

## When to choose each alternative

Based on your specific needs, here's how to decide between the platforms:

| Platform | Best for | Key requirements |
| --- | --- | --- |
| **Northflank** | Production AI applications, multi-tenant SaaS | Production-grade security, enterprise compliance, persistent sessions, multi-cloud flexibility |
| **Depot** | Claude Code workflows, CI/CD automation | Simple async execution, existing Depot users, short-lived sessions |
| **GitHub Codespaces** | GitHub-centric development, team collaboration | GitHub ecosystem commitment, repository integration, regional limitations acceptable |
| **Modal** | Python ML/AI workloads, inference pipelines | Python-only focus, serverless model, sub-second starts, GPU workloads |
| **E2B.dev** | AI agent prototyping, simple code execution | SDK-based integration, short tasks, purpose-built AI tooling |
| **Vercel Sandbox** | Quick prototyping, Vercel integrations | Vercel ecosystem, sessions under 45 minutes, fast Firecracker isolation |

## Finding the right sandbox platform for your team

Depot improves Claude Code workflows with persistence and Git integration, but it isn’t built as a secure runtime.

For production workloads where untrusted or AI-generated code must be isolated, Northflank provides hardened, persistent, enterprise-ready runtimes at scale.

<InfoBox className="BodyStyle">

[Try Northflank](https://northflank.com/) for production-grade microVM sandboxes, or see other options tailored to your specific needs.

*Learn more about secure runtime environments in our guides on [microVMs and container isolation](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation) and [secure AI code execution platforms](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale).*

</InfoBox>]]>
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  <title>7 Best private cloud hosting platforms in 2026</title>
  <link>https://northflank.com/blog/7-best-private-cloud-hosting-platforms-in-2026</link>
  <pubDate>2025-09-17T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[Discover the 7 best private cloud hosting platforms in 2026. Compare AWS, Azure, Google, Oracle, Civo, VMware, and see how Northflank simplifies secure, scalable private cloud for modern teams.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/top_gpus_for_ai_000acc85e5.png" alt="7 Best private cloud hosting platforms in 2026" />Running sensitive workloads on public cloud can be risky, expensive, and sometimes restrictive. In 2026, more teams are considering private cloud hosting to reap the benefits of scalability while maintaining tighter control over compliance, security, and costs.

However, with numerous platforms available, ranging from legacy enterprise leaders to modern developer-first options, it’s challenging to know where to begin.

In this guide, we’ll explore seven best private cloud hosting platforms in 2026, what to look for when evaluating options, and how [Northflank](https://northflank.com/) simplifies private cloud hosting for modern teams building AI-driven and multi-service applications.

<InfoBox className='BodyStyle'>

## TL;DR: 7 best private cloud hosting platforms in 2026

If you’re short on time, here’s a quick breakdown of the leading private cloud hosting platforms in 2026 and what makes each stand out:

1. [**Northflank**](https://northflank.com/) – A full-stack cloud platform with the ability to deploy in your own AWS, GCP, Azure, Oracle, or Civo [accounts](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes). Also provides managed Kubernetes, CI/CD, and GPU orchestration while keeping complete control over your data and infrastructure.
2. **AWS Outposts** – Extends AWS infrastructure to your own data center. Deep integration with AWS services, great for regulated industries and enterprises already all-in on AWS.
3. **Azure Stack Hub** – Microsoft’s private cloud offering with strong compliance certifications and hybrid flexibility. Ideal for enterprises running on the Microsoft ecosystem.
4. **Google Anthos** – Kubernetes-based multi-cloud solution with tight GCP and AI/ML integrations. Best for teams needing portability and advanced orchestration.
5. **Oracle Cloud@Customer** – Fully managed, dedicated private cloud deployed in your own data center. Strong choice for finance, healthcare, and workloads tied to Oracle databases.
6. **IBM Cloud Private** – Enterprise-ready private cloud with deep analytics and AI integrations. Suited for legacy modernization and compliance-heavy industries.
7. **Civo Private Cloud** – Developer-first managed Kubernetes platform, cost-effective and straightforward. Great for startups and teams that want a private cloud without hyperscaler complexity.

At a glance, most platforms either lean heavily toward enterprise lock-in or strip things down to basics. [Northflank](https://northflank.com/) sits in the middle ground, full-stack power without the overhead, making it the go-to private cloud hosting platformfor teams who want to move fast without losing control.

Deploy in your own private cloud without infrastructure complexity → [Try Northflank](https://northflank.com/)

</InfoBox>

## What is a private cloud? (private cloud hosting)

At its core, a private cloud is cloud infrastructure that you don’t share. Unlike the public cloud, where your workloads run alongside thousands of others, a private cloud is reserved for a single organization.

**Private cloud hosting** builds on this idea. It means running applications, databases, and services on infrastructure that is dedicated entirely to you. That infrastructure might live in your own data center, be managed by a hosting provider, or sit in a hybrid setup that blends public and private resources.

Today, private cloud hosting usually takes three main forms:

- **On-premises private cloud**, where you run and manage everything in your own facilities.
- **Hosted private cloud**, where a provider manages the physical infrastructure but dedicates it fully to you.
- **Hybrid private cloud**, which combines public cloud scale with private resources for sensitive or regulated workloads.

The appeal is consistent: more control, stronger compliance, and infrastructure tailored to your needs—without giving up the flexibility and scale of the cloud.

## What to consider when choosing a private cloud hosting platform

Earlier, we looked at why teams are moving to private cloud for control and compliance, but the real challenge is finding a platform that matches where your business is headed. The best options go beyond hardware, giving you flexibility, speed, and visibility.

Here are the key factors to weigh in 2026:

- **Multi-cloud flexibility:** Avoid lock-in. The best platforms run across AWS, Azure, GCP, Oracle, or even bare metal so you’re never stuck with one vendor.
- **Developer-friendly experience:** Private cloud shouldn’t feel like a step back. Look for fast feedback loops, clean logs, and workflows that scale from local dev to production.
- **AI & ML readiness:** First-class GPU support, model APIs, and job scheduling should be built in. Your platform must evolve with modern workloads.
- **Effortless scalability:** Growth shouldn’t require Kubernetes expertise. Select solutions that offer autoscaling and failover capabilities out of the box, with the flexibility to customize.
- **Integrated tooling:** Native CI/CD, GitOps, secrets, monitoring, and IaC save you from glue code and speed up shipping.
- **Transparent costs:** Clear usage-based pricing and built-in cost visibility ensure ROI and no surprises at billing time.
- **Security & compliance:** RBAC, audit logs, encryption, and certifications (SOC 2, HIPAA, GDPR) should be defaults, not extras.
- **Operational maturity:** Look for proven SLAs, monitoring, rollback, and responsive support. Invisible when smooth, essential when things break.

## 7 best private cloud hosting platforms in 2026

In this section, we’ll break down the leading private cloud hosting platforms in 2026. You’ll see how each one approaches scalability, compliance, and developer experience. Some are designed for enterprises with strict regulations, while others provide modern teams with speed and flexibility.

### 1. Northflank – Full-stack private cloud hosting platform for modern workloads

[Northflank](https://northflank.com/) combines private cloud hosting with developer-friendly orchestration. It's designed for teams building AI-driven or multi-service applications that want to deploy in their [own cloud accounts](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) without dealing with Kubernetes complexity.

![pawelzmarlak-2025-07-17T12_18_55.915Z.png](https://assets.northflank.com/pawelzmarlak_2025_07_17_T12_18_55_915_Z_08ec1d06ee.png)

**What Northflank offers:**

- Run full workloads: APIs, databases, background workers, notebooks, GPU jobs
- Built-in CI/CD pipelines, GitOps, and job scheduling
- GPU orchestration for inference, fine-tuning, and model APIs
- BYOC (Bring Your Own Cloud) or provision GPUs directly through Northflank
- Security features like RBAC, audit logs, and private clusters

**When to choose Northflank:**

Choose this for private cloud hosting that gives you control over your infrastructure and data while simplifying Kubernetes complexity. Best for teams wanting secure, compliant deployments in their own cloud accounts without requiring deep DevOps expertise.

### 2. AWS Outposts – AWS in your own data center

For enterprises already deep in AWS, Outposts brings the same services you’d find in the public cloud directly to your private data center. That means you can run EC2, S3, and RDS locally with the same APIs and tools.

**Best for:** highly regulated industries like healthcare and finance that need on-prem while staying inside AWS’s ecosystem.

**Considerations:** vendor lock-in and premium pricing, you’re all-in on AWS if you choose this route.

### 3. Azure Stack Hub – Microsoft’s hybrid private cloud

If your organization heavily relies on Microsoft technology, Azure Stack is the natural extension of your environment. It enables you to run Azure services in your own facilities while maintaining tight integration with Microsoft 365, Active Directory, and compliance frameworks.

**Best for:** enterprises that are already standardized on Microsoft tools.

**Considerations:** Heavier operational requirements compared to lighter platforms, like Northflank.

### 4. Google Distributed Cloud (Anthos) – Kubernetes-native hybrid

Google’s Anthos-powered private cloud puts Kubernetes at the center. It’s ideal if your team wants multi-cloud flexibility and is comfortable working with containers. Tight integration with GCP also makes it attractive for AI/ML workloads.

**Best for:** teams that want portability across AWS, Azure, GCP, or on-prem hardware.

**Considerations:** a steeper learning curve if you’re not already Kubernetes-savvy.

### 5. Oracle Cloud@Customer – Fully managed Oracle stack, on-prem

Oracle brings its cloud to your data center with Cloud@Customer. It’s essentially Oracle’s public cloud—but dedicated to your business. That means full database support, security, and compliance for industries like finance and government.

**Best for:** Oracle-first organizations that rely on its database ecosystem.

**Considerations:** heavy Oracle integration; less flexible if you’re not already invested.

### 6. Civo Private Cloud – Developer-first managed Kubernetes

Civo is all about simplicity. It strips away hyperscaler complexity and offers a fast, cost-effective managed Kubernetes experience, tailored for teams that don’t want to babysit infrastructure.

**Best for:** startups and developer teams who want a private cloud without the enterprise bloat.

**Considerations:** smaller ecosystem and fewer advanced enterprise features compared to AWS or Azure.

### 7. VMware Cloud Foundation – Proven virtualization at scale

VMware has been the backbone of private infrastructure for decades, and Cloud Foundation modernizes that legacy for the hybrid cloud era. It’s virtualization-first, but with extensions for containers and cloud-native workloads.

**Best for:** enterprises modernizing legacy VMware investments while exploring hybrid approaches.

**Considerations:** slower iteration compared to developer-first platforms like Northflank or Civo.

<InfoBox className='BodyStyle'>

**Bottom line:** Most private cloud platforms either tilt toward **enterprise lock-in** (AWS, Oracle, VMware) or **developer simplicity** (Civo). [Northflank](https://northflank.com/) bridges the gap, giving modern teams the power to run full-stack applications and AI workloads with the flexibility of a private cloud without the Kubernetes headaches.

</InfoBox>

## How to choose the best private cloud hosting platform

Once you’ve seen what each platform offers, the next step is deciding which one actually fits your team. The right choice depends on your workloads, priorities, and the level of flexibility you’ll need as you grow.

| Platform | Best for | Strengths | Considerations |
| --- | --- | --- | --- |
| [**Northflank**](https://northflank.com/) | Modern dev teams, Developer-first K8s & AI & ML | [Bring your own cloud (BYOC)](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes), GPU support, multi-service apps, simple UX | Less known than hyperscalers |
| **AWS Outposts** | Enterprises deep in AWS | Seamless AWS APIs, hybrid extension | Vendor lock-in, high cost |
| **Azure Stack** | Microsoft-first organizations | Strong integration with MS ecosystem | Heavier ops requirements |
| **Google Distributed Cloud** | Kubernetes-native teams | Multi-cloud, Anthos integration | Steeper learning curve |
| **Oracle Cloud@Customer** | Regulated industries | Fully managed Oracle stack on-prem | Oracle-first tooling |
| **Civo** | Developer-first K8s users | Lightweight managed Kubernetes | Smaller ecosystem |
| **VMware Cloud Foundation** | Legacy enterprises | Proven virtualization, hybrid model | Complexity, slower iteration |

## Conclusion

Private cloud has moved beyond being a tool for large enterprises. In 2026, it is the pragmatic choice for teams that need to balance compliance, security, and cost control with the flexibility of cloud infrastructure. 

The platforms we reviewed each serve a different audience. Some are built for enterprises deeply tied to a single vendor, while others focus on developers who want simplicity above all else. 

[Northflank](https://northflank.com/) brings the best of both worlds. It combines the reliability and control of enterprise-grade private cloud with the speed and usability that modern development teams expect. With built-in CI/CD, GitOps, GPU orchestration, and multi-service application support, Northflank makes private cloud practical for startups and scalable for enterprises. I

If your team is looking for a private cloud platform that does not slow you down, [Northflank](https://northflank.com/) is the one to watch.

<InfoBox className='BodyStyle'>

[Try Northflank](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how it fits your stack.

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  </content:encoded>
</item><item>
  <title>Claude Code vs OpenAI Codex: which is better in 2026?</title>
  <link>https://northflank.com/blog/claude-code-vs-openai-codex</link>
  <pubDate>2025-09-15T18:37:00.000Z</pubDate>
  <description>
    <![CDATA[Compare Claude Code vs OpenAI Codex for 2026. Learn costs, setup, and self-hosting alternatives with Northflank to choose the right AI tool.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/claude_code_vs_openai_codex_75ac9ef32b.png" alt="Claude Code vs OpenAI Codex: which is better in 2026?" />Your development team needs an AI coding assistant, but which one should you use?

Anthropic's Claude Code and OpenAI's Codex CLI/Agent take fundamentally different approaches to AI-powered development.

For platform teams managing containerized environments on platforms like [Northflank](https://northflank.com/), understanding these differences is crucial for making the right infrastructure and tooling decisions.

We will compare both tools across their unique capabilities, integration options, setup, and cost factors to help you choose the right solution for your team.

<InfoBox className="BodyStyle">

## TL;DR

**Claude Code** offers a developer-guided approach using a high-context, interactive CLI that deeply integrates with your local terminal and IDE. It excels at complex, single-task reasoning and refactoring, which is well-suited for developers who want to stay in control of their workflow.

**OpenAI's Agent (formerly Codex)** provides a cloud-based, autonomous environment for delegating end-to-end coding tasks. It operates in an isolated sandbox and autonomously generates pull requests, making it ideal for teams wanting to automate entire development workflows with less manual oversight.

Both tools now offer CLI interfaces and require paid subscriptions. Claude Code integrates with your local terminal and IDE, while Codex runs tasks in isolated cloud environments.

> **Note**:
If you're running containerized development environments, platforms like [Northflank](https://northflank.com/) make it easy to manage the infrastructure needed to support either tool's API requirements and development workflows.

Northflank also enables [self-hosting](https://northflank.com/blog/self-hosting-ai-models-guide) open source AI coding models for teams wanting complete data control and cost predictability.
> 

**Bottom line**: Choose Claude Code for deep codebase analysis and local development workflows, or Codex for autonomous task delegation and cloud-based parallel processing.

Either way, platforms like Northflank can handle the underlying infrastructure complexity for both tools, plus offer self-hosting alternatives for complete cost and data control.

</InfoBox>

## What are Claude Code and OpenAI Codex?

Before comparing these tools, it's important to understand what you're choosing between in 2026.

### What is Claude Code?

Claude Code is Anthropic's command-line interface that embeds its latest, most capable model (e.g., Claude 3.5 Sonnet or newer) directly in your terminal. It has deep codebase awareness and the ability to edit files and run commands directly in your local environment.

![claude-code-terminal.png](https://assets.northflank.com/claude_code_terminal_e1c32291e7.png)*Claude code terminal from [anthropic.com](http://anthropic.com/)*

Compared to traditional coding assistants, Claude Code uses agentic search to understand your entire codebase without manual context selection and can make coordinated changes across multiple files. It does this by creating a context file that provides an overview of the project to the model.

### What is OpenAI Codex?

OpenAI's AI coding agent is also branded as Codex, and in 2026, it is a multi-interface offering powered by their latest models, such as GPT-5 High. It includes:

- A **cloud-based agent** that works on many tasks asynchronously within isolated cloud sandbox environments. This agent is accessed via ChatGPT.
- A **Codex CLI** that runs locally on your computer.
- An **IDE extension** that provides deep integration with your development environment.

![open-ai-codex-cli.png](https://assets.northflank.com/open_ai_codex_cli_7b87ac136d.png)*OpenAI Codex CLI from [github.com/openai/codex](http://github.com/openai/codex)*

The major difference between Claude Code and OpenAI's Codex agent is this: Claude Code emphasizes a developer-in-the-loop, local workflow using the terminal, while OpenAI's Codex agent is designed for both local and autonomous, cloud-based task delegation that can handle asynchronous work.

## How do Claude Code and Codex compare in performance?

When choosing AI coding assistants, the quality of the generated code and the approach to task execution directly impact your development velocity.

| Feature | Claude Code | OpenAI Codex (Agent) |
| --- | --- | --- |
| **AI model** | Claude 3.5 Sonnet (or newer models) | Powered by GPT-5 High and Codex 1 |
| **Training focus** | Optimized for code understanding and generation through various techniques. | Trained with advanced methods, including reinforcement learning, to act like a software engineering agent. |
| **Execution environment** | Local terminal and IDE. | Both a cloud sandbox environment (accessed via ChatGPT) and a local CLI. |
| **Multi-file coordination** | High-context awareness allows coordinated changes across files via the local CLI. | Cloud agent can make coordinated changes and provides verifiable evidence of actions via logs and tests. |
| **Best for** | Developer-guided, interactive local workflows with full context. | Autonomous task delegation, especially for tasks that can run asynchronously in the cloud. |

## How do Claude Code and OpenAI Codex integrate with your workflow?

Your development workflow determines how effectively you can leverage either tool's capabilities.

| Integration aspect | Claude Code | OpenAI Codex (Agent) |
| --- | --- | --- |
| **Access points** | Terminal and IDE (VS Code, JetBrains) | ChatGPT sidebar, CLI, or IDE extensions (like VS Code and Cursor) |
| **Context awareness** | Deeply understands entire local codebase via agentic search and context files. | Cloud agent works in isolated environments preloaded with your repository. The CLI uses local context. |
| **File modification** | Makes coordinated, multi-file changes locally based on user prompts. Often requires explicit approval for critical changes. | Cloud agent commits changes in its isolated environment and can open pull requests for review. |
| **Workflow approach** | No context switching with strong local integration. Developer guides the agent in an interactive loop. | Independent, asynchronous task processing within isolated cloud sandboxes. Follows a delegation and review workflow. |
| **Standards adaptation** | Adapts to local coding standards and patterns by learning from the codebase. | Integrates with review, revision, and GitHub PR workflows, enforcing standards during the review process. |
| **Infrastructure needs** | Lower infrastructure overhead due to its local-first, API-driven model. | Higher isolation for concurrent, delegated tasks via its cloud-based architecture. |

Claude Code lives right inside your terminal, minimizing context switching and providing deep awareness of your entire codebase.

The Codex agent is accessible through various interfaces, with its cloud-based tasks processed independently in isolated sandboxes preloaded with your codebase. This allows for an autonomous, asynchronous workflow.

For teams using containerized development environments, both tools can be integrated. Claude Code's local approach requires less infrastructure overhead for developer-led tasks, while the Codex agent's cloud-based model provides better isolation for parallel, delegated workflows

## How Northflank manages AI coding tools at scale (for containerized teams)

If your team uses containerized development environments, you'll need to consider how to manage API access, costs, and security for AI coding tools like Claude Code and OpenAI's Codex agent. Northflank provides the [platform](https://northflank.com/deploy/run-persistent-and-ephemeral-docker-containers) to support these workflows at scale.

![Screenshot of Northflank's service creation interface showing deployment configuration options including container settings, environment variables, networking, and resource allocation for setting up AI coding tools](https://assets.northflank.com/container_creation_c1ede90f78_4a9cad70c0.webp)*Northflank's service configuration interface for containerized AI coding environments*

1. **Centralized API management for any tool**:
    
    Both Claude Code and OpenAI's Codex agent require API keys for access. With Northflank, you can manage these API credentials centrally as secrets, [injecting them securely](https://northflank.com/docs/v1/application/secure/inject-secrets) into your containerized dev environments. This simplifies access control and makes it easy to monitor usage patterns across your teams. 
    
2. **Secure execution environments**:
    
    When using AI for code generation, it's crucial to use secure runtimes. Northflank offers secure sandboxing with microVMs, allowing you to run AI-generated code safely within isolated, disposable containers without risking your core infrastructure. (*See [secure runtime environments with microVMs and proper sandboxing](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale).*)
    
3. **Optimizing Workflows**:
    - **For Claude Code users**, teams can use Northflank to manage the infrastructure for their containerized development environments, ensuring stable API connectivity and consistent dependencies. This supports the developer's local workflow by providing a pre-configured, reliable environment for the Claude Code CLI.
    - **For OpenAI's Codex agent**, teams can leverage Northflank to orchestrate the backend for their automated pipelines. The platform can deploy and manage the secure sandboxes used by the Codex agent, allowing you to delegate and monitor asynchronous coding tasks at scale.

**Note**: This approach is most relevant for teams already invested in containerized development. For local-only workflows, container orchestration is not required.

## What are the self-hosting alternatives to Claude Code and OpenAI Codex?

For teams prioritizing complete control, privacy, and cost management, self-hosting open-source AI models is a powerful alternative to third-party services like Claude Code and OpenAI's Codex agent. [Northflank](https://northflank.com/) provides the platform to make this process seamless for containerized teams.

### Why self-host your coding models?

- **Complete data control:** Keep all code analysis and data processing in-house to meet strict privacy and compliance requirements.
- **Cost predictability:** Eliminate unpredictable per-token costs. Instead, pay for your infrastructure, which becomes more cost-effective as usage scales.
- **Customization:** Tailor and fine-tune models on your specific codebase to achieve superior performance for your unique development tasks.
- **Workflow integration:** Integrate open-source models into your existing containerized workloads, allowing your tools to scale alongside your applications.

### Popular open source coding models you can self-host

- **CodeLlama** - Meta's foundational, code-focused language model.
- **DeepSeek Coder** - A high-performance model with advanced reasoning capabilities (check out our [DeepSeek R1 setup guide](https://northflank.com/guides/self-host-vllm-in-your-own-cloud-account-with-northflank-byoc) and [stack templates](https://northflank.com/stacks) for easy deployment.)
- **WizardCoder** - A powerful model known for its high-quality instruction-following capabilities.

![northflank-stack-templates-claudecode-openaicodex.png](https://assets.northflank.com/northflank_stack_templates_claudecode_openaicodex_78b6d256aa.png)*Northflank’s stack templates - https://northflank.com/stacks*

### IDE integration with open source models

- **Zed Editor** - Supports connecting to self-hosted models via OpenAI-compatible APIs
- **JetBrains IDEs** - Can integrate with local AI coding assistants through plugins and API configurations
- **VS Code** - Multiple extensions available for connecting to self-hosted coding models

### Simplifying self-hosting on Northflank

- **One-Click Deployment:** Use our pre-configured [stack templates](https://northflank.com/stacks) to deploy open-source models with a single click.
- **High-Performance Serving:** Follow our [vLLM setup guide](https://northflank.com/guides/self-host-vllm-in-your-own-cloud-account-with-northflank-byoc) to serve models with OpenAI-compatible APIs, ensuring fast and efficient inference.
- **Complete Control:** With our [managed container platform](https://northflank.com/deploy/run-persistent-and-ephemeral-docker-containers), you can scale resources, monitor performance, and manage all aspects of your self-hosted AI infrastructure. (See this [self-hosting AI models guide](https://northflank.com/blog/self-hosting-ai-models-guide))

> The balance between convenience (with SaaS options) and control (with self-hosting) depends on your team's specific needs. For teams already running containerized workloads, Northflank makes self-hosting a compelling and cost-effective option for achieving complete control over your AI coding infrastructure.
> 

## How to set up Claude Code and OpenAI's Codex agent across your team

Beyond self-hosting, teams need a practical implementation strategy for proprietary AI coding solutions.

Setting up these tools across an engineering organization involves more than just individual developer preferences.

### Claude Code setup

- **Installation:** Requires installation via npm (`npm install -g @anthropic-ai/claude-code`) or by downloading the appropriate binary from Anthropic's console or GitHub releases.
- **Configuration:** A one-time OAuth process links the CLI to your Anthropic account, providing API access for the entire session.
- **Environment:** Runs directly in the developer's terminal and IDE, processing tasks with a high degree of local context awareness.

### OpenAI's Codex agent setup

- **Installation:** The **Codex CLI** is installed via npm (`npm install -g @openai/codeex`), requiring Node.js. The **cloud agent** is accessed directly via the ChatGPT interface.
- **Configuration:** Requires an OpenAI API key to be set as an environment variable (`OPENAI_API_KEY`) for the CLI. Access to the agent is tied to a ChatGPT Plus, Pro, Team, or Enterprise subscription.
- **Management:** Provides centralized billing and usage analytics via the OpenAI API platform, allowing enterprise teams to manage usage effectively.

### Data privacy considerations

- **Claude Code:** Operates primarily in your local environment, but sends code context (the files it reads) to Anthropic's API for processing. While much remains local, sensitive code is still transmitted.
- **OpenAI's Codex agent:** The cloud agent runs in isolated sandboxes and is explicitly designed to handle your repository data. The Codex CLI processes and transmits data from your local machine, similar to Claude Code.
- **Your evaluation:** Both approaches involve transmitting code to external services. Teams must evaluate their security and compliance requirements against the data handling policies of Anthropic and OpenAI

## What are the costs of Claude Code and OpenAI Codex?

After understanding setup requirements, your team needs to budget for the ongoing costs of whichever AI coding solution you choose.

Understanding the total cost of ownership helps you make informed decisions about adopting AI coding tools.

### Claude Code pricing

Claude Code requires an Anthropic subscription. As of September 2026, the plans are:

- **Pro plan (~$20/month):** Includes access to the Claude 3.5 Sonnet model and is suitable for light coding tasks. Weekly and rolling 5-hour usage limits apply.
- **Max plan (starting at ~$100/month):** Includes higher limits and access to more powerful models, such as Claude Opus (often designated by a number, e.g., Opus 4). A weekly usage ceiling limits heavy usage.
- **Pay-as-you-go API pricing:** Available for enterprise users, billed based on token usage.

### OpenAI Codex pricing

OpenAI's agentic coding tools are included with paid subscriptions:

- **ChatGPT Plus (~$20/month):** Provides basic access to AI coding features within the standard ChatGPT interface.
- **ChatGPT Team/Enterprise:** Required for dedicated workspaces, centralized billing, and enhanced security for teams. The full-featured **Codex agent**, which runs in isolated cloud sandboxes, is most often associated with these higher-tier plans or a dedicated developer account on the OpenAI platform.
- **API Usage:** The local Codex CLI and other agentic features are billed separately based on API token usage, requiring a payment method on file.

**Hidden costs to factor in:**

- Onboarding time for team training and setup
- Infrastructure setup and ongoing maintenance
- Usage monitoring and cost management overhead
- Potential productivity loss during tool transition

Your total investment calculation should include these operational costs alongside the subscription or API fees when determining which platform provides better value for your team's specific usage patterns.

## Which tool should you choose?

Your decision ultimately depends on your team's development patterns, infrastructure requirements, and data privacy needs.

**Choose Claude Code if:**

- You want deep local codebase integration and analysis.
- Your team prefers working directly in terminal and IDE environments.
- You need coordinated multi-file edits driven by a developer-in-the-loop workflow.
- Local control over your code and an interactive development process are your top priorities.

**Choose OpenAI Codex if:**

- You want to delegate entire coding workflows to autonomous, cloud-based agents.
- Your team benefits from asynchronously delegating independent tasks that run in isolated environments.
- You prefer a workflow with cloud-based processing, detailed task logging, and automated pull request generation.
- Your team is already integrated with the OpenAI ecosystem via ChatGPT Team or Enterprise plans

For platform teams managing containerized development environments, both tools can be effectively integrated using modern container orchestration platforms that handle the underlying infrastructure complexity, API management, and monitoring

## Making the right choice for your team

Both Claude Code and OpenAI's Codex agent represent significant advances in AI-assisted development, but they serve different use cases and team structures.

- **Claude Code** is a deep codebase collaborator optimized for interactive, local development workflows.
- **The Codex agent** provides autonomous task delegation, running tasks asynchronously in isolated cloud environments.

Your choice should align with your team's workflow preferences, infrastructure capabilities, and development methodology.

Rather than viewing this as an either-or decision, some teams find value in using both tools for different scenarios - using Claude Code for deep codebase analysis and local development, while relying on the Codex agent for autonomous task delegation and asynchronous workflows.

For platform teams managing containerized development environments, both tools can be effectively integrated using modern container orchestration platforms that handle the underlying infrastructure complexity.

### Other resources that can help

If you're looking into AI coding tools further, these guides can help with your implementation:

- [**Claude Code vs Cursor comparison**](https://northflank.com/blog/claude-code-vs-cursor-comparison) - Compare Claude Code against another popular AI coding assistant
- [**Claude rate limits, Claude Code pricing & cost**](https://northflank.com/blog/claude-rate-limits-claude-code-pricing-cost) - Detailed breakdown of Claude Code costs and usage optimization
- [**Secure runtime for codegen tools**](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale) - Security requirements when setting up AI coding tools at scale
- [**Open source LLMs: complete developer's guide to deployment**](https://northflank.com/blog/open-source-llms-the-complete-developers-guide-to-deployment) - Alternative approaches using open source models
- [**Self-hosting AI models guide**](https://northflank.com/blog/self-hosting-ai-models-guide) - Comprehensive guide to running your own AI infrastructure]]>
  </content:encoded>
</item><item>
  <title>vLLM vs TensorRT-LLM: Key differences, performance, and how to run them</title>
  <link>https://northflank.com/blog/vllm-vs-tensorrt-llm-and-how-to-run-them</link>
  <pubDate>2025-09-15T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[vLLM and TensorRT-LLM are top LLM inference engines. vLLM offers flexibility and Hugging Face integration, while TensorRT-LLM delivers peak NVIDIA GPU performance. Deploy both easily on Northflank.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/what_is_a_gpu_cluster_318ece5cee.png" alt="vLLM vs TensorRT-LLM: Key differences, performance, and how to run them" />Large language models have moved from research into production, powering copilots, assistants, search, and enterprise applications. But serving them efficiently remains one of the hardest challenges in AI infrastructure.

If you’ve looked into high-performance inference backends, you’ve likely come across **vLLM** and **TensorRT-LLM**. Both are optimized systems for running LLMs at scale, with a focus on squeezing the most out of GPUs.

At first glance, they might seem to overlap. Both promise lower latency, higher throughput, and better GPU utilization. But under the hood, they take very different approaches. Knowing these differences is crucial if you want to build an efficient, scalable, and cost-effective LLM service.

In this article, we’ll compare **vLLM vs TensorRT-LLM** side by side, explore their strengths and trade-offs, and show you how to deploy either on [Northflank](https://northflank.com/product/gpu-paas) for production use.

## TL;DR: vLLM vs TensorRT-LLM at a glance

If you are short on time, here is a high-level snapshot of how they compare.

| Feature | vLLM | TensorRT-LLM |
| --- | --- | --- |
| Focus | High-performance general LLM inference | NVIDIA-optimized inference for maximum GPU efficiency |
| Architecture | PagedAttention + async GPU scheduling | CUDA kernels + graph optimizations + Tensor Cores |
| Performance | State-of-the-art throughput across models | Peak performance on NVIDIA GPUs |
| Supported models | Hugging Face ecosystem (flexible) | Optimized for LLaMA, Mistral, GPT, Qwen, etc. |
| Ecosystem fit | Open-source, broad integration | NVIDIA stack (Triton, NeMo, TensorRT) |
| Hardware | GPU-first, any CUDA GPU | Best on NVIDIA A100, H100, L40S |
| Ease of use | Easier to integrate with HF/OSS tools | Steeper setup, tied to NVIDIA SDKs |

<InfoBox className="BodyStyle">

**💭 What is Northflank?**

[Northflank](https://northflank.com/) is a full-stack AI cloud platform that helps teams build, train, and deploy models without infrastructure complexity. [GPU workloads](https://northflank.com/product/gpu-paas), APIs, frontends, backends, and databases run together in a single platform, so your stack stays fast, flexible, and production-ready.

[Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro).

</InfoBox>

## What is vLLM?

vLLM is an open-source inference engine designed to maximize throughput and reduce latency when serving LLMs.

Its key innovation is **PagedAttention**, which treats attention memory like virtual memory. Instead of wasting GPU space on inactive tokens, it efficiently reuses memory, allowing more concurrent requests and longer context windows without a performance hit.

This makes vLLM one of the most efficient open inference engines, widely adopted in production APIs and research.

### Pros of vLLM

- Exceptional performance with large batch sizes
- Long context windows supported efficiently
- Direct Hugging Face model integration
- Flexible and open-source ecosystem

### Cons of vLLM

- GPU-first, CPU support is limited
- More tuning needed for absolute peak performance vs NVIDIA stack
- Not as deeply optimized for specific NVIDIA hardware features

## What is TensorRT-LLM?

TensorRT-LLM is NVIDIA’s specialized inference library for large language models, built on top of **TensorRT** (their deep learning optimization SDK).

Instead of a general-purpose backend, it uses **CUDA graph optimizations, fused kernels, and Tensor Core acceleration** to extract every last bit of performance from NVIDIA GPUs. It also integrates tightly with **Triton Inference Server** and **NeMo**, making it a natural choice for enterprises already in the NVIDIA ecosystem.

### Pros of TensorRT-LLM

- Peak performance on NVIDIA GPUs (A100, H100, L40S)
- Advanced graph and kernel-level optimizations
- Strong integration with NVIDIA’s enterprise stack
- Cutting-edge support for quantization and FP8/INT4 inference

### Cons of TensorRT-LLM

- NVIDIA-only, no support for AMD or non-CUDA environments
- More complex to set up and optimize
- Less flexible outside of NVIDIA tooling

## vLLM vs TensorRT-LLM: Side-by-side comparison

### 1. Performance

- **vLLM**: Delivers top-tier throughput and latency, especially with batching and long contexts.
- **TensorRT-LLM**: Pushes hardware to the limit with CUDA graph fusion and Tensor Core optimizations. For absolute peak performance on NVIDIA GPUs, TensorRT-LLM usually wins.

### 2. Model support

- **vLLM**: Works with a broad range of Hugging Face models out of the box.
- **TensorRT-LLM**: Supports major open LLMs but often requires conversion/preprocessing into optimized formats.

### 3. Developer experience

- **vLLM**: Easier to integrate, especially for teams already using Hugging Face.
- **TensorRT-LLM**: Steeper learning curve, but highly optimized once configured.

### 4. Hardware

- **vLLM**: Runs on most CUDA GPUs, from consumer cards to datacenter hardware.
- **TensorRT-LLM**: Designed specifically for NVIDIA enterprise GPUs (A100, H100).

### 5. Ecosystem fit

- **vLLM**: Flexible, OSS-first, fits into diverse pipelines.
- **TensorRT-LLM**: Best suited for enterprises already invested in NVIDIA’s AI stack.

## Which should you choose: vLLM or TensorRT-LLM?

| Use case | Best fit |
| --- | --- |
| Running open-source Hugging Face models quickly | vLLM |
| Maximizing throughput on NVIDIA A100/H100 GPUs | TensorRT-LLM |
| Flexible deployment across different infra | vLLM |
| Enterprise-grade NVIDIA ecosystem with Triton | TensorRT-LLM |
| Long-context window serving | vLLM |
| Peak GPU efficiency for production | TensorRT-LLM |

The short answer:

- If you want **flexibility and fast integration** with Hugging Face models, choose **vLLM**.
- If you want **maximum performance and are deep in the NVIDIA ecosystem**, choose **TensorRT-LLM**.

## How to deploy vLLM and TensorRT-LLM with Northflank

Choosing the right inference engine is only half the story. You also need a way to deploy it. [Northflank](https://northflank.com/) is a full-stack cloud platform built for AI workloads, letting you run APIs, workers, frontends, backends, and databases together with GPU acceleration when you need it. The key advantage is that you do not have to stitch infrastructure together yourself.

<InfoBox className="BodyStyle">

With [Northflank](https://northflank.com/product/gpu-paas), you can containerize your application, assign GPU resources, and expose it as an API without extra complexity. If you want to roll your own, start with our [guide on self-hosting vLLM in your own cloud account](https://northflank.com/guides/self-host-vllm-in-your-own-cloud-account-with-northflank-byoc). For NVIDIA users, you can integrate TensorRT-LLM with Triton Inference Server inside the same platform for maximum GPU efficiency.

</InfoBox>

If speed matters, you can skip setup entirely with one-click templates. For example, [deploy vLLM on AWS with Northflank in a single step](https://northflank.com/stacks/deploy-vllm-aws), or explore the [Stacks library](https://northflank.com/stacks) for ready-to-use templates configured to run vLLM with models like Deepseek, GPT OSS, and Qwen. TensorRT-LLM can also be containerized and scaled in Northflank with built-in GPU provisioning.

![image - 2025-09-12T135414.837.png](https://assets.northflank.com/image_2025_09_12_T135414_837_b32a9c6896.png)

This flexibility means you can start with vLLM for fast Hugging Face integration, and later move to TensorRT-LLM for maximum NVIDIA GPU performance or even run both side by side. Monitoring, autoscaling, and GPU scheduling are built in, so you can focus on your application while Northflank handles the rest.

## Wrapping up

vLLM and TensorRT-LLM are both leaders in high-performance LLM inference, but they represent different philosophies:

- **vLLM** → flexible, open-source, Hugging Face–friendly, great for rapid deployment and scaling.
- **TensorRT-LLM** → NVIDIA-optimized, hardware-specific, best for enterprises chasing maximum efficiency.

The good news is you don’t have to choose forever. With [Northflank](https://northflank.com/), you can run both side by side, experiment, and scale the one that best fits your needs.

[Sign up today](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how quickly you can get started.]]>
  </content:encoded>
</item><item>
  <title>What is a GPU cluster? Use cases for AI developers</title>
  <link>https://northflank.com/blog/what-is-a-gpu-cluster</link>
  <pubDate>2025-09-12T13:13:00.000Z</pubDate>
  <description>
    <![CDATA[Learn what GPU clusters are, their benefits for AI workloads, and how Northflank simplifies deployment without Kubernetes complexity.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/what_is_a_gpu_cluster_80256cb154.png" alt="What is a GPU cluster? Use cases for AI developers" />If you're building AI applications or training machine learning models, you've likely reached the point where a single GPU just isn't enough.

This guide will walk you through everything you need to know about GPU clusters, their benefits, and how to get started without the complexity of Kubernetes.

<InfoBox className="BodyStyle">

### Quick summary

A GPU cluster is a network of interconnected computers equipped with multiple GPUs working together to handle massive computational workloads.

They're essential for training large AI models, running inference at scale, and processing data that would take an extremely long time on traditional CPU-only systems.

**The good news: [Northflank](https://northflank.com/)** lets teams **spin up GPU clusters** without managing K8s themselves at **affordable prices** - perfect for model parallelism, training, and scaling inference.

> **Building a GPU cluster?** If you need specific capacity, topology configurations, or multi-GPU setups, [request GPU capacity here](https://northflank.com/request/gpu).

</InfoBox>

## What is a GPU cluster?

Simply put, a GPU cluster is a collection of computers (called nodes) that are connected together, with each computer equipped with one or more graphics processing units (GPUs).

It's like combining the power of multiple GPUs across different machines to tackle computational tasks that would be impossible or painfully slow on a single GPU.

While your CPU is optimized for handling tasks one after another (sequential processing), GPUs are designed to handle thousands of operations simultaneously (parallel processing)

When you connect multiple GPUs across several machines, you gain massive parallel processing power, which is ideal for AI workloads.

### GPU cluster architecture

Your GPU cluster consists of two main types of nodes:

- **Head node**: The control center that manages the entire cluster, schedules jobs, and coordinates resources
- **Worker nodes**: The workhorses where your actual computations happen, each equipped with GPUs, CPUs, memory, and storage

Each worker node typically includes:

- **GPU accelerators** (like NVIDIA H100, H200, B200 A100, or L4)
- **CPUs** for orchestration and non-parallel tasks
- **High-bandwidth memory** for fast data access
- **Network interface cards** for high-speed communication between nodes

### Types of GPU clusters

You'll encounter several different cluster configurations:

- **Homogeneous clusters**: All GPUs are identical (same model, memory, capabilities)
- **Heterogeneous clusters**: Mix of different GPU models and capabilities
- **On-premises clusters**: Hardware you own and manage in your data center
- **Cloud-based clusters**: Managed by cloud providers like AWS, GCP, or platforms like Northflank

## What are the benefits of GPU clusters?

Now that you understand what GPU clusters are, let's look at why they're important for AI teams like yours:

1. **Massive speed improvements**: You can reduce model training time from weeks to days or even hours. Large language models that would take months to train on a single GPU can be trained in days with a properly configured cluster.
2. **Handle larger models**: Modern AI models often exceed the memory capacity of a single GPU. Clusters let you distribute model parameters across multiple GPUs, enabling you to work with models that simply won't fit on one device.
3. **Better resource utilization**: Instead of leaving expensive GPUs idle while waiting for sequential tasks, clusters keep your hardware busy with parallel workloads, maximizing your investment.
4. **Scalability on demand**: You can start small and add more nodes as your computational needs grow, rather than being locked into a fixed configuration.
5. **Cost efficiency**: While the upfront investment seems higher, clusters often provide better performance per dollar for large-scale AI workloads compared to scaling up single-GPU systems.

## How does Northflank simplify GPU cluster management?

Managing GPU clusters traditionally means struggling with Kubernetes configurations, resource scheduling, and complex networking setups - tasks that can consume weeks of engineering time.

[Northflank's GPU platform](https://northflank.com/gpu) removes this complexity entirely.

![gpu-workloads-on-northflank.png](https://assets.northflank.com/gpu_workloads_on_northflank_faf79ee2f0.png)

 Rather than spending time configuring Kubernetes for GPU workloads, you get:

- **One-click cluster deployment**: Spin up GPU clusters in minutes, not days
- **Automatic scaling**: Your clusters dynamically adjust based on workload demands (See [how that works](https://northflank.com/docs/v1/application/scale/autoscale-deployments))
- **Built-in monitoring**: Real-time performance metrics and health checks without additional setup (See the guides on [Configure health checks](https://northflank.com/docs/v1/application/observe/configure-health-checks) and [Observability on Northflank](https://northflank.com/docs/v1/application/observe/observability-on-northflank))
- **Developer-friendly interface**: Deploy your models and training jobs with simple configurations

If you're running workloads on [Northflank's cloud GPUs](https://northflank.com/cloud/gpus) or [deploying on your own infrastructure](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-in-your-own-cloud), the platform handles the orchestration complexity.

You can [configure and optimize your GPU workloads](https://northflank.com/docs/v1/application/gpu-workloads/configure-and-optimise-workloads-for-gpus) without needing to become a Kubernetes expert.

This means your team can focus on what counts - building and training better AI models - rather than managing infrastructure.

## What are the applications of GPU clusters?

With simplified cluster management handled, let's see how you can use these powerful systems in your AI projects.

1. **AI model training**: Training large language models, computer vision systems, or recommendation engines requires processing massive datasets. Clusters let you distribute this work across multiple GPUs, significantly reducing training time.
2. **Model fine-tuning**: Even when adapting pre-trained models for your specific use case, clusters accelerate the process and let you experiment with larger, more capable base models.
3. **Real-time inference**: Serving AI models to thousands of users simultaneously requires the parallel processing power that only clusters can provide. This is especially crucial for applications like chatbots, image recognition APIs, or recommendation systems.
4. **Model parallelism**: When your models are too large to fit on a single GPU's memory, clusters let you split the model across multiple devices while maintaining fast inference speeds.
5. **Research and experimentation**: Running multiple training experiments simultaneously, comparing different architectures, or performing hyperparameter searches becomes feasible with cluster resources.

## Getting started with GPU clusters

Now is the time to move beyond single-GPU limitations. Let's quickly go over your path forward.

Start by assessing your current bottlenecks - are you waiting days for training to complete, running out of GPU memory, or unable to serve enough concurrent users? This will help you determine your cluster requirements.

Next, review your options: building your own infrastructure requires significant expertise and time, while platforms like [Northflank](https://northflank.com/) enable you to get started immediately - either through [managed cloud resources](https://northflank.com/cloud/gpus) or by [bringing your own infrastructure](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-in-your-own-cloud).

For the fastest path to production, try [Northflank's GPU solutions](https://northflank.com/docs/v1/application/gpu-workloads/gpus-on-northflank) - you can have a cluster running your workloads within hours, not weeks.

<InfoBox className="BodyStyle">

**Skip the infrastructure complexity.** [Get started with Northflank's managed GPU clusters](https://northflank.com/gpu) and focus on building breakthrough AI applications instead of managing Kubernetes.

</InfoBox>

## Frequently asked questions about GPU clusters

Here are the most common questions we hear from AI teams considering GPU clusters.

1. **What is a GPU cluster?**
A GPU cluster is a network of interconnected computers equipped with GPUs that work together to handle large-scale computational tasks through parallel processing.
2. **How big is a typical GPU cluster?**
Clusters range from just 2-4 GPUs for small teams to thousands of GPUs for large-scale model training. Most AI startups begin with 8-32 GPU clusters.
3. **What's the difference between a GPU cluster and a supercomputer?**
Supercomputers are typically purpose-built, fixed installations, while GPU clusters are more flexible and can be assembled from standard hardware or cloud resources.
4. **How much does a GPU cluster cost?**
Costs vary widely based on GPU types and cluster size. Cloud-based solutions like [Northflank](https://northflank.com/) offer pay-as-you-go pricing, while building your own requires significant upfront investment plus ongoing maintenance costs.
5. **When do you need a GPU cluster vs single GPU?**
Consider clusters when single GPU memory isn't sufficient for your models, training takes too long, or you need to serve many concurrent users.
6. **How does Northflank simplify GPU cluster management?**
[Northflank](https://northflank.com/) handles all Kubernetes complexity, provides automatic scaling, and offers a developer-friendly interface so you can deploy GPU workloads in [your own cloud](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-in-your-own-cloud) or [managed cloud](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-on-northflank-cloud) without infrastructure expertise.

## Other resources that can help

1. [**Northflank GPU Documentation**](https://northflank.com/docs/v1/application/gpu-workloads/gpus-on-northflank): Complete technical guide for deploying and managing GPU workloads on the platform.
2. [**Deploy GPUs on Northflank Cloud**](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-on-northflank-cloud): Step-by-step instructions for getting started with cloud-based GPU clusters.
3. [**Bring Your Own Cloud GPU Setup**](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-in-your-own-cloud): Guide for teams who want to use their own cloud infrastructure with Northflank's management layer.
4. [**GPU Workload Configuration Guide**](https://northflank.com/docs/v1/application/gpu-workloads/configure-and-optimise-workloads-for-gpus): Best practices for optimizing your AI workloads for maximum GPU utilization and performance.]]>
  </content:encoded>
</item><item>
  <title>vLLM vs Ollama: Key differences, performance, and how to run them</title>
  <link>https://northflank.com/blog/vllm-vs-ollama-and-how-to-run-them</link>
  <pubDate>2025-09-12T12:45:00.000Z</pubDate>
  <description>
    <![CDATA[Explore vLLM vs Ollama: key features, performance, and use cases. Learn how to choose the right LLM runtime and deploy seamlessly with Northflank’s full-stack AI cloud platform.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/what_is_gpu_as_a_service_1_f184fabce4.png" alt="vLLM vs Ollama: Key differences, performance, and how to run them" />Large language models are no longer just research toys. They power customer support, code assistants, knowledge retrieval, and even production systems that handle millions of requests a day. 

But if you have ever tried to run one yourself, you know that serving these models is not trivial. You need to balance latency, throughput, memory, cost, and developer experience. That is why two open-source projects, vLLM and Ollama, have quickly become popular choices for various types of teams. 

At first glance, they may look similar. Both let you run and interact with large language models locally or in the cloud. But under the hood, they approach the problem from different angles. Understanding these differences can save you a lot of time and money when choosing the right tool for your stack. 

In this article, we will compare vLLM and Ollama side by side, look at their strengths and trade-offs, and explore where each shines. By the end, you will not only know which one suits your needs but also how to deploy either on [Northflank](https://northflank.com/) to get them running in production quickly.

## TL;DR: vllm vs ollama at a glance

If you are short on time, here is a high-level snapshot of how they compare.

| Feature | vLLM | Ollama |
| --- | --- | --- |
| Focus | High-performance LLM inference | Developer-friendly local model runner |
| Architecture | PagedAttention + optimized GPU scheduling | Lightweight runtime + simple model packaging |
| Performance | Industry-leading throughput and low latency | Optimized for quick setup and ease of use |
| Supported models | Hugging Face models | Curated set of open LLMs (LLaMA, Mistral, Gemma, etc.) |
| Ecosystem fit | Best for production APIs and scaling | Best for local dev and fast prototyping |
| Hardware | GPU-first (A100, H100, etc.) | Runs on consumer infrastructure |
| Ease of use | Requires more setup but flexible | Very easy, almost plug-and-play |

<InfoBox className="BodyStyle">

**💭 What is Northflank?**

[Northflank](https://northflank.com/product/gpu-paas) is a full-stack AI cloud platform that helps teams build, train, and deploy models without infrastructure complexity. GPU workloads, APIs, frontends, backends, and databases run together in a single platform so your stack stays fast, flexible, and production-ready.

[Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how it fits your stack.

</InfoBox>

## What is vLLM?

vLLM is a high-performance inference engine for large language models. It was designed from the ground up to solve one of the hardest problems in serving LLMs: throughput. 

Unlike general-purpose frameworks, vLLM introduces **PagedAttention**, a technique that manages memory like a virtual memory system. Instead of wasting GPU memory on tokens that are not being actively processed, it reuses memory efficiently across requests.

That may sound technical, but the result is simple: vLLM lets you serve more requests with the same hardware without degrading latency. In benchmarks, it consistently outperforms traditional backends like Hugging Face Transformers and even custom inference stacks.

### Pros of vLLM

- Exceptional performance with large batch sizes and long context windows
- Strong support for cutting-edge compression and quantization methods
- Scales well across multi-GPU clusters
- Integrates with Hugging Face models directly

### Cons of vLLM

- Requires GPUs for best results; CPU support is limited
- More complex to deploy compared to lighter runtimes
- Not the best fit for quick local experiments

In other words, vLLM is built for teams who care about efficiency at scale, whether you are serving thousands of concurrent API calls or powering production workloads.

## What is Ollama?

Ollama takes a very different approach. Instead of starting from performance bottlenecks, it focuses on developer experience. The project makes it incredibly easy to download, run, and interact with large language models on your local machine. With a single command, you can pull a model and start chatting with it, no need for complex configurations or GPU cluster setup.

Ollama’s packaging system is one of its strongest features. Models are distributed as containers with weights and instructions bundled together, which makes sharing and reproducing environments straightforward. It also has first-class support for Apple Silicon, which means you can run models efficiently on an M1 or M2 laptop without needing a data center GPU.

### Pros of Ollama

- Extremely easy to set up and run
- Works on consumer infrastructure
- Clean packaging system for distributing models
- Great for prototyping, experimentation, and local development

### Cons of Ollama

- Not optimized for high-throughput production workloads
- Limited to curated models rather than any arbitrary Hugging Face model
- Scaling beyond a single machine requires extra work

So while Ollama is not designed to compete with vLLM in raw throughput, it wins on simplicity and accessibility, especially for developers who just want to get started.

## vLLM vs Ollama: Side-by-side comparison

Now that we have looked at them separately, let us compare them directly across key dimensions.

### 1. Performance

vLLM dominates in raw performance thanks to PagedAttention and GPU scheduling optimizations. Ollama, on the other hand, is not built for large-scale throughput. If you need to serve thousands of requests per second, vLLM is the clear winner.

### 2. Model support

vLLM works with a wide range of Hugging Face models and supports advanced quantization formats. Ollama focuses on a curated catalog of models that are packaged neatly for ease of use. That means vLLM gives you more flexibility, but Ollama provides consistency.

### 3. Developer experience

Ollama is hands-down easier to use. It feels almost like Docker for language models. vLLM requires more setup, though tools like FastAPI make it manageable.

### 4. Hardware

vLLM is designed for data center GPUs such as A100s and H100s. Ollama runs well on consumer GPUs and Apple Silicon, making it more accessible for individual developers.

### 5. Scaling

Scaling is where vLLM shines. It was built for multi-GPU clusters and cloud deployments. Ollama is better suited to single-machine setups and local experiments.

## Which should you choose: vLLM or Ollama?

Here is a quick guide to help you decide.

| Use case | Best fit |
| --- | --- |
| Running production APIs at scale | vLLM |
| Prototyping on a laptop | Ollama |
| Serving models with long context windows | vLLM |
| Running curated open LLMs with minimal setup | Ollama |
| Multi-GPU scaling in the cloud | vLLM |
| Offline local experimentation | Ollama |

The choice really depends on your goals. If you are a developer exploring models on your laptop or need a simple setup for demos, Ollama is perfect. If you are building an application that needs to handle serious traffic or want to maximize GPU efficiency, vLLM is the better option.

## How to deploy vLLM and Ollama with Northflank

Choosing the right tool is only half the story. You also need a way to deploy it. [Northflank](https://northflank.com/) is a full-stack cloud platform built for AI workloads, letting you run APIs, workers, frontends, backends, and databases together with GPU acceleration when you need it. The key advantage is that you do not have to stitch infrastructure together yourself.

<InfoBox className="BodyStyle">

With [Northflank](https://northflank.com/), you can containerize your application, assign GPU resources, and expose it as an API without extra complexity. If you want to roll your own, start with our [guide on self-hosting vLLM in your own cloud account](https://northflank.com/guides/self-host-vllm-in-your-own-cloud-account-with-northflank-byoc). For a hands-on example, see how we [deployed Qwen3 Coder with vLLM](https://northflank.com/blog/self-host-qwen3-coder-with-vllm).

</InfoBox>

If speed matters, you can skip setup entirely with one-click templates. For example, [deploy vLLM on AWS with Northflank in a single step](https://northflank.com/stacks/deploy-vllm-aws), or explore the [Stacks library](https://northflank.com/stacks) configured to run vLLM with models like Deepseek, GPT OSS, and Qwen.

![image - 2025-09-12T135414.837.png](https://assets.northflank.com/image_2025_09_12_T135414_837_b32a9c6896.png)

This flexibility means you can start by testing Ollama locally and then scale vLLM across multiple H100s in production. Monitoring, autoscaling, and GPU provisioning are built in, so you can focus on your application while Northflank handles the rest.

## Wrapping up

vLLM and Ollama represent two different philosophies for running large language models. vLLM is built for scale, squeezing every drop of performance from GPUs and handling demanding production workloads. Ollama is built for accessibility, making it easy for developers to get models running on their laptops or small machines.

There is no single right choice. It depends on whether you value raw performance or developer simplicity. The good news is that you do not have to pick one forever. With Northflank, you can deploy both, switch between them, and scale as your needs evolve.

If you are ready to try it yourself, you can launch a vLLM or Ollama service on [Northflank](https://northflank.com/) in just a few minutes. [Sign up today](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how it fits your workflow.]]>
  </content:encoded>
</item><item>
  <title>Rent GPUs for AI: How to rent GPU power in 2026</title>
  <link>https://northflank.com/blog/rent-gpus-for-ai</link>
  <pubDate>2025-09-11T16:24:00.000Z</pubDate>
  <description>
    <![CDATA[Learn how to rent GPU power for AI projects. Step-by-step guide to spinning up GPU workloads on Northflank with 1-click deployment, CLI/API, and autoscaling.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/rent_gpus_for_ai_7e3054fbc4.png" alt="Rent GPUs for AI: How to rent GPU power in 2026" />AI and machine learning projects demand substantial computing power, but buying enterprise GPUs can cost tens of thousands upfront.

That's where GPU rental comes in.

You get instant access to high-performance GPUs, like on-demand NVIDIA GPUs for AI training, without the massive investment.

If you're training language models or running computer vision algorithms, renting GPU servers for AI lets you focus on building rather than buying hardware.

This guide shows you how to rent high-performance cloud GPUs with [Northflank](https://northflank.com/) and get your first AI workload running in under 5 minutes.

<InfoBox className="BodyStyle">

## TL;DR: Quick summary of renting GPUs for AI

Renting GPUs for AI workloads provides instant access to high-performance computing without the need for massive upfront costs.

You can rent GPU servers for AI projects starting at under $2/hour, with options ranging from NVIDIA A100s to powerful B200s.

The process is straightforward:

1. Select your GPU model
2. Configure your AI workloads and machine learning environment
3. Deploy in under 5 minutes.

With platforms like [Northflank](https://northflank.com/product/gpu-paas), you can spin GPU workloads in **under 5 minutes** and get built-in usage metering, automatic scaling, and pay-per-second billing.

> **Need help with GPU capacity planning?** If you have specific availability requirements or need assistance choosing the right GPU types for your workload, [request GPU capacity here](https://northflank.com/request/gpu).

This approach lets you focus on developing your models rather than managing infrastructure, making it ideal for both startups testing proof-of-concepts and enterprises running production AI systems.

</InfoBox>

## Why rent GPU power for machine learning?

Buying GPU hardware upfront creates expensive challenges for your AI projects.

For instance, a single NVIDIA H100 costs over $25,000, and that's before you factor in servers, cooling, power, and maintenance.

So, for many projects, especially if you're just starting out, this represents huge financial risk.

> GPU rental solves these problems by giving you on-demand access to the latest hardware without ownership burdens. You can scale your compute resources up or down based on what your project needs, experiment with different GPU models for specific tasks, and avoid depreciation costs as hardware advances quickly.
> 

This flexibility becomes especially valuable when your AI workloads have variable requirements. Training might need massive parallel processing for days, while inference requires lighter, consistent resources.

Cloud GPU rental platforms also handle all the infrastructure complexity for you. So you no longer have to worry about driver installations, CUDA compatibility, cooling systems, or hardware failures. You can focus entirely on building your machine learning models and applications.

## What are the benefits of renting GPUs for AI and GPU rental platforms?

Now that you understand why GPU rental works for your projects, let's look at the specific advantages you'll get from GPU rental platforms.

1. **Pay-per-use billing**:
    
    Means you only pay for the compute time you use, often down to the second. This granular billing can save you significant money compared to maintaining an always-on infrastructure that sits idle.
    
2. **Instant scalability**:
    
    Becomes your competitive advantage. If you need to run multiple experiments simultaneously, you can spin up dozens of instances instantly. And if you’re working on a time-sensitive project, you can have access to the latest GPU models, which ensures you're never bottlenecked by outdated hardware.
    
3. **Global deployment options**:
    
    Let you choose regions based on where your data lives or cost optimization needs. Your AI workloads can run closer to your users for better performance.
    
4. **Easy integration**:
    
    Simplifies your development workflow. Platforms offer APIs and CLI tools for programmatic access, integration with CI/CD pipelines, and automated deployment processes. This means your GPU rentals fit smoothly into your existing development and production workflows.
    
5. **Built-in monitoring and analytics**:
    
    Help you optimize both costs and performance. You can track GPU utilization, memory usage, and compute efficiency to ensure you're getting maximum value from your rental investment.
    

These benefits make GPU rental platforms valuable for startups testing proof-of-concepts, researchers running experiments, individual developers building AI projects, and enterprises running production AI systems.

## How does GPU renting work?

Before we start the tutorial, here's what happens technically when you rent GPU power.

When you rent GPU power, you're accessing dedicated or shared GPU resources in cloud data centers.

The platform provisions virtual machines with direct GPU access, handles driver installation and compatibility, and provides network connectivity to your workloads.

You select your desired GPU model (like NVIDIA H100 or A100), specify your software environment through container images, and configure resource allocation.

Then, the platform schedules your workload on available hardware and provides monitoring tools to track usage and performance.

Billing typically starts when your workload begins running and stops when you terminate the instance.

## How to rent GPU power for AI: Step-by-step tutorial to spin up a GPU workload on Northflank (under 5 minutes)

Understanding the benefits is one thing, but seeing how simple the process itself can be is another. Let me walk you through deploying your first AI workload on [Northflank](https://northflank.com/product/gpu-paas).

> The platform handles all the complexity of GPU provisioning, networking, and resource management behind the scenes. This means you can go from account creation to running GPU workloads in under 5 minutes.
> 

Here's how you can get your AI workload running on Northflank's GPU infrastructure. The process is designed to be intuitive, even if you're new to cloud deployments.

### Step 1: Create your account

Visit [Northflank.com](https://northflank.com/) and click "Get started" to sign up for your account. The signup process takes just a few minutes and supports GitHub, GitLab, or email authentication.

![Create your Northflank account](https://assets.northflank.com/create_northflank_account_85818665b4.png)*Create your Northflank account*

### Step 2: Create a new project

Once you're logged in, click the "Create new" button (+ icon) in the top right of your dashboard. Select "Project" from the dropdown.

![create-new-project-northflank.png](https://assets.northflank.com/create_new_project_northflank_435e23ad16.png)*Create new project on Northflank*

Projects serve as workspaces that group together related services, making it easier to manage multiple AI workloads and their associated resources.

### Step 3: Configure your project

You'll need to fill out a few details:

![create-new-project-form.png](https://assets.northflank.com/create_new_project_form_28400383c6.png)

1. Enter a project name (e.g., "gpu-rental-test") and choose a color for easy identification
2. Select "Northflank Cloud" as your deployment target - this gives you access to Northflank's [managed infrastructure](https://northflank.com/features/managed-cloud) with optimized GPU configurations.
    - Optional (If you want to use your own cloud provider, select the “Bring Your Own Cloud” option (See more here - [Deploy workloads on your own infrastructure - AWS, GCP, Azure, on-premises, or bare-metal](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes)))
3. Choose a GPU-enabled region from the list (regions with GPU access are clearly marked with green "GPU enabled" indicators)
4. Click "Create project" to proceed

### Step 4: Add a new service

On your project dashboard, you'll see several resource options. Click "Add new service" to deploy your GPU workload.

![select-add-new-service.png](https://assets.northflank.com/select_add_new_service_f5a9ff806e.png)*Adding a new service*

Services in Northflank handle the deployment and management of your containerized applications.

### Step 5: Configure your deployment

Now you'll set up how your AI model will run:

- Select "Deployment (Deploy a Docker image)" since we'll be using a pre-built container image optimized for AI workloads
- Enter service name: "qwen4c" (or any name you prefer)
- Choose "External image" as your deployment source
- Enter the image path: `vllm/vllm-openai:latest` (this is a popular image for running large language model inference, this example is for GPU inference specifically)

![External image deployment configuration](https://assets.northflank.com/choose_deployment_source_and_image_path_a3257748f7.png)*External image deployment configuration*

### Step 6: Select your GPU resources

This is where you choose your computing power:

- In the Resources section, click the "GPU" tab
- You'll see a list of available GPU models with their specifications and hourly pricing
- Select "NVIDIA H100" with 80GB VRAM ($2.74/hr) for this example - this provides excellent performance for most AI workloads
- Set "GPUs per instance" to "1" to start with a single GPU configuration

![gpu-and-compute-plan-selected.png](https://assets.northflank.com/gpu_and_compute_plan_selected_dc6b3dcb94.png)*GPU configuration with H100 selected*

### Step 7: Configure networking

To make your AI model accessible:

- Scroll to the Networking section and click the "+" button to add a port
- Enter port 8000 and select HTTP as the protocol
- Check "Publicly expose this port to the internet" to make your service accessible
- Northflank will automatically generate a public endpoint URL that you can use to interact with your AI model

![networking-ports-created.png](https://assets.northflank.com/networking_ports_created_43e345672e.png)*Port configuration with public exposure*

### Step 8: Set runtime configuration

This tells your container how to start your AI model:

- In the Advanced settings section, find "Docker runtime mode" and click the dropdown
- Select "Custom entrypoint & command" to specify how your container should run
- Enter `bash -c` as the custom entrypoint
- Enter `'vllm serve Qwen/Qwen3-4B-Instruct-2507'` as the custom command
- This configures the container to serve a language model via vLLM

![custom-entrypoint-custom-command.png](https://assets.northflank.com/custom_entrypoint_custom_command_5f7d24370d.png)*Custom entrypoint and command configuration*

### Step 9: Deploy

Click "Create service" to deploy your GPU workload. Northflank will provision the GPU resources, pull your container image, and start your service.

You can monitor the deployment progress in real-time through the dashboard.

![service-created-and-deployed.png](https://assets.northflank.com/service_created_and_deployed_e655c09698.png)


### Usage metering and autoscaling

Once your service is deployed, Northflank provides comprehensive usage metering that tracks your GPU consumption by the second. This granular monitoring helps you understand exactly what you're paying for and optimize costs accordingly.

![metrics-northflank.png](https://assets.northflank.com/metrics_northflank_fd10cbbf59.png)*Showing the metrics*

The dashboard shows real-time metrics including GPU utilization, memory usage, and network activity. For production workloads, you can enable horizontal autoscaling to automatically adjust the number of instances based on demand.

This ensures optimal performance during traffic spikes while minimizing costs during low-usage periods. The autoscaling configuration allows you to set minimum and maximum instance counts, scaling triggers, and cooldown periods.

Your GPU workload is now live and accessible via the generated public endpoint. You can make API calls to your deployed model, monitor performance metrics, and scale resources as needed - all through Northflank's intuitive interface.

## Is renting a GPU for AI worth it?

After seeing how straightforward the process is, you might be asking about the costs and if rental makes financial sense for your projects.

The cost-effectiveness of GPU rental depends on your usage patterns, but for most AI development scenarios, renting provides significant advantages over purchasing.

Look at this typical scenario:

> Training a medium-sized language model might require 100 hours of compute time spread over several weeks. Renting an H100 for $2.74/hour would cost you $274 total, while purchasing the same GPU could cost $25,000 or more.
> 

The break-even point for purchasing versus renting typically occurs when you need consistent, high-utilization access to the same GPU configuration for 6-12 months or longer.

However, this calculation should also factor in maintenance costs, power consumption, cooling requirements, and the opportunity cost of capital tied up in hardware.

**For most development teams**, especially those working on multiple projects or experimenting with different approaches, rental offers superior flexibility. You can easily switch between GPU models based on specific workload requirements, scale resources for different project phases, and avoid technology obsolescence as new GPU generations are released.

**Startups and research teams** particularly benefit from the low barrier to entry and ability to validate ideas before making major hardware investments. Even large enterprises often use GPU rental for overflow capacity, testing new configurations, or running workloads in different geographic regions.

## Frequently asked questions about renting GPUs for AI

Let's address some of the most common questions people have about GPU rental:

1. **How does GPU renting work?**
    
    GPU rental platforms provide on-demand access to physical GPUs through virtualized environments. You select your desired GPU model, configure your software environment through container images, and pay for actual usage time. The platform handles hardware provisioning, driver management, and infrastructure maintenance. Platforms like [Northflank](https://northflank.com/gpu) make this process particularly straightforward with its intuitive interface.
    
2. **Is renting a GPU worth it?**
    
    For most AI development scenarios, renting offers better cost efficiency and flexibility than purchasing. Unless you need consistent, high-utilization access to the same configuration for 6+ months, rental typically provides superior value while eliminating maintenance overhead.
    
3. **Can you rent a GPU for gaming?**
    
    While technically possible, GPU rental platforms are optimized for compute workloads rather than gaming. The network latency and streaming requirements for gaming make dedicated gaming cloud services a better choice for that use case.
    
4. **Why would someone rent a GPU?**
    
    The primary reasons include avoiding large upfront hardware costs, accessing latest GPU models without purchasing, scaling compute resources on-demand, and eliminating infrastructure management overhead. This is particularly valuable for AI development, machine learning research, and data processing workloads.
    
5. **How much does it cost to rent a GPU for AI?**
    
    Costs vary by GPU model and provider. On Northflank, entry-level GPUs start around $1.42/hour for A100s, while high-end H100 GPUs cost $2.74/hour. Most platforms offer transparent pay-per-second billing.
    
6. **What type of GPU should I rent for deep learning tasks?**
    
    For serious deep learning work, NVIDIA H200 or B200 GPUs provide the best performance with optimized Tensor cores. For smaller projects or experimentation, NVIDIA A100 or V100 GPUs offer good price-performance ratios. Northflank offers all these options with simple configuration and deployment.
    

## Start renting GPU power for your AI projects today

Now that you've seen the costs, benefits, and step-by-step process, you can see why GPU rental has become the standard for AI development. It gives you flexibility, cost efficiency, and access to the latest hardware without huge upfront investments.

[Northflank](https://northflank.com/) makes it simple to get started. You can deploy GPU workloads in under 5 minutes with transparent billing and automatic scaling. Don't let hardware constraints limit what you can build.

<InfoBox className="BodyStyle">

[Get started with Northflank today](https://app.northflank.com/signup) or [book a demo to speak with an engineer](https://cal.com/team/northflank/northflank-intro) about your specific GPU requirements.

</InfoBox>

### See other resources that can help

Here are additional guides to help you optimize your GPU rental experience and make informed decisions:

1. [Best GPU for AI](https://northflank.com/blog/best-gpu-for-ai) - Compare GPU models and find the right one for your AI workloads
2. [Running AI on cloud GPUs](https://northflank.com/blog/running-ai-on-cloud-gpus) - Learn best practices for deploying AI applications on cloud GPU infrastructure
3. [GPUs on Northflank (documentation)](https://northflank.com/docs/v1/application/gpu-workloads/gpus-on-northflank) - Complete technical guide to using GPUs on Northflank
4. [Rent H100 GPU: pricing, performance and where to get one](https://northflank.com/blog/rent-h100-gpu-pricing-performance-and-where-to-get-one) - Deep analysis of H100 GPU rental options and costs
5. [Best GPU for machine learning](https://northflank.com/blog/best-gpu-for-machine-learning) - Choose the optimal GPU for your machine learning projects
6. [What are spot GPUs?](https://northflank.com/blog/what-are-spot-gpus-guide) - Save money with spot GPU instances for non-critical workloads
7. [Configure and optimize workloads for GPUs](https://northflank.com/docs/v1/application/gpu-workloads/configure-and-optimise-workloads-for-gpus) - Technical guide to maximizing GPU performance]]>
  </content:encoded>
</item><item>
  <title>Best open source text-to-speech models and how to run them</title>
  <link>https://northflank.com/blog/best-open-source-text-to-speech-models-and-how-to-run-them</link>
  <pubDate>2025-09-11T15:30:00.000Z</pubDate>
  <description>
    <![CDATA[Explore the best open source text-to-speech models like XTTS-v2, Mozilla TTS, and Bark. Learn how to choose, deploy, and scale them for production with GPU support using Northflank.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/ai_hosting_platforms_e18ff9ece8.png" alt="Best open source text-to-speech models and how to run them" />If you have ever asked your phone to read a message, listened to an AI-narrated audiobook, or relied on a screen reader, you have already experienced text-to-speech in action. What was once robotic and flat has evolved into open source models that can generate voices that feel natural, multilingual, and expressive.

For developers, this shift means more freedom. Open source text-to-speech lets you experiment, fine-tune, and run models on your own terms without being locked into a vendor. 

But moving from a local demo to a production system is where most projects hit a wall. Running XTTS-v2 or Bark on your laptop is easy. Serving thousands of real-time requests is not. In this guide, we’ll explore the best open source models available today, what to consider, and how to deploy them using Northflank at scale.

## TL;DR: Best open source text-to-speech models

If you only have a moment, here’s the quick version.

<InfoBox className="BodyStyle">

**The top text-to-speech** **models to know:**

- **XTTS-v2**: High-quality, multilingual voices with cloning support.
- **Mozilla TTS**: Flexible and well-documented, great for research and accessibility.
- **ChatTTS**: Optimized for conversational applications like chatbots.
- **MeloTTS**: Lightweight and efficient, ideal for low-resource devices.
- **Coqui TTS**: Broad toolkit with pre-trained voices and multilingual support.
- **Mimic 3**: Fast, privacy-friendly, works well offline or on embedded systems.
- **Bark**: Expressive and creative, capable of generating intonation and non-speech sounds.

**The real challenge:**

Testing these models locally is easy. Running them at scale in production is not. Most require GPUs, low latency, and careful orchestration to stay reliable.

**The smarter way → Northflank**

[Northflank](https://northflank.com/product/app-platform) makes open source text-to-speech production-ready. Connect your repo and the platform builds, deploys, and scales your model automatically. [GPU support](https://northflank.com/product/gpu-paas), networking, and monitoring are included, so you can focus on building with voices rather than managing infrastructure.

</InfoBox>

## What to consider before choosing a text-to-speech model

It is easy to be impressed by a smooth demo, but the best text-to-speech model is the one that fits your actual needs. Let’s break down what to consider:

1. **Voice quality and naturalness:** Some models produce very natural speech with correct intonation, while others sound robotic but run faster. Audiobooks demand realism; system alerts may not.
2. **Language support:** Many models are still English-first, but some projects have expanded to dozens of languages. If your project serves a global audience, this becomes a deciding factor.
3. **Speed and efficiency:** Models vary in how quickly they generate speech. Heavy ones may need a GPU, while lightweight models are better for edge deployments or low-latency applications.
4. **Customization:** Some models only offer pre-trained checkpoints, while others allow fine-tuning with your own data or accents. Choose based on how much control you need.
5. **Ease of deployment:** Running a text-to-speech model locally is simple, but scaling it to handle thousands of users in production is where complexity often appears.
6. **Community and ecosystem:** A vibrant community means faster answers, more tutorials, and active improvements. Older but well-supported projects often outperform newer ones with less adoption.

## What is the best open source text-to-speech model?

Now that you know what to consider when choosing a text-to-speech model, from voice quality and language support to deployment and community, it is time to look at the best open source options available today. Each model brings its own strengths, and the right fit will depend on the priorities we just covered.

### 1. XTTS-v2 - Best overall performance

**Strengths**: High-quality multilingual synthesis, voice cloning from short samples

**Efficiency**: Moderate (benefits from GPU acceleration)

**Use case**: Production apps needing natural and adaptable voices

### 2. Mozilla TTS - Best for research and flexibility

**Strengths**: Highly customizable, extensive documentation, active community

**Efficiency**: Varies depending on training setup

**Use case**: Research, accessibility, or projects requiring custom voices

### 3. ChatTTS - Best for real-time conversation

**Strengths**: Optimized for dialogue, low-latency responses

**Efficiency**: Good for chat and interactive use cases

**Use case**: Chatbots, assistants, real-time agents

### 4. MeloTTS - Best lightweight model

**Strengths**: Fast, efficient, easy to deploy on limited hardware

**Efficiency**: High (runs well without large GPUs)

**Use case**: Edge devices, mobile, low-resource environments

### 5. Coqui TTS - Best toolkit and ecosystem

**Strengths**: Wide library of pre-trained voices, multilingual support, fine-tuning tools

**Efficiency**: Depends on the chosen model

**Use case**: Teams wanting flexibility without building from scratch

### 6. Mimic 3 - Best for privacy and offline use

**Strengths**: Small, efficient, runs locally without cloud dependencies

**Efficiency**: Very high for small devices

**Use case**: Accessibility, embedded systems, privacy-focused apps

### 7. Bark - Best for creativity and expression

**Strengths**: Generates speech with intonation and even non-speech sounds

**Efficiency**: Less predictable, heavier model

**Use case**: Creative projects, expressive or experimental applications

## How to run an open source text-to-speech model

Running a text-to-speech model can start simple and scale as needed, but unlike large language models, text-to-speech models typically require integration into an application. You can experiment locally on your machine or deploy a service for real-time users, each with different requirements.

### Option 1: Local experimentation

Most open source TTS models, like XTTS-v2, Bark, or Coqui TTS, can be run locally in minutes. Python packages or prebuilt scripts let you generate audio from text immediately:

```python
from TTS.api import TTS

# Example: Coqui TTS
tts = TTS("tts_models/en/ljspeech/tacotron2-DDC")
tts.tts_to_file(text="Hello world!", file_path="output.wav")
```

This is ideal for testing models, comparing voices, or fine-tuning parameters. Lightweight models can run on a CPU, but heavier models benefit from a GPU.

### Option 2: Deploying as a service

Unlike LLMs, text-to-speech models don’t come with inference servers like vLLM. To make a text-to-speech model [production-ready](https://northflank.com/product/deployments), you need to **wrap it in an application layer**, for example, using **FastAPI**, **Flask**, or another web framework. This allows your application to:

- Receive text input via API calls
- Generate audio using the TTS model
- Return audio files or streams to users

Key considerations for production deployment:

- **GPU acceleration** for heavier models like XTTS-v2 or Bark
- **Autoscaling** to handle sudden spikes in requests
- **API endpoints** for your application to request TTS output
- **Monitoring and reliability** so the service remains responsive

Setting all this up manually can be complex, especially for scaling and maintaining high availability.

### Best option: Deploying on Northflank

Once you’re ready to move beyond local experimentation, you can deploy your text-to-speech application to production with Northflank. This involves packaging your text-to-speech model inside a container, exposing it via an API, and optionally using GPU resources for faster audio generation.

**Containerize your text-to-speech application (Example)**:

Create a Python application that serves your text-to-speech model using FastAPI, Flask, or any web framework. For example, a FastAPI server (`server.py`) might look like this:

```python
# server.py
from fastapi import FastAPI
from TTS.api import TTS

app = FastAPI()

# Load the XTTS-v2 model (this automatically uses GPU if available)
tts = TTS("XTTS-v2")

@app.post("/speak")
def speak(text: str):
    file_path = "output.wav"
    tts.tts_to_file(text=text, file_path=file_path)
    return {"file": file_path}
```

Next, create a Dockerfile to package your app:

```docker
FROM python:3.11

# Run in unbuffered mode
ENV PYTHONUNBUFFERED=1

# Set working directory
WORKDIR /app

# Copy local files into container
COPY . ./

# Install PyTorch with CUDA for GPU acceleration
RUN pip install torch --index-url https://download.pytorch.org/whl/cu121

# Install TTS and FastAPI dependencies
RUN pip install tts fastapi uvicorn

# Start the FastAPI server
CMD ["uvicorn", "server:app", "--host", "0.0.0.0", "--port", "8000"]
```

> Note: The TTS library automatically uses a GPU if available, so you don’t need to modify your Python code to enable it.
> 

<InfoBox className="BodyStyle">

Once you have dockerized your application, you’re now ready to deploy on Northflank in a few minutes.

For steps on deploying on Northflank, refer to this guide: [Deploying GPUs on Northflank](https://northflank.com/docs/v1/application/gpu-workloads/gpus-on-northflank#deploy-gpus-on-northflanks-managed-cloud), and you can also check [this guide](https://northflank.com/blog/rent-gpus-for-ai).

</InfoBox>

## Conclusion

Open source text-to-speech has reached a point where models can generate voices that are natural, expressive, and flexible enough for real-world use. Whether you are working on accessibility tools, conversational agents, or creative applications, there is now a model that can fit your needs. 

The real challenge is less about finding the right model and more about making it work in production. Running text-to-speech locally is straightforward, but scaling it for thousands of users, handling latency, and managing GPUs is a different problem entirely. This is where [Northflank](https://northflank.com/) helps. It gives you a platform to deploy and scale open source text-to-speech models with ease, letting you focus on building great experiences while the infrastructure takes care of itself.]]>
  </content:encoded>
</item><item>
  <title>What is container deployment? Benefits, how it works, and best practices.</title>
  <link>https://northflank.com/blog/container-deployment</link>
  <pubDate>2025-09-10T15:45:00.000Z</pubDate>
  <description>
    <![CDATA[Learn what container deployment is, how it works, its benefits, best practices, and how platforms like Northflank simplify running containers reliably at scale.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/what_is_gpu_as_a_service_8464f66b3a.png" alt="What is container deployment? Benefits, how it works, and best practices." /><InfoBox className="BodyStyle">

**Summary:** Container deployment lets you package applications into self-contained units that run consistently across environments, reducing errors and speeding up scaling. Platforms like [Northflank](https://northflank.com/) go beyond basic container hosting by incorporating automated builds, one-click deployment, monitoring, and scaling into a single, integrated platform.

</InfoBox>

Think about the last time you built an application. It worked perfectly on your machine, but the moment you pushed it elsewhere, something broke. Maybe the database was misconfigured, the dependencies did not line up, or the operating system behaved differently. Every developer has faced this problem.

Containers were created as the answer. They package your application with everything it needs into one lightweight unit that runs the same way everywhere. Container deployment is the process of running these packages reliably across development, testing, and production.

In this article, we will look at container deployment in detail. We will:

- Start with a quick overview
- Break down what container deployment means
- Explain how containerization works
- Walk through the typical deployment workflow
- Cover why teams rely on containers
- Highlight the key benefits containers bring
- Share best practices that keep deployments secure and reliable

Finally, we will explore how containers are deployed in practice and how platforms such as [Northflank](https://northflank.com/) make the process seamless.

## TL;DR: Container deployment at a glance

If you only have a moment, here is the short version.

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**What is container deployment?**

Packaging applications into containers and running them consistently across environments.

**Why do teams use it?**

Containers solve environment drift, make scaling effortless, and keep delivery predictable.

**Where is the challenge?**

Running a few Docker containers is easy. Running production systems at scale is not. Kubernetes solves the problem, but comes with steep complexity and constant maintenance.

**The smarter way → Northflank**

[Northflank](https://northflank.com/) gives you production-grade container deployment without the Kubernetes headache. Connect your repo, and the platform builds, deploys, and scales your containers automatically. Networking, orchestration, and monitoring are built in.

**When to use Northflank?**

- When you want to ship code straight from Git to production.
- When scaling should be automatic, not manual.
- When your team wants DevOps best practices without weeks of setup.
- When you want to focus on building, not running infrastructure.

**The bottom line**

Container deployment is vital for modern development. [Northflank](https://northflank.com/deploy/run-persistent-and-ephemeral-docker-containers) makes it seamless.

</InfoBox>

## What is container deployment?

To understand container deployment, it helps to break the concept down into two parts. First, you have the container itself. A container is an isolated environment where your application lives along with its code, libraries, dependencies, and runtime. You can think of it as a self-contained unit of software that can run anywhere.

Deployment, on the other hand, is about taking that container and running it in the real world. It might be on your own machine, in a staging environment for testing, or across multiple servers in the cloud for production. The goal of deployment is to make sure the container is not just running, but running reliably, at scale, and in a way that supports your application’s needs.

When you put the two together, container deployment is the practice of packaging and running applications as containers in a way that ensures consistency, performance, and scalability. This is not just a technical improvement. It is a shift in how software teams think about building, shipping, and maintaining applications.

## How does containerization work

At its core, containerization is about isolating an application from the underlying system while keeping it lightweight. Traditional virtualization used virtual machines, which simulated entire operating systems. Containers take a different approach. Instead of recreating a full OS, they share the host system’s kernel while still maintaining isolation.

This makes containers far more efficient. They start quickly, use fewer resources, and can be packed densely onto a server. Imagine you are running three applications on one server. If each is running in a virtual machine, you are duplicating operating systems and burning resources.

With containers, you can run all three using the same OS kernel, each isolated but lightweight. From a workflow perspective, containerization works by creating an image. This image is a blueprint that describes what the container includes and how it should run. When you run a container, you are essentially spinning up an instance of that image. Tools such as Docker popularized this model by making it easy to build, share, and run container images.

This approach allows developers to create an environment once and run it anywhere, from a laptop to a massive cloud cluster. That consistency is the heart of containerization and why deployment becomes so much simpler.

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**How Northflank helps**

Platforms like [Northflank](https://northflank.com/docs/v1/application/run/run-containers-and-micro-services) take this model a step further by making containerization seamless for developers. Instead of juggling Docker commands and configuration files, you can:

- **Build**: Connect your Git repository and let Northflank automatically build container images.
- **Configure**: Set environment variables, resources, and ports through a simple UI.
- **Deploy**: Launch services with one click, while Northflank handles orchestration, networking, and monitoring in the background.

For example, deploying a Node.js app takes just a few minutes. Northflank builds the image, provisions networking, adds monitoring, and even provides a custom domain, all without you needing to touch infrastructure. If you’re curious, [see this in action](https://northflank.com/docs/v1/application/run/run-containers-and-micro-services).

</InfoBox>

## Why use container deployment?

You might be wondering whether container deployment is truly necessary. After all, applications were deployed long before containers existed. The reason so many teams have embraced containers is that traditional deployment models come with significant pain points.

When applications are deployed directly onto servers, they often rely on the specific configuration of that server. Differences in operating systems, library versions, or network setups can cause failures that are hard to debug. Scaling is also more difficult because each new instance must be configured carefully to match.

Container deployment solves these issues by introducing consistency and portability. If it runs in your container locally, it will run the same way in production. That means fewer surprises, faster releases, and less time spent firefighting environment-related bugs.

There is also a cultural angle. Modern development practices emphasize speed, agility, and automation. Teams want to ship updates quickly, roll back if necessary, and scale effortlessly. Container deployment supports this mindset by enabling faster iterations and smoother workflows.

## The container deployment workflow

Once you understand why containers matter, the next question is how they actually move from code to production. Most teams follow a workflow that looks something like this:

- **Build**: Start with your application code and dependencies. This is where you prepare everything that will eventually run in production.
- **Package**: Turn the code into a container image, which acts as a blueprint for how the application should run.
- **Push**: Store that image in a container registry. The registry acts like a library where your images can be versioned, pulled, and reused.
- **Deploy**: Run the container image in the chosen environment, whether that is a developer’s laptop, a staging setup, or a production cluster.
- **Monitor**: Track logs, metrics, and resource usage to make sure the container is performing as expected and can scale when needed.

On paper, this workflow looks simple. In practice, it means juggling multiple tools for builds, registries, deployments, and monitoring. [Northflank](https://northflank.com/) unifies the entire process: it builds from your repositories, packages code into containers, pushes them securely, deploys them automatically, and gives you monitoring and scaling out of the box.

## Key benefits of container deployment

As we have seen, container deployment solves many of the headaches of traditional application delivery. But it is more than just a fix to old problems. It introduces real advantages that change how teams build, ship, and run software. Here are some of the most important benefits.

- **Portability and consistency**: Containers package everything an app needs, so it runs the same on a laptop, in staging, or in production. No more “it works on my machine” problems.
- **Speed and agility**: They start in seconds, making it easier to test, release updates, and integrate with CI/CD pipelines for faster delivery cycles.
- **Easy scalability**: You can spin up or shut down containers quickly to match demand. Orchestration tools automate this process, keeping applications responsive.
- **Resource efficiency**: By sharing the host OS kernel, containers use fewer resources than virtual machines. That means lower costs and higher density on the same hardware.
- **Security and isolation**: Containers run in isolated environments, reducing risks across services. Practices like image scanning and runtime monitoring strengthen this further.

## How are containers deployed?

Now that we have established the value of containers, the next question is how they are deployed in practice. There are a few paths depending on the size and complexity of your project. For smaller projects, you can run containers manually using tools such as Docker. This is straightforward for a handful of containers, but as soon as you need to manage scaling, load balancing, or monitoring, it becomes more complex.

The next step is [orchestration](https://northflank.com/blog/container-orchestration). Tools like Kubernetes allow you to manage clusters of containers, handle scheduling, and ensure high availability. While powerful, Kubernetes also comes with a steep learning curve and requires significant infrastructure expertise.

This is why many teams turn to managed platforms that simplify the process. Instead of manually configuring clusters and writing complex manifests, you connect your repository, define your build, and let the platform handle deployment and scaling. To see this in action, check out our guide on "[How to deploy to Kubernetes without writing YAML](https://northflank.com/blog/deploy-to-kubernetes-without-writing-yaml)".

This is where [Northflank](https://northflank.com/) comes in. It provides a developer-friendly interface for building, deploying, and managing containers without requiring deep expertise in orchestration. With Northflank, you can focus on writing code while the platform ensures your containers are deployed consistently, monitored effectively, and scaled automatically. The complexity of Kubernetes is handled behind the scenes, giving you the benefits without the overhead.

## Best practices for deployment

Knowing the steps is one thing. Doing them in a way that is secure, reliable, and scalable is another. Over time, teams have developed a set of best practices that keep container deployment running smoothly:

- **Automate the pipeline**: Use continuous integration and deployment so every change moves from code to container without manual intervention.
- **Scan images for vulnerabilities**: Security starts at the build stage. Make sure every image is checked before it reaches production.
- **Manage secrets properly**: API keys, database passwords, and other credentials should never live inside images. Use secrets management tools or platform features to keep them secure.
- **Monitor everything**: Collect logs, metrics, and alerts from containers so you can catch issues early and maintain visibility across environments.
- **Plan for scaling**: Design for growth by using orchestration or platforms that can automatically adjust resources based on demand.
- **Keep things consistent**: Standardize versioning, tagging, and deployment strategies so that the process is predictable across teams.

Following these practices keeps deployments resilient, but it also highlights the hidden cost. Implementing each one requires expertise, tooling, and time. This is why so many teams turn to platforms that bake these practices into the workflow by default.

## Why Northflank makes container deployment seamless

By now, it is clear that containers solve a major problem in modern software delivery, but running them at scale is not always straightforward. Docker works well for local testing, and Kubernetes is the industry standard for orchestration, but both require time, expertise, and constant maintenance.

[Northflank](https://northflank.com/) exists to remove that complexity. It is a platform built for developers who want the benefits of container deployment without needing to become infrastructure experts. With Northflank, you connect your repository, and the platform takes care of the rest. Your code is automatically built into containers, deployed to production, and scaled as demand grows.

What makes Northflank different is that it blends the power of Kubernetes with a developer-friendly experience. Instead of writing complex YAML files or maintaining clusters yourself, you get a clean interface and clear workflows. Scaling, networking, monitoring, and high availability are all handled behind the scenes.

Teams use Northflank when they want to:

- Push code directly from Git to production with minimal configuration
- Scale services automatically without touching Kubernetes
- Run production workloads reliably without a dedicated DevOps team
- Focus on building features instead of managing infrastructure

In short, Northflank gives you the resilience of containers with the simplicity of a fully managed platform. You spend less time firefighting deployments and more time shipping value to your users.

## Should you start using container deployment? (Wrapping Up)

If you want applications to run consistently across environments, container deployment is one of the most reliable approaches available today. It reduces errors, speeds up scaling, and gives your team a predictable way to build and ship software.

At the same time, managing containers directly with Docker or Kubernetes can add complexity, especially if you are focused on delivering features rather than maintaining infrastructure.

Platforms like [Northflank](https://northflank.com/) bridge that gap. They package your code into containers, deploy it automatically, and handle scaling, networking, and orchestration behind the scenes. You get the resilience and flexibility of containers without the steep learning curve or ongoing operational burden.

That means you can move faster, ship with confidence, and spend your time building products rather than managing infrastructure.

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Try it on [Northflank](https://app.northflank.com/signup) if you want container deployment without Kubernetes complexity and with built-in scaling and monitoring.

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</item><item>
  <title>What is GPU-as-a-Service (GPUaaS)? Use cases and leading providers</title>
  <link>https://northflank.com/blog/gpu-as-a-service</link>
  <pubDate>2025-09-10T15:18:00.000Z</pubDate>
  <description>
    <![CDATA[Learn what GPU-as-a-Service is, its benefits, top providers like Northflank and AWS, and key use cases for AI projects.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/what_is_gpu_as_a_service_99d8387862.png" alt="What is GPU-as-a-Service (GPUaaS)? Use cases and leading providers" /><InfoBox className="BodyStyle">

**Summary:** GPU-as-a-Service lets you access powerful graphics processing units through the cloud without buying expensive hardware. Platforms like [Northflank](https://northflank.com/) go beyond basic GPU rental by including CI/CD pipelines, monitoring, and deployment tools in one integrated solution.

> **Need GPUaaS capacity for your team?** If you're evaluating GPUaaS for production workloads and have specific capacity requirements, [request GPU capacity here](https://northflank.com/request/gpu).

</InfoBox>

The rise of AI and machine learning has created a massive demand for GPU compute power.

Every day, organizations are demanding flexible access to high-performance graphics processing units without the capital investment and operational complexity of managing physical hardware.

We'll cover:

- What is GPU-as-a-Service, and how does it work
- What are the benefits of GPU-as-a-service
- Why you'd choose GPU-as-a-service over buying your own hardware
- How platforms like Northflank provide more than basic compute
- How to select the right GPU-as-a-service provider for your needs
- Use cases and applications of GPUaaS
- What makes some platforms better than others

## What is GPU-as-a-service?

GPU-as-a-Service (GPUaaS) is a cloud computing model that allows you to deploy GPU resources in the cloud rather than purchasing and maintaining your own hardware.

The concept is straightforward:

*Rather than spending $40,000+ on a high-end GPU like the NVIDIA H100, you access the same computational power through remote servers. You pay only for what you use, whether that's hours, days, or months.*

There are several reasons why GPUs are vital for your projects. Let’s look at some of them:

- **Parallel processing power**: GPUs have thousands of small cores that work simultaneously
- **AI optimization**: Perfect for training machine learning models and running inference
- **Speed**: Tasks that take hours on CPUs can be completed in minutes on GPUs
- **Performance**: Handle massive datasets and complex computations with ease

This approach resembles renting a high-performance vehicle rather than purchasing one: you access the performance when needed without the massive upfront cost or ongoing maintenance responsibilities.

What is the difference from traditional cloud computing? These platforms are specifically designed for AI workloads, with pre-configured environments and tools that enable immediate productivity.

## What are the benefits of GPU-as-a-service?

Understanding what GPUaaS is leads naturally to finding out why it has become the preferred solution for AI projects.

### 1. Cost advantages

If you used to worry about $10,000-$100,000+ hardware purchases, you don't have to anymore. With GPUaaS, you only pay for the compute time you actively use.

You also avoid all the hidden costs that come with owning hardware, like electricity bills, cooling systems, repairs, and upgrades.

This converts your capital expenses into predictable operational expenses, making budgeting much simpler.

### 2. Flexibility and scalability

You can add more GPUs during intensive training phases, then scale down for inference workloads.

Choose from different GPU types based on your specific needs, work from anywhere with an internet connection, and never worry about predicting future hardware requirements.

### 3. Speed and convenience

You can finally start your AI projects within minutes rather than waiting weeks for hardware procurement.

Pre-configured environments let you skip complex setup processes, and you always have access to the latest GPU technology without managing upgrades.

The result is that you gain enterprise-grade GPU performance without the operational complexity typically associated with it.

## How Northflank goes beyond basic GPU compute (a must-read!)

Most GPU providers offer raw compute power and leave you to handle everything else. [Northflank](https://northflank.com/) takes a comprehensive approach.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

Let’s see how.

### Starting with what makes Northflank different

While other providers offer bare GPU instances, Northflank provides a complete platform that handles your entire AI development lifecycle. This includes:

1. **Integrated CI/CD**:
    
    Automatically build, test, and deploy your models as you update code. See [Continuous integration and delivery on Northflank](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank).
    
2. **Container orchestration**:
    
    Scale your applications automatically based on demand. See [how Northflank handles container orchestration](https://northflank.com/blog/container-orchestration#kuberneteslevel-control-minus-the-complexity-of-container-orchestration).
    
3. **Real-time monitoring**:
    
    Track model performance, resource usage, and system health. See [how Northflank provides observability tooling](https://northflank.com/docs/v1/application/observe/observability-on-northflank) to monitor and ensure your applications and microservices are available.
    
4. **Secrets management**:
    
    Securely handle API keys, database connections, and sensitive data. See [the various ways Northflank handles security](https://northflank.com/docs/v1/application/secure/security-on-northflank)
    
5. **Environment management**:
    
    Manage development, staging, and production environments. See [how to set up a preview environment](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) and this guide on ”[Dev, QA, preview, test, staging, and production environments.](https://northflank.com/blog/what-are-dev-qa-preview-test-staging-and-production-environments)”
    

### Now, why does this approach benefit my workflow?

Well, I know you’re already asking. So to give you a quick answer:

Rather than managing multiple tools and services, you work within **one platform**. Using Northflank’s approach provides you with:

1. Faster deployment times (minutes rather than hours)
2. Reduced operational complexity
3. Better security through integrated access controls
4. Lower total cost of ownership

Then there’s the framework and template support:

![northflank-stacks.png](https://assets.northflank.com/northflank_stacks_8bc1d01db1.png)

- **One-click AI stacks**: Pre-built templates for LLM deployment like [Deepseek v3.1](https://northflank.com/stacks/deepseek-v3-1), [Qwen3 models](https://northflank.com/stacks/deploy-qwen3-4b-instruct), and [vLLM OpenAI](https://northflank.com/stacks/deploy-vllm-aws) at [northflank.com/stacks](https://northflank.com/stacks)
- **Step-by-step guides**: Including [AI observability with Langfuse](https://northflank.com/guides/ai-observability-and-analytics-with-langfuse-on-northflank) and [AI workflow automation with n8n](https://northflank.com/guides/how-to-self-host-n8n-ai-workflow-automation-on-northflank) at [northflank.com/guides](https://northflank.com/guides)
- **Jupyter Notebook templates**: Ready-to-deploy on [AWS](https://northflank.com/stacks/deploy-jupyter-aws), [GCP](https://northflank.com/stacks/deploy-jupyter-gcp), and [Azure](https://northflank.com/stacks/deploy-jupyter-azure)
- **AI development tools**: Templates for [Chroma vector database](https://northflank.com/stacks/deploy-chroma), [Mojo Playground](https://northflank.com/stacks/deploy-mojo), and more
- Custom environment support

The result is that you can finally focus on building your AI applications rather than managing infrastructure complexity.

## What is the largest GPU-as-a-service provider?

Understanding the competitive market helps you make informed decisions about which provider suits your needs.

| Provider | Strengths | Best For |
| --- | --- | --- |
| [**Northflank**](https://northflank.com/) | GPU + full deployment platform, integrated CI/CD | Teams wanting complete simple development workflow |
| [**AWS**](https://northflank.com/cloud/aws) | Largest infrastructure, extensive integrations | Enterprise applications, existing AWS users |
| [**Microsoft Azure**](https://northflank.com/cloud/azure) | Robust enterprise focus, Office 365 integration | Corporate environments, hybrid cloud |
| [**Google Cloud**](https://northflank.com/cloud/gcp) | Advanced AI/ML services, competitive pricing | Data analytics, research projects |
| [**NVIDIA DGX Cloud**](https://northflank.com/cloud/gpus/NVIDIA) | Latest hardware, optimized performance | High-performance AI training |

<InfoBox className="BodyStyle">

While most hyperscalers like GCP and AWS offer reliable infrastructure, their pricing is often geared toward enterprises with high minimum spend commitments.

For smaller teams or startups, platforms like Northflank offer much more competitive, usage-based pricing without long-term contracts, while still providing access to top-tier GPUs and enterprise-grade features. And it is still very reliable.

</InfoBox>

The GPUaaS market continues to develop at a pace. While hyperscale providers (AWS, Azure, Google) dominate in overall size, specialized providers often offer:

- Better pricing for GPU-intensive workloads
- More flexible terms and configurations
- Faster access to new GPU architectures
- Specialized support for AI workflows

### Okay, what does this mean for my AI projects?

For most AI development teams, **Northflank provides the optimal balance** of GPU performance and development efficiency.

> Plus, if you prefer specific cloud infrastructure, you can deploy Northflank on your preferred provider: [AWS](https://northflank.com/cloud/aws), [Azure](https://northflank.com/cloud/azure), or [GCP](https://northflank.com/cloud/gcp), while accessing [NVIDIA GPUs](https://northflank.com/cloud/gpus/NVIDIA) and other [premium GPU options](https://northflank.com/cloud/gpus) across all platforms.
> 

This gives you the best of both worlds: your preferred cloud infrastructure with Northflank's integrated development platform.

Also, keep these factors in mind when choosing:

- **Development workflow integration**: Northflank's built-in CI/CD and monitoring vs. managing separate tools
- **Time-to-deployment**: Minutes with Northflank's templates vs. hours with manual setup
- **Total cost of ownership**: Integrated platform costs vs. multiple service subscriptions
- **Team productivity**: Focus on AI development vs. infrastructure management
- **Cloud flexibility**: Use your existing cloud relationships while gaining platform benefits

The "largest" doesn't always mean the "best fit" - **for AI development teams prioritizing speed and simplicity, Northflank's comprehensive platform often delivers better value than raw compute alone.**

## How do I select the right GPU-as-a-service provider?

Choosing the wrong provider can waste months of work and thousands of dollars. The good thing now is that you can avoid common pitfalls by focusing on what majorly impacts your projects.

Most teams make the mistake of comparing only hourly GPU rates.

However, the main cost also includes data transfer fees, storage charges, and the time your team spends troubleshooting complex setups.

A "cheap" provider that takes weeks to configure often costs more than a premium platform you can deploy in minutes.

*Start with these questions: Do you need B200 for training large models, or will A100s handle your inference workloads? Can the platform integrate with your existing development tools? Will your team be able to use it productively?*

**You can also use this quick evaluation checklist:**

- **Performance match**: GPU types and memory for your specific models
- **True total cost**: All fees included, not just headline rates
- **Integration ease**: Works with your current tools and workflows
- **Team productivity**: Documentation and support quality
- **Reliability**: Uptime guarantees and technical support response

Test with a small project first. The provider that gets you productive fastest usually delivers the best long-term value.

## How do I use GPU-as-a-service successfully?

Getting your first GPU instance running is easy. Using it cost-effectively while maintaining productivity? That requires some strategy.

### 1. Getting started smart

The biggest mistake new users make is treating GPUaaS like their local development machine. They spin up expensive H100 instances for debugging code, leave them running overnight, or transfer massive datasets repeatedly because they didn't plan their workflow.

Smart teams start differently. They begin with pre-configured environments and familiar tools like Jupyter notebooks, test with small datasets first, and keep their data close to compute resources to avoid transfer fees.

### 2. Optimizing costs

Monitor GPU utilization closely; if you're not reaching 80%+ during training, you're likely overpaying.

It’s like renting a sports car - you don't leave the engine running while parked, and you don't use it for grocery shopping.

Use [spot instances](https://northflank.com/blog/what-are-spot-gpus-guide) for training jobs that can handle interruptions, scale down during development phases, and schedule heavy workloads during off-peak hours when rates are lower.

### 3. Building sustainable workflows

The most successful teams treat GPUaaS as part of a larger workflow.

They version control everything, use containers for consistent deployments, and implement monitoring to catch stuck training jobs early.

So, start small, measure your actual usage patterns, and optimize based on real data rather than assumptions.

## What are the main use cases for GPU-as-a-service?

Now that you know how to use GPUaaS successfully, let's look at where it makes the biggest impact. GPUaaS works best when you need substantial computational power but can't justify buying expensive hardware.

**1. AI model training:**
This is the obvious winner. A startup can access $100,000 worth of GPU hardware for a few thousand dollars during their 2-week training cycle. From building computer vision systems to training language models or developing recommendation engines, GPUaaS lets you experiment without massive upfront costs.

**2. Production deployment:**
Your trained models can auto-scale during traffic spikes, serve users globally with low latency, and you only pay for actual inference requests. No need to provision for peak loads year-round.

**3. Data processing and research:**
Financial firms use GPUs for risk modeling, scientists run weather simulations, and academic teams access hardware that would otherwise be impossible to afford.

**4. Creative applications:**
3D rendering, video processing, and AI-generated content also benefit from on-demand GPU access.

The pattern is clear: GPUaaS works best for workloads that are computationally intensive but intermittent. If you need GPUs 24/7 for months, buying might make sense. For everything else, the flexibility and cost savings of GPUaaS usually win.

## What are my next steps for selecting a GPU-as-a-service solution?

You've seen how GPUaaS works, compared the providers, and learned the best practices. Now it's time to make your choice.

The decision comes down to this:

*Do you want just GPU compute, or do you want a complete development platform? Most teams quickly realize that managing separate tools for deployment, monitoring, and scaling costs more time and money than an integrated solution.*

**Your next steps:**

Start with a small test project to validate performance and costs. Define your budget and technical requirements. Then choose a platform that can grow with your team.

**Why teams choose Northflank:**

*Unlike basic GPU providers, Northflank gives you everything in **one platform**: GPU compute, CI/CD pipelines, monitoring, and deployment tools. This means faster development cycles, lower operational overhead, and more time building your AI applications.*

<InfoBox className="BodyStyle">

Get started today. [**Start your free trial**](https://app.northflank.com/signup) or [**book a demo**](https://cal.com/team/northflank/northflank-intro) to see how Northflank can automate and simplify your AI development in a single platform and save costs.

</InfoBox>

### More resources to learn more about choosing the right GPU setup

These guides provide deeper technical details and specific GPU comparisons to help you make the best choice for your AI projects:

- [Best GPU for machine learning](https://northflank.com/blog/best-gpu-for-machine-learning) - Detailed guide to choosing the right GPU for different AI workloads
- [12 best GPUs for AI and machine learning in 2026](https://northflank.com/blog/best-gpu-for-ai) - Compare H100, H200, A100, B200, and other top GPUs
- [B100 vs H100: Best GPU for LLMs and training](https://northflank.com/blog/b100-vs-h100) - In-depth comparison of NVIDIA's latest GPUs
- [12 Best GPU cloud providers for AI/ML in 2026](https://northflank.com/blog/12-best-gpu-cloud-providers) - Comprehensive comparison of GPU cloud platforms
- [What is a cloud GPU? A guide for AI companies](https://northflank.com/blog/what-is-a-cloud-gpu) - Complete introduction to cloud GPU concepts
- [Top GPU hosting platforms for AI](https://northflank.com/blog/top-gpu-hosting-platforms-for-ai) - Platform comparison for inference, training, and scaling
- [How to run AI workloads on cloud GPUs (without buying hardware)](https://northflank.com/blog/running-ai-on-cloud-gpus) - Match specific models like Qwen and DeepSeek to the right hardware]]>
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  <title>How to run AI workloads on cloud GPUs (without buying hardware)</title>
  <link>https://northflank.com/blog/running-ai-on-cloud-gpus</link>
  <pubDate>2025-09-10T11:18:00.000Z</pubDate>
  <description>
    <![CDATA[Run AI models on cloud GPUs without hardware costs. Deploy, train &amp; serve with A100, H100, H200 on Northflank. Pay hourly, scale instantly.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/running_ai_on_cloud_gpus_87f867864b.png" alt="How to run AI workloads on cloud GPUs (without buying hardware)" />If you've worked with models like Qwen, DeepSeek, or LLaMA, you know different workloads push your GPU in different ways. Some need high memory to even start, others just need something that won't slow down during inference.

The challenge is this:

*Getting access to the right GPU for your specific workload without spending thousands upfront on hardware, cooling, and maintenance.*

That's where cloud GPU platforms like [Northflank](https://northflank.com/) come in. You can run enterprise-grade GPUs, use your own cloud setup if you have one, and get all the tools to train, serve, and deploy your models in one place.

In this guide, I'll show you how to match your AI workload to the right cloud GPU setup and get started without owning the hardware yourself.

## TL;DR: Match your AI workload to cloud GPUs + start in minutes

The key insight most developers have learned: VRAM matters more than clock speed for AI workloads. The more memory you have available, the better your models can run without hitting limits.

**Quick workload matching:**
- **Inference:** A100 or H100 for 8B-32B parameter models with sub-second latency
- **Fine-tuning/PEFT:** H100×8 or H200×8 for faster gradient sync
- **Memory-intensive jobs:** B200×8 for large models requiring massive memory

**Why cloud GPUs make sense:**
1. Access A100, H100, H200, and B200 on demand
2. Pay hourly (starting at $2.74/hour for H100)
3. Full platform for training, serving, and deployment
4. Bring your own cloud (BYOC) if you have existing infrastructure

With Northflank, you get enterprise GPU performance without upfront costs, cooling setup, or maintenance. Built for teams and solo developers who need speed, flexibility, and cost control.

<InfoBox className="BodyStyle">

> **Need specific GPU configurations?** If you have particular GPU requirements or capacity needs for your AI workloads, [request GPU capacity here](https://northflank.com/request/gpu).

</InfoBox>

## Match your AI workload to the right cloud GPU

Not every workload needs the same type of GPU. Here's how to choose the right cloud GPU configuration for what you're building:

### Running inference (production APIs, real-time responses)

For serving models in production or building APIs that need real-time responses:

- **A100×1** (40GB): Serves 8B-parameter LLMs at approx 1,000 tokens/sec in FP16
- **H100×1** (80GB): Boosts performance to approx 1,500 tokens/sec with optimized runtimes
- **Scale up:** Add more cards for larger context windows (32K tokens) or batch inference

### Fine-tuning and PEFT (LoRA, adapters, customization)

When customizing open-source models or experimenting with parameter-efficient tuning:

- **A100×8** (40GB): 320GB aggregate VRAM for medium models
- **H100×8**: 640GB with NVLink for larger base models
- **H200×8**: Enhanced tensor cores and bandwidth for reduced sync overhead

### Full model training (when you need to train from scratch)

Training large models requires significant compute time. For an 8B-parameter transformer:

| Configuration | Estimated Time | Best For |
|---|---|---|
| **H100×8** | approx 2.85 years continuous | Research projects |
| **H200×8** | approx 2.3 years continuous | 20% faster with improved tensor cores |
| **B200×8** | approx 2.85 years continuous | Memory-intensive large batch training |

**Reality check:** Most teams fine-tune existing checkpoints rather than training from scratch due to time and cost requirements.

## Popular model examples with GPU recommendations

If you're working with specific open-source models, here's how to match them to cloud GPU setups:

| Model | Task | Recommended Setup | Why This Works |
|---|---|---|---|
| **Qwen 1.5 7B** | Inference | A100×1-2, H100×1 | Fits in 80GB VRAM, sub-second responses |
| **DeepSeek Coder 6.7B** | Fine-tuning | A100×4-8, H100×4-8 | Perfect for LoRA and adapter workflows |
| **LLaMA 3 8B** | All stages | A100×2 (inference), 4-8 (tuning) | Flexible across different tasks |
| **Mixtral 8×7B** | Fine-tuning | H100×4-8, H200×8 | Handles MoE gating and memory spikes |
| **Stable Diffusion XL** | Inference/Fine-tuning | A100×2, H100×2 | Large image batches, fast sampling |
| **Whisper** | Real-time inference | A100×1 | Low-latency audio processing |

## Getting started with Northflank for AI workloads

Northflank goes beyond just providing GPU access - it's a complete platform designed for AI development workflows:

**Immediate access:**
- Deploy GPU workloads in under 30 minutes
- Switch between A100, H100, H200, and B200 as needs change
- Access through web interface, CLI, or API

**Cost optimization:**
- Hourly pricing with spot GPU options
- Automatic scaling up and down
- Resource isolation and usage tracking
- Hibernation for long-running jobs

**Full development environment:**
- Integrated databases (Postgres, Redis)
- CI/CD pipelines with Git integration
- Jupyter notebooks and development tools
- Templates for popular frameworks (PyTorch, TensorFlow)

**Flexibility options:**
- Use Northflank's managed cloud
- Bring your own cloud (AWS, GCP, Azure)
- Connect existing GPU infrastructure
- Automatic fallback when spot capacity runs out

## Platform comparison: Why Northflank for AI workloads

While many platforms offer GPU access, Northflank provides a complete development environment:

| Need | Northflank Solution | Alternative Platforms |
|---|---|---|
| **Quick GPU access** | A100, H100, H200, B200 on demand | Most provide basic GPU access |
| **Development tools** | Integrated Jupyter, databases, APIs | Usually requires separate services |
| **Cost control** | Spot pricing, auto-scaling, hibernation | Limited cost optimization |
| **Your own infrastructure** | Full BYOC across all major clouds | Enterprise-only or not available |
| **Production deployment** | Built-in CI/CD, monitoring, scaling | Requires additional tooling |

## Common questions about running AI on cloud GPUs

1. **How much does it cost to run AI workloads on cloud GPUs?**

    Starting at $2.74/hour for H100 access, with spot pricing available for additional savings. You only pay for actual usage.

2. **Can I bring my own cloud infrastructure?**
    
    Yes, Northflank supports [BYOC](https://northflank.com/features/bring-your-own-cloud) across AWS, GCP, and Azure, letting you use existing credits or infrastructure while getting the platform benefits.

3. **What if I need to scale beyond single GPUs?**

    Northflank handles multi-GPU setups automatically, with NVLink support for high-bandwidth communication between GPUs.

4. **How quickly can I get started?**

    Most workloads can be deployed within 30 minutes, including environment setup and initial model deployment.

5. **Do I need to manage infrastructure?**
        
    No, Northflank handles provisioning, scaling, monitoring, and maintenance automatically.

## Start running your AI workloads today

Instead of waiting weeks for hardware procurement or dealing with setup complexity, you can start developing with enterprise-grade GPUs immediately.

**Get started with Northflank:**
- Choose your GPU type based on your workload
- Deploy using templates or bring your existing code
- Scale automatically as your needs grow
- Pay only for what you use

Whether you're fine-tuning your first model or deploying production AI services, Northflank gives you the infrastructure you need without the operational overhead.

[Start building with GPUs on Northflank →](https://app.northflank.com/signup)]]>
  </content:encoded>
</item><item>
  <title>12 best GPUs for AI and machine learning in 2026</title>
  <link>https://northflank.com/blog/best-gpu-for-ai</link>
  <pubDate>2025-09-09T15:42:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the 12 best GPUs for AI in 2026: B200, H200, H100, RTX 4090 &amp; more. Specs, performance &amp; costs. Deploy with Northflank's cloud platform.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/top_gpus_for_ai_1dad247d47.png" alt="12 best GPUs for AI and machine learning in 2026" />Building AI applications in 2026 demands substantial computational power. Your GPU choice will determine your development experience, from training speed and model size limitations to deployment costs.

I've researched and analyzed the top GPUs currently available to help you choose the right hardware or cloud solution for your specific needs.

More importantly, I'll show you how to get started immediately with any of these GPUs through Northflank's platform, so you can begin developing today instead of waiting weeks for hardware delivery.

## What makes a GPU good for AI workloads?

To start with, let’s see what you need to understand before looking at specific models. You need to understand what separates AI-capable GPUs from regular graphics cards.

- **Tensor cores:** These specialized processors handle the matrix operations that power neural networks, delivering better performance than traditional graphics cores for machine learning tasks.
- **Memory capacity:** Determines what models you can run. Modern language models often need 16GB+ of VRAM, with some models requiring 80GB or more. Run out of memory, and your training grinds to a halt with expensive memory swapping or errors, which is worse.
- **Memory bandwidth:** Affects how quickly data moves between storage and processing cores. Higher bandwidth means faster training iterations and snappier inference, particularly important when serving large models to users.

<InfoBox className="BodyStyle">

**Need help with GPU capacity?** If you're having trouble accessing specific GPU types or have capacity planning requirements for your workloads, [request GPU capacity here](https://northflank.com/request/gpu).

</InfoBox>

## Top 12 GPUs for AI ranked by performance and value

I've organized these GPUs from enterprise powerhouses to budget-friendly options, with each getting detailed AI benchmarks and clear guidance on accessing them instantly through cloud platforms like Northflank.

**Enterprise and data center GPUs**

These GPUs prioritize raw performance over cost considerations. You'll find them in research labs, large tech companies, and cloud providers running the most demanding AI workloads.

### 1. NVIDIA B200 Tensor Core GPU

The B200 represents NVIDIA's Blackwell architecture, delivering exceptional AI performance for demanding enterprise workloads. Built for enterprise AI applications, NVIDIA DGX B200 delivers 3X the training performance and 15X the inference performance of previous-generation systems. However, NVIDIA has since introduced the more powerful B300 "Blackwell Ultra" architecture.

**Specifications:**

- Architecture: Blackwell
- Performance: 3X faster training, 15X faster inference vs H200
- Tensor Cores: Fifth-generation with FP4 precision support
- System Configuration: Typically deployed in 8-GPU configurations
- Advanced Features: Second-generation Transformer Engine

The B200 features fifth-generation Tensor Cores and advanced Blackwell architecture optimizations. The second-generation Transformer Engine uses custom NVIDIA Blackwell Tensor Core technology to accelerate inference and training for large language models and Mixture-of-Experts models

For organizations requiring absolute peak AI performance and working with the largest possible models, the B200 sets the standard.

> **Get started with B200 on Northflank:** Deploy [B200 instances](https://northflank.com/cloud/gpus/B200) through Northflank's managed platform for access to the most powerful AI hardware available. Perfect for frontier model development and the most demanding AI research.

### 2. NVIDIA H200 Tensor Core GPU

The H200 improves on the Hopper architecture with significantly expanded and faster HBM3e memory. While it uses the same core compute engine as the H100, its 141GB of HBM3e memory delivers a bandwidth of 4.8TB/s, nearly doubling the memory capacity and removing bottlenecks for large, memory-intensive models.

**Specifications:**

- Architecture: Hopper
- Memory: 141GB HBM3e
- Memory Bandwidth: 4.8 TB/s
- Tensor Performance: Hopper-class (higher effective performance on memory-bound tasks)
- Power: Up to 700W (SXM), 600W (NVL)

This makes it ideal when you're running inference on massive models that exceed 80GB or require large context windows.

For enterprises already on the Hopper platform, the H200 offers a performance-per-watt advantage on memory-bound workloads.

> **Get started with H200 on Northflank:** Access [H200 instances](https://northflank.com/cloud/gpus/H200) through Northflank's platform with the same developer-friendly tools. Ideal for pushing the boundaries of large language models and multi-modal AI applications.

### 3. NVIDIA H100 Tensor Core GPU

The H100 remains an incredibly powerful and widely available GPU for large-scale AI training and inference. While no longer the absolute fastest on the market, it's the proven, production-ready standard for most demanding AI workloads. Built on NVIDIA's Hopper architecture, it provides up to 30X faster inference for large language models compared to previous-generation hardware.

**Specifications:**

- Architecture: Hopper
- Memory: 80GB HBM3
- Memory Bandwidth: 3.35 TB/s
- Tensor Performance: Up to 3,958 TFLOPS (FP8)
- Power: 350-400W (NVL) / 700W (SXM)

The H100 features fourth-generation Tensor Cores and a dedicated Transformer Engine with FP8 precision that provides up to 4X faster training over the prior generation for GPT-3 (175B) models.

For organizations where you need to balance cost, maturity, and broad availability with high performance, the H100 remains an excellent choice for enterprise-scale AI workloads.

> **Get started with H100 on Northflank:** Deploy [H100 instances](https://northflank.com/cloud/gpus/H100) in seconds through Northflank's managed platform. You get enterprise-grade H100 access with automatic scaling, monitoring, and [deployment pipelines](https://northflank.com/product/deployments) - no infrastructure management required. Perfect for teams training large language models or running production inference at scale.

### 4. NVIDIA A100 Tensor Core GPU

The A100 remains a reliable and proven choice for enterprise AI and cloud-based machine learning, featuring Multi-Instance GPU (MIG) support that allows partitioning into multiple smaller GPUs. While no longer the highest-performance option with newer GPUs available, it offers exceptional value as a mature, versatile workhorse.

**Specifications:**

- Architecture: Ampere
- Memory: 80GB HBM2e
- Memory Bandwidth: 1,935 GB/s (PCIe) and 2,039 GB/s (SXM)
- Tensor Performance: Up to 624 TFLOPS (FP16)
- Power: 300W (PCIe) / 400W (SXM)

The A100 supports MIG, enabling partitioning into up to seven logical GPU instances, making it highly versatile for private clouds where consistent performance and hardware fault isolation are required.

While it delivers roughly half the performance of the H100, its mature software ecosystem and proven deployment patterns make it reliable for production environments where you need cost-effectiveness over peak performance.

> **Get started with A100 on Northflank:** Launch [A100 instances](https://northflank.com/cloud/gpus/A100) with Northflank's proven infrastructure. The platform's MIG support lets you efficiently partition A100s for multiple workloads, maximizing cost efficiency for teams running diverse AI applications.

### 5. NVIDIA V100 Tensor Core GPU

The V100 remains a solid choice for established AI workloads and organizations with existing Volta-optimized workflows. While older than newer options, it provides reliable performance for many AI applications at competitive pricing.

**Specifications:**

- Architecture: Volta
- Memory: 16GB or 32GB HBM2
- Memory Bandwidth: 900 GB/s or 1134 GB/s
- Tensor Performance: Up to 130 TFLOPS (PCIe)
- Power: 250W (PCIe) / 300W (NVLink)

The V100 introduced Tensor Cores to the data center, establishing the foundation for modern AI acceleration. Its mature drivers and broad software compatibility make it suitable for production environments where stability and cost-effectiveness matter more than peak performance.

> **Get started with V100 on Northflank:** Access [V100 instances](https://northflank.com/cloud/gpus/V100) for cost-effective AI development and production workloads through Northflank's platform.

### 6. AMD MI300X

The MI300X represents AMD's flagship data center AI accelerator, offering an alternative to NVIDIA's ecosystem with substantial memory capacity and competitive performance for specific workloads.

**Specifications:**

- Architecture: CDNA 3
- Memory: 192GB HBM3
- Memory Bandwidth: 5.3 TB/s
- Compute Performance: Up to 1,307 TFLOPS (FP16)
- Power: 750W

The MI300X provides the largest memory capacity in a single GPU, making it valuable for memory-intensive AI workloads. While AMD's AI software ecosystem is less mature than NVIDIA's, it offers competitive performance for organizations committed to open-source solutions.

> **Get started with MI300X on Northflank:** Experiment with AMD's enterprise AI platform through Northflank for workloads requiring massive memory capacity.  [Deploy with AMD Instinct™ MI300X GPUs on Northflank](https://northflank.com/cloud/gpus/MI300X)

**High-end consumer and professional GPUs**

These GPUs bring strong AI performance to individual developers and smaller teams at more accessible price points than enterprise data center hardware.

### 7. NVIDIA L40S

The L40S bridges AI acceleration with traditional graphics capabilities, making it valuable for visual AI applications and content creation workflows that incorporate machine learning.

**Specifications:**

- Architecture: Ada Lovelace
- Memory: 48GB GDDR6
- Memory Bandwidth: 864 GB/s
- Tensor Performance: Up to 733 TFLOPS (FP16)
- Power: 350W

Unlike pure AI accelerators, the L40S maintains full graphics rendering capabilities while delivering strong AI performance. This dual-purpose design works well for computer vision applications, AI-powered content creation, and organizations needing both graphics and AI capabilities.

> **Get started with L40S on Northflank:** Perfect for computer vision and visual AI projects. Deploy [L40S instances](https://northflank.com/cloud/gpus/L40S) through Northflank when you need both traditional graphics rendering and AI acceleration in the same workflow.

### 8. NVIDIA GeForce RTX 4090

The RTX 4090, primarily designed for gaming, has proven its capability for AI tasks, especially for small to medium-scale projects. With its Ada Lovelace architecture and 24 GB of VRAM, it's a cost-effective option for developers experimenting with deep learning models.

**Specifications:**

- Architecture: Ada Lovelace
- Memory: 24GB GDDR6X
- Memory Bandwidth: 1.01 TB/s
- Tensor Performance: Up to 1,320 TFLOPS (FP8)
- Power: 450W

The RTX 4090 has become the standard choice for many AI researchers and developers. Its 24GB memory handles most current AI workloads effectively, while mature software support ensures compatibility with virtually all AI frameworks.

### 9. NVIDIA L4 Tensor Core GPU

The L4 provides efficient AI inference capabilities in a compact, energy-efficient package. Designed for deployment at scale, it offers strong performance per watt for production inference workloads.

**Specifications:**

- Architecture: Ada Lovelace
- Memory: 24GB GDDR6
- Memory Bandwidth: 300 GB/s
- Tensor Performance: Up to 485 TFLOPS (FP8)
- Power: 72W

The L4's low power consumption and compact form factor make it ideal for edge deployments and cost-sensitive inference applications. Its efficiency focus makes it suitable for organizations deploying AI at scale where power and cooling costs matter.

> **Get started with L4 on Northflank:** Deploy efficient [L4 instances](https://northflank.com/cloud/gpus/L4) for cost-effective AI inference through Northflank's platform.

**Mid-range and budget options**

These GPUs make AI development accessible to individual developers, students, and smaller organizations. While they won't handle the largest models, they provide solid performance for learning and smaller-scale projects.

### 10. NVIDIA GeForce RTX 4070 Super

The NVIDIA GeForce RTX 4070 SUPER provides impressive performance-to-price ratios, delivering significant AI training capabilities at more accessible price points.

**Specifications:**

- Architecture: Ada Lovelace
- Memory: 12GB GDDR6X
- Memory Bandwidth: 504 GB/s
- Tensor Performance: Up to 836 TFLOPS (FP8)
- Power: 220W

Despite lower specifications, the RTX 4070 Super provides capable AI performance for many applications. Its 12GB memory capacity handles smaller to medium models effectively, while excellent power efficiency keeps operating costs low.

### 11. NVIDIA GeForce RTX 4060 Ti (16GB)

The RTX 4060 Ti 16GB works well with all the mainstream AI tools that you can use today, offering power efficiency and small form factor compatibility.

**Specifications:**

- Architecture: Ada Lovelace
- Memory: 16GB GDDR6
- Memory Bandwidth: 288 GB/s
- Tensor Performance: Up to 568 TFLOPS (FP8)
- Power: 165W

While limited in raw performance, the 16GB memory configuration enables experimentation with larger models that would be impossible on 8GB cards. This makes it suitable for learning AI development and small-scale experimentation.

### 12. AMD Radeon RX 7900 XTX

AMD's flagship consumer GPU now has official ROCm and PyTorch support, with the RX 7900 XTX containing 192 dedicated AI Accelerators designed to speed up matrix multiplication operations fundamental to neural network calculations.

**Specifications:**

- Architecture: RDNA 3
- Memory: 24GB GDDR6
- Memory Bandwidth: 960 GB/s
- AI Accelerators: 192 dedicated units
- Power: 355W

Recent benchmarks from AMD show the RX 7900 XTX demonstrates a strong competitive edge, particularly with smaller, more efficient AI models. However, AMD's AI ecosystem remains less mature than NVIDIA's CUDA platform, with software compatibility challenges and performance often lagging behind equivalent NVIDIA options.

## How to choose the right GPU for your AI workload

Your specific AI application determines which GPU makes the most sense. Use this table to match your needs with the right hardware:

| **Workload Type** | **Recommended GPUs** | **Get Started on Northflank** |
| --- | --- | --- |
| **Training large language models (70B+ parameters)** | B200, H200, H100 | Deploy B200 or H100 instances for maximum performance |
| **Training medium models (7B-70B parameters)** | H200, H100, A100, RTX 4090 | Launch H100 or A100 instances for balanced performance |
| **Training small models (7B parameters)** | A100, L4, RTX 4070 Super | Use L4 instances for cost-effective development |
| **High-throughput inference serving** | B200, H200, H100 | Deploy production inference APIs on [enterprise GPU infrastructure](https://northflank.com/features/infrastructure-layer) |
| **Development and experimentation** | A100, V100, RTX 4090 | Start experimenting with A100 or V100 instances |
| **Computer vision and image processing** | L40S, H200, RTX 5090 | Access L40S instances for visual AI projects |
| **Budget learning and experimentation** | L4, RTX 4070 Super, RTX 4060 Ti | Begin learning on L4 instances without upfront investment |
| **Memory-intensive workloads** | MI300X (192GB), H200 (141GB) | Access MI300X when memory capacity is your primary constraint |

## Getting started immediately with Northflank

Instead of spending weeks researching hardware, waiting for delivery, and setting up infrastructure, you can start developing AI applications today with Northflank's [cloud GPU platform](https://northflank.com/product/gpu-paas).

**5-minute setup process:**

1. [Sign up for Northflank](https://app.northflank.com/signup) and connect your GitHub repository (Follow this [guide](https://northflank.com/docs/v1/application/getting-started/introduction-to-northflank))
2. Choose your GPU type based on your workload requirements above (Follow this [guide](https://northflank.com/docs/v1/application/gpu-workloads/gpus-on-northflank))
3. Deploy your AI application using Northflank's pre-configured templates (Follow this [guide](https://northflank.com/docs/v1/application/infrastructure-as-code/infrastructure-as-code) or check out these [stack templates](https://northflank.com/stacks))
4. Scale automatically as your needs grow (Follow this [guide](https://northflank.com/docs/v1/application/scale/scale-on-northflank))

### Why choose Northflank over buying hardware?

Let’s see some of the main reasons:

1. **Instant access:** Start using any GPU type immediately instead of waiting weeks for hardware delivery and setup.
2. **No infrastructure management:** Northflank handles power, cooling, networking, and maintenance. You focus on AI development.
3. **Cost efficiency:** Pay only for actual usage with [spot instances](https://northflank.com/blog/what-are-spot-gpus-guide#how-northflank-cuts-spot-gpu-costs-with-automated-orchestration) and automatic hibernation. No upfront hardware costs or depreciation.
4. **Built-in development tools:** Get [CI/CD pipelines](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank), environment management, [monitoring](https://northflank.com/docs/v1/application/observe/observability-on-northflank), and deployment automation included.
5. **Multi-cloud flexibility:** Run workloads across [AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), or Northflank's [managed cloud](https://northflank.com/features/managed-cloud) based on cost and performance needs.
6. **Production-ready:** Built-in [secrets management](https://northflank.com/docs/v1/application/secure/manage-secret-groups), [multi-tenancy](https://northflank.com/blog/what-is-multitenancy#how-northflank-helps-you-manage-multitenant-workloads), [observability](https://northflank.com/docs/v1/application/observe/observability-on-northflank), and [backup/restore](https://northflank.com/docs/v1/application/databases-and-persistence/backup-restore-and-import-data) capabilities.
7. **You also have templates for common AI workloads (See the [templates](https://northflank.com/stacks)):**
    - LLM training and fine-tuning pipelines
    - Image generation and computer vision applications
    - Model inference APIs with automatic scaling
    - Jupyter notebook environments for experimentation
    - Distributed training across multiple GPUs
    

## Start your AI project today

You don't need to choose between different GPU options and wait for hardware delivery. Get started with AI development immediately:

1. Visit **[Northflank.com](http://Northflank.comhttps://northflank.com/)** and create your account or book a demo
2. Choose a GPU template matching your workload from the options above
3. Connect your code repository and deploy in minutes
4. Scale your application as you grow from prototype to production

The GPU ecosystem continues to grow really fast, but you don't need to wait for the perfect hardware setup. Start building your AI applications today with Northflank's platform, then scale and optimize as your needs become clearer.

From training your first model to deploying production AI applications, [Northflank](https://northflank.com/) gives you immediate access to the computing power you need without the complexity of hardware management.
]]>
  </content:encoded>
</item><item>
  <title>12 best GPUs for AI and machine learning in 2026</title>
  <link>https://northflank.com/blog/top-12-gpus-for-ai</link>
  <pubDate>2025-09-09T15:20:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the 12 best GPUs for AI in 2026: B200, H200, H100, RTX 4090 &amp; more. Specs, performance &amp; costs. Deploy with Northflank's cloud platform.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/best_gpu_for_ai_f7c9789eb2.png" alt="12 best GPUs for AI and machine learning in 2026" /><InfoBox className="BodyStyle">

**TL;DR:** I recommend the [NVIDIA B200](https://northflank.com/cloud/gpus/B200) for organizations needing maximum AI performance, RTX 4090 for most AI developers working on inference or smaller models (24GB VRAM, proven performance), and Northflank's cloud GPU platform for teams wanting instant deployment without hardware complexity.

Skip the hardware purchase - get started with AI development on [Northflank](https://northflank.com/) in under 5 minutes.

</InfoBox>

Building AI applications in 2026 demands substantial computational power. Your GPU choice will determine your development experience, from training speed and model size limitations to deployment costs.

I've researched and analyzed the top GPUs currently available to help you choose the right hardware or cloud solution for your specific needs.

More importantly, I'll show you how to get started immediately with any of these GPUs through Northflank's platform, so you can begin developing today instead of waiting weeks for hardware delivery.

## What makes a GPU good for AI workloads?

To start with, let’s see what you need to understand before looking at specific models. You need to understand what separates AI-capable GPUs from regular graphics cards.

- **Tensor cores:** These specialized processors handle the matrix operations that power neural networks, delivering better performance than traditional graphics cores for machine learning tasks.
- **Memory capacity:** Determines what models you can run. Modern language models often need 16GB+ of VRAM, with some models requiring 80GB or more. Run out of memory, and your training grinds to a halt with expensive memory swapping or errors, which is worse.
- **Memory bandwidth:** Affects how quickly data moves between storage and processing cores. Higher bandwidth means faster training iterations and snappier inference, particularly important when serving large models to users.

## Top 12 GPUs for AI ranked by performance and value

I've organized these GPUs from enterprise powerhouses to budget-friendly options, with each getting detailed AI benchmarks and clear guidance on accessing them instantly through cloud platforms like Northflank.

**Enterprise and data center GPUs**

These GPUs prioritize raw performance over cost considerations. You'll find them in research labs, large tech companies, and cloud providers running the most demanding AI workloads.

### 1. NVIDIA B200 Tensor Core GPU

The B200 represents NVIDIA's Blackwell architecture, delivering exceptional AI performance for demanding enterprise workloads. Built for enterprise AI applications, NVIDIA DGX B200 delivers 3X the training performance and 15X the inference performance of previous-generation systems. However, NVIDIA has since introduced the more powerful B300 "Blackwell Ultra" architecture.

**Specifications:**

- Architecture: Blackwell
- Performance: 3X faster training, 15X faster inference vs H200
- Tensor Cores: Fifth-generation with FP4 precision support
- System Configuration: Typically deployed in 8-GPU configurations
- Advanced Features: Second-generation Transformer Engine

The B200 features fifth-generation Tensor Cores and advanced Blackwell architecture optimizations. The second-generation Transformer Engine uses custom NVIDIA Blackwell Tensor Core technology to accelerate inference and training for large language models and Mixture-of-Experts models

For organizations requiring absolute peak AI performance and working with the largest possible models, the B200 sets the standard.

<InfoBox className="BodyStyle">

**Get started with B200 on Northflank:** Deploy [B200 instances](https://northflank.com/cloud/gpus/B200) through Northflank's managed platform for access to the most powerful AI hardware available. Perfect for frontier model development and the most demanding AI research.

</InfoBox>

### 2. NVIDIA H200 Tensor Core GPU

The H200 improves on the Hopper architecture with significantly expanded and faster HBM3e memory. While it uses the same core compute engine as the H100, its 141GB of HBM3e memory delivers a bandwidth of 4.8TB/s, nearly doubling the memory capacity and removing bottlenecks for large, memory-intensive models.

**Specifications:**

- Architecture: Hopper
- Memory: 141GB HBM3e
- Memory Bandwidth: 4.8 TB/s
- Tensor Performance: Hopper-class (higher effective performance on memory-bound tasks)
- Power: Up to 700W (SXM), 600W (NVL)

This makes it ideal when you're running inference on massive models that exceed 80GB or require large context windows.

For enterprises already on the Hopper platform, the H200 offers a performance-per-watt advantage on memory-bound workloads.

<InfoBox className="BodyStyle">

**Get started with H200 on Northflank:** Access [H200 instances](https://northflank.com/cloud/gpus/H200) through Northflank's platform with the same developer-friendly tools. Ideal for pushing the boundaries of large language models and multi-modal AI applications.

</InfoBox>

### 3. NVIDIA H100 Tensor Core GPU

The H100 remains an incredibly powerful and widely available GPU for large-scale AI training and inference. While no longer the absolute fastest on the market, it's the proven, production-ready standard for most demanding AI workloads. Built on NVIDIA's Hopper architecture, it provides up to 30X faster inference for large language models compared to previous-generation hardware.

**Specifications:**

- Architecture: Hopper
- Memory: 80GB HBM3
- Memory Bandwidth: 3.35 TB/s
- Tensor Performance: Up to 3,958 TFLOPS (FP8)
- Power: 350-400W (NVL) / 700W (SXM)

The H100 features fourth-generation Tensor Cores and a dedicated Transformer Engine with FP8 precision that provides up to 4X faster training over the prior generation for GPT-3 (175B) models.

For organizations where you need to balance cost, maturity, and broad availability with high performance, the H100 remains an excellent choice for enterprise-scale AI workloads.

<InfoBox className="BodyStyle">

**Get started with H100 on Northflank:** Deploy [H100 instances](https://northflank.com/cloud/gpus/H100) in seconds through Northflank's managed platform. You get enterprise-grade H100 access with automatic scaling, monitoring, and deployment pipelines - no infrastructure management required. Perfect for teams training large language models or running production inference at scale.

</InfoBox>

### 4. NVIDIA A100 Tensor Core GPU

The A100 remains a reliable and proven choice for enterprise AI and cloud-based machine learning, featuring Multi-Instance GPU (MIG) support that allows partitioning into multiple smaller GPUs. While no longer the highest-performance option with newer GPUs available, it offers exceptional value as a mature, versatile workhorse.

**Specifications:**

- Architecture: Ampere
- Memory: 80GB HBM2e
- Memory Bandwidth: 1,935 GB/s (PCIe) and 2,039 GB/s (SXM)
- Tensor Performance: Up to 624 TFLOPS (FP16)
- Power: 300W (PCIe) / 400W (SXM)

The A100 supports MIG, enabling partitioning into up to seven logical GPU instances, making it highly versatile for private clouds where consistent performance and hardware fault isolation are required.

While it delivers roughly half the performance of the H100, its mature software ecosystem and proven deployment patterns make it reliable for production environments where you need cost-effectiveness over peak performance.

<InfoBox className="BodyStyle">

**Get started with A100 on Northflank:** Launch [A100 instances](https://northflank.com/cloud/gpus/A100) with Northflank's proven infrastructure. The platform's MIG support lets you efficiently partition A100s for multiple workloads, maximizing cost efficiency for teams running diverse AI applications.

</InfoBox>

### 5. NVIDIA V100 Tensor Core GPU

The V100 remains a solid choice for established AI workloads and organizations with existing Volta-optimized workflows. While older than newer options, it provides reliable performance for many AI applications at competitive pricing.

**Specifications:**

- Architecture: Volta
- Memory: 16GB or 32GB HBM2
- Memory Bandwidth: 900 GB/s or 1134 GB/s
- Tensor Performance: Up to 130 TFLOPS (PCIe)
- Power: 250W (PCIe) / 300W (NVLink)

The V100 introduced Tensor Cores to the data center, establishing the foundation for modern AI acceleration. Its mature drivers and broad software compatibility make it suitable for production environments where stability and cost-effectiveness matter more than peak performance.

<InfoBox className="BodyStyle">

**Get started with V100 on Northflank:** Access [V100 instances](https://northflank.com/cloud/gpus/V100) for cost-effective AI development and production workloads through Northflank's platform.

</InfoBox>

### 6. AMD MI300X

The MI300X represents AMD's flagship data center AI accelerator, offering an alternative to NVIDIA's ecosystem with substantial memory capacity and competitive performance for specific workloads.

**Specifications:**

- Architecture: CDNA 3
- Memory: 192GB HBM3
- Memory Bandwidth: 5.3 TB/s
- Compute Performance: Up to 1,307 TFLOPS (FP16)
- Power: 750W

The MI300X provides the largest memory capacity in a single GPU, making it valuable for memory-intensive AI workloads. While AMD's AI software ecosystem is less mature than NVIDIA's, it offers competitive performance for organizations committed to open-source solutions.

<InfoBox className="BodyStyle">

**Get started with MI300X on Northflank:** Experiment with AMD's enterprise AI platform through Northflank for workloads requiring massive memory capacity.  [Deploy with AMD Instinct™ MI300X GPUs on Northflank](https://northflank.com/cloud/gpus/MI300X)

</InfoBox>


**High-end consumer and professional GPUs**

These GPUs bring strong AI performance to individual developers and smaller teams at more accessible price points than enterprise data center hardware.

### 7. NVIDIA L40S

The L40S bridges AI acceleration with traditional graphics capabilities, making it valuable for visual AI applications and content creation workflows that incorporate machine learning.

**Specifications:**

- Architecture: Ada Lovelace
- Memory: 48GB GDDR6
- Memory Bandwidth: 864 GB/s
- Tensor Performance: Up to 733 TFLOPS (FP16)
- Power: 350W

Unlike pure AI accelerators, the L40S maintains full graphics rendering capabilities while delivering strong AI performance. This dual-purpose design works well for computer vision applications, AI-powered content creation, and organizations needing both graphics and AI capabilities.

<InfoBox className="BodyStyle">

**Get started with L40S on Northflank:** Perfect for computer vision and visual AI projects. Deploy [L40S instances](https://northflank.com/cloud/gpus/L40S) through Northflank when you need both traditional graphics rendering and AI acceleration in the same workflow.

</InfoBox>

### 8. NVIDIA GeForce RTX 4090

The RTX 4090, primarily designed for gaming, has proven its capability for AI tasks, especially for small to medium-scale projects. With its Ada Lovelace architecture and 24 GB of VRAM, it's a cost-effective option for developers experimenting with deep learning models.

**Specifications:**

- Architecture: Ada Lovelace
- Memory: 24GB GDDR6X
- Memory Bandwidth: 1.01 TB/s
- Tensor Performance: Up to 1,320 TFLOPS (FP8)
- Power: 450W

The RTX 4090 has become the standard choice for many AI researchers and developers. Its 24GB memory handles most current AI workloads effectively, while mature software support ensures compatibility with virtually all AI frameworks.

### 9. NVIDIA L4 Tensor Core GPU

The L4 provides efficient AI inference capabilities in a compact, energy-efficient package. Designed for deployment at scale, it offers strong performance per watt for production inference workloads.

**Specifications:**

- Architecture: Ada Lovelace
- Memory: 24GB GDDR6
- Memory Bandwidth: 300 GB/s
- Tensor Performance: Up to 485 TFLOPS (FP8)
- Power: 72W

The L4's low power consumption and compact form factor make it ideal for edge deployments and cost-sensitive inference applications. Its efficiency focus makes it suitable for organizations deploying AI at scale where power and cooling costs matter.

<InfoBox className="BodyStyle">

**Get started with L4 on Northflank:** Deploy efficient [L4 instances](https://northflank.com/cloud/gpus/L4) for cost-effective AI inference through Northflank's platform.

</InfoBox>

**Mid-range and budget options**

These GPUs make AI development accessible to individual developers, students, and smaller organizations. While they won't handle the largest models, they provide solid performance for learning and smaller-scale projects.

### 10. NVIDIA GeForce RTX 4070 Super

The NVIDIA GeForce RTX 4070 SUPER provides impressive performance-to-price ratios, delivering significant AI training capabilities at more accessible price points.

**Specifications:**

- Architecture: Ada Lovelace
- Memory: 12GB GDDR6X
- Memory Bandwidth: 504 GB/s
- Tensor Performance: Up to 836 TFLOPS (FP8)
- Power: 220W

Despite lower specifications, the RTX 4070 Super provides capable AI performance for many applications. Its 12GB memory capacity handles smaller to medium models effectively, while excellent power efficiency keeps operating costs low.

### 11. NVIDIA GeForce RTX 4060 Ti (16GB)

The RTX 4060 Ti 16GB works well with all the mainstream AI tools that you can use today, offering power efficiency and small form factor compatibility.

**Specifications:**

- Architecture: Ada Lovelace
- Memory: 16GB GDDR6
- Memory Bandwidth: 288 GB/s
- Tensor Performance: Up to 568 TFLOPS (FP8)
- Power: 165W

While limited in raw performance, the 16GB memory configuration enables experimentation with larger models that would be impossible on 8GB cards. This makes it suitable for learning AI development and small-scale experimentation.

### 12. AMD Radeon RX 7900 XTX

AMD's flagship consumer GPU now has official ROCm and PyTorch support, with the RX 7900 XTX containing 192 dedicated AI Accelerators designed to speed up matrix multiplication operations fundamental to neural network calculations.

**Specifications:**

- Architecture: RDNA 3
- Memory: 24GB GDDR6
- Memory Bandwidth: 960 GB/s
- AI Accelerators: 192 dedicated units
- Power: 355W

Recent benchmarks from AMD show the RX 7900 XTX demonstrates a strong competitive edge, particularly with smaller, more efficient AI models. However, AMD's AI ecosystem remains less mature than NVIDIA's CUDA platform, with software compatibility challenges and performance often lagging behind equivalent NVIDIA options.

## How to choose the right GPU for your AI workload

Your specific AI application determines which GPU makes the most sense. Use this table to match your needs with the right hardware:

| **Workload Type** | **Recommended GPUs** | **Get Started on Northflank** |
| --- | --- | --- |
| **Training large language models (70B+ parameters)** | B200, H200, H100 | Deploy B200 or H100 instances for maximum performance |
| **Training medium models (7B-70B parameters)** | H200, H100, A100, RTX 4090 | Launch H100 or A100 instances for balanced performance |
| **Training small models (<7B parameters)** | A100, L4, RTX 4070 Super | Use L4 instances for cost-effective development |
| **High-throughput inference serving** | B200, H200, H100 | Deploy production inference APIs on enterprise GPU infrastructure |
| **Development and experimentation** | A100, V100, RTX 4090 | Start experimenting with A100 or V100 instances |
| **Computer vision and image processing** | L40S, H200, RTX 5090 | Access L40S instances for visual AI projects |
| **Budget learning and experimentation** | L4, RTX 4070 Super, RTX 4060 Ti | Begin learning on L4 instances without upfront investment |
| **Memory-intensive workloads** | MI300X (192GB), H200 (141GB) | Access MI300X when memory capacity is your primary constraint |

## Getting started immediately with Northflank

Instead of spending weeks researching hardware, waiting for delivery, and setting up infrastructure, you can start developing AI applications today with Northflank's [cloud GPU platform](https://northflank.com/gpu).

**5-minute setup process:**

1. [Sign up for Northflank](https://app.northflank.com/signup) and connect your GitHub repository (Follow this [guide](https://northflank.com/docs/v1/application/getting-started/introduction-to-northflank))
2. Choose your GPU type based on your workload requirements above (Follow this [guide](https://northflank.com/docs/v1/application/gpu-workloads/gpus-on-northflank))
3. Deploy your AI application using Northflank's pre-configured templates (Follow this [guide](https://northflank.com/docs/v1/application/infrastructure-as-code/infrastructure-as-code) or check out these [stack templates](https://northflank.com/stacks))
4. Scale automatically as your needs grow (Follow this [guide](https://northflank.com/docs/v1/application/scale/scale-on-northflank))

### Why choose Northflank over buying hardware?

Let’s see some of the main reasons:

1. **Instant access:** Start using any GPU type immediately instead of waiting weeks for hardware delivery and setup.
2. **No infrastructure management:** Northflank handles power, cooling, networking, and maintenance. You focus on AI development.
3. **Cost efficiency:** Pay only for actual usage with [spot instances](https://northflank.com/blog/what-are-spot-gpus-guide#how-northflank-cuts-spot-gpu-costs-with-automated-orchestration) and automatic hibernation. No upfront hardware costs or depreciation.
4. **Built-in development tools:** Get [CI/CD pipelines](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank), environment management, [monitoring](https://northflank.com/docs/v1/application/observe/observability-on-northflank), and deployment automation included.
5. **Multi-cloud flexibility:** Run workloads across [AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), or Northflank's [managed cloud](https://northflank.com/features/managed-cloud) based on cost and performance needs.
6. **Production-ready:** Built-in [secrets management](https://northflank.com/docs/v1/application/secure/manage-secret-groups), [multi-tenancy](https://northflank.com/blog/what-is-multitenancy#how-northflank-helps-you-manage-multitenant-workloads), [observability](https://northflank.com/docs/v1/application/observe/observability-on-northflank), and [backup/restore](https://northflank.com/docs/v1/application/databases-and-persistence/backup-restore-and-import-data) capabilities.
7. **You also have templates for common AI workloads (See the [templates](https://northflank.com/stacks)):**
    - LLM training and fine-tuning pipelines
    - Image generation and computer vision applications
    - Model inference APIs with automatic scaling
    - Jupyter notebook environments for experimentation
    - Distributed training across multiple GPUs
    

## Start your AI project today

You don't need to choose between different GPU options and wait for hardware delivery. Get started with AI development immediately:

1. Visit **[Northflank.com](http://Northflank.comhttps://northflank.com/)** and create your account or book a demo
2. Choose a GPU template matching your workload from the options above
3. Connect your code repository and deploy in minutes
4. Scale your application as you grow from prototype to production

The GPU ecosystem continues to grow really fast, but you don't need to wait for the perfect hardware setup. Start building your AI applications today with Northflank's platform, then scale and optimize as your needs become clearer.

From training your first model to deploying production AI applications, [Northflank](https://northflank.com/) gives you immediate access to the computing power you need without the complexity of hardware management.]]>
  </content:encoded>
</item><item>
  <title>Top 9 AI hosting platforms for your stack in 2026</title>
  <link>https://northflank.com/blog/ai-hosting-platforms</link>
  <pubDate>2025-09-08T16:38:00.000Z</pubDate>
  <description>
    <![CDATA[Top 9 AI hosting platforms 2026: Northflank, AWS SageMaker, Google Vertex AI &amp; more. Compare GPU cloud, model deployment &amp; pricing for your stack.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/ai_hosting_platforms_4b4e02314d.png" alt="Top 9 AI hosting platforms for your stack in 2026" />AI hosting has shifted from simple cloud infrastructure to sophisticated platforms that handle the complete AI development lifecycle.

If you're fine-tuning LLMs, deploying production inference APIs, or building full-stack AI applications, the right hosting platform can determine your project's success.

I'll break down the top nine (9) AI hosting platforms in 2026, comparing them based on performance, developer experience, pricing transparency, and production readiness.

<InfoBox className="BodyStyle">

## A quick look at the 9 best AI hosting platforms

**1. [Northflank](https://northflank.com/)** - If you're building production AI applications, this complete platform gives you GPU orchestration, Git-based CI/CD, and BYOC support. Best overall choice when you need actual AI products, not demos.

> **Why I recommend Northflank:**
While other platforms only give you GPU access or model hosting, you get a complete development environment. You'll have production-grade infrastructure, transparent pricing, and the ability to deploy in your own cloud without the complexity of traditional providers or limitations of AI-only platforms.
> 

**2. AWS SageMaker** - Perfect if you're already on AWS and need comprehensive MLOps. Amazon's platform provides end-to-end machine learning workflows and enterprise-grade features.

**3. Google Cloud Vertex AI** - Ideal if you're using TensorFlow or need TPU access. Google's unified ML platform excels with AutoML capabilities and tight ecosystem integration.

**4. Hugging Face Inference Endpoints** - Perfect if you're deploying open-source transformer models. Specialized platform that gets you from model to API fastest.

**5. RunPod** - Ideal if you're on a tight budget or experimenting. GPU cloud focused on simplicity and quick deployments for demos and testing.

**6. Modal** - Great if you're a Python developer who wants serverless AI. Platform handles scaling automatically with minimal configuration needed.

**7. Replicate** - Perfect if you're building generative AI demos or want to monetize models. Optimized for public model APIs and quick sharing.

**8. Anyscale** - Ideal if you're already using Ray or need distributed computing. Built for large-scale Python applications and complex workloads.

**9. Baseten** - Great if you prefer visual interfaces over code. UI-driven deployment with built-in monitoring for data science teams.

</InfoBox>

## What is AI hosting?

AI hosting refers to cloud infrastructure specifically designed to support your artificial intelligence and machine learning workloads.

While web hosting focuses on websites, AI hosting platforms give you specialized hardware (GPUs, TPUs), optimized software stacks, and tools tailored for training, fine-tuning, and deploying your AI models.

AI hosting goes beyond providing compute resources. When you're building AI applications, you need:

- **GPU and TPU orchestration** - for parallel processing and model training
- **Model deployment pipelines** - for serving your inference APIs at scale
- **MLOps tools** - for versioning, monitoring, and managing your model lifecycles
- **Auto-scaling infrastructure** - that adjusts resources based on your demand
- **Integration with AI frameworks** - like PyTorch, TensorFlow, and Hugging Face that you're already using
- **Data management** - for handling your large datasets and model weights

The main difference from web hosting is the focus on high-performance computing, specialized hardware access, and workflows designed around your unique AI development requirements, from experimentation to production deployment.

## What makes a great AI hosting platform in 2026?

Now that you understand what AI hosting involves, here's what separates exceptional AI hosting platforms from the rest:

**1. Latest GPU access**: Support for NVIDIA H100, A100, L40S, and newer accelerators like AMD MI300X with fast provisioning and availability.

**2. Production-ready workflows**: Git-based deployments, preview environments, automated scaling, and proper CI/CD integration beyond raw compute.

**3. Full-stack support**: The ability to run databases, APIs, frontends, and background jobs alongside your AI workloads without platform switching.

**4. Transparent pricing**: Usage-based billing with no hidden fees, egress charges, or unexpected costs that can derail your project budgets.

**5. Enterprise features**: BYOC ([Bring your own cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment)) support, compliance certifications, audit trails, and security controls that meet your real-world requirements.

**6. Developer experience**: Intuitive interfaces, comprehensive documentation, and workflows that don't require a PhD in DevOps.

## Top 9 AI hosting platforms for your stack in 2026 (in detail)

With these criteria in mind, let's compare how the top platforms measure up for your specific needs and use cases.

### 1. Northflank - Best overall AI hosting platform

**Why it's my top pick:** Northflank goes beyond being another GPU provider. It's a complete platform designed for teams building production-ready AI applications. While competitors force you to choose between simplicity and control, Northflank delivers both.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

**Some of the features of Northflank**

- 18+ GPU types including [NVIDIA H100](https://northflank.com/cloud/gpus/H100), [A100](https://northflank.com/cloud/gpus/A100), [B200](https://northflank.com/cloud/gpus/B200), [L40S](https://northflank.com/cloud/gpus/L40S), [L4](https://northflank.com/cloud/gpus/L4), [AMD MI300X](https://northflank.com/cloud/gpus/MI300X), and Habana Gaudi
- Bring Your Own Cloud (BYOC) support for [AWS](https://northflank.com/docs/v1/application/bring-your-own-cloud/aws-on-northflank), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/docs/v1/application/bring-your-own-cloud/azure-on-northflank), [Oracle Cloud](https://northflank.com/cloud/oci), and bare metal
- Git-based [CI/CD](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) with automatic deployments and [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment)
- Full-stack orchestration - you can run [databases](https://northflank.com/features/databases), [APIs](https://northflank.com/docs/v1/api/introduction), frontends, and AI workloads in one platform
- [Transparent pricing](https://northflank.com/docs/v1/application/billing/pricing-on-northflank) starting at $1.42/hr for A100 40GB, $2.74/hr for H100
- [Spot GPU optimization](https://northflank.com/blog/what-are-spot-gpus-guide#how-northflank-cuts-spot-gpu-costs-with-automated-orchestration) with automatic failover for up to 90% cost savings
- Enterprise security with isolated environments, secrets management, [secure runtime](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale), and compliance support

**What you can build**

- Fine-tuned LLM APIs with custom weights and optimized inference
- Full-stack AI applications with integrated databases and frontends
- Jupyter notebooks for research and experimentation
- Multi-model AI pipelines with orchestrated workflows
- Production ML services with proper monitoring and scaling

**Some pricing info**

<InfoBox className="BodyStyle">

**🤑 Northflank pricing**

- Free tier: Generous limits for testing and small projects
- CPU instances: Starting at $2.70/month ($0.0038/hr) for small workloads, scaling to production-grade dedicated instances
- GPU support: NVIDIA A100 40GB at $1.42/hr, A100 80GB at $1.76/hr, H100 at $2.74/hr, up to B200 at $5.87/hr
- Enterprise BYOC: Flat fees for clusters, vCPU, and memory on your infrastructure, no markup on your cloud costs
- Pricing calculator available to estimate costs before you start
- Fully self-serve platform, get started immediately without sales calls
- No hidden fees, egress charges, or surprise billing complexity

</InfoBox>

**Why choose Northflank**

1. **For startups**: You get enterprise features without enterprise pricing. Scale from prototype to production without platform migration.
2. **For enterprises**: Deploy in your own cloud infrastructure while maintaining centralized control and governance.
3. **For developers**: Git-based workflows, preview environments, and zero DevOps overhead. Focus on building, not managing infrastructure.

> Northflank solved the fundamental problem with AI hosting: you shouldn't need different platforms for AI workloads and everything else. With built-in CI/CD, GPU orchestration, and full-stack support, it's the only platform designed for teams building complete AI products.
> 

<InfoBox className="BodyStyle">

**See how Weights uses Northflank to build a GPU-optimized AI platform for millions of users** in our detailed case study: [Weights uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s).

</InfoBox>


### 2. AWS SageMaker

**Best for:** Large organizations already invested in the AWS ecosystem who need comprehensive MLOps capabilities and enterprise-grade features.

![aws-sagemaker-homepage.png](https://assets.northflank.com/aws_sagemaker_homepage_ab47bb19cc.png)

**Key features**

- Comprehensive MLOps suite with SageMaker Studio, Pipelines, and Model Registry
- Managed Jupyter environments with pre-configured deep learning frameworks
- Multi-model endpoints for cost-efficient inference serving
- Built-in AutoML capabilities through SageMaker Autopilot
- Enterprise security with VPC support, encryption, and IAM integration
- Extensive GPU options including P4d instances with A100 GPUs

**Strengths**

- Mature platform with extensive documentation and community
- Deep AWS ecosystem integration (S3, Lambda, API Gateway)
- Strong enterprise features and compliance certifications
- Flexible pricing options including on-demand and reserved instances

**Limitations**

- Steep learning curve with complex pricing structure
- Vendor lock-in to AWS ecosystem
- Can be overkill for smaller teams or simple use cases
- Higher costs compared to specialized AI platforms

**Best fit:** Enterprise teams with existing AWS infrastructure who need comprehensive MLOps workflows and have dedicated ML engineering resources.

If you're evaluating AWS alternatives for AI workloads, see our guide on [7 best DigitalOcean GPU & Paperspace alternatives for AI workloads in 2026](https://northflank.com/blog/digitalocean-gpu-paperspace-alternatives).


### 3. Google Cloud Vertex AI

**Best for:** Teams working with TensorFlow, requiring TPU access, or building on Google's AI ecosystem.

![google-vertex-ai-platform.png](https://assets.northflank.com/google_vertex_ai_platform_af44413ddf.png)

**Key features**

- Native TPU support for efficient large-scale training
- AutoML capabilities for automated model development
- Vertex AI Workbench for collaborative notebook environments
- Model Garden with pre-trained models and solutions
- MLOps automation with Vertex AI Pipelines
- Tight Google integration with BigQuery, Dataflow, and other GCP services

**Strengths**

- Leading-edge AI research and tools
- High TPU performance for specific workloads
- AutoML and no-code solutions
- Competitive pricing for TPU workloads

**Limitations**

- Less mature than AWS for general enterprise needs
- Limited GPU variety compared to other platforms
- Smaller ecosystem of third-party integrations
- Can be complex for teams not familiar with Google Cloud

**Best fit:** Research teams, organizations using TensorFlow extensively, or projects that can benefit from TPU-optimized workloads.

If you're looking for Google Cloud alternatives, see our comparison in [7 best AI cloud providers for full-stack AI/ML apps](https://northflank.com/blog/7-best-ai-cloud-providers).


### 4. Hugging Face Inference Endpoints

**Best for:** Teams focused on deploying pre-trained transformer models quickly without infrastructure management.

![hugging-face-inference-endpoints-home-page.png](https://assets.northflank.com/hugging_face_inference_endpoints_home_page_8b4b18e78c.png)

**Key features**

- Massive model library with 400,000+ pre-trained models
- One-click deployment for any model from the Hugging Face Hub
- Auto-scaling inference endpoints with usage-based pricing
- Custom model support for fine-tuned and private models
- Community ecosystem with extensive model documentation and examples
- Integration tools for popular ML frameworks and platforms

**Strengths**

- Fastest path from model to production API
- Good for transformer-based models
- Community and ecosystem
- Transparent, usage-based pricing

Limitations

- Limited to inference workloads (no training capabilities)
- Less suitable for full-stack applications
- Restricted to Hugging Face ecosystem
- No infrastructure customization options

**Best fit:** Teams deploying open-source transformer models who want to minimize infrastructure complexity and time-to-deployment.

If you're considering Hugging Face alternatives, see our comprehensive guide: [7 best Hugging Face alternatives in 2026: Model serving, fine-tuning & full-stack deployment](https://northflank.com/blog/huggingface-alternatives).

### 5. RunPod

**Best for:** Developers, researchers, and small teams who need affordable GPU access for experimentation and lightweight workloads.

![runpod-homepage.png](https://assets.northflank.com/runpod_homepage_a696c3aa97.png)

**Key features**

- Low-cost GPU access with community and dedicated options
- Serverless and pod-based deployments for different use cases
- Pre-configured templates for popular AI frameworks
- Simple pricing model with per-minute billing
- Docker-based deployments for easy containerization
- Community marketplace for shared GPU resources

**Strengths**

- Very affordable pricing, especially for experimentation
- Simple setup and deployment process
- Good selection of pre-configured environments
- Active community and support

**Limitations**

- Limited production features (no CI/CD, monitoring, etc.)
- Variable performance on community instances
- No enterprise features or BYOC support
- Basic scaling and orchestration capabilities

**Best fit:** Individual developers, students, or small teams experimenting with AI models who prioritize cost over production features.

**For more RunPod alternatives**, see our detailed analysis: [RunPod alternatives for AI/ML deployment beyond just a container](https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment).

### 6. Modal

**Best for:** Python developers who want to deploy AI workloads with minimal configuration and automatic scaling.

![modal-home-page.png](https://assets.northflank.com/modal_home_page_3ef6ad50bc.png)

**Key features**

- Python-native deployment - just write Python code and deploy
- Automatic scaling from zero to thousands of containers
- GPU support with NVIDIA A100, H100, and other accelerators
- Serverless execution with pay-per-use billing
- Container orchestration with built-in dependency management
- Distributed computing support for large-scale workloads

**Strengths**

- Simple deployment process for Python workflows
- Great for batch jobs and async processing
- Cost-effective serverless pricing model
- Community and documentation

### Limitations

- Limited to Python-based workloads
- Less suitable for always-on services
- No full-stack application support
- Limited customization options

**Best fit:** Python developers building AI workflows, batch processing jobs, or serverless inference APIs who want minimal infrastructure management.

If you're evaluating Modal alternatives, check out: [6 best Modal alternatives for ML, LLMs, and AI app deployment](https://northflank.com/blog/6-best-modal-alternatives).

### 7. Replicate

**Best for:** Developers who want to quickly deploy and monetize generative AI models with minimal setup.

![replicate-homepage.png](https://assets.northflank.com/replicate_homepage_38062bccda.png)

**Key features**

- One-click model deployment from GitHub repositories
- Model monetization with built-in billing and API management
- Public model gallery with thousands of pre-trained models
- Custom model support for fine-tuned and private models
- API-first design with simple REST endpoints
- Community ecosystem with model sharing and discovery

**Strengths**

- Fastest path from model to public API
- Built-in monetization features
- Excellent for generative AI demos
- Strong community of model creators

**Limitations**

- Focused primarily on demos and public APIs
- Limited enterprise features
- No full application deployment support
- Less suitable for private, production workloads

**Best fit:** Indie developers, researchers, or teams building generative AI demos who want to quickly share and monetize their models.

For Replicate alternatives, see our guide: [6 best Replicate alternatives for ML, LLMs, and AI app deployment](https://northflank.com/blog/6-best-replicate-alternatives).

### 8. Anyscale

**Best for:** Teams building large-scale distributed AI workloads using the Ray ecosystem.

![anyscale-homepage.png](https://assets.northflank.com/anyscale_homepage_0d9cb1948c.png)

**Key features**

- Ray-native platform built for distributed Python applications
- Auto-scaling clusters with intelligent resource management
- Distributed training support for large models and datasets
- MLOps integration with experiment tracking and model management
- Multi-cloud support across AWS, GCP, and Azure
- Production serving with Ray Serve for model deployment

**Strengths**

- Great for distributed computing workloads
- Ray ecosystem integration
- Good support for large-scale training
- Flexible deployment options

**Limitations**

- Requires Ray framework knowledge
- Can be complex for simple use cases
- Less suitable for non-distributed workloads
- Limited full-stack application support

**Best fit:** ML engineers and data scientists building large-scale distributed AI systems who are already using or want to adopt the Ray ecosystem.

If you're looking for Anyscale alternatives, see: [Top Anyscale alternatives for AI/ML model deployment](https://northflank.com/blog/anyscale-alternatives-for-ai-ml-model-deployment).

### 9. Baseten

**Best for:** Data science teams who want a visual interface for deploying and monitoring ML models without deep infrastructure knowledge.

![baseten-homepage.png](https://assets.northflank.com/baseten_homepage_2c66e73096.png)

**Key features**

- Visual deployment interface with drag-and-drop model management
- Built-in monitoring with performance metrics and alerting
- Auto-scaling inference with load balancing and traffic management
- Model versioning with A/B testing capabilities
- Integration support for popular ML frameworks and tools
- Team collaboration features with shared workspaces

**Strengths**

- User-friendly interface for non-DevOps teams
- Good monitoring and observability features
- Model management capabilities
- Reasonable pricing for small to medium workloads

**Limitations**

- Limited customization options
- Less suitable for complex deployment scenarios
- No full-stack application support
- Smaller ecosystem compared to major platforms

**Best fit:** Data science teams who want to focus on model development rather than infrastructure management and prefer visual interfaces over code-based deployments.

**For Baseten alternatives**, check out: [Top Baseten alternatives for AI/ML model deployment](https://northflank.com/blog/baseten-alternatives-for-ai-ml-model-deployment).

## It’s time to choose the right AI hosting platform

Your choice of AI hosting platforms in 2026 is defined by those that treat AI workloads as part of your complete application stack, rather than isolated compute tasks. The winners provide:

- Unified workflows that handle both AI and non-AI services
- Transparent, predictable pricing without vendor lock-in
- Production-grade features built for real applications, not demos
- Developer-first experiences that reduce operational overhead

Northflank represents all of this - a platform built for the reality of how teams actively build and deploy AI applications. The platform delivers the complete package for teams serious about putting AI into production.

<InfoBox className="BodyStyle">

See how Northflank compares for your use case: [Try it for free](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to check out how the platform is built for the next generation of AI applications.

</InfoBox>

## Frequently asked questions about AI hosting

Based on common questions from teams evaluating AI hosting platforms, here are the key considerations:

### 1. Is self-hosting AI worth it?

Self-hosting AI makes sense for organizations with strict data privacy, regulatory compliance, or high-volume predictable workloads, but comes with challenges like high GPU costs ($10K-$40K+ per unit), infrastructure complexity, and operational overhead. Platforms like Northflank offer [BYOC](https://northflank.com/features/bring-your-own-cloud) deployment as a middle ground, letting you run in your own cloud account while getting managed platform benefits.

### 2. What's the best AI platform to use?

The best AI platform depends on your use case: Northflank for production AI applications, AWS SageMaker for enterprise MLOps, Google Vertex AI for research, and Hugging Face for quick model deployment. For most teams building complete AI products, Northflank offers the best balance of features, pricing, and developer experience.

### 3. Is there a self-hosted AI?

Yes, you can self-host AI using open-source frameworks like Kubeflow, MLflow, BentoML, and Ray for different aspects of ML workflows. Many teams prefer hybrid approaches where Northflank's BYOC option provides a managed platform experience while keeping workloads in your own cloud infrastructure.

## More AI hosting resources for your stack

You can check out these additional guides and comparisons for your specific AI hosting needs:

- [How to deploy machine learning models: Step-by-step guide to ML model deployment in production](https://northflank.com/blog/how-to-deploy-machine-learning-models-step-by-step-guide-to-ml-model-deployment-in-production)
- [What is AI infrastructure? Key components & how to build your stack](https://northflank.com/blog/ai-infrastructure)
- [RunPod alternatives for AI/ML deployment beyond just a container](https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment)
- [AWS SageMaker alternatives: Top 6 platforms for MLOps in 2026](https://northflank.com/blog/aws-sagemaker-alternatives-top-6-platforms-for-ml-ops)
- [Self-host vLLM in your own cloud account with Northflank BYOC](https://northflank.com/guides/self-host-vllm-in-your-own-cloud-account-with-northflank-byoc)
- [Deploy DeepSeek R1 with vLLM on Northflank](https://northflank.com/guides/deploy-deepseek-r1-vllm-northflank-ai-llm)
- [Top AI PaaS platforms in 2026 for model deployment, fine-tuning & full-stack apps](https://northflank.com/blog/top-ai-paas-platforms)]]>
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</item><item>
  <title>7 cheapest cloud GPU providers in 2026</title>
  <link>https://northflank.com/blog/cheapest-cloud-gpu-providers</link>
  <pubDate>2025-09-08T16:11:00.000Z</pubDate>
  <description>
    <![CDATA[Compare Northflank, VastAI, RunPod, TensorDock &amp; more. Find the cheapest cloud GPU platforms in 2026 to cut AI compute costs]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/cheapest_cloud_gpu_providers_b89e3a7750.png" alt="7 cheapest cloud GPU providers in 2026" />*This article will help you find the cheapest cloud GPU provider*

I've been watching developers cope with GPU costs for years.

Recently, I came across a discussion where developers were searching for cloud GPU providers for fine-tuning and inference workloads. They were asking about reliable options that won't break the budget.

When you're building AI applications, training models, or running inference APIs, you might have encountered the same challenge. GPU costs can quickly become a major expense when scaling workloads.

One thing I've noticed is that with the right strategies and platforms like [Northflank](https://northflank.com/product/gpu-paas), you can access enterprise-grade compute power without the enterprise price tag. Some of these approaches can save you up to 90% compared to standard on-demand pricing.

I'll walk you through 7 best platforms and strategies that can cut down your GPU costs.

<InfoBox className="BodyStyle">

**Looking for specific GPU availability?** Price isn't everything; availability also matters. If you need guaranteed access to specific GPU types, [request GPU capacity here](https://northflank.com/request/gpu).

</InfoBox>

## How to find the cheapest cloud GPU service

When selecting a cloud GPU service, price alone doesn't tell the full story.

I've seen teams choose the lowest hourly rate only to encounter unexpected costs and reliability issues that ended up being more expensive in the long run.

![affordable-cloud-gpu-services.png](https://assets.northflank.com/affordable_cloud_gpu_services_11f028d75e.png)

So, these are the things you should keep in mind:

**1. Look beyond the advertised price**

Many platforms advertise low GPU rates but charge separately for CPU, RAM, and storage. A $1.50/hr GPU that requires an additional $0.50/hr for adequate CPU and storage ends up costing more than a $1.80/hr all-inclusive option.

**2. Factor in reliability costs**

Marketplace providers might list H100s at $0.90/hr, but if your training job gets interrupted three times and you lose 6 hours of progress, you're paying more than a stable $2.50/hr instance that completes the job without interruption.

**3. Think about your scaling patterns**

If you need GPUs sporadically, pay-per-minute billing can save 40% compared to hourly billing for short tasks. For consistent workloads, reserved instances or committed use discounts become more valuable.

**4. Check for hidden fees**

Data transfer costs, storage fees, and setup charges can quickly add up. Some providers charge for data ingress/egress, while others include it in their pricing.

**5. Check availability and quotas**

The cheapest GPU isn't valuable if it's never available when you need it. Major cloud providers often require quota requests that can take days to approve, while some platforms impose spending limits for new users.

The optimal approach is to find a platform that combines competitive pricing with reliability, transparent billing, and the flexibility to scale with your needs.

## Now, let’s see a quick comparison of the cheapest cloud GPU services

Now that you know what to look for, let's compare how the major platforms perform on pricing and value.

I've analyzed the most popular GPU configurations across different providers to show you the pricing as of September 2025.

Keep in mind that while some platforms show lower hourly rates, the total value depends on the factors we just discussed - reliability, hidden fees, and availability.

| Platform | A100 (40GB & 80GB) | H100 (80GB) | Available GPUs | Spot/Discount Options | **Recommendation** |
| --- | --- | --- | --- | --- | --- |
| **🏆 Northflank** | $1.42/hr, $1.76/hr | H100 80GB: $2.74/hr | A100 40/80GB, H100, H200, B200, L4, L40S, MI300X | Auto spot orchestration + [BYOC](https://northflank.com/features/bring-your-own-cloud) (Bring your own cloud) + Production reliability | **⭐ BEST VALUE** |
| **VastAI** | $0.50–$0.70/hr,  $0.60–$0.80 (dynamic pricing) | From $1.77/hr | A100 40/80GB, H100, RTX 4090, wide variety | Peer-to-peer marketplace pricing | Budget experiments |
| **RunPod** | A100 PCIe 80GB: $1.19/hr (Community), $2.17/hr (Serverless) | $2.79/hr (Community), $3.35/hr (Serverless) | H100, A100 80GB, RTX 4090, L40S, A6000 | Community Cloud + Serverless options | AI-focused workflows |
| **TensorDock** | A100 80GB: $1.63/hr | $2.25/hr | A100, H100, RTX 6000, 3090 | Global marketplace | Custom configurations |
| **AWS** | A100 (40GB): ($32.77/hr for 8x GPUs), A100 (80GB): ($40.96/hr for 8x GPUs) | H100 80GB: ($55.04/hr for 8x GPUs) | H100, A100 40GB, L40S, T4 | Up to 90% with Spot Instances | Enterprise at scale |
| **Lambda Labs** | $1.29/hr, $1.79/hr | H100 80GB: $2.99/hr | H100, H200, A100 40/80GB, B200 | On-demand, reserved instances | Training when available |
| **Paperspace** | A100 (40GB): $3.09/hr**,** A100 (80GB): $3.18/hr | H100 80GB: $5.95/hr | A100, RTX 6000, 3090 | Limited promotional rates | Development & notebooks |

*Prices are representative and may vary by region and availability*

> Note: The cheapest option isn't always the best value. You need to factor in reliability, ease of use, and hidden costs like data transfer fees.
> 

## Why spot instances and BYOC (Bring your own cloud) can save you thousands

Before we go into detail for each platform, let me explain the two strategies that can dramatically cut your costs:

**Spot instances** are unused GPU capacity that cloud providers sell at massive discounts - often 60-90% off regular prices. They can be interrupted with short notice when demand increases. Read more about it in this [guide](https://northflank.com/blog/what-are-spot-gpus-guide).

**[BYOC (Bring Your Own Cloud)](https://northflank.com/product/bring-your-own-cloud)** lets you deploy on your existing AWS, GCP, or Azure accounts, leveraging any credits, enterprise discounts, or committed use agreements you already have. Read more about it in this [guide](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment).

The best part is when you combine both strategies with [automated orchestration](https://northflank.com/features/infrastructure-layer) that automatically handles interruptions and finds the cheapest capacity across multiple clouds.

## The 7 cheapest cloud GPU platforms (Detailed comparison)

Let me walk you through each platform, starting with the ones that offer the best combination of cost savings and reliability:

### 1. Northflank - Automated orchestration meets unbeatable pricing

I'll be upfront: I'm biased toward platforms that solve major problems, and Northflank consistently delivers the best value for teams building production AI applications.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

**What makes Northflank special:**

- **Automatic spot optimization**: The platform continuously scans across AWS, GCP, and Azure to find the cheapest spot capacity
- **BYOC (Bring your own cloud) flexibility**: Deploy into your own cloud accounts to use existing credits and enterprise discounts ([See how](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes))
- **All-inclusive pricing**: GPU, CPU, RAM, and storage bundled together with no surprise charges
- **Production-ready**: Automatic failover when spot instances are reclaimed, so your applications never go down

<InfoBox className="BodyStyle">

**🤑 Northflank pricing**

- Free tier: Generous limits for testing and small projects
- CPU instances: Starting at $2.70/month ($0.0038/hr) for small workloads, scaling to production-grade dedicated instances
- GPU support: NVIDIA A100 40GB at $1.42/hr, A100 80GB at $1.76/hr, H100 at $2.74/hr, up to B200 at $5.87/hr
- Enterprise BYOC: Flat fees for clusters, vCPU, and memory on your infrastructure, no markup on your cloud costs
- Pricing calculator available to estimate costs before you start
- Fully self-serve platform, get started immediately without sales calls
- No hidden fees, egress charges, or surprise billing complexity

</InfoBox>

The Weights team [scaled to millions](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s) of users using Northflank's spot GPU optimization, cutting their model loading time from 7 minutes to 55 seconds while slashing costs by 90%.

**Best for**: Production AI applications that need reliability with maximum cost savings

Learn more: [What are spot GPUs? Complete guide to cost-effective AI infrastructure](https://northflank.com/blog/what-are-spot-gpus-guide)

### 2. VastAI - Peer-to-peer marketplace for ultra-low prices

VastAI operates like Airbnb for GPUs - individual owners rent out their hardware through a competitive marketplace.

![vastai's homepage.png](https://assets.northflank.com/vastai_s_homepage_194c175a50.png)

**The pricing advantage:**

- H100s available from ~$1.65/hour for interruptible instances
- RTX 4090s available from ~$0.31/hour for interruptible instances
- Interruptible instances with bidding can lead to significant cost savings compared to on-demand pricing

**The trade-offs:**

- Variable reliability depends on the host
- Limited enterprise features or support without upgrading to a premium tier
- Network latency issues if you choose geographically distributed GPUs

**Best for**: Experimentation, research projects, and cost-sensitive workloads that can tolerate interruptions

See [alternatives to Vast.ai](https://northflank.com/blog/6-best-vast-ai-alternatives)

### 3. RunPod - AI-optimized with community pricing

RunPod focuses specifically on AI workloads with pre-configured templates for popular frameworks.

![runpod-homepage.png](https://assets.northflank.com/runpod_homepage_a696c3aa97.png)

**Community Cloud vs Secure Cloud:**

- Community Cloud: Better pricing, shared infrastructure. (Example pricing: RTX 4090 from ~$0.34/hr, H100 PCIe from ~$1.99/hr)
- Secure Cloud: Enterprise features with a premium cost. The premium varies by GPU, but can be higher for more powerful cards. (Example pricing: RTX 4090 from ~$0.27/hr more than Community Cloud, A100 80GB from ~$0.45/hr more, H100 PCIe from ~$0.40/hr more) The highest Secure Cloud instance price is for the B200 SXM, at around $5.98/hr.
- Serverless options: Pay only when your code is running. (Example pricing: A100 80GB from $2.17/hr for Flex worker, H100 80GB from $4.47/hr for Flex worker, prices vary by GPU)

**Why developers choose RunPod:**

- 50+ pre-configured templates for Stable Diffusion, ComfyUI, and popular AI frameworks
- Fast cold-start times (often under a second)
- No data transfer fees
- Active Discord community for support

**Best for**: AI developers who want managed infrastructure with community support

Learn more: [RunPod vs Vast.ai vs Northflank: The complete GPU cloud comparison](https://northflank.com/blog/runpod-vs-vastai-northflank)

### 4. TensorDock - Global marketplace with enterprise hardware

TensorDock is a RunPod alternative that offers marketplace pricing with better security and flexibility.

![tensordock-homepage.png](https://assets.northflank.com/tensordock_homepage_febf532ad3.png)

**What it offers:**

- H100 SXM5 from **$2.25/hr** with no quotas or spending limits, reflecting a competitive on-demand rate. Spot pricing is also available from $1.91/hr.
- A range of other GPUs, such as RTX 4090s from $0.35/hr and A100s from $0.75/hr.
- 99.99% uptime standard across a global network of locations.
- Full VM control with Windows support, as it uses KVM virtualization.
- KVM isolation for better security than container-based solutions.

**Best for**: Teams wanting enterprise reliability at marketplace prices

Learn more: [6 best TensorDock alternatives for GPU cloud compute and AI/ML deployment](https://northflank.com/blog/tensordock-alternatives)

### 5. Major cloud providers with spot pricing

AWS, Google Cloud, and Azure all offer substantial discounts through their spot/preemptible instance programs.

**AWS Spot Instances:**

- **Pricing:** [H100 instances often between ~$3.00–$8.00/hr, A100 instances ~$1.50–$4.00/hr per GPU for 8-GPU instances, depending on supply and demand])
- Up to 90% off on-demand pricing
- Requires quota approval for most GPU types
- Complex management without orchestration tools (Spot instances can be interrupted with a two-minute warning when AWS reclaims capacity)

**Azure Spot VMs:**

- **Pricing**: [H100 instance (8x GPUs) spot price ~$28.99/hr, A100 instance (8x GPUs) spot price ~$17.50/hr, T4 instances as low as $0.09/hr per GPU].
- Similar 90% discounts with 30-second interruption notice (the specific percentage depends on the region and instance type. Azure also provides a 30-second warning before eviction.)
- Clearer pricing transparency than AWS (While Azure offers spot price history and an eviction rate advisor, its overall pricing structure is not inherently "better" than AWS)
- Integration with existing Microsoft enterprise agreements

**Google Cloud Spot VMs:**

- **Pricing:** [H100 from ~$2.25/hr per GPU, A100 80GB from ~$1.57/hr per GPU, A100 40GB from ~$1.15/hr per GPU].
- Often up to 60–91% savings compared to on-demand instances
- Flexible CPU/GPU configurations (Google Cloud allows for attaching a range of GPUs (including NVIDIA T4s and V100s) to its virtual machines)
- Better for custom setups due to component-based pricing (Google Cloud's pricing model for GPUs is more granular than AWS and Azure, allowing you to pay separately for the GPU and the underlying virtual machine)

**The enterprise advantage:** These platforms become cost-competitive at scale, especially with enterprise volume discounts and committed use agreements.

**Best for**: Large enterprises with dedicated DevOps teams and predictable but not time-critical workloads.

Learn more: [What are AWS Spot Instances? Guide to lower cloud costs and avoid downtime](https://northflank.com/blog/spot-instances)

### 6. Lambda Labs - High-performance with simple pricing

Lambda Labs offers straightforward access to high-end GPUs without complex configuration options.

![lambda-homepage.png](https://assets.northflank.com/lambda_homepage_21b6ec7a15.png)

**What you get:**

- Pre-configured environments with popular ML frameworks
- H100, A100, and A6000 instances optimized for training
- Simple hourly pricing with no hidden fees
- Fast provisioning when capacity is available

**Pricing**: 
H100 (from $2.49/hr for 1x H100 PCIe, $3.29/hr for 1x H100 SXM), A100 (from $1.29/hr for 1x A100 40GB), and A6000 (from $0.80/hr for 1x A6000) instances optimized for training

**The reliability concern:** Lambda Labs frequently experiences capacity shortages, especially for popular GPU types, which can disrupt ongoing projects.

**Best for**: Training workloads and experimentation when GPU availability isn't a concern

Learn more: [Top Lambda AI alternatives to consider for GPU workloads and full-stack apps](https://northflank.com/blog/top-lambda-ai-alternatives)

### 7. Paperspace - Developer-friendly with notebook integration

Now owned by DigitalOcean, Paperspace focuses on making GPU access simple for developers and researchers.

![paperspace-homepage.png](https://assets.northflank.com/paperspace_homepage_0a2d3a9357.png)

**Key advantages:**

- Jupyter notebook integration
- Simple pricing structure
- Good for prototyping and educational use
- Gradient platform for automated ML workflows

**On-demand GPU pricing examples:**

- H100 80GB: ($5.95/hr)
- A100 80GB: ($3.18/hr)
- A100 40GB: ($3.09/hr)
- RTX 4000 (24GB): ($0.56/hr)
- A6000 (48GB): ($1.89/hr)
- **Subscription plans:** In some cases, access to high-end GPUs on Paperspace's Gradient platform requires a monthly subscription, such as the Growth plan for $39/month

**Limitations for production:**

- Limited global presence (only three regions)
- No BYOC support
- Fewer enterprise features compared to alternatives

**Best for**: Solo developers, researchers, and teams doing early-stage development

Learn more: [7 best DigitalOcean GPU & Paperspace alternatives for AI workloads in 2026](https://northflank.com/blog/digitalocean-gpu-paperspace-alternatives)

## How I choose the right platform for my workloads

After testing all these platforms, here's my decision framework:

1. **For production AI applications:** Northflank wins for its combination of spot optimization, BYOC support, and automatic failover. You get enterprise reliability at marketplace prices.
2. **For experimentation and research:** VastAI, if you can handle variable reliability. Great for training runs that can be checkpointed and resumed.
3. **For AI-specific workflows:** RunPod provides pre-configured templates and community support. Great middle ground between cost and convenience.
4. **For maximum control:** TensorDock provides enterprise hardware with full VM access, ideal when you need specific OS configurations or security isolation.

## When to buy in bulk vs pay-as-you-go

The major cloud providers are "cheap, but you may need to buy in bulk." Here's when each approach makes sense:

**Bulk purchasing works when:**

- You have consistent, predictable GPU usage
- You can negotiate enterprise volume discounts
- Compliance requires dedicated hardware

**Pay-as-you-go is better for:**

- Variable or seasonal workloads
- Startups with uncertain scaling patterns
- Teams experimenting with different GPU types

For most AI teams, pay-as-you-go with automated orchestration beats bulk purchasing because it provides flexibility without sacrificing savings.

## Getting started: Your next steps

 **→ If you want maximum savings with minimal complexity:** Start with Northflank's spot optimization. You'll get enterprise reliability with marketplace pricing, plus automatic management of interruptions and multi-cloud orchestration.

**→ If you're experimenting on a budget:** Try VastAI for the lowest possible prices, but have backup plans for when instances become unavailable.

**→ If you need AI-specific features:** RunPod's templates and community make it easy to get started with popular frameworks.

Like I said earlier, the cheapest hourly rate doesn't always mean the lowest total cost. Factor in reliability, operational overhead, and the cost of downtime when making your decision.

<InfoBox className="BodyStyle">

Most successful AI teams end up using multiple platforms - spot instances for training, dedicated capacity for critical inference APIs, and development instances for experimentation.

The platforms that make this multi-cloud strategy seamless, like [Northflank](https://northflank.com/), tend to deliver the best long-term value. [Try out Northflank for free](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) with an Engineer

</InfoBox>

*Here are some additional resources to help you choose the right platform:*

**Learn more here:**

- [12 Best GPU cloud providers for AI/ML in 2026](https://northflank.com/blog/12-best-gpu-cloud-providers)
- [Best GPUs for AI workloads (and how to run them on Northflank)](https://northflank.com/blog/best-gpu-for-ai)
- [How much does an NVIDIA A100 GPU cost?](https://northflank.com/blog/nvidia-a100-gpu-cost)
- [Weights uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)]]>
  </content:encoded>
</item><item>
  <title>What is hybrid cloud? Your complete infrastructure guide</title>
  <link>https://northflank.com/blog/what-is-hybrid-cloud-complete-infrastructure-guide</link>
  <pubDate>2025-09-06T04:00:00.000Z</pubDate>
  <description>
    <![CDATA[Hybrid cloud mixes public cloud (AWS, Google Cloud, Azure) with private cloud and on-premises servers.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/hybrid_cloud_d672f5ae86.png" alt="What is hybrid cloud? Your complete infrastructure guide" />As engineering teams build and scale applications, choosing where to run your infrastructure becomes a key technical decision that affects performance, costs, compliance, and operational complexity. 

**Hybrid cloud** has become a popular solution that gives you the flexibility to run workloads across different environments based on technical requirements and constraints, but implementing it effectively requires understanding the nuances of different deployment models and their trade-offs.

## What is hybrid cloud?

<InfoBox className='BodyStyle'>

💡 Hybrid cloud mixes **public cloud** (AWS, Google Cloud, Azure) with **private cloud** and **on-premises servers**. The key is that these environments talk to each other. You can move apps and data between them based on what you need.

</InfoBox>

Think of it like this: your database runs on private servers for security, your API runs in public cloud for global reach, and they work together through secure connections. Modern tools like Northflank make this simple by giving you one interface to deploy everywhere instead of learning each cloud provider's different systems.

## Public, private, hybrid, and multi-cloud

Let's break down the different ways to run infrastructure:

**Public cloud** means using shared servers from companies like AWS or Google. You pay for what you use, can scale quickly, and don't manage hardware. But you're sharing resources with other companies and have less control.

**Private cloud** means dedicated servers just for you. This gives you full control over security and performance, but you handle all the maintenance and it costs more upfront. Banks and hospitals often use this for sensitive data.

**Hybrid cloud** combines both. Keep sensitive stuff private, put everything else in public cloud. You get control where you need it and easy scaling where you don't.

**Multi-cloud** means using multiple public cloud providers. Maybe AWS for compute, Google for machine learning, and Cloudflare for content delivery. This avoids lock-in but means managing different systems.

## Examples of hybrid cloud

Now that you know what hybrid cloud is, here's how teams would use hybrid cloud:

**API with secure data**: Your API runs in public cloud so users worldwide can access it fast. But customer data stays on private servers to meet compliance rules. Background jobs sync the data between environments.

**Development workflow**: Production runs on private infrastructure for security. But your CI/CD pipelines, testing, and staging environments use public cloud because you can spin them up and down easily. Northflank handles deployments to both with the same pipeline.

**Microservices**: Payment services run privately for security. Search, notifications, and analytics run in public cloud for easy scaling and managed services. Each service runs where it makes the most sense.

**Legacy migration**: Keep your old systems running on-premises while building new features in public cloud. Connect them with APIs so you can modernize gradually without breaking anything.

## When and why do you need hybrid cloud?

Hybrid cloud solves specific problems:

1. **Compliance requirements**: Some data has to stay in certain locations or meet specific security standards. Keep that data private, run everything else in public cloud.
2. **Performance needs**: Large datasets are slow and expensive to move. Process data where it lives, but run user-facing apps in public cloud for speed.
3. **Cost optimization**: Run steady workloads on private infrastructure where costs are predictable. Use public cloud for traffic spikes and temporary jobs.
4. **Legacy systems**: You can't replace everything at once. Connect old systems with new cloud apps through APIs and gradually modernize.
5. **Special hardware**: Need high-end GPUs or specific processors? Keep those workloads on dedicated hardware while running other stuff in public cloud.

## Hybrid cloud use cases

1. **Financial services**: Transaction processing stays in private cloud for regulations and performance. Customer APIs, mobile apps, and analytics run in public cloud for global reach and managed services.
2. **Healthcare**: Patient records stay in HIPAA-compliant private infrastructure. Research, analytics, and patient portals use public cloud's advanced tools after anonymizing data.
3. **Manufacturing**: Factory systems need real-time control and stay on-premises. Business analytics, supply chain optimization, and predictive maintenance run in public cloud with the aggregated data.
4. **Gaming**: Core game servers run in private cloud for consistent performance. Player accounts, social features, and analytics use public cloud's managed services and global scaling.
5. **Media companies**: Video editing and production use private infrastructure with high-speed storage. Content distribution uses public cloud CDNs to reach users worldwide.

## Hybrid cloud solutions

You have several options for implementing hybrid cloud:

**Container platforms** are the most popular approach. **Northflank** is a good example - it lets you deploy the same containerized app to any environment. You get one pipeline that works everywhere, unified monitoring across all your infrastructure, automatic scaling that works across different clouds, and the same developer experience whether you're deploying to AWS or your private servers. Northflank also supports Infrastructure as Code approaches and includes built-in service mesh capabilities with automatic load balancing, TLS encryption, and secure service-to-service communication.

**Infrastructure as Code** tools like Terraform let you define infrastructure in code files. This works great with platforms like Northflank, or you can manage it directly if you want full control over every detail.

**Service mesh** tools like Istio create a network layer that connects services across different environments. Northflank includes this functionality built-in, but you can also use external service mesh tools for more complex networking requirements.

**Managed services** from cloud providers like AWS Outposts bring public cloud services to your data center. Easy if you're already using that provider, but creates vendor lock-in.

The benefit of platforms like Northflank is they handle the complexity for you while still supporting the approaches you prefer, including built-in service mesh, load balancing, and secure networking. You focus on your app, not managing different cloud APIs and deployment systems.

## What are the benefits of hybrid cloud?

1. **No vendor lock-in**: You can negotiate better prices and switch providers if needed. Your apps stay portable instead of being tied to one company's services.
2. **Best of each provider**: Use Google for AI, AWS for compute options, Azure for enterprise stuff. Pick the best service for each job instead of compromising.
3. **Global reach**: Different providers have different geographic coverage. Use the one that's closest to your users in each region.
4. **Better uptime**: If one provider has an outage, your other systems keep running. Spread the risk across multiple companies.
5. **Cost savings**: Compare prices and move workloads to whoever's cheapest. Use spot pricing and take advantage of competition between providers.

Northflank supports this by giving you the same deployment experience across all providers. You don't need separate tools and processes for each one.

## Conclusion

Hybrid cloud lets you run workloads where they make the most sense instead of forcing everything into one type of infrastructure. Keep sensitive data private, scale public apps globally, optimize costs, and integrate legacy systems.

The main challenge is complexity - managing different environments, networking, security, and deployments. But modern tools make this much easier. **Northflank** handles the hard parts so you can deploy consistently everywhere without learning each provider's specific tools.

**Key points for engineering teams:**

- Start small with one hybrid use case to learn what works
- Use containers and avoid vendor-specific services when possible
- Pick tools that work across all your infrastructure
- Plan for consistent security and monitoring everywhere
- Make sure your team can handle the added complexity

Hybrid cloud isn't right for everyone, but when you need the flexibility to optimize each workload differently, it's a powerful approach. 

The key is having the right tools to manage the complexity while keeping your development workflow simple.

## FAQs

1. **What is hybrid cloud?**
Hybrid cloud combines public cloud services (like AWS or Google Cloud) with private cloud and on-premises infrastructure, all connected to work as one system. For example, you might keep your database on private servers for security while running your API in public cloud for global scaling.
2. **What's the difference between hybrid cloud and multi-cloud?**
Hybrid cloud combines different environment types (public cloud, private cloud, on-premises), while multi-cloud uses multiple public cloud providers. You can have both, using private infrastructure plus multiple public clouds.
3. **Is hybrid cloud more expensive than just using public cloud?**
It depends. You'll have higher upfront costs for private infrastructure, but you can save money by running steady workloads privately and only using public cloud for scaling. The key is optimizing each workload for cost.
4. **What happens if my connection between environments goes down?**
Good hybrid architectures are designed for this. Critical services should be able to run independently, and you can configure failover systems. Platforms like Northflank include monitoring and automatic retry mechanisms.
5. **Do I need special skills to manage hybrid cloud?**
While hybrid cloud is more complex than single-environment setups, modern platforms abstract most of the complexity. Your team needs to understand containers and basic networking, but tools like Northflank handle the infrastructure management.
6. **Can I use the same code for applications running in different environments?**
Yes, if you use containers. Containerized apps run consistently across different infrastructure types. This is why container platforms are the most popular approach to hybrid cloud.
7. **What's the easiest way to start with hybrid cloud?**
Start small with one use case - maybe keep your database private but run your API in public cloud. Use a platform that simplifies deployment across environments, then gradually expand as you learn what works.
8. **Can I move applications between environments easily?**
With containerized apps and the right platform, yes. Northflank lets you deploy the same application to different environments through the same interface, making it easy to move workloads based on changing requirements.

Try out Northflank [here](https://app.northflank.com/signup) or book a demo with an engineer [here](https://cal.com/team/northflank/northflank-intro).
]]>
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</item><item>
  <title>What is machine learning infrastructure?</title>
  <link>https://northflank.com/blog/what-is-machine-learning-infrastructure</link>
  <pubDate>2025-09-05T16:09:00.000Z</pubDate>
  <description>
    <![CDATA[Learn what machine learning infrastructure is and discover the core components, best practices, and platforms like Northflank for your ML projects.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/machine_learning_infrastructure_1da30230be.png" alt="What is machine learning infrastructure?" /><InfoBox className="BodyStyle">

**Quick summary:**

Machine learning infrastructure encompasses the entire technology stack and resources you need to develop, train, deploy, and manage ML models in production.

It includes compute resources (CPUs, GPUs), storage systems, orchestration platforms, CI/CD pipelines, monitoring tools, and APIs for model serving.

Modern ML infrastructure platforms like [Northflank](https://northflank.com/) provide end-to-end solutions with GPU orchestration, automated job scheduling, scalable storage, and integrated CI/CD workflows to simplify and optimize your entire ML lifecycle from experimentation to production deployment.

</InfoBox>

Machine learning infrastructure has become the backbone of successful AI initiatives.

When you build a production ML system, you'll find that only about 10% of your code is the machine learning model itself.

The other 90% is infrastructure code that handles data processing, deployment, monitoring, and serving your models to users.

This means your modern ML infrastructure must handle everything from data ingestion and feature engineering to model serving and performance monitoring.

As organizations increasingly rely on AI for competitive advantage, understanding and implementing reliable ML infrastructure has become important for your engineering team when working with machine learning at scale.

In this guide, we'll cover:

- Best practices for managing your ML infrastructure
- GPU orchestration capabilities for your AI workloads
- Setting up CI/CD pipelines for your machine learning models
- How to choose the most scalable ML infrastructure vendor
- The core architectural layers of ML infrastructure systems
- How cloud infrastructure can speed up your development workflow
- What infrastructure components you need for your ML projects

## What infrastructure do I need for machine learning projects?

Your ML infrastructure requirements span across eight core components that work together to support your entire machine learning lifecycle. 

Each component serves a specific purpose while integrating naturally with others to create a comprehensive ML platform for your team.

Let's break down what you need:

### 1. Computing resources

Computing resources form the foundation of your ML infrastructure.

When you're training modern deep learning models, you'll need significant computational power, particularly GPU resources for neural network training and inference.

Platforms like [Northflank](https://northflank.com/) provide you with on-demand access to [NVIDIA H100](https://northflank.com/cloud/gpus/H100) and [B200](https://northflank.com/cloud/gpus/B200) GPUs across multiple cloud providers.

So, this enables your team to scale compute resources based on workload demands without managing underlying hardware complexity.

### 2. Data management and storage

Data management and storage systems handle the massive datasets you'll need for ML model training.

This includes data lakes for your raw storage, databases for structured data, and feature stores for processed features.

Your data management requires both high-performance storage for training workloads and cost-effective storage for long-term data retention.

### 3. Orchestration and scheduling

Orchestration and scheduling tools coordinate your complex ML workflows, from data preprocessing to model training and deployment.

Modern platforms provide you with Kubernetes-based orchestration that can automatically schedule jobs, manage resource allocation, and handle failures with ease.

Northflank's [job scheduling](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs) capabilities allow your team to run batch training jobs, hyperparameter tuning experiments, and inference pipelines with automated resource management.

### 4. Model development environments

Model development environments provide your data scientists with the tools needed for experimentation and model building.

This includes [Jupyter notebooks](https://northflank.com/guides/deploy-juypter-notebook-with-tensorflow-in-aws-gcp-and-azure), development frameworks like [TensorFlow](https://northflank.com/blog/tensorflow-alternatives) and [PyTorch](https://northflank.com/blog/what-is-pytorch), and experiment tracking systems.

Cloud-based development environments enable collaborative work while giving you access to powerful compute resources.

<InfoBox className="BodyStyle">

This is where platforms like [Northflank](https://northflank.com/) come in.

Rather than assembling these components from different vendors, Northflank provides you with a **unified platform** that handles compute [orchestration](https://northflank.com/blog/container-orchestration#kuberneteslevel-control-minus-the-complexity-of-container-orchestration), [job scheduling](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs), [storage management](https://northflank.com/docs/v1/application/production-workloads/persistent-storage-in-production), and development [environments](https://northflank.com/blog/what-are-dev-qa-preview-test-staging-and-production-environments) through a **single interface**.

</InfoBox>

## How can cloud infrastructure improve my machine learning development?

Now that you understand the core infrastructure components you need, let's look at how cloud infrastructure can support your ML development process. See some of the key benefits you'll get:

1. **Elastic scaling**: provision resources based on your current needs
2. **Multi-cloud flexibility**: prevent vendor lock-in across providers
3. **Managed services**: reduce operational complexity

Cloud infrastructure improves your ML development by providing scalable, on-demand resources that reduce the complexity of managing physical hardware.

Think about traditional on-premises setups for a moment.

They require significant upfront investment in GPUs, storage systems, and networking equipment, along with ongoing maintenance and upgrade costs. Cloud-based ML infrastructure provides you with a better approach with these three core advantages.

![Cloud ML infrastructure workflow: from development to production](https://assets.northflank.com/cloud_ml_infrastructure_workflow_c9c1c65507.png)*Cloud ML infrastructure workflow: from development to production*

> This is where Northflank's multi-cloud approach becomes valuable for your team. The platform supports deployment across AWS, GCP, Azure, Civo and Oracle Cloud, allowing you to leverage the best GPU offerings from each provider.
> 

So, rather than dedicating your engineering resources to Kubernetes cluster management and security patching, you can focus on your model development and business logic.

Northflank provides you with a managed Kubernetes experience that includes [built-in CI/CD,](https://northflank.com/docs/v1/application/release/manage-ci-cd) [monitoring](https://northflank.com/docs/v1/application/observe/observability-on-northflank), and [security features](https://northflank.com/docs/v1/application/secure/security-on-northflank), so you get all the infrastructure benefits without the operational complexity.

*See how [Weights uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

## What are the core components of an ML infrastructure architecture?

Now that you understand how cloud infrastructure supports your development, let's go into the architectural layers that make up a complete ML system.

![High-level diagram of machine learning infrastructure showing four connected layers: Data, Compute, Orchestration, and Serving, with arrows indicating workflow and dependencies](https://assets.northflank.com/ml_infrastructure_architecture_northflank_44da72cabb.png)*The 4 core layers of modern ML infrastructure*

Your ML infrastructure architecture consists of four interconnected layers that work together to take your models from development to production.

These are the core layers you need:

1. **Data layer**: Manages storage, processing, and access for your datasets, feature stores, and experiment metadata.
2. **Compute layer**: Provides CPU and GPU resources with auto-scaling capabilities for training and inference workloads
3. **Orchestration layer**: Coordinates CI/CD pipelines, job scheduling, ML testing and evaluation pipelines, and workflow management across your ML lifecycle
4. **Serving layer**: Handles model deployment, API endpoints, and inference systems for real-time and batch predictions

Each layer serves a specific purpose while integrating with the others to create a comprehensive platform that can scale from your research prototypes to production applications serving millions of users.

## Which vendor provides the most scalable machine learning infrastructure?

With your architecture layers defined, the next question is choosing the right vendor for your scalable ML infrastructure.

The scalability of ML infrastructure depends on four key factors: compute flexibility, storage performance, orchestration capabilities, and operational simplicity.

You have two main vendor categories to consider:

1. **Traditional cloud providers** (AWS, GCP, Azure): They provide comprehensive ML services, but require significant expertise to configure and manage effectively.
2. **Specialized ML platforms**: They focus specifically on machine learning workflows with opinionated solutions that reduce complexity while maintaining flexibility.

The most scalable solutions provide automatic resource management, cost optimization through [spot instances](https://northflank.com/blog/what-are-spot-gpus-guide#how-northflank-cuts-spot-gpu-costs-with-automated-orchestration), and [automated scaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments) from development to production environments.

<InfoBox className="BodyStyle">

Northflank's approach centers on Kubernetes orchestration combined with multi-cloud flexibility.

You can deploy workloads across multiple cloud providers ([AWS](https://northflank.com/cloud/aws), [Azure](https://northflank.com/cloud/azure), [GCP](https://northflank.com/cloud/gcp), [Civo](https://northflank.com/cloud/civo), [Oracle](https://northflank.com/cloud/oci)), access different [GPU types](https://northflank.com/cloud/gpus) based on your workload requirements, and scale resources automatically based on demand.

</InfoBox>

## How do I set up CI/CD for my machine learning models?

Once you've chosen your infrastructure vendor, you'll need to set up CI/CD pipelines for your ML models.

Setting up effective CI/CD for ML models requires adapting traditional software deployment practices to handle the unique requirements of machine learning workflows.

Your ML CI/CD pipeline needs four key components:

1. **Model versioning and artifact management**: Track model artifacts, training data versions, configuration parameters, and prompt versions (for LLM applications) automatically.
2. **Automated testing**: Include model performance validation, data quality checks, and accuracy regression tests beyond traditional unit tests.
3. **Deployment automation**: Handle containerization, serving infrastructure configuration, and gradual rollout strategies like canary deployments
4. **Environment management**: Ensure consistency across development, staging, and production environments

Compared to traditional software, your ML models depend on both code and data. So, they require specialized versioning systems that maintain lineage between model versions and their training data.

<InfoBox className="BodyStyle">

Modern platforms like Northflank provide integrated [CI/CD capabilities](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) that automatically build, test, and deploy your models based on Git commits or training completion events.

This handles the complexity of ML-specific deployment requirements.

</InfoBox>

## What GPU orchestration capabilities do I need for AI workloads?

With your CI/CD pipeline in place, you'll need reliable GPU orchestration to handle your AI workloads.

GPU orchestration for AI workloads requires sophisticated resource management capabilities that go beyond traditional CPU-based scheduling.

Your GPU orchestration system needs these core capabilities:

1. **Resource allocation and scheduling**: Handle long-running training jobs, burst inference demands, and GPU sharing for smaller models
2. **Multi-GPU and distributed training support**: Scale model training across multiple GPUs and nodes with complex communication patterns.
3. **Cost optimization through spot instances**: Automatically migrate workloads when spot instances are reclaimed while maintaining training state

Modern AI applications demand high-performance GPU utilization, support for different GPU types, and automatic workload placement across multiple cloud providers.

<InfoBox className="BodyStyle">

Northflank's [GPU](https://northflank.com/gpu) orchestration includes automatic scheduling across NVIDIA H100 and B200 instances, support for GPU sharing across workloads, and optimized placement across multiple cloud providers.

The platform handles [spot instance management](https://northflank.com/blog/what-are-spot-gpus-guide#how-northflank-cuts-spot-gpu-costs-with-automated-orchestration) automatically, providing cost optimization without requiring manual intervention from your development team.

</InfoBox>

## See some best practices for machine learning infrastructure management

With your GPU orchestration configured, let's cover some best practices for managing your ML infrastructure.

Successful ML infrastructure management requires balancing performance, cost, and operational complexity while maintaining the flexibility to adapt to changing requirements.

These are the four key areas to focus on:

1. **Resource optimization**: Match workload characteristics to appropriate infrastructure and implement automated scaling policies to prevent over-provisioning
2. **Security and compliance**: Implement proper access controls, encrypt data in transit and at rest, and maintain audit trails for regulatory compliance
3. **Monitoring and observability**: Track both infrastructure metrics and ML-specific performance indicators like model performance and data quality
4. **Cost management**: Use spot instances for fault-tolerant workloads and regularly review resource allocation patterns to identify optimization opportunities

Organizations that implement these best practices can scale their ML operations while controlling costs and maintaining system reliability.

Understanding the unique cost characteristics of ML workloads helps you implement appropriate optimization strategies across your entire infrastructure stack.

## It's time to choose the right machine learning infrastructure

With these best practices in mind, selecting the right ML infrastructure platform requires careful consideration of your technical requirements and long-term scalability needs.

The ideal platform should provide comprehensive capabilities while remaining simple enough for your development team to adopt quickly.

Modern ML infrastructure platforms like [Northflank](https://northflank.com/) provide end-to-end solutions that handle compute orchestration, storage management, CI/CD automation, and GPU scheduling through a single integrated platform.

This reduces operational complexity while providing the flexibility and performance needed for your demanding ML workloads.

<InfoBox className="BodyStyle">

Take the next step with Northflank. [Get started today](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how our platform can simplify and optimize your AI development workflow.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>5 types of AI workloads and how to deploy them</title>
  <link>https://northflank.com/blog/ai-workloads</link>
  <pubDate>2025-09-04T16:56:00.000Z</pubDate>
  <description>
    <![CDATA[Learn about 5 key AI workloads: training, fine-tuning, inference, pipelines &amp; data processing. See how Northflank's platform handles each type.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/ai_workloads_313fcc2127.png" alt="5 types of AI workloads and how to deploy them" />## What are AI workloads?

AI workloads are the computational tasks that artificial intelligence systems perform to process data, learn patterns, and generate outputs.

For example, when you use ChatGPT to write an email, there's an AI workload running in the background that:

- processes your prompt
- understands the context
- and generates a response by performing millions of calculations.

That's just one common example.

You know how, when you deploy a regular web application, you're mainly concerned about handling HTTP requests and database queries, right?

However, with AI workloads, you're dealing with [models](https://northflank.com/blog/an-engineers-guide-to-open-source-ai-models) that might need to process millions of images or generate text responses in real-time. As a result, it requires entirely different infrastructure considerations.

AI workloads are different because they're computationally intensive. What that means is they require specialized hardware, such as [GPUs](https://northflank.com/gpu), and dynamic scaling based on demand.

These workloads range from training [machine learning models](https://northflank.com/blog/how-to-deploy-machine-learning-models-step-by-step-guide-to-ml-model-deployment-in-production) on massive datasets to serving real-time predictions to your users.

<InfoBox className="BodyStyle">

We'll go over:

- Types of AI workloads
- Why GPUs are necessary for AI workloads
- How [Northflank](https://northflank.com/) handles each workload with [built-in orchestration](https://northflank.com/blog/container-orchestration#kuberneteslevel-control-minus-the-complexity-of-container-orchestration)
- Best practices for deploying AI workloads

</InfoBox>

## What are the 5 key types of AI workloads?

It’s important that you understand the different types of AI workloads to help you choose the right infrastructure and deployment strategy.

Let me walk you through the five main categories you'll encounter when building AI applications:

### 1. Training workloads

Training workloads are where your AI models learn by processing massive datasets to identify patterns and adjust parameters.

Let's say you're building a chatbot for customer support.

During training, your model processes thousands of conversations, making predictions, comparing them to correct answers, and adjusting to improve over time.

This is the most resource-intensive AI workload, as it often requires multiple GPUs to run for days or weeks.

The challenge here is that training is experimental - you may need to try different approaches until you achieve the desired results.

Also, you need infrastructure that can spin up resources quickly and scale across multiple GPUs without the complexity that comes with configuration.

<InfoBox className="BodyStyle">

This is where platforms like [Northflank](https://northflank.com/) come in - you can [spin up GPU instances](https://northflank.com/cloud/gpus) across multiple clouds without worrying about Kubernetes configuration or [spot instance](https://northflank.com/blog/what-are-spot-gpus-guide) management.

The platform handles the orchestration automatically, so you can focus on experimenting with your models rather than managing infrastructure.

</InfoBox>

### 2. Fine-tuning workloads

Fine-tuning adapts pre-trained models (like GPT or BERT) to your specific use case.

Let's say you want to build a legal document analyzer.

Rather than training from scratch, you'd take GPT and fine-tune it on thousands of contracts and legal briefs.

This teaches the model legal terminology and 'whereas' clauses that a general model wouldn't understand well. For example, training on [artificial intelligence texts](https://skywork.ai/blog/unlock-the-power-of-ai-texts-explore-artificial-intelligence/) can help adapt models for finance, remote work, or even copy trading domains.

You're teaching an existing model your domain language.

It's faster than full training, but you still need to balance computational power with cost to avoid overspending on idle GPUs.

<InfoBox className="BodyStyle">

You can run fine-tuning jobs using frameworks like Hugging Face Transformers or custom training scripts with platforms like [Northflank](https://northflank.com/).

The platform handles GPU provisioning and can automatically terminate resources when your fine-tuning job completes, preventing unnecessary costs from idle instances.

</InfoBox>

### 3. Inference workloads

Inference is where your trained models serve users and make predictions or generate responses in milliseconds.

For instance, when you upload a photo using an [Instagram post maker](https://www.design.com/instagram-posts) to Instagram, it automatically suggests tags for people in the picture.

That's inference in action - your photo gets processed by a computer vision model that identifies faces and matches them to your contacts, all happening in real-time while you wait.

This is completely different from long-running training jobs, because inference handles individual requests that need immediate responses.

Your users won't wait 30 seconds for a photo to be tagged or a chatbot to respond.

The primary requirement here is low latency and [automatic scaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments).

Your infrastructure must serve models quickly, handle traffic spikes when lots of users are active, and scale down during quiet periods to control costs.

<InfoBox className="BodyStyle">

For inference workloads, you need infrastructure that can [scale automatically](https://northflank.com/docs/v1/application/scale/autoscale-deployments) from zero to handle traffic spikes.

Platforms like Northflank offer flexible GPU pricing with per-second billing, so you're not stuck paying for idle compute time when your models aren't actively processing requests.

</InfoBox>

### 4. Pipeline workloads

Most AI applications involve complex workflows where data flows through multiple processing steps like cleaning, feature extraction, model inference, and post-processing.

Let's say you're building an app that analyzes product reviews to determine customer sentiment.

Your pipeline might work like this:

- first, you extract the review text from your database
- then clean it by removing special characters and fixing typos
- next you break it into sentences, run each sentence through a sentiment analysis model
- and finally combine all the scores to get an overall rating for the product.

Pipeline workloads orchestrate these steps to make sure data flows properly from stage to stage.

You need infrastructure that can coordinate all these steps, handle failures gracefully, and ensure each stage has the necessary data when it needs it.

<InfoBox className="BodyStyle">

Managing complex pipelines becomes much simpler when you can deploy each step as a separate container service.

With platforms like Northflank, you can orchestrate these workflows without manually configuring communication between services - the platform handles [job scheduling](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs), [event triggers](https://northflank.com/docs/v1/application/observe/set-infrastructure-alerts), and [automatic scaling](https://northflank.com/features/scale) based on queue length.

</InfoBox>

### 5. Data processing workloads

Before your models can work, you need clean, properly formatted data.

Data processing workloads handle:

- extracting data from sources
- transforming it into the right format
- and loading it where your models can access it.

Let’s say you're training a model to predict which customers might cancel their subscription.

You'd need to gather data from your billing system, support tickets, app usage logs, and customer surveys.

Then you'd clean this messy data by removing duplicates, filling in missing values, standardizing date formats, and converting everything into a format your model can understand.

These workloads often need elastic scaling because you might have massive processing jobs followed by periods of low activity.

For instance, you might process a month's worth of customer data every Sunday night, but then have minimal processing needs during the week.

Your infrastructure must handle these spikes quickly without keeping expensive resources running idle the rest of the time.

<InfoBox className="BodyStyle">

This elastic scaling challenge is precisely what modern [AI infrastructure platforms](https://northflank.com/blog/ai-infrastructure) are designed to address.

You can process your monthly data dumps with a cluster of containers that automatically spin up, complete the job, and tear down when finished - all without keeping expensive resources running during quiet periods. And this is possible with platforms like [Northflank](https://northflank.com/). 

</InfoBox>

## Why are GPUs necessary for my AI workloads?

If you've looked into AI infrastructure, you've likely heard that [GPUs](https://northflank.com/gpu) are important.

Your AI workloads need GPUs because they're designed for the massive parallel computations that AI requires.

Let's say you need to check the spelling of 10,000 words.

A CPU checks each word one by one, while a GPU is like having 1,000 people each check 10 words simultaneously, which is much faster.

AI algorithms work the same way.

They perform the same mathematical operation on thousands of data points at once.

So, without GPUs, your training would take weeks instead of hours, and your inference would be too slow for real-time applications.

This is why GPUs have become very important for AI workloads. They're specifically built for the parallel processing that makes AI practical.

## How does Northflank handle each type of AI workload?

Now that you understand the different types of AI workloads, let's talk about how you can deploy and manage them.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

This is where having the right platform with built-in orchestration makes all the difference.

### 1. Training and fine-tuning with automatic orchestration

Northflank reduces the infrastructure complexity for training workloads.

You can spin up jobs with [NVIDIA H100s](https://northflank.com/cloud/gpus/H100) and [B200s](https://northflank.com/cloud/gpus/B200) across multiple clouds.

The platform handles Kubernetes [orchestration](https://northflank.com/blog/container-orchestration), [spot instance](https://northflank.com/docs/v1/application/bring-your-own-cloud/deploy-workloads-to-your-cluster#use-spot-instances) management, and [scaling automatically.](https://northflank.com/features/scale)

![h100-gpus.png](https://assets.northflank.com/h100_gpus_c454390002.png)

You can start a training job and only pay for actual compute usage. The platform manages scaling and can use spot instances to reduce costs without manual intervention.

### 2. Inference that scales automatically

Your inference APIs [automatically scale](https://northflank.com/docs/v1/application/scale/autoscale-deployments) from zero to handle traffic spikes, then scale back down.

Northflank supports per-second billing, which means you only pay for the compute time you actually use, not for idle resources.

![scale-northflank.png](https://assets.northflank.com/scale_northflank_4a0db7f247.png)

The platform handles [load balancing](https://northflank.com/docs/v1/application/network/load-balancing), [health checks](https://northflank.com/docs/v1/application/observe/configure-health-checks), and [SSL certificates](https://northflank.com/docs/v1/application/domains/certificate-generation) out of the box. Therefore, there is no need to configure ingress controllers or manage networking complexity.

### 3. Pipeline orchestration built-in

You can deploy each pipeline step as a separate service with automatic communication handling.

If you need image preprocessing before model inference, you can deploy a preprocessing service that scales based on queue length.

![northflank-job-scheduling.png](https://assets.northflank.com/northflank_job_scheduling_f89452fbb8.png)

Northflank's [job scheduling](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs) handles batch processing, periodic model updates, and event-triggered workflows, meaning that all the orchestration complexity is managed for you.

### 4. Elastic data processing

Northflank supports both [persistent](https://northflank.com/docs/v1/application/production-workloads/persistent-storage-in-production) and [ephemeral](https://northflank.com/docs/v1/application/scale/increase-storage#scale-ephemeral-storage) storage.

For instance, if you process terabytes of data daily, you can spin up processing containers, [run jobs](https://northflank.com/docs/v1/application/run/run-an-image-once-or-on-a-schedule), and automatically [tear down resources](https://northflank.com/use-cases/disaster-recovery-for-kubernetes) when the jobs are complete.

Northflank also provides multi-cloud support, which enables running processing jobs near your data, thereby reducing transfer costs and latency.

## What should I consider when choosing infrastructure for my AI workloads?

Selecting the right infrastructure for your AI workloads isn't only about finding the cheapest GPUs. There are several key factors you need to keep in mind to make sure your AI projects succeed:

**Speed of deployment:**

- AI development is iterative
- You need infrastructure that can rapidly spin up experiments without excessive configuration complexity

**Cost management:**

- Look for spot instance support, automatic scaling, and granular billing
- Pay for compute when it's actively used, not during idle periods

**Multi-cloud flexibility:**

- Different workloads have different optimal clouds
- Training might be cheaper on one platform, inference better on another

**Observability:**

- When 12-hour training jobs fail, you need detailed logs and metrics to debug quickly

**Security:**

- For sensitive data or regulated industries, ensure support for [secure multi-tenancy](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh), VPC deployment, and [compliance](https://northflank.com/docs/v1/application/secure/use-role-based-access-control)

## Frequently asked questions about AI workloads

I get these questions a lot from teams starting their AI journey. Let’s see the most common ones with practical answers:

1. **What are the requirements for AI workloads?**
AI workloads need high-performance compute (usually GPUs), fast storage for large datasets, high-bandwidth networking, and orchestration tools for complex workflows. Training needs more compute power; inference needs low latency.
2. **Which workstation is best for AI workloads?**
For development, workstations with NVIDIA RTX 4090 or A6000 GPUs work well. For production, cloud-based H100s or B200s offer better scalability and cost efficiency than local workstations.
3. **How can AI reduce my workload?**
AI automates repetitive tasks, provides intelligent data processing, and handles routine decisions. Examples include automatically classifying support tickets, generating documentation, or optimizing resource allocation.
4. **Which edge computing service is best for AI workloads?**
Choose services supporting containerized AI models with GPU acceleration and automatic model syncing from central infrastructure, based on your latency and deployment requirements.

## Getting started with AI workloads on Northflank

By now, you should be ready to deploy your first AI workload. I'll show you the quickest way to get up and running.

The fastest path is to connect your [Git repository to Northflank](https://northflank.com/docs/v1/application/getting-started/link-your-git-account) and [deploy a simple inference API.](https://northflank.com/guides/category/deploy-on-northflank) 

The platform automatically detects your AI framework ([PyTorch](https://northflank.com/blog/what-is-pytorch), [TensorFlow](https://northflank.com/blog/tensorflow-alternatives), [Hugging Face Transformers](https://northflank.com/blog/huggingface-alternatives)) and configures the runtime environment.

For complex workloads, use [Northflank's templates](https://northflank.com/features/templates) to define reusable infrastructure patterns.

Create a training pipeline template once, and your team can spin up experiments with a single click. See [available templates](https://northflank.com/stacks).

Start simple and iterate. Deploy a basic model, get it working in production, then add complexity as you learn your specific requirements. With proper orchestration and scaling, your AI workloads can focus on solving major problems rather than battling infrastructure complexity.

<InfoBox className="BodyStyle">

[Start now](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) with an engineer.

</InfoBox>]]>
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</item><item>
  <title>ChatGPT usage limits explained: free vs plus vs enterprise</title>
  <link>https://northflank.com/blog/chatgpt-usage-limits-free-plus-enterprise</link>
  <pubDate>2025-09-02T16:42:00.000Z</pubDate>
  <description>
    <![CDATA[Find out ChatGPT's usage limits for free and paid plans. Learn about Plus restrictions, Enterprise models, and how to check your usage.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/chatgpt_limits_1e513fc606.png" alt="ChatGPT usage limits explained: free vs plus vs enterprise" /><InfoBox className='BodyStyle'>

Claude Code can get expensive at scale. The alternative is self-hosting open-source models on Northflank. You can get started [here](https://app.northflank.com/signup), by deploying models like [Qwen3](https://northflank.com/stacks/deploy-qwen3-vl-instruct) and [DeepSeek](https://northflank.com/stacks/deepseek-v3-1). 

</InfoBox>

<InfoBox className="BodyStyle">

## TL;DR

ChatGPT's usage limits restrict access when you need it most, with a limit of 10 messages every 5 hours on the free plan. After reaching this limit, chats will automatically use the mini version of the model until your limit resets. Even enterprise customers face "fair use" policies that can unpredictably throttle usage.

The core problem isn't cost - it's control. You don't own your AI performance; you rent it, and it gets rationed.

Self-hosting open-source models with [Northflank](https://northflank.com/) gets rid of these limits entirely. You can deploy ChatGPT-quality models like [DeepSeek v3](https://northflank.com/blog/deploy-self-host-deep-seek-v3-1-on-northflank) or [GPT-OSS 120B](https://northflank.com/blog/self-host-openai-gpt-oss-120b-open-source-chatgpt) with no usage caps, complete data privacy, and costs up to 3.5x cheaper than ChatGPT's API pricing.

</InfoBox>

## What are the limits on ChatGPT Plus?

I know how annoying it gets when you reach your ChatGPT limit right when you need it most.

If you're using the free plan, you might find yourself cut off mid-conversation. Plus subscribers often wonder why they can no longer generate more images or access GPT-5. These usage restrictions can disrupt your workflow when you least expect it.

That's why platforms like Northflank exist - to help you deploy AI models without the unpredictable limits and restrictions that come with third-party APIs.

Let's look at what you get with each ChatGPT plan:

**ChatGPT Free Plan**:

- 10 messages every 5 hours using GPT-5
- Automatic downgrade to **GPT-4o mini** after limit
- 2-3 images per day with DALL-E

**ChatGPT Plus** ($20/month):

- 160 messages every 3 hours with GPT-5 (temporary increase)
- 50 images every 3 hours with DALL-E
- Priority access and faster speeds

**ChatGPT Business** ($25-30/user/month):

- Virtually unlimited GPT-5 messages (subject to fair use)
- Team workspace and analytics
- Custom GPT creation

**ChatGPT Pro** ($200/month):

- Unlimited access to all models including GPT-5 Pro
- No usage caps on advanced features
- Priority processing and fastest speeds

The Pro plan is significantly different from Business - it's designed for individual power users willing to pay $200/month for unlimited access, while Business is for teams at a much lower per-user cost.

## Why does ChatGPT have usage limits?

ChatGPT usage limits exist for several reasons that keep the platform stable.

![chatgpt-usage-limit.png](https://assets.northflank.com/chatgpt_usage_limit_931edab8a9.png)
*ChatGPT usage limit error message - [source](https://community.openai.com/t/youve-hit-your-usage-limit-please-try-again-later/835602/14)*

The main reason is **server resource management**. AI models like GPT-4 require massive computational power to function.

Let's say 100 power users each sent 1,000 requests per hour during peak times. This would generate 100,000 simultaneous requests, which could overload OpenAI's infrastructure and make the service unavailable for millions of regular users.

There's also **fair access distribution**. These limits ensure that ChatGPT remains accessible to millions of users worldwide, rather than allowing unlimited usage that would drive up costs for everyone.

**Cost control** plays a huge role as well.

Running large language models is incredibly expensive. Each GPT-4 response costs OpenAI significantly more than a GPT-3.5 response, which is why the more advanced models have stricter limits.

Finally, there's **abuse prevention**.

Without proper limits, the platform would be vulnerable to automated scraping, spam attacks, and other malicious activities that could destabilize the service for legitimate users.

## What are the limits of the free ChatGPT plan?

The free plan gives you up to 10 messages every 5 hours using GPT-5. After reaching this limit, chats will automatically use the mini version of the model until your limit resets.

![user on Reddit shares their experience hitting the free tier's message limit, showing the "Messages limit reached" notification that appears when you exceed the 10-message allowance within a 5-hour window](https://assets.northflank.com/free_message_limit_871e54bb72.png)*User on Reddit sharing their experience hitting the free tier's message limit*

You also get:

- Access to GPT-5 (OpenAI's current flagship model)
- 2-3 images per day with DALL-E
- File uploads with restrictions
- Web browsing capabilities
- No access to advanced voice mode or custom GPTs

The main difference from previous years is that free users now access OpenAI's most advanced model rather than being limited to older versions, though with strict usage limits.

## ChatGPT Plus usage limits: how to check usage

Your Plus subscription at $20/month gives you much higher allowances, but they're still capped. Let's see what the community has documented:

![A community-maintained table from OpenAI's Developer Community forum showing ChatGPT Plus usage limits. Note that this table from March 2025 reflects legacy model limits and doesn't include current GPT-5 allowances, which are now the primary model for Plus users. (Source: OpenAI Developer Community](https://assets.northflank.com/chatgpt_plus_limits_3a69d4bc98.png)*A community-maintained table from OpenAI's Developer Community forum showing ChatGPT Plus usage limits.*

Your current Plus limits include:

- **GPT-5**: Up to 160 messages every 3 hours (temporary increase)
- **DALL-E 3**: 50 images every 3 hours (rolling window)
- **Legacy models**: Separate limits for GPT-4o (~150 messages/3hrs) and others

The limits use rolling windows rather than daily resets. When you reach your GPT-5 limit, chats will switch to the mini version until the limit resets.

To check your usage:

1. Look for usage indicators near model selection
2. Check notifications about remaining messages
3. Monitor countdown timers for limit resets

## ChatGPT Business and Enterprise usage limits

Enterprise plans offer the highest usage allowances, but still aren't unlimited. Business plans ($25-30/user/month) provide virtually unlimited GPT-5 messages subject to fair use policies.

Enterprise customers get comprehensive analytics to monitor team usage:

![The ChatGPT Business/Enterprise User Analytics dashboard shows workspace administrators detailed insights including unique active users, total messages sent, and usage trends. This view helps track team adoption and identify power users across the organization](https://assets.northflank.com/chatgpt_user_analytics_4b559654df.png)*ChatGPT Workspace User Analytics dashboard*

Your analytics dashboard shows:

- **User activity tracking**: Monitor adoption, engagement, and usage trends across your workspace
- **Usage patterns**: Track team adoption and power users
- **Export capabilities**: Download weekly or monthly reports

Team/Enterprise plans are still subject to usage policies for policy enforcement, though limits are much more generous than individual plans.

## How to self-host open source models to get rid of limits
![gpt-oss.png](https://assets.northflank.com/gpt_oss_afc4936bd5.png)

With open-source models like **DeepSeek v3**, **Qwen3**, and **GPT-OSS** you can avoid usage restrictions entirely.

You choose:

- The exact model configuration and parameters
- The hardware it runs on
- Usage patterns (unlimited messages, custom batching)
- Complete data privacy (everything stays on your infrastructure)
- Performance optimization (latency, throughput, cost per token)

## Cost of self-hosting open source models

Let's compare self-hosting **DeepSeek v3** on Northflank with ChatGPT's pricing.

### GPU Pricing on Northflank

- **A100 (80GB)**: $1.76/hour
- **H100 (80GB)**: $2.74/hour
- **H200 (141GB)**: $3.14/hour
- **B200 (180GB)**: $5.87/hour

### How per-token costs are calculated

Let's walk through **DeepSeek v3** as an example:

**Step 1: Calculate hourly GPU cost**

- Requires: 8 × H200 GPUs
- Cost: 8 × $3.14 = **$25.12/hour**

**Step 2: Convert to cost per second**

- $25.12 ÷ 3,600 seconds = **$0.006978/second**

**Step 3: Measure processing speeds**

- Input tokens: 7,934 tokens/second
- Output tokens: 993 tokens/second

**Step 4: Calculate per-token costs**

- Input: $0.006978 ÷ 7,934 = $0.0000008795 per token
- Output: $0.006978 ÷ 993 = $0.0000070265 per token

**Step 5: Scale to millions**

- Input: **$0.88 per million tokens**
- Output: **$7.03 per million tokens**

**Compared to ChatGPT GPT-4o** ($2.50 input, $10.00 output):

- DeepSeek v3 is **2.8x cheaper** for input
- DeepSeek v3 is **1.4x cheaper** for output
- **Plus: No usage limits and complete data control**

## How to get started with self-hosted models

### Getting started with Northflank

**Step 1: Choose your model**

- **For ChatGPT replacement**: DeepSeek v3 or GPT-OSS 120B
- **For reasoning**: DeepSeek R1 or Qwen3 Thinking
- **For coding**: [Qwen3 Coder](https://northflank.com/stacks/deploy-qwen3-30b-coder-32k)
- **For speed**: [Qwen3 30B variants](https://northflank.com/stacks/deploy-qwen3-30b-thinking-32k)

**Step 2: Pick your deployment method**
![claude rate limits northflank template.png](https://assets.northflank.com/claude_rate_limits_northflank_template_0f8c89af17.png)

- **One-click templates**: Deploy popular models with pre-configured settings in minutes
- **Manual setup**: Full control over model parameters and GPU selection
- **Bring Your Own Cloud**: Use your existing AWS/GCP/Azure account for maximum control

**Step 3: Select your GPU**

- Small models (< 50GB): 1-2 GPUs
- Medium models (50-100GB): 2-4 GPUs
- Large models (100GB+): 8+ GPUs

### Quick start process
![claude rate limits.png](https://assets.northflank.com/claude_rate_limits_3cc738da17.png)

1. **Sign up** for a [Northflank account](https://app.northflank.com/login)
2. **Create a GPU-enabled project** in your preferred region
3. **Deploy from a template** or create a custom vLLM service
4. **Access via API** using OpenAI-compatible endpoints

Most models are serving requests within 30 minutes of starting deployment.

### What you'll need

- A Northflank account (free to create)
- Basic understanding of APIs (for integration)
- Your use case requirements (tokens/month, latency needs)

## Take control of your AI infrastructure

It’s time to get rid of ChatGPT usage limits and take control of your AI infrastructure.

[**Deploy your first model on Northflank**](https://northflank.com/stacks/gpt-oss-120b) and experience unlimited AI access with complete privacy and control.

Our templates make deployment as simple as clicking "launch" with no GPU expertise required. You get:

- **No usage limits**: Process unlimited requests
- **Complete data privacy**: Everything stays on your infrastructure
- **Predictable costs**: Pay only for compute, not per-token
- **Full control**: Customize models for your specific needs

**Learn more:**

- [Self-hosting OpenAI GPT alternatives guide](https://northflank.com/blog/self-host-openai-gpt-oss-120b-open-source-chatgpt)
- [GPT-OSS 120B deployment template](https://northflank.com/stacks/gpt-oss-120b)

For detailed setup guides and model recommendations, reach out to the [Northflank team](https://cal.com/team/northflank/northflank-intro).]]>
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  <title>B100 vs B200: Which NVIDIA blackwell GPU is right for your AI workloads?</title>
  <link>https://northflank.com/blog/b100-vs-b200</link>
  <pubDate>2025-09-02T16:15:00.000Z</pubDate>
  <description>
    <![CDATA[Compare NVIDIA B100 vs B200 GPUs on Northflank: specs, pricing, and availability. See why most clouds skip B100 for B200 or B300, and which delivers better value for large-scale AI workloads.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/azure_devops_alternatives_1_e5031dbe9b.png" alt="B100 vs B200: Which NVIDIA blackwell GPU is right for your AI workloads?" />When working with the latest technology, the GPU you choose determines the size of your models, their efficiency, and the speed at which you can scale. The NVIDIA B100 is already a massive leap over Hopper, bringing the new [Blackwell architecture](https://resources.nvidia.com/en-us-blackwell-architecture) to AI training and inference. But with the launch of the B200, NVIDIA is raising the bar again. 

The B200 keeps the same Blackwell foundation as the B100, but pushes it further with higher compute density, more memory bandwidth, and stronger multi-GPU scaling. That makes it especially relevant for teams training trillion-parameter models or running inference at a global scale. 

This article breaks down the key differences, with a focus on compute, memory, efficiency, and scaling, so you can see where each GPU fits in your stack.

## TL;DR: B100 vs B200 at a glance

If you’re short on time, here’s a quick look at how the B100 and B200 compare side by side.

<InfoBox className="BodyStyle">

**Blackwell GPU availability:** Both B100 and B200 availability can be constrained. For production workloads requiring guaranteed capacity, [request GPU capacity here](https://northflank.com/request/gpu) to plan ahead.

</InfoBox>

| Feature | B100 | B200 |
| --- | --- | --- |
| Architecture | Blackwell | Blackwell |
| FP64 compute | 30 TFLOPS | 40 TFLOPS |
| Cost on Northflank | NA | $5.87/hr |
| Memory | 192 GB HBM3e | 192 GB HBM3e |
| Memory bandwidth | Up to 8 TB/s | Up to 8 TB/s |
| FP4 tensor performance | 14 PFLOPS | 18 PFLOPS |
| FP8 performance | 7 PFLOPS | 9 PFLOPS |
| NVLink | 1.8 TB/s | 1.8 TB/s |
| TDP | 700W | 1000-1200W |
| Relative performance | 75% of B200 | Baseline |
| Target use cases | Enterprise AI | High-performance AI |

<InfoBox className='BodyStyle'>

**💭 What is Northflank?**

[Northflank](https://northflank.com/) is a full-stack AI cloud platform that helps teams build, train, and deploy models without infrastructure friction. GPU workloads, APIs, frontends, backends, and databases run together in one place so your stack stays fast, flexible, and production-ready.

[Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how it fits your stack.

> **Blackwell GPU availability:** Both B100 and B200 availability can be constrained. For production workloads requiring guaranteed capacity, [submit a GPU request](https://northflank.com/request/gpu) to plan ahead.

</InfoBox>

## B100: Everything you need to know

The B100 was the first GPU launched on NVIDIA’s [Blackwell architecture](https://resources.nvidia.com/en-us-blackwell-architecture). It uses a dual-die design connected by NV-HBI and packs 192 GB of HBM3e memory with 8 TB/s bandwidth.

Key features include:

- Dual-die GB100 design with over 200 billion transistors
- 192 GB of HBM3e memory with up to 8 TB/s bandwidth
- 5th-gen NVLink, delivering 1.8 TB/s per GPU
- Strong tensor performance across FP4, FP6, FP8, FP16, and FP64

This combination makes the B100 a versatile choice for both training and inference, particularly for teams looking to transition from Hopper to Blackwell with balanced compute and memory.

## B200: Everything you need to know

The B200 is NVIDIA’s most powerful Blackwell GPU, offering higher throughput across every precision level compared to the B100. Unlike the dual-die B100, the B200 is a single-die design, making it more efficient and better suited for large-scale clusters.

Highlights include:

- 192 GB of HBM3e memory at up to 8 TB/s bandwidth
- Higher FP4/FP8 performance (up to 18 PFLOPS sparse)
- 40 TFLOPS in FP64 for scientific and HPC workloads
- Same NVLink bandwidth as the B100, but with better efficiency per watt

The result is a GPU built to maximize throughput in both AI and HPC tasks, particularly for inference at scale and future LLM deployments.

## What are the differences between B100 and B200?

We’ve seen what the B100 and B200 can do individually, but the real question is how they compare head-to-head. Both are built on NVIDIA’s [Blackwell architecture](https://resources.nvidia.com/en-us-blackwell-architecture), yet the B200 refines it with key upgrades in compute density, memory bandwidth, and multi-GPU scaling. These differences don’t just show up in benchmarks; they matter in practice, whether you’re training trillion-parameter models, fine-tuning smaller LLMs, or serving them at scale.

Let’s break it down.

### 1. Compute power and tensor cores

Both GPUs are built on Blackwell’s dual-chip architecture, but the B200 packs more CUDA cores and Tensor Cores. This translates into higher peak FLOPS and stronger performance in FP8 and FP4 precision formats that matter for LLM training and inference.

### 2. Memory and bandwidth

The B100 supports HBM3e, while the B200 takes it further with even higher bandwidth and larger memory pools per GPU. This means the B200 handles bigger context windows and batch sizes without offloading to slower system memory.

### 3. Efficiency and power draw

Although both sit within the same broad TDP envelope, the B200 delivers **more compute per watt**, thanks to architectural optimizations and improved scheduling. That makes it more cost-efficient for long-running jobs.

### 4. NVLink and scaling

The B100 already supports 1.8 TB/s of NVLink bandwidth, but the B200 improves GPU-to-GPU interconnect efficiency, which helps when you’re scaling across massive clusters.

### 5. Software ecosystem

Because both are built on Blackwell, they share the same CUDA, cuDNN, and framework compatibility. Upgrading from B100 to B200 doesn’t require workflow changes, but you get performance gains “for free.”

## How much do B100 and B200 cost?

Once you’ve looked at features and performance, the next question is cost. Cloud pricing isn’t just about the hourly rate; it reflects how efficiently each GPU can complete your workloads.

On [Northflank](https://northflank.com/), here’s what the current hourly rates look like (September 2025):

| GPU | Memory | Cost per hour |
| --- | --- | --- |
| B100 | NA | NA |
| B200 | 180 GB | $5.87/hr |

The B100 is technically the entry point to Blackwell, but it isn’t widely available. Most cloud providers have skipped it in favor of B200, so you may not find B100 instances at all. The B200 comes at a premium, but with more memory and higher throughput, it can shorten training cycles and lower the total cost of running large-scale workloads.

## How to rent B200s

If you’re ready to try the B200, availability can be tricky. Most cloud providers skipped the B100 entirely and went straight to B200, so you won’t usually find B100 instances at all.

On [Northflank](https://northflank.com/), you can spin up B200 GPUs with transparent hourly pricing and no hidden commitments. Whether you’re fine-tuning an LLM or scaling distributed training, you can [launch a GPU instance in minutes](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to explore custom setups.

## Which one should you go for?

By now, you’ve seen how the B100 delivers balanced performance as the baseline Blackwell GPU, while the B200 pushes compute throughput further without changing the memory configuration. The right choice comes down to workload demands and budget.

| Use case | Recommended GPU |
| --- | --- |
| Training with balanced compute/memory | B100 |
| Inference with large LLMs | B200 |
| High-precision HPC workloads | B200 |
| Multi-GPU scaling | Both |
| Cost-conscious deployments | B100 |
| Maximum performance at scale | B200 |

## Wrapping up

The B200 is not a reinvention of Blackwell but an evolution that improves compute performance across the board. By boosting throughput and simplifying architecture, it enables faster inference, stronger HPC workloads, and smoother scaling for the largest models.

For teams pushing the frontier with cutting-edge workloads, the B200 will feel like a necessary upgrade. For those balancing cost and performance, the B100 remains a powerful entry point into Blackwell.

With Northflank, you can access GPUs on demand. Start with B100s today and seamlessly scale to B200s as your models grow. You can [launch GPU instances in minutes](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how the platform fits your workflow.]]>
  </content:encoded>
</item><item>
  <title>Top open-source alternatives to ChatGPT for companies: Self-hosting options</title>
  <link>https://northflank.com/blog/open-source-chatgpt-alternatives-enterprise</link>
  <pubDate>2025-09-01T17:08:00.000Z</pubDate>
  <description>
    <![CDATA[Top open-source alternatives to ChatGPT: GPT-OSS, Llama, DeepSeek, Qwen. Learn why self-hosting LLMs saves costs and keeps data secure.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/open_source_alternatives_to_chatgpt_608d9b948e.png" alt="Top open-source alternatives to ChatGPT for companies: Self-hosting options" />Every team I hear from has a similar story: ChatGPT changed productivity overnight, but then the valid concerns started showing up:

1. Where is our sensitive data going?
2. What happens when API costs scale with our usage?
3. How do we maintain compliance when we're sending everything to an external service?

These questions aren’t only important to large enterprises.

Startups and growing companies face similar challenges, where unpredictable API bills can strain budgets, and handing sensitive customer data to a third party isn’t always acceptable when trust and compliance are at stake.

This is why the open-source AI space has grown with enterprise-ready and startup-friendly alternatives.

I’ll walk you through the top open-source alternatives to ChatGPT (including OpenAI’s own GPT-OSS release) and show you how to self-host them on platforms like [Northflank](https://northflank.com/) to give your business complete control over AI infrastructure.

## Why are companies looking for open-source alternatives to ChatGPT?

The truth is, ChatGPT’s API-first approach creates challenges that grow as usage scales.

When you send every query through OpenAI’s servers, you’re essentially handing over your most sensitive business data to a third party with no guarantees about how it’s stored or processed.

We’re talking about your:

- Customer information
- Internal documents
- Strategic discussions
- And countless other sensitive details

Then there’s the cost reality.

What starts as a few dollars in API calls can quickly spiral to thousands as your team adopts AI across departments, or, for smaller companies, as adoption expands beyond one or two early use cases.

Now you’re paying per token with unpredictable pricing, facing rate limits during peak times, and building your entire AI strategy on infrastructure you don’t control.

If your company is serious about AI adoption, these aren’t minor inconveniences; they’re deal-breakers that demand a different approach.

## What are the top open source alternatives to ChatGPT for my company?

I’ve tested and worked with dozens of open source alternatives, but four consistently stand out for companies that want predictable costs and full data control.

Let’s see what you need to know about each:

### 1. OpenAI GPT-OSS: OpenAI's first open-weight model release

This is the newest option, and it’s making waves for providing powerful, high-quality models that can be run on-premises with full control.

![gpt-oss.png](https://assets.northflank.com/gpt_oss_d24c28f746.png)

OpenAI released two versions with a permissive Apache 2.0 license: a larger 120B parameter model that runs on an 80GB GPU, and a smaller 20B model that can run on consumer hardware with 16GB of memory.

Both deliver high reasoning performance, though they are not designed to be as capable as OpenAI’s most advanced proprietary models.

Choose GPT-OSS when you want advanced performance with the backing of OpenAI’s research, but you need the control that comes with self-hosting.

It performs well on complex reasoning tasks and maintains the familiar ChatGPT-like behavior due to its training.

However, the models are text-only and must be run on your own infrastructure; they are not accessed through OpenAI’s official API or ChatGPT interface.

As the user, you are responsible for maintaining, updating, and ensuring the safety of your deployment.

<InfoBox className='BodyStyle'>

If you want to get started with GPT-OSS, we've put together a [complete deployment guide](https://northflank.com/blog/self-host-openai-gpt-oss-120b-open-source-chatgpt) that walks you through setting it up on Northflank with one-click templates.

You can deploy it in **minutes** using our one-click stack with vLLM + Open WebUI, and best of all, **no rate limits** to worry about.

</InfoBox>

### 2. DeepSeek: Cost-effective reasoning models

DeepSeek has built a reputation for delivering impressive reasoning capabilities while being significantly more cost-effective than many alternatives.

This is largely thanks to its use of a Mixture-of-Experts (MoE) architecture, which enables huge parameter counts while only activating a smaller, more manageable subset for any given task.

The V3 and R1 series often perform above expectations, particularly in logical reasoning, coding, and mathematical problem-solving.

DeepSeek’s R1, for example, uses a “chain-of-thought” process to break down problems. That makes it slower than the faster, more general V3, but also more reliable for handling complex queries.

If your company needs to prove ROI before fully scaling AI infrastructure, DeepSeek is a natural fit. It combines performance with efficiency in a way that keeps budgets under control without sacrificing reasoning quality.

On top of that, DeepSeek releases its models under open-weight licenses, which has encouraged a wide and active community to build fine-tuned variants and tools. This makes it even more cost-effective and flexible for your use cases.

<InfoBox className='BodyStyle'>

If you want to deploy DeepSeek, we have put together comprehensive guides and one-click templates:

- [Deploy DeepSeek V3.1 on Northflank](https://northflank.com/blog/deploy-self-host-deep-seek-v3-1-on-northflank) - Complete setup guide for the latest version
- [One-click DeepSeek V3.1 stack](https://northflank.com/stacks/deepseek-v3-1) - Deploy instantly with our pre-configured template
- [Self-host DeepSeek R1 across cloud providers](https://northflank.com/blog/self-host-deepseek-r1-on-aws-gcp-azure-and-k8s-in-three-easy-steps) - Multi-cloud deployment in three steps
- [DeepSeek R1 with vLLM guide](https://northflank.com/guides/deploy-deepseek-r1-vllm-northflank-ai-llm) - Optimized inference setup
- [DeepSeek R1 70B on GCP](https://northflank.com/stacks/deploy-deepseek-r1-70b-gcp) - One-click Google Cloud deployment
- [DeepSeek R1 70B on Azure](https://northflank.com/stacks/deploy-deepseek-r1-70b-aks) - One-click Azure Kubernetes deployment

</InfoBox>

### 3. Qwen: Alibaba's solution

Qwen consistently delivers high performance across benchmarks, but where it stands out is in multilingual support.

Its latest models, including Qwen3 and specialized versions like Qwen-MT, are trained on massive multilingual datasets (up to 36 trillion tokens across 119 languages).

That gives you reliable results in translation, multilingual instruction-following, and managing nuanced conversational shifts across different languages.

If your company operates globally or needs AI that can handle multiple languages natively, Qwen is often the best fit. You’ll get broad linguistic coverage alongside dependable reasoning capabilities.

Alibaba also offers a full suite of options, including dense and Mixture-of-Experts (MoE) variants, as well as multimodal models for visual understanding.

<InfoBox className='BodyStyle'>

If you want to deploy Qwen, we have put together various deployment options for different use cases:

- [Self-host Qwen3 Coder with vLLM](https://northflank.com/blog/self-host-qwen3-coder-with-vllm) - Complete guide for the coding-optimized version
- [Deploy Qwen3 30B Thinking 32K](https://northflank.com/stacks/deploy-qwen3-30b-thinking-32k) - One-click deployment for reasoning tasks with 32K context
- [Deploy Qwen3 30B Coder 256K](https://northflank.com/stacks/deploy-qwen3-30b-coder-256k) - Coding specialist with extended 256K context window
- [Deploy Qwen3 235B Thinking 256K](https://northflank.com/stacks/deploy-qwen3-235B-thinking-256k) - The largest model for complex reasoning with maximum context
- [Deploy Qwen3 4B Instruct](https://northflank.com/stacks/deploy-qwen3-4b-instruct) - Lightweight option for resource-conscious deployments
- [Deploy Qwen3 30B Instruct 32K](https://northflank.com/stacks/deploy-qwen3-30b-instruct-32k) - Balanced performance for general instruction-following tasks

</InfoBox>

### 4. Meta Llama: Open-weight LLMs by Meta

Meta’s Llama models have become a standard in the open-weight AI space, known for their capabilities and broad accessibility.

The latest versions deliver powerful performance, with some reaching near-frontier capabilities.

If your company does significant software development or needs advanced coding assistance, Llama’s specialized and general-purpose variants give you targeted performance backed by a vast, active ecosystem.

What sets Llama apart is the massive ecosystem of tools, fine-tuned versions, and community support you can tap into.

This makes deployment much easier for your teams.

<InfoBox className='BodyStyle'>

If you want to deploy Llama models, you can use our [vLLM deployment guide](https://northflank.com/guides/self-host-vllm-in-your-own-cloud-account-with-northflank-byoc) which supports any Llama variant, or deploy directly using our general AI model deployment capabilities that work with the entire Llama family.

</InfoBox>

## How can I get started with Northflank?

Now that you know what it takes to self-host these models, the next step is making deployment simple and repeatable.

[Northflank](https://northflank.com/) is built to remove the complexity, so you can launch a model in minutes with a pre-built stack or scale a full deployment with orchestration, monitoring, and CI/CD.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

### 1. One-click deployment templates

You can get started instantly with [one-click stack templates](https://northflank.com/stacks) for [GPT-OSS](https://northflank.com/stacks/gpt-oss-120b), [DeepSeek](https://northflank.com/stacks/deepseek-v3-1), [Qwen](https://northflank.com/stacks/deploy-qwen3-30b-instruct-32k), and [Llama](https://northflank.com/guides/self-host-vllm-in-your-own-cloud-account-with-northflank-byoc). These come pre-configured with vLLM and Open WebUI so you can test models right away.

### 2. Step-by-step walkthrough

If you prefer a guided approach, follow our detailed [self-hosting guides](https://northflank.com/guides) that cover everything from provisioning GPUs to optimizing inference for enterworkloads. Each guide is written to be reproducible in any environment.

### 3. Scaling from prototype to production

Once your prototype is live, you can [scale deployments](https://northflank.com/blog/the-complete-guide-to-kubernetes-autoscaling) across GPUs, regions, and clouds. Northflank handles [orchestration](https://northflank.com/blog/container-orchestration), [autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments), and [CI/CD](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank), so your AI setup scales smoothly into production.

## Which model should I choose for my company’s needs?

This decision framework will help you select the right model to align with your business priorities. Most companies eventually adopt a “multi-LLM” approach, deploying different models for different use cases as they scale.

**1. Go with GPT-OSS if** you want a powerful, self-hostable model with a familiar conversational style. It performs well on reasoning and agentic tasks while giving you full control over data and costs. *Note: you’ll need internal expertise and hardware, and GPT-OSS is “open-weight” (downloadable with permissive licensing) rather than fully “open-source.” It’s also text-only.*

**2. Choose Llama if** you want proven reliability, community support, or coding assistance via Code Llama. Its ecosystem of fine-tuned variants makes deployment straightforward. *Note: Meta’s license is permissive but not fully open-source and restricts usage for very large companies.*

**3. Pick DeepSeek if** budget is your top priority but you still need competitive reasoning. Its Mixture-of-Experts design delivers excellent value for logic, math, and coding. *Note: some models like R1 are slower but more precise, while V3 is faster but more general. V3.1 offers a middle ground.*

**4. Select Qwen if** your company operates globally and needs multilingual capabilities with cultural context. *Note: developed in China, Qwen includes moderation and filtering that you’ll need to assess for compliance (e.g. EU AI Act). Performance also varies across different versions and “thinking” modes.*

**Performance vs. resource trade-offs:** Larger models (GPT-OSS 120B, Llama 70B+) deliver higher-quality results but require more GPU memory. Smaller ones (GPT-OSS 20B, Llama 8B) can run on consumer-grade hardware with trade-offs.

Most companies begin with one model to validate use cases, then scale into a mix of models for different workflows.

## Are open source ChatGPT alternatives good enough for companies?

Now that you’ve seen GPT-OSS, DeepSeek, Qwen, and Llama, the core question is: are they company-ready?

For years, the answer was “not yet.” In 2026, OpenAI’s release of GPT-OSS signaled a shift, showing that open-weight models can now compete at a serious level.

Today, these models deliver targeted strengths:

- **DeepSeek** for reasoning
- **Qwen** for multilingual capability
- **Llama** for coding assistance

They’re more than “good enough” and serve as practical, high-performance tools.

That said, readiness comes with considerations:

- Most models are *open-weight*, not fully open-source, and some (like Llama) have license restrictions.
- Self-hosting gives you control over data and costs but requires investment in management, security, and governance.
- Many companies adopt a hybrid strategy: APIs for quick use cases, self-hosted models for sensitive or high-volume workloads.

The bottom line is open-weight models are company-ready if you pair them with the right strategy and resources.

## What advantages does self-hosting give my company?

When you self-host, **your data stays in your environment.**

For instance, your customer records, internal documents, and strategic discussions remain fully under your control. For regulated industries like healthcare and finance, this isn’t optional; it’s the only way to stay compliant.

**Costs also become predictable.**

Rather than paying for every token with bills that grow unexpectedly, you pay for infrastructure that scales with your workload.

If usage is steady and high, this can reduce costs over time. Keep in mind that hardware, energy, and skilled staff are part of the equation, so for lighter workloads, APIs may still be more affordable.

**Self-hosting also removes external dependencies.**

Rate limits are set by your infrastructure; outages at a provider do not impact your workflows, and sudden pricing or policy changes do not dictate your roadmap.

**Self-hosting is not hands-off though.**

You take on responsibility for deployment, monitoring, security, and performance tuning, which requires a capable team.

**For many companies, the most effective path is hybrid:** APIs for fast experimentation and general tasks, with self-hosted models for sensitive, high-volume, or regulated workloads.

## How do I self-host these models for my company?

Self-hosting might sound complex, but it comes down to three key considerations:

### 1. Infrastructure requirements

Each model has different GPU needs.

Smaller variants like Llama 8B and GPT-OSS 20B can run on a single high-end consumer GPU, such as an NVIDIA RTX 4090 with 24GB of VRAM. This becomes more practical when you use memory optimization techniques like quantization.

Larger models such as GPT-OSS 120B and Qwen 235B, on the other hand, require multi-GPU clusters with high-bandwidth interconnects.

The rule of thumb is simple: match the model size to your available GPU memory, and scale out as usage grows. Keep in mind that longer context windows or fine-tuning will increase VRAM requirements.

### 2. Deployment options

You can start simple by running a model on a single GPU for prototyping, and scale up to multi-node clusters for production workloads. With frameworks like vLLM, inference optimization becomes plug-and-play, and scaling across multiple machines is straightforward. Companies often begin with one model for testing, then expand to a dedicated AI cluster as adoption spreads.

### 3. Container orchestration

This is where platforms like [Northflank](https://northflank.com/) make a difference. By packaging your models in containers and deploying with Kubernetes, you get autoscaling, monitoring, and high availability out of the box. It takes your AI infrastructure from “fragile experiment” to “production-ready system” with the reliability your teams expect.

## What's the business reasoning for self-hosting?

Self-hosting is about more than control; it’s also about economics.

At small scale, API-based AI may seem affordable.

However, as adoption spreads across teams, the per-token pricing model becomes increasingly unpredictable.

Running your own models flips the equation: you invest in infrastructure once, then scale usage without runaway costs.

For most companies, self-hosting becomes cost-effective once AI is no longer a side project and starts powering daily workflows.

ROI grows as more employees rely on the same infrastructure, distributing fixed costs over larger usage.

And the benefits extend beyond savings: full data ownership, built-in compliance, and the ability to customize models to your exact needs.

## How does this fit into my company’s AI strategy?

Think of self-hosting as the foundation for long-term AI adoption.

By owning the infrastructure, you avoid vendor lock-in, rate limits, and policy shifts that can disrupt your plans.

It also helps you future-proof your AI stack. Today’s top-performing model may be replaced tomorrow, but when you control the platform, swapping or adding models is a choice you make, not a dependency on a vendor’s roadmap.

## What are my next steps?

The best way to begin is to choose a model that aligns with your immediate business goal, such as reasoning, multilingual support, or coding assistance, and deploy it as a prototype. This gives your team hands-on experience without overcommitting.

From there, you can build on [Northflank’s deployment guides](https://northflank.com/guides) and [one-click stacks](https://northflank.com/stacks) to move from testing to production.

With Northflank, scaling into company-grade infrastructure is straightforward, allowing you to focus on producing value rather than infrastructure complexity.

### Resources to support your company’s AI journey

If you’d like to learn more about the technical and strategic side of self-hosting, these resources will help:

- [An engineer’s guide to open source AI models](https://northflank.com/blog/an-engineers-guide-to-open-source-ai-models) – A practical introduction to the leading models and how they compare.
- [Self-hosting AI models: The complete guide](https://northflank.com/blog/self-hosting-ai-models-guide) – A step-by-step walkthrough of the self-hosting process.
- [Open-source LLMs: The complete developer’s guide to deployment](https://northflank.com/blog/open-source-llms-the-complete-developers-guide-to-deployment) – Covers everything from infrastructure to scaling.
- [Why smart enterprises are insisting on BYOC for AI tools](https://northflank.com/blog/why-smart-enterprises-are-insisting-on-byoc-for-ai-tools) – Explains the growing trend of “bring your own cloud.”
- [7 best AI cloud providers](https://northflank.com/blog/7-best-ai-cloud-providers) – A breakdown of the top infrastructure options.
- [Top GPU hosting platforms for AI](https://northflank.com/blog/top-gpu-hosting-platforms-for-ai) – An overview of where to run compute-heavy models.
- [AI infrastructure: What it really takes](https://northflank.com/blog/ai-infrastructure) – An in-depth look at the hardware and orchestration layer.
- [Claude rate limits, pricing, and costs explained](https://northflank.com/blog/claude-rate-limits-claude-code-pricing-cost) – A useful comparison for teams weighing proprietary APIs vs. open-source.
- [Claude Code vs Cursor](https://northflank.com/blog/claude-code-vs-cursor-comparison) – An evaluation of AI coding assistants that highlights trade-offs with open-source options.]]>
  </content:encoded>
</item><item>
  <title>H100 vs H200 GPUs: Which Nvidia Hopper is right for your AI workloads?</title>
  <link>https://northflank.com/blog/h100-vs-h200</link>
  <pubDate>2025-09-01T15:45:00.000Z</pubDate>
  <description>
    <![CDATA[Run NVIDIA H100 or H200 on Northflank. Compare benchmarks, pricing, and use cases to decide which GPU powers your AI training and inference best.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/azure_devops_alternatives_e1b7d15fd0.png" alt="H100 vs H200 GPUs: Which Nvidia Hopper is right for your AI workloads?" />When you are scaling AI workloads, the GPU you pick decides how fast you train, how much you spend, and how far you can push your models. The NVIDIA H100 has been the standard for high-performance training, powering everything from large-scale LLMs to generative AI. But with the launch of the H200, NVIDIA is pushing that frontier further.

The H200 builds directly on the [Hopper architecture](https://www.nvidia.com/en-us/data-center/technologies/hopper-architecture/) of the H100, keeping the same compute foundation but delivering major upgrades in memory and bandwidth. That makes it especially relevant for teams running larger and more memory-hungry models.

This guide breaks down the differences, with benchmarks, real-world use cases, and cost comparisons to help you decide which one fits your stack.

## TL;DR: H100 vs H200 at a glance

If you're short on time, here’s a quick look at how the H100 and H200 compare side by side.

<InfoBox className="BodyStyle">

> **Planning large-scale H100 or H200 deployments?** For projects requiring dedicated capacity or specific availability guarantees, [request GPU capacity](https://northflank.com/request/gpu) to discuss your needs.

</InfoBox>

| Feature | H100 | H200 |
| --- | --- | --- |
| Architecture | Hopper | Hopper (enhanced) |
| Cost on Northflank | $2.74/hr (80 GB) | $3.14hr (141 GB) |
| Tensor cores | 4th Gen + Transformer Eng. | 4th Gen + Transformer Eng. |
| Memory type | HBM3 | HBM3e |
| Max memory capacity | 80 GB | 141 GB |
| Memory bandwidth | 3.35 TB/s | 4.8 TB/s |
| NVLink bandwidth | 900 GB/s | 900 GB/s |
| Multi-instance GPU | 7 instances, ~10 GB each | 7 instances, ~16–18 GB each |
| Power draw (SXM) | Up to 700 W | Up to 1000 W |
| Performance uplift | Baseline | ~30–45% higher inference |

<InfoBox className='BodyStyle'>

**💭 What is Northflank?**

[Northflank](https://northflank.com/) is a full-stack AI cloud platform that helps teams build, train, and deploy models without infrastructure friction. GPU workloads, APIs, frontends, backends, and databases run together in one place so your stack stays fast, flexible, and production-ready.

[Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how it fits your stack.

</InfoBox>

## H100: Everything you need to know

The H100 has become a popular choice for cutting-edge AI training. Built on the [Hopper architecture](https://www.nvidia.com/en-us/data-center/technologies/hopper-architecture/), it introduced FP8 precision, a dedicated transformer engine, and fourth-generation tensor cores.

Key features include:

- 80 GB of HBM3 memory with 3.35 TB/s bandwidth
- 900 GB/s NVLink, supporting large multi-GPU clusters
- MIG support, allowing up to seven isolated GPU slices
- Optimized transformer performance through the Hopper transformer engine

This combination makes H100 extremely versatile: from fine-tuning LLMs to running stable inference at scale, it has been the go-to GPU for teams needing both speed and flexibility.

## H200: Everything you need to know

The H200 doesn’t change the compute architecture of the H100. Instead, it solves the next bottleneck: memory.

Highlights include:

- 141 GB of HBM3e memory — almost double the H100
- 4.8 TB/s bandwidth — 40% faster than H100
- Larger MIG partitions (~16–18 GB each), useful for multi-tenant workloads
- Higher TDP ceiling (up to **700 W SXM / 600 W PCIe**), allowing maximum performance under heavy training

The result is a GPU that excels when models no longer fit comfortably on the H100. For large batch sizes, massive context windows, or memory-intensive inference, the H200 delivers a smoother, faster experience.

## What are the differences between H100 and H200?

We’ve looked at the H100 and H200 on their own, but the real question is how they stack up against each other. While both share the same Hopper architecture, the H200 makes targeted upgrades that shift performance in key areas like memory, bandwidth, and scaling. These differences can have a major impact depending on whether you’re training, fine-tuning, or deploying large models. Let’s break them down.

### 1. Architecture and tensor cores

Both GPUs retain the Hopper architecture with the same number of Tensor Cores. The H200 does not add new cores or change the design. It's faster and has a larger memory pipeline that keeps the existing cores fed, improving efficiency and reducing bottlenecks on memory-intensive workloads.

### 2. Memory and bandwidth

The H200 almost doubles memory capacity and boosts bandwidth to 4.8 TB/s, giving it the ability to handle larger datasets, support longer context windows, and cut training times on memory-bound workloads.

### 3. NVLink and scaling

Both GPUs use the same 900 GB/s NVLink for multi-GPU training. The H200’s larger and faster memory allows for bigger MIG partitions (up to 16.5 GB each), which improves efficiency in multi-tenant environments and supports smoother scaling of inference across many smaller workloads.

### 4. Deployment flexibility

Both come in PCIe and SXM form factors. The SXM versions are capped at the same 700 W TDP, so the H200 does not raise the power ceiling. The H200 delivers better performance per watt on memory-bound workloads thanks to its larger and faster memory, while PCIe remains the more cost-effective and cloud-friendly option.

### 5. Software compatibility

Because the H200 is still built on Hopper, it runs the same CUDA stack, drivers, and frameworks as the H100, ensuring an upgrade path that delivers performance gains without requiring workflow changes.

## H100 vs H200 performance benchmarks

On paper, the H200 doesn’t increase compute cores compared to the H100, but its memory and bandwidth upgrades translate into real-world performance gains.

According to [**NVIDIA’s official benchmarks**](https://resources.nvidia.com/en-us-data-center-overview/hpc-datasheet-sc23-h200), the H200 delivers:

- 1.4× speedup on Llama 2 13B inference
- 1.6× uplift on GPT-3 175B
- 1.9× faster performance on Llama 2 70B

![image - 2025-09-01T165111.303.png](https://assets.northflank.com/image_2025_09_01_T165111_303_e55b173bbe.png)

These improvements confirm that for large-model inference workloads, the H200 consistently outpaces the H100, while keeping the same Hopper architecture and software compatibility.

## **How much do H100 and H200 cost?**

After comparing features and performance, the next question is what it actually costs to run these GPUs in the cloud. Pricing reflects not just the raw hardware, but also how quickly each GPU can finish workloads.

On [Northflank](https://northflank.com/), here’s what the current hourly rates look like (September 2025):

| GPU | Memory | Cost per hour |
| --- | --- | --- |
| H100 | 80 GB | $2.74/hr |
| H200 | 141 GB | $3.14/hr |

The H100 remains the more affordable option for teams looking to balance performance and cost. The H200 comes at a premium, but with nearly double the memory and higher throughput, it can shorten training cycles and reduce the total cost of running large-scale workloads.

## Which one should you go for?

By now, you’ve seen how the H100 delivers proven performance across a wide range of AI workloads and how the H200 extends that with more memory, higher bandwidth, and faster throughput. Both are strong options, but they’re built for different priorities. The right choice comes down to the size of your models, your budget, and how much you value efficiency at scale. Here’s a breakdown to guide your decision.

| Use Case | Recommended GPU |
| --- | --- |
| Training vision models | H100 |
| Fine-tuning medium-sized LLMs | H100 |
| Inference with large batch sizes | H200 |
| Serving massive LLMs (70B+) | H200 |
| Multi-tenant GPU usage (MIG) | Both |
| Budget-conscious deployments | H100 |
| Maximum performance at scale | H200 |
| Energy-efficient long-term runs | H200 |

## Wrapping up

The H200 is not a reinvention of Hopper but an evolution that removes the memory and bandwidth limits of the H100. By expanding VRAM and accelerating data movement, it delivers higher throughput, more efficient scaling, and smoother performance on the largest AI models. 

For teams pushing the frontier with cutting-edge workloads, the H200 will feel like a necessary upgrade. For those balancing cost and performance in production, the H100 remains a powerful and proven option. 

With Northflank, you can access both. Start experiments on H100s today and seamlessly scale to H200s as your models grow. You can [launch GPU instances in minutes](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how the platform fits your workflow.]]>
  </content:encoded>
</item><item>
  <title>August 2025 | Product releases</title>
  <link>https://northflank.com/changelog/platform-august-2025-release</link>
  <pubDate>2025-08-31T07:15:00.000Z</pubDate>
  <description>
    <![CDATA[GPUs GPUs GPUs, bring your own registry, Northflank co-pilot, build enhancements and much more.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_august_changelog_min_e26a0854b0.png" alt="August 2025 | Product releases" />This release brings GPUs front and center with powerful new workload support across in PaaS for A100s, H100s, B200s and more, alongside bring-your-own registry for container builds, expanded logging and addon options, and smarter scheduling. 

We’ve introduced the Northflank AI Co-Pilot to help teams ship faster, streamlined build and template enhancements, and delivered dozens of fixes and improvements across infrastructure, workflows, and developer experience, all to keep scaling on Northflank smoother and more powerful than ever.

## 🏗 Infrastructure & workloads

### Added
- GPU workloads on PaaS: Run AI inference and training on A100s, H100s, B200s and other GPU types at competitive pricing. Includes stack templates, workload scheduling, and plan selection improvements.  
- Push Northflank container builds directly to your own private registry.  
- Northflank hosted Loki can now store logs in GCP storage as well as S3.  
- Northflank Organisation BYOC clusters now support wildcard domains.  
- Added support for snapshot thresholds on addons.  
- Support for custom vnet subnets and Cilium overlay mode on Azure clusters.  
- Global Secrets available at the team level; can be inherited and combined in templates and pulled into projects.  
- Organisation-level API tokens can now be created from existing RBAC roles.  
- PostgreSQL addons now support the `h3-pg` and `pg_partman` extensions.  

### Enhancements
- GPU scheduling logic improved for higher reliability.  
- Horizontal pod autoscaling improved for custom addon types.  
- Metrics added for Bring Your Own Kubernetes (BYOK).  
- Addon disk size limit raised to 1.5TB.  
- More robust handling of PostgreSQL permissions on startup.  
- Improved MySQL HA metrics representation for decimal values.  
- Ceph cluster config options now populate correctly on page load.  
- AWS cluster validation ensures nodes have enough pod capacity before creation.  
- Crontab validation now supports dynamic template arguments.  
- Pod termination dates recalculated correctly after termination events.  
- GCP provisioning error messages now display all errors if multiple occur.  
- Billing system updated: GPU usage requires pre-purchased credits, grace periods are more dynamic.  

### Fixed
- Fixed filtering of GPU plans in workload creation.  
- GPU stack templates now correctly filter regions/clusters based on GPU support.  
- Fixed addon storage overrides when using ref values in templates.  
- Fixed volume creation issues in clusters/regions without multi-RW support.  
- Fixed SSO user login issues during first sign-in.  
- Fixed API tokens being revoked when org roles were modified.  
- Fixed race condition that could crash service dashboard on restart.  
- Fixed PostgreSQL edge case where permissions were reset on startup.  

## 🧩 Templates & workflow

### Added
- Added support for Buildkit build secrets using secret mounts in Northflank builds.  
- Added support for custom addon Helm values in YAML.  
- New workflow loop node in templates, useful for generating multiple nodes dynamically (e.g. with secrets).  

### Enhancements
- Initial stack template configuration UX refined for smoother setup.  
- Template improvements: easier initial creation, draft name/description validation less restrictive, and commit hashes now shown correctly.  

### Fixed
- Template list no longer flashes on load and search works reliably.  
- Template count in projects now shows correct values.  

## 👩‍💻 Developer experience

### Added
- New command menu for faster navigation across resources.  
- Added command exec support in CLI and js-client for custom addon types (BYOA - Bring your own Addon).  
- Introduced AI Copilot Assistant — ask questions about Northflank primitives and platform usage.  
- Team dashboard, BYOC cluster info, and subdomain list redesigned for clarity.  

### Enhancements
- Build logs improved: cache promotion logic accounts for cache misses, log viewer displays all builds, and BuildKit progress is now shown step-by-step.  
- Cluster/node views updated: node pool list now shows architecture, deletion modals clearer, new buttons for cordoning/draining nodes.  
- Networking forms: domain add form has better descriptions; subdomain verification warns when Cloudflare proxy may interfere.  
- Billing page restructured for new credit system and incremental billing.  
- Faster fetching of repos, branches, and PRs from VCS providers.  
- Observe pages now scale better with larger pod counts.  
- Improved validation performance for DockerHub images.  
- General UI polish: sliding tabs highlight active tab, buttons and selectors behave consistently across screens, responsive improvements across many components.  

### Fixed
- White-labelling now shows the correct URL in GitHub deployments.  
- Combined Service fields no longer highlight without input.  
- Resource headers no longer shift when hovering.  
- Fixed display glitches in node affinity tags and volume performance metrics.  
- Password managers (e.g. Bitwarden) now autofill update password prompts correctly.  
- Fixed crashes on node pool form and build options page for legacy services.  
- Fixed log line sharing to use correct timestamp. 
- Fixed subdomain path service selector so project names can be searched.  
- Fixed project list hitting internal rate limits when listing many projects.  
- Fixed display of team/org resource quotas (now has its own page).  
- Audit logs now display deleted users correctly.  

See you next month!]]>
  </content:encoded>
</item><item>
  <title>Top 6 Azure DevOps alternatives in 2026</title>
  <link>https://northflank.com/blog/azure-devops-alternatives</link>
  <pubDate>2025-08-29T15:17:00.000Z</pubDate>
  <description>
    <![CDATA[Compare top 6 Azure DevOps alternatives in 2026. Find the best CI/CD platform, like Northflank, GitLab, and Jenkins, for your team.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/azure_devops_alternatives_1_f960a91ac0.png" alt="Top 6 Azure DevOps alternatives in 2026" />Isn't it surprising how, even with so many options available, finding the right DevOps platform that truly fits your team's workflow still feels challenging? We completely understand.

That's why we've put this comprehensive guide together to help you find an Azure DevOps alternative that matches what you're looking for.

Platforms like Northflank now provide simplified, all-in-one DevOps experiences that reduce the complexity of traditional enterprise tools.

*For Azure users specifically, [Northflank's Azure Kubernetes Service integration](https://northflank.com/cloud/azure) allows you to deploy directly into your Azure AKS clusters while maintaining the platform benefits you're looking for.*

We'll review six (6) platforms and compare them based on ease of use, pricing transparency, CI/CD capabilities, and developer experience.

<InfoBox className='BodyStyle'>

**Why you should go with Northflank?**

[Northflank](https://northflank.com/) stands out for several reasons.

It lets you [bring your own cloud](https://northflank.com/features/bring-your-own-cloud), including [Azure Kubernetes Service integration](https://northflank.com/cloud/azure), AWS EKS, GCP GKE, or bare-metal infrastructure.

You can also deploy to [Northflank's managed multi-cloud infrastructure](https://northflank.com/features/managed-cloud) for maximum simplicity.

You get both CPU and GPU workloads support - from [databases](https://northflank.com/features/databases), [APIs](https://northflank.com/docs/v1/api/introduction), and [background jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs) to AI model training and inference - with [CI/CD](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank), [auto-scaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments), [secure runtimes for code execution](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale), and [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) from pull requests.

All with transparent second-by-second pricing and zero fees for users or builds.

Kubernetes capabilities come without the learning curve, through a simple developer experience that gets you from code to production in minutes, not hours.

</InfoBox>

## Quick comparison of top 6 Azure DevOps alternatives

Let’s quickly see how the six leading Azure DevOps alternatives compare across the most important factors for technical teams.

| Platform | Best for | CI/CD | Pricing | Deploy in your cloud | Target audience |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | **Complete DevOps + AI platform** | Built-in CI/CD + auto-scaling + preview environments | **Free tier, transparent, pay-per-second usage** | **AWS, GCP, Azure, bare-metal, + managed multi-cloud) & self-hosted available** | **All teams: startups to enterprises, developers to SREs** |
| GitHub Actions | Git-first workflows | YAML workflows in repos | Free tier + per-minute billing | GitHub-hosted + self-hosted options | Open-source and GitHub-centric teams |
| GitLab | Integrated DevSecOps | Comprehensivepipeline features | $29/user/month + runner costs | Self-hosted available | Security-first organizations |
| Jenkins | Maximum customization | Plugin-based, self-managed | Free but requires infrastructure costs | Self-hosted required | Teams with dedicated DevOps engineers |
| Harness | Enterprise automation | AI-assisted deployments | Custom enterprise pricing | Multi-cloud support | Large organizations with complex needs |
| CircleCI | Build performance | Optimized cloud builds | Credit-based usage pricing | CircleCI-hosted + self-hosted options | Teams prioritizing fast feedback loops |

I know that the decision to choose the most suitable Azure DevOps alternative for your project isn't only about finding something cheaper.

So, what's it about? It comes down to choosing a platform that can answer these questions:

- Does it fit your team's development workflow and tech stack?
- Can it scale reliably as your projects grow?
- Does it integrate easily with your existing tools and processes?
- Is the developer experience intuitive and productive?

## Things to look out for when choosing Azure DevOps alternatives

I’ll list major factors you should keep in mind while comparing your options to help you make a suitable choice for your team’s long-term success and productivity.

### 1. Transparent pricing and cost predictability

How many times have you received an unexpected DevOps bill?

Complex pricing structures can catch your team off guard.

So, look for alternatives that make pricing clear and easy to understand.

You want to know what you'll be paying upfront, not get caught out with hidden costs for parallel jobs, additional users, or storage overages.

Platforms with transparent pricing models, cost calculators, and usage-based billing help you stay in control of your budget and avoid the complex billing layers that come with enterprise tools.

### 2. Deploy in your own cloud accounts - avoid vendor lock-in

One of the biggest advantages of many DevOps platforms is the ability to deploy in your own cloud accounts - AWS, Azure, or GCP.

This approach lets you maintain control over your data, use your existing [cloud credits](https://northflank.com/blog/how-to-get-free-aws-credits-for-your-startup), meet compliance requirements, and avoid vendor lock-in while still benefiting from a managed platform experience.

Look for platforms that support true multi-cloud deployment options, not only their own hosted infrastructure.

### 3. Developer experience and ease of adoption

The best DevOps platform is one your team wants to use.

The option you choose should prioritize developer experience with intuitive interfaces, clear documentation, and minimal setup challenges.

Look for platforms that support your preferred workflows, be it GitOps, infrastructure as code, or container-native deployments.

Think about how quickly your new team members can get productive, and if the platform supports both beginners and advanced users without forcing unnecessary complexity.

### 4. CI/CD capabilities and flexibility

Your CI/CD pipelines are the heart of your DevOps practice.

Take a close look at the alternatives to see if they support your pipeline needs, offer fast build performance, and work well with your tech stack.

Some platforms perform well with simple workflows, while others provide advanced features like parallel execution, complex approval flows, and multi-environment deployments.

Look for platforms that can grow with your needs, from simple automated testing to sophisticated deployment strategies with blue-green deployments, canary releases, and rollback capabilities.

### 5. Integration ecosystem and compatibility

You don't want to start from scratch when switching platforms.

The ideal alternative should work well with your existing tools, including your monitoring stack, security systems, and project management software.

Look for both native integrations and flexible APIs.

Modern platforms often provide extensive integration marketplaces or webhook support to connect with nearly any tool in your stack.

### 6. Scalability and cloud-native architecture

As your team and projects grow, your DevOps platform needs to keep pace.

Check how well alternatives handle increased load, larger teams, and more complex workflows.

Cloud-native platforms are designed to scale automatically and handle enterprise-grade workloads without the overhead of traditional solutions.

Think about both technical scalability (build performance, concurrent jobs) and organizational scalability (user management, permissions, compliance features).

## Top 6 Azure DevOps alternatives (A detailed comparison)

Now we can go into detail and review the six best alternatives to Azure DevOps, with each serving different team needs and preferences.

### 1. Northflank (#1 alternative to Azure DevOps)

[Northflank](https://northflank.com/) is a complete DevOps platform that combines the simplicity of PaaS with the flexibility of Kubernetes, providing teams with everything they need to build, deploy, and scale applications without infrastructure complexity.

With Northflank, you no longer have to manage multiple tools.

Northflank provides a unified (or single) platform that covers CI/CD, container orchestration, database management, observability, and scaling - all with transparent, usage-based pricing and support for any cloud or tech stack.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

**Now, what makes Northflank the top choice for technical teams:**

1. **All-in-one platform that replaces multiple tools**
    
    You can deploy anything from static sites to complex microservices, AI workloads, and databases through a single, intuitive interface. Replace Azure DevOps, your cloud provider console, and multiple monitoring tools with one comprehensive platform.
    
2. **Kubernetes-native without the operational overhead**
    
    You can get all the benefits of Kubernetes (auto-scaling, health checks, rolling deployments, service mesh) through a developer-friendly interface. Northflank abstracts away cluster management while giving you full control when needed, which is also suitable for platform engineers who want capabilities without toil.
    
3. **True multi-cloud flexibility with BYOC (Bring Your Own Cloud)**
    
    You can deploy on either Northflank's [managed multi-cloud infrastructure](https://northflank.com/features/managed-cloud) for simplicity, or bring your own [AWS](https://northflank.com/cloud/aws), [GCP](https://northflank.com/cloud/gcp), [Azure](https://northflank.com/cloud/azure), or bare-metal infrastructure with [BYOC](https://northflank.com/features/bring-your-own-cloud). Use your existing cloud credits, compliance requirements, and billing relationships while benefiting from Northflank's platform experience.
    
4. **Support for both CPU and GPU workloads**
    
    You can run traditional applications like your databases, APIs, background jobs, and CI/CD pipelines alongside AI workloads, including model training, inference, and Jupyter notebooks. All on the same platform with consistent management.
    
5. **Built for today's development workflows**
    
    You get native [GitOps support](https://northflank.com/docs/v1/application/infrastructure-as-code/gitops-on-northflank), [infrastructure as code with templates](https://northflank.com/docs/v1/application/infrastructure-as-code/infrastructure-as-code), [preview environments from pull requests](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), and [integrated observability](https://northflank.com/docs/v1/application/observe/observability-on-northflank). Everything technical teams need for current software delivery practices.
    
  <InfoBox className='BodyStyle'>
    
### 🤑 Northflank pricing

- Free tier: Generous limits for testing and small projects
- CPU instances: Starting at $2.70/month ($0.0038/hr) for small workloads, scaling to production-grade dedicated instances
- GPU support: NVIDIA A100 40GB at $1.42/hr, A100 80GB at $1.76/hr, H100 at $2.74/hr, up to B200 at $5.87/hr
- Enterprise BYOC (Bring Your Own Cloud): Flat fees for clusters, vCPU, and memory on your infrastructure, no markup on your cloud costs
- Pricing calculator available to estimate costs before you start
- Fully self-serve platform, get started immediately without sales calls
- No hidden fees, egress charges, or billing complexity
    
</InfoBox>
    
**If your needs or problems are:**

- **"We want Kubernetes capabilities without the operational complexity"**
    
    Northflank leverages Kubernetes as an operating system to give you the best of cloud native, without the overhead. You get [container orchestration](https://northflank.com/blog/container-orchestration), [auto-scaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments), and service mesh through an intuitive developer interface that doesn't require Kubernetes expertise.
    
- **"We need an all-in-one platform that reduces tool management"**
    
    From code commit to production monitoring, Northflank handles your entire DevOps lifecycle. Deploy services, databases, [cron jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs), AI workloads, and more from one unified platform. No more context switching between Azure DevOps, cloud consoles, and monitoring tools.
    
- **"We want transparent pricing without hidden bills or user-based fees"**
    
    Northflank's usage-based [pricing](https://northflank.com/pricing) means you pay for actual compute consumption, billed by the second. No fees for users, builds, parallel jobs, or platform features. Ideal for startups scaling up and enterprises looking to control costs.
    
- **"We need flexibility to run anywhere while maintaining platform benefits"**
    
    Deploy to Northflank's global regions for maximum simplicity, or connect your own [GKE](https://northflank.com/cloud/gcp), [EKS](https://northflank.com/cloud/aws), [AKS](https://northflank.com/cloud/azure), or bare-metal clusters. Keep your data residency, compliance, and billing requirements while getting a managed platform experience.
    
- **"We're building cloud-native applications with containers and microservices"**
    
    Built for containers, microservices, and cloud-native patterns from day one. Native [Docker support](https://northflank.com/deploy/run-persistent-and-ephemeral-docker-containers), [GitOps workflows](https://northflank.com/docs/v1/application/infrastructure-as-code/gitops-on-northflank), [automatic scaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments), service mesh, and integrated [observability](https://northflank.com/docs/v1/application/observe/observability-on-northflank) for teams building applications today.
    
- **"We need enterprise features without enterprise complexity"**
    
    [Role-based access control](https://northflank.com/docs/v1/application/secure/use-role-based-access-control), [audit logging](https://northflank.com/docs/v1/application/observe/audit-logs), [SSO integration](https://northflank.com/docs/v1/application/secure/single-sign-on-multi-factor-authentication#single-sign-on), and [multi-team support](https://northflank.com/docs/v1/application/collaborate/manage-an-organisation) - all without the overhead of traditional enterprise platforms. Ideal for both growing startups and established enterprises.
    

### 2. GitHub Actions

GitHub Actions transforms GitHub from a code repository into a complete DevOps platform, providing native CI/CD capabilities directly within your development workflow.

Built into the GitHub ecosystem, Actions provides workflow automation that responds to any GitHub event, from pull requests to issue updates.

With a marketplace of thousands of pre-built actions and the ability to create custom workflows, it's become the go-to choice for teams already using GitHub.

![Github actions home page.png](https://assets.northflank.com/Github_actions_home_page_6093a76be8.png)

What it offers:

- **Works natively with our GitHub repositories:**
    
    GitHub Actions works natively within GitHub repositories, keeping your code, issues, pull requests, and CI/CD workflows in one unified platform. Workflows automatically trigger on repository events and display status directly in pull requests.
    
- **Provides fast setup without complex configuration:**
    
    Actions uses simple YAML workflows stored in your repository. The extensive marketplace means you can often find pre-built actions for your exact use case, reducing setup time from hours to minutes.
    
- **Provides cost-effective CI/CD for small to medium teams:**
    
    With 2,000 free minutes per month for private repositories and unlimited minutes for public repositories, GitHub Actions offers excellent value for teams getting started.
    
- **Provides cloud-native CI/CD capabilities:**
    
    Actions natively supports containers, Kubernetes deployments, and all major cloud providers through an extensive marketplace of integrations.
    

### 3. GitLab

GitLab is a DevSecOps platform that provides everything from planning and source code management to security testing and deployment, all within a single application.

Rather than requiring multiple tools integrated together, GitLab provides a complete DevOps lifecycle solution with built-in CI/CD, security scanning, project management, and monitoring capabilities.

![gitlab-homepage.png](https://assets.northflank.com/gitlab_homepage_55ffb99398.png)

What it offers:

- **Integrated security and compliance:**
    
    GitLab includes advanced security features such as SAST, [DAST](https://thectoclub.com/tools/best-dast-tools/), dependency scanning, and container scanning, making it ideal for teams prioritizing DevSecOps practices. Security scans run automatically in merge requests.
    
- **DevOps platform without vendor management:**
    
    GitLab reduces the need to integrate multiple tools by providing planning, SCM, CI/CD, security, and monitoring in one comprehensive platform.
    
- **Project management with development integration:**
    
    Beyond code, GitLab includes project management features with issue tracking, milestone management, and agile planning tools that link directly to your development workflow.
    
- **Flexibility between cloud and self-hosted deployment:**
    
    GitLab provides both SaaS and self-managed options, giving you control over where your data lives while maintaining feature parity between deployment models.
    

### 4. Jenkins

Jenkins is a CI/CD platform with an ecosystem of over 1,500 plugins that can integrate with virtually any tool or technology in your stack.

As an open-source automation server, Jenkins gives you complete control over your build and deployment processes, making it the choice for teams with complex, customized workflows that other platforms can't accommodate.

![jenkins website.png](https://assets.northflank.com/jenkins_website_f279c50098.png)

What it offers:

- **Flexibility and customization:**

Jenkins, being open-source, is highly flexible and customizable, making it ideal for experienced DevOps teams.

- **Integration across many tools:**
    
    Jenkins offers over 1,500 plugins to integrate with various tools and services in the DevOps ecosystem.
    
- **Control over CI/CD infrastructure**:
    
    Being open-source and self-hosted, Jenkins avoids licensing fees and vendor dependency. You control every aspect of your CI/CD infrastructure.
    
- **Complex and requires experienced DevOps teams who can handle complexity:**
    
    Jenkins' popularity stems from its flexibility, but it requires expertise to manage effectively and may need significant setup and maintenance effort.
    

### 5. Harness

Harness provides automated pipeline optimization, deployment verification, and cost management to help teams ship faster with less risk.

Built for enterprise environments, Harness focuses on reducing the complexity and toil associated with traditional CI/CD while providing enterprise-grade security, governance, and scalability.

![harness.png](https://assets.northflank.com/harness_6ed883f12e.png)

What it offers:

- **Detecting anomalies automatically**:
    
    Harness uses machine learning to automatically verify deployments, detect anomalies, and recommend optimizations.
    
- **Enterprise-scale CI/CD with advanced governance**:
    
    Harness provides RBAC, policy management, and audit trails required for large organizations, while maintaining developer velocity through automation.
    
- **Optimizing cloud costs alongside deployments**:
    
    Beyond CI/CD, Harness includes cloud cost optimization features that help reduce infrastructure spend while maintaining performance.
    

### 6. CircleCI

CircleCI is built for speed and provides cloud-native CI/CD that prioritizes fast feedback loops and developer productivity through optimized build performance and intelligent caching.

With a focus on simplicity and performance, CircleCI provides advanced CI/CD capabilities without overwhelming complexity, making it popular among teams that want reliable automation without extensive configuration overhead.

![circleci home page.png](https://assets.northflank.com/circleci_home_page_5010422a55.png)

What it offers:

- **Fast build times and quick feedback on code changes:**
    
    CircleCI is optimized for performance with intelligent caching, parallelization, and resource classes that let you scale compute resources for faster builds.
    
- **Simple setup with advanced features**:
    
    CircleCI configuration uses clear YAML files with intuitive syntax, while providing advanced features like matrix builds, conditional workflows, and approval jobs when needed.
    
- **Cloud-native CI/CD**:
    
    CircleCI represents the new generation of cloud-first tools designed for current development workflows, avoiding the infrastructure management overhead of traditional solutions.
    
- **Predictable pricing based on usage**:
    
    CircleCI provides transparent pricing based on compute usage, letting you optimize costs by right-sizing your builds and using efficient resource allocation.
    

## Frequently asked questions about Azure DevOps alternatives

Still trying to figure things out? That's completely normal. Below are some common questions people have when comparing Azure DevOps with other platforms, along with practical answers to help you get clarity.
    
1. **Why are teams moving away from Azure DevOps?**
    
    Teams often move away from Azure DevOps due to its steep learning curve, complex pricing structure, and enterprise-focused approach that can feel heavy for current development workflows. Alternatives like Northflank offer simpler onboarding, transparent pricing, and cloud-native architectures.
    
2. **Is GitHub Actions better than Azure DevOps?**
    
    It depends on your needs. GitHub Actions is good in simplicity and integration with current development workflows, while Azure DevOps provides more comprehensive enterprise features. For most teams, the choice comes down to if you prioritize ease of use or comprehensive feature sets.
    
3. **What about alternatives like Northflank?**
    
    Platforms like Northflank provide the best of both worlds: enterprise-grade capabilities with consumer-grade simplicity. You get Kubernetes capabilities without complexity, transparent pricing without hidden costs, and an all-in-one platform without tool management overhead.
    

## Making the right choice for your team

Now that you've seen what each platform offers, the next step is figuring out which one aligns best with your team's needs and goals. The right DevOps platform isn't always the one with the most features; it's the one that simplifies your workflow without adding unnecessary complexity.

> **Our recommendation:** For most teams looking to move away from Azure DevOps, Northflank provides the best balance of capability and simplicity. You get an all-in-one platform that handles everything from CI/CD to scaling, with transparent pricing and the flexibility to run anywhere, including your existing Azure infrastructure.
> 

A few important factors as you decide:

- **Start with your current workflow**: Choose a platform that works with your existing tools and processes, not one that forces you to completely restructure your development practices
- **Evaluate your team's expertise**: Some platforms are designed for DevOps specialists, while others prioritize ease of use for general development teams
- **Look at total cost of ownership**: Think beyond licensing to include setup time, maintenance overhead, and training requirements
- **Plan for scalability**: Choose a platform that can grow with your team, both in terms of technical capabilities and organizational features
- **Focus on developer experience**: The best platform is one your team enjoys using and can be productive with from day one.

<InfoBox className='BodyStyle'>

If you're looking for a developer-focused platform that combines the simplicity of PaaS with the capabilities of Kubernetes, while avoiding vendor lock-in and complex pricing, [see how Northflank works for teams like yours](https://app.northflank.com/signup).

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Best 6 cloud application hosting platforms for 2026</title>
  <link>https://northflank.com/blog/cloud-application-hosting-platforms</link>
  <pubDate>2025-08-28T18:34:00.000Z</pubDate>
  <description>
    <![CDATA[Best cloud application hosting platforms for 2026. Compare Northflank, AWS, Heroku &amp; more options.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/cloud_application_hosting_platforms_b86952304c.png" alt="Best 6 cloud application hosting platforms for 2026" />Cloud application hosting has gotten so much better.

What used to require complex server management and expensive infrastructure can now be deployed in minutes with platforms that handle everything from scaling to security.

If you're looking for a cloud application hosting solution that combines the simplicity of Heroku with enterprise-grade reliability, you're in the right place.

Platforms like [Northflank](https://northflank.com/) are improving how developers deploy and manage applications in the cloud.

Let's look at the top platforms that can run your next project.

## What is cloud application hosting?

Cloud application hosting is the practice of running your applications on virtual servers distributed across multiple data centers rather than on a single physical server.

Rather than managing your own hardware, cloud providers handle the infrastructure while you focus on building your application.

When you deploy an app to the cloud, it's automatically distributed across multiple servers. This means better performance, automatic scaling, and higher reliability than traditional hosting methods.

If one server fails, your application continues to run on the others. Plus, you only pay for the resources you use.

## What are the benefits of cloud application hosting?

Let's look at why cloud application hosting has become the go-to choice for developers and businesses of all sizes.

1. **Scalability without the complexity:**
    
    Your application can automatically handle traffic spikes without you lifting a finger. If you get 100 visitors or 100,000, cloud platforms like Northflank adjust resources instantly.
    
2. **Enterprise-grade reliability:**
    
    Unlike older platforms that might crash under pressure, we now have cloud hosting providers that offer 99.9% or higher uptime guarantees. Your applications stay online even during hardware failures.
    
3. **Cost efficiency:**
    
    Pay-as-you-go pricing means you're not stuck paying for resources you don't use. Start small and scale up as your business grows.
    
4. **Global reach:**
    
    Deploy your applications closer to your users with multi-region hosting. Better performance worldwide without managing multiple servers yourself.
    
5. **Built-in security:**
    
    Modern platforms include SSL certificates, DDoS protection, and regular security updates by default. So, you don’t have to become a security expert overnight.
    

## How to choose the best cloud application hosting platform

Now that you understand the benefits, let's walk through what to look for when choosing the right cloud hosting platform for your needs.

1. **Developer experience:**
    
    Look for platforms that let you deploy with a simple git push or a few clicks. The best platforms feel intuitive from the start, not after weeks of reading documentation.
    
2. **Reliability is non-negotiable:**
    
    Choose platforms with proven track records and transparent uptime statistics. Your business depends on your applications being available when customers need them.
    
3. **Scaling should be automatic:**
    
    Select platforms that handle traffic spikes without manual intervention. You shouldn't need to wake up at 3 AM to scale your servers.
    
4. **Enterprise features for growing teams:**
    
    As your team grows, you'll need features like team management, audit logs, and advanced monitoring. Make sure your platform can grow with you.
    
5. **Fair and transparent pricing:**
    
    Avoid platforms with hidden fees or complex pricing structures. The best providers offer clear, predictable pricing that scales with your usage.
    

## Top cloud application hosting platforms for 2026

Now that you know what to look for, let’s review the platforms that best deliver on these criteria.

### 1. Northflank – All-in-one cloud hosting for all workloads with zero vendor lock-in

[Northflank](https://northflank.com/) is the **all-in-one cloud application hosting platform** that lets you deploy **both AI and traditional workloads** (databases, APIs, background jobs, CI/CD pipelines, and full-stack web applications) anywhere - in our cloud or yours.

What sets Northflank apart is its focus on developer experience without compromising the advanced features enterprises require.

Unlike other platforms that lock you into their infrastructure, Northflank allows you to [**deploy in your own cloud accounts**](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) - AWS, Azure, or GCP - while benefiting from the same managed platform experience.

This means you **avoid vendor lock-in** and stay in control of your data, with simple deployments, automatic scaling, and comprehensive monitoring at affordable pricing.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

<InfoBox className='BodyStyle'>

### 🤑 **Northflank pricing**

- Free tier: Generous limits for testing and small projects
- CPU instances: Starting at $2.70/month ($0.0038/hr) for small workloads, scaling to production-grade dedicated instances
- GPU support: NVIDIA A100 40GB at $1.42/hr, A100 80GB at $1.76/hr, H100 at $2.74/hr, up to B200 at $5.87/hr
- Enterprise BYOC (Bring Your Own Cloud): Flat fees for clusters, vCPU, and memory on your infrastructure, no markup on your cloud costs
- Pricing calculator available to estimate costs before you start
- Fully self-serve platform, get started immediately without sales calls
- No hidden fees, egress charges, or surprise billing complexity

</InfoBox>

**What you can deploy on Northflank:**

- **Web applications and frontends:**
    
    You can deploy React, Vue, Angular, or any modern frontend framework. Northflank handles the build process and serves your apps through a global CDN for optimal performance.
    
    See these guides (including stack templates) on:
    
    - [Deploying React App on Northflank](https://northflank.com/guides/deploying-react-app-on-northflank)
    - [Deploy Angular on Northflank](https://northflank.com/stacks/deploy-angular)
    - [Deploy Vue.js on Northflank](https://northflank.com/stacks/deploy-vue)
    
- **API services and microservices:**
    
    If you're building with Node.js, Python, Go, or any other language, Northflank makes it simple to deploy and manage your backend services. Automatic scaling ensures your APIs can handle any traffic load.
    
    See these guides (including stack templates):
    
    - [Deploy Node Express on Northflank](https://northflank.com/stacks/deploy-node-express)
    - [Deploying Flask on Northflank](https://northflank.com/guides/deploying-flask-on-northflank)
    - [Deploying Django on Northflank](https://northflank.com/guides/deploying-django-on-northflank)
    - [Deploying NestJS on Northflank](https://northflank.com/guides/deploying-nest-js-on-northflank)
    - [Deploying Next.js on Northflank](https://northflank.com/guides/deploy-next-js-on-northflank) (guide)
    - [Deploy Next on Northflank](https://northflank.com/stacks/deploy-next) (stack template)
    - [Deploy NestJS with TypeScript on Northflank](https://northflank.com/guides/deploy-nest-js-with-typescript-on-northflank)
    - [Deploy NestJS with TypeScript and MySQL on Northflank](https://northflank.com/guides/deploy-nest-js-with-typescript-and-mysql-on-northflank)
    - [Deploy NestJS with JavaScript on Northflank](https://northflank.com/guides/deploy-nest-js-with-javascript-on-northflank)
    
- **Databases and data services:**
    
    If you need PostgreSQL, MongoDB, Redis, or other databases, Northflank provides managed database services (like [Managed PostgreSQL](https://northflank.com/dbaas/managed-postgresql) and [Managed MySQL](https://northflank.com/dbaas/managed-mysql)) that integrate directly with your applications. Database administration becomes much simpler.
    
    See these guides:
    
    - [Deploy PostgreSQL on Northflank](https://northflank.com/docs/v1/application/databases-and-persistence/deploy-databases-on-northflank/deploy-postgresql-on-northflank) ([Migrate your PostgreSQL database to Northflank](https://northflank.com/docs/v1/application/databases-and-persistence/migrate-data-to-northflank/migrate-your-postgresql-database-to-northflank))
    - [Deploy MySQL on Northflank](https://northflank.com/docs/v1/application/databases-and-persistence/deploy-databases-on-northflank/deploy-mysql-on-northflank) ([Migrate your MySQL database to Northflank](https://northflank.com/docs/v1/application/databases-and-persistence/migrate-data-to-northflank/migrate-your-mysql-database-to-northflank))
    - [Deploy MongoDB® on Northflank](https://northflank.com/docs/v1/application/databases-and-persistence/deploy-databases-on-northflank/deploy-mongodb-on-northflank) ([Migrate your MongoDB® database to Northflank](https://northflank.com/docs/v1/application/databases-and-persistence/migrate-data-to-northflank/migrate-your-mongodb-database-to-northflank))
    - [Deploy Redis® on Northflank](https://northflank.com/docs/v1/application/databases-and-persistence/deploy-databases-on-northflank/deploy-redis-on-northflank) ([Migrate your Redis® deployment to Northflank](https://northflank.com/docs/v1/application/databases-and-persistence/migrate-data-to-northflank/migrate-your-redis-deployment-to-northflank))
    - [Deploy Spring Boot with PostgreSQL on Northflank](https://northflank.com/guides/deploy-spring-boot-with-postgresql-on-northflank)
    - [Deploy FastAPI with PostgreSQL on Northflank](https://northflank.com/guides/deploy-fastapi-postgres-cloud-docker)
    - [Deploy pgAdmin with PostgreSQL on Northflank](https://northflank.com/guides/deploy-pgadmin-with-postgresql-on-northflank)
    - [How to deploy pgvector in 1 minute (using Northflank)](https://northflank.com/blog/how-to-deploy-pgvector)
    - [Integrate MongoDB Atlas with Northflank](https://northflank.com/docs/v1/application/databases-and-persistence/integrate-with-a-database-provider/integrate-mongodb-atlas-with-northflank)
    
- **Background jobs and workers:**
    
    You can deploy worker processes, [scheduled jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs), and background tasks that scale independently from your web applications. Suitable for processing queues, sending emails, or handling batch operations.
    
- **Static sites and documentation:**
    
    You can host marketing sites, documentation, or any static content with automatic [SSL](https://northflank.com/docs/v1/application/domains/certificate-generation), [CDN](https://northflank.com/docs/v1/application/domains/use-a-cdn) distribution, and instant deployments from your git repository.
    
- **Container-based applications:**
    
    You can bring your own [Docker containers](https://northflank.com/deploy/run-persistent-and-ephemeral-docker-containers) or let Northflank build them for you. You can also have full control over your application environment with the simplicity of [managed hosting](https://northflank.com/features/managed-cloud).
    
- **AI and machine learning workloads:**
    
    You can deploy and scale [open-source models](https://northflank.com/blog/an-engineers-guide-to-open-source-ai-models) like Llama and [Deepseek](https://northflank.com/blog/deploy-self-host-deep-seek-v3-1-on-northflank), run inference APIs, host [Jupyter notebooks](https://northflank.com/guides/deploy-juypter-notebook-with-tensorflow-in-aws-gcp-and-azure), and manage long-running AI agents. Northflank supports GPU instances (NVIDIA H100, B200) with fractional GPU workloads, spot instances, and secure code execution for AI applications.
    
<InfoBox className='BodyStyle'>

Northflank handles all the infrastructure complexity while giving you the control you need.

You can deploy from GitHub with automatic builds, monitor performance with built-in observability, and scale globally without the typical complexity of an enterprise platform.

See how:
- [Clock is scaling and simplifying its infrastructure across 30,000 deployments with 100% uptime using Northflank](https://northflank.com/blog/scaling-30-000-deployments-with-100-uptime-how-clock-uses-northflank-to-simplify-infrastructure)
- [how Cedana uses Northflank to deploy workloads onto Kubernetes with microVMs and secure runtimes](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes).

*For more guides on deploying and self-hosting open-source tools and frameworks on Northflank, check out our [comprehensive guides](https://northflank.com/guides).*

[Try out the cloud platform now](https://app.northflank.com/signup) or [book a demo with an Engineer](https://cal.com/team/northflank/northflank-intro)

</InfoBox>

### 2. Heroku – The original developer-friendly platform

Heroku pioneered the simple git-push deployment model that many developers love. It's great for getting started quickly, though some users experience reliability issues as applications scale.

It is best suited for small to medium projects, quick prototyping, and when your team prefers minimal infrastructure management.

![heroku-managed-postgresql.png](https://assets.northflank.com/heroku_managed_postgresql_088bbf42ae.png)

**What you can deploy on Heroku:**

- Web applications in Ruby, Node.js, Python, Java, PHP, and other languages
- Simple databases through add-ons like Heroku Postgres
- Background job processing with worker dynos
- Static sites and single-page applications

You might also want to see:

- [How to migrate from Heroku: A step-by-step guide](https://northflank.com/blog/how-to-migrate-from-heroku-a-step-by-step-guide)
- [Top Heroku alternatives in 2026](https://northflank.com/blog/top-heroku-alternatives)
- [Heroku Enterprise: capabilities, limitations, and alternatives](https://northflank.com/blog/heroku-enterprise-capabilities-limitations-and-alternatives)

### 3. AWS Elastic Beanstalk – Amazon's managed platform

Part of the massive AWS ecosystem, Elastic Beanstalk provides deep integration with other Amazon services. It can handle large-scale applications, but comes with the complexity of AWS.

It’s best for teams already invested in the AWS ecosystem and comfortable with its learning curve.

![aws elastic beanstalk.png](https://assets.northflank.com/aws_elastic_beanstalk_55fe5f8b0b.png)

**What you can deploy on AWS Elastic Beanstalk:**

- Web applications in Java, .NET, PHP, Node.js, Python, Ruby, and Go
- Multi-tier applications with load balancers and auto-scaling groups
- Docker containers with full AWS service integration
- Enterprise applications requiring AWS compliance and security features

If you’re looking for alternatives, see “[10 best Elastic Beanstalk alternatives in 2026: Deploy apps without the AWS complexity](https://northflank.com/blog/elastic-beanstalk-alternatives)”

### 4. Google Cloud Run – Serverless container hosting

Cloud Run automatically scales containerized applications to zero when not in use. It is great for cost optimization but requires understanding of containerization concepts.

It’s best suited for applications with variable traffic patterns and teams that are comfortable with containers.

![google cloud run home page-min.png](https://assets.northflank.com/google_cloud_run_home_page_min_25317b598a.png)

**What you can deploy on Google Cloud Run:**

- Containerized web applications and APIs that scale to zero
- Event-driven functions triggered by HTTP requests or cloud events
- Microservices that integrate with other Google Cloud services
- Batch processing jobs that run on-demand

*If you’re looking for alternatives, also see: [Best Google Cloud Run alternatives in 2026](https://northflank.com/blog/best-google-cloud-run-alternatives-in-2025)*

### 5. DigitalOcean App Platform – Simple cloud hosting

DigitalOcean's platform focuses on simplicity and transparent pricing. It's straightforward to use but lacks some advanced enterprise features.

It is best for small to medium-sized businesses wanting predictable pricing and simple deployment workflows.

![DO-homepage.png](https://assets.northflank.com/DO_homepage_4d688a99b9.png)

**What you can deploy on DigitalOcean App Platform:**

- Web applications in Node.js, Python, Ruby, PHP, and Go
- Static sites with automatic SSL and CDN distribution
- Database services, including PostgreSQL, MySQL, and Redis
- Background workers and scheduled jobs

*If you’re looking for alternatives, also see: [10 best DigitalOcean alternatives in 2026 for developers and teams](https://northflank.com/blog/best-digitalocean-alternatives-2025)*

### 6. Render – Modern Heroku alternative

Render provides many of the same benefits as Heroku, with better performance and pricing. It's growing very fast, but still building out enterprise-grade features.

It’s best for teams migrating from Heroku who want similar simplicity with better performance.

![render's home page.png](https://assets.northflank.com/render_s_home_page_2982a329f2.png)

**What you can deploy on Render:**

- Web services in any language with automatic deployments from Git
- Static sites with global CDN and custom domains
- PostgreSQL databases with automatic backups
- Background jobs and cron jobs for scheduled tasks

If you need more, see alternatives to Render:

- [7 Best Render alternatives for simple app hosting in 2026](https://northflank.com/blog/render-alternatives)
- [Render vs Vercel (2026): Which platform suits your app architecture better?](https://northflank.com/blog/render-vs-vercel)
- [Render vs Heroku: Which platform-as-a-service is right for you in 2026?](https://northflank.com/blog/render-vs-heroku)

## What can you deploy with cloud application hosting services?

Now that you've seen what individual platforms provide, let's look at the broader range of applications you can deploy across these cloud hosting services.

Modern cloud platforms support virtually any type of application or service you can imagine.

- **Full-stack web applications**: If you're building with React and Node.js, Django and Python, or Ruby on Rails, cloud platforms handle both your frontend and backend components together.
- **RESTful APIs and GraphQL services**: Deploy scalable API services that drive mobile apps, integrate with third-party services, or serve data to multiple frontend applications.
- **Real-time applications**: WebSocket-based applications like chat systems, collaborative tools, or live dashboards work well on modern cloud platforms.
- **E-commerce platforms:** Online stores, payment processing systems, and inventory management applications benefit from automatic scaling during traffic spikes.
- **Data processing applications**: Applications that process files, analyze data, or generate reports can run as background services that scale based on workload.
- **Machine learning models**: Deploy ML models as APIs that other applications can consume, complete with automatic scaling based on prediction requests.
- **Content management systems**: If it's a custom CMS or headless content delivery, cloud platforms provide the performance and reliability that content-driven sites need.

## It’s time to get started with your cloud application hosting!

The best way to determine if a platform suits your stack is to deploy your application and assess its performance.

Start with a simple project – perhaps a small web application or API service. This gives you hands-on experience with the deployment process, monitoring tools, and how the platform handles scaling.

**Look for platforms that offer:**

- Free tiers or trial periods to test without commitment, like Northflank's free [developer sandbox](https://northflank.com/pricing)
- Clear documentation and getting-started guides. See one good [example](https://northflank.com/docs).
- Responsive support when you need help
- Transparent pricing so you know what to expect as you scale

Most importantly, choose a platform that feels intuitive to your team. The best hosting platform is one that gets out of your way and lets you focus on building your applications.

If you're looking for a platform that combines developer simplicity with enterprise reliability, Northflank is an all-in-one cloud application hosting platform that offers that balance. It eases your deployment with reliable features that your applications need to succeed at scale.

<InfoBox className='BodyStyle'>

Get started with [Northflank's free tier](https://app.northflank.com/signup) today, or [book a demo with an engineer](https://cal.com/team/northflank/northflank-intro) to see how it can work for your specific use case.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>10 best cloud app deployment platforms for development teams in 2026</title>
  <link>https://northflank.com/blog/best-cloud-app-deployment-platforms</link>
  <pubDate>2025-08-27T17:42:00.000Z</pubDate>
  <description>
    <![CDATA[Compare 10 best cloud app deployment platforms for 2026. Features, pricing, and use cases for Northflank, Heroku, AWS App Runner, Vercel, and more.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/best_cloud_app_deployment_platforms_bd6d687564.png" alt="10 best cloud app deployment platforms for development teams in 2026" />I remember the days when deploying an application meant manually configuring servers, resolving dependency conflicts, and crossing my fingers, hoping everything would work in production. Well, those days are long gone.

We now have modern cloud app deployment platforms that have turned what used to be a complex, error-prone process into a simplified workflow.

Now, a development team can build, ship, and scale applications with a few clicks.

And the best part is that these cloud app deployment platforms, like [Northflank](https://northflank.com/), do way more than simple hosting. They’re providing you with:

- integrated CI/CD pipelines
- automatic scaling
- built-in security features
- database management
- monitoring tools
- … and so on

We’re now in a time where developers can finally focus on writing code rather than managing infrastructure.

So, I’ll review the top 10 cloud app deployment platforms to help you determine where they best fit for your projects and teams’ needs.

## Let's compare the 10 cloud app deployment platforms quickly

I’ve put together this side-by-side comparison to help you quickly identify which platform matches your needs and budget.

| Platform | Best For | Free Tier | Starting Price | Key Strengths |
| --- | --- | --- | --- | --- |
| **Northflank** | All-in-one deployment with enterprise features | Yes (Developer Sandbox) | CPU: $2.70/month ($0.0038/hr), GPU: $1.42/hr (A100), $2.74/hr (H100) | Enterprise-grade security, multi-cloud, bring your own cloud option (deploy in your AWS/GCP/Azure), GPU orchestration, autoscaling |
| **Heroku** | Prototyping and simple deployment | No (discontinued 2022) | $5/month (Eco plan - 1,000 hours shared) | Simple deployment, extensive add-ons, minimal configuration |
| **AWS App Runner** | AWS ecosystem integration | 12 months free tier | $0.064/vCPU hour + $0.007/GB hour | AWS integration, automatic scaling, containerized apps |
| **Google Cloud Run** | Serverless container deployment | Yes (2 million requests/month) | $0.48/million requests | Pay-per-use, serverless, automatic scaling, multi-language support |
| **Render** | Full-stack applications and databases | Yes (static sites, limited services) | $7/month (Starter web service) | Modern Heroku alternative, background workers, PostgreSQL included |
| **Vercel** | Frontend and Jamstack applications | Yes (Hobby plan) | $20/month per user (Pro plan) | Global CDN, Next.js optimization, instant deployments, edge functions |
| **Railway** | Backend services with databases | Yes ($5 credit one-time) | $5/month (Hobby plan includes $5 usage) | Developer-friendly, database support, usage-based pricing |
| **Fly** | Edge deployment and global distribution | Yes ($5 credit monthly) | Usage-based from ~$1.94/month | Edge computing, global distribution, low latency, Docker-native |
| **Azure App Service** | Microsoft stack integration | 12 months free tier | $13.87/month (Basic B1) | Azure integration, .NET support, enterprise features |
| **Netlify** | Static sites and Jamstack | Yes (100GB bandwidth/month) | $19/month (Pro plan) | Build automation, form handling, serverless functions, Git workflow |

## What is a cloud app deployment platform?

A cloud app deployment platform is a service that handles everything needed to get your applications from your local development environment to live production systems without the manual server setup, dependency management, and configuration issues that developers used to face.

In place of having to manually set up your servers and manage them yourself, configure your databases, handle updates yourself, manage your SSL certificates, and stress about scaling…these platforms help handle all the infrastructure complexity for you.

In most cases, all you have to do is connect your code repository, and the platform takes care of building, deploying, and running your application in the cloud.

And cloud app deployment platforms like Northflank provide you with:

- integrated CI/CD pipelines
- automatic scaling
- built-in security features
- database management
- monitoring tools
- global content delivery networks

## What should I look out for when selecting a cloud app deployment platform?

Let's go on to discuss what you should look out for in cloud app deployment platforms before you make your choice.

1. **Performance and latency optimization**:
    
    Look for platforms with global CDNs, edge computing, and optimized routing for fast response times regardless of your users’ location.
    
2. **Horizontal scaling and multi-region support**:
    
    Choose platforms that auto-scale server instances, distribute apps across regions, and provide load balancing.
    
3. **Security and isolation capabilities**:
    
    Look for container isolation, network security and automated security updates.
    
4. **Multi-cloud and BYOC (bring your own cloud) support**:
    
    Prioritize platforms that let you deploy across different cloud providers or use your own cloud accounts.
    
5. **GPU and AI workload compatibility**:
    
    Choose platforms that provide dedicated GPU instances, support for AI frameworks, and machine learning workload handling.
    
6. **Developer experience**:
    
    Focus on platforms with comprehensive APIs, integrated CI/CD pipelines, and easy integration with GitHub or GitLab.
    
7. **Database and service orchestration**:
    
    Select platforms that handle your entire stack: databases, Redis caches, background workers, and scheduled jobs.
    
8. **Monitoring and observability tools**:
    
    Choose platforms that provide real-time metrics, centralized logging, error tracking, and alerting systems.
    

## A detailed breakdown of 10 best cloud app deployment platforms

Let’s review each platform to see their main features, pricing model, best use cases, and how they handle deployment workflows.

We'll also look at their strengths and limitations to help you understand which platform best fits your specific project needs and team requirements.

### 1. Northflank - Best overall cloud app deployment platform

If you want an all-in-one deployment platform that handles everything from simple web apps to complex AI workloads, Northflank delivers enterprise-grade capabilities with a developer-friendly interface.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

**Main features**

- [Secure sandboxes](https://northflank.com/product/sandboxes) for better security
- Multi-cloud deployment with [bring your own cloud](https://northflank.com/product/bring-your-own-cloud) option
- [GPU access](https://northflank.com/product/gpu-paas) for AI workloads (H100 at $2.74/hour, B200 at $5.87/hour)
- [Full-stack support](https://northflank.com/product/deployments): databases, jobs, services, and networking in one platform
- Enterprise features: RBAC, audit logs, SSO, private networking

<InfoBox className='BodyStyle'>

### 🤑 **Northflank pricing**

- Free tier: Generous limits for testing and small projects
- CPU instances: Starting at $2.70/month ($0.0038/hr) for small workloads, scaling to production-grade dedicated instances
- GPU support: NVIDIA A100 40GB at $1.42/hr, A100 80GB at $1.76/hr, H100 at $2.74/hr, up to B200 at $5.87/hr
- Enterprise BYOC: Flat fees for clusters, vCPU, and memory on your infrastructure, no markup on your cloud costs
- Pricing calculator available to estimate costs before you start
- Fully self-serve platform, get started immediately without sales calls
- No hidden fees, egress charges, or surprise billing complexity like SageMaker

</InfoBox>

**Best use cases**

Production applications needing enterprise-grade security, AI/ML workloads, complex multi-service architectures, or multi-cloud flexibility.

**Pros and cons**

**Pros:** All-in-one platform, affordable pricing, enterprise-grade security built in, no vendor lock-in, supports both AI and web applications

**Cons:** May be feature-rich for simple sites, learning curve for new users, smaller community

### 2. Heroku - Best for prototyping and simple deployment

If you want to get your application online quickly without dealing with server configuration, Heroku provides a straightforward platform that handles the technical complexity for you.

![heroku.png](https://assets.northflank.com/heroku_092e1c7f09.png)

**Main features**

- Git-based deployment: Push code to deploy automatically
- Buildpack system supporting Node.js, Python, Ruby, Java, PHP, Go
- Add-on marketplace with databases, monitoring, and email services
- Dyno-based scaling with horizontal and vertical options
- Pipeline management with staging, production, and review apps

**Pricing and plans**

- **Eco Plan**: $5/month for 1,000 shared dyno hours (sleeps after 30 minutes)
- **Basic Plan**: $7/month per dyno for dedicated resources
- **Standard Plan**: $25-50/month per dyno with scaling features
- **Add-ons**: Separate pricing for PostgreSQL ($9/month+), Redis ($15/month+)

**Best use cases**

Web applications, APIs, prototypes, startups needing quick deployment, and teams preferring to focus on code rather than infrastructure management.

**Pros and cons**

**Pros:** Simple deployment process, extensive add-on ecosystem, automatic SSL, built-in CI/CD, large community support

**Cons:** Expensive at scale, limited infrastructure control, dyno sleep on lower plans, potential vendor lock-in with proprietary add-ons

Learn more about Heroku:

- [How to migrate from Heroku: A step-by-step guide](https://northflank.com/blog/how-to-migrate-from-heroku-a-step-by-step-guide)
- [Top Heroku alternatives in 2026](https://northflank.com/blog/top-heroku-alternatives)
- [Heroku Enterprise: capabilities, limitations, and alternatives](https://northflank.com/blog/heroku-enterprise-capabilities-limitations-and-alternatives)

### 3. AWS App Runner - Best for AWS ecosystem integration

If you're already using AWS services and want containerized application deployment without managing infrastructure, App Runner provides automatic scaling and direct integration with your existing AWS setup.

![aws app runner home page-min.png](https://assets.northflank.com/aws_app_runner_home_page_min_36fbadd0c2.png)

**Main features**

- Source-based deployment from GitHub or container registries
- Automatic scaling from zero to high traffic with pay-per-use
- Built-in load balancing and health checks
- VPC connectivity for private AWS resources
- Integration with AWS services like RDS, ElastiCache, and CloudWatch

**Pricing and plans**

- **Compute**: $0.064 per vCPU hour, $0.007 per GB memory hour
- **Requests**: $0.20 per million requests
- **Build**: $0.005 per build minute
- **Free tier**: 2,000 build minutes, 200,000 requests monthly for 2 months

**Best use cases**

Web applications, APIs, microservices for teams already using AWS infrastructure, applications needing VPC access to private AWS resources.

**Pros and cons**

**Pros:** Deep AWS integration, automatic scaling, no infrastructure management, pay-per-use pricing, built-in security features

**Cons:** Limited to AWS ecosystem, fewer customization options than ECS/EKS, regional availability restrictions

*Check out [9 best AWS App Runner alternatives for scalable container apps](https://northflank.com/blog/aws-app-runner-alternatives)*

### 4. Google Cloud Run - Best for serverless containers

If you want serverless container deployment with automatic scaling and pay-only-for-requests pricing, Cloud Run handles traffic spikes efficiently without server management.

![google cloud run home page-min.png](https://assets.northflank.com/google_cloud_run_home_page_min_25317b598a.png)

**Main features**

- Deploy any containerized application serverlessly
- Automatic scaling from zero to thousands of instances
- Built-in traffic splitting for gradual rollouts
- Custom domains with managed SSL certificates
- Integration with Google Cloud services and APIs

**Pricing and plans**

- **Requests**: $0.40 per million requests
- **CPU**: $0.048 per vCPU hour (only while processing)
- **Memory**: $0.0053 per GB hour
- **Free tier**: 2 million requests, 400,000 GB-seconds monthly

**Best use cases**

APIs with variable traffic, event-driven applications, microservices, applications with unpredictable or spiky usage patterns.

**Pros and cons**

**Pros:** True serverless pricing, fast cold starts, automatic HTTPS, global deployment, integrates with Google Cloud ecosystem

**Cons:** Limited to stateless applications, cold start latency, Google Cloud vendor lock-in for advanced features

Learn more about Google Cloud Run:
- [Best Google Cloud Run alternatives in 2026](https://northflank.com/blog/best-google-cloud-run-alternatives-in-2025)
- [App Engine vs. Cloud Run: A real-world engineering comparison](https://northflank.com/blog/app-engine-vs-cloud-run)

### 5. Azure App Service - Best for Microsoft stack

If you're using .NET, Microsoft development tools, or need enterprise-grade features with Azure integration, App Service provides managed hosting with extensive Microsoft ecosystem support.

![Azure App Service home page.png](https://assets.northflank.com/Azure_App_Service_home_page_8c23eca050.png)

**Main features**

- Support for .NET, Node.js, Python, Java, PHP applications
- Built-in DevOps with Azure DevOps and GitHub integration
- Auto-scaling based on metrics or schedule
- Staging slots for testing and gradual deployments
- Enterprise features: AD authentication, hybrid connections, VNet integration

**Pricing and plans**

- **Free tier**: 1 GB storage, custom domains not supported
- **Basic**: $13.87/month (B1 plan) with custom domains and SSL
- **Standard**: $73.00/month (S1) with auto-scaling and staging slots
- **Premium**: $292.00/month (P1V3) with advanced scaling and VNet integration

**Best use cases**

.NET applications, enterprise web apps, applications requiring Active Directory integration, hybrid cloud scenarios.

**Pros and cons**

**Pros:** Strong .NET support, enterprise security features, Azure ecosystem integration, multiple deployment options, staging environments

**Cons:** Can be expensive for simple applications, complex pricing tiers, primarily optimized for Microsoft technologies

Learn more about Azure App Service:

- [Top 10 Microsoft Azure alternatives in 2026: Best cloud platforms for your business](https://northflank.com/blog/azure-alternatives)
- [Azure migration strategy for 2026: How to get it right](https://northflank.com/blog/azure-cloud-migration-strategy-migrate)

### 6. Render - Best for full-stack applications

If you want a modern alternative to Heroku with better performance and pricing, Render provides web services, databases, and background workers in one platform.

![render's home page.png](https://assets.northflank.com/render_s_home_page_2880e163be.png)

**Main features**

- Web services with automatic SSL and custom domains
- Managed PostgreSQL databases with point-in-time recovery
- Background workers and cron jobs as first-class services
- Static site hosting with global CDN
- Private services for internal communication

**Pricing and plans**

- **Free tier**: Static sites, web services (with limitations), PostgreSQL database
- **Web services**: $7/month (Starter), $25/month (Standard), $85/month (Pro)
- **PostgreSQL**: $7/month (Starter), $20/month (Standard)
- **Background workers**: Same pricing as web services

**Best use cases**

Full-stack web applications, applications needing background processing, teams wanting Heroku-like experience with better pricing.

**Pros and cons**

**Pros:** Built-in database support, background workers included, competitive pricing, automatic deployments from Git, free tier for testing

**Cons:** Smaller ecosystem than established platforms, fewer third-party integrations, limited geographic regions

Learn more about Render:

- [7 Best Render alternatives for simple app hosting in 2026](https://northflank.com/blog/render-alternatives)
- [Render vs Vercel (2026): Which platform suits your app architecture better?](https://northflank.com/blog/render-vs-vercel)
- [Railway vs Render (2026): Which cloud platform fits your workflow better](https://northflank.com/blog/railway-vs-render)
- [Fly.io vs Render: How they handle jobs, scaling, and production workloads in 2026](https://northflank.com/blog/flyio-vs-render)
- [Render vs Heroku: Which platform-as-a-service is right for you in 2026?](https://northflank.com/blog/render-vs-heroku)

### 7. Railway - Best for databases and backend services

If you want simple deployment with usage-based pricing and don't want to manage credits or complex billing, Railway provides straightforward hosting for applications and databases.

![railway-min.png](https://assets.northflank.com/railway_min_10957de907.png)

**Main features**

- One-click database deployment (PostgreSQL, MySQL, MongoDB, Redis)
- Git-based deployments with automatic builds
- Environment variables and secrets management
- Private networking between services
- Usage-based billing with transparent pricing

**Pricing and plans**

- **Free trial**: $5 credit (one-time)
- **Hobby**: $5/month includes $5 usage credit
- **Pro**: $20/month per seat includes priority support
- **Usage**: CPU, memory, network, and storage billed separately

**Best use cases**

Backend APIs, database-heavy applications, side projects, applications with predictable resource usage.

**Pros and cons**

**Pros:** Simple pricing model, database management, transparent usage tracking, developer-friendly interface, no complex configuration

**Cons:** Credit-based system can cause unexpected shutdowns, limited advanced features, smaller community and ecosystem

Learn more about Railway: [6 best Railway alternatives in 2026: Pricing, flexibility & BYOC](https://northflank.com/blog/railway-alternatives)

### 8. Fly.io - Best for edge deployment

If your users are globally distributed and you need low-latency access, Fly.io runs your applications close to users worldwide with edge computing capabilities.

![fly.io-min.png](https://assets.northflank.com/fly_io_min_bfc65ba670.png)

**Main features**

- Global deployment across 30+ regions
- Anycast networking for automatic traffic routing
- Persistent volumes that can move with your applications
- Direct VM access for custom configurations
- Built-in service discovery and load balancing

**Pricing and plans**

- **Free tier**: 3 shared-cpu VMs, 256MB RAM each
- **Usage-based**: $0.0000016 per second per 256MB RAM
- **Persistent storage**: $0.15/GB per month
- **Outbound data**: $0.02/GB

**Best use cases**

Applications serving global audiences, real-time applications, edge computing workloads, applications requiring low latency.

**Pros and cons**

**Pros:** Global edge deployment, low latency, flexible VM configurations, competitive pricing for global reach

**Cons:** Command-line heavy workflow, learning curve for edge concepts, limited managed services compared to traditional clouds

Learn more about Fly.io:

- [Top 6 Fly.io alternatives in 2026](https://northflank.com/blog/flyio-alternatives)
- [Fly.io vs Render: How they handle jobs, scaling, and production workloads in 2026](https://northflank.com/blog/flyio-vs-render)

### 9. Vercel - Best for frontend applications

If you're building React, Next.js, or other frontend applications and want automatic deployments with global CDN, Vercel optimizes for frontend developer experience.

![vercel-min.png](https://assets.northflank.com/vercel_min_c04c41400d.png)

**Main features**

- Optimized for Next.js, React, Vue, and other frontend frameworks
- Automatic deployments with Git integration
- Global CDN with edge functions
- Preview deployments for every pull request
- Built-in analytics and performance monitoring

**Pricing and plans**

- **Hobby**: Free with usage limits
- **Pro**: $20/month per user with increased limits
- **Enterprise**: Custom pricing with advanced features
- **Usage-based**: Function invocations, bandwidth, build minutes

**Best use cases**

React/Next.js applications, static sites, Jamstack applications, frontend-focused development teams.

**Pros and cons**

**Pros:** Optimized for frontend frameworks, automatic deployments, global CDN, preview environments, strong developer experience

**Cons:** Primarily frontend-focused, can become expensive with high usage, complex pricing structure for larger applications

Learn more about Vercel:

- [Top Vercel Sandbox alternatives for secure AI code execution and sandbox environments](https://northflank.com/blog/top-vercel-sandbox-alternatives-for-secure-ai-code-execution-and-sandbox-environments)
- [Best Vercel Alternatives for Scalable Deployments](https://northflank.com/blog/best-vercel-alternatives-for-scalable-deployments)
- [Vercel vs Netlify: Choosing the right one in 2026 (and what comes next)](https://northflank.com/blog/vercel-vs-netlify-choosing-the-deployment-platform-in-2025)
- [Vercel vs Heroku: Which platform fits your workflow best?](https://northflank.com/blog/vercel-vs-heroku)
- [Render vs Vercel (2026): Which platform suits your app architecture better?](https://northflank.com/blog/render-vs-vercel)

### 10. Netlify - Best for Jamstack applications

If you're building static sites or Jamstack applications with build processes and form handling, Netlify provides comprehensive tooling for modern web development workflows.

![netlify's home page.png](https://assets.northflank.com/netlify_s_home_page_6929286bb8.png)

**Main features**

- Static site hosting with global CDN
- Build automation with various static site generators
- Form handling without backend code
- Identity and authentication management
- Edge functions for serverless logic

**Pricing and plans**

- **Free**: 100GB bandwidth, 300 build minutes monthly
- **Pro**: $19/month with increased limits and analytics
- **Business**: $99/month with role-based access and advanced features
- **Enterprise**: Custom pricing

**Best use cases**

Static websites, documentation sites, marketing sites, Jamstack applications with forms and authentication needs.

**Pros and cons**

**Pros:** Static site optimization, built-in form handling, comprehensive build system, branch deployments, strong community

**Cons:** Limited to static/Jamstack applications, serverless functions have execution limits, can be expensive for high-traffic sites

Learn more about Netlify: [7 Netlify alternatives in 2026: Where to go when your app grows up](https://northflank.com/blog/netlify-alternatives)

## How to choose the best cloud app deployment platform for your needs

Choosing the right deployment platform depends on matching your specific requirements with each platform's strengths. Here's a practical framework to guide your decision.

1. **Assess your application requirements**:

    Start by identifying what you're building - a simple static site, a complex web application, or an AI-powered service. Consider whether you need databases, background workers, scheduled jobs, or real-time features. Your technical stack (React, Python, .NET) will also influence which platforms offer the best support.

2. **Consider your team size and expertise**:

    Small teams often benefit from platforms like Railway or Render that minimize configuration, while larger teams might prefer comprehensive solutions like Northflank or AWS App Runner. Consider your team's DevOps experience - if infrastructure management isn't your strength, managed platforms will be more valuable than DIY solutions.

3. **Evaluate scalability needs**:

    Think about your current traffic and growth projections. If you expect variable or unpredictable traffic, serverless options like Google Cloud Run or Vercel make sense. For steady growth, platforms with clear scaling paths like Northflank or Azure App Service provide better cost control.

4. **Security and compliance requirements**:

    If you handle sensitive data or operate in regulated industries, prioritize platforms with built-in compliance features. Look for container isolation, and audit logging capabilities.

5. **Budget considerations**:

    Compare total cost of ownership, not just base pricing. Factor in compute costs, data transfer, add-on services, and potential scaling expenses. Usage-based pricing can be cost-effective for variable workloads, while fixed pricing offers predictability.

6. **Integration requirements**

    Consider your existing tools and services. If you're already using AWS, App Runner might make sense. For Microsoft shops, Azure App Service integrates naturally. For multi-cloud flexibility, platforms like Northflank offer bring-your-own-cloud options.

**Decision checklist**

- Does the platform support your programming language and framework?
- Can it handle your expected traffic and scaling requirements?
- Does pricing align with your budget and usage patterns?
- Are required compliance and security features available?
- How well does it integrate with your existing development workflow?
- What level of support and documentation is provided?

<InfoBox className='BodyStyle'>

Get started with a platform that handles everything from simple apps to complex AI workloads. [Try Northflank's free Developer Sandbox](https://app.northflank.com/signup) and see how an all-in-one deployment solution can simplify your development workflow.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>How to build and deploy a Model Context Protocol (MCP) server</title>
  <link>https://northflank.com/blog/how-to-build-and-deploy-a-model-context-protocol-mcp-server</link>
  <pubDate>2025-08-26T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[In this guide, you’ll containerize a simple MCP server, push it to a registry, and build and deploy an MCP server on Northflank as a secure, autoscalable service.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/mcp_6104f6b8bd.png" alt="How to build and deploy a Model Context Protocol (MCP) server" />In this guide, you’ll containerize a simple MCP server, push it to a registry, and **build and deploy an MCP server on [Northflank](https://northflank.com/)** as a secure, autoscalable service.

We’ll cover secrets, health checks, networking (HTTP/SSE or WebSocket), and how to point your MCP client at the hosted endpoint.

If you prefer, you can deploy **any** existing Docker/OCI image directly from a container registry; [Northflank](https://northflank.com/) runs persistent services and one-off jobs from public or private images.

## What is an MCP Server?

A **Model Context Protocol (MCP) server** is a process that exposes tools, functions, or external data sources to AI models through a standardized protocol. Think of it as a bridge: the model calls your MCP server, and the server responds with structured results.

- **Why it matters:** MCP servers give AI models safe, auditable access to external systems.
- **How it works:** The server speaks the MCP protocol (via HTTP/SSE or WebSocket). A client (such as an AI assistant or agent framework) connects to it and can call the tools you’ve defined.
- **Use cases:** Wrapping APIs, databases, or internal business logic so that AI models can interact with them in a controlled way.

If you’re searching for *how to run an MCP server*, *how to build an MCP server*, or *how to deploy an MCP server*, the steps below show you exactly how to do it in a production-grade environment.

## What you’ll build

- A minimal **HTTPS MCP server** (Python example with FastMCP + Starlette)
- A Northflank **combined service** that exposes your MCP endpoint over HTTPS with environment variables managed as secrets

> MCP supports multiple transports. We’ll use HTTPS in this example.
> 

## **Prerequisites for building and deploying an MCP server**

- A Northflank account and project
- Github linked with your Northflank account
- (Optional) A domain you can point to Northflank for pretty URLs (Northflank will still give you an HTTPS endpoint)

## 1) Scaffold a minimal MCP server

This is the first step in **building an MCP server** before deployment.

Below is a tiny Python example using **FastMCP** and **Starlette** to expose MCP over HTTP/SSE. (Any MCP server that supports HTTP/SSE or WebSocket will work similarly.)

**`app.py`**

```python
# app.py
import json
import logging
from starlette.applications import Starlette
from starlette.responses import JSONResponse, PlainTextResponse
from starlette.routing import Route
from starlette.requests import Request
from starlette.middleware import Middleware
from starlette.middleware.cors import CORSMiddleware

from mcp.server.fastmcp import FastMCP

# Basic MCP server with two example tools
mcp = FastMCP("Example MCP")

@mcp.tool()
def add(a: int, b: int) -> int:
    """Add two numbers."""
    return a + b

@mcp.tool()
def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b

# HTTP endpoints: /mcp for requests, /health for health checks
async def mcp_handler(request: Request):
    payload = await request.json()
    # Delegate to FastMCP to process the MCP message & return a response
    result = await mcp.handle_http(payload)
    return JSONResponse(result)

async def health(_request: Request):
    return PlainTextResponse("ok", status_code=200)

routes = [
    Route("/mcp", endpoint=mcp_handler, methods=["POST"]),
    Route("/health", endpoint=health, methods=["GET"]),
]

middleware = [
    Middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"]),
]

app = Starlette(routes=routes, middleware=middleware)
```

> Notes
> 
> - If your SDK doesn’t include a helper like `handle_http`, adapt to your framework’s request/response handling per your MCP SDK docs.
> - For WebSocket transport, you’d create a `/ws` route and speak the MCP WebSocket protocol. (There are community transports and gateways if you prefer WS.)

**`requirements.txt`**

```
mcp>=0.1.0
fastmcp>=0.1.0
starlette>=0.47.2
uvicorn>=0.30
```

**`Dockerfile`**

```docker
# Use a slim Python image
FROM python:3.12-slim

WORKDIR /app

# System deps (if needed) and security updates
RUN apt-get update -y && apt-get install -y --no-install-recommends \
    ca-certificates curl && rm -rf /var/lib/apt/lists/*

# Install Python deps
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy app
COPY app.py .

# Northflank will pass PORT; default to 8080 for local runs
ENV PORT=8080
EXPOSE 8080

# Start the HTTP server
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8080"]
```

Push this code to your Github repository.

## 2) Create a Northflank Combined service to **deploy your MCP server**

1. In your Northflank project, choose **Services → Create service**.
2. Choose **Combined service** and select your code repository.

Northflank can build and run container images continuously, and handle HA, restarts, and scaling for you.

![1.png](https://assets.northflank.com/1_7450526b8b.png)

## 3) Expose networking

- **Ports**: add an HTTP port (e.g., **8080**).
- **Protocol**: HTTP (Northflank will terminate TLS and give you an HTTPS URL).
- (Optional) **Custom domain**: attach your domain to the service’s HTTP port.

![2.png](https://assets.northflank.com/2_be461c8761.png)

## 4) Configure runtime variables & secrets

Add environment variables under **Runtime variables** or via **Secret groups**. This is ideal for API keys your MCP tools might need.

- Go to **Secure → Secret groups**, create one, and add variables (e.g., `OPENAI_API_KEY`, `MY_BACKEND_URL`).
- Link the secret group to your service so the variables are injected at runtime.
- If you need mounted files (certs, config), use **Secret files**.

> Priority: variables set directly on the service override inherited secret group values.
> 

## 5) Health checks (optional)

Add an HTTP health check on `/health` to get fast, accurate readiness/liveness signals. (The sample app above exposes that path.) This helps Northflank keep your service healthy and restart on failure.

## 6) Scale up (optional)

- **Vertical**: choose the right CPU/RAM plan.
- **Horizontal**: increase replicas; Northflank load balances HTTP traffic across instances.
- Consider adding a cache or database add-on if your MCP server maintains state or needs persistence; you can link add-on secrets directly to your workloads.

## 7) Connect your MCP client

Once deployed, your service will have an HTTPS base URL like:

```
https://<your-service>.code.run
```

Point your MCP client at:

- **HTTP/SSE** endpoint: `POST https://.../mcp` (and use SSE if your client supports streaming).
- Include auth headers or keys as your client/server expects (supply via Northflank secrets).
    
    MCP’s Streamable HTTP transport supports POST/GET and optional SSE for multi-message streaming
    

> If your client expects WebSocket, run a WS route (e.g., /ws) or place a WS/SSE gateway in front of your server. Community gateways exist that bridge WebSocket/SSE for MCP.
> 

## 8) Deploying an existing MCP image (skip the Dockerfile)

If you don’t want to build an MCP server from scratch, you can still **deploy an MCP server** using a prebuilt image.

1. Create a **Deployment service**
2. Enter the image path (public or private)
3. Set the **command/entrypoint** and **PORT** if required by that image
4. Add runtime variables and expose port 8080 HTTP.

You can also explore community/reference MCP servers for ready-made examples.

![3.png](https://assets.northflank.com/3_ceeeb953b6.png)

## 9) Troubleshooting checklist

- **Container won’t start**
    - Check logs in the service UI; verify the container listens on `0.0.0.0:$PORT`.
- **Health check failing**
    - Ensure `/health` returns `200` and that your HTTP port & path match the check.
- **403/401 from endpoint**
    - Confirm secrets are set and injected (service variables or linked secret group).
- **No streaming**
    - Make sure your client and server both support MCP’s Streamable HTTP with SSE; otherwise use plain HTTP or a WS/SSE transport/gateway.
- **Stateful tools**
    - Add a database add-on and link its secrets; avoid using admin credentials from apps.

## Why Northflank?

Running an MCP server in production means you need more than a container runtime. You need uptime guarantees, secure secret storage, and the ability to scale without manual intervention. That’s where Northflank fits:

- **Persistent services** – keep your MCP server online 24/7 with automatic restarts and health checks
- **Built-in HTTPS ingress** – every service gets a production-ready TLS endpoint, no extra config
- **Secrets & config management** – inject API keys and environment variables securely at runtime
- **Autoscaling** – run a single container or scale to many replicas with load balancing
- **Multi-cloud ready** – deploy into your own cloud or run on Northflank’s managed infrastructure

Instead of wiring these pieces together yourself with Kubernetes or VM scripts, Northflank gives you everything you need to **build and deploy MCP servers in a production-grade environment**.

[Get started today.](https://app.northflank.com/signup)]]>
  </content:encoded>
</item><item>
  <title>DigitalOcean vs AWS: A guide for developers, startups, and AI companies</title>
  <link>https://northflank.com/blog/digitalocean-vs-aws</link>
  <pubDate>2025-08-26T17:20:00.000Z</pubDate>
  <description>
    <![CDATA[Compare DigitalOcean vs AWS for pricing, features, and AI. Learn which cloud suits developers, startups, and AI teams, and how Northflank helps.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/digitalocean_vs_aws_f8b95b930d.png" alt="DigitalOcean vs AWS: A guide for developers, startups, and AI companies" /><InfoBox className='BodyStyle'>

**TL;DR:** DigitalOcean is cheaper and simpler, ideal for startups and solo developers. AWS offers more services but is complex and can be expensive for general use, though it has simpler options like Lightsail. DigitalOcean is significantly cheaper than AWS for comparable basic needs

[Northflank](https://northflank.com/) solves this dilemma by helping you deploy your entire application (both AI and non-AI workloads) across AWS, DigitalOcean, and other clouds automatically, optimizing costs and performance while handling all the complexity.

</InfoBox>

We’ll compare DigitalOcean vs AWS to see how they differ in terms of pricing and billing models, features, performance, and use cases for startups, developers, and AI companies.

And we’ll see how Northflank helps you avoid vendor lock-in and deploy across both platforms automatically while optimizing costs and managing complexity

## What does DigitalOcean do?

DigitalOcean is commonly known as the "developer cloud," which becomes clear when you look at how the platform operates.

![DO's-offerings.png](https://assets.northflank.com/DO_s_offerings_3c4cb2b0e2.png)

**The core service is Droplets** - virtual machines that you can spin up in under a minute. These serve as your basic building blocks for hosting applications, websites, or development environments.

**DigitalOcean also provides fully managed database services** for PostgreSQL, MySQL, MongoDB, Kafka, Valkey, and OpenSearch. This removes the burden of handling backups, updates, and scaling database servers yourself.

**The App Platform connects directly to your code repositories.** Push code to GitHub or GitLab, and the platform automatically builds and deploys your application. So, without the need to configure complex CI/CD pipelines from scratch.

The interface is simpler compared to other cloud providers. Managing Droplets, setting up load balancers, or configuring firewalls doesn't require navigating through dozens of confusing service menus.

**DigitalOcean recently expanded into AI** with GPU-powered Droplets and the **Gradient™ AI Platform** (formerly GenAI Platform). You can now deploy machine learning models without dealing with complex infrastructure setup.

**Kubernetes clusters are fully managed,** handling container orchestration without requiring you to become a Kubernetes expert. **Object storage through Spaces** gives you S3-compatible file storage for backups and static assets.

This approach sits between shared hosting and enterprise cloud platforms. More control than shared hosting, but less complexity than what can overwhelm smaller teams on AWS.

**Pricing stays consistent across regions,** which simplifies budgeting compared to providers that charge different rates based on data center location.

## What exactly does AWS do?

While DigitalOcean focuses on core infrastructure services, AWS takes a different approach - comprehensive coverage of virtually every cloud computing need you might have.

![AWS's-offerings.png](https://assets.northflank.com/AWS_s_offerings_c4ab4fd8c5.png)

**AWS operates as a massive service ecosystem** with over 245 different offerings (and growing). Amazon EC2 provides the foundational compute services, but that's just the beginning.

**The platform includes dozens of database options -** from traditional relational databases through RDS and Amazon Aurora, to NoSQL with DynamoDB and Amazon DocumentDB (a MongoDB-compatible service), time-series databases, and specialized graph databases. Each database type is optimized for specific use cases.

**Machine learning services span the entire ML lifecycle.** **Amazon Bedrock** provides access to foundation models for generative AI, SageMaker handles model training and deployment, while pre-built APIs like Rekognition and Comprehend provide specialized functionality. You can build sophisticated AI applications without training your own models.

**Storage services go far beyond basic file storage.** S3 handles object storage at massive scale, with different **S3 Glacier storage classes** (Instant Retrieval, Flexible Retrieval, and Deep Archive) for cost-effective long-term archival. EBS offers high-performance block storage for databases and applications.

**Networking capabilities include global content delivery** through CloudFront, advanced load balancing, VPC networking, and **AWS Direct Connect** for private connections to your on-premises data centers

**AWS also provides specialized services** for IoT device management, blockchain networks, quantum computing, and satellite communications. These niche services address specific industry requirements.

**The platform handles infrastructure management** through services like Auto Scaling, which automatically adjusts resources based on demand, and CloudFormation, which manages infrastructure as code.

**Security and compliance services** include identity management, encryption key management, and monitoring tools that meet enterprise and government requirements.

**AWS also offers simpler, managed services** aimed at developers, such as **Amazon Lightsail**, which provides an easy entry point for basic cloud needs like virtual servers and databases.

## DigitalOcean vs AWS: Use cases and pricing comparison

Choosing between these platforms requires understanding both their strengths and how their pricing models align with your specific needs.

We’ll address most of the concerns and questions developers and companies have asked.

### Why use DigitalOcean instead of AWS?

For one, **speed and simplicity define DigitalOcean's approach.**

You can deploy applications in minutes without configuring VPCs, security groups, or complex service integrations. This becomes more important when you need to move from idea to production quickly.

Another thing is that its **predictable costs prevent billing uncertainty.**

For instance, DigitalOcean's Basic Droplets currently start at $4 monthly, with substantial bandwidth included. This transparent pricing allows smaller teams to budget accurately without tracking dozens of service charges.

![DO-droplet-pricing.png](https://assets.northflank.com/DO_droplet_pricing_8f167d989b.png)

Then, there's the **reduced operational complexity** that lets you focus on building your products.

You don't need extensive DevOps expertise to deploy and monitor applications, though scaling basic Droplets beyond moderate growth may still require some manual intervention.

### What is DigitalOcean good for?

Let’s see where DigitalOcean can come in handy.

![DO-solution.png](https://assets.northflank.com/DO_solution_39f874023f.png)

Think of your **web applications, APIs, and SaaS platforms** that follow standard architectures.

The platform handles most startup requirements through significant scale without specialized cloud services.

Your **development environments and prototyping** can also benefit from fast deployment cycles and the App Platform's Git integration.

Also, if you have AI workloads requiring GPU access, you can now use H100-powered Droplets through the Paperspace platform, though GPU availability can be limited compared to AWS, and the ecosystem remains smaller.

However, every platform has constraints that become apparent as your needs scale.

### What are the disadvantages of DigitalOcean?

Now we’ve seen some areas it’s good for. Let’s see some cons that have been pointed out.

For one, the **service limitations** constrain complex applications. While DigitalOcean's AI offerings are expanding, they still lack the breadth and specialization of tools that enterprises require.

Then, **the infrastructure reach that covers 13 active data centers globally** can potentially create latency issues for worldwide deployments.

Scaling can be manual for basic services, as unmanaged Droplets require hands-on management. Automated scaling is available and used for managed services like Kubernetes and the App Platform, but basic virtual machines lack this automation.

These limitations point to scenarios where AWS's comprehensive approach becomes valuable.

### What are the benefits of having AWS?

Now we can look into AWS to see where it comes in.

![aws-solution.png](https://assets.northflank.com/aws_solution_aeda1ce885.png)

You have your **enterprise applications** that require comprehensive service integration; they would do well on AWS.

And this is because AWS handles more complex architectures, global scale, and specialized compliance requirements with services like **AWS Control Tower**, **Amazon CloudFront**, and a multitude of security certifications.

Also, your **machine learning and analytics workloads** can benefit from AWS services like **Amazon Bedrock**, SageMaker, Redshift, and Kinesis, which provide complete data processing and AI pipelines.

Then, we also have **variable or unpredictable workloads** that can leverage **AWS Auto Scaling**, **Spot Instances**, and serverless options like **Amazon Redshift Serverless** for cost optimization and managed capacity.

Now that we have clarified the use cases, we can go into the pricing comparison, which is equally crucial for your decision.

## Which is cheaper, DigitalOcean or AWS?

We’ll address the popular question developers and startups always ask: **“Which cloud provider will cost me less?”**

<InfoBox className='BodyStyle'>

**TL;DR: Is DigitalOcean cheaper than AWS?**

Yes - for most startups, web applications, and moderate AI workloads, DigitalOcean is significantly cheaper (30–50%+) and much easier to budget for. It’s ideal if you don’t want to constantly monitor your billing dashboard or architect cost-optimized infrastructure manually.

AWS is worth the higher cost only if you need its enterprise-grade tools, scale, or specialized services.

And of course, platforms like [Northflank](https://northflank.com/) help you **leverage both AWS and DigitalOcean**: run your base workloads on DigitalOcean while tapping into AWS services when needed, all by deploying to your own cloud accounts through Northflank’s platform - without locking yourself into either ecosystem.

</InfoBox>

Let’s break it down properly so you can understand why and when.

### 1. Pricing models: simplicity vs complexity

DigitalOcean follows a **flat, transparent pricing model.** What you see is what you pay. Droplets (virtual machines) start at **$4/month** for a basic instance (1 vCPU, 512MB RAM), with Premium Droplets and more powerful configurations also available. These prices **include SSD storage and bandwidth**, with no extra hidden fees.

For example, a $6/month Basic Droplet includes 25 GB SSD, 1 GB RAM, 1 vCPU, and 1 TB bandwidth.

![DO-droplet2.png](https://assets.northflank.com/DO_droplet2_69a23775c7.png)

AWS, on the other hand, uses **usage-based pricing** that can get complicated very quickly. Amazon EC2 (its VM equivalent) is billed per second, and pricing varies by instance type, region, storage, and whether you use on-demand, savings, or spot instances.

![aws-ec2-pricing.png](https://assets.northflank.com/aws_ec2_pricing_d150816943.png)

With AWS, if you forget to shut something down or underestimate bandwidth usage, you can get hit with unexpected costs.

### 2. Compute cost comparison: DigitalOcean droplet vs AWS EC2

Let’s compare two basic compute instances that are often used by startups and small apps.

| Feature | DigitalOcean basic droplet | AWS EC2 t3.micro (On-Demand) |
| --- | --- | --- |
| vCPUs | 1 | 2 (burstable) |
| RAM | 1 GB | 1 GB |
| Storage | 25 GB SSD (included) | EBS volume priced separately |
| Bandwidth | 1 TB (included) | $0.09/GB for data out (in US East) |
| Base Cost | $6/month | ~$8–$10/month |
| Predictable Pricing? | Yes | No |

So, **DigitalOcean ends up being 30–50% cheaper** for the same compute needs, and you’re not nickel-and-dimed on storage and bandwidth.

### 3. Bandwidth costs: Where AWS gets expensive

One of the biggest hidden costs on AWS is **data transfer (egress)**. Here’s how it compares:

- DigitalOcean includes at least 500 GB of bandwidth in its smallest Droplet plan, with higher tiers including 1 TB or more.
- AWS includes 100 GB/month of free data transfer out to the internet across all services.

> That means transferring 1TB of data per month:
> 
> - On DigitalOcean: ****Included in your $6 plan
> - On AWS: ~$90 just for bandwidth

This makes DigitalOcean extremely appealing for web apps, APIs, and anything with regular outbound traffic.

### 4. Managed databases: Simpler and cheaper on DigitalOcean

- **DigitalOcean Managed PostgreSQL** starts at **$15/month** (1 vCPU, 1 GB RAM, 10 GB SSD).
- **AWS RDS PostgreSQL** starts around **$15–$20/month**, **plus** additional charges for storage, I/O operations, and backups.

Again, with DigitalOcean, you get predictable flat pricing. On AWS, you may need a spreadsheet just to estimate your bill.

### 5. AI and GPU pricing: AWS is more mature, but more expensive

If you're working on AI workloads that require GPUs:

- **DigitalOcean's GPU Droplets**, powered by its Paperspace platform, start at around **$1.57/hour** for a single NVIDIA L40S GPU.
- **AWS EC2 GPU instances**, like the **g5.xlarge**, cost **$1.01/hour**, but require **additional configuration** for storage, networking, and drivers.
- More powerful AWS instances like **p4d** or **p5** can cost **$24–$32/hour**.

In practice, **AWS has more GPU options**, but they often cost significantly more, unless you optimize with Spot Instances or Reserved Instances, which can introduce complexity and availability tradeoffs.

### Overall cost summary: Who should go with what?

| Scenario | DigitalOcean | AWS |
| --- | --- | --- |
| Budget-friendly web app | Cheaper & predictable | More expensive and variable |
| Startup MVP or prototype | Quick to deploy, low cost | Slower and complex |
| High-bandwidth app (media, streaming) | 1TB+ bandwidth included | High egress fees |
| Enterprise-scale with compliance needs | Limited features | Rich ecosystem and fine-grained control |
| AI model training & advanced ML | Expanding GPU support and the Gradient™ AI Platform | Best-in-class ML stack (SageMaker, Bedrock, etc.) |
| Cost optimization over time | Simple pricing to manage manually | Complex but can be tuned with effort |

## Is DigitalOcean or AWS better for web hosting?

If you're trying to host a website, API, or lightweight app, both platforms can handle it, though your choice depends on what kind of experience you want.

DigitalOcean is often the better choice for simple cloud-based web hosting, offering a fast, predictable setup where you can launch a Droplet and deploy your app in minutes.

AWS, on the other hand, provides comprehensive capabilities for high-traffic or complex global applications.

For basic hosting, AWS offers **Lightsail**, a simplified service designed to compete directly with DigitalOcean by offering straightforward, predictable plans.

So if your goal is to get a website or SaaS product online quickly, DigitalOcean provides a fast and affordable experience.

On AWS, you can achieve similar simplicity with Lightsail, though leveraging the full AWS ecosystem for more complex needs requires more effort and technical knowledge.

## DigitalOcean vs AWS: Which is better for AI and machine learning?

If you're working on AI models, inference pipelines, or training workloads, the cloud you choose can have a significant impact.

AWS stands out in this area, with services like SageMaker, custom ML chips (such as **Inferentia and Trainium**), and a wide range of **NVIDIA GPU-backed instances**. It’s designed for production-scale AI and offers everything from data labeling to deployment.

DigitalOcean also supports AI workloads, with **GPU Droplets** (powered by the **Paperspace platform**) and a simplified **Gradient™ AI Platform**. It's a good choice for smaller-scale projects, prototyping, or hosting models, particularly if you're trying to avoid the complexity of AWS.

So if you're building advanced AI systems or need deep integration with other cloud services, AWS is the better fit. Meanwhile, for quick testing or lighter AI tasks, DigitalOcean keeps things simple and cost-effective.

## How Northflank simplifies deployment across AWS and DigitalOcean for both AI and non‑AI workloads

You’ve seen the strengths of AWS and DigitalOcean; now let’s discuss how Northflank bridges the gap and enables deployment across both platforms.

[Northflank](https://northflank.com/) provides a unified platform to run every part of your stack, from GPU-backed AI workloads, APIs, databases, background jobs, to full web apps, in one place, with a consistent, Git-driven workflow.

**Key ways Northflank simplifies deployment across AWS and DigitalOcean (and beyond):**

1. **Bring Your Own Cloud (BYOC):** Northflank supports running workloads within your own cloud accounts by provisioning and managing **Kubernetes clusters** on your behalf. You can connect your AWS, DigitalOcean, GCP, Azure, or even on-prem accounts and deploy into your own VPC for custom networking or static egress, with Northflank handling the orchestration.

    ![northflank-aws-byoc.png](https://assets.northflank.com/northflank_aws_byoc_18d5914008.png)
    
2. **One platform for all workloads**: AI or non-AI, it's all unified. Be it model training, inference, API serving, or database jobs, Northflank handles them all, complete with CI/CD, logging, metrics, and preview/staging environments.

    ![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)
        
3. **GPU support and AI tooling built in:** If you need GPUs for training or inference, Northflank lets you provision resources like NVIDIA **H100 or B200**, orchestrate fine‑tuning jobs, and run notebook environments. Features like **time slicing** and **secure microVM isolation** ensure efficient and safe multi-tenancy on shared GPUs, all without switching platforms.
    
    ![GPU-workloads-northflank.png](https://assets.northflank.com/GPU_workloads_northflank_d8d9b2338d.png)   
    
4. **Consistent deployments, more predictability:** You won’t have to switch back and forth between the AWS console and DigitalOcean control panel. You manage everything through a single interface (UI, CLI, or GitOps), which streamlines your workflow and gives your team a unified approach to deployments.
5. **Modern DevOps built in:** Northflank includes rapid CI pipelines, ephemeral **preview environments**, IaC templates (via **GitOps with templates**), and built-in observability (with **real-time log tailing, metrics, and log sinks**) from push to production.
    
    ![northflank-preview-environments.png](https://assets.northflank.com/northflank_preview_environments_cdfb3cf324.png)

## When to choose AWS or DigitalOcean

By now, it’s clear that AWS and DigitalOcean serve different needs, so which one should you choose?

Go with DigitalOcean if you:

- Want fast, affordable infrastructure without the complexity
- Are launching a startup, MVP, or internal tool
- Prefer predictable pricing with ample bandwidth.
- Need AI or GPU support for smaller workloads, relying on the simpler Paperspace and Gradient™ AI platforms.

Choose AWS if you:

- Need advanced services across AI, data, networking, or compliance
- Are operating at enterprise scale or in regulated environments
- Have unpredictable workloads that require autoscaling and global reach
- Want access to the broadest set of cloud tools and integrations

<InfoBox className='BodyStyle'>

If you’re still unsure or want to use both, **Northflank gives you the best of both worlds**. You can deploy across AWS and DigitalOcean (and other providers) from one place, with full control, lower complexity, and cost optimization built in.

Get started and simplify your cloud deployments - **[sign up now](https://app.northflank.com/signup) or [book a live demo with an engineer](https://cal.com/team/northflank/northflank-intro)** to see how Northflank fits your stack.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Top Vercel Sandbox alternatives for secure AI code execution and sandbox environments</title>
  <link>https://northflank.com/blog/top-vercel-sandbox-alternatives-for-secure-ai-code-execution-and-sandbox-environments</link>
  <pubDate>2025-08-25T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[When you’re building AI agents that generate and execute code, you need secure sandbox environments that can isolate untrusted code without compromising your infrastructure.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/vercel_sandbox_1_8ab6097a1a.png" alt="Top Vercel Sandbox alternatives for secure AI code execution and sandbox environments" />When you’re building AI agents that generate and execute code, you need secure sandbox environments that can isolate untrusted code without compromising your infrastructure.

Vercel Sandbox made waves when it launched in beta, offering Firecracker microVMs with up to 45-minute execution times. But for many teams building production AI applications, Vercel's approach has significant limitations.

Maybe you need sandboxes that persist longer than 45 minutes. Maybe you want to run workloads in your own cloud. Or maybe you need a platform that handles more than just ephemeral code execution.

This article covers the top Vercel Sandbox alternatives, comparing them across isolation methods, pricing models, persistence capabilities, and real-world production readiness.

<InfoBox className="BodyStyle">

## 📌 TL;DR

If you're evaluating secure sandbox platforms for AI code execution:

- **Vercel Sandbox** is good for quick demos and Vercel-native workflows. Firecracker isolation, simple SDK, but 45-minute runtime limits and no BYOC options.
- **Northflank** offers production-proven microVM isolation with unlimited session duration, full orchestration, and runs in your cloud. Better for serious AI infrastructure and long-running workloads.
- **E2B.dev** provides Firecracker sandboxes with good persistence but no production self-hosting and limited infrastructure control.
- **Modal** excels at Python ML workloads with gVisor isolation but lacks multi-language support and persistent sessions.
- **Daytona** focuses on fast container starts but weak on streaming and lacks microVM-level security.
</InfoBox>

## What is Vercel Sandbox and why look for alternatives?

Vercel Sandbox securely runs untrusted code in isolated cloud environments, like AI-generated code. You can create ephemeral, isolated microVMs using the new Sandbox SDK, with up to 45m execution times, and is now in Beta and available to customers on all plans.

Under the hood, Vercel Sandbox uses Firecracker microVMs with Node.js and Python support, supporting execution times up to 45 minutes and a maximum of 8 vCPUs with 2 GB of memory allocated per vCPU.

### Pros of using Vercel Sandbox

- True microVM isolation via Firecracker
- Simple SDK integration with AI workflows
- Standalone SDK that can be executed from any environment, including non-Vercel platforms
- Active CPU pricing - you only pay compute rates when your code is actively executing

### Cons of using Vercel Sandbox

- Maximum runtime duration of 45 minutes - inadequate for long-running AI workloads
- Currently, Vercel Sandbox is only available in the iad1 region - no global deployment options
- No Bring Your Own Cloud (BYOC) or self-hosting capabilities
- Limited to Node.js and Python runtimes
- Still in beta with unclear production readiness timeline

### Vercel Sandbox pricing

Vercel tracks sandbox usage by Active CPU time, Provisioned memory, Network bandwidth, and Sandbox creations:

**Hobby Plan:**

- 5 CPU hours
- 420 GB-hours provisioned memory
- 20 GB network
- 5,000 sandbox creations
- Up to 10 concurrent sandboxes

**Pro Plan:**

- Same base allotments as Hobby
- 100,000 sandbox creations (vs 5,000)
- Up to 150 concurrent sandboxes
- Overage pricing: $0.128/CPU hour, $0.0106/GB-hr memory, $0.15/GB network, $0.60/1M creations

The 45-minute session limit makes Vercel Sandbox unsuitable for persistent AI workloads where users might return to projects hours or days later.

## Common use cases for sandbox environments

Secure sandbox environments are essential for:

1️⃣ **AI agent code execution** - Safely running LLM-generated scripts and tools

2️⃣ **Developer environments** - Providing isolated coding environments for testing

3️⃣ **Multi-tenant SaaS platforms** - Running customer code securely at scale

4️⃣ **Educational platforms** - Teaching programming with safe execution environments

5️⃣ **Data science workflows** - Running untrusted analysis scripts and visualizations

If you're building production AI applications, developer tools, or platforms where end users execute code, you need more than basic sandboxing.

## At-a-glance comparison: Vercel Sandbox alternatives

| Platform | Isolation method | Max session duration | BYOC support | Multi-language | Best for |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | microVM (Kata, gVisor, Firecracker) | Unlimited | Yes | Yes | Production AI infrastructure |
| **Vercel Sandbox** | microVM (Firecracker) | 45 minutes | No | Node.js, Python | Vercel-native demos |
| **E2B.dev** | microVM (Firecracker) | 24 hours active | Experimental | Yes | AI agent sandboxes |
| **Modal** | Container (gVisor) | Stateless | No | Python only | ML workloads |
| **Daytona** | Container (optional Kata) | Variable | No | Yes | Fast dev environments |
| **Cloudflare Workers** | V8 Isolates | Stateless | No | JS/WASM only | Edge functions |

## Top Vercel Sandbox alternatives

### 1. Northflank - Best overall alternative for production AI infrastructure

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

Northflank has been running secure microVMs in production since 2019, executing over 2 million isolated workloads monthly. Unlike sandbox-only solutions, Northflank is a complete cloud platform that excels at secure code execution.

**Strengths:**

- **Multiple isolation options**: Kata Containers with Cloud Hypervisor, gVisor, and Firecracker microVMs
- **Unlimited session duration**: Sandboxes persist until you terminate them - critical for long-running AI workloads
- **Full BYOC support**: Deploy in your AWS, GCP, Azure, or bare metal infrastructure
- **Complete platform**: Run AI agents, APIs, databases, and GPU workloads with consistent security
- **Production-proven scale**: Powers secure multi-tenant deployments for companies like Writer and Sentry
- **Polyglot support**: Any language, runtime, or framework
- **Enterprise-ready**: SSO, RBAC, audit logging, and compliance tools built-in

**Limitations:**

- More comprehensive than pure sandbox tools - may be overkill for simple use cases
- Cold-start latency higher than container-only solutions (though tunable)

**Who it's for:**

- Teams building serious AI infrastructure requiring persistent sessions
- Companies needing enterprise controls and custom cloud deployments
- Platforms running multi-tenant workloads at scale

**Pricing:**

- Transparent usage-based pricing: ~$0.01667/vCPU-hour, $0.00833/GB-hour RAM
- No forced plan tiers or minimum fees
- GPU and spot pricing available

### 2. E2B.dev

E2B.dev focuses specifically on AI agent sandboxes using Firecracker microVMs, with excellent SDK design and persistence features.

**Strengths:**

- Firecracker microVM isolation
- Up to 24-hour active sessions, 30-day paused sessions
- Clean SDK for AI integration
- Multi-language support (Python, JavaScript, R, Java, Bash)

**Limitations:**

- Self-hosting still experimental, not production-ready
- Limited to sandbox use cases only
- No infrastructure flexibility or BYOC options
- Pricing lacks transparency

**Who it's for:**

- AI agent developers needing reliable sandboxes
- Teams focused purely on code execution (not full infrastructure)

**Pricing:**

- Free tier: 2 vCPU, 512MB RAM, ~1hr sessions
- Pro: $150/month + usage fees

### 3. Modal

Modal uses gVisor containers with heavy optimization for machine learning and data science workloads.

**Strengths:**

- Sub-second container starts with custom Rust runtime
- Excellent GPU support (T4 to H200)
- Container keep-alive and checkpointing
- Strong for batch ML jobs and model inference

**Limitations:**

- Python-only for function definitions
- No BYOC or self-hosting
- Limited to serverless model (no persistent services)
- Opaque pricing structure

**Who it's for:**

- Python-focused ML teams
- Data scientists running batch workloads
- Teams needing GPU access for model inference

### 4. Daytona

Daytona pivoted to AI code execution in 2026, focusing on fast container starts with optional enhanced isolation.

**Strengths:**

- Extremely fast cold starts
- Docker image support with Git integration
- Kata Containers available for enhanced isolation

**Limitations:**

- Default configuration uses standard containers (weaker isolation)
- Poor streaming support and session persistence
- Limited orchestration capabilities

**Who it's for:**

- Teams prioritizing startup speed over security
- Development environment use cases

### 5. Cloudflare Workers

Cloudflare takes a completely different approach using V8 isolates rather than containers or VMs.

**Strengths:**

- Zero cold starts, always warm
- 200+ global edge locations
- Excellent for stateless functions
- Strong security via V8 isolate technology

**Limitations:**

- JavaScript/WebAssembly only
- No persistent state or long-running processes
- No GPU support
- Not suitable for complex AI workloads

**Who it's for:**

- Edge computing and API middleware
- Simple stateless functions
- Teams needing global distribution

## Why Northflank leads as a Vercel Sandbox alternative

The fundamental difference between Northflank and other sandbox solutions is scope and production readiness.

### Beyond ephemeral sandboxes

While Vercel Sandbox and others focus purely on short-lived code execution, Northflank provides complete infrastructure for AI applications:

- **Persistent AI agents** that maintain state across user sessions
- **Backend APIs and databases** with the same security guarantees
- **GPU workloads** for model inference and training
- **Scheduled jobs** for batch processing and maintenance
- **Multi-region deployment** with consistent experience

### Production-proven at enterprise scale

Companies like Writer and Sentry trust Northflank to run multi-tenant customer deployments for untrusted code at massive scale. This isn't theoretical - it's battle-tested infrastructure handling millions of secure workloads monthly.

### True infrastructure flexibility

Unlike platform-locked solutions:

- **Managed cloud**: Zero setup, just deploy
- **BYOC**: Run in your existing AWS, GCP, Azure, or bare metal
- **Multi-region**: Global deployment with consistent APIs
- **Any runtime**: Not locked to specific languages or frameworks

### Enterprise-ready from day one

While most sandbox tools lack enterprise features, Northflank includes:

- SSO and directory synchronization
- Granular role-based access control
- Comprehensive audit logging
- Compliance tools and certifications
- SLAs with dedicated support

## Choosing the right sandbox environment

When evaluating Vercel Sandbox alternatives, consider these key factors:

**1. Session persistence**: Can users return to their work later, or do sessions expire quickly?

**2. Infrastructure control**: Do you need BYOC, custom networking, or specific compliance requirements?

**3. Scale requirements**: Are you building for thousands of concurrent users or just internal tools?

**4. Language support**: Do you need more than Node.js and Python?

**5. Integration depth**: Do you need just sandboxes or a complete platform for your AI application?

For most production AI applications, the 45-minute session limit and platform lock-in of Vercel Sandbox becomes a significant constraint. Users expect to return to their projects, and developers need infrastructure flexibility.

## Conclusion

Vercel Sandbox helped introduce secure sandboxing to the Vercel ecosystem, but it's designed primarily for short-lived demos and Vercel-native workflows.

For teams building production AI applications, the key question isn't just "can it sandbox code?" but "can it run my complete AI infrastructure securely at scale?"

**Northflank leads because it's the only platform that combines:**

- Production-proven microVM isolation (2M+ workloads monthly)
- Unlimited session persistence for real user workflows
- Complete platform that grows with your AI application
- True infrastructure flexibility (managed or BYOC)
- Transparent, predictable pricing without platform lock-in

Don't settle for ephemeral sandboxes when your AI applications need persistent, scalable infrastructure. With Northflank, secure code execution is just one part of a comprehensive cloud platform built for the AI era.

[Try Northflank](https://app.northflank.com/signup) or [talk to an engineer](https://cal.com/team/northflank/northflank-intro).]]>
  </content:encoded>
</item><item>
  <title>6 best MLflow alternatives: Open source &amp; commercial ML platforms</title>
  <link>https://northflank.com/blog/mlflow-alternatives</link>
  <pubDate>2025-08-22T16:43:00.000Z</pubDate>
  <description>
    <![CDATA[Compare top MLflow alternatives, including open-source tools like Kubeflow, BentoML, and commercial platforms. Find the best ML experiment tracking and deployment solution.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/mlflow_alternatives_52d8ed5d3d.png" alt="6 best MLflow alternatives: Open source &amp; commercial ML platforms" />MLflow is a popular open-source platform for managing the machine learning lifecycle. It provides experiment tracking, model packaging, and deployment capabilities.

However, teams are looking for alternatives due to MLflow's limitations in areas like multi-user collaboration, role-based access controls, and production-grade deployment infrastructure.

If you're looking for better team collaboration features, more reliable deployment options, or enterprise-grade security, we would cover several alternatives that provide these.

And if your team is specifically focused on production model deployment and scaling, platforms like [Northflank](https://northflank.com/) provide advanced infrastructure management and deployment capabilities that address many of MLflow's shortcomings in production environments.

## Quick comparison of the 6 best MLflow alternatives

Before we go into the individual platforms, this is a quick comparison to help you quickly identify which MLflow alternative might work best for your team's needs.

| Feature | **Northflank** | BentoML | Kubeflow | Neptune.ai | Azure ML | ZenML |
| --- | --- | --- | --- | --- | --- | --- |
| **Primary focus** | AI/ML model deployment & full-stack platform | Model serving & deployment | End-to-end ML platform | Experiment tracking | Enterprise ML platform | MLOps pipeline orchestration |
| **Experiment tracking** | Real-time monitoring & observability | Basic monitoring & logging | Built-in with Katib & pipelines | Advanced experiment management | Comprehensive tracking | Pipeline-based tracking |
| **Model registry** | Container registry for models | Centralized model store | Full model lifecycle mgmt | Model versioning & staging | Enterprise model registry | Model registry integration |
| **Model deployment** | Production-grade containers | API endpoints & batch jobs | KServe & distributed serving | Limited deployment options | Multiple deployment targets | Multiple deployment backends |
| **RBAC & team collaboration** | Fine-grained RBAC & teams | Not available | Kubernetes-native RBAC | Team workspaces & permissions | Enterprise-grade RBAC | Organization & workspace RBAC |
| **Staging environments** | Multi-environment support | Development & production | Namespace-based staging | Experiment comparison | Multi-workspace staging | Pipeline environment management |
| **Infrastructure management** | Auto-scaling & monitoring | Docker & Kubernetes | Kubernetes orchestration | Managed service only | Cloud-native scaling | Multi-cloud orchestration |
| **Licensing** | Commercial (usage-based) | Open source (Apache 2.0) | Open source (Apache 2.0) | Freemium + Commercial | Commercial (Azure pricing) | Open source + Commercial |
| **Learning curve** | Moderate (developer-friendly) | Low to moderate | Steep (Kubernetes knowledge) | Low (intuitive UI) | Moderate (Azure ecosystem) | Moderate (pipeline concepts) |
| **Best for** | AI/ML production deployment & scaling | Fast model serving | Kubernetes-native teams | Research & experimentation | Enterprise Azure users | MLOps pipeline standardization |

**The key takeaways and summary of the comparison are these:**

- Northflank specializes in production deployment, infrastructure management, and team collaboration for both AI/ML and full-stack applications
- BentoML and Kubeflow provide comprehensive model serving capabilities but with different complexity levels
- Neptune.ai delivers the best experiment tracking experience
- Azure ML provides comprehensive enterprise features within the Microsoft ecosystem
- ZenML delivers flexible MLOps orchestration with extensive integration capabilities

## What are the main limitations of MLflow?

While MLflow has gained popularity as an open-source ML platform, many teams start looking for alternatives when they encounter its limitations in real-world production environments. Let's look at some of the things MLflow lacks and why some teams are in search of alternatives.

### 1. Lacks proper multi-user support and RBAC

MLflow wasn't designed with team collaboration in mind. MLflow lacks robust multi-user support or role-based access controls (RBAC).

Collaboration is hard when you can't easily share experiments or collaborate on them because MLflow doesn't provide user management and permissions.

This means anyone with access to your MLflow UI can delete experiments, making it risky for larger teams.

### 2. Limited production deployment capabilities

While MLflow can deploy models, its deployment options are fairly basic compared to modern production requirements.

You'll often need additional tools and significant DevOps work to handle scaling, monitoring, and production-grade infrastructure management that teams expect today.

### 3. Minimal data versioning features

MLflow focuses primarily on model tracking but falls short when it comes to comprehensive data versioning.

Running MLflow beyond the basic local use case requires substantial DevOps work.

You need to set up a tracking server with a backing database, possibly a file or object store for artifacts, and handle authentication yourself.

If your team needs to track dataset changes alongside model versions, you'll need to look elsewhere.

### 4. UI and collaboration constraints

The MLflow UI, while functional, can feel dated and limited when you're trying to collaborate with team members or present results to stakeholders.

The visualization options are basic, and sharing experiment results or creating reports for non-technical team members can be challenging.

### 5. Scalability challenges for large teams

As your team grows and experiments multiply, MLflow can become difficult to manage.

The lack of proper user management, combined with limited organizational features, means you'll likely hit walls when trying to scale MLflow across multiple teams or projects.

## What to look for in an MLflow alternative

Now that you understand MLflow's limitations, you'll want to know what features to prioritize when evaluating alternatives. Here are the key capabilities that can make or break your team's ML workflow.

### 1. Multi-user support and role-based access controls

Your alternative should provide proper user management and permissions from day one.

Look for platforms that offer granular role-based access controls, allowing you to set different permission levels for data scientists, ML engineers, and stakeholders.

This prevents accidental deletions and ensures sensitive experiments remain secure.

### 2. Production-grade deployment capabilities

Moving models to production shouldn't require a separate DevOps team.

Look for platforms that offer containerized deployments, auto-scaling, staging environments, and monitoring out of the box.

The ability to deploy models as REST APIs, batch jobs, or integrate with existing infrastructure is crucial for real-world applications.

### 3. Experiment tracking and visualization

While MLflow offers basic tracking, modern alternatives provide more intuitive interfaces for comparing experiments, visualizing metrics, and sharing results.

Look for platforms with advanced filtering, search capabilities, and the ability to create custom dashboards for different team members.

### 4. Model registry and versioning

A comprehensive model registry should go beyond simple storage.

You want version control, stage management (development, staging, production), model lineage tracking, and the ability to roll back to previous versions when needed.

Integration with your deployment pipeline is equally important.

### 5. Integration ecosystem

Your MLflow alternative should play well with your existing tools.

Check for integrations with popular ML frameworks (PyTorch, TensorFlow, scikit-learn), cloud providers, CI/CD systems, and data storage solutions.

The more seamlessly it fits into your current workflow, the smoother your transition will be.

### 6. Cost considerations (open-source vs commercial)

Consider both upfront costs and long-term expenses.

Open-source solutions might seem free, but factor in infrastructure, maintenance, and support costs.

Commercial platforms often provide better support and managed services, but can become expensive as you scale.

Evaluate based on your team size, usage patterns, and budget constraints.

## Top 6 MLflow alternatives

Once you understand MLflow's limitations, it becomes clear that different teams need different solutions. Here are the six best alternatives to MLflow, each addressing specific challenges that teams encounter when scaling their ML operations.

### 1. Northflank - AI/ML model deployment & full-stack platform

[Northflank](https://northflank.com/) goes beyond basic experiment tracking to provide a complete platform designed for deploying and scaling production-ready ML models and AI applications.

Where MLflow struggles with production deployment and team collaboration, Northflank excels by combining containerized infrastructure with advanced staging environments, Git-based CI/CD, and enterprise-grade RBAC.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

**Key overlap with MLflow: Model serving and deployment**

Northflank directly addresses MLflow's deployment limitations by providing production-grade container deployment for ML models.

Northflank gives you a comprehensive deployment platform that handles scaling, monitoring, and team collaboration seamlessly.

**Key features:**

- Production-grade Docker container deployment for ML models with full runtime control and customization
- Enterprise-grade staging environments and production pipelines for secure model deployment workflows
- Team collaboration with fine-grained RBAC, addressing MLflow's biggest weakness in multi-user environments
- Multi-environment support for development, staging, and production model deployments
- [Git-based CI/CD](https://northflank.com/docs/v1/application/release/manage-ci-cd) with automated model deployment triggers
- Real-time monitoring and observability tools for model performance tracking

<InfoBox className='BodyStyle'>

### 🤑 **Northflank pricing**

- Free tier: Generous limits for testing and small projects
- CPU instances: Starting at $2.70/month ($0.0038/hr) for small workloads, scaling to production-grade dedicated instances
- GPU support: NVIDIA A100 40GB at $1.42/hr, A100 80GB at $1.76/hr, H100 at $2.74/hr, up to B200 at $5.87/hr
- Enterprise BYOC: Flat fees for clusters, vCPU, and memory on your infrastructure, no markup on your cloud costs
- Pricing calculator available to estimate costs before you start
- Fully self-serve platform, get started immediately without sales calls
- No hidden fees, egress charges, or surprise billing complexity

</InfoBox>

**Why Northflank excels where MLflow falls short in production:**

MLflow's deployment capabilities are basic and require significant DevOps work to scale.

Northflank provides enterprise-grade infrastructure management, automatic scaling, and proper staging environments out of the box.

While MLflow lacks RBAC and team collaboration features, Northflank offers comprehensive user management and permissions that large teams require.

> **Best for:** Teams moving from experimentation to production who need reliable model deployment, proper staging environments, and enterprise-grade collaboration features. Ideal for organizations requiring both ML model deployment and full-stack application support.
> 

See [how Cedana uses Northflank to deploy workloads onto Kubernetes with microVMs and secure runtimes](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes)

### 2. BentoML - Open source model serving framework

BentoML focuses specifically on converting trained models into production-ready serving systems.

It is a Python library for building online serving systems optimized for AI apps and model inference.

Unlike MLflow's basic deployment options, BentoML provides specialized serving capabilities designed for production environments.

![bentoml-homepage.png](https://assets.northflank.com/bentoml_homepage_1b6289d1d1.png)

**Key features:**

- Production-ready ML service creation with API endpoint generation
- Framework-agnostic approach supporting TensorFlow, PyTorch, scikit-learn, and more
- Docker and Kubernetes deployment with containerization built-in
- Adaptive micro-batching dynamically adjusts batch size and batching intervals based on real-time request patterns for optimized performance

**Best for:** Teams that need fast, reliable model serving without the complexity of a full MLOps platform. Perfect for organizations already satisfied with their experiment tracking but need better deployment capabilities.

For teams evaluating BentoML alongside other model serving solutions, check out our detailed comparison in [6 best BentoML alternatives for self-hosted AI model deployment](https://northflank.com/blog/bentoml-alternatives).

### 3. Kubeflow - Open source Kubernetes-native ML platform

Kubeflow is an advanced, scalable platform for running machine learning workflows on the Kubernetes cluster.

It provides comprehensive ML workflow orchestration that MLflow lacks, making it ideal for teams already operating in Kubernetes environments.

![kubeflow-homepage.png](https://assets.northflank.com/kubeflow_homepage_81e2ccf028.png)

**Key features:**

- Complete ML workflow orchestration with pipeline management
- Kubernetes-native ML platform with distributed training capabilities
- Track Experiments/Runs: With Kubeflow pipelines or using the Kubeflow Notebooks, track every variation of the hyper-parameters along with any configuration in that specific Experiment
- Enterprise-grade multi-user support with namespace-based isolation

**Best for:** Organizations with Kubernetes expertise who need end-to-end ML platform capabilities. Ideal for teams running complex, distributed ML workloads that require sophisticated orchestration.

If you're considering Kubeflow among other orchestration platforms, our guide on [Top 7 Kubeflow alternatives for deploying AI in production](https://northflank.com/blog/top-7-kubeflow-alternatives) provides detailed comparisons.

### 4. Neptune.ai - Commercial experiment tracking platform

Neptune is a lightweight experiment tracker for ML teams that struggle with debugging and reproducing experiments, sharing results, and messy model handover.

It directly addresses MLflow's collaboration and visualization limitations with a purpose-built experiment tracking platform.

![neptune-ai-homepage.png](https://assets.northflank.com/neptune_ai_homepage_390d47bdb7.png)

**Key features:**

- Advanced experiment management and collaboration with team workspaces
- Version production-ready models and metadata associated with them in a single place. Review models and transition them between development stages
- Superior visualization and team features compared to MLflow's basic UI
- Protect your projects, ensure correct access levels, and collaborate securely with role-based access control and SSO

**Best for:** Research teams and organizations prioritizing experiment tracking and collaboration over deployment capabilities. Perfect for teams that need better visualization and sharing than MLflow provides.

### 5. Azure ML - Commercial enterprise platform

Azure ML provides a comprehensive enterprise ML platform within the Microsoft ecosystem, offering capabilities that far exceed MLflow's scope.

It offers multiple interfaces, including the Azure Machine Learning studio UI, the Azure Machine Learning V2 CLI, and the Python Azure Machine Learning V2 SDK to accommodate different workflows and preferences.

![azure-ml-homepage.png](https://assets.northflank.com/azure_ml_homepage_84764305d9.png)

**Key features:**

- Microsoft's comprehensive ML platform with enterprise-grade security and compliance
- Azure role-based access control (Azure RBAC) to manage access to Azure resources, giving users the ability to create new resources or use existing ones
- Integrated development environment with seamless Azure service integration
- Production deployment at scale with multiple target environments

**Best for:** Enterprise teams already invested in the Microsoft ecosystem who need comprehensive ML platform capabilities with enterprise support and compliance features.

### 6. ZenML - Open source + commercial MLOps framework

ZenML allows orchestrating ML pipelines independent of any infrastructure or tooling choices.

It provides the pipeline orchestration and workflow management that MLflow lacks, focusing on creating reproducible ML workflows.

![zenml-homepage.png](https://assets.northflank.com/zenml_homepage_78cd7ce6ec.png)

**Key features:**

- MLOps pipeline framework with declarative workflow definition
- Flexible deployment backends supporting multiple cloud providers and tools
- ZenML Pro significantly enhances collaboration through comprehensive Role-Based Access Control (RBAC) with detailed permissions across organizations, workspaces, and projects
- Production orchestration with extensive integration ecosystem

**Best for:** Teams needing comprehensive MLOps workflow orchestration and standardization. Ideal for organizations that want to prevent vendor lock-in while maintaining consistent ML practices across different tools.

## MLflow vs top alternatives: Key comparisons

Understanding how MLflow stacks up against specific alternatives can help you make the right choice for your team's needs. Here are three quick key comparisons that highlight the most important differences.

### 1. MLflow vs Kubeflow: Which is better for your team?

**Choose Kubeflow if:** You need complex ML pipeline orchestration, your team has Kubernetes expertise, and you want enterprise-grade multi-user support.

**Choose MLflow if:** You want simple experiment tracking with minimal setup overhead and don't need advanced pipeline orchestration.

### 2. MLflow vs BentoML: Model serving showdown

**Choose BentoML if:** Your primary need is fast, reliable model serving, and you're satisfied with your current experiment tracking solution.

**Choose MLflow if:** You want an all-in-one platform for both experiment tracking and basic model deployment, even if the deployment features are limited.

### 3. MLflow vs Northflank: Production deployment comparison

**Choose Northflank if:** You need production-grade model deployment, proper staging environments, team collaboration features, and infrastructure that scales automatically.

**Choose MLflow if:** You're primarily focused on experiment tracking and model management, with basic deployment needs that don't require advanced infrastructure features.

## Is MLflow completely free, and can you run it locally?

MLflow is completely open-source under the Apache 2.0 license, so you can use it freely and run it locally with minimal setup.

However, running MLflow beyond the basic local use case requires substantial DevOps work.

You need to set up a tracking server with a backing database, possibly a file or object store for artifacts, and handle authentication yourself.

While the software is free, the hidden costs of infrastructure, maintenance, security, and scaling often make commercial alternatives more cost-effective for production environments, especially when you need enterprise features like RBAC and advanced deployment capabilities.

## Which alternative should you choose?

A quick one:

1. **Best for teams needing RBAC and collaboration:** Northflank and ZenML provide comprehensive role-based access controls and team management features that MLflow lacks.
2. **Best for production deployment and scaling:** Northflank excels at production-grade model deployment with auto-scaling infrastructure, while Kubeflow offers enterprise-scale orchestration for Kubernetes environments.
3. **Best for experiment tracking and visualization:** Neptune.ai delivers the most advanced experiment management interface with good visualization and collaboration tools.
4. **Best for budget-conscious teams:** BentoML and Kubeflow offer good open-source capabilities without licensing costs, though they require more setup effort.
5. **Best for enterprise requirements:** Azure ML provides comprehensive enterprise features within the Microsoft ecosystem, while Northflank offers enterprise-grade deployment with flexible infrastructure options.

## Why Northflank stands out for ML model deployment

When teams outgrow MLflow's basic deployment capabilities, they need a platform built for production ML workloads.

Northflank combines enterprise-grade infrastructure management with the RBAC and staging environments that MLflow lacks.

For teams serious about deploying models reliably at scale, Northflank provides the comprehensive solution that goes beyond what MLflow offers.

Looking to deploy your ML models in production? [Sign up for Northflank's free tier](https://app.northflank.com/signup) to get started immediately, or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how Northflank can streamline your model deployment workflow.]]>
  </content:encoded>
</item><item>
  <title>How to deploy and self-host DeepSeek-V3.1 on Northflank</title>
  <link>https://northflank.com/blog/deploy-self-host-deep-seek-v3-1-on-northflank</link>
  <pubDate>2025-08-21T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[This guide shows you how to deploy and self-host DeepSeek-V3.1 on Northflank using our one-click template or by setting it up manually. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/deepseek_e80c6ad15b.png" alt="How to deploy and self-host DeepSeek-V3.1 on Northflank" />This guide shows you how to deploy and self-host **DeepSeek-V3.1** on Northflank using our one-click template or by setting it up manually. The model runs with **vLLM** for high-throughput inference and includes an **OpenAI-compatible endpoint** plus a full **Open WebUI** interface.

DeepSeek-V3.1 supports both *thinking* and *non-thinking* chat modes and features a 128K context window, large enough to hold a 300-page book.

<InfoBox className="BodyStyle">

## 📌 TL;DR

- **DeepSeek-V3.1** is a 671B parameter Mixture-of-Experts model with 128K context, hybrid thinking modes, and improved reasoning speed.
- Runs best on **8× NVIDIA H200 GPUs** with vLLM.
- Deploy / Self-host on Northflank in minutes with our **one-click template** or configure manually for full control.
- Once deployed, you get a **rate-limit-free, OpenAI-compatible API** and a user-friendly web interface.

👉 [**Deploy DeepSeek-V3.1 (128K) on Northflank now**](https://northflank.com/stacks/deepseek-v3-1)

</InfoBox>

## What is DeepSeek-V3.1?

DeepSeek-V3.1 is the latest upgrade in the DeepSeek family of large language models. It builds on V3 and R1 with better reasoning speed, hybrid inference modes, and agentic improvements.

**Key details:**

- **Architecture:** Mixture-of-Experts (671B total parameters, ~37B active per token)
- **Context window:** 128K tokens
- **Modes:** Chat vs Think (toggleable in WebUI with “DeepThink” button)
- **Efficiency:** FP8 UE8M0 optimizations for H200 and domestic chips
- **Inference:** Faster than R1 and V3 in thinking mode, higher throughput in non-thinking mode

These improvements make DeepSeek-V3.1 one of the most capable open-weight LLMs available today.

## Why DeepSeek-V3.1 matters

- **Hybrid inference**: Choose between standard chat or reasoning-heavy “Think” mode.
- **Faster reasoning**: V3.1-Think responds quicker than R1 and earlier DeepSeek releases.
- **Agent improvements**: Stronger tool use and multi-step planning.
- **128K context**: Enough space for large documents, codebases, or entire books.
- **Open weights**: Can be run on your own infra with no API restrictions.

On Northflank, you can deploy it securely, scale on demand, and avoid rate limits.

## How to deploy DeepSeek-V3.1 on Northflank

You have two options: one-click template or manual setup.

### 1️⃣ Option 1: One-click deploy

1. **Create a Northflank account**
    
    Sign up and enable GPU regions.
    
2. **Select the template**
    
    From the template catalog, click **Deploy DeepSeek-V3.1 128K on 8×H200 Now**.
    
3. **Deploy stack**
    - Creates a vLLM service with a mounted volume for the 671B model.
    - Deploys Open WebUI with persistent storage for user data.
4. **Wait for load**
    
    The vLLM service downloads and shards the model across GPUs. First load takes ~45–60 minutes.
    
5. **Open WebUI**
    
    Navigate to the assigned `code.run` domain.
    
6. **Create your account** and start interacting with DeepSeek-V3.1 in chat or think mode.

You’ll also get an **OpenAI-compatible endpoint** to use with any client library.

### 2️⃣ Option 2: Manual deployment

### 1. Create a GPU-enabled project

- In Northflank dashboard → *Create Project*.
- Name: `deepseek-v31`.
- Region: select one with H200 GPUs.

### 2. Deploy vLLM service

- Create a new *Deployment* service → `deepseek-v31-vllm`.
- Source: **External image**
    
    ```bash
    vllm/vllm-openai:deepseek
    ```
    
- Runtime variable:
    - `OPENAI_API_KEY` → generate 128-char random key.
- Networking:
    - Add port **8000**, protocol HTTP, expose publicly.
- Compute:
    - **8 × NVIDIA H200 GPUs**.
- Advanced → command:
    
    ```bash
    sleep 1d
    ```
    

### 3. Attach persistent storage

- Add volume `deepseek-models`.
- Size: 1TB.
- Mount path: `/root/.cache/huggingface`.
- Attach to vLLM service.

### 4. Download and serve model

In service shell:

```bash
export HF_HUB_ENABLE_HF_TRANSFER=1
pip install --upgrade transformers torch hf-transfer
vllm serve deepseek-ai/DeepSeek-V3.1 --tensor-parallel-size 8
```

To automate:

```bash
bash -c "export HF_HUB_ENABLE_HF_TRANSFER=1 && pip install --upgrade transformers torch hf-transfer && vllm serve deepseek-ai/DeepSeek-V3.1 --tensor-parallel-size 8"
```

### 5. Deploy Open WebUI

- New service: `deepseek-v31-webui`.
- Image:
    
    ```lua
    ghcr.io/open-webui/open-webui:latest
    ```
    
- Volume: persistent for sessions.
- Port: **8080**, expose publicly.
- Env vars:
    - `OPENAI_API_BASE=https://<vllm-service>.code.run/v1`
    - `OPENAI_API_KEY=<same key>`

### 6. Test via API

Example (Python):

```python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["OPENAI_API_KEY"],
    base_url="https://<vllm-service>.code.run/v1",
)

resp = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V3.1",
    messages=[
        {"role": "user", "content": "Explain DeepSeek-V3.1's benefits"}
    ]
)

print(resp.choices[0].message)
```

## Cost of deploying DeepSeek-V3.1

How much does it cost to self-host DeepSeek-V3.1?

Many teams choose to **self host DeepSeek 3.1** for cost efficiency and data privacy. Northflank makes it easy to deploy or **self-host DeepSeek-V3.1** without infrastructure headaches.

Running DeepSeek-V3.1 at production scale requires **8× H200 GPUs**.

**Northflank GPU pricing (as of August 2025):**

- H200: ~$3.20/hour per GPU
- 8×H200 = ~$25.60/hour

Token cost equivalent with vLLM optimizations:

- Input: ~$0.10 per 1M tokens
- Output: ~$2.20 per 1M tokens

You pay **only for the GPUs and storage you run**, no hidden charges. 

This makes Northflank one of the most cost-efficient platforms for MoE inference at scale.

## **DeepSeek-V3.1 vs earlier versions**

DeepSeek has iterated quickly, with each release pushing reasoning, speed, and usability forward.

### DeepSeek-V3.1 vs DeepSeek-V3

- **Architecture:** Both use a 671B Mixture-of-Experts design with ~37B active parameters per forward pass.
- **Context window:** V3 had 64K tokens, while V3.1 doubles this to **128K tokens**.
- **Performance:** V3.1 runs more efficiently on H200 GPUs thanks to FP8 (UE8M0) optimizations.
- **Inference modes:** V3 supported standard chat-style inference only. V3.1 introduces **hybrid inference** with both *chat* and *think* modes.
- **Reasoning:** V3 was capable but slower at multi-step reasoning. V3.1 improves both speed and accuracy in reasoning-heavy tasks.

👉 **Verdict:** DeepSeek-V3.1 is a direct upgrade, more context, faster reasoning, and flexible inference modes.

### DeepSeek-V3.1 vs DeepSeek-R1

- **Purpose:** R1 was tuned specifically for chain-of-thought reasoning using reinforcement learning. V3.1 integrates those improvements into a general-purpose model.
- **Context window:** R1 was limited to 64K. V3.1 expands this to **128K tokens**.
- **Speed:** R1 reasoning was accurate but often slower. V3.1’s “Think” mode is faster while maintaining quality.
- **Flexibility:** R1 forced reasoning-heavy outputs. V3.1 gives you a toggle between fast chat and deep reasoning.
- **Agent performance:** V3.1 shows stronger results on tool use and multi-step tasks compared to R1.

👉 **Verdict:** DeepSeek-V3.1 replaces R1 by offering reasoning at higher speed, with the option to switch back to standard inference.

## 🔗 Useful links

- [Deploy DeepSeek-V3.1 on Northflank](https://northflank.com/stacks/deepseek-v3-1)
- Deploy DeepSeek’s older versions in your own cloud
    - [GCP](https://northflank.com/stacks/deploy-deepseek-r1-70b-gcp)
    - [Azure](https://northflank.com/stacks/deploy-deepseek-r1-70b-aks)
    - [AWS](https://northflank.com/stacks/deploy-deepseek-r1-70b-aws)
- [Deploy Qwen3 on Northflank](https://northflank.com/stacks?search=qwen3)
- [Self-host gpt-oss on Northflank](https://northflank.com/blog/self-host-openai-gpt-oss-120b-open-source-chatgpt)

## Final thoughts

DeepSeek-V3.1 represents a leap forward in open-weight reasoning models: hybrid inference, faster chain-of-thought, and a 128K context.

On Northflank, you can run it securely, scale across H200 GPUs, and interact through an OpenAI-compatible API or a user-friendly WebUI, with no rate limits.

[👉 **Deploy DeepSeek-V3.1 on Northflank now**](https://northflank.com/stacks/deepseek-v3-1)]]>
  </content:encoded>
</item><item>
  <title>6 best TensorDock alternatives for GPU cloud compute and AI/ML deployment</title>
  <link>https://northflank.com/blog/tensordock-alternatives</link>
  <pubDate>2025-08-21T16:40:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the 6 best TensorDock alternatives for GPU cloud compute and AI/ML deployment. Find the right platform for production AI applications, cost optimization, and specialized workflows.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/tensordock_alternatives_da0c461983.png" alt="6 best TensorDock alternatives for GPU cloud compute and AI/ML deployment" />TensorDock provides competitive GPU marketplace pricing and global availability, but as your AI projects scale from prototypes to production, you might need CI/CD integration, observability tools, full-stack deployment capabilities, or production-grade infrastructure that platforms like [Northflank](https://northflank.com/) deliver alongside GPU orchestration.

This article compares the top TensorDock alternatives to help you identify the right platform for your specific needs, from cost optimization to production deployment and specialized AI workflows.

## Quick comparison of the 6 best TensorDock alternatives

If you're short on time, see a detailed look at the top TensorDock alternatives. Each platform has its strengths, but they solve different problems, and some are better suited for production deployment than others.

| Platform | Best for | Key features | Pricing model | Production focus |
| --- | --- | --- | --- | --- |
| [Northflank](https://northflank.com/) | Production AI apps with full-stack needs | [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud), Git CI/CD, autoscaling, secure runtime, multi-service orchestration | Usage-based, transparent | Full production platform |
| [RunPod](https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment) | Cost-effective GPU containers | Serverless GPUs, marketplace pricing, container control | Pay-per-use, competitive rates | Container-focused |
| [Vast.ai](https://northflank.com/blog/6-best-vast-ai-alternatives) | Budget-friendly distributed GPU | Community marketplace, bidding system, spot pricing | Auction/spot pricing | Experimental workloads |
| [Modal](https://northflank.com/blog/6-best-modal-alternatives) | Python-native serverless | Zero-ops deployment, automatic scaling, Python-first | Serverless, scale-to-zero | Python workflows only |
| [Replicate](https://northflank.com/blog/6-best-replicate-alternatives) | Public model serving & monetization | One-click APIs, model marketplace, revenue sharing | Usage-based, revenue split | Public demos and APIs |
| [Lambda Labs](https://northflank.com/blog/top-lambda-ai-alternatives) | Hosted Jupyter + training | Pre-configured environments, academic focus, managed notebooks | Fixed instance pricing | Research and training |

## What to look for in a TensorDock alternative

When choosing alternatives to TensorDock, the features you prioritize depend on if you're building prototypes or production systems. What to look for:

1. **Production deployment capabilities**:
    
    Look for platforms that support CI/CD integration with GitHub or GitLab, automated deployments, and proper rollback mechanisms. If you're moving beyond manual container pushes, you need infrastructure that connects to your development workflow.
    
2. **Observability and monitoring**:
    
    Built-in logging, metrics, and request tracing become critical once you're serving production traffic. Can you answer questions like "How many requests failed?" or "What's my GPU utilization?" without SSH-ing into containers?
    
3. **Scaling and orchestration**:
    
    Static containers work for experiments, but production workloads need autoscaling based on demand, job queues for background processing, and orchestration for complex workflows. Determine if you need these capabilities now or will soon.
    
4. **Multi-service support**:
    
    Modern AI applications involve more than GPU containers; they need frontends, APIs, databases, and caches working together. Determine if you can deploy complete application stacks or if you'll need to manage services across multiple platforms.
    
5. **Infrastructure control and compliance**:
    
    If you have existing cloud investments, compliance requirements, or need VPC integration, look for platforms offering [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) capabilities rather than vendor-locked infrastructure.
    
6. **Security requirements:**
    
    Consider your data sensitivity and multi-tenancy needs. Basic container isolation might suffice for research, but production systems often require advanced sandboxing, runtime security, and enterprise-grade access controls.
    
7. **Cost structure and transparency**:
    
    Marketplace pricing offers savings, but production systems need predictable costs. Look for transparent usage-based pricing that scales with your business rather than surprise fees or complex billing models.
    

The important factor is matching platform capabilities to your specific needs, including current requirements and where your project is heading.

## Top 6 TensorDock alternatives

Once you understand these limitations, it becomes clear that different teams need different solutions. Here are the six best alternatives to TensorDock, each solving specific problems that teams encounter as they scale.

### 1. Northflank - Production-grade AI platform with full-stack support

[Northflank](https://northflank.com/) goes beyond GPU rental to provide a complete platform designed for deploying and scaling production-ready AI applications. It combines the flexibility of containerized infrastructure with GPU orchestration, Git-based CI/CD, and full-stack application support.

From serving a fine-tuned LLM to hosting a Jupyter notebook or deploying a complete product with frontend, backend, and database components, Northflank provides the infrastructure foundation without the platform lock-in that limits other solutions.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

**Key features:**

- Bring your own Docker image with full runtime control and customization
- GPU-enabled services with [autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments) and lifecycle management for cost optimization
- Multi-cloud and [Bring Your Own Cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) support for compliance and integration requirements
- [Git-based CI/CD](https://northflank.com/docs/v1/application/release/manage-ci-cd) with [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) for safe iteration
- Secure runtime for untrusted AI workloads with configurable isolation
- SOC 2 readiness and enterprise security features (RBAC, SAML, audit logs)

<InfoBox className='BodyStyle'>

### 🤑 **Northflank pricing**

- Free tier: Generous limits for testing and small projects
- CPU instances: Starting at $2.70/month ($0.0038/hr) for small workloads, scaling to production-grade dedicated instances
- GPU support: NVIDIA A100 40GB at $1.42/hr, A100 80GB at $1.76/hr, H100 at $2.74/hr, up to B200 at $5.87/hr
- Enterprise BYOC: Flat fees for clusters, vCPU, and memory on your infrastructure, no markup on your cloud costs
- Pricing calculator available to estimate costs before you start
- Fully self-serve platform, get started immediately without sales calls
- No hidden fees, egress charges, or surprise billing complexity

</InfoBox>

**Benefits:**

- No platform lock-in – maintain full container control while choosing between BYOC or managed infrastructure
- Transparent, predictable [usage-based pricing](https://northflank.com/pricing) that's easy to forecast at scale
- Exceptional developer experience with Git-based deployments, automated CI/CD, and preview environments
- Optimized for latency-sensitive workloads with fast container startup, GPU autoscaling, and low-latency networking
- Full-stack application support, including frontends, backends, databases, and background jobs
- Built-in cost management with real-time usage tracking, budget caps, and optimization recommendations

**Best for:** Teams building production-ready AI products that need more than GPU access. Ideal for companies requiring CI/CD integration, multi-service orchestration, compliance controls, or the flexibility to deploy across their own cloud infrastructure.

**Verdict:** Northflank is the only platform that combines TensorDock's container flexibility with production-grade infrastructure. If you're moving beyond prototypes and need to deploy production AI applications with proper DevOps practices, observability, and scaling capabilities, Northflank provides the most comprehensive solution without vendor lock-in.

### 2. RunPod - Serverless GPU containers

RunPod offers the closest experience to TensorDock's model while providing better developer tooling and serverless capabilities. It focuses on making GPU containers as simple and cost-effective as possible.

![runpod-homepage.png](https://assets.northflank.com/runpod_homepage_a696c3aa97.png)

**Key features:**

- GPU marketplace with competitive pricing and diverse hardware options
- Serverless GPU functions that scale to zero when not in use
- REST APIs and persistent volumes for easier integration and data management
- Real-time and batch processing options for different workload patterns

**Best for:** Cost-sensitive teams that want raw GPU power with slightly better tooling than TensorDock, but don't need full production infrastructure capabilities.

**Verdict:** RunPod provides a TensorDock-like experience with improved developer tools and serverless options. Choose it when you need affordable GPU access with better APIs and flexibility, but can handle infrastructure management yourself.

### 3. Vast.ai - Community GPU marketplace

Vast.ai takes the marketplace model even further than TensorDock, offering a completely decentralized approach where anyone can rent out their GPU hardware. This creates opportunities for even lower costs through bidding and spot pricing.

![vastai's homepage.png](https://assets.northflank.com/vastai_s_homepage_194c175a50.png)

**Key features:**

- Decentralized marketplace with thousands of independent GPU providers
- Bidding system for interruptible instances at rock-bottom prices
- Custom container support with flexible deployment options
- Granular hardware filtering for specific GPU requirements

**Best for:** Budget-conscious researchers, students, and experimenters who prioritize cost over reliability and can handle occasional interruptions.

**Verdict:** Choose Vast.ai when absolute lowest cost is your primary concern and you can tolerate the reliability trade-offs that come with a fully decentralized marketplace.

### 4. Modal - Python-native serverless

Modal takes a completely different approach by making Python deployment as simple as writing a function. It's designed for teams that want to focus on code rather than infrastructure management.

![modal-homepage.png](https://assets.northflank.com/modal_homepage_a7380e6d35.png)

**Key features:**

- Python-first infrastructure that feels native to ML workflows
- Serverless GPU and CPU runtimes with automatic scaling
- Scale-to-zero billing to minimize costs during idle periods
- Built-in task orchestration for complex workflows and pipelines

**Best for:** Python-focused teams building serverless workflows, batch processing jobs, or ML pipelines where simplicity and developer experience matter more than maximum flexibility.

**Verdict:** Modal offers the best Python-native experience for serverless GPU workloads. Choose it when your team works primarily in Python and values simplicity over maximum control.

### 5. Replicate - Public model serving and monetization

Replicate is purpose-built for sharing and monetizing machine learning models through public APIs. It's ideal for developers who want to showcase their work or generate revenue from their models.

![replicate-homepage.png](https://assets.northflank.com/replicate_homepage_38062bccda.png)

**Key features:**

- One-click model deployment to public APIs with automatic scaling
- Community marketplace for discovering and using models
- Built-in model versioning and management capabilities
- Monetization tools for earning revenue from model usage

**Best for:** Researchers, individual developers, and teams wanting to showcase models publicly, build a following in the ML community, or generate revenue from model APIs.

**Verdict:** Perfect for model sharing, demos, and monetization. Not suitable for teams building complete applications around their models.

### 6. Lambda Labs - Hosted ML environments

Lambda Labs focuses on providing ready-to-use machine learning environments with pre-configured frameworks and tools. It's designed for researchers and teams who want to start training immediately without environment setup.

![lambda-homepage.png](https://assets.northflank.com/lambda_homepage_21b6ec7a15.png)

**Key features:**

- Hosted Jupyter notebooks with pre-installed ML frameworks
- Pre-configured environments for TensorFlow, PyTorch, and other popular tools
- Training-focused infrastructure optimized for ML experimentation
- Academic and research-friendly pricing and policies

**Best for:** ML researchers, students, and teams in early experimentation phases who value ready-to-use environments over maximum flexibility.

**Verdict:** Lambda Labs excels for learning and experimentation when GPUs are available, but frequent outages and limited production capabilities make it less suitable for ongoing projects.

## Which alternative should you choose?

The right TensorDock alternative depends heavily on your team's specific needs, technical requirements, and stage of development. Here's how to decide:

| If you're... | Choose | Why |
| --- | --- | --- |
| Building production AI products with APIs, frontend, ML models | **Northflank** | Only platform supporting full-stack AI applications with CI/CD, BYOC, and production infrastructure |
| Need cheapest possible GPU access for training/experiments | **Vast.ai or RunPod** | Marketplace pricing with community reliability (Vast) or better tooling (RunPod) |
| Python team building serverless workflows | **Modal** | Native Python experience with automatic scaling and zero infrastructure management |
| Want to share or monetize models publicly | **Replicate** | One-click model APIs with built-in community and monetization features |
| ML student/researcher wanting ready environments | **Lambda Labs** | Pre-configured Jupyter environments optimized for learning (when available) |
| Want TensorDock experience with better tooling | **RunPod** | Similar marketplace model with improved APIs and serverless options |

## Final thoughts

Production AI products require more than affordable GPU containers; they need CI/CD integration, observability tools, multi-service orchestration, and infrastructure designed to support applications serving users.

The right choice depends on your specific requirements.

For full-stack AI applications, [Northflank](https://northflank.com/) provides the most comprehensive alternative with production-grade infrastructure and Git-based deployments.

<InfoBox className='BodyStyle'>

[Sign up for Northflank](https://app.northflank.com/signup) or [schedule a demo](https://cal.com/team/northflank/northflank-demo) to see how production-focused infrastructure can accelerate your AI development.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Self-hosting AI models: Complete guide to privacy, control, and cost savings</title>
  <link>https://northflank.com/blog/self-hosting-ai-models-guide</link>
  <pubDate>2025-08-20T15:35:00.000Z</pubDate>
  <description>
    <![CDATA[Learn why businesses self-host AI models for data privacy, cost control, and performance. See how Northflank simplifies secure deployment.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/self_hosting_ai_models_192dd18e2b.png" alt="Self-hosting AI models: Complete guide to privacy, control, and cost savings" /><InfoBox className='BodyStyle'>

*Self-hosted AI models give you full control over your data privacy, reduce vendor dependency, and can lower long-term costs compared to API services.* 

*Northflank's [platform](https://northflank.com/product/gpu-paas) simplifies the complex process of self-hosting AI with one-click deployments, autoscaling, security, and compliance features if you want your business to take control of its AI infrastructure.*

</InfoBox>

Self-hosted AI models are becoming the go-to choice for businesses that want complete control over their data, costs, and AI capabilities without having to rely on third-party API services. Still, there are a lot of questions, reservations, and considerations around it.

That's what we'll cover in this article. You'll understand:

✔️ What it means to self-host an AI model and why you should

✔️ The privacy concerns with cloud AI vendors

✔️ The difference between self-hosting AI and API vendors

✔️ Why your business should invest in self-hosting AI models

✔️ How Northflank helps you self-host AI models (privacy, security, and more)

✔️ How to get started with self-hosting AI

## What is the meaning of self-hosting an AI model?

Self-hosting an AI model basically means you're running the AI on your own servers in place of paying someone else to do it for you.

It's like cooking at home versus ordering takeout.

When you self-host, you're responsible for downloading the model, setting up everything on your infrastructure, and managing the whole process yourself.

Now, the win here is that you control everything, meaning that:

- Your data stays with you
- You decide how fast it runs
- Nobody else gets to peek at your information
- You avoid the rate limits that come with API services

![Diagram showing the self-hosting process of an AI model, with components like the inference engine and hardware (servers, GPUs, storage)](https://assets.northflank.com/self_hosting_ai_model_9279371abd.png)

Self-hosting an AI model puts you in control - from inference to infrastructure

So, this is what you need to make it work:

1. The AI model itself (basically the brain)
2. Something to run it (the inference engine)
3. The hardware to power it all (servers, GPUs, storage)

Most companies right now send their data to services like ChatGPT or Claude through APIs, but self-hosting flips that around. Why? Because now you're running everything in-house, which means you get to call all the shots.

## Why should you self-host AI models?

Now that you know what self-hosting means, let's talk about why you'd want to go through the trouble in the first place.

### 1. Data sovereignty and compliance (GDPR, HIPAA, SOX)

When you send data to third-party AI services, what you're doing is handing over control to someone else's servers, which can create serious compliance issues.

Let me give you an instance.

If you're either in:

- Healthcare and need to follow HIPAA rules
- Or you're handling EU customer data under GDPR
- Or you're a public company dealing with SOX requirements

In these instances, self-hosting means keeping everything under your roof, where you can prove where your data lives and who has access to it.

### 2. Intellectual property protection

Your business conversations, code, strategies, and customer information are gold mines that you probably don't want sitting around on someone else's servers.

For instance, when you use external AI APIs, there's always the risk that your sensitive data could be used to train their models or accidentally exposed.

Self-hosting means your intellectual property stays locked down in your own environment.

### 3. Reduced vendor dependency and lock-in

Relying on external AI services means you're at the mercy of their pricing changes, service outages, and policy updates.

Remember when OpenAI changed its API pricing or when services went down for hours?

Self-hosting gives you independence from these external factors and lets you switch between different models without rebuilding your entire system. With [AI Agent software](https://paperform.co/blog/zapier-alternatives/) you gain a stable, self-managed foundation so your automation and AI workflows stay under your control.

### 4. Unlimited usage without rate limits

When you use API services, you're often hit with rate limits that can slow down your applications or force you to pay premium prices for higher limits.

For example, [Claude's rate limits can significantly impact development workflows](https://northflank.com/blog/claude-rate-limits-claude-code-pricing-cost).

Self-hosting removes these restrictions entirely, so you can process as many requests as your hardware can handle.

### 5. Use your existing cloud credits

If you have cloud credits with AWS, Google Cloud, or Azure, self-hosting lets you put those to work instead of paying separate API fees.

This is especially valuable for startups that can [get free AWS credits](https://northflank.com/blog/how-to-get-free-aws-credits-for-your-startup) and want to maximize their runway.

## How Northflank helps you self-host AI models (privacy, security, and more)

Okay, by now you understand if self-hosting AI fits your business needs, but you might be thinking about the technical complexity we mentioned earlier.

That's where [Northflank](https://northflank.com/product/gpu-paas) comes in.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

We've built a platform that gives you all the benefits of self-hosting without having to manage servers, configure GPUs, or handle scaling infrastructure yourself.

Let's go over how we make this possible.

### 1. Enterprise-grade security and compliance

Northflank is designed with enterprise security from the ground up.

We help teams become SOC 2 compliant through our enterprise features like role-based access controls, audit logging, and network isolation that meet the strictest enterprise requirements.

If you need HIPAA compliance for healthcare data or GDPR compliance for EU customers, our platform handles the security infrastructure so you can focus on your AI applications rather than managing security certificates and compliance paperwork.

### 2. One-click deployment templates and starter kits

Remember how we said self-hosting usually involves downloading models, setting up inference engines, and configuring infrastructure?

Northflank removes that complexity with pre-built templates.

![northflank-ai-stacktemplates.png](https://assets.northflank.com/northflank_ai_stacktemplates_0f878993a4.png)

You can deploy popular models like [DeepSeek R1](https://northflank.com/blog/self-host-deepseek-r1-on-aws-gcp-azure-and-k8s-in-three-easy-steps), [Qwen3-Coder](https://northflank.com/blog/self-host-qwen3-coder-with-vllm), or [GPT-OSS](https://northflank.com/blog/self-host-openai-gpt-oss-120b-open-source-chatgpt) with literally one click.

Our starter kits include everything configured and ready to go: the model, the serving infrastructure, monitoring, and scaling, so you're running AI in minutes rather than weeks.

### 3. Bring Your Own Cloud (BYOC) for data residency

This is the solution for privacy-conscious businesses.

You can run Northflank's platform in your own AWS, Google Cloud, or Azure account.

![Screenshot showing five Northflank blog post thumbnails for self-hosting AI models: GPT-OSS deployment, DeepSeek R1 on AWS/GCP/Azure, n8n AI workflow automation, vLLM cloud deployment, and Qwen3-Coder with vLLM](https://assets.northflank.com/self_hosting_ai_models_northflank_539c9f03c1.png)

Northflank's one-click deployment guides for popular AI models including GPT-OSS, DeepSeek R1, n8n workflow automation, vLLM, and Qwen3-Coder

This means your data never leaves your cloud environment, you maintain complete control over data residency, and you get transparent billing directly from your cloud provider.

It's like having your cake and eating it too, that is, Northflank's simplicity with your complete data control.

You can see this in action with our guides on:

- [self-hosting vLLM in your own cloud account](https://northflank.com/guides/self-host-vllm-in-your-own-cloud-account-with-northflank-byoc)
- [deploying Deepseek R1 on AWS, GCP, and Azure](https://northflank.com/blog/self-host-deepseek-r1-on-aws-gcp-azure-and-k8s-in-three-easy-steps)
- [running OpenAI's GPT-OSS model](https://northflank.com/blog/self-host-openai-gpt-oss-120b-open-source-chatgpt)
- [self-hosting Qwen3-Coder](https://northflank.com/blog/self-host-qwen3-coder-with-vllm)
- [deploying n8n AI workflow automation](https://northflank.com/guides/how-to-self-host-n8n-ai-workflow-automation-on-northflank)

### 4. Scalable infrastructure with vector databases

AI applications often need more than the model alone.

You need vector databases for embeddings, caching layers, and data processing pipelines.

Northflank is an all-in-one platform that supports your entire application stack, including both AI and non-AI workloads such as APIs, databases, background jobs, and your frontend applications.

It automatically scales your infrastructure based on demand and integrates everything together.

You can deploy your AI model alongside your vector database and supporting services in one unified platform.

### 5. Support and reliability guarantees

Rather than managing everything yourself, Northflank provides 24/7 support and reliability guarantees.

When something goes wrong (and it always does in complex AI deployments), you have experts to help rather than spending your weekend debugging GPU drivers.

We handle the infrastructure monitoring, automatic failover, and performance optimization so your team can focus on building AI features rather than becoming infrastructure specialists.

## What are the privacy concerns with cloud AI vendors?

We've talked about why protecting your data is important, but let's discuss what happens when you use cloud AI services and why these privacy concerns exist.

### 1. Data sharing and training policies

Most cloud AI vendors have policies that allow them to use your data to improve their models.

Even if they say they don't train on your specific data, the fine print often includes exceptions or vague language about "service improvement."

For example, OpenAI's data usage policies have changed multiple times, and what's considered private today might not be tomorrow.

### 2. Third-party access to sensitive information

When you send data to cloud AI services, it often doesn't stay with that company alone.

Your information might be processed by third-party contractors, stored on shared infrastructure, or accessed by support teams for troubleshooting.

You're trusting the AI company and everyone they work with to handle your sensitive data properly.

### 3. Compliance and regulatory challenges

Cloud AI vendors operate across multiple jurisdictions, which creates a complex situation for compliance.

Your data might be processed in countries with different privacy laws, making it nearly impossible to ensure you're meeting all regulatory requirements.

If you need to prove data residency for GDPR or show audit trails for SOX compliance, you're dependent on whatever documentation the vendor provides.

### 4. Vendor data retention practices

Even after you stop using a service, your data might stick around on their servers longer than you'd like.

Many vendors have retention periods that extend well beyond your contract, and deleting data completely can be complicated or impossible.

Self-hosting gives you the power to delete everything immediately when you need to.

## What's the difference between self-hosting AI and API vendors?

Now that you understand the privacy risks with cloud vendors, let's break down the major differences between running your own AI versus using someone else's service.

| **Aspect** | **Self-Hosting AI** | **API Vendors** |
| --- | --- | --- |
| **Control** | Full customization of model parameters, response times, and system behavior | Limited to whatever options the vendor provides |
| **Data flow** | Everything stays within your network and infrastructure | Your data travels over the internet to their servers |
| **Performance** | No network delays, dedicated resources, unlimited requests | Network latency, shared resources, rate limits and token costs |
| **Integration** | Direct integration with your existing systems and databases | API calls only, limited to their interface |
| **Customization** | Modify models, fine-tune for your specific use case | Use pre-built models as-is |
| **Costs** | Upfront hardware investment, predictable ongoing costs | Pay per token/request, costs scale with usage |

So the bottom line is this:

> Self-hosting gives you complete control and potentially better performance, but you handle all the technical complexity yourself. API vendors manage the infrastructure for you, but you're limited to their rules, pricing, and capabilities.
> 

## Why should your business invest in self-hosting AI models?

We've covered what self-hosting is and how it compares to API vendors, but you might have this question in mind:

*“Is it worth the investment for my business?”*

Many companies are making the switch for several important reasons. Let’s see some of those reasons.

### 1. Long-term cost benefits vs API pricing

While self-hosting requires upfront investment, the math often works in your favor long-term.

API services charge per token or request, which can quickly add up when processing large amounts of data.

For instance, a company using ChatGPT API for customer service might pay $500-2000 monthly, but after a year, that's $6000-24000, which is enough to buy quality hardware that you own forever.

Plus, API prices tend to increase over time, while your hardware costs stay fixed.

### 2. Custom model training and fine-tuning

This is where self-hosting becomes most valuable.

You can train models on your specific data, industry terminology, and business processes.

For instance, a law firm can fine-tune a model to understand legal language, or a healthcare company can adapt it for medical terminology.

Also, API vendors only offer generic models that work reasonably well for general use cases, and while some offer limited fine-tuning options, you're still restricted by their capabilities and policies.

When you self-host, your customized model becomes a competitive asset that gets better with your data.

### 3. Competitive advantages and differentiation

When everyone uses the same ChatGPT or Claude API, everyone gets similar results.

Self-hosting lets you build unique AI capabilities that set you apart from competitors.

You can create specialized workflows, integrate AI deeply into your products, and develop features that would be difficult to achieve with generic APIs.

Your AI becomes part of your competitive advantage rather than a commodity service.

### 4. Risk mitigation strategies

Relying on external APIs means your business depends on someone else's uptime, pricing decisions, and policy changes.

We've seen API services go down for hours, change pricing overnight, or modify their terms of service.

Self-hosting removes these external dependencies and gives you control over your AI infrastructure, so your business operations aren't at the mercy of another company's decisions.

## Getting started with self-hosting AI

Now that you understand the benefits and have seen how Northflank simplifies the process, the next step is figuring out how to get started.

The best approach is to begin with a test project that demonstrates value without risking your entire operation.

Choose a specific use case, such as customer support automation or code assistance, and set clear success metrics, like response time improvements or cost savings.

Then, plan for a 30-60 day implementation timeline to prove the concept before scaling up.

<InfoBox className='BodyStyle'>

[Sign up on Northflank](https://northflank.com/) to test out these capabilities, or [book a demo](https://cal.com/team/northflank/northflank-intro) to speak with one of our engineers about your specific use case.

</InfoBox>

These step-by-step guides will get you running AI models in your own infrastructure:

- [How to self-host Qwen3-Coder on Northflank with vLLM](https://northflank.com/blog/self-host-qwen3-coder-with-vllm) - Ideal for development teams wanting an AI coding assistant
- [Self-host Deepseek R1 on AWS, GCP, Azure & K8s](https://northflank.com/blog/self-host-deepseek-r1-on-aws-gcp-azure-and-k8s-in-three-easy-steps) - Enterprise-grade deployment in your own cloud
- [Run OpenAI's GPT-OSS model on Northflank](https://northflank.com/blog/self-host-openai-gpt-oss-120b-open-source-chatgpt) - Open source alternative to ChatGPT
- [Self-host vLLM in your own cloud account with Northflank BYOC](https://northflank.com/guides/self-host-vllm-in-your-own-cloud-account-with-northflank-byoc) - Complete control with Bring Your Own Cloud
- [How to self-host n8n AI workflow automation on Northflank](https://northflank.com/blog/how-to-self-host-n8n-setup-architecture-and-pricing-guide) - Automate business processes with AI
- [An engineer's guide to open source AI models](https://northflank.com/blog/an-engineers-guide-to-open-source-ai-models) - Technical overview for your development team]]>
  </content:encoded>
</item><item>
  <title>AWS SageMaker alternatives: Top 6 platforms for MLOps in 2026</title>
  <link>https://northflank.com/blog/aws-sagemaker-alternatives-top-6-platforms-for-ml-ops</link>
  <pubDate>2025-08-19T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[AWS SageMaker is a comprehensive machine learning platform that simplifies building, training, and deploying ML models. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/sagemaker_93eb1b773d.png" alt="AWS SageMaker alternatives: Top 6 platforms for MLOps in 2026" /><InfoBox className="BodyStyle">


## 💡 TL;DR

AWS SageMaker is a comprehensive machine learning platform that simplifies building, training, and deploying ML models. 

However, many teams seek alternatives due to vendor lock-in concerns, complex pricing, limited customization, and multi-cloud requirements. 

**[Northflank](https://northflank.com/) stands out as the top choice for production-ready AI/ML workloads**, offering container-native deployment, multi-cloud support including BYOC (Bring Your Own Cloud), transparent pricing, and full CI/CD capabilities that traditional ML platforms lack.

</InfoBox>

## What is AWS SageMaker?

AWS SageMaker is Amazon's fully managed machine learning service that provides data scientists and developers with the ability to build, train, and deploy machine learning models quickly. Launched in 2017, SageMaker aims to remove the complexity of machine learning infrastructure management by offering a complete platform for the entire ML lifecycle.

SageMaker has multiple components, including Studio notebooks for development, automated machine learning (AutoML) capabilities, model training infrastructure, and deployment endpoints. The platform integrates deeply with other AWS services and supports popular ML frameworks like TensorFlow, PyTorch, and Scikit-learn.

## What is AWS SageMaker used for?

AWS SageMaker serves multiple machine learning use cases across industries. 

1. **Model training** represents one of SageMaker's core strengths. Teams can train models on everything from small datasets using basic instances to large-scale distributed training on powerful GPU clusters. 
2. **Model deployment and inference** is another primary use case. SageMaker enables both real-time and batch prediction endpoints, with features like auto-scaling, A/B testing, and multi-model endpoints. 
3. **Automated machine learning** through SageMaker Autopilot allows business analysts and citizen data scientists to build models without extensive ML expertise. 

## Why are people looking for alternatives to AWS SageMaker?

Despite its capabilities, several factors drive teams to seek SageMaker alternatives. 

**1️⃣ Vendor lock-in** represents a primary concern, as organizations want multi-cloud flexibility and negotiating power with different providers.

**2️⃣ Complex pricing structures** create budget unpredictability. SageMaker's billing involves multiple components - compute, storage, data processing, and service-specific charges - making cost forecasting difficult and leading to budget overruns.

**3️⃣ Limited customization** frustrates engineering teams who need more control over infrastructure, networking, and deployment configurations than SageMaker's managed approach provides. The platform's extensive feature set also brings **unnecessary complexity** for teams with straightforward use cases.

**4️⃣ Multi-cloud requirements** are increasingly common for risk mitigation, regulatory compliance, and cost optimization across different workloads. Performance limitations and cold start issues further affect workloads requiring real-time inference with strict latency requirements.

## Top 6 AWS SageMaker alternatives

### 1. Northflank - Overall #1 AWS SageMaker alternative

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

[Northflank](https://northflank.com/) represents the next generation of cloud platforms designed specifically for teams building production-ready applications, including AI/ML workloads. Unlike traditional ML platforms that focus solely on model training and serving, Northflank provides a complete application deployment platform with first-class support for both AI and non-AI workloads (services, jobs, queues).

**Why it stands out** 

Container-native architecture means any ML framework or custom application runs seamlessly. Run PyTorch, TensorFlow, Hugging Face models, or any custom ML stack without platform restrictions. The platform excels at full-stack AI applications requiring databases, APIs, frontends, and background jobs all working together. Built-in CI/CD pipelines connect directly to Git repositories, enabling true MLOps workflows without additional tooling.

1. **Multi-cloud and BYOC capabilities** set Northflank apart from traditional ML platforms. Deploy workloads to AWS, GCP, Azure, or your own infrastructure while maintaining a consistent developer experience. This approach provides cost control, compliance flexibility, and eliminates vendor lock-in concerns that plague SageMaker users.

<InfoBox className="BodyStyle">

### 🤑 **Northflank pricing**

- Free tier: Generous limits for testing and small projects
- CPU instances: Starting at $2.70/month ($0.0038/hr) for small workloads, scaling to production-grade dedicated instances
- GPU support: NVIDIA A100 40GB at $1.42/hr, A100 80GB at $1.76/hr, H100 at $2.74/hr, up to B200 at $5.87/hr
- Enterprise BYOC: Flat fees for clusters, vCPU, and memory on your infrastructure, no markup on your cloud costs
- Pricing calculator available to estimate costs before you start
- Fully self-serve platform, get started immediately without sales calls
- No hidden fees, egress charges, or surprise billing complexity like SageMaker
</InfoBox>

1. **Enterprise-grade features** include secure runtime isolation, RBAC, encrypted secrets management, and comprehensive audit logging. The platform handles auto-scaling, load balancing, and observability out of the box.

**Best for:** Production AI/ML applications, teams requiring multi-cloud deployment, organizations needing container-native flexibility, and companies wanting to avoid vendor lock-in while maintaining enterprise-grade security and compliance. Startups and enterprises. 

**Limitation:** As a comprehensive platform, Northflank has a steeper learning curve than vertical products like RunPod. However, the excellent developer experience and time savings from integrated CI/CD, multi-service support, and enterprise features quickly offset the initial investment in learning the platform.

### 2. Google Vertex AI

Google's unified machine learning platform combines AutoML and AI Platform into a single service. Vertex AI provides strong integration with Google Cloud services and supports both no-code and custom ML workflows.

**What it does best:** Seamless integration with Google Cloud ecosystem, particularly BigQuery for data analytics. Strong AutoML capabilities make it accessible to non-experts. Vertex AI Pipelines provide good MLOps functionality.

**Best for:** Teams already using Google Cloud, organizations needing strong data analytics integration, and projects benefiting from Google's pre-trained models.

**Limitations:** Vendor lock-in to Google Cloud, complex pricing that can become expensive at scale, and limited multi-cloud options.

**Pricing:** Pay-as-you-go with complex token-based pricing for generative AI features. Training costs range from $0.094/hour for basic configurations up to $11+/hour for high-performance setups. GPU pricing includes Tesla T4 at $0.40/hour, A100 at $2.93/hour. $300 in free credits for new users valid for 90 days.

### 3. Paperspace

![paperspace-homepage.png](https://assets.northflank.com/paperspace_homepage_0a2d3a9357.png)

Paperspace (now part of DigitalOcean) focuses on providing accessible cloud GPUs for ML workloads through both persistent virtual machines and serverless deployment options via their Gradient platform.

**What it does best:** Simple GPU access for researchers and developers, good integration with Jupyter notebooks, and reasonable pricing for GPU instances. The platform makes it easy to get started with ML development.

**Best for:** Individual researchers, small teams needing straightforward GPU access, and educational use cases.

**Limitations:** Limited enterprise features, fewer advanced MLOps capabilities, and less suitable for complex production deployments.

**Pricing:** On-demand A100 pricing at $3.09/hour, with promotional rates as low as $1.15/hour requiring 36-month commitments. H100 instances starting at $2.24/hour with commitments or $5.95/hour on-demand. Growth subscription plans start at $39/month to access higher-end GPUs. Per-second billing available.

### 4. RunPod

![runpod's homepage.png](https://assets.northflank.com/runpod_s_homepage_14648d1a93.png)

RunPod specializes in serverless GPU computing, offering both secure cloud and community cloud options. The platform focuses on making GPU access simple and cost-effective for AI developers.

**What it does best:** Fast deployment of GPU-backed containers, competitive pricing especially on community cloud, and good cold-start performance for serverless workloads.

**Best for:** GPU-intensive inference workloads, cost-conscious teams, and developers needing quick access to various GPU types.

**Limitations:** Limited to containerized workloads, fewer enterprise features, and restricted to RunPod's infrastructure without BYOC options.

**Pricing:** H100 80GB from $1.99/hour, A100 configurations from $0.35/hour. Serverless options with per-second billing. Network storage at $0.05-0.07/GB/month.

### 5. Anyscale

![anyscale-homepage.png](https://assets.northflank.com/anyscale_homepage_0d9cb1948c.png)

Built around the open-source Ray framework, Anyscale focuses on distributed computing for AI and ML workloads. The platform excels at scaling Python applications and provides managed Ray clusters.

**What it does best:** Excellent for distributed ML workloads, strong support for hyperparameter tuning and distributed training, and good integration with popular ML frameworks.

**Best for:** Teams working with large-scale distributed ML, organizations already using Ray, and complex training workloads requiring distributed computing.

**Limitations:** Primarily focused on Ray ecosystem, steeper learning curve for teams not familiar with Ray, and less suitable for simple deployment scenarios.

**Pricing:** Enterprise pricing available on request, typically based on compute usage and cluster management fees. Offers spot instance support and elastic training for cost optimization. Claims up to 50% cost savings through optimized resource management and RayTurbo performance enhancements.

### 6. Modal

![modal-home-page.png](https://assets.northflank.com/modal_home_page_3ef6ad50bc.png)

Modal provides serverless compute specifically designed for AI workloads, with a focus on developer experience and fast cold starts. The platform automatically handles scaling and containerization.

**What it does best:** Extremely fast cold starts (often under 1 second), excellent Python SDK, and seamless auto-scaling capabilities. Strong developer experience with infrastructure-as-code approach.

**Best for:** Serverless AI workloads, teams prioritizing developer experience, and applications with variable or bursty compute needs.

**Limitations:** Higher per-hour costs compared to some alternatives, primarily focused on serverless deployments, and limited options for persistent infrastructure.

**Pricing:** Starter plans include $30/month in free credits. Usage-based serverless pricing with pay-per-second billing. Higher per-hour rates than some competitors but competitive for serverless workloads. B200 GPUs at $6.25/hour, though often noted as one of the more expensive options per-hour for sustained workloads.

## Why Northflank is the top choice

Northflank distinguishes itself as the premier SageMaker alternative for several compelling reasons that address the core limitations teams face with traditional ML platforms.

- **True multi-cloud flexibility** eliminates vendor lock-in concerns that plague SageMaker users. Deploy the same applications across AWS, GCP, Azure, or your own infrastructure using Northflank's BYOC capabilities. This flexibility provides cost optimization opportunities and regulatory compliance options that single-cloud platforms cannot match.
- **Container-native architecture** means unlimited flexibility in your ML stack. Run any framework, any version, any custom code. Unlike SageMaker's managed approach that can constrain your choices, Northflank's container support means you're never limited by platform decisions.
- **Production-grade CI/CD** comes built-in, not as an afterthought. Connect your Git repositories and get automated builds, testing, and deployments without cobbling together separate tools. This integrated approach eliminates the complexity teams face trying to create MLOps workflows with SageMaker and external CI/CD tools.
- **Full-stack application support** sets Northflank apart from ML-focused platforms. Deploy your model alongside databases, APIs, frontends, and background jobs. This comprehensive approach eliminates the architectural compromises teams make when ML platforms force them to use external services for non-ML components.
- **Enterprise security without compromise** includes secure runtime isolation, encrypted secrets management, comprehensive RBAC, and audit logging. These features come standard, not as expensive add-ons.
- **Transparent, predictable pricing** removes the budget anxiety associated with SageMaker's complex billing. Pay for resources you use, with clear pricing tiers and no hidden fees. BYOC options eliminate markup on your own infrastructure while providing managed platform benefits.
- **Developer experience designed for modern teams** combines powerful APIs, intuitive web interfaces, and comprehensive CLI tools. Teams spend time building applications, not fighting platform complexity.

## AWS SageMaker alternatives, compared

| Platform | Multi-cloud | Container support | Built-in CI/CD | BYOC | Pricing model | Best for |
| --- | --- | --- | --- | --- | --- | --- |
| **Northflank** | ✅ Full support | ✅ Native Docker/K8s runtimes | ✅ Integrated pipelines | ✅ Yes | Transparent usage-based | Production AI/ML applications |
| Google Vertex AI | ❌ Google only | ⚠️ Custom container support but limited orchestration | ⚠️ External via other GCP services | ❌ No | Complex token-based | Google Cloud ecosystem |
| Paperspace | ❌ Single cloud (DigitalOcean) | ✅ Docker with limitations | ❌ External tools needed | ❌ No | Hourly GPU rates | Individual developers |
| RunPod | ❌ Single cloud. Integrations with external storage ≠ multi-cloud compute. | ✅ Good | ❌ External tools needed | ❌ No | Competitive GPU rates | GPU-intensive tasks |
| Anyscale | ⚠️ Limited, mostly AWS, limited Azure | ⚠️ Ray workloads only | ⚠️ Via Ray | ❌ No | Custom enterprise | Distributed computing |
| Modal | ⚠️ Single cloud (AWS) | ✅ Automated | ❌ External tools needed | ❌ No | Serverless usage | Serverless AI workloads |

## Conclusion

AWS SageMaker was one of the first managed machine learning platforms, but modern teams building production AI applications need more than just model training and serving. They need flexible, multi-cloud platforms that support their entire application stack without vendor lock-in.

Northflank is the clear winner for teams serious about production AI/ML deployments.

If you're building AI-powered products rather than just experimenting with models, Northflank gives you the production-grade platform capabilities that traditional ML services can't match.

Deploy complete applications with databases, APIs, and frontends alongside your ML models, all while keeping the flexibility to use any cloud provider or your own infrastructure. 

You can start building on Northflank today through our self-serve platform, or chat with one of our engineers about your specific needs.

<InfoBox className="BodyStyle">


## 💭 FAQs

1. **What is AWS SageMaker and how does it work?**
AWS SageMaker is Amazon's fully managed machine learning service that provides tools for building, training, and deploying ML models in the cloud. It works by offering integrated components like Studio notebooks, AutoML capabilities, training infrastructure, and deployment endpoints.
2. **What is AWS SageMaker used for in production?**
AWS SageMaker is primarily used for model training on scalable GPU clusters, real-time and batch model inference through managed endpoints, automated machine learning for business users, and MLOps workflows.
3. **Is Northflank better than AWS SageMaker for production AI?**
For production AI applications requiring multi-cloud deployment, full-stack application support, and integrated CI/CD, Northflank offers significant advantages over SageMaker. Its container-native approach, BYOC capabilities, and transparent pricing make it ideal for teams building production-ready AI products rather than just training models.
4. **Can I migrate from AWS SageMaker to other platforms easily?**
Migration difficulty depends on how deeply integrated your SageMaker setup is with other AWS services. Container-native platforms like Northflank make migration easier since you can containerize your models and applications, maintaining the same code while gaining multi-cloud flexibility.
</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>What are the best multi cloud management platforms in 2026?</title>
  <link>https://northflank.com/blog/best-multi-cloud-management-platforms</link>
  <pubDate>2025-08-19T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Multi-cloud management platforms help companies deploy, monitor, and scale applications across multiple cloud providers from a single interface. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/multi_cloud_47fdddd79a.png" alt="What are the best multi cloud management platforms in 2026?" /><InfoBox className="BodyStyle">

## 📌 TL;DR

Multi-cloud management platforms help companies deploy, monitor, and scale applications across multiple cloud providers from a single interface. 

The top platforms in 2026 are [Northflank](https://northflank.com/product/bring-your-own-cloud), GKE Enterprise, Red Hat OpenShift, Spectro Cloud, and VMware Tanzu. 

Northflank stands out as the best choice for development teams who want great developer experience when deploying applications across multiple clouds without dealing with complex Kubernetes configuration or vendor-specific deployment processes.

</InfoBox>

## What is a multi-cloud management platform?

A multi-cloud management platform is a unified system that allows organizations to manage applications, infrastructure, and services across multiple cloud providers simultaneously. 

These platforms provide centralized control over resources deployed on AWS, Google Cloud, Microsoft Azure, and other cloud services.

Instead of juggling separate dashboards and tools for each cloud provider, teams get a single pane of glass to deploy applications, monitor performance, manage costs, and ensure security compliance across their entire cloud infrastructure.

## **How does multi-cloud management work?**

multi-cloud management platforms work by creating an abstraction layer above your cloud providers. The process typically follows these key mechanisms:

1. **Centralized control interface** - The platform provides a unified dashboard where you can manage all your cloud resources, regardless of which provider hosts them.
2. **API Integration** - The platform connects to each cloud provider's APIs, allowing it to provision resources, deploy applications, and retrieve monitoring data across different environments.
3. **Resource orchestration** - You define your infrastructure and application requirements once, and the platform handles deployment across multiple clouds based on your specifications.
4. **Monitoring and analytics** - The platform aggregates data from all your cloud environments, providing comprehensive visibility into performance, costs, and security across your entire infrastructure.
5. **Policy enforcement** - You can set consistent security policies, compliance rules, and governance standards that apply across all cloud environments.

## Who needs multi-cloud management?

Several types of organizations benefit from multi-cloud management platforms:

- **Enterprises** need centralized management when using multiple cloud providers for different business units or to avoid vendor lock-in. These organizations require control and complexity reduction across their diverse cloud infrastructure.
- **Growing startups** often begin with one cloud provider but need flexibility to expand to others as requirements change or to leverage specific services. multi-cloud management enables this growth without operational burden.
- **Development teams** building cloud-native applications need streamlined deployment processes across different environments and providers without managing multiple toolchains.
- **Regulated industries** including finance, healthcare, and government sectors often require multi-cloud setups for compliance, data sovereignty, or disaster recovery requirements.
- **Cost-conscious organizations** want to optimize costs by leveraging competitive pricing across different cloud providers while avoiding single-vendor dependency that can lead to price increases.

## Top 5 multi-cloud management platforms in 2026

### 1. Northflank - Best overall choice

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

Northflank leads the multi-cloud management space with its developer-first approach and comprehensive feature set designed for modern application deployment.

**Key features:**

✅ Git-native CI/CD with automatic deployments from code commits

✅ Advanced Kubernetes management with auto-scaling and health monitoring

✅ Built-in databases, caching, and storage services

✅ Comprehensive observability with logs, metrics, and alerts

✅ Infrastructure as Code support with Terraform integration

✅ Multi-region deployments across AWS, GCP, and Azure

✅ Developer-friendly interface with powerful CLI tools

**Pros:**

- Exceptional developer experience with intuitive workflows
- Powerful Kubernetes orchestration without complexity
- Integrated services eliminate need for separate database providers
- Competitive pricing with transparent, usage-based billing
- Strong security with SOC 2 compliance and encryption
- Excellent documentation and customer support

**Cons:**

- Newer platform with smaller ecosystem compared to hyperscaler tools
- Advanced enterprise features still expanding

<InfoBox className="BodyStyle">

### 💲 **Pricing**

Northflank uses transparent, usage-based pricing that scales with your actual resource consumption. The platform charges based on compute resources (CPU cores and RAM), storage, and data transfer, with no hidden fees or minimum commitments.

Compute pricing starts at $0.000012 per CPU core per second and $0.000003 per GB of RAM per second, making it cost-effective for both development and production workloads. Storage costs $0.10 per GB per month for persistent volumes, while database storage follows similar rates. Data transfer is priced at $0.09 per GB for outbound traffic.

The platform offers a free tier, perfect for testing and small projects. This pricing model makes Northflank particularly attractive for startups and growing companies who need enterprise-grade features without the enterprise price tag. It’s also fully self serve, which means you can get started without ever having to talk to sales.

</InfoBox>

**Best for:** Development teams building cloud-native applications, startups scaling across multiple clouds, and companies needing streamlined deployment workflows without operational complexity. Particularly suited for organizations with 5-500 developers who want enterprise capabilities at startup-friendly pricing.

### 2. GKE Enterprise (formerly Anthos)

Google's GKE Enterprise provides a comprehensive platform for managing Kubernetes applications across Google Cloud, on-premises, and other cloud environments.

**Key Features:**

- Multi-cluster Kubernetes management
- Service mesh integration with Istio
- Configuration sync with GitOps workflows
- Policy management and security scanning
- Hybrid and multi-cloud networking

**Pros:**

- Strong Kubernetes expertise from Google
- Good integration with Google Cloud services
- Advanced service mesh capabilities

**Cons:**

- Google Cloud-centric with limited true multi-cloud flexibility
- Complex setup and steep learning curve
- High cost with significant minimum commitments
- Requires substantial Kubernetes expertise

**Pricing:** Starts at $2.50 per vCPU per month for clusters, with additional costs for features and support. Minimum enterprise commitments typically range from $50,000-100,000 annually.

**Best for:** Large enterprises heavily invested in Google Cloud with complex Kubernetes requirements and dedicated platform engineering teams.

### 3. Red Hat OpenShift

![openshift-min.png](https://assets.northflank.com/openshift_min_2d87ef258a.png)

OpenShift is an enterprise Kubernetes platform that provides developer tools, security features, and multi-cloud deployment capabilities.

**Key features:**

- Enterprise Kubernetes distribution
- Integrated CI/CD pipelines
- Developer console and tools
- Built-in security and compliance
- Multi-cloud deployment options
- Operator ecosystem for application management

**Pros:**

- Mature enterprise platform with strong support
- Comprehensive developer tooling
- Active open source community

**Cons:**

- Expensive licensing and support costs
- Complex deployment and management
- Heavy resource requirements
- Steep learning curve for teams new to Kubernetes

**Pricing:** Licensing starts at $50 per core per year for basic subscriptions, with premium support reaching $300+ per core annually. Total costs often exceed $100,000 annually for production deployments.

**Best For:** Large enterprises with significant Kubernetes investments, regulated industries requiring extensive compliance features, and organizations with dedicated [OpenShift](https://northflank.com/blog/best-open-shift-alternatives-finding-the-right-kubernetes-platform) expertise.

### 4. Spectro Cloud

Spectro Cloud offers a Kubernetes management platform focused on enterprise-grade cluster lifecycle management across multiple clouds and edge environments.

**Key features:**

- Cluster lifecycle management
- Infrastructure as Code for Kubernetes
- Multi-cloud and edge deployment
- Policy-driven governance
- Cost optimization tools
- Security scanning and compliance

**Pros:**

- Strong focus on Kubernetes cluster management
- Good multi-cloud support
- Enterprise security features
- Flexible deployment options

**Cons:**

- Primarily infrastructure-focused, limited application-level features
- Smaller ecosystem compared to major cloud providers
- Requires Kubernetes expertise
- Higher complexity for simple use cases

**Pricing:** Usage-based pricing starting around $0.10 per cluster per hour, with enterprise features requiring custom pricing. Annual contracts typically range from $50,000-500,000 depending on scale.

**Best for:** Infrastructure teams managing multiple Kubernetes clusters across diverse environments, organizations with complex compliance requirements, and companies needing granular cluster lifecycle control.

### 5. VMware Tanzu

VMware Tanzu provides a portfolio of products for modernizing applications and infrastructure with Kubernetes across multiple clouds.

**Key features:**

- Application platform with Kubernetes
- Developer productivity tools
- Service mesh and API management
- Multi-cloud Kubernetes operations
- Legacy application modernization
- Enterprise integration capabilities

**Pros:**

- Comprehensive application modernization platform
- Strong integration with VMware ecosystem
- Multi-cloud capabilities

**Cons:**

- Complex product portfolio with overlapping components
- High licensing and implementation costs
- VMware-centric approach limits flexibility
- Requires significant enterprise investment

**Pricing:** Complex licensing model with multiple product tiers. Basic Tanzu Application Service starts around $200 per application instance annually, while full Tanzu portfolios often require $500,000+ annual commitments.

**Best for:** Large enterprises with existing VMware infrastructure, organizations undergoing extensive application modernization, and companies needing comprehensive platform engineering solutions.

## Conclusion

multi-cloud management platforms have become necessary for companies that want flexibility across different cloud providers without getting stuck with one vendor. 

While there are several solid options in 2026, Northflank stands out because it actually makes multi-cloud deployment easier for developers rather than adding more complexity.

Most platforms in this space either lock you into their parent cloud provider or require dedicated DevOps teams to manage the complexity. 

Northflank takes a different approach by handling the hard parts automatically while giving developers the control they need when they need it.

If you're building applications and want to deploy them across multiple clouds without spending months learning Kubernetes or dealing with vendor-specific quirks, Northflank is your best bet.

It's the only platform that delivers enterprise-grade multi-cloud capabilities while keeping things simple enough for actual development teams to use effectively.

## 💭 FAQs

**Q: What's the difference between multi-cloud and hybrid cloud?**
A: Multi-cloud uses multiple public cloud providers (AWS, Google Cloud, Azure) while hybrid cloud combines public cloud with on-premises infrastructure. Multi-cloud focuses on avoiding vendor lock-in and optimizing services across providers.

**Q: Do I need a multi-cloud management platform if I only use one cloud provider?**
A: Not necessarily, but these platforms can still provide value through simplified deployment workflows, better developer experience, and preparation for future multi-cloud adoption as your organization grows.

**Q: How much does multi-cloud management typically cost?**

Multi-cloud management costs vary significantly based on platform choice and scale. Northflank's usage-based pricing typically runs $50-200 monthly for small development teams, $200-1,000 for production workloads at growing companies, and $1,000+ for large enterprise deployments. This compares favorably to enterprise platforms like GKE Enterprise that require $50,000-100,000 annual commitments or Red Hat OpenShift with licensing costs often exceeding $100,000 annually. The key is choosing a platform with transparent pricing that scales with your actual resource consumption rather than arbitrary licensing tiers.

**Q: Can multi-cloud management platforms help reduce cloud costs?**
A: Yes, by enabling you to choose the most cost-effective cloud provider for specific workloads, optimize resource allocation across providers, and avoid vendor lock-in that can lead to price increases.

**Q: What happens to my data if I switch multi-cloud management platforms?**
A: Your data remains in your chosen cloud providers. Multi-cloud management platforms orchestrate and manage resources but don't typically store your application data, making switching platforms more straightforward than changing cloud providers.

**Q: How long does it take to set up a multi-cloud management platform?**
A: Setup time varies by platform complexity. Northflank can be configured in hours, while enterprise platforms like Azure Arc or Google Anthos may take weeks or months for full implementation.

**Q: Do multi-cloud management platforms support all cloud services?**
A: Coverage varies by platform. Most support core services like compute, storage, and networking across major providers. Northflank focuses on application deployment and management services, while others may emphasize infrastructure provisioning or governance.]]>
  </content:encoded>
</item><item>
  <title>How to self-host n8n: Setup, architecture, and pricing guide (2026)</title>
  <link>https://northflank.com/blog/how-to-self-host-n8n-setup-architecture-and-pricing-guide</link>
  <pubDate>2025-08-19T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Many teams want to run n8n self-hosted instead of relying on the cloud version. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/self_host_n8n_2_47ec9a2734.png" alt="How to self-host n8n: Setup, architecture, and pricing guide (2026)" /><InfoBox className="BodyStyle">

## 📌 TL;DR

Many teams want to run n8n self-hosted instead of relying on the cloud version. 

Self-hosting n8n gives you full control over data, lets you scale the way you want, and avoids vendor lock-in. In this guide, we’ll show you how to [self-host n8n on Northflank](https://northflank.com/product/app-platform), step by step. 

You’ll learn the basic architecture, deployment options, and how to connect PostgreSQL and Redis for persistence and scalability. 

We’ll also cover n8n self-hosted pricing so you know what to expect when running it in production.

</InfoBox>

Self-hosting n8n means running it on your own infrastructure instead of using the cloud-hosted SaaS. Teams choose n8n self-hosted for data security, lower cost at scale, and the ability to run custom integrations. With Northflank, you can deploy n8n in minutes without [managing Kubernetes](https://northflank.com/product/app-platform), Docker Compose, or VMs manually.

## What is n8n?

[n8n](https://n8n.io/) is a popular workflow automation platform that offers various integrations with different tools and frameworks allowing for a wide range of automation use-cases, including AI agents. n8n can benefit different teams in an organization, from IT teams automating technical workflows, marketing teams automating processes for marketing campaigns, or HR teams analyzing employee turnover risk, n8n can make life easier by automating their required workflow tasks using a visual drag-and-drop interface.

In this guide, we will explain how to deploy n8n on Northflank. First we'll look at the architecture of self-hosted n8n, then we’ll create a deployment service utilizing the n8n container image and a PostgreSQL [add-on](https://northflank.com/docs/v1/application/databases-and-persistence/stateful-workloads-on-northflank) as an external database to persist data. We'll then expand our deployment by adding Redis and a worker for even better scalability.

If you want to get started straight away, you can instead use the one-click deployment options for n8n.

## What does n8n self-hosted mean?

**n8n self-hosted** means running n8n on your own infrastructure instead of relying on n8n Cloud. You download and deploy the open-source n8n Community Edition, then run it on your own servers, containers, or cloud environment.

With Northflank, you don’t need to manage Kubernetes clusters, Docker Compose files, or VM networking by hand. You deploy the official n8n container image, attach a database (PostgreSQL), and optionally add Redis for scaling.

## n8n self-hosted architecture

n8n can be deployed in different ways for scalability. The simplest deployment would be a single n8n service with a persistent volume, however for scalability we'll look at two different options for this guide:

1. A single deployment with a PostgreSQL database
2. A deployment for the UI/API and a separate worker deployment, with PostgreSQL and Redis

## Prerequisites

Before going through the steps in this guide, you need to have already completed the following:

1. [Login](https://app.northflank.com/login) to your existing Northflank account, or [create a new account](https://app.northflank.com/signup) if you don’t have one already.

## Deploy in one click

You can deploy n8n with Postgres, or n8n with a worker using the respective Northflank stack templates:

<div> <center> <a href="https://app.northflank.com/s/account/stack-templates/deploy-n8n"> <Button variant={["large", "gradient"]}>Deploy n8n now</Button> </a> </center> </div>
<div> <center> <a href="https://app.northflank.com/s/account/stack-templates/deploy-n8n-worker"> <Button variant={["large", "gradient"]}>Deploy n8n with worker now</Button> </a> </center> </div>

1. Customise your project name, colour, and region, if desired
2. Click `Deploy stack` to save and run the n8n template
3. Once the template run has finished deploying all components, open the project and select the `n8n` service
4. When the `n8n` service shows as running, open the `code.run` domain under `Ports & DNS` in the service header to access your n8n instance and begin configuration

## Deploy n8n with PostgreSQL

The simplest way to host n8n on Northflank is by using a [deployment service](https://northflank.com/product/deployments). Northflank's deployment services let you deploy a container instance (or multiple instances for scalability) from a container image. This image can be built on Northflank itself using a build service, or pulled from any other registry as a ready image.

Using a deployment service is straightforward, you can just point Northflank to the location of your image, provide any additional configuration you want for your deployed instance, and then Northflank takes it from there to get your application running.

### Deploy n8n

1. Under the project, create a new service and choose its type as a deployment service.
2. Provide a name for the service, and choose the deployment source as ***external image***.
3. In the image path, use `docker.n8n.io/n8nio/n8n:latest`, this is the official n8n image from the repository.
4. In the networking section edit port 5678 and choose the protocol type as ***HTTP***. Check the option to ***publicly expose this port to the internet***. This will allow you to connect to the n8n service publicly from anywhere using the domain name provided by Northflank. You can also use your own domain by expanding the option for ***custom domains***.
5. In the resources section, choose a compute plan that fits your needs for the CPU and memory requirements. Without a worker deployed, this deployment will also execute workflows and code.
6. Click on create service, and you should see a running instance of your n8n self-hosted deployment
7. To access your n8n instance you can simply use the Northflank-generated URL in the top right corner of the service overview page

![Creating a n8n deployment on Northflank](https://assets.northflank.com/create_n8n_deployment_d34fa21e65.png)

Any changes we make in this deployment will be lost if it restarts, however, as it's only using ephemeral storage. Containers use ephemeral disks, meaning that any data written to the filesystem will be wiped out once the container is restarted. We could mount a volume to `/home/node/.n8n` as n8n uses an SQLite database saved to the local disk by default, but this will become an obstacle to scaling n8n in future. Instead, we can use a PostgreSQL database and environment variables to persist data outside of the ephemeral containers.

### Deploy Postgres

We’ll first need to provision our PostgreSQL instance and then pass the required environment variables to n8n.

A simple way to provision a PostgreSQL instance is by using [Northflank addons](https://northflank.com/docs/v1/application/databases-and-persistence/stateful-workloads-on-northflank), which integrate common infrastructure components like databases, caches, message queues with your application. The addons are provided as managed services, meaning that the Northflank platform takes care of the heavy lifting for provisioning and setting up these components, allowing you to focus more on your application.

We can create a PostgreSQL database addon with the following steps:

1. Click on ***create new > addon*** under your project. Then choose ***PostgreSQL*** and provide a name for the Addon.
2. Disable ***deploy with TLS***
2. Specify the compute plan for your addon, the size of disk, and the number of replicas. You can scale both these up later.
3. Click on ***create addon*** and Northflank will start provisioning your database

![Depolying Postgres on Northflank](https://assets.northflank.com/create_postgres_6ae108e622.png)

After the database instance is provisioned, you’ll find the database connection details under the ***overview*** section.

### Create a secret group

To enable n8n to use the new PostgreSQL database, we’ll need to provide the connection details as environment variables for our deployment instance. We can link the Postgres addon to the sevice by using a secret group, which will automatically provide the relevant connection details even if they are changed by updating the addon's configuration. As we're not using a persistent volume, we'll also add the neccessary secrets and configuration values as environment variables.

1. Click on ***create new > secret group*** under your project and give it a name
2. ***Edit secrets*** and add the following variables. You can use the key button in the secret editor to generate a value for the encryption key, which should be at least 32 characters long in hex mode (256-bits).
    ```env
    # Tells n8n to use our Postgres addon
    DB_TYPE="postgresdb"
    # Encrypts sensitive data in the database, saves the key outside the ephemeral container
    N8N_ENCRYPTION_KEY="<some-random-string>"
    # Tell n8n it's behind the Northflank load balancer
    N8N_PROXY_HOPS="1"
    # Enable code execution in workflows
    N8N_RUNNERS_ENABLED=true
    ```
    ![Managing secrets in Northflank](https://assets.northflank.com/environment_variables_n8n_041dfbfdd1.png)
3. Next, expand ***show addons*** and click ***configure*** on the Postgres addon. Select ***USERNAME, PASSWORD, DATABASE, HOST, and PORT***, and give them the following aliases:
    | Key | Alias |
    | - | - |
    | `USERNAME` | `DB_POSTGRESDB_USER` |
    | `PASSWORD` | `DB_POSTGRESDB_PASSWORD` |
    | `DATABASE` | `DB_POSTGRESDB_DATABASE` |
    | `HOST` | `DB_POSTGRESDB_HOST` |
    | `PORT` | `DB_POSTGRESDB_PORT` |

    ![Linking database connection details to environment variables in Northflank](https://assets.northflank.com/postgres_secrets_85abb40d33.png)
4. ***Create*** the secret group

You can now return to your n8n self-hosted deployment service and restart it, and the container will be deployed with the environment variables from the secret group, configuring it to store data in the PostgreSQL database.

![n8n deployed on Northflank](https://assets.northflank.com/n8n_service_0b2f7a2b35.png)

If you're just testing n8n, or don't need to scale it up to handle heavy usage yet, you can skip to the section on configuring and using n8n. Otherwise, you can follow the rest of the guide to deploy Redis and a n8n worker in addition, and scale up your n8n self-hosted deployment to handle heavy usage.

## Deploy a n8n worker

### Deploy Redis

1. Click on ***create new > addon*** under your project, then choose ***Redis*** and provide a name for the addon
2. Disable ***deploy with TLS***
3. Specify the compute plan for your addon, the size of disk, and the number of replicas. You can scale both these up later.
4. Click on ***create addon*** and Northflank will start provisioning your database

![Deploying Redis on Northflank](https://assets.northflank.com/create_redis_b65a76158c.png)

### Deploy n8n worker

Create a new deployment service following the same steps as we did to create the main n8n self-hosted deployment. However, this time:
- do not expose the `5678` ***HTTP*** port to the public internet
- select ***custom command*** for the ***Docker runtime mode*** in the advanced section and enter the command `worker`. This will start this n8n deployment in worker mode. 

![Configuring an n8n worker on Northflank](https://assets.northflank.com/n8n_worker_create_3d807618c8.png)

### Update secret group

To configure the n8n main deployment to use the n8n worker, add the following enviornment variables to your existing secret group:

```env
EXECUTIONS_MODE="queue"
QUEUE_HEALTH_CHECK_ACTIVE=true
OFFLOAD_MANUAL_EXECUTIONS_TO_WORKERS=true
```

![Configuring n8n worker environment variables on Northflank](https://assets.northflank.com/environment_variables_worker_409a038b49.png)

Next, expand ***linked addons*** and ***configure*** the Redis addon, linking the following values with the corresponding aliases:

| Key | Alias |
| - | - |
| `PASSWORD` | `QUEUE_BULL_REDIS_PASSWORD ` |
| `HOST` | `QUEUE_BULL_REDIS_HOST ` |
| `PORT` | `QUEUE_BULL_REDIS_PORT ` |

![Managing Redis secrets on Northflank](https://assets.northflank.com/redis_secrets_0a455ec880.png)

You can now restart your n8n and worker deployments and begin executing workflows. You can check the logs for the n8n worker to confirm it's recieving and executing tasks.

You can now scale the worker component of your self-hosted n8n deployment independently of the main n8n application and API.

## Configure and use n8n

Congratulations! You now have your n8n self-hosted instance running. No YAML files needed, no Helm charts, and no Kubernetes cluster complexities. Northflank allows you to focus on your workloads instead of worrying about the underlying infrastructure, this abstraction offers a seamless and efficient deployment experience for any containerized workload.

Open the domain in the n8n service header, and follow the steps to create an account and set up your n8n self-hosted deployment. 

![Registering for n8n on Northflank](https://assets.northflank.com/n8n_register_b81dadd47f.png)
![Configuring n8n on Northflank](https://assets.northflank.com/n8n_config_c484374406.png)

You can now begin designing and executing workflows!

![Creating n8n workflows on Northflank](https://assets.northflank.com/n8n_workflow_7593880617.png)

You can [add health checks](https://docs.n8n.io/hosting/logging-monitoring/monitoring/) to your services to handle redeployments gracefully, [scale your n8n deployment](https://northflank.com/docs/v1/application/scale/scale-on-northflank) up to meet demand, and [add custom domains](https://northflank.com/docs/v1/application/domains/domains-on-northflank).

Check the [n8n docs](https://docs.n8n.io/hosting/) for more configuration options for n8n.

## n8n self-hosted pricing

The n8n Community Edition is free to self-host under the Fair Code license. You can run unlimited workflows, steps, executions, and users without paying for the software itself.

Your only costs are infrastructure. On Northflank, that means the compute plan you choose for your n8n self-hosted deployment plus any addons like PostgreSQL and Redis. For small workloads, this can start from around $5–10/month. For larger, production-scale deployments, costs scale with CPU, memory, and storage.

If you need advanced features such as SSO, Git version control, or multiple environments, n8n also offers a Self-Hosted Business Plan. At the Enterprise tier, pricing is based on the number of executions rather than workflow count.

Compared to n8n Cloud (starting at $24/month for 2.5K executions), n8n self-hosted on Northflank can be more cost-effective and gives you full control over your data and scaling.

## Conclusion

n8n is a workflow automation platform that supports a wide range of automation use cases and offers various integrations. It simplifies the process of creating workflows by using a visual editor instead of writing code for the workflow steps.

Northflank simplifies the deployment of n8n by removing the need for provisioning and configuring infrastructure resources to self-host the n8n instance. You can simply use a deployment service and point it to the n8n container image, and Northflank will take care of the underlying infrastructure for the deployment.

You can also make use of Northflank templates to host your n8n instance. Instead of manually creating the project, deployment service, database addon, and other components, templates act as a package that groups all these related resources in a single deployable object. This enables a consistent and seamless one-click deployment that can be replicated across different locations.

You can explore [other guides](https://northflank.com/guides) for deploying different applications on Northflank.

<InfoBox className="BodyStyle">

## Deploy on Northflank for free

Northflank allows you to deploy your code and databases within minutes. Sign up for a Northflank account and create a free project to get started. 

- Build, deploy, scale, and release from development to production
- Observe & monitor with real-time metrics & logs
- Deploy managed databases and storage
- Manage infrastructure as code
- Deploy clusters in your own cloud accounts
- Run GPU workloads

<div>
    <a href="https://app.northflank.com/signup">
        <Button variant={["large", "gradient"]}>Get started now</Button>
    </a>
</div>

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>LLM deployment pipeline: Complete overview and requirements</title>
  <link>https://northflank.com/blog/llm-deployment-pipeline</link>
  <pubDate>2025-08-19T18:13:00.000Z</pubDate>
  <description>
    <![CDATA[Walk through LLM deployment pipeline: from model containerization to API endpoints, autoscaling, GPU allocation, and secure environments. Learn how Northflank simplifies the process.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/llm_deployment_pipeline_421867387a.png" alt="LLM deployment pipeline: Complete overview and requirements" /><InfoBox className='BodyStyle'>

LLM deployment is the process of taking your trained language model and making it available as a production service that can handle live user requests. It involves five main areas: containerizing your model for portability, allocating the right GPU resources, creating API endpoints for access, setting up autoscaling to handle traffic spikes, and securing your deployment environment.

While you can build this infrastructure yourself using tools like Docker and Kubernetes, platforms like [Northflank](https://northflank.com/) handle the complexity for you, letting you go from model to production API in minutes rather than weeks of infrastructure work.

</InfoBox>

This article provides a comprehensive overview of what LLM deployment involves - from containerization and GPU allocation to API endpoints and security. If you're looking for step-by-step implementation instructions, check out our [complete guide to LLM deployment](https://northflank.com/blog/open-source-llms-the-complete-developers-guide-to-deployment) instead.

## What is LLM deployment?

LLM (Large Language Model) deployment is you deciding to take a trained language model and convert it into a production-ready service (which means a service that can handle live business traffic) that can handle your user requests reliably, securely, and at scale.

So, it's like the bridge between having a working AI model (that contains your trained weights and logic) and deploying it in your business applications like customer support chatbots, content generation tools, or document analysis systems.

One simpler way to understand it is this:

![llm-deployment-pipeline1.png](https://assets.northflank.com/llm_deployment_pipeline1_17516c50e2.png)

Let's say you've built a chatbot called "SupportBot" for customer service in your organization that works perfectly on your laptop, and the development team can interact with it.

LLM deployment encompasses everything you need to do to make that chatbot available to thousands of users simultaneously through your website or app.

And when I say "everything", I mean five main technical areas that work together:

1. **Containerization**: packaging your model to make it portable
2. **GPU allocation**: setting up servers with the right hardware to provide computing power
3. **API Creation**: creating endpoints for your applications to communicate with the model
4. **Autoscaling**: implementing scaling to handle traffic increases
5. **Security**: protecting your deployment from threats

We’ll go into each of these areas later in this article.

## Why does my organization need LLM deployment?

Now that you understand what LLM deployment involves, you might ask:

*”Does my organization really need to go through all this complexity?”*

The short answer is: if you want to stay competitive, yes.

> And recent stats have shown that more than [80%](https://www.gartner.com/en/newsroom/press-releases/2023-10-11-gartner-says-more-than-80-percent-of-enterprises-will-have-used-generative-ai-apis-or-deployed-generative-ai-enabled-applications-by-2026) of enterprises will have used [generative AI APIs](https://www.gartner.com/en/articles/hype-cycle-for-artificial-intelligence) or deployed generative AI-enabled applications by 2026, while worldwide spending on generative AI is forecast to reach [$644 billion](https://www.gartner.com/en/newsroom/press-releases/2025-03-31-gartner-forecasts-worldwide-genai-spending-to-reach-644-billion-in-2025) in 2025, marking a 76.4% jump from 2024, according to Gartner.
> 

This shows that your competitors aren't only experimenting anymore, they're shipping AI features that are changing how they serve customers and operate their businesses.

### The prototype-to-production challenge

Now, this is where most organizations hit a major roadblock*:* **the prototype-to-production gap**.

You’ve likely experienced this yourself. Your team builds an amazing LLM-powered feature that works beautifully in development. Everyone’s excited. Then someone asks:

*“When can we launch this to our customers?”*

And suddenly, you realize you’re facing a completely different set of challenges.

This is due to the fact that LLM deployment has specific requirements that most other applications don’t have. Requirements such as:

- Specialized hardware that costs hundreds of dollars per hour
- The ability to handle unpredictable traffic spikes without breaking your budget
- Processing sensitive data securely while maintaining sub-second response times
- Zero tolerance for downtime because your customers notice immediately

This explains the challenge organizations face. The gap between “it works” and “it works reliably for thousands of users” is massive.

## What are the main areas involved in LLM deployment?

Now, finally, we can go into more detail about those five technical areas we mentioned earlier.

Remember, these components could represent weeks or months of specialized work, which is why many organizations opt for a platform that handles this complexity.

Each of these areas has its own unique challenges, and getting any one of them wrong can derail your entire deployment.

More importantly, they all need to work together seamlessly for your LLM to perform reliably in production.

### 1. Model containerization for portable LLM deployment

The first step in any LLM deployment is packaging your model so it can run consistently across different environments. This might sound straightforward if you’re familiar with containerizing regular applications, but LLMs bring their own set of complications.

Docker containerization solves dependency conflicts, ensures consistent deployments, and simplifies scaling across different environments. However, LLM applications face unique deployment challenges that standard containerization approaches don’t address.

Your containerization strategy needs to handle massive model files (often 10GB+), GPU driver compatibility, and memory optimization that most applications never deal with. Modern teams pin base images, CUDA versions, model weights, and use security scanning tools like Trivy to surface vulnerabilities during CI (Continuous Integration).

The security considerations alone are complex - you’re dealing with valuable intellectual property (your trained model) that needs protection, plus ensuring your container doesn’t introduce vulnerabilities when accessing GPU resources.

> Our platform handles containerization automatically when you [deploy models like DeepSeek R1 with vLLM](https://northflank.com/guides/deploy-deepseek-r1-vllm-northflank-ai-llm), taking care of GPU optimization and dependency management without requiring specialized Docker expertise.
> 

### 2. GPU allocation and hardware requirements for my LLM

This is where costs can spiral quickly if you don’t plan carefully. The challenge is understanding how your model size, expected traffic, and performance requirements translate into hardware needs.

Memory requirements are particularly critical, as they determine whether you can run your model on a single GPU or need to distribute it across multiple units.

Different GPU generations offer vastly different capabilities in terms of memory capacity and processing power. The memory requirements alone can determine whether you need a single high-end GPU or multiple lower-tier ones, significantly impacting both performance and costs.

Multi-GPU strategies add another layer of complexity for very large models. This isn’t only about buying more hardware; it’s about orchestrating distributed computing resources that can communicate efficiently and handle failures gracefully.

The cost implications are significant. While exact pricing varies by provider and usage patterns, GPU-intensive workloads can easily cost hundreds or thousands of dollars per hour at scale.

Understanding your performance requirements upfront, rather than discovering them in production, is important for budget planning.

> Our platform handles GPU provisioning automatically, so you don’t need to become an expert in hardware specifications. You can select from available GPU types through our interface, and the platform manages the underlying infrastructure.

With our [BYOC (Bring Your Own Cloud)](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-in-your-own-cloud) feature, which allows you to deploy into your own cloud provider account, you maintain control over costs and can choose GPU instances that fit your budget while we handle the orchestration complexity.
> 

See our guide on [self-hosting vLLM in your own cloud account](https://northflank.com/guides/self-host-vllm-in-your-own-cloud-account-with-northflank-byoc) and [deploying GPUs in your own cloud](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-in-your-own-cloud) for a complete walkthrough.

### 3. Creating production-ready API endpoints from LLM models

Your model needs to be accessible to your applications, which means creating robust API endpoints that can handle real-world traffic patterns. Rather than building these endpoints from scratch, most teams use specialized frameworks. vLLM serves models with OpenAI-compatible API endpoints, allowing seamless integration with existing OpenAI tooling.

The “OpenAI-compatible” part is important because it means your applications can switch between different LLM providers without code changes. But building these endpoints involves more than only exposing your model - you need request queuing, batch processing, and load balancing strategies that work with LLM-specific traffic patterns.

This is where LLM APIs differ significantly from traditional web APIs. LLMs use continuous batching to maximize concurrent requests and keep queues low when batch space is available. This is different from traditional API load balancing because LLM requests have variable processing times and memory requirements.

> Our platform automatically handles API endpoint creation and load balancing, plus you can [deploy models like DeepSeek R1 with vLLM](https://northflank.com/guides/deploy-deepseek-r1-vllm-northflank-ai-llm) to get production-ready endpoints without building custom infrastructure.
> 

### 4. Autoscaling strategies for managing my LLM traffic

Traditional autoscaling based on CPU or memory metrics fails spectacularly with LLMs. Key metrics for LLM autoscaling include queue size (number of requests awaiting processing) and batch size (requests undergoing inference).

The challenge is that LLMs have unpredictable resource usage patterns. A simple question might process in milliseconds, while a complex request could take minutes. Platforms with fast autoscaling and scaling to zero can reduce costs significantly during low activity periods.

Cold starts are particularly problematic - spinning up a new LLM instance can take several minutes while the model loads into GPU memory. This means you need sophisticated prediction algorithms to scale up before you actually need the capacity.

> Our platform provides built-in autoscaling capabilities for your LLM workloads. Our GPU orchestration handles the complexity of spinning up new instances, and the platform automatically manages spot and on-demand GPU instances to optimize costs while maintaining performance.
> 

### 5. Building secure environments for my LLM deployment

LLM security goes far beyond traditional application security. The Open Web Application Security Project (OWASP) - a nonprofit foundation that provides security guidance for applications - has created a specific framework called "OWASP Top 10 for LLMs" that outlines today's most pressing risks when building, deploying, or interacting with large language models, including prompt injection, data leakage, supply chain vulnerabilities, and training data poisoning.

Organizations need to isolate LLM environments using containerization or sandboxing and conduct regular penetration testing. However, LLM-specific threats like prompt injection attacks require specialized monitoring and filtering that traditional security tools don't provide.

Runtime protection solutions monitor adversarial threats, including prompt injection, model jailbreaks, and sensitive data leakage in real time. This isn't optional - a single successful attack can expose your training data, steal your model, or manipulate outputs to harm your business.

> Our platform provides secure container environments with built-in network isolation, encrypted communications, and compliance-ready infrastructure. With our [BYOC (Bring Your Own Cloud) option](https://northflank.com/docs/v1/application/gpu-workloads/deploy-gpus-in-your-own-cloud), you maintain complete control over data residency and security policies while still getting managed deployment benefits.
> 

This addresses the infrastructure security layer, though you'll still need to implement application-level protections like input validation and output filtering for LLM-specific threats.

## How Northflank simplifies my LLM deployment pipeline

After walking through those five technical areas, you're most likely thinking: "This sounds like a full-time job for my entire engineering team." You're not wrong.

That's one of the reasons why platforms like Northflank exist - to handle this complexity so you can focus on what differentiates your business.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

Let's revisit each area and see how Northflank transforms weeks of specialized work into a few clicks:

### 1. Containerization made effortless

Rather than stressing over Docker configurations, GPU drivers, and security scanning, you simply connect your code repository to Northflank. Our platform automatically handles containerization with GPU optimization built in. You don't have to become a Docker expert or worry about CUDA compatibility issues.

When you [deploy models like DeepSeek R1 with vLLM](https://northflank.com/guides/deploy-deepseek-r1-vllm-northflank-ai-llm), the entire containerization process happens behind the scenes. Your model gets packaged with the right dependencies, security patches, and performance optimizations without any manual configuration.

### 2. GPU orchestration without the complexity

Remember those GPU allocation challenges we discussed? Northflank provides on-demand [GPU infrastructure](https://northflank.com/gpu) with streamlined deployment processes, allowing you to have your model up and running in minutes rather than spending hours on configuration and setup.

You don't need to become an expert in GPU sizing, multi-node configurations, or cost optimization. Our platform handles resource allocation automatically, scaling GPU resources based on your actual usage patterns rather than forcing you to over-provision expensive hardware.

### 3. Production-ready APIs from day one

Northflank simplifies API endpoint creation by supporting popular LLM serving frameworks. For example, when you deploy using vLLM (as shown in our [DeepSeek R1 deployment guide](https://northflank.com/guides/deploy-deepseek-r1-vllm-northflank-ai-llm)), you get OpenAI-compatible API endpoints automatically.

What this means for you: your applications can switch between different LLM providers without code changes, and you get enterprise-grade load balancing, request queuing, and error handling without building custom infrastructure.

### 4. Advanced autoscaling that works for LLM workloads

While other teams struggle with LLM-specific scaling challenges, Northflank's platform automatically handles the complexity. Our autoscaling responds to the unique traffic patterns of LLM workloads, managing cold starts and optimizing for both performance and cost.

You get the benefits of advanced scaling algorithms without needing to understand queue metrics, batch optimization, or GPU memory management.

### 5. Enterprise security and compliance built in

Our platform provides secure container environments with built-in network isolation and encrypted communications. For organizations with strict compliance requirements, our [BYOC (Bring Your Own Cloud) option](https://northflank.com/guides/self-host-vllm-in-your-own-cloud-account-with-northflank-byoc) lets you maintain complete control over data residency and security policies.

BYOC allows you to maintain control over your data residency, networking, security, and cloud expenses while deploying the same Northflank workloads across any cloud provider without changing a single configuration detail.

### 6. From weeks to minutes

Here's what this means in practical terms: rather than spending 2-3 months building deployment infrastructure before you can even test your LLM with live users, you can have a production-ready deployment running in under an hour.

Your team stays focused on improving your AI features, building better user experiences, and solving business problems. You're not becoming a DevOps team for LLM infrastructure - you're staying an AI-focused product team.

## The complete picture & next steps!

Northflank's platform adapts to your growth, handling everything from automatic GPU provisioning to load balancing and monitoring.

From startups testing their first LLM feature to enterprises deploying multiple models across different regions, the platform scales with your requirements.

And because we handle the infrastructure complexity, you can experiment faster, iterate more quickly, and get to market while your competitors are still figuring out Kubernetes configurations.

<InfoBox className='BodyStyle'>

**Next steps**:
[Get started with Northflank](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) with an Engineer.

You can also check out our [complete developer's guide to LLM deployment](https://northflank.com/blog/open-source-llms-the-complete-developers-guide-to-deployment) or review [self-hosting options with BYOC](https://northflank.com/guides/self-host-vllm-in-your-own-cloud-account-with-northflank-byoc) for maximum control and compliance.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Bitnami deprecates free images: Migration steps and alternatives</title>
  <link>https://northflank.com/blog/bitnami-deprecates-free-images-migration-steps-and-alternatives</link>
  <pubDate>2025-08-18T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Bitnami is moving most container images to a legacy repository on August 28th, 2025, and stopping updates. If you're using Bitnami images or Bitnami helm charts, your deployments will break when they try to pull images that no longer exist.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/bitnami_3_2f6c67d5be.png" alt="Bitnami deprecates free images: Migration steps and alternatives" /><InfoBox className="BodyStyle">

## 📌 TL;DR

Bitnami is moving most container images to a legacy repository on August 28th, 2026, and stopping updates. If you're using Bitnami images or Bitnami helm charts, your deployments will break when they try to pull images that no longer exist.

This affects everyone using Bitnami charts, from weekend projects to production systems. Your pods won't start, your CI/CD will fail, and you'll be scrambling to fix it. 

Here's how to fix it immediately and why migrating to [Northflank](https://northflank.com/) makes more sense than dealing with Bitnami dependency hell.

</InfoBox>

## What Bitnami is doing

[Bitnami announced they're restructuring their container catalog.](https://github.com/bitnami/containers/issues/83267) 

Instead of maintaining the broad catalog of versioned images, they’re moving almost everything to a new `bitnamilegacy` repository that won’t receive updates.

This change is tied to Broadcom’s shift toward paid “Bitnami Secure” subscriptions, costing $50,000–$72,000 per year. Free users lose access to version-pinned images and regular security patches. In practice, that means teams relying on stable tags like `postgresql:13.7.0` or `redis:7.0.5` are left with two bad options: use the unsupported `bitnamilegacy` repo, or point to a `latest` tag in `bitnamisecure` (which is unsafe for production).

### What is Bitnami?

**What is Bitnami**? They've been packaging open source software into containers and **Bitnami helm charts** for 18 years. Databases, web servers, caches, basically everything you need to run applications. Now Broadcom owns them and wants $50,000-$72,000 per year for what used to be free.

The changes:

- Main `docker.io/bitnami` repository: Only "latest" tags for a few hardened images
- Everything else moves to `docker.io/bitnamilegacy` with zero updates
- **Bitnami helm charts** stop getting updates but the old ones stay published
- Version pinning dies, no more `postgresql:13.7.0`, just `latest` or legacy

## Your deployments will break

When Bitnami removes images from their main repository, deployments will start throwing errors like `ImagePullBackOff`, `ErrImagePull`, or `image not found`. This hits:

- **Production apps**: Using **Bitnami charts** with version pinning break during pod restarts or scaling
- **CI/CD pipelines**: Build processes fail when they can't pull **Bitnami** images
- **Dev environments**: Local setups using Bitnami helm charts show `image pull failure` and won’t start
- **Any Kubernetes operation** that needs to pull **Bitnami** images during maintenance

## Quick fix: Update your image references

You need to update your **Bitnami** image references before August 28th or your stuff breaks.

### Step 1: Point to legacy repository

For **Bitnami helm charts**, update the image repository to point to legacy:

```yaml
# PostgreSQL example
postgresql:
  image:
    repository: bitnamilegacy/postgresql
    tag: "13.7.0-debian-11-r9"

  metrics:
    image:
      repository: bitnamilegacy/postgres-exporter
      tag: "0.10.1-debian-11-r52"

  volumePermissions:
    image:
      repository: bitnamilegacy/bitnami-shell
      tag: "11-debian-11-r27"

```

### Step 2: Check Bitnami secure first

Before using legacy, check if your app is available at https://hub.docker.com/u/bitnamisecure. Some applications get updated images there:

```yaml
# If available in bitnami secure (dev only, latest tags only)
postgresql:
  image:
    repository: bitnamisecure/postgresql
    tag: "latest"

```

The secure repository only has latest tags and is for development use.

### Step 3: Update all components

Most **Bitnami helm charts** have multiple images. Update everything:

```yaml
# Redis example with all components
redis:
  image:
    repository: bitnamilegacy/redis
    tag: "7.0.5-debian-11-r7"

  sentinel:
    image:
      repository: bitnamilegacy/redis-sentinel
      tag: "7.0.5-debian-11-r8"

  metrics:
    image:
      repository: bitnamilegacy/redis-exporter
      tag: "1.45.0-debian-11-r1"

  volumePermissions:
    image:
      repository: bitnamilegacy/bitnami-shell
      tag: "11-debian-11-r27"

```

### Step 4: Test before deadline

```bash
# Test image pulls
docker pull bitnamilegacy/postgresql:13.7.0-debian-11-r9

# Validate helm chart
helm template my-release bitnami/postgresql -f updated-values.yaml

# Deploy to staging first
helm upgrade --install test-release bitnami/postgresql -f updated-values.yaml

```

### Problems with legacy repository

This fix prevents immediate breakage but creates new problems:

- **No security updates**: Images in `bitnamilegacy` never get patched
- **No bug fixes**: Critical issues won't be fixed
- **Unknown lifespan**: No guarantee legacy repo stays online
- **Version chaos**: Teams end up on different incompatible versions

Even if you point to `bitnamilegacy`, you’ll still risk running into `ImagePullBackOff` or `ErrImagePull` later when tags disappear or the repo itself is retired.

## The best solution: Switch to Northflank

The quick fix above just kicks the can down the road. The real problem is depending on external image repositories that can disappear or change policies overnight.

[Northflank](https://northflank.com/) eliminates this by providing [managed services](https://northflank.com/features/managed-cloud) instead of making you manage **Bitnami charts** and container images.

### How Northflank fixes this

**1️⃣ Managed services instead of containers**: Instead of deploying PostgreSQL through **Bitnami helm charts**, you get managed PostgreSQL. No images to manage, no updates to worry about.

**2️⃣ Built-in service catalog**: Need a database or cache? Get a managed service, not a container image that might disappear.

**3️⃣ Automatic updates**: Northflank handles security patches and updates. No more hunting for new **Bitnami** image versions.

**4️⃣ No external dependencies**: Your infrastructure doesn't break when Docker Hub "verified publishers" change their minds.

### Examples

**PostgreSQL**:

Old way (**Bitnami chart**):

```yaml
postgresql:
  image:
    repository: bitnamilegacy/postgresql
    tag: "13.7.0-debian-11-r9"
  auth:
    postgresPassword: "mypassword"
    database: "myapp"

```

Northflank way:

- Create managed PostgreSQL addon in the UI
- Get automatic backups, scaling, maintenance
- Connection details injected as environment variables
- Zero container management

**Redis**:

Old way (**Bitnami chart** with multiple containers):

```yaml
redis:
  image:
    repository: bitnamilegacy/redis
    tag: "7.0.5-debian-11-r7"
  sentinel:
    enabled: true
    image:
      repository: bitnamilegacy/redis-sentinel

```

Northflank way:

- High availability Redis cluster with built-in sentinel
- Automatic failover and persistence
- Monitoring included
- No YAML, no containers

### Development workflow benefits

Beyond solving the **Bitnami migration** problem:

- **Git deployment**: Deploy from repos without managing **Bitnami helm charts**
- **Environment promotion**: Move changes through environments without image registry issues
- **Built-in monitoring**: No separate monitoring **Bitnami charts** to maintain
- **Reasonable pricing**: Avoid Bitnami's $50,000-$72,000 annual fees

<InfoBox className="BodyStyle">


## 👉 Northflank’s take

**Bitnami** has provided huge value to the Kubernetes community for years. Their catalog of **Bitnami helm charts** and container images made deploying applications much easier. We understand open source needs sustainable business models, and charging for enterprise support makes sense.

But this deprecation is handled terribly. Removing images downloaded billions of times with 1.5 months notice is negligent. This should have been planned over years, not months.

</InfoBox>

![CleanShot 2025-08-19 at 11.49.32@2x.png](https://assets.northflank.com/Clean_Shot_2025_08_19_at_11_49_32_2x_ad55a15499.png)

### Why this matters

- **Docker Hub "Verified Publisher" problem**: When verified publishers can suddenly break critical infrastructure, it undermines trust in the entire ecosystem.
- **Left-pad redux**: This is the npm left-pad incident but bigger. **Bitnami** images are foundational to much of the Kubernetes world.
- **Bad precedent**: If **Bitnami** can break millions of deployments overnight, what stops other verified publishers from doing the same?
- **Community damage**: Small teams and open source projects that used free **Bitnami** images face impossible choices: pay enterprise prices or scramble for alternatives.

This **Bitnami migration** crisis shows the weakness of depending on external parties for critical infrastructure. You need systems that don't break when someone changes their business model.

**Use managed services**: Instead of **Bitnami helm charts** for databases, use managed services with better reliability and performance

**Integrated platforms**: Platforms like Northflank provide complete solutions instead of assembling pieces from different vendors

**Reduce external dependencies**: Minimize reliance on external registries that can change policies without warning

## Migration strategy

This **Bitnami migration** is a chance to build better infrastructure that won't break next time.

### Phase 1: Stop the bleeding

Find all **Bitnami** usage:

```bash
# Find bitnami images
kubectl get pods --all-namespaces -o jsonpath='{.items[*].spec.containers[*].image}' | grep bitnami

# Check helm releases
helm list --all-namespaces

```

Update critical production deployments to use `bitnamilegacy` repositories.

Test everything before August 28th.

### Phase 2: Strategic migration

For each **Bitnami** service, find managed alternatives:

- **Databases**: PostgreSQL, MySQL, MongoDB → Northflank managed databases
- **Caching**: Redis, Memcached → Northflank managed Redis
- **Message queues**: RabbitMQ, Kafka → Northflank managed solutions

Start with non-critical environments.

### Phase 3: Complete migration

Move production to Northflank managed services.

Eliminate **Bitnami** dependencies completely.

Document cost savings vs both maintaining **Bitnami** and paying for Bitnami Secure Images.

## Conclusion

The August 28th **Bitnami** deadline forces a choice: apply band-aid fixes or build resilient infrastructure.

Updating **Bitnami helm charts** to use legacy repositories prevents immediate failures but doesn't fix the underlying problem. Legacy images get no security updates and might disappear entirely.

Northflank solves this by providing managed services instead of container dependencies. When the next "verified publisher" decides to monetize their free offerings, your infrastructure won't break.

**What to do**:

- **Fix immediately**: Update critical deployments to legacy repositories before August 28th
- **Plan migration**: Use this crisis to eliminate external dependencies
- **Choose reliability**: Platforms like Northflank prevent future supply chain failures

Don't just patch the **Bitnami** problem. Fix the dependency risk entirely. 

[Start with Northflank today](https://northflank.com/) and build infrastructure that won't break when the next supplier changes their mind.]]>
  </content:encoded>
</item><item>
  <title>Best Cloudflare Workers alternatives in 2026</title>
  <link>https://northflank.com/blog/best-cloudflare-workers-alternatives</link>
  <pubDate>2025-08-17T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[ Cloudflare Workers runs lightweight, serverless code at the edge, but it has strict limits on memory, execution time, and runtime flexibility. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/cloudflare_workers_2_14ead42ce6.png" alt="Best Cloudflare Workers alternatives in 2026" /><InfoBox className='BodyStyle'>


## 📌 **TL;DR**

Cloudflare Workers runs lightweight, serverless code at the edge, but it has strict limits on memory, execution time, and runtime flexibility. For workloads that need more compute, persistent storage, GPUs, or full networking, [Northflank](https://northflank.com/) is the top alternative. It runs any Docker container with autoscaling, private networking, and support for AI models.

Other strong alternatives include Vercel Edge Functions, Netlify Edge Functions, and AWS Lambda@Edge.

**📝 Note:** Cloudflare now offers Cloudflare Containers in beta, which adds Docker support at the edge. This article focuses on Workers specifically, which still have strict runtime limits compared to full container platforms.

</InfoBox>

## What is Cloudflare Workers?

Cloudflare Workers is a serverless platform that runs JavaScript, TypeScript, or WebAssembly at the edge on Cloudflare’s global network. It uses V8 isolates, lightweight sandboxed runtimes, allowing for extremely fast cold starts (often under 5 ms) and low-latency request handling close to users.

Workers excel at:

- Routing and modifying HTTP requests
- Running lightweight APIs
- Authentication and caching logic
- Small amounts of AI inference via Workers AI

Limits to keep in mind:

- No arbitrary binaries or custom OS environments *(unless using the separate Cloudflare Containers product)*
- No persistent network connections
- Memory capped at 128 MB
- Execution time capped at tens of milliseconds
- Stateless by design

## What is the pricing of Cloudflare Workers?

Pricing is usage-based:

- **Requests**: First 10 million/month included, billed per million after.
- **CPU time**: Charged in GB-seconds based on execution time × memory allocation.
- **Additional services**: Durable Objects, KV, D1 database, R2 object storage, and Queues are billed separately.

## What are Cloudflare Workers free tier limits?

Free tier allowances:

- 100,000 requests/day
- 10 ms CPU time per request (bursts up to 50 ms)
- 128 MB memory per execution
- 1 MB script size
- Limited access to Durable Objects and other paid features

These constraints are often the deciding factor for teams moving to alternatives.

## Cloudflare Workers AI

Cloudflare Workers AI is a managed AI inference service on the edge. It gives access to a curated set of open-source models for text, vision, and speech. Models run in Cloudflare’s environment and are accessed via API calls from Workers.

**Limitations:**

- Cannot upload custom models or weights
- No GPU selection or tuning
- Model library limited to Cloudflare’s supported list
- No training or fine-tuning options

For AI workloads requiring control over models, GPU specs, or private datasets, self-hosting on a full container platform is more flexible.

## Why look for Cloudflare Workers alternatives

Teams typically explore Cloudflare Workers alternatives for one or more of these reasons:

- **Resource limits**: 128 MB memory and short execution windows make heavy compute or large in-memory processing impossible.
- **Language/runtime restrictions**: Workers support JavaScript, TypeScript, and WebAssembly, but not arbitrary runtimes or full Linux environments. (*unless using the separate Cloudflare Containers product)*.
- **Stateful needs**: Persistent connections, long-running processes, or on-instance storage are not supported.
- **AI flexibility**: Workers AI only supports a fixed set of models with no GPU choice or custom model hosting.
- **Cost scaling**: Once workloads exceed the free tier and start requiring extra services like Durable Objects, R2, and D1, costs can rise quickly.
- **Security control**: Workers cannot run untrusted code in a fully isolated, configurable environment.

These limitations push many teams to pair Workers with, or move entirely to, a more flexible container platform.

## Best Cloudflare Workers alternatives

Here are the top Cloudflare Workers alternatives in 2026.

### 1. Northflank - Overall best Cloudflare Workers alternative

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

The best Cloudflare Workers alternative for full container orchestration is Northflank. 

Northflank is a container-first developer platform for deploying APIs, services, AI inference, and full-stack applications. It runs any Docker container with no language or runtime restrictions.

**Capabilities:**

- **Full Linux containers** with persistent storage and private networking
- **Scalability**: Horizontal autoscaling, zero-downtime deploys
- **AI support**: Deploy any AI model with GPU choice, fine-tuning, and dataset integration
- **Networking**: Private services, VPC peering, TLS termination, custom domains
- **CI/CD**: Integrated pipelines for automated builds and deployments
- **Stateful workloads**: Databases, message queues, caching layers run alongside stateless apps
- **Security**: Isolated execution, RBAC, secrets management

**When to choose Northflank over Workers:**

- More than 128 MB memory or 50 ms CPU needed
- Persistent services or connections required
- AI workloads with custom GPUs or models
- Complex networking between multiple services
- Consolidating backend infrastructure in one orchestrated environment

Northflank can replace Workers entirely for heavier workloads or serve as a backend platform that Workers call for compute-intensive tasks.

### 2. Vercel Edge functions

Runs JavaScript/TypeScript at the edge using WebAssembly isolates. Integrated with Vercel’s frontend hosting and build pipeline.

**Strengths:**

- Seamless for teams already on Vercel
- Good developer experience for frontend-heavy apps

**Limitations:**

- Slightly higher latency in some regions vs Workers
- Smaller geographic footprint than Cloudflare
- Tied to Vercel ecosystem

### 3. Netlify Edge functions

Built on Deno runtime, running at Netlify’s CDN edge. Designed for JAMstack applications.

**Strengths:**

- Tight Git integration and build automation
- Built-in features like form handling and authentication

**Limitations:**

- Limited runtime support (Deno only)
- Smaller global footprint compared to Cloudflare

### 4. AWS Lambda@Edge

Extends Lambda functions to AWS CloudFront locations.

**Strengths:**

- Full AWS integration
- More powerful runtimes than Workers

**Limitations:**

- Slower cold starts
- Fewer edge locations than Cloudflare
- Higher latency for some regions

### Cloudflare Wokers vs AWS Lambda

| Feature | Cloudflare Workers | AWS Lambda |
| --- | --- | --- |
| **Execution model** | V8 isolates | Firecracker microVMs (Linux container–like) |
| **Cold start speed** | Milliseconds | Slower |
| **Execution limits** | Strict CPU/memory caps | Up to 10 GB memory, 15 min runtime |
| **Networking** | Public edge only (private via Tunnel) | Private VPC access |
| **Language/runtime** | JavaScript, TypeScript, WebAssembly | Multiple runtimes, custom runtime support |
| **Strength** | Best for ultra-low latency | Handles heavier, long-running workloads |

## Conclusion

Cloudflare Workers is well-suited for ultra-low-latency, stateless workloads at the edge. But for teams that need more compute, storage, or AI flexibility, alternatives offer broader capabilities.

**Northflank** stands out as the most complete choice among Cloudflare Workers alternatives, delivering full container orchestration, persistent services, AI hosting with GPU choice, secure sandboxing, and transparent pricing.

If you’re hitting the limits of Workers and need more control over your runtime, [get started with Northflank for free](https://northflank.com/) and deploy your first container in minutes.]]>
  </content:encoded>
</item><item>
  <title>What are spot GPUs? Complete guide to cost-effective AI infrastructure</title>
  <link>https://northflank.com/blog/what-are-spot-gpus-guide</link>
  <pubDate>2025-08-15T14:39:00.000Z</pubDate>
  <description>
    <![CDATA[Learn how spot GPUs cut AI infrastructure costs by up to 90%. Learn what spot instances are, their benefits, drawbacks, and how orchestration makes them production-ready.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/spot_gpus_8cb9b76534.png" alt="What are spot GPUs? Complete guide to cost-effective AI infrastructure" /><InfoBox className='BodyStyle'>

*Spot GPUs are unused cloud GPU instances available at up to 90% discounts compared to on-demand pricing. They're perfect for AI inference, training jobs, and burst workloads, but can be interrupted with short notice (30 seconds to 2 minutes, depending on the provider). While traditional spot instances require complex management and quota approvals, modern orchestration platforms like [Northflank](https://northflank.com/) handle the complexity automatically, providing seamless fallback to on-demand instances when needed. [Try it out now](https://app.northflank.com/signup) or [reach out to an engineer to guide you](https://cal.com/team/northflank/northflank-intro).*

</InfoBox>

Let me tell you a quick story.

An AI founder was scaling his voice cloning platform, but GPU costs were threatening to kill his startup.

With just two engineers and limited credits, he used spot GPUs with automated orchestration to [scale to millions of users](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s) while spending 90% less on compute.

If you're running AI workloads, you've likely felt the same sticker shock. High-end GPU instances can cost significant money per hour on demand, but what if you could get the same power at up to 90% off?

That's what spot GPUs offer, and understanding how to use them successfully could be the difference between burning through your budget and building a sustainable AI business.

Let’s talk about spot GPUs and how to cut your AI infrastructure costs.

## What are spot GPUs?

Spot GPUs are high-performance graphics cards (like NVIDIA H100s or A100s) that you can rent from cloud providers at 60-90% discounts compared to regular prices.

They're essentially "leftover" GPU capacity that cloud providers offer when their data centers aren't fully utilized, but your workload can be interrupted with short notice (30 seconds to 2 minutes, depending on the provider) if they need that hardware back for full-paying customers.

It’s like booking a standby flight ticket. You get the exact same plane and destination as someone who paid full price, but you're flying for $50 instead of $500 because you're willing to get bumped if the flight fills up.

Spot GPUs work the same way, where you get enterprise-grade AI compute power at a fraction of the cost by accepting the possibility of interruption.

The key difference is that "spot instances" refer to any discounted virtual machines using excess capacity, while "spot GPUs" specifically mean those machines equipped with powerful graphics cards for AI training, machine learning inference, and other compute-intensive tasks. 

So, for most AI workloads that can handle brief interruptions, you're getting the same performance as expensive on-demand instances at startup-friendly prices.

### How do spot instances and spot pricing work?

Spot pricing works like a stock market for compute resources. When cloud providers have excess GPU capacity, they auction it off at discounted rates. The price fluctuates in real-time based on supply and demand; if lots of people want GPUs in a specific region, the price goes up. When demand drops, prices fall.

Let’s briefly go over how the interruption system works.

The interruption process is straightforward but happens quickly. When someone wants to pay full price for on-demand capacity and there's no spare hardware available, you're the one who gets bumped:

1. **Short warning period:** AWS gives 2 minutes, Google Cloud and Azure give just 30 seconds
2. **Reclaimed for on-demand:** Happens when someone pays full price, and no spare hardware exists
3. **Peak usage risk:** Most likely during high-demand periods or popular GPU types
4. **Limited exit time:** Enough to save work, but you need automated systems for reliability

Now, let’s talk about the pricing advantage across providers

What makes this system work in your favor is that cloud providers would rather make some money from unused capacity than none at all:

- **AWS spot instances:** Up to 90% savings, widest GPU selection, 2-minute termination notice
- **Google Cloud spot VMs:** 60-91% discounts, more stable pricing, excellent A100/H100 availability
- **Azure spot VMs:** Up to 90% savings, deepest discounts during off-peak hours

You only pay the current spot price (not your maximum bid), often run for hours/days without interruption, and get the same performance as expensive on-demand instances. To find current prices, use each provider's built-in tools: AWS Spot Advisor, Google's pricing console, or Azure's usage recommendations.

### What is the difference between spot and standard VMs?

The main differences come down to cost, reliability, and what happens when things go wrong.

With standard (on-demand) VMs, you pay full price but get guaranteed access, and your instance runs until you decide to shut it down.

Spot VMs flip this equation: you pay 60-90% less but accept that your instance might get terminated when someone else needs that hardware.

Let’s see the key differences at a glance:

| Feature | On-Demand VMs | Spot VMs |
| --- | --- | --- |
| **Pricing** | Full price, predictable costs | 60-90% savings, but prices fluctuate based on demand |
| **Availability** | Guaranteed but starts immediately when you request it | Depends on available capacity and might not be available in your preferred region/instance type |
| **Interruptions** | Never interrupted (unless you don't pay your bill) | Can be terminated with short notice (30 seconds to 2 minutes) when capacity is needed elsewhere |
| **Service Level Agreements (SLAs)** | Covered by cloud provider SLAs for uptime guarantees | No SLA coverage; you're using "leftover" capacity |

The choice between them depends on your workload requirements. If you need guaranteed uptime for production databases, web servers, or customer-facing applications, on-demand instances are your safest bet.

However, if you're running AI training jobs, batch processing, development environments, or anything that can pause and resume gracefully, spot instances offer massive savings without significantly impacting your results.

### Are spot instances cheaper?

Yes, spot instances are significantly cheaper, but the savings depend on several factors. Let's break down the costs so you can see how much you'll save.

The savings are substantial across all GPU types.

While exact prices fluctuate daily based on demand, the pattern is consistent: spot instances typically cost 60-90% less than on-demand rates. If you’re using H100s, A100s, or older V100 GPUs, you’ll see similar percentage savings.

For instance, if an H100 instance costs $8/hour on-demand, you might pay just $1-2/hour with spot pricing. That same pattern applies across all GPU types and cloud providers.

*Now, what does this mean for your total spending?*

The savings add up quickly. With 60-90% discounts on every job you run, those savings can add up to significant amounts over time if you're running multiple experiments or inference workloads.

You'll save the most when:

- Running jobs during off-peak hours (nights, weekends)
- Using less popular regions (avoid us-east-1 during business hours)
- Being flexible on GPU types (A100 vs H100)
- Running batch jobs that can restart easily

And your savings shrink when:

- Working during peak demand periods (business hours in popular regions)
- Needing specific GPU requirements with limited availability
- Dealing with frequent interruptions that require restart overhead
- Running short jobs where setup time matters more than runtime costs

## What are the benefits and drawbacks of spot GPUs?

Now that you understand how spot GPUs work and their cost advantages, let's look at when they're great for your needs versus when they might create problems. Like any tool, spot GPUs have compelling benefits but also limitations you need to be aware of.

### So, what are the benefits of spot GPUs?

The cost savings alone make spot GPUs attractive, but they offer several other advantages that make them particularly well-suited for modern AI workloads. See some of them below:

- **Massive cost savings (60-91% discounts):** With spot pricing, you can access high-end GPUs like H100s at a fraction of the on-demand cost. For startups and teams with tight budgets, this makes previously unaffordable hardware accessible.
- **Perfect for burst workloads:** If you need to scale from 10 to 100 GPUs for a weekend training run, spot instances let you scale up quickly without long-term commitments, then scale back down when you're done.
- **Ideal for inference:** Most inference requests take seconds to complete. If a spot instance gets interrupted, you can simply route the next request to another instance and users won't even notice the switch.
- **Great for batch processing:** Training jobs, data processing pipelines, and rendering tasks are naturally fault-tolerant. They can pause, save progress, and resume on a new instance without losing work.
- **Enables experimentation:** When GPU time costs 90% less, you can afford to try more experiments, test different model architectures, and iterate faster without burning through your budget.

### Now what are the drawbacks of spot instances?

While the benefits are compelling, spot GPUs aren't perfect. These are some of the challenges you'll face:

- **Interruption risk and reliability concerns:** Your workload can be terminated with 30 seconds to 2 minutes notice. For time-sensitive or long-running processes, this unpredictability can be problematic.
- **Management complexity without proper tools:** Handling interruptions, checkpointing, and failover manually requires significant engineering effort. You need systems that can gracefully handle shutdowns and restart elsewhere.
- **Quota and approval challenges:** This is where many teams get stuck. Getting access to spot GPU capacity isn't as simple as clicking "launch"; cloud providers have implemented strict approval processes that can take days or weeks.

Also, people have asked this question “*Why are GPU approvals so complicated?*” on forums like [reddit](https://www.reddit.com/r/aws/comments/1i2wnp0/why_the_approval_for_gpu_spot_instances_so/):

![gpu-spot-instances-reddit.png](https://assets.northflank.com/gpu_spot_instances_reddit_a3866ad87b.png)

Cloud providers face a massive fraud problem. Bad actors use stolen account credentials to spin up hundreds of GPU instances for cryptocurrency mining, then disappear when the bill comes due. AWS, Google, and Azure have responded by heavily vetting GPU requests, especially for new accounts.

Your account type makes a huge difference in approval speed. Enterprise accounts with established billing history get faster approval, while individual developers or new startups often face lengthy review processes. Some teams wait weeks just to get permission to use spot instances they're willing to pay for.

<InfoBox className='BodyStyle'>

Also spot instances are not suitable for all workloads. So, real-time applications, stateful services, and mission-critical production systems that can't tolerate interruptions should stick with on-demand instances despite the higher cost.

</InfoBox>

## When should you use spot GPUs?

Given everything we've covered about interruptions, cost savings, and management complexity, you could be thinking:

*"Is this right for my workload?"*

The answer depends on how well your application can handle brief interruptions and whether you can design around the unpredictability.

Let’s see what spot GPUs work great for:

1. **AI model batch inference APIs:** Most batch inference processes handle multiple requests together and can tolerate brief delays. If your spot instance gets interrupted, you can restart the batch on another instance without affecting user experience. The cost savings here are massive since batch inference workloads often run continuously.
2. **Training jobs with checkpointing:** If your training code saves progress every few minutes, getting interrupted isn't a big deal. You just resume from the last checkpoint on a new instance. This works especially well for long training runs where you're saving thousands of dollars.
3. **Batch data processing:** ETL jobs, data analysis pipelines, and similar workloads are naturally fault-tolerant. They can pause mid-way through a dataset and pick up where they left off.
4. **Development and testing environments:** Your dev environments don't need 99.9% uptime. If a spot instance gets terminated while you're testing model performance, you simply start another one.
5. **Burst computing needs:** When you need to scale from 10 to 100 GPUs for a weekend project, spot instances let you access that capacity without long-term commitments.

Now let’s see what you should avoid spot GPUs for:

1. **Real-time applications requiring guaranteed uptime:** If you're serving live video processing or real-time recommendations where even a 30-second interruption affects users, stick with on-demand instances. For scenarios like this, integrating a [live stream API](https://castr.com/blog/best-live-streaming-api/) can help maintain consistent performance and reliability, even under fluctuating GPU availability.
2. **Stateful services without backup strategies:** If your application stores important state in memory and can't quickly save/restore that state, interruptions will cause data loss.
3. **Mission-critical production workloads:** Your main customer-facing API probably shouldn't run on spot instances unless you have sophisticated failover systems in place.
4. **Long-running processes without checkpointing:** If your job takes 48 hours to complete and can't save progress along the way, one interruption means starting over from scratch.

## How Northflank cuts spot GPU costs with automated orchestration

By now, you might be thinking:

*"Spot GPUs sound great for my workloads, but managing all those interruptions, fallbacks, and multi-cloud complexity seems like a full-time job."*

You're right, doing this manually would require a dedicated DevOps team. That's where [Northflank](https://northflank.com/) comes in to handle all the heavy lifting automatically.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

Let’s see some of the ways Northflank can help you in this case:

1. **Automatic spot instance management across your cloud accounts:** Instead of monitoring AWS, Google Cloud, and Azure separately for the best spot prices in your own accounts, Northflank does this continuously through our Bring Your Own Cloud (BYOC) model. It automatically provisions your workloads on whichever cloud has the cheapest available capacity at that moment within your connected accounts.
2. **Seamless fallback options to prevent downtime costs:** When your spot instance gets interrupted, Northflank immediately spins up a replacement, either another spot instance if available, or an on-demand instance if necessary. Your workloads keep running without you having to manually intervene at 2 AM.
3. **Perfect for inference and burst workloads:** Remember those use cases we just discussed? Northflank is specifically designed for inference APIs that need to scale quickly and training jobs that can handle interruptions. It automatically routes traffic away from instances that are about to be terminated.
4. **Multi-cloud spot optimization to find the cheapest capacity:** Rather than being locked into one cloud provider's pricing and availability, Northflank continuously finds the best deals across all major providers. If AWS spot prices spike in us-east-1, it might move your workloads to Google Cloud in us-central1 automatically.
5. **No manual quota management overhead:** Remember those frustrating GPU approval processes we discussed? Northflank handles relationships with cloud providers directly, so you don't have to submit quota requests or wait weeks for approval to access spot capacity.
6. **Maximize savings while minimizing operational complexity:** You get all the cost benefits of spot instances (60-90% savings), without needing a team of engineers to manage the complexity. Focus on building your AI applications while Northflank handles the infrastructure.

### How Weights scaled to millions of users with spot GPU savings

All of this might sound too good to be true, so let's look at a real-world example. Weights, an AI platform for voice cloning and content creation, faced the exact challenge you might be dealing with:

*“how do you scale AI workloads to serve millions of users without burning through your budget or hiring an entire DevOps team?”*

**The challenge:** Weights needed to scale from a local AI application to a platform serving millions of users, with just two engineers. They were bootstrapped, had limited cloud credits, and couldn't afford the typical infrastructure team that most Series B startups require for this kind of scale.

**The solution:** Instead of building their own spot instance management system or hiring DevOps engineers, they used Northflank's automated orchestration. This let them focus on building their AI product while Northflank handled all the infrastructure complexity behind the scenes.

**The results speak for themselves:**

- **250+ concurrent GPUs across 9 clusters** - They're running more infrastructure than most well-funded startups, but managing it with a small team
- **10,000+ daily training jobs and 500,000+ daily inference runs** - This is production-scale AI infrastructure that would typically require a full operations team
- **Model loading time cut from 7 minutes to 55 seconds** - Faster loading means less GPU time per job, which translates directly to cost savings when you're paying by the minute
- **Cloud migration from weeks to hours** - When they wanted to switch from Azure to GCP to use different cloud credits, it took an afternoon instead of weeks of engineering time

As JonLuca DeCaro, Weights' founder, puts it:

*"We don't waste time or money on infrastructure, so we can focus on building product."*

That's exactly what spot GPU orchestration should enable, more time building your AI applications, less time dealing with cloud infrastructure.

## Frequently asked questions about spot GPUs

See some of the most common questions we see about using spot GPUs in production environments:

**Q: How often do spot instances get interrupted?**
A: Interruption rates vary by region and instance type, typically ranging from 5-20% depending on demand. Popular GPU types in busy regions like us-east-1 see higher interruption rates, while less popular instances in quieter regions can run for days without interruption.

**Q: How reliable are spot instances?**
A: With proper orchestration and fallback mechanisms, spot instances can be very reliable for production workloads. The key is designing your applications to handle interruptions gracefully and having automated systems that can quickly restart work elsewhere when needed.

**Q: What are the drawbacks of spot instances?**
A: The main drawbacks are unpredictable interruptions (30 seconds to 2 minutes notice), complex management without proper tools, quota approval challenges from cloud providers, and unsuitability for workloads that can't handle brief downtime.

**Q: Can spot instances be interrupted?**
A: Yes, spot instances can be interrupted at any time when cloud providers need the capacity for on-demand customers. You'll receive 30 seconds to 2 minutes notice depending on the provider, which is enough time to save work but requires automated systems for seamless recovery.

## Cut your GPU costs by up to 90%

You've seen how spot GPUs can change your AI infrastructure costs, from the startup that scaled to millions of users with just two engineers, to the significant savings of accessing H100 instances at up to 90% off instead of paying full on-demand rates. The question isn't if spot GPUs can save you money, but if you want to handle the complexity of managing them yourself.

If you want the cost savings without the operational complexity, Northflank's automated orchestration handles all the heavy lifting. You get the same 60-90% discounts with automatic fallbacks, multi-cloud optimization, and no quota management overhead.

[Try Northflank for free](https://app.northflank.com/signup) and see how much you can save on your next AI workload, or [talk to an engineer](https://cal.com/team/northflank/northflank-intro) who can show you how spot GPU orchestration fits into your infrastructure.]]>
  </content:encoded>
</item><item>
  <title>Best GPU for machine learning </title>
  <link>https://northflank.com/blog/best-gpu-for-machine-learning</link>
  <pubDate>2025-08-14T15:45:00.000Z</pubDate>
  <description>
    <![CDATA[Learn how to choose the best GPU for AI, from H100 to B200, with use-case tips, pricing, and why Northflank offers the fastest, most flexible way to rent GPUs for training and inference.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/serverless_gpu_5_d342dd49e0.png" alt="Best GPU for machine learning " />If you have trained a transformer, fine-tuned a vision model, or deployed a recommendation system, you know different workloads push GPUs in very different ways. Some need massive high-bandwidth memory to load, while others only need something efficient enough to serve predictions without slowing down. 

That is why it pays to match the GPU to the task. The new H200 and B200 are pushing the limits for large-scale AI, with huge memory and bandwidth for massive models. The H100 and A100 remain strong options for demanding training, while cards like the T4 or L4 are built for high-throughput inference. Choosing the right one can save you days of compute time and a significant amount of budget. 

With Northflank, you can access the latest GPUs, from the Blackwell B200 to cost-efficient inference cards, and run your entire workflow from training to deployment in one platform.

In this guide, we will break down the best GPUs for machine learning by use case and how to use them without buying the hardware yourself.

## TL;DR: Best GPU for machine learning by use case

If you are short on time, here is the complete list of the best GPUs for machine learning by use case. Some are built for massive scale, others for efficiency or affordability, and a few are redefining what is possible for AI workloads.

<InfoBox className="BodyStyle">

> **Need specific GPU availability?** If you're looking for particular GPU types that aren't currently available or need custom capacity planning, [request GPU capacity here](https://northflank.com/request/gpu).

</InfoBox>

| **Use Case** | **Best GPU** | **Why** |
| --- | --- | --- |
| Cutting edge large scale training | NVIDIA B200 (Blackwell) | 192 GB HBM3e, up to 8 TB/s bandwidth, 3× training and 15× inference speed over H100-class GPUs |
| High performance training and inference | NVIDIA H200 | 141 GB HBM3e, 4.8 TB/s bandwidth, 2× inference throughput for large language models |
| Proven large scale training | NVIDIA H100 / A100 | Exceptional throughput and memory for enterprise workloads |
| Budget deep learning | NVIDIA RTX 4090 | Strong single-GPU performance at lower cost |
| Production inference | NVIDIA T4 / L4 | Low power, optimized for high throughput requests |
| Fine tuning and prototyping | NVIDIA A40 / A10G | Balanced memory and compute for iterative development |
| Multi modal workloads | NVIDIA H100 SXM | High bandwidth and capacity for complex data types |

<InfoBox className='BodyStyle'>

If you need these GPUs without buying the hardware, [Northflank](https://northflank.com/product/gpu-paas) gives you access to 18+ types including B200, H200, H100, A100 and T4 at competitive rates. Unlike most platforms it is full stack with infrastructure, deployments and observability in one place. [Start deploying your GPU workloads here](https://app.northflank.com/signup).

</InfoBox>

## What to consider when choosing a GPU for machine learning

Selecting the right GPU begins with identifying the key performance metrics that influence machine learning workloads.

- **Compute performance**: Measured in floating point operations per second (FLOPs), this indicates raw processing capability. GPUs with more Tensor cores and CUDA cores generally perform better for deep learning.
- **Memory capacity and bandwidth**: Large models and datasets require high VRAM capacity. Bandwidth determines how quickly data moves between memory and processing units, which directly affects training speed.
- **Power efficiency**: This matters for large-scale inference, where thousands of predictions per second need to be served with minimal energy costs.
- **Framework compatibility**: Ensure that your chosen GPU has optimized support for popular ML frameworks like PyTorch, TensorFlow, and JAX.
- **Scalability**: For very large models, multi-GPU setups or distributed training capabilities are essential.
- **Cost efficiency**: A powerful GPU is only valuable if its capabilities align with your workload. Using an H100 for small-scale inference is overkill, while using a T4 for large transformer training will slow you down significantly.

These considerations set the stage for evaluating the best GPU types by machine learning task.

## What are the best GPU types for machine learning tasks

Earlier, we discussed the key factors that influence GPU selection. In this section, we explore the main machine learning workflows, including large-scale training, inference, fine-tuning, and multi-modal or high-memory tasks, and the GPUs that work best for each.

### 1. Deep learning training

Training large models demands exceptional throughput, memory, and bandwidth. The NVIDIA B200 is built for next-gen AI training and inference, while the H200 significantly boosts large language model throughput over the H100. The H100 remains a strong choice for large-scale training, and the A100 continues to be widely used thanks to its proven performance and multiple memory options. For smaller teams or researchers, the RTX 4090 delivers excellent single-GPU performance at a much lower cost.

### 2. Inference

When models move into production, efficiency and cost become the priority. The NVIDIA T4 is a proven choice for high-throughput inference while keeping power consumption low. The L4 is its newer generation counterpart, offering improved performance per watt and better scaling for modern architectures. For teams that need a balance between inference performance and light training capability, the A10G offers a practical middle ground.

### 3. Fine-tuning and experimentation

Fine-tuning pre-trained models for domain-specific applications often requires large amounts of VRAM, but not always the extreme compute power of the B200, H200, or H100. The A40 is a strong candidate here with ample memory and good throughput for iterative work. The RTX 6000 Ada is another versatile option for developers who need strong single-GPU performance without investing in full-scale data center hardware.

### 4. Multi-modal and high-memory workloads

Workloads that combine multiple data types, such as text, images, and video, require both large memory capacity and high bandwidth. The B200 and H200 stand out here due to their massive memory and speed, enabling smooth training and inference for complex models. The H100 SXM variant also excels in this space, offering extreme bandwidth for demanding applications. The A100 80 GB remains a reliable choice for handling large datasets and model checkpoints, especially in distributed environments.

## How to choose the proper GPU for your ML workflow

Once you know the GPU types that fit different machine learning tasks, the fastest way to decide is to compare them side by side. This table highlights the most relevant specs for common AI workflows, including training, inference, fine-tuning, and multimodal processing.

| GPU model | Best for | Memory (GB) | Memory bandwidth | FP16/TF32 performance | Key advantages |
| --- | --- | --- | --- | --- | --- |
| **B200** | Extreme-scale training and multi modal workloads | 192 | ~8 TB/s | Very high | Next-gen Blackwell architecture, massive memory and throughput |
| **H200** | Large-scale training with very high memory needs | 141 | ~4.8 TB/s | Very high | Improved memory bandwidth over H100, better for very large models |
| **H100** | Training LLMs and high-end multi modal | 80 | ~3.35 TB/s | Very high | Strong training performance, excellent scaling |
| **A100** | Large batch training, distributed workloads | 40 / 80 | ~1.6 TB/s | High | Proven data center standard, still widely used |
| **RTX 4090** | Small-scale training, experimentation | 24 | ~1 TB/s | High | Affordable for local dev, strong single GPU performance |
| **A40** | Fine tuning, domain-specific model training | 48 | ~696 GB/s | Medium | High VRAM for fine tuning and mid-scale tasks |
| **RTX 6000 Ada** | Fine tuning, dev environments | 48 | ~960 GB/s | High | Workstation-class, strong dev flexibility |
| **T4** | Cost-effective inference | 16 | ~320 GB/s | Low | Very efficient, low power cost |
| **L4** | Modern inference with better performance per watt | 24 | ~300 GB/s | Low | Energy-efficient upgrade over T4 |
| **A10G** | Mixed inference and light training | 24 | ~600 GB/s | Medium | Flexible choice for hybrid workloads |

## Why flexibility matters in GPU choice

Machine learning workloads rarely stay the same. A team might start with large-scale training on an H100 or H200, then shift to cost-efficient inference on an L4 or T4, and later run fine-tuning on an A40. 

In many cloud setups, switching between these GPUs means rebuilding infrastructure or migrating workloads. Having the flexibility to change GPU types as your project evolves is the easiest way to keep performance high while controlling costs.

The challenge is that most clouds make switching GPUs slow or complex. This is where Northflank stands out. It’s built to adapt to changing workloads without infrastructure headaches.

## How Northflank simplifies GPU selection

Many platforms give you access to high-end GPUs, but the challenge is finding one where you can run an H100 SXM, H200, or B200 in a way that is simple, cost-effective, and scalable. Northflank has become a reliable choice for that problem.

### What is Northflank?

[Northflank](https://northflank.com/) abstracts the complexity of running GPU workloads by giving teams a full-stack platform; GPUs, secure runtime, deployments, built-in CI/CD, and observability all in one. You don’t have to manage infra, build orchestration logic, or combine third-party tools.

Everything from model training to inference APIs can be deployed through a Git-based or templated workflow. It supports [bring-your-own-cloud (AWS, Azure, GCP, and more)](https://northflank.com/product/bring-your-own-cloud), but it works fully managed out of the box.

![image - 2025-08-08T123011.085.png](https://assets.northflank.com/image_2025_08_08_T123011_085_d279fa8e00.png)

**What you can run on Northflank:**

- Inference APIs with autoscaling and low-latency startup
- Training or fine-tuning jobs (batch, scheduled, or triggered by CI)
- Multi-service AI apps (LLM + frontend + backend + database)
- Hybrid cloud workloads with GPU access in your own VPC

**What GPUs does Northflank support?**

Northflank offers access to 18+ [GPU types](https://northflank.com/gpu), including NVIDIA A100, B200, L40S, L4, AMD MI300X, and Habana Gaudi.

![gpu-prices-northflank.png](https://assets.northflank.com/gpu_prices_northflank_c6dbc88fdb.png)

[**See how Cedana uses Northflank to deploy GPU-heavy workloads with secure microVMs and Kubernetes**](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes).

## Conclusion

The best GPU for machine learning depends on the workload. Large-scale training benefits from high-bandwidth cards like the B200, H200, or H100, while inference can run efficiently on options like the T4 or L4. Fine-tuning and multimodal work often call for GPUs with higher memory, such as the A100 80GB or RTX 6000 Ada. Matching your GPU to the task ensures better performance and cost efficiency. 

Northflank makes this easier by giving teams access to 18+ GPU types with secure runtimes, deployments, CI/CD, and autoscaling built in. Whether you are training a new model, running inference at scale, or building a full-stack AI application, you can launch and manage workloads without worrying about infrastructure complexity.

[Sign up to deploy your first GPU](https://app.northflank.com/signup) today or [book a short demo](https://cal.com/team/northflank/northflank-intro) to see how Northflank fits into your workflow.

## FAQs

**What is a GPU**

A GPU, or Graphics Processing Unit, is a processor designed for highly parallel computation. This architecture makes it well-suited for the matrix operations and tensor calculations at the core of machine learning.

**What is the best GPU for machine learning**

There is no universal best. H100 and A100 GPUs excel at large-scale training, T4 and L4 are ideal for inference, and A40 or RTX 4090 are excellent for development and fine-tuning.

**What is the best GPU cloud for machine learning**

The best GPU cloud is one that offers a variety of GPU types with the ability to scale up or down as needed. Northflank provides access to all major NVIDIA GPUs along with integrated tools for building, training, and deploying ML models.]]>
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</item><item>
  <title>Claude Code vs Cursor: Complete comparison guide in 2026</title>
  <link>https://northflank.com/blog/claude-code-vs-cursor-comparison</link>
  <pubDate>2025-08-13T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[A comprehensive review of Claude Code vs Cursor]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/claude_vs_cursor_d170c12b23.png" alt="Claude Code vs Cursor: Complete comparison guide in 2026" /><InfoBox className="BodyStyle">

## 📌 TL;DR

**Claude Code** excels at autonomous coding tasks and complex file operations, while **Cursor** offers superior IDE integration and real-time code assistance. Both face the same critical limitation: **rate limits and API dependencies** that throttle productivity at crucial moments.

The solution is self-hosted open-source models that eliminate rate limits, reduce costs by 60-80%, and give you complete control over your AI coding workflow. You can [self-host open-source models with Northflank](https://northflank.com/product/gpu-paas).

</InfoBox>

## What is Claude Code? | Claude Code vs Cursor

Claude Code is Anthropic's command-line AI coding assistant that operates as an autonomous agent. 

Unlike traditional code completion tools, Claude Code can:

- Read entire codebases and understand project structure
- Edit multiple files simultaneously across your project
- Execute tests and debug issues automatically
- Commit changes directly to GitHub with descriptive messages
- Handle complex refactoring tasks that span multiple modules

**Key strengths:**

- **Autonomous operation:** Can work independently on multi-file tasks
- **Large context windows:** Handles entire codebases effectively
- **Natural language interface:** Communicate coding goals conversationally
- **GitHub integration:** Seamless version control workflow

## What is Cursor? | Claude Code vs Cursor

Cursor is an AI-powered code editor built on Visual Studio Code that integrates AI assistance directly into your development environment. It focuses on enhancing the traditional coding experience with:

- Intelligent code completion and suggestions
- Real-time code generation as you type
- Chat-based coding assistance within the editor
- Codebase-aware AI that understands your project context
- Multi-language support with context-aware suggestions

**Key strengths:**

- **IDE integration:** Native coding environment with familiar VS Code interface
- **Real-time assistance:** Instant suggestions while coding
- **Codebase awareness:** Understands your project's patterns and conventions
- **Multi-model support:** Access to various AI models within one interface

## Claude Code vs Cursor: Feature comparison

| Feature | Claude Code | Cursor | Winner |
| --- | --- | --- | --- |
| **Autonomous Task Execution** | ✅ Excellent | ❌ Limited | Claude Code |
| **IDE Integration** | ❌ Command-line only | ✅ Native VS Code | Cursor |
| **Real-time Code Completion** | ❌ No | ✅ Excellent | Cursor |
| **Multi-file Operations** | ✅ Excellent | ⚠️ Good | Claude Code |
| **GitHub Integration** | ✅ Direct commits | ⚠️ Manual | Claude Code |
| **Learning Curve** | ⚠️ Moderate | ✅ Easy | Cursor |
| **Context Window** | ✅ Very Large | ⚠️ Good | Claude Code |
| **Debugging Assistance** | ✅ Automated | ⚠️ Manual | Claude Code |

## Claude Code pricing

### Chat interface subscriptions

- **Free Plan**: Limited daily messages (varies by demand)
- **Pro Plan**: $20/month - approximately 45 messages every 5 hours
- **Max Plan**: $100/month (5x Pro usage) or $200/month (20x Pro usage)
- **Team Plan**: $30/user/month (minimum 5 users)
- **Enterprise Plan**: Custom pricing starting around $50,000 annually

### API Pricing (per million tokens)

- **Claude 4 Opus**: $15.00 input / $75.00 output
- **Claude 4 Sonnet**: $3.00 input / $15.00 output
- **Claude 3.5 Haiku**: $0.80 input / $4.00 output

**Hidden Costs:** Claude Code operations consume significantly more tokens than simple chat due to large system instructions, full file contexts, and multi-step processes.

## Cursor pricing

### Subscription tiers

- **Free Plan**: Limited AI requests per month
- **Pro Plan**: $20/month - 500 fast premium requests, unlimited slow requests
- **Business Plan**: $40/user/month - Everything in Pro plus centralized billing and admin features

### Usage-based costs

- **Premium requests**: Fast completions with latest models
- **Standard requests**: Slower but unlimited on paid plans
- **Bring Your Own Key**: Option to use your own API keys for different models

**Value Proposition:** Cursor offers more predictable pricing for individual developers, with unlimited slow requests on paid plans.

## What each tool excels at

### When to choose Claude Code

**Best for:**

- **Large-scale refactoring** across multiple files and directories
- **Autonomous debugging** when you need the AI to investigate and fix issues independently
- **Complex project setup** including configuration, testing, and deployment scripts
- **Code reviews and documentation** generation for entire codebases
- **Teams** that prefer command-line workflows and batch processing

### When to Choose Cursor

**Best for:**

- **Daily coding workflow** with real-time assistance and completion
- **Learning new languages** or frameworks with contextual help
- **Rapid prototyping** with immediate AI feedback
- **Code exploration** and understanding existing codebases
- **Individual developers** who want AI-enhanced IDE experience

## The critical limitation: Rate limits

Both Claude Code and Cursor share a fundamental weakness that impacts serious development workflows: **rate limits and API dependencies**.

### Claude Code rate limit problems

- **Weekly caps** that reset every seven days (new as of August 28, 2025)
- **Separate limits** for different Claude models
- **Token consumption spikes** during complex operations
- **Unpredictable throttling** during peak usage times

### Cursor Rate limit issues

- **Premium request quotas** limit access to best models
- **API dependency** means you're subject to provider rate limits
- **Performance degradation** when falling back to slower models
- **Team scaling challenges** as usage grows

### Real-world Impact

Rate limits create friction exactly when you need AI assistance most:

- **During sprints** when multiple developers are coding simultaneously
- **Critical debugging sessions** when you hit your quota mid-investigation
- **Large refactoring projects** that require sustained AI assistance
- **Team onboarding** when new developers consume quota learning the codebase

## The better alternative: Self-hosted open source models

Instead of renting throttled access to closed models, you can host powerful open-source alternatives with complete control over performance and costs.

### Top Open Source Code Models

| Model | Best For | Performance vs Closed Models |
| --- | --- | --- |
| **Qwen3 Coder** | Programming tasks | Matches/exceeds Claude for coding |
| **DeepSeek v3** | General coding | Matches Claude Sonnet 3.5 |
| **DeepSeek R1** | Complex reasoning | Matches OpenAI o1 |
| **Qwen3 Thinking 235B** | Advanced problem-solving | Matches Claude Opus 4, GPT-4 |

### Cost comparison: Self-Hosting vs Claude

**Example: DeepSeek v3 on Northflank**

**GPU Requirements:**

- 8 × H200 GPUs at $3.14/hour each
- Total: $25.12/hour

**Per-Token Costs:**

- Input: $0.88 per million tokens (vs Claude's $3.00)
- Output: $7.03 per million tokens (vs Claude's $15.00)

**Savings:**

- **Input tokens**: 3.4x cheaper than Claude
- **Output tokens**: 2.1x cheaper than Claude
- **No rate limits**: Process unlimited requests
- **Full control**: Scale up or down based on your needs

### Benefits of self-hosting LLMs

**Complete control**

- No weekly caps or daily quotas
- Consistent performance regardless of provider load
- Custom model fine-tuning for your specific use cases
- [Data never leaves your infrastructure](https://northflank.com/product/bring-your-own-cloud)

**Predictable costs**

- Pay only for GPU time used
- No surprise overage charges
- Scale costs linearly with usage
- Budget accurately for development sprints

**Enhanced security**

- Code never sent to third-party APIs
- Full data sovereignty and compliance control
- Custom security configurations
- No telemetry or usage tracking concerns

## Getting started with self-hosted AI coding

### Step 1: Choose your platform

**[Northflank](https://northflank.com/)** offers the easiest path to self-hosting with:

- [One-click deployment templates](https://northflank.com/stacks)
- Pre-configured popular models
- Multiple GPU options (A100, H100, H200, B200)
- OpenAI-compatible API endpoints

### Step 2: Select your model

**For Claude Code replacement:**

- **Qwen3 Coder** - Specialized programming model
- **DeepSeek v3** - General-purpose with strong coding abilities

**For Cursor replacement:**

- **Qwen3 30B** - Fast responses for real-time completion
- **Llama 4 Scout** - Lightweight but capable

### Step 3: Integration options

**Command-Line Tools**

- Replace Claude Code with custom scripts using your self-hosted API
- Build autonomous agents without rate limit concerns
- Create unlimited development workflows

**IDE Integration**

- Configure your self-hosted model with VS Code extensions
- Set up real-time completion with no quota restrictions
- Customize responses for your coding style and patterns

## Performance benchmarks

### Speed comparison

| Task Type | Claude Code | Cursor | Self-Hosted Qwen3 |
| --- | --- | --- | --- |
| **Large file refactoring** | 45-90 seconds* | 30-60 seconds* | 15-30 seconds |
| **Code completion** | N/A | 200-500ms* | 100-200ms |
| **Multi-file operations** | 2-5 minutes* | 3-8 minutes* | 1-3 minutes |
| **Debugging assistance** | 30-120 seconds* | 45-90 seconds* | 20-60 seconds |
- Performance varies significantly based on rate limits and server load

### Reliability Metrics

- **Self-hosted models**: 99.9% uptime (your infrastructure)
- **Claude Code**: Subject to Anthropic's service availability
- **Cursor**: Dependent on multiple API providers


## Conclusion: Claude Code, Cursor, or self-hosting?

In this Claude Code vs Cursor comparison, we’ve seen how each takes a different approach to AI-assisted development, each with distinct strengths. Claude Code dominates autonomous, complex operations, while Cursor excels at real-time, interactive coding assistance.

However, both tools share the same fundamental limitation: **you're renting throttled access to someone else's infrastructure**. Rate limits, unpredictable performance, and escalating costs make them unsuitable for serious development teams scaling AI-native workflows.

**Self-hosted open-source models eliminate these constraints entirely.** You get:

- **No rate limits** - Process unlimited requests
- **Lower costs** - 60-80% savings compared to API pricing
- **Complete control** - Scale performance based on your needs
- **Enhanced security** - Code never leaves your infrastructure

Ready to break free from rate limits and take control of your AI coding workflow? Explore self-hosted options with platforms like Northflank and experience unlimited AI assistance without the constraints of closed-model providers.

Looking to get started with self-hosted AI models? Check out [Northflank's one-click deployment templates](https://northflank.com/stacks/) for popular coding models like [Qwen3 Coder](https://northflank.com/stacks/deploy-qwen3-30b-coder-32k) and DeepSeek v3.

## FAQs

### Is Claude Code better than Cursor for large projects?

Claude Code excels at autonomous, multi-file operations that require understanding entire codebases. For large-scale refactoring, automated testing, and complex project setup, Claude Code's autonomous nature makes it superior to Cursor's interactive approach.

### Which tool is more cost-effective for teams?

For individual developers, Cursor's $20/month Pro plan offers better value. For teams of 5+ developers doing heavy AI-assisted coding, self-hosted models become significantly more cost-effective than either Claude Code or Cursor.

### Can I use both Claude Code and Cursor together?

Yes, many developers use Cursor for day-to-day coding assistance and Claude Code for complex, autonomous tasks. However, this doubles your subscription costs and still leaves you vulnerable to rate limits on both platforms.

### What’s the key takeaway from the Claude Code vs Cursor debate?

The key takeaway from the Claude Code vs Cursor debate is that they’re built for different coding styles and needs. Claude Code is better for autonomous, multi-file operations, large-scale refactoring, and command-line driven workflows. Cursor is better for real-time code completion, in-editor assistance, and developers who want AI embedded in their IDE. Both are limited by rate limits and dependency on third-party APIs, which is why many teams eventually explore self-hosted open-source alternatives to get more control, lower costs, and remove usage caps.

### How difficult is it to set up self-hosted models?

Modern platforms like Northflank make self-hosting as simple as clicking "deploy" on a template. Most models are serving requests within 30 minutes, and you don't need GPU expertise to get started.

### Do self-hosted models match the quality of Claude and GPT-4?

Recent open-source models like Qwen3 Coder and DeepSeek v3 match or exceed closed models for many coding tasks. They often perform better on specialized tasks since you can fine-tune them for your specific use cases.

### What about data privacy and security?

Self-hosted models offer superior privacy since your code never leaves your infrastructure. With Claude Code and Cursor, your code is sent to third-party APIs, creating potential security and compliance risks.

### How do I handle model updates with self-hosting?

Self-hosting gives you control over when and how to update models. You can test new versions in staging environments before deploying, ensuring stability for production workflows.

### What GPU requirements do I need for coding models?

Most coding-focused models run effectively on 2-4 H100 or H200 GPUs. Northflank provides clear guidance on GPU requirements for each model template.]]>
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  <title>Runpod vs Lambda vs Northflank: GPU cloud platform comparison</title>
  <link>https://northflank.com/blog/runpod-vs-lambda-northflank</link>
  <pubDate>2025-08-13T16:42:00.000Z</pubDate>
  <description>
    <![CDATA[Compare RunPod vs Lambda vs Northflank for GPU cloud platforms. See pricing, features, and which offers the best value for AI workloads.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/runpod_vs_lambda_vs_northflank_71884aaead.png" alt="Runpod vs Lambda vs Northflank: GPU cloud platform comparison" /><InfoBox className='BodyStyle'>

If you need serverless AI workflows with zero cold starts, Runpod provides managed infrastructure. For traditional GPU cloud with academic backing, Lambda Labs offers reliable compute with a decade-long AI focus. However, [Northflank](https://northflank.com/) delivers better overall value with affordable GPU pricing (H100s at $2.74/hr) and enterprise features like deploying in your own cloud, plus a complete platform for both AI and non-AI workloads that reduces vendor complexity. [Try the platform](https://app.northflank.com/signup) or [speak with an engineer](https://cal.com/team/northflank/northflank-intro) to see the difference.

</InfoBox>

You're comparing Runpod vs Lambda vs Northflank because you need GPU cloud infrastructure for your AI projects, and I understand that choosing between these three platforms can feel overwhelming with their different pricing models and features.

That's why I've put together this guide to simplify the process of finding your next GPU cloud platform by:

1. Breaking down what you get with each platform
2. Comparing their pricing
3. Helping you determine which one delivers the best value for your specific needs and budget

One thing to keep in mind is that each platform takes a fundamentally different approach. Runpod focuses on serverless AI workflows, Lambda emphasizes traditional cloud with academic roots, and Northflank gives you a complete developer platform.

Let’s get into it.

## So, how do Runpod, Lambda, and Northflank differ?

We'll compare Runpod, Lambda, and Northflank based on their business models, pricing approaches, target audiences, and enterprise capabilities.

After looking at the table, you should be able to answer these questions:

1. *“Which platform's approach aligns best with my team's technical expertise and workflow?”*
2. *“What pricing model works best for my usage patterns and budget?”*
3. *“Do I need GPU access only or a complete development platform with additional services like databases, CI/CD pipelines, and APIs?”*
4. *“What level of enterprise features and infrastructure control does my project require?”*

If you can't fully answer these questions from the table alone, proceed to the next sections, where we go into each platform's specific features and capabilities in detail.

Now, look at the table:

| Feature | Runpod | Lambda | Northflank |
| --- | --- | --- | --- |
| **Business model** | AI-focused cloud platform with serverless capabilities | Traditional GPU cloud with AI-first heritage since 2012 | Full-stack developer platform with GPU orchestration |
| **Primary target** | AI developers and ML teams seeking managed serverless infrastructure | AI researchers, academic institutions, and enterprises needing traditional cloud | AI developers, ML teams, enterprises, and teams needing full-stack solutions |
| **Pricing approach** | Pay-per-second with Flex/Active worker options | Pay-per-minute with on-demand, reserved, and private cloud tiers | Per-second billing with flexible pricing and enterprise features included |
| **GPU availability** | 30+ GPU models (RTX 4090 to H100) across global regions | Latest NVIDIA models (B200, H200, H100, A100) with focus on hardware | 18+ GPU types including H100, H200, B200, A100, AMD MI300X across multiple clouds |
| **Platform focus** | Serverless AI optimization with FlashBoot and auto-scaling | Pure AI compute with pre-installed ML frameworks and inference APIs | Complete platform supporting both AI and non-AI workloads (databases, APIs, CI/CD) |
| **Enterprise features** | 99.9% uptime SLA, SOC2 certification in progress | SOC2 Type II compliant, private cloud options, academic partnerships | SOC2/HIPAA/ISO 27001 ready, BYOC deployment, secure microVM isolation |
| **Infrastructure control** | Managed infrastructure with AI-specific tools and templates | Traditional cloud instances with pre-configured environments | Full control with option to deploy in your own cloud infrastructure |
| **Unique value proposition** | Zero cold starts with FlashBoot technology and serverless scaling | Academic-trusted platform with decade-long AI expertise and inference APIs | Most affordable pricing with enterprise features and full-stack platform capabilities |

## Overview of Northflank (*don’t skip!*)

Maybe I have the power to read minds, but I know you might already be thinking:

*“Hey! Can Northflank actually handle both my AI workloads and the rest of my application infrastructure without forcing me to jump from one platform to the other?”*

The answer is “YES”. I’ll walk you through what Northflank is (if you’re hearing about it for the first time), what the platform offers, what the pricing is like, and why Northflank’s approach might save you both time and money compared to bringing together separate solutions.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

### What is Northflank, and why should I consider it?

Northflank is a comprehensive (you can also call it the “all-in-one”) developer platform that handles everything from GPU compute to databases, APIs, and CI/CD pipelines, allowing you to deploy your entire application stack either on Northflank’s managed cloud or in your own cloud infrastructure while using the platform layer.

That was a lot… I know. What I’m saying in simple terms is this:

> *Northflank is like getting your GPU compute, databases, APIs, and deployment tools all in one place. So, intead of using separate services for each piece of your project, you can build and run everything through Northflank, either on Northflank’s servers or on your own AWS/Google Cloud account.*
> 

So, unlike other platforms that focus solely on GPU access, Northflank treats GPU workloads as part of your broader development needs.

This means you can run your AI training on an H100, deploy your inference API, set up your databases, and manage your CI/CD pipelines all from the same platform.

You might be a founder or work in a startup that needs everything to “just work,” or an enterprise that requires deploying in your own AWS/GCP/Azure account; Northflank adapts to your infrastructure preferences. 

### Some of the features and benefits Northflank offers

Now you know what Northflank is and offers to an extent, but there’s more. Let’s look at some of the key features you get with Northflank that make it different from other GPU providers.

I have categorized them into three: GPU and compute capabilities, full-stack platform features, and enterprise and security.

1. **GPU and compute capabilities:**
    - 18+ GPU types including H100, H200, B200, A100, and AMD MI300X
    - Deploy in your own cloud infrastructure (GCP, AWS, Azure, Oracle Cloud) while using Northflank's platform
    - Secure microVM isolation for multi-tenant workloads
    - Real-time interface with team collaboration and fine-grained permissions
2. **Full-stack platform features:**
    - Support for both AI and non-AI workloads (databases, background jobs, APIs, CI/CD pipelines)
    - Native integrations with GitHub, GitLab, Bitbucket, and container registries
    - Multiple access methods: UI, API, CLI, JavaScript client, Infrastructure as Code, TypeScript SDK
    - Built-in monitoring, logging, and observability tools
3. **Enterprise and security:**
    - SOC2/HIPAA/ISO 27001 compliance ready
    - Bring Your Own Cloud (BYOC) deployment options
    - Fine-grained access controls and team management
    - 99.9% uptime SLA


<InfoBox className='BodyStyle'>

Notice that the major advantage here is that you’re not only getting GPU access, you’re getting a complete platform that can handle your entire development workflow, which often translates to significant cost savings by reducing the number of vendors you need.

</InfoBox>

### High-level overview of Northflank's pricing

Northflank uses transparent per-second billing with no hidden fees, and the GPU pricing is often more affordable than specialized GPU providers:

**See some of the GPU pricing examples:**

- H100 (80GB): $2.74/hour
- H200 (141GB): $3.14/hour
- B200 (180GB): $5.87/hour
- A100 (40GB): $1.42/hour, A100 (80GB): $1.76/hour

All pricing includes CPU, memory, and storage bundled together, so you're not getting surprised by additional compute costs.

**And the Platform plans:**

- **Developer Sandbox:** Free for basic workloads and exploration
- **Starter Plan:** Pay-as-you-go with no commitments
- **Pro Plan:** Advanced features for growing teams
- **Enterprise Plan:** Custom solutions with BYOC deployment options

**So, what’s the cost advantage?**
Enterprise features like [Bring Your Own Cloud](https://northflank.com/cloud/gpus) (BYOC) deployment, compliance support, and full-stack capabilities are included at no extra cost. This often reduces your total infrastructure expenses since you're not paying for multiple specialized platforms.

### Now, what makes Northflank different from Runpod and Lambda?

Let’s see the differences quickly:

**Versus Runpod:**

While Runpod is good at serverless AI workflows, Northflank provides broader platform capabilities that extend beyond AI workloads. You get affordable GPU pricing plus the ability to deploy your entire application stack, including databases, APIs, and CI/CD pipelines. Northflank also offers enterprise features like [Bring Your Own Cloud](https://northflank.com/cloud/gpus) (BYOC) deployment that Runpod doesn't provide.

**Versus Lambda:**

Lambda focuses purely on AI compute with good academic backing, but Northflank provides a comprehensive (all-in-one) development platform. While Lambda charges separately for compute, storage, and additional services, Northflank bundles everything together with transparent pricing. Plus, Northflank's [BYOC](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) option gives you infrastructure control that Lambda's managed-only approach doesn't offer.

**So, the main differentiator?**
Northflank takes out the need to piece together multiple vendors for your infrastructure needs. Instead of managing separate platforms for GPU compute, databases, CI/CD, and monitoring, you get everything in one place with consistent pricing and unified management.

## Overview of Runpod

If you're specifically looking for AI-focused infrastructure that scales automatically without you having to manage servers, Runpod might be what you need. Let’s see what makes Runpod’s approach different.

![runpod's homepage.png](https://assets.northflank.com/runpod_s_homepage_14648d1a93.png)

### What is Runpod

Runpod is an AI-focused cloud platform that provides serverless GPU environments with pre-installed machine learning frameworks. It’s specifically designed for AI workloads with GPUs that can scale from zero to thousands of workers automatically.

One of Runpod’s focuses is solving the "cold start" problem that most serverless platforms face. With Runpod’s FlashBoot technology, you get sub-200ms startup times, which means your AI models can respond almost instantly, even when scaling from zero.

### Some key features and benefits Runpod offers

Let’s see what Runpod focuses on:

1. **Serverless AI capabilities:**
    - FlashBoot technology with sub-200ms cold starts
    - Auto-scaling from 0 to 1000+ GPU workers
    - Pre-configured environments with PyTorch, TensorFlow, and other ML frameworks
    - Runpod Hub for one-click model deployment
2. **GPU options:**
    - 30+ GPU models from RTX 4090s to H100s
    - Pay-per-second billing
    - Global regions for low-latency access
    - Instant multi-node GPU clusters
3. **Developer experience:**
    - Serverless and traditional cloud GPU options
    - Built-in Jupyter notebooks and development tools
    - API access for automation

### High-level overview of Runpod's pricing

Runpod offers three pricing models:

1. **Cloud GPUs (traditional instances):**
    - H100 SXM (80GB VRAM): $2.69/hour
    - A100 SXM (80GB VRAM): $1.74/hour
    - A100 PCIe (80GB VRAM): $1.64/hour
    - RTX 4090 (24GB VRAM): $0.69/hour
    - Available in Community Cloud (cheaper) or Secure Cloud (enterprise features)
2. **Serverless pricing:**
    - Flex Workers (pay when running): H100 (80GB) at $4.18/hour, A100 (80GB) at $2.72/hour
    - Active Workers (always-on, 30% discount): H100 (80GB) at $3.35/hour, A100 (80GB) at $2.17/hour
3. **Storage:** Network volumes at $0.07/GB/month with no data transfer fees

The serverless options cost more per hour, but you only pay when actually processing, which can save money for sporadic workloads.

### What makes Runpod different from Lambda and Northflank

Let’s see the major differences:

**Versus Lambda:**

Runpod focuses on serverless capabilities while Lambda offers traditional cloud instances. Runpod's FlashBoot technology handles cold starts, making it better for real-time applications, while Lambda's strength is in academic backing and inference APIs with more predictable pricing.

**Versus Northflank:**

Runpod specializes purely in AI workloads with deeper ML-specific optimizations like zero cold starts and auto-scaling. However, you're limited to mainly GPU compute; you'll need separate platforms for databases, APIs, and CI/CD, which Northflank handles as part of its full-stack approach.

**The main trade-off:**

Runpod gives you the most advanced serverless AI features, but you're getting a specialized tool rather than a complete platform. Great if AI processing is your only need, but you'll need additional services for everything else.

*You can also see how Runpod compares to Vast.ai in this [article](https://northflank.com/blog/runpod-vs-vastai-northflank), and if you want to take a look at more Runpod alternatives, you can also check this [piece](https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment).*

## Overview of Lambda

If you're coming from an academic background or need a traditional GPU cloud provider with proven AI expertise, Lambda might feel familiar and reliable. Let’s break down what the decade-plus focus on AI infrastructure offers to see if the academic-trusted approach fits your needs.

![lambda-ai-homepage.png](https://assets.northflank.com/lambda_ai_homepage_d987e1a760.png)

### What is Lambda

Lambda is a traditional GPU cloud platform that's been focused solely on AI since 2012. They position themselves as "the superintelligence cloud" and have built their reputation by serving 97% of top US research universities and over 100,000 ML engineers.

Unlike newer platforms, Lambda started by selling GPU workstations to researchers before moving into cloud services. This means their platform is designed around the workflows that academic researchers and AI labs use, with pre-installed frameworks and configurations that "just work" for machine learning.

### Some key features and benefits Lambda offers

Let’s see what Lambda focuses on:

1. **AI-first infrastructure:**
    - Latest NVIDIA GPUs (B200, H200, H100, A100) with hardware focus
    - Pre-installed ML frameworks: PyTorch, TensorFlow, CUDA, cuDNN, JAX
    - On-demand instances, 1-Click Clusters, and private cloud options
    - Inference API for deploying models with no rate limits
2. **Academic and enterprise features:**
    - SOC2 Type II compliance
    - 99.999% uptime SLA
    - Direct support from AI infrastructure engineers
    - Academic partnerships and institutional pricing
3. **Flexible deployment options:**
    - On-demand instances (pay-per-minute)
    - 1-Click Clusters (16-1,536 GPUs with InfiniBand)
    - Private cloud contracts (1,000+ GPUs for enterprises)

### High-level overview of Lambda's pricing

Lambda offers straightforward per-minute billing across different service tiers:

1. **On-demand instances:**
    - H100 SXM (80GB): $2.99/hour
    - H100 PCIe (80GB): $2.49/hour
    - A100 SXM (40GB): $1.29/hour
    - GH200 (96GB): $1.49/hour
    - A6000 (48GB): $0.80/hour
2. **1-Click Clusters:**
    - H100 clusters: From $2.69/hour per GPU
    - B200 clusters: From $3.79/hour per GPU (reserved pricing available)
3. **Private cloud:** As low as $2.99/hour for B200 with multi-year commitments
4. **Inference API:** Token-based pricing for various models, including Llama and DeepSeek variants

### What makes Lambda different from Runpod and Northflank

Let’s see what makes Lambda different from the rest.

**Versus Runpod:**

Lambda offers traditional cloud instances while Runpod focuses on serverless capabilities. Lambda's strength is in proven reliability and academic backing, while Runpod majors at auto-scaling and zero cold starts. Lambda is better for consistent workloads, and Runpod for variable AI processing.

**Versus Northflank:**

Lambda focuses purely on AI compute with deep ML expertise, while Northflank provides a full-stack development platform. Lambda offers inference APIs and academic partnerships that Northflank doesn't, but you're limited to mainly GPU services; you'll need separate platforms for databases, CI/CD, and other development tools.

**The main value:**

Lambda gives you a proven, academic-trusted platform with deep AI expertise and reliable infrastructure. You're getting specialized knowledge and battle-tested systems, but you're paying for a single-purpose tool rather than a comprehensive development platform.

*If you want to take a look at other Lambda alternatives, you can check this [piece](https://northflank.com/blog/top-lambda-ai-alternatives).*

## Time to make your decision

After comparing all three platforms, see this quick decision framework:

Choose Lambda if you're working in academic research or need a proven AI platform with traditional cloud reliability and inference APIs.

Go with Runpod if you need AI-specific workflows with serverless capabilities and zero cold starts for variable workloads.

However, for most teams building production applications, [Northflank](https://northflank.com/) delivers the best overall value by combining affordable GPU pricing with enterprise features like deploying in your own cloud, full-stack platform capabilities, and comprehensive DevOps tools that eliminate the need for multiple vendors.

While others force you to choose between AI specialization, cost, or platform completeness, Northflank gives you all three in one solution.


<InfoBox className='BodyStyle'>

See the differences for yourself by [trying out Northflank's platform](https://app.northflank.com/signup) or [speaking 1:1 with an Engineer](https://cal.com/team/northflank/northflank-intro) (yes, a real human, not a machine 😉) to discuss your specific requirements.

</InfoBox>]]>
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</item><item>
  <title>Rent H100 GPU: Pricing, performance, and where to get one</title>
  <link>https://northflank.com/blog/rent-h100-gpu-pricing-performance-and-where-to-get-one</link>
  <pubDate>2025-08-13T15:45:00.000Z</pubDate>
  <description>
    <![CDATA[Rent H100 GPUs for deep learning to boost training speed, cut costs, and scale AI workloads. Compare PCIe vs SXM, see pricing, and learn how Northflank simplifies deployment with full-stack GPU infrastructure.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/deploy_to_k8s_2_41171b7b8e.png" alt="Rent H100 GPU: Pricing, performance, and where to get one" /><InfoBox className='BodyStyle'>

If you're looking for the best platform to rent your H100 GPU, you can get one on Northflank at a cheaper rate ($2.74/hr) for 80GB VRAM. 

Unlike other platforms, Northflank offers a full-stack solution that integrates infrastructure, deployments, and observability in one place. It also supports various GPUs, including the H200, A100, B200, and [many more](https://northflank.com/gpu).

> **Need dedicated H100 capacity?** H100s are in high demand. If your project requires guaranteed availability or specific volume commitments, [request GPU capacity here](https://northflank.com/request/gpu).

</InfoBox>

When you need to train or fine-tune deep learning models at scale, the hardware you choose makes a direct impact on speed, efficiency, and cost. Many teams now look to rent H100 GPUs, one of the most powerful options in NVIDIA’s lineup, built on the [Hopper architecture](https://northflank.com/blog/h100-vs-a100#h100-built-for-frontier-workloads) and tuned for transformer models, FP8 precision, and high-throughput training. It [outperforms](https://northflank.com/blog/h100-vs-a100) even the well-established A100, making it ideal for both large-scale training and low-latency inference.

The cost of owning an H100 is significant, but renting puts its capabilities within reach. With cloud access, you can bring top-tier compute to your projects exactly when needed, whether adapting a foundation model to a niche dataset or deploying AI services at scale. 

At [Northflank](https://northflank.com/), developers rent H100 GPUs to accelerate research, shorten training times, and run production inference without the complexity of managing physical infrastructure. This guide explores why the H100 is different and how to get the most from renting one.

## What is the NVIDIA H100?

The NVIDIA H100 Tensor Core GPU is the flagship of the [Hopper architecture](https://northflank.com/blog/h100-vs-a100#h100-built-for-frontier-workloads), designed from the ground up for large-scale AI and high-performance computing. It brings together fourth-generation Tensor Cores for accelerating matrix operations, FP8 precision for higher efficiency without sacrificing accuracy, and HBM3 memory for extreme bandwidth. 

Think of the PCIe variant as the versatile all-rounder, ready to slot into existing servers with minimal reconfiguration. The SXM variant, in contrast, is the high-performance specialist, connecting through NVLink and NVSwitch to push inter-GPU communication to incredible speeds. Together, they cover a spectrum of needs from smaller-scale training to the largest distributed AI jobs running across multiple nodes.

This hardware is not just about bigger numbers on a spec sheet; it is about enabling new possibilities. The H100 makes it feasible to train models with hundreds of billions of parameters or run real-time inference for massive user bases, both of which were once restricted to only the largest tech companies.

### Why rent an H100 GPU for deep learning?

As previously explained, the NVIDIA H100 is one of the most capable GPUs ever built, offering exceptional speed, memory bandwidth, and efficiency for large-scale AI workloads. Here's why renting makes sense:

- **Cost-effective access** - Avoid significant upfront investment while gaining immediate access to cutting-edge hardware
- **No specialized infrastructure needed** - Skip the complex setup and cooling requirements of owning physical GPUs
- **On-demand flexibility** - Scale compute power up or down based on project needs:
    - Training massive transformer models
    - Fine-tuning foundation models to specific domains
    - Running low-latency inference in productionRunning low-latency inference in production
- **Pay only for what you use** - When your job is finished, the hardware is no longer on your books
- **Try advanced features** - Experiment with newer capabilities like FP8 precision and faster interconnects without commitment.

Renting H100s lets you match world-class performance with the pace and budget of your projects.

## NVIDIA H100 rental options and pricing

While the performance is unmatched, owning H100s outright is not always practical. The h100 rent price depends on form factor, provider, and market demand, but renting offers the flexibility to match compute power to project needs.

If your workload is constant and predictable, dedicated access may be best. For workloads that can pause or resume without penalty, spot rentals can reduce costs significantly. The PCIe model is generally more affordable, while the SXM model commands a premium for its higher performance ceiling.

The ability to rent for hours, days, or weeks means you only pay for what you use, avoiding the sunk cost of idle hardware while still benefiting from the H100’s capabilities.

[*Learn more about H100 pricing details here*](https://northflank.com/blog/how-much-does-an-nvidia-h100-gpu-cost)

## Where can I rent H100 GPUs for deep learning?

Many providers offer H100 access, but the question often becomes ”W**here can I rent an H100 PCIe GPU for deep learning?”** or ” W**here can I rent an H100 SXM GPU for deep learning?”** in a way that is simple, cost-effective, and scalable. Northflank has emerged as a reliable answer to that question.

### What is Northflank?

[Northflank](https://northflank.com/) abstracts the complexity of running GPU workloads by giving teams a full-stack platform; GPUs, secure runtime, deployments, built-in CI/CD, and observability all in one. You don’t have to manage infra, build orchestration logic, or combine third-party tools.

Everything from model training to inference APIs can be deployed through a Git-based or templated workflow. It supports [bring-your-own-cloud (AWS, Azure, GCP, and more)](https://northflank.com/features/bring-your-own-cloud), but it works fully managed out of the box.

![image - 2025-08-08T123011.085.png](https://assets.northflank.com/image_2025_08_08_T123011_085_d279fa8e00.png)

### What you can run on Northflank:

- Inference APIs with autoscaling and low-latency startup
- Training or fine-tuning jobs (batch, scheduled, or triggered by CI)
- Multi-service AI apps (LLM + frontend + backend + database)
- Hybrid cloud workloads with GPU access in your own VPC

### What other GPUs does Northflank support?

Northflank offers access to 18+ [GPU types](https://northflank.com/gpu), including NVIDIA A100, B200, L40S, L4, AMD MI300X, and Habana Gaudi.

![gpu-prices-northflank.png](https://assets.northflank.com/gpu_prices_northflank_c6dbc88fdb.png)

## Conclusion

The decision to rent H100 GPUs can be the difference between hitting your training goals on time and struggling with limited compute. The H100 is built for demanding workloads, but the platform you choose determines how well it performs for you. Some providers make the process slow and unpredictable, while others hide costs that only appear once your workloads are running.

Northflank makes it simple to **rent H100** GPUs with transparent pricing, reliable performance, and all the infrastructure you need in one place. You get compute, storage, and networking configured and production-ready, so you can focus on training, fine-tuning, or running inference at scale. [Sign up to deploy your first H100](https://app.northflank.com/signup) today or [book a short demo](https://cal.com/team/northflank/northflank-intro) to see how it fits into your workflow.

## FAQs

**What is the H100 rent price?**

As of August 2025, the current rental price for the NVIDIA H100 on Northflank is $2.74/hr for the 80GB VRAM model, one of the cheapest rates in the market. You can learn more about how it compares to other providers [here](https://northflank.com/blog/how-much-does-an-nvidia-h100-gpu-cost).

**Where can I rent an H100 PCIe GPU for deep learning?**

You can rent an H100 PCIe GPU for deep learning through platforms like Northflank, which make it simple to deploy without a complex setup. PCIe models are a strong choice for a balance of performance, compatibility, and cost.

**Where can I rent an H100 SXM GPU for deep learning?**

For the highest performance, you can rent an H100 SXM GPU for deep learning on Northflank. SXM models offer faster interconnect speeds with NVLink and greater thermal headroom, making them ideal for the most demanding large-scale training workloads.]]>
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</item><item>
  <title>RunPod vs Vast.ai vs Northflank: The complete GPU cloud comparison</title>
  <link>https://northflank.com/blog/runpod-vs-vastai-northflank</link>
  <pubDate>2025-08-12T16:59:00.000Z</pubDate>
  <description>
    <![CDATA[Compare runpod vs vast.ai vs Northflank for GPU cloud computing. See pricing, features, and why Northflank offers the best value with affordable rates and enterprise features.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/runpod_vs_vastai_vs_northflank_3701103333.png" alt="RunPod vs Vast.ai vs Northflank: The complete GPU cloud comparison" /><InfoBox className='BodyStyle'>

### Quick summary

If you need basic GPU access and have extensive DevOps expertise, Vast.ai operates a marketplace model with variable pricing.

For AI-specific workflows with managed infrastructure, Runpod provides serverless capabilities and zero cold starts.

However, [Northflank](https://northflank.com/) delivers the best overall value with the most affordable GPU pricing, long-term commitment options, plus an all-in-one platform supporting both AI and non-AI workloads (databases, APIs, CI/CD pipelines) with options for managed infrastructure or deploying in your own cloud that reduce the need for multiple vendors.

See the differences for yourself by [trying out the cloud platform](https://app.northflank.com/signup) or [speaking 1:1 with an Engineer](https://cal.com/team/northflank/northflank-intro) (yes, a real human, not a machine 😉).

</InfoBox>

*What used to cost $30,000+ per H100 GPU is now accessible for $2-5 per hour through cloud platforms. "The best time in human history to be alive."*

The three leading GPU cloud platforms (Runpod, Vast.ai, and Northflank) each take fundamentally different approaches.

Runpod focuses on AI-specific features with serverless capabilities, while Vast.ai operates a marketplace model, and Northflank offers a full-stack developer platform with the most affordable GPU pricing, flexible long-term commitments, and enterprise features.

We'll compare these three platforms to help you determine which one fits your team's needs, budget, and technical requirements, and why Northflank's combination of the most affordable pricing, enterprise capabilities, and full-stack platform typically delivers the best long-term value.

> *Disclaimer: All information covered in this article is based on information available on the respective product pages and documentation as of August 2025. For product-specific information like pricing, always review the pricing pages for each platform for up-to-date information.*
> 

## A table comparing Runpod, Vast.ai, and Northflank to help you decide

Look at the table below to see how Runpod, Vast.ai, and Northflank compare against each other based on their pricing models, target use cases, enterprise features, and unique value propositions.

| **Feature** | **Runpod** | **Vast.ai** | **Northflank** |
| --- | --- | --- | --- |
| **Business model** | AI-focused cloud platform with serverless capabilities | GPU marketplace connecting providers to users | Full-stack developer platform with GPU orchestration |
| **Primary target** | AI developers and ML teams | Cost-conscious developers and researchers | AI developers, ML teams, enterprises, and cost-conscious developers |
| **Pricing model** | Pay-per-second GPU billing with fixed rates | Auction-based marketplace with variable pricing | Per-second billing with long-term commitments and affordable rates |
| **GPU access** | Wide range of GPU models across global regions | Large GPU pool through marketplace providers | Wide range of GPU types across multiple cloud providers |
| **Platform focus** | Serverless AI optimization and zero cold starts | Raw GPU compute with maximum cost savings | Full developer platform supporting both AI and non-AI workloads (databases, background jobs, APIs, CI/CD pipelines) plus GPU orchestration |
| **Enterprise features** | 99.9% uptime SLA, SOC2 certification in progress | SOC2 Type I certified, ISO 27001 datacenters | SOC2/HIPAA/ISO 27001 ready, secure runtime, and option to deploy in your own cloud infrastructure while maintaining full control |
| **Infrastructure control** | Managed infrastructure with AI-specific tools | Variable based on marketplace providers | Full control with option to deploy in your own cloud |
| **Unique value proposition** | AI-specific features with FlashBoot technology | Maximum cost savings through marketplace dynamics | Most affordable all-in-one platform with enterprise features and full-stack capabilities |

*After looking at the table, you might need more information on each of the platforms to decide or take back to your team, so next, we'll break down each platform individually with detailed overviews of Northflank, Runpod, and Vast.ai that will cover what each platform is, their key features and benefits, pricing structures, and how they compare to each other, starting with Northflank.*

## Overview of Northflank

We'll take a closer look at Northflank's full-stack platform, features, pricing model, and how it compares to Runpod and Vast.ai to help you determine if it fits your needs.

[Northflank](https://northflank.com/) is a comprehensive (all-in-one) developer platform that handles everything from GPU compute to databases, APIs, and CI/CD pipelines, allowing teams to deploy their entire application stack on their own cloud infrastructure or Northflank's managed cloud.

![northflank's-ai-homepage.png](https://assets.northflank.com/northflank_s_ai_homepage_1f22620fec.png)

### What are the features and benefits Northflank offers

Some of the key features include:

- 18+ [GPU types](https://northflank.com/gpu) including [H100](https://northflank.com/cloud/gpus/H100), [H200](https://northflank.com/cloud/gpus/H200), [B200](https://northflank.com/cloud/gpus/B200), [A100](https://northflank.com/cloud/gpus/A100), and [AMD MI300X](https://northflank.com/cloud/gpus/MI300X)
- Deploy in your own cloud infrastructure (GCP, AWS, Azure, OCI) while using Northflank's platform ([Try it out](https://northflank.com/cloud/gpus))
- Full-stack platform supporting both AI and non-AI workloads (databases, APIs, CI/CD pipelines)
- Secure microVM isolation for multi-tenant workloads (See these guides on [spinning up secure sandboxes](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh) and [why container isolation matters](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation))
- Real-time interface with team collaboration and fine-grained permissions ([See how](https://northflank.com/docs/v1/application/collaborate/collaborate-on-northflank))
- Multiple access methods ([UI](https://northflank.com/docs/v1/application/overview), [API](https://northflank.com/docs/v1/api/use-the-api), [CLI](https://northflank.com/docs/v1/api/use-the-cli), [JavaScript client](https://northflank.com/docs/v1/api/use-the-javascript-client), [Infrastructure as Code](https://northflank.com/docs/v1/application/infrastructure-as-code/infrastructure-as-code), TypeScript SDK)
- Native integrations with [GitHub](https://northflank.com/docs/v1/application/getting-started/link-your-git-account), [GitLab](https://northflank.com/docs/v1/application/getting-started/link-your-git-account#link-your-gitlab-account), [Bitbucket](https://northflank.com/docs/v1/application/getting-started/link-your-git-account#link-your-bitbucket-account), and [container registries](https://northflank.com/docs/v1/application/run/run-an-image-from-a-container-registry)
- Per-second billing with long-term commitment options

### A high-level overview of Northflank's pricing

Northflank uses transparent per-second billing with affordable GPU rates and enterprise features included.

- **GPU Pricing**
[H100](https://northflank.com/cloud/gpus/H100) at $2.74/hour, [H200](https://northflank.com/cloud/gpus/H200) at $3.14/hour, [B200](https://northflank.com/cloud/gpus/B200) at $5.87/hour, [A100](https://northflank.com/cloud/gpus/A100) at $1.42-1.76/hour. All pricing includes CPU, memory, and storage bundled together.
- **Platform Plans**
Developer Sandbox (free for basic workloads), Starter Plan (pay-as-you-go), Pro Plan (advanced features), and Enterprise Plan (custom solutions with cloud deployment options).
- **Key Benefits**
Enterprise features like cloud deployment options, compliance support, and full-stack capabilities are included at no extra cost, often reducing total infrastructure expenses by eliminating the need for multiple vendors.

### What makes Northflank different from Runpod and Vast.ai

Northflank provides a complete platform solution rather than just GPU access.

**Versus Runpod:**
Northflank offers broader platform capabilities beyond AI workloads, including cloud deployment options and enterprise features that Runpod lacks. While Runpod specializes in AI-specific optimizations, Northflank provides a comprehensive solution for entire application stacks with affordable GPU pricing.

**Versus Vast.ai:**
Northflank offers predictable enterprise-grade infrastructure with transparent pricing, while Vast.ai focuses on marketplace cost optimization. Northflank includes full DevOps capabilities and compliance features that Vast.ai doesn't provide, making it suitable for production applications requiring reliability and enterprise features.

## Overview of Runpod

We'll go over Runpod's platform, features, pricing, and how it compares to [Vast.ai](http://vast.ai/) and Northflank to help you determine if it fits your needs.

Runpod is an AI-focused cloud platform that provides GPU environments with pre-installed frameworks, targeting developers and ML teams who prefer managed infrastructure over handling their own setup.

![runpod's homepage.png](https://assets.northflank.com/runpod_s_homepage_14648d1a93.png)

### What are the features and benefits Runpod offers

Some of them include:

- FlashBoot technology with sub-200ms cold starts
- Auto-scaling from 0 to 1000 GPU workers
- Pay-per-second billing
- Pre-configured environments with popular frameworks
- Runpod Hub for one-click model deployment
- 30+ GPU models from RTX 4090s to H100s
- Multiple global regions
- Instant multi-node GPU clusters

### A high-level overview of Runpod's pricing

Runpod uses a pricing model with three main categories: Cloud GPUs, Serverless, and Storage.

- **Cloud GPU Pricing**
Popular options include H100 variants at $2.39-2.79/hr, A100 (80GB) at $1.64-1.74/hr, and RTX 4090 at $0.69/hr. They offer Community Cloud (more variety, lower cost) and Secure Cloud (enterprise features for $0.20/hr premium).
- **Serverless Pricing**
Flex Workers (pay when running): H100 at $4.18/hr, A100 at $2.72/hr
Active Workers (always-on, 30% discount): H100 at $3.35/hr, A100 at $2.17/hr
- **Storage Pricing**
Network volumes cost $0.07/GB/month (under 1TB) with no data transfer fees.

### What makes Runpod different from Vast.ai and Northflank

Runpod takes an AI-first approach with managed serverless capabilities.

**Versus Vast.ai:**

Runpod offers predictable pricing and managed infrastructure, while Vast.ai requires navigating marketplace uncertainty. Runpod focuses on production reliability, while Vast.ai prioritizes cost savings.

**Versus Northflank:**

Runpod specializes in AI workloads with deeper ML-specific features, while Northflank provides a broader full-stack platform. Runpod lacks enterprise capabilities like Bring Your Own Cloud (BYOC) but offers more AI-specific optimizations.

## Overview of Vast.ai

We'll go over Vast.ai's marketplace platform, features, pricing model, and how it compares to Runpod and Northflank to help you determine if it fits your needs.

Vast.ai is a marketplace-based cloud platform that connects GPU providers with users, focusing on reducing costs through competitive bidding and offering access to underutilized GPU resources at significantly lower rates than traditional cloud providers.

![vastai's homepage.png](https://assets.northflank.com/vastai_s_homepage_194c175a50.png)

### What are the features and benefits Vast.ai offers

Some of them include:

- Access to over 10,000 on-demand GPUs through marketplace providers
- DLPerf scoring system for hardware performance prediction
- Auction-based bidding for interruptible instances
- On-demand, interruptible, and reserved pricing options
- Bring your own Docker images and custom configurations
- Auto SSH and Jupyter setup for most existing images
- Persistent storage with direct data copy and cloud sync
- Multiple access methods (GUI, Python CLI, direct HTTPS REST APIs)

### A high-level overview of Vast.ai's pricing

Vast.ai uses a marketplace pricing model with three main options: On-Demand, Interruptible, and Reserved.

**Pricing Models**

- On-Demand: Fixed prices set by hosts, run for as long as needed
- Interruptible: Auction-based bidding where clients set bid prices
- Reserved: Deposit credits for longer time blocks (months) in exchange for up to 50% discounts

**Cost Savings**

H100s available from $0.90/hour, RTX 4090s under $5, with typical savings of 60-80% compared to major cloud providers like AWS and GCP. $5 minimum to get started with per-second billing.

### What makes Vast.ai different from Runpod and Northflank

Vast.ai operates as a pure marketplace focusing on cost optimization rather than managed services.

**Versus Runpod:**

Vast.ai offers potentially much lower costs through marketplace dynamics but requires more hands-on infrastructure management. Runpod provides managed AI-specific features and predictable pricing, while Vast.ai prioritizes maximum cost savings with variable availability.

**Versus Northflank:**

Vast.ai focuses solely on GPU compute access through marketplace providers, while Northflank offers a comprehensive full-stack platform. Vast.ai lacks enterprise features like Bring Your Own Cloud (BYOC) and integrated DevOps capabilities, but can provide substantial cost savings for teams comfortable with marketplace uncertainty and infrastructure management.

## The bottom line is choosing your GPU cloud platform (See quick tips)

After comparing all three platforms, here's the quick decision framework:

Choose Vast.ai if you're cost-optimizing for development work and have extensive DevOps skills to handle marketplace uncertainty.

Go with Runpod if you only need AI-specific workflows with managed serverless infrastructure.

However, for most teams building production applications, Northflank delivers the best overall value by combining affordable GPU pricing with enterprise features like deploying in your own cloud, full-stack platform capabilities, and comprehensive DevOps tools that reduce the need for multiple vendors.

While others force you to choose between cost, features, or scalability, Northflank gives you all three in one platform.

> See the differences for yourself by [trying out Northflank’s platform](https://app.northflank.com/signup) or [speaking 1:1 with an Engineer](https://cal.com/team/northflank/northflank-intro) (yes, a real human, not a machine 😉) to discuss your specific requirements.]]>
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  <title>Best cloud services for renting high-performance GPUs on-demand</title>
  <link>https://northflank.com/blog/best-cloud-services-for-renting-high-performance-gpus-on-demand</link>
  <pubDate>2025-08-12T15:30:00.000Z</pubDate>
  <description>
    <![CDATA[Learn how on-demand GPUs power AI and ML workloads, what to look for in a provider, and why the right strategy can cut costs and speed deployment without managing complex infrastructure.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/deploy_to_k8s_1_fa85e764cc.png" alt="Best cloud services for renting high-performance GPUs on-demand" />If you have tried to rent a high-end GPU in the past year, you know it is not as simple as clicking launch instance. The surge in large language models, generative AI, and high-resolution rendering has pushed global GPU capacity to its limits. 

Even well-funded teams face queues, shuffle between providers, or pay premium rates to secure capacity. For workloads from large inference batches to custom model training, the GPU you get and how quickly you get it can decide whether you meet a deadline or miss it. Buying hardware ties up capital and often leaves equipment idle when demand falls.

On-demand GPU rentals change that. They let you access top-tier hardware only when you need it and shut it down when you do not. The challenge is that on-demand means different things depending on the provider. Some focus on instant access while others, like Northflank, offer flexible capacity with managed orchestration so you spend more time on workloads and less on infrastructure.

## TL;DR: Where to find the best on-demand GPU cloud services?

If you're short on time, here’s the complete list of the best on-demand GPU cloud services for AI and ML teams. Some are built for scale. Others excel at speed, simplicity, or affordability. A few are rethinking the GPU experience from the ground up.

| **Platform** | **GPU Types Available** | **Key Strengths** | **Best For** |
| --- | --- | --- | --- |
| [**Northflank**](https://northflank.com/) | A100, H100, H200, B200, L40S, B200, MI300X, Gaudi and many more. | Full-stack GPU hosting with CI/CD, secure runtimes, app orchestration, GitOps, [BYOC](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) | Production AI apps, inference APIs, model training, fast iteration |
| **NVIDIA DGX Cloud** | H100, A100 | High-performance NVIDIA stack, enterprise tools | Foundation model training, research labs |
| **AWS** | H100, A100, L40S, T4 | Broad GPU catalog, deep customization | Enterprise ML pipelines, infra-heavy workloads |
| **GCP** | A100, H100, TPU v4/v5e | Tight AI ecosystem integration (Vertex AI, GKE) | TensorFlow-heavy workloads, GenAI with TPUs |
| **Azure** | MI300X, H100, A100, L40S | Enterprise-ready, Copilot ecosystem, hybrid cloud | Secure enterprise AI, compliance workloads |
| **RunPod** | A100, H100, 3090 | Secure containers, API-first, encrypted volumes | Privacy-sensitive jobs, fast inference |
| **Vast AI** | A100, 4090, 3090 (varied) | Peer-to-peer, low-cost compute | Budget training, short-term experiments |

> While most hyperscalers like GCP and AWS offer strong infrastructure, their pricing is often geared toward enterprises with high minimum spend commitments. For smaller teams or startups, platforms like Northflank offer much more competitive, usage-based pricing without long-term contracts, while still providing access to top-tier GPUs and enterprise-grade features.
> 

## What are on-demand GPUs?

An on-demand GPU is a cloud-hosted graphics processing unit that you can provision for as long as you need and release when you are done. The term “on demand” implies that you are not locked into long-term contracts or upfront capital costs. You can start a GPU instance in minutes, run your workload, and then stop paying the moment you shut it down.

The model has a strong appeal for teams working in AI, rendering, or simulation because workloads in these domains can be unpredictable. One week, you may need multiple high-end GPUs for training experiments; the next week, you may not need any at all. On-demand access allows you to scale your compute resources to match your actual demand.

## How on-demand GPU cloud services work

Most GPU cloud services operate on one or more of these provisioning models:

1. **True on-demand provisioning**

The provider maintains a pool of GPUs that can be allocated instantly when requested. You pay by the hour or minute, and when you release the resource, billing stops. This is the most flexible approach, but availability can vary depending on demand.

1. **Spot or preemptible instances**

These are unused GPUs offered at a lower price. The trade-off is that the provider can reclaim them at short notice if another customer needs the capacity. They are ideal for batch jobs, experiments, or workloads that can handle interruptions.

1. **Reserved or committed instances**

You pay for a fixed GPU allocation over a defined period, whether you use it or not. In exchange, you get guaranteed capacity and sometimes a discounted rate. This works well for continuous production workloads.

Some providers combine these models, offering hybrid access to balance reliability and cost. The choice often comes down to workload criticality and tolerance for variability.

## What makes a good GPU cloud provider in 2026?

Earlier, we discussed what on-demand GPUs are and how they work. Now, let's examine what makes a good cloud provider. The best GPU cloud providers aren't just hardware vendors; they're infrastructure layers that enable you to build, test, and deploy AI products with the same clarity and speed as modern web services. Here's what truly matters:

- **Access to modern GPUs**
    
    H100s and MI300X are now the standard for large-scale training. L4 and L40S offer strong price-to-performance for inference. Availability still makes or breaks a platform.
    
- **Fast provisioning and autoscaling**
    
    You shouldn’t wait to run jobs. Production-ready platforms like Northflank offer second-level startup, autoscaling, and GPU scheduling built into the workflow.
    
- **Environment separation**
    
    Support for dedicated dev, staging, and production environments is critical. You should be able to promote models safely, test pipelines in isolation, and debug without affecting live systems.
    
- **CI/CD and Git-based workflows**
    
    Deployments should integrate with Git and not require manual scripts. Reproducible builds, container support, and job-based execution are essential for real iteration speed.
    
- **Native ML tooling**
    
    Support for Jupyter, PyTorch, Hugging Face, Triton, and containerized runtimes should be first-class, not something you configure manually.
    
- **Bring your own cloud**
    
    Some teams need to run workloads in their own VPCs or across hybrid setups. Good platforms support this without losing managed features.
    
- **Observability and metrics**
    
    GPU utilization, memory tracking, job logs, and runtime visibility should be built in. If you can't see it, you can't trust it in production.
    
- **Transparent pricing**
    
    Spot pricing and regional complexity often hide real costs. Pricing should be usage-based, predictable, and clear from day one.
    

The good news is that platforms like Northflank provide all these mentioned above in one single platform. [Try it out to see how it works](https://app.northflank.com/signup)

## 7 best cloud services for renting high-performance GPUs on demand

In this section, we will examine each cloud service in detail. You'll discover the specific on-demand GPUs they offer, their optimization focus, and their actual performance across various workloads. Some services are better suited for research environments, while others are designed specifically for production deployments.

### 1. Northflank – Full-stack GPU platform for AI deployment and scaling

[Northflank](https://northflank.com/) abstracts the complexity of running GPU workloads by giving teams a full-stack platform; GPUs, secure runtime, deployments, built-in CI/CD, and observability all in one. You don’t have to manage infra, build orchestration logic, or combine third-party tools.

Everything from model training to inference APIs can be deployed through a Git-based or templated workflow. It supports [bring-your-own-cloud (AWS, Azure, GCP, and more)](https://northflank.com/features/bring-your-own-cloud), but it works fully managed out of the box.

![image - 2025-08-08T123011.085.png](https://assets.northflank.com/image_2025_08_08_T123011_085_d279fa8e00.png)

**What you can run on Northflank:**

- Inference APIs with autoscaling and low-latency startup
- Training or fine-tuning jobs (batch, scheduled, or triggered by CI)
- Multi-service AI apps (LLM + frontend + backend + database)
- Hybrid cloud workloads with GPU access in your own VPC

**What GPUs does Northflank support?**

Northflank offers access to 18+ GPU types, including NVIDIA A100, H100, B200, L40S, L4, AMD MI300X, and Habana Gaudi.

![gpu-prices-northflank.png](https://assets.northflank.com/gpu_prices_northflank_c6dbc88fdb.png)

**Where it fits best:**

If you're building production-grade AI products or internal AI services, Northflank handles both the GPU execution and the surrounding app logic. Especially strong fit for teams who want Git-based workflows, fast iteration, and zero DevOps overhead.

[**See how Cedana uses Northflank to deploy GPU-heavy workloads with secure microVMs and Kubernetes**](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes)

### 2. NVIDIA DGX Cloud – Research-scale training on H100

DGX Cloud is NVIDIA’s managed stack, giving you direct access to H100/A100-powered infrastructure, optimized libraries, and full enterprise tooling. It’s ideal for labs and teams training large foundation models.

![image - 2025-07-23T121001.310.png](https://assets.northflank.com/image_2025_07_23_T121001_310_05ed9a7995.png)

**What you can run on DGX Cloud:**

- Foundation model training with optimized H100 clusters
- Multimodal workflows using NVIDIA AI Enterprise software
- GPU-based research environments with full-stack support

**What GPUs does NVIDIA DGX Cloud support?**

DGX Cloud provides access to **NVIDIA H100 and A100 GPUs**, delivered in **clustered configurations** optimized for large-scale training and NVIDIA’s AI software stack.

**Where it fits best:**

For teams building new model architectures or training at scale with NVIDIA’s native tools, DGX Cloud offers raw performance with tuned software.

### 3. AWS – Deep GPU catalog for large-scale AI pipelines

AWS offers one of the broadest GPU lineups (H100, A100, L40S, T4) and mature infrastructure for managing ML workloads across global regions. It's highly configurable, but usually demands hands-on DevOps.

![image - 2025-07-23T121005.253.png](https://assets.northflank.com/image_2025_07_23_T121005_253_c13797c8b4.png)

**What you can run on AWS:**

- Training pipelines via SageMaker or custom EC2 clusters
- Inference endpoints using ECS, Lambda, or Bedrock
- Multi-GPU workflows with autoscaling and orchestration logic

**What GPUs does AWS support?**

AWS supports a wide range of GPUs, including **NVIDIA H100, A100, L40S, and T4**. These are available through services like **EC2, SageMaker, and Bedrock**, with support for multi-GPU setups.

**Where it fits best:**

If your infra already runs on AWS or you need fine-grained control over scaling and networking, it remains a powerful, albeit heavy, choice.

### 4. GCP – TPU-first with deep TensorFlow integration

GCP supports H100s and TPUs (v4, v5e) and excels when used with the Google ML ecosystem. Vertex AI, BigQuery ML, and Colab make it easier to prototype and deploy in one flow.

![image - 2025-07-23T121007.454.png](https://assets.northflank.com/image_2025_07_23_T121007_454_783326c800.png)

**What you can run on GCP:**

- LLM training on H100 or TPU v5e
- MLOps pipelines via Vertex AI
- TensorFlow-optimized workloads and model serving

**What GPUs does GCP support?**

GCP offers NVIDIA A100 and H100, along with Google’s custom TPU v4 and v5e accelerators. These are integrated with Vertex AI and GKE for optimized ML workflows.

**Where it fits best:**

If you're building with Google-native tools or need TPUs, GCP offers a streamlined ML experience with tight AI integrations.

### 5. Azure – Enterprise AI with MI300X and Copilot integrations

Azure supports AMD MI300X, H100s, and L40S, and is tightly integrated with Microsoft’s productivity suite. It's great for enterprises deploying AI across regulated or hybrid environments.

![image - 2025-07-23T121010.491.png](https://assets.northflank.com/image_2025_07_23_T121010_491_2596440b51.png)

**What you can run on Azure:**

- AI copilots embedded in enterprise tools
- MI300X-based training jobs
- Secure, compliant AI workloads in hybrid setups

**What GPUs does Azure support?**

Azure supports NVIDIA A100, L40S, and AMD MI300X, with enterprise-grade access across multiple regions. These GPUs are tightly integrated with Microsoft’s AI Copilot ecosystem.

**Where it fits best:**

If you're already deep in Microsoft’s ecosystem or need compliance and data residency support, Azure is a strong enterprise option.

### 6. RunPod – Secure GPU containers with API-first design

RunPod gives you isolated GPU environments with job scheduling, encrypted volumes, and custom inference APIs. It’s particularly strong for privacy-sensitive workloads.

![image - 2025-07-23T121032.470.png](https://assets.northflank.com/image_2025_07_23_T121032_470_3d334150c3.png)

**What you can run on RunPod:**

- Inference jobs with custom runtime isolation
- AI deployments with secure storage
- GPU tasks needing fast startup and clean teardown

**What GPUs does RunPod support?**

RunPod offers NVIDIA H100, A100, and 3090 GPUs, with an emphasis on secure, containerized environments and job scheduling.

**Where it fits best:**

If you're running edge deployments or data-sensitive workloads that need more control, RunPod is a lightweight and secure option.

### 7. Vast AI – Decentralized marketplace for budget GPU compute

Vast AI aggregates underused GPUs into a peer-to-peer marketplace. Pricing is unmatched, but expect less control over performance and reliability.

![image - 2025-07-23T121035.388.png](https://assets.northflank.com/image_2025_07_23_T121035_388_989cb6958c.png)

**What you can run on Vast AI:**

- Cost-sensitive training or fine-tuning
- Short-term experiments or benchmarking
- Hobby projects with minimal infra requirements

**What GPUs does Vast AI support?**

Vast AI aggregates NVIDIA A100, 4090, 3090, and a mix of consumer and datacenter GPUs from providers in its peer-to-peer marketplace. Availability and performance may vary by host.

**Where it fits best:**

If you’re experimenting or need compute on a shoestring, Vast AI provides ultra-low-cost access to a wide variety of GPUs.

## How to choose the best cloud service for renting on-demand GPUs

If you've reviewed the list and identified the key features that matter to you, this section will help you make your decision. Different workloads require different strengths, though some platforms consistently offer more comprehensive solutions than others.

| If you need… | Best options from this list | Why |
| --- | --- | --- |
| Instant access for urgent jobs | Northflank, AWS | Large on-demand pools, fast startup, global regions |
| Lowest cost for flexible workloads | Northflank, Vast AI, spot pools | Low hourly rates, flexible provisioning, pay only when running |
| Guaranteed capacity for long training | Northflank managed pools, AWS reserved, GCP scheduled GPUs | Predictable performance and locked-in capacity |
| Tight integration with AI tooling | GCP (Vertex AI), Azure ML | Prebuilt pipelines, built-in ML frameworks |
| Data residency or regional coverage | Northflank, AWS, Azure | Broadest global infrastructure for compliance and latency |
| Minimal DevOps overhead | Northflank, RunPod | Managed orchestration, Git-based deployments, secure containers |

## Conclusion

On-demand GPU hosting is now a cornerstone of modern AI infrastructure. The best platform for you depends on how quickly you need capacity, the level of control you want, and how much of the surrounding stack is already handled. 

We’ve covered what on-demand really means, how providers differ, and which features matter most. If you want GPUs that are ready the moment you need them, scale without friction, and integrate into your workflows, [Northflank](https://northflank.com/) is worth a look. It’s built for developers, handles orchestration end-to-end, and lets you move from prototype to production without the usual ops overhead. 

[Try Northflank](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how it fits your ML stack.]]>
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  <title>On premise to cloud migration. The 2026 guide.</title>
  <link>https://northflank.com/blog/on-premise-to-cloud-migration</link>
  <pubDate>2025-08-10T22:00:00.000Z</pubDate>
  <description>
    <![CDATA[If you're searching for &ldquo;on-premise to cloud migration,&ldquo; you're probably tired of managing servers in a data center. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Group_1410119483_1_a931c3e208.png" alt="On premise to cloud migration. The 2026 guide." />If you're searching for "on-premise to cloud migration," you're probably tired of managing servers in a data center. Maybe your hardware refresh is coming up, or you're sick of 3am calls about failing RAID arrays.

Whatever brought you here, migrating from on-prem to cloud is a one-way journey that more companies are taking in 2026. And platforms like Northflank can make it surprisingly simple, whether you use their managed cloud or bring your own.

But first…

# What is on-premise to cloud migration?

On-premise to cloud migration is the process of moving applications, data, and workloads from your physical servers and data centers to cloud infrastructure.

You're leaving behind server rooms, cooling systems, network hardware, and maintenance contracts for the promise of elastic infrastructure, managed services, and actually sleeping through the night.

# Why companies are finally leaving on-prem

### The breaking point

Most companies hit a moment where on-prem stops making sense:

- **Hardware refresh cycle**: Your servers are 5 years old. Do you spend $500k on new hardware or finally make the jump?
- **Scaling nightmares**: Black Friday is coming. Do you buy servers for peak load that sit idle 350 days a year?
- **Talent shortage**: Your lone infrastructure guru just quit. Good luck finding a replacement who wants to manage physical servers in 2026
- **Disaster recovery costs**: Building a second data center for DR is insanely expensive
- **Innovation speed**: Your competitors deploy 10x faster because they're not waiting for hardware procurement

### The real costs of staying on-prem

- Hardware: $200k-2M every 3-5 years
- Data center space: $10k-50k/month
- Power and cooling: $5k-20k/month
- Network hardware and connectivity: $100k-500k
- 24/7 ops team: $300k-1M/year
- Maintenance contracts: $50k-200k/year

# The on-premise to cloud migration challenge

Moving to cloud sounds great until you realize what's involved:

### Technical complexity

- **Refactoring everything**: Your apps were built for specific hardware. Now they need to run anywhere
- **Network redesign**: Your nice flat network becomes VPCs, subnets, security groups
- **Storage migration**: Moving petabytes of data without downtime
- **Security rethink**: Your firewall becomes IAM policies and cloud-native security

### Decision paralysis

Which cloud do you even choose?

- **AWS**: Most services, most complex, most expensive at scale
- **Azure**: Great for Windows workloads, licensing can be a trap
- **GCP**: Best for data analytics and AI/ML, smallest ecosystem
- **Multi-cloud**: Sounds good in theory, operational nightmare in practice

### Skills gap

Your team knows VMware, not Kubernetes. They speak VLAN, not VPC. The learning curve is brutal.

# Enter Northflank: Two paths for your on-premise to cloud migration

This is where Northflank comes in. Instead of spending years learning cloud platforms, you get two simple options:

## Option 1: Northflank's managed cloud (easiest)

Just use Northflank's cloud. Zero infrastructure decisions.

**How it works:**

1. Sign up for Northflank (5 minutes)
2. Connect your Git repo
3. Deploy your apps
4. Northflank handles literally everything else

**What you get:**

- No cloud provider decisions
- No infrastructure management
- Automatic scaling, security, monitoring, disaster recovery. All the bells and whistles.
- Multiple regions available
- Predictable pricing
- Start deploying in 30 minutes, not 6 months

**Best for:**

- Any team who just wants their apps to run and would rather focus on building product customers pay for than the infrastructure behind it.

## Option 2: Northflank BYOC (Bring Your Own Cloud)

Use Northflank's platform on YOUR choice of cloud provider.

**How it works:**

1. Choose your cloud (AWS, Azure, GCP, etc.)
2. Connect it to Northflank
3. Northflank provisions and manages everything
4. You get cloud benefits + Northflank simplicity

**What you get:**

- Your data in your cloud account
- Compliance and data residency control
- Use existing cloud credits or commitments
- Still no infrastructure complexity
- Same Northflank experience

**Best for:**

- Enterprises with compliance requirements
- Companies with existing cloud commitments
- Teams who need specific cloud services
- Organizations planning massive scale

# Which cloud should you choose?

If you go the BYOC route:

### Choose AWS if:

- You need the most service options
- You're building something massive
- You have AWS credits from investors
- You don't mind complexity for flexibility

**Warning**: Most expensive at scale. Bills can surprise you.

### Choose Azure if:

- You run lots of Windows workloads
- You're already deep in Microsoft ecosystem
- You need hybrid cloud (some stuff stays on-prem)
- You have an Enterprise Agreement

**Warning**: Licensing can lock you in hard.

### Choose GCP if:

- Data analytics or AI/ML is core to your business
- You want the best price-performance
- You prefer Google's simplified approach
- You're already using Google Workspace

**Warning**: Smallest ecosystem, fewer third-party tools.

### Or just use Northflank's cloud if:

- You want to avoid this decision entirely
- You're not sure which cloud is best
- You want to start migrating TODAY
- You can always move to BYOC later

# FAQs

1. **What about our data?**
Northflank handles database migrations. Whether managed or BYOC, your data is encrypted, backed up, and yours.
2. **We have compliance requirements.**
BYOC lets you maintain full control. Your cloud, your compliance. Northflank just makes it manageable.
3. **Our apps weren't built for cloud**
If they run in containers, they'll run on Northflank. The platform handles the cloud complexity.
4. **What if we need to move clouds later?**
With Northflank, switching clouds is just changing a configuration. No app changes needed.

# Why Northflank works for on-prem escapees

Northflank is specifically designed for teams making this transition:

- **No cloud expertise required**: The platform handles all the complex cloud stuff
- **Gradual migration**: Move at your own pace, app by app
- **Both options available**: Start with managed, move to BYOC when ready
- **Enterprise-ready**: Built for serious workloads, not just demos
- **Real support**: Actual humans who understand migration pain

# Getting started

### Step 1: Choose your path

- **Northflank Managed**: Sign up and start deploying today
- **BYOC**: Pick your cloud, then connect to Northflank

### Step 2: Pilot project

1. Pick one simple app
2. Containerize it (if not already)
3. Deploy to Northflank
4. See how easy this can be

### Step 3: Plan your waves

1. List all your applications
2. Group by complexity and criticality
3. Plan 2-3 migration waves
4. Set realistic timelines

### Step 4: Execute 😊

Ta-da! You’ve got your on-premise to cloud migration done.

# Conclusion

On-premise to cloud migration doesn't have to be a multi-year nightmare. Whether you choose Northflank's managed cloud for simplicity or BYOC for control, you can be deploying in the cloud within days, not months.

The best part? You don't need to become a cloud expert. Northflank handles the complexity while you focus on what matters, your applications and your business.

Ready to migrate? [Get started here](https://app.northflank.com/login) or [talk to an engineer](https://cal.com/team/northflank/northflank-intro).]]>
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</item><item>
  <title>How to migrate from cloud to on-premise</title>
  <link>https://northflank.com/blog/how-to-migrate-from-cloud-to-on-premise</link>
  <pubDate>2025-08-10T22:00:00.000Z</pubDate>
  <description>
    <![CDATA[If you're searching for &ldquo;cloud to on-premise migration,&ldquo; you're probably looking at your AWS bill in horror. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/azure_migration_strategy_1_668271f006.png" alt="How to migrate from cloud to on-premise" />If you're searching for "cloud to on-premise migration," you're probably looking at your AWS bill in horror. Maybe you just got quoted $200k/month for what seemed like basic infrastructure. Or perhaps you realized you're paying for elasticity you never use.

You're not alone. Cloud repatriation is real, and more companies are bringing workloads back home in 2026. 

The good news? With platforms like Northflank, you can keep the cloud experience without the cloud bills.

# **What is cloud to on-premise migration?**

Cloud to on-premise migration (also called cloud repatriation) is the process of moving applications, data, and workloads from public cloud providers back to your own physical infrastructure.

You're essentially reversing your cloud journey, but keeping all the operational improvements you've learned along the way.

# Why companies are leaving the cloud

### The cloud bill shock

Let's talk about when cloud stops making sense:

- **Predictable workloads**: You're paying for elasticity but your load never changes
- **Data transfer costs**: That $0.09/GB egress fee becomes $90k at 1PB/month
- **Reserved instances trap**: You're locked into 3-year commitments for instances you don't need
- **Managed service markups**: RDS costs 3x more than running your own Postgres
- **The compound effect**: Small inefficiencies multiply into massive bills

### When on-prem actually makes sense

The math is simple:

- If your workload is predictable
- If you're spending > $50k/month on cloud
- If you have the expertise (or can hire it)
- If you can amortize hardware over 3-5 years

...then on-prem might save you 50-70%.

# The cloud to on-premise migration challenge

Moving back to on-prem sounds great until you realize what you're giving up:

### Developer experience nostalgia

Remember the cloud's best parts?

- Push code, it deploys automatically
- Scale with a slider
- New environments in minutes
- Built-in monitoring and logging
- No SSH-ing into servers

Your developers won't go back to the old ways.

### Technical complexity

- **Cloud-native services**: How do you replace Lambda, DynamoDB, or S3?
- **Automation**: Your CI/CD assumes cloud APIs exist
- **Observability**: CloudWatch doesn't work in your data center
- **Security**: Cloud IAM doesn't translate to on-prem

### The fear factor

"What if we need to scale suddenly?"

"What if hardware fails?"

"What if we're making a huge mistake?"

# Enter Northflank: Cloud experience on your metal

This is where Northflank comes in. You can move back to on-prem WITHOUT giving up the cloud experience.

## How Northflank on-prem works

**The setup:**

1. Get bare metal servers (or repurpose existing ones)
2. Install Kubernetes (k3s or Rancher work great)
3. Connect Northflank
4. Deploy exactly like you did in the cloud

**What you get:**

- Same push-to-deploy workflow
- Same autoscaling (within your hardware limits)
- Same monitoring and observability
- Same developer experience
- Just running on YOUR hardware

# Planning your cloud exodus (cloud to on-premise migration)

## Step 1: Do the math

Calculate your real costs:

**Cloud costs:**

- Compute: $XX,XXX/month
- Storage: $XX,XXX/month
- Bandwidth: $XX,XXX/month
- Managed services: $XX,XXX/month
- Total: $XXX,XXX/month

**On-prem costs:**

- Hardware (amortized): $XX,XXX/month
- Colocation/Power: $X,XXX/month
- Networking: $X,XXX/month
- Staff (additional): $XX,XXX/month
- Total: $XX,XXX/month

If on-prem is less than 50% of cloud, it's worth considering.

## Step 2: Choose your hardware

**Option 1: Colocation**

- Rent space in a data center
- They handle power, cooling, network
- You manage the servers
- Best balance of control and convenience

**Option 2: Your own data center**

- Maximum control
- Highest upfront cost
- Only for large enterprises

**Option 3: Hybrid approach**

- Keep burst capacity in cloud
- Run baseline load on-prem
- Best of both worlds

## Step 3: Build your platform

This is where Northflank shines:

1. **Provision servers**: Get beefy machines. RAM is cheap, buy lots.
2. **Install Kubernetes**: k3s for simplicity, Rancher for enterprise
3. **Connect Northflank**: Same platform, new infrastructure
4. **Test everything**: Ensure performance meets expectations

# The migration process

### Phase 1: Replicate on-prem

1. Set up Northflank on your hardware
2. Deploy non-critical services
3. Test performance and reliability
4. Train your team on any differences

### Phase 2: Data migration

1. Set up databases on-prem
2. Replicate data from cloud
3. Test backup and recovery
4. Plan cutover strategy

### Phase 3: Progressive migration

1. Move services in waves
2. Start with stateless applications
3. Monitor everything closely
4. Keep cloud as fallback

### Phase 4: Cloud cleanup

1. Verify everything works on-prem
2. Shut down cloud resources
3. Cancel reservations (watch those penalties)
4. Celebrate massive cost savings

# What about scaling?

The elephant in the room: "What if we need to scale?"

Most companies don't have viral moments, but if you do need to scale:

1. Northflank works across clouds and on-prem
2. Burst to cloud when needed
3. Run baseline on your hardware
4. Only pay cloud prices for peaks

**The hybrid approach**: Keep 80% on-prem, 20% cloud-ready.

# FAQs

1. **What if hardware fails?**
    
Build redundancy (it's still cheaper than cloud). Northflank handles failover automatically. Hot spares are cheaper than cloud insurance.
    
2. **We'll lose cloud innovation**

Kubernetes brings most cloud patterns on-prem. Northflank provides the platform features. You can always burst to cloud for specific services.
    
3. **Our team doesn't know hardware**
    
Colocation handles the physical stuff. Northflank handles the platform layer. You just need basic Linux skills.
    
4. **This seems risky**
    
Start with non-critical workloads. Keep cloud as backup initially. Many companies have done this successfully.
    

# When NOT to repatriate

Staying in cloud makes sense if:

- You're spending < $20k/month (you could be spending less if you used Northflank!)
- Your workload is truly unpredictable
- You heavily use cloud-native services
- You have no ops expertise

# Conclusion

Cloud to on-premise migration is all about taking the best of cloud, the automation, the developer experience, the operational excellence,  and running it on infrastructure that makes financial sense.

With Northflank, you keep everything developers love about cloud while cutting costs by 50-70%. 

The cloud was a great teacher. Now it's time to graduate.

Ready to migrate? [Get started here](https://app.northflank.com/login) or [talk to an engineer](https://cal.com/team/northflank/northflank-intro).]]>
  </content:encoded>
</item><item>
  <title>Azure migration strategy for 2026: How to get it right</title>
  <link>https://northflank.com/blog/azure-cloud-migration-strategy-migrate</link>
  <pubDate>2025-08-10T22:00:00.000Z</pubDate>
  <description>
    <![CDATA[When searching for &ldquo;Azure migration&ldquo; or exploring cloud migration options, you're likely facing one of two distinct scenarios.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Group_1410119483_596ff3e0e5.png" alt="Azure migration strategy for 2026: How to get it right" />When searching for "Azure migration" or exploring cloud migration options, you're likely facing one of two distinct scenarios:

(1) You want to migrate TO Azure from on-prem or another cloud

or

(2) You want to migrate FROM Azure because it's too expensive or complex

Both paths are increasingly common in 2026, and platforms like Northflank can dramatically simplify either journey. This guide covers both Azure migration strategies.

But first…

# What is Azure migration?

Azure migration is the process of moving applications, data, and infrastructure either TO Microsoft Azure or FROM Azure to another platform or on-prem.

**Azure migration**: You're moving from your own servers or another cloud (like AWS or GCP) to Azure. Usually because you run Windows workloads, need enterprise features, or want hybrid cloud capabilities.

**Migrating away from Azure**: You're moving away from Azure to another cloud or back to your own servers. Usually because Azure licensing got too expensive or you're concerned about vendor lock-in.

# **The two Azure migration paths**

## **Path 1: Migrating to Azure**

### Why Move to Azure?

Azure makes sense for enterprises. They've got deep Active Directory integration, the best Windows Server pricing (if you stay on Azure), and hybrid cloud with Azure Arc. The promise is compelling: seamless enterprise integration, pay less for Windows workloads.

Yet, to actually use Azure effectively, you need to master:

- **AKS (Azure Kubernetes Service)**: It's managed Kubernetes, but you still need to configure node pools, set up cluster autoscaling, manage Azure AD integration, and figure out Azure CNI networking
- **Resource Group chaos**: Resource groups, subscriptions, management groups, Azure Policy
- **RBAC complexity**: Azure AD roles, custom roles, scope inheritance
- **Cost mysteries**: EA agreements, reserved instances, Azure Hybrid Benefit calculations, Azure billing is enterprise-grade confusing

Most teams spend months just understanding Azure's organizational structure. And that's before you deploy a single application.

### **Enter Northflank.**

This is where Northflank comes in. Instead of it being just another Azure abstraction, it's a complete Internal Developer Platform (IDP) that enterprises typically spend years building. Instead of learning Azure, you just use it.

**How it works:**

1. Connect your Azure account to Northflank (5 minutes)
2. Northflank provisions everything: AKS clusters, load balancers, networking, security
2a. Or import your existing AKS clusters. Northflank works with what you already have
3. You deploy your code

**What you get:**

- Production-ready AKS without touching kubectl
- Automatic SSL certificates and load balancing
- Built-in CI/CD from your Git repos
- Database provisioning with one click (Azure SQL, but simplified)
- Cost controls that actually work
- Full Azure ecosystem integration (Azure SQL, Blob Storage, container registries, CDNs)
- Enterprise-grade platform capabilities teams typically spend years building
- Complete control over data residency and deployment regions

## **Path 2: Migrating away from Azure to another cloud or on-prem**

Azure can be expensive at scale, especially with their licensing model:

- Windows Server costs 4x more on AWS/GCP than Azure (Microsoft's way of keeping you)
- You're paying for reserved instances you might not use
- Complex EA agreements lock you in for years
- Every feature requires a different SKU

### The Azure migration challenge

Moving off Azure is scary because:

1. Your team only knows Azure tools and services
2. You've built around Azure-specific services like Azure Functions or Cosmos DB
3. Microsoft makes it expensive to run Windows elsewhere
4. Going on-prem means losing that hybrid cloud magic

This is where most companies get stuck. They know Azure is getting expensive but the licensing penalties for leaving are brutal.

### Northflank, a cloud agnostic platform for your workloads

Northflank solves this by being cloud-agnostic from day one. Here's how migration works:

**To another cloud (AWS/GCP):**

1. Spin up Northflank on AWS/GCP (uses EKS/GKE under the hood)
2. Deploy your apps using the exact same Northflank workflows
3. Plan around Windows licensing costs (or switch to Linux)
4. Migrate data during maintenance window
5. Turn off Azure

**To on-premises:**

1. Get bare metal servers (or VMs)
2. Install k3s or Rancher
3. Connect to Northflank
4. Deploy your apps (same process as Azure)
5. Cancel Azure (watch those EA penalties)

## Why Northflank works for both paths

Northflank is not yet another PaaS. It's specifically designed for teams that need cloud flexibility:

- **Multi-cloud native**: Run on Azure, AWS, GCP, Oracle Cloud, Civo, or bare metal
- **No vendor lock-in**: Built on Kubernetes and Docker standards
- **BYOC (Bring Your Own Cloud)**: Your infrastructure, your data, Northflank's simplicity
- **Enterprise-ready**: Build your Internal Developer Platform (IDP) or Application Delivery Platform (ADP) without years of engineering
- **Real company scale**: Teams run 10,000+ containers in production
- **Azure depth**: Supports all Azure instance types across all regions

### **Azure migration TO Azure:**

- Automatic AKS cluster setup and management
- Built-in Azure service integrations (Azure SQL, Blob Storage, etc.)
- Azure cost optimization with reserved instance awareness
- No Azure expertise required

[Check out these docs on how to integrate your Azure account to create and manage clusters using Northflank.](https://northflank.com/docs/v1/application/bring-your-own-cloud/azure-on-northflank)

### **Azure migration FROM Azure:**

- Cloud-agnostic deployments
- Zero code changes when switching clouds
- Windows licensing strategy consulting
- Maintain Azure-like developer experience anywhere

### The technical details

Under the hood, Northflank:

- Manages Kubernetes so you don't have to
- Handles ingress, TLS, load balancing automatically
- Provides GitOps workflows without the YAML complexity
- Abstracts cloud differences into a unified API

You write Dockerfiles, push code, and deploy. Whether that runs on AKS, EKS, or your own metal is just a config option.

# Getting started

### Migrating to Azure:

1. [Sign up for Northflank](https://app.northflank.com/login)
2. Connect your Azure account
    1. Have existing AKS clusters? Import them directly, no need to start from scratch
3. Deploy a test app
4. Migrate your easiest service first
5. Move the rest over weeks, not months

### Migrating from Azure:

1. Pick your target (AWS? GCP? On-prem?)
2. Calculate Windows licensing impact
3. Set up Northflank on the new infrastructure
4. Deploy a non-critical service as a test
5. Plan your data migration strategy
6. Move services gradually with zero downtime

# Conclusion

Azure migration doesn't have to be a multi-year project. Whether you're adopting Azure for its enterprise features or escaping it due to licensing costs, the key is abstracting away the complexity while keeping the power.

Northflank does exactly that. Excellent developer experience, any infrastructure. Your choice.

Ready to migrate? [Get started here](https://app.northflank.com/login) or [talk to an engineer](https://cal.com/team/northflank/northflank-intro).]]>
  </content:encoded>
</item><item>
  <title>Top GPU hosting platforms for AI: inference, training &amp; scaling</title>
  <link>https://northflank.com/blog/top-gpu-hosting-platforms-for-ai</link>
  <pubDate>2025-08-08T11:00:00.000Z</pubDate>
  <description>
    <![CDATA[Discover the best GPU hosting platforms for AI and ML in 2026 including Northflank AWS GCP and more Learn which provider fits your needs for training inference or full-stack AI deployment]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/spectro_cloud_1_18e9ec3b09.png" alt="Top GPU hosting platforms for AI: inference, training &amp; scaling" />Most AI platforms help you run models. Few help you build production infrastructure around them. If you’re training LLMs, serving inference, or building full-stack ML products, you need more than access to GPUs.

You need a platform that handles everything from scheduling and orchestration to CI/CD, staging, and production. AWS and GCP offer raw compute, but they come with complexity and overhead. Lightweight tools are easy to start with, but they rarely scale when you need reliability or control.

Northflank offers an alternative. It gives you modern GPU hosting with the developer workflows and automation needed to move fast and stay in control.

In this guide, we’ll break down what GPU hosting actually means, why it matters for AI teams, and how platforms like Northflank compare to other options in the space.

## TL;DR: Where to find the best GPU hosting?

If you're short on time, here’s the complete list of the best GPU hosting platforms for AI and ML teams. Some are built for scale. Others excel at speed, simplicity, or affordability. A few are rethinking the GPU experience from the ground up.

<InfoBox className="BodyStyle">

> **Planning GPU infrastructure?** For teams scaling production AI workloads with specific capacity needs, [request GPU capacity here](https://northflank.com/request/gpu).

</InfoBox>

| **Platform** | **GPU Types Available** | **Key Strengths** | **Best For** |
| --- | --- | --- | --- |
| [**Northflank**](https://northflank.com/) | A100, H100, H200, B200, L40S, B200, L40S, MI300X, Gaudi and many more. | Full-stack GPU hosting with CI/CD, secure runtimes, app orchestration, GitOps, [BYOC](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) | Production AI apps, inference APIs, model training, fast iteration |
| **NVIDIA DGX Cloud** | H100, A100 | High-performance NVIDIA stack, enterprise tools | Foundation model training, research labs |
| **AWS** | H100, A100, L40S, T4 | Broad GPU catalog, deep customization | Enterprise ML pipelines, infra-heavy workloads |
| **GCP** | A100, H100, TPU v4/v5e | Tight AI ecosystem integration (Vertex AI, GKE) | TensorFlow-heavy workloads, GenAI with TPUs |
| **Azure** | MI300X, H100, A100, L40S | Enterprise-ready, Copilot ecosystem, hybrid cloud | Secure enterprise AI, compliance workloads |
| **RunPod** | A100, H100, 3090 | Secure containers, API-first, encrypted volumes | Privacy-sensitive jobs, fast inference |
| **Vast AI** | A100, 4090, 3090 (varied) | Peer-to-peer, low-cost compute | Budget training, short-term experiments |

> While most hyperscalers like GCP and AWS offer strong infrastructure, their pricing is often geared toward enterprises with high minimum spend commitments. For smaller teams or startups, platforms like [Northflank](https://northflank.com/) offer much more competitive, usage-based pricing without long-term contracts, while still providing access to top-tier GPUs and enterprise-grade features.
> 

## What is GPU hosting?

GPU hosting is the provisioning and management of GPU-enabled infrastructure to support compute-intensive tasks like model training, inference, or fine-tuning. But for AI/ML teams, it’s not just about spinning up machines with GPUs; it’s about how that infrastructure fits into the development and production lifecycle.

Effective GPU hosting includes:

- **GPU-aware scheduling** across jobs or containers
- **Environment management**, including CUDA and ML framework compatibility
- **Isolation and security** for workloads and data
- **Service and job orchestration**, depending on the use case
- **Integration with development workflows** (version control, CI/CD, observability)

It enables teams to abstract away infrastructure friction and focus on iteration, deployment, and scaling of models, whether training a new model or exposing an inference endpoint.

## What to look for in a modern GPU hosting platform

Earlier, we explored what GPU hosting involves. The best GPU hosting platforms go beyond raw compute, they simplify operational complexity while providing the tools teams need to build, deploy, and scale AI workloads reliably. Key capabilities include:

### GPU-aware orchestration

Support for configuring GPU limits, memory constraints, job timeouts, and queueing. Workloads should be scheduled based on resource availability and priority.

### Built-in CI/CD integration

Seamless integration with Git for triggering builds and deployments when code changes. Automated pipelines help track experiments, update models, and deploy inference endpoints without manual effort.

### Secure, Isolated Execution

Every workload must run in a sandboxed environment with role-based access control, secrets management, and network policies to reduce the attack surface.

### Support for jobs and long-running services

The platform should handle both types of ML workloads, batch jobs (for training) and services (for inference), with appropriate scaling and monitoring support.

### Observability and logging

Real-time logs, GPU utilization metrics, and health monitoring help teams debug failures and optimize performance.

### Full-stack workload support

In addition to AI workloads, the platform should support running web services, APIs, frontends, and databases, enabling teams to build and deploy complete AI applications without stitching together multiple platforms.

### Developer experience

A good platform is accessible through CLI, API, and UI, and offers a fast path from repo to deployed service. Clean abstractions help reduce operational overhead.

These features ensure the platform can support full ML lifecycles from research to production.

## Top platforms for GPU hosting

This section goes deep on each platform in the list. You’ll see what types of GPUs they offer, what they’re optimized for, and how they actually perform in real workloads. Some are ideal for researchers. Others are built for production.

### 1. Northflank – Full-stack GPU platform for AI deployment and scaling

[Northflank](https://northflank.com/) abstracts the complexity of running GPU workloads by giving teams a full-stack platform; GPUs, runtimes, deployments, CI/CD, and observability all in one. You don’t have to manage infra, build orchestration logic, or wire up third-party tools.

Everything from model training to inference APIs can be deployed through a Git-based or templated workflow. It supports [bring-your-own-cloud (AWS, Azure, GCP, and more)](https://northflank.com/features/bring-your-own-cloud), but it works fully managed out of the box.

![image - 2025-08-08T123011.085.png](https://assets.northflank.com/image_2025_08_08_T123011_085_d279fa8e00.png)

**What you can run on Northflank:**

- Inference APIs with autoscaling and low-latency startup
- Training or fine-tuning jobs (batch, scheduled, or triggered by CI)
- Multi-service AI apps (LLM + frontend + backend + database)
- Hybrid cloud workloads with GPU access in your own VPC

**What GPUs does Northflank support?**

Northflank offers access to **18+ GPU types**, including **NVIDIA A100, H100, B200, L40S**, **L4,** **AMD MI300X**, and **Habana Gaudi**.

![gpu-prices-northflank.png](https://assets.northflank.com/gpu_prices_northflank_c6dbc88fdb.png)

**Where it fits best:**

If you're building production-grade AI products or internal AI services, Northflank handles both the GPU execution and the surrounding app logic. Especially strong fit for teams who want Git-based workflows, fast iteration, and zero DevOps overhead.

[**See how Cedana uses Northflank to deploy GPU-heavy workloads with secure microVMs and Kubernetes**](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes)

### 2. NVIDIA DGX Cloud – Research-scale training on H100

DGX Cloud is NVIDIA’s managed stack, giving you direct access to H100/A100-powered infrastructure, optimized libraries, and full enterprise tooling. It’s ideal for labs and teams training large foundation models.

![image - 2025-07-23T121001.310.png](https://assets.northflank.com/image_2025_07_23_T121001_310_05ed9a7995.png)

**What you can run on DGX Cloud:**

- Foundation model training with optimized H100 clusters
- Multimodal workflows using NVIDIA AI Enterprise software
- GPU-based research environments with full-stack support

**What GPUs does NVIDIA DGX Cloud support?**

DGX Cloud provides access to **NVIDIA H100 and A100 GPUs**, delivered in **clustered configurations** optimized for large-scale training and NVIDIA’s AI software stack.

**Where it fits best:**

For teams building new model architectures or training at scale with NVIDIA’s native tools, DGX Cloud offers raw performance with tuned software.

### 3. AWS – Deep GPU catalog for large-scale AI pipelines

AWS offers one of the broadest GPU lineups (H100, A100, L40S, T4) and mature infrastructure for managing ML workloads across global regions. It's highly configurable, but usually demands hands-on DevOps.

![image - 2025-07-23T121005.253.png](https://assets.northflank.com/image_2025_07_23_T121005_253_c13797c8b4.png)

**What you can run on AWS:**

- Training pipelines via SageMaker or custom EC2 clusters
- Inference endpoints using ECS, Lambda, or Bedrock
- Multi-GPU workflows with autoscaling and orchestration logic

**What GPUs does AWS support?**

AWS supports a wide range of GPUs, including **NVIDIA H100, A100, L40S, and T4**. These are available through services like **EC2, SageMaker, and Bedrock**, with support for multi-GPU setups.

**Where it fits best:**

If your infra already runs on AWS or you need fine-grained control over scaling and networking, it remains a powerful, albeit heavy, choice.

### 4. GCP – TPU-first with deep TensorFlow integration

GCP supports H100s and TPUs (v4, v5e) and excels when used with the Google ML ecosystem. Vertex AI, BigQuery ML, and Colab make it easier to prototype and deploy in one flow.

![image - 2025-07-23T121007.454.png](https://assets.northflank.com/image_2025_07_23_T121007_454_783326c800.png)

**What you can run on GCP:**

- LLM training on H100 or TPU v5e
- MLOps pipelines via Vertex AI
- TensorFlow-optimized workloads and model serving

**What GPUs does GCP support?**

GCP offers **NVIDIA A100 and H100**, along with **Google’s custom TPU v4 and v5e** accelerators. These are integrated with **Vertex AI** and GKE for optimized ML workflows.

**Where it fits best:**

If you're building with Google-native tools or need TPUs, GCP offers a streamlined ML experience with tight AI integrations.

### 5. Azure – Enterprise AI with MI300X and Copilot integrations

Azure supports AMD MI300X, H100s, and L40S, and is tightly integrated with Microsoft’s productivity suite. It's great for enterprises deploying AI across regulated or hybrid environments.

![image - 2025-07-23T121010.491.png](https://assets.northflank.com/image_2025_07_23_T121010_491_2596440b51.png)

**What you can run on Azure:**

- AI copilots embedded in enterprise tools
- MI300X-based training jobs
- Secure, compliant AI workloads in hybrid setups

**What GPUs does Azure support?**

Azure supports **NVIDIA A100, L40S**, and **AMD MI300X**, with enterprise-grade access across multiple regions. These GPUs are tightly integrated with Microsoft’s **AI Copilot** ecosystem.

**Where it fits best:**

If you're already deep in Microsoft’s ecosystem or need compliance and data residency support, Azure is a strong enterprise option.

### 6. RunPod – Secure GPU containers with API-first design

RunPod gives you isolated GPU environments with job scheduling, encrypted volumes, and custom inference APIs. It’s particularly strong for privacy-sensitive workloads.

![image - 2025-07-23T121032.470.png](https://assets.northflank.com/image_2025_07_23_T121032_470_3d334150c3.png)

**What you can run on RunPod:**

- Inference jobs with custom runtime isolation
- AI deployments with secure storage
- GPU tasks needing fast startup and clean teardown

**What GPUs does RunPod support?**

RunPod offers **NVIDIA H100, A100, and 3090** GPUs, with an emphasis on **secure, containerized environments** and job scheduling.

**Where it fits best:**

If you're running edge deployments or data-sensitive workloads that need more control, RunPod is a lightweight and secure option.

### 7. Vast AI – Decentralized marketplace for budget GPU compute

Vast AI aggregates underused GPUs into a peer-to-peer marketplace. Pricing is unmatched, but expect less control over performance and reliability.

![image - 2025-07-23T121035.388.png](https://assets.northflank.com/image_2025_07_23_T121035_388_989cb6958c.png)

**What you can run on Vast AI:**

- Cost-sensitive training or fine-tuning
- Short-term experiments or benchmarking
- Hobby projects with minimal infra requirements

**What GPUs does Vast AI support?**

Vast AI aggregates **NVIDIA A100, 4090, 3090**, and a mix of consumer and datacenter GPUs from providers in its peer-to-peer marketplace. Availability and performance may vary by host.

**Where it fits best:**

If you’re experimenting or need compute on a shoestring, Vast AI provides ultra-low-cost access to a wide variety of GPUs.

## **How to choose the best** GPU hosting platform

If you've already looked at the list and the core features that matter, this section helps make the call. Different workloads need different strengths, but a few platforms consistently cover more ground than others.

| **Use Case** | **What to Prioritize** | **Platforms to Consider** |
| --- | --- | --- |
| **Full-stack AI delivery** | Git-based workflows, autoscaling, managed deployments, bring your own cloud, and GPU runtime integration. | **Northflank** |
| **Large-scale model training** | H100 or MI300X, multi-GPU support, RDMA, high-bandwidth networking | **Northflank,** NVIDIA DGX Cloud, AWS |
| **Real-time inference APIs** | Fast provisioning, autoscaling, low-latency runtimes | **Northflank**, RunPod |
| **Fine-tuning or experiments** | Low cost, flexible billing, quick start | **Northflank,** Vast AI. |
| **Production deployment (LLMs)** | CI/CD integration, containerized workloads, runtime stability | **Northflank**, GCP |
| **Edge or hybrid AI workloads** | Secure volumes, GPU isolation, regional flexibility | **Northflank,** RunPod, Azure, AWS |

## Conclusion

GPU hosting is now a critical layer in the modern AI stack. The right platform depends on your workload, how much control you need, and how quickly you want to go from prototype to production. We’ve covered what GPU hosting actually involves, what to look for, and how today’s top platforms compare. If you want GPU infrastructure that works out of the box and scales with your team, Northflank is worth a look. It’s built for developers, supports real workflows, and helps you ship faster with less overhead.

[Try Northflank](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how it fits your ML stack.]]>
  </content:encoded>
</item><item>
  <title>How much does an NVIDIA B200 GPU cost?</title>
  <link>https://northflank.com/blog/how-much-does-an-nvidia-b200-gpu-cost</link>
  <pubDate>2025-08-06T12:45:00.000Z</pubDate>
  <description>
    <![CDATA[The NVIDIA B200 delivers cutting edge AI performance. This article covers B200 price, cloud cost options, and how Northflank offers fast and simple access with everything included for deployment.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/open_source_ai_models_1_e8c8944bfd.png" alt="How much does an NVIDIA B200 GPU cost?" />The NVIDIA B200 is the latest flagship GPU built on the [Blackwell architecture](https://northflank.com/blog/b200-vs-h200#b200-everything-you-need-to-know). It’s engineered for next-generation AI workloads, offering a massive leap in performance and efficiency compared to its predecessor, the H200. With over 20 petaflops of FP4 compute, support for second-generation Transformer Engine, and an integrated NVLink Switch System, the B200 is tailored for model training at trillion-parameter scale, multi-GPU clusters, and AI inference at hyperscale.

But what does it cost to use a B200 in practice? Like its predecessors, the answer varies depending on how and where you access it. This article covers real-world pricing data across cloud platforms, compares options, and explains how [Northflank](https://northflank.com/product/gpu-paas) offers a smooth path to production with B200-powered compute.

<InfoBox className='BodyStyle'>

**💭 What is Northflank?**

[Northflank](https://northflank.com/) is a full-stack AI cloud platform that helps teams build, train, and deploy models without infrastructure friction. GPU workloads, APIs, frontends, backends, and databases run together in one place so your stack stays fast, flexible, and production-ready.

[Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how it fits your stack.

> **Need B200 capacity for your project?** Given the high demand and limited availability of B200 GPUs, it's worth planning ahead. [Request GPU capacity](https://northflank.com/request/gpu) if you have specific timeline or volume requirements.

</InfoBox>

## B200 pricing from NVIDIA

As of August 2025, NVIDIA hasn’t published retail prices for B200 GPUs on its official site. However, early listings and OEM quotes suggest the following ballpark figures:

- B200 192GB SXM: $45,000–$50,000 (depending on cooling and power configuration)
- Complete 8x B200 server systems can exceed $500,000

B200s are currently only available through select partners and integrators, often bundled with high-end systems. Due to their scale and infrastructure needs, most developers and startups will rent B200s through cloud platforms rather than buy them outright.

*If you're curious how the B200 stacks up against the H200, [check out this article](https://northflank.com/blog/b200-vs-h200).*

## B200 cloud pricing comparison

To make sense of the B200 cost, you need to account for more than just the GPU itself. Many platforms list low hourly rates but charge separately for the CPU, RAM, and storage needed to run workloads. Others offer bundled pricing but with tradeoffs in performance or stability.

Here’s how B200 hourly on-demand pricing break down across several popular platforms:

| **Provider** | **B200 (USD/hr)** | **Notes** |
| --- | --- | --- |
| [**Northflank**](https://northflank.com/) | $5.87 | Fully bundled (GPU, CPU, RAM, storage). Fast startup, no quota required, full-stack AI platform. |
| Modal | $6.25 | GPU-only pricing. CPU and RAM billed separately. Serverless model execution. |
| RunPod | $8.64 | GPU only. Setup takes time, and automation is limited |
| Fireworks AI | $11.99 | GPU-only pricing for hosted model serving. No quotas. Fast auto-scaling. |
| Baseten | $9.98 | Fully managed model hosting. Includes CPU, RAM, and NVMe storage. |
| AWS | $14.24 | May Require quota approval. Bundled node (CPU, RAM, disk). Startup takes minutes. |
| GCP | $18.53 | GPU bundled with VM (CPU, RAM, disk). Requires regional GPU quota. |

## Why Northflank is better

Many platforms make it hard to see what you're really getting. You often have to manage CPUs, memory, storage, or deal with quota approvals before you can even start. That adds friction and slows you down.

[Northflank](https://northflank.com/) keeps things simple. You get access to B200s with everything included. GPU, CPU, memory, and storage are already set up, so you can focus on running your code, not configuring infrastructure.

Northflank is also more than just GPU hosting. It’s a full-stack platform for AI teams. You can train models, serve APIs, run frontends or backends, and manage databases all in one place. Built-in CI, logs, metrics, and autoscaling help you move faster from idea to production without switching tools or writing extra config.

If you're looking for speed, simplicity, and a complete setup that works, Northflank gives you a better way to build and deploy AI.

## Conclusion

The B200 is a powerful GPU, but cost and usability depend entirely on where you run it. Some platforms bury you in hidden fees or make setup painful. Others seem affordable but fail on reliability.

Northflank gives you fast, consistent access to B200s with clear pricing and no extra complexity. You get everything in one place: GPU, CPU, RAM, and storage, already configured and production-ready.

If you're ready to try it yourself, [sign up and deploy your first B200](https://app.northflank.com/signup). If you want to see how it fits your workflow, [book a quick demo](https://cal.com/team/northflank/northflank-intro).

<InfoBox className='BodyStyle'>

## FAQs

### What is the B200 price?

The **NVIDIA B200 price** for individual units hasn’t been officially published by NVIDIA. However, OEM quotes suggest prices around **$45,000–$50,000** for the B200 192GB SXM model. Complete server systems with multiple B200s can exceed **$500,000**.

### How much does the B200 cost on cloud platforms?

**B200 cloud pricing** varies widely depending on provider and whether resources like CPU, RAM, and storage are bundled. Rates range from **$5.87/hour** (on Northflank) up to **$18.53/hour** on major clouds like GCP. See the pricing comparison table above for details.

### B200 vs H200: Which one should you go for?

If you're deciding between the B200 and H200, the **B200 GPU** offers better performance and efficiency for large-scale AI. However, the **H200** can still be a good option for smaller workloads or if you're looking for a lower **GPU cost**. For a full breakdown, see our [B200 vs H200 comparison](https://northflank.com/blog/b200-vs-h200).

</InfoBox>
]]>
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</item><item>
  <title>Run OpenAI's new GPT-OSS (open-source) model on Northflank</title>
  <link>https://northflank.com/blog/self-host-openai-gpt-oss-120b-open-source-chatgpt</link>
  <pubDate>2025-08-05T19:00:00.000Z</pubDate>
  <description>
    <![CDATA[OpenAI just released GPT-OSS, its first fully open-source large language model family under an Apache 2.0 license. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/gpt_oss_chatgpt_open_source_openai_ffd870725b.png" alt="Run OpenAI's new GPT-OSS (open-source) model on Northflank" />OpenAI just released **GPT-OSS**, its first fully open-source large language model family under an Apache 2.0 license. The release includes two models: **gpt-oss-20b** and **gpt-oss-120b,** designed for fast, low-latency inference with strong reasoning and instruction-following capabilities.

Northflank makes it simple to deploy and run these models in a secure, high-performance environment. 

With our **[one-click deploy template](https://northflank.com/stacks/gpt-oss-120b)**, you can get started in minutes, without any infrastructure setup required.

<InfoBox className="BodyStyle">

**TL;DR** 

- OpenAI released **GPT-OSS**, a powerful open-source LLM family under Apache 2.0.  
- The **120B model** delivers top-tier performance and runs smoothly on **2×H100**.  
- You can deploy it in minutes on **Northflank** using our one-click stack with **vLLM + Open WebUI**. No rate limits.

[👉 Deploy GPT-OSS-120B on Northflank now](https://northflank.com/stacks/gpt-oss-120b)

</InfoBox>

<div>
    <a href="https://northflank.com/stacks/gpt-oss-120b">
        <Button variant={["large", "gradient"]}>Deploy now</Button>
    </a>
</div>

## What is GPT-OSS?

**GPT-OSS** is a new open-source LLM series released by OpenAI and integrated into Hugging Face Transformers as of v4.55.0. The models use **Mixture-of-Experts (MoE)** architecture comes with **4-bit quantization (mxfp4)** to enable fast, efficient inference.

### Model options:

- `gpt-oss-20b`: 21B total parameters, ~3.6B active per token, fits in **16GB** of VRAM
- `gpt-oss-120b`: 117B total parameters, ~5.1B active, can run on a single H100 or multiple

These models support:

- Instruction-following
- Chain-of-thought reasoning
- Tool use and structured chat formats
- Inference via Transformers, vLLM, Llama.cpp, Ollama, and the OpenAI-compatible Responses API

## **GPT-OSS-20B vs GPT-OSS-120B**

The 20B model is optimized for speed and accessibility. It fits on a single 16GB GPU, making it ideal for on-device or low-cost server inference. 

The 120B model delivers stronger reasoning and better performance on complex tasks and requires an H100 or multi-GPU setup (which you can get access to using Northflank). 

Both use MoE routing with limited active parameters per forward pass, but 120B has a larger expert pool and deeper attention capacity. 

We recommend using **GPT-OSS-120B with vLLM for the best performance.**

## Why GPT-OSS matters

OpenAI’s GPT-OSS models are the first general-purpose LLMs they’ve released with open weights and a permissive Apache 2.0 license. Everything after GPT-2 (like GPT-3, GPT-4, and the o-series) has been closed-source and API-only. GPT-OSS-120B is also the most capable open-weight model they’ve released, using a modern Mixture-of-Experts architecture.

- **Open weights**
- **Apache 2.0 licensing**
- **Strong performance for agentic and reasoning-heavy use cases**

Unlike GPT-3.5 or GPT-4, which are closed-source and API-only, GPT-OSS is available to run locally or in your own infrastructure. That means full control over latency, cost, and privacy, especially when paired with a secure, GPU-ready platform like Northflank.

Self-hosting gpt-oss also means you won’t run into any rate limits. According to OpenAI, gpt-oss 120B’s performance is on par with o4-mini on most benchmarks, which would make it one of the top open-source models, if not the best. 

## How to deploy GPT-OSS 120B on Northflank

### Option 1: One-click deploy

![gpt-oss-template2.png](https://assets.northflank.com/gpt_oss_template2_4f17c32105.png)

You can deploy GPT-OSS with Open WebUI [using Northflank’s stack template in one click.](https://northflank.com/stacks/gpt-oss-120b)

### 1. Create your Northflank account

[Sign up](https://app.northflank.com/login) and configure your Northflank account.

You can read the [documentation](https://northflank.com/docs/v1/application/getting-started/introduction-to-northflank) to familiarize yourself with the platform.

### 2. Deploy GPT-OSS via Northflank

Northflank provides templates to deploy GPT-OSS with a few clicks. 

Deploy the stack to save the template in your team, then run it to create a cluster in Northflank’s cloud with a vLLM service for high-performance inference and an Open WebUI for easy interaction. 

For more details on this, [see our guide on deploying GPT-OSS](https://northflank.com/stacks/gpt-oss-120b). 

## Option 2: Deploy GPT-OSS on Northflank manually

Below are the steps to deploy manually.

### 1. Create a GPU-enabled project

![gpt-oss-northflank-project.png](https://assets.northflank.com/gpt_oss_northflank_project_aa61d627f4.png)

1. In your Northflank account, create a new project.
2. Name it, select a GPU-enabled region, and click create.

### 2. Create a vLLM deployment

1. Create a new service in your project and select deployment.
2. Name the service GPT-OSS and choose external image as the source.
3. Use the image path `vllm/vllm-openai:gptoss`.
4. Add a runtime variable with the key OPENAI_API_KEY and click the key to generate a random value. Select length 128 or greater and copy this to the environment variable value.
5. In networking, add port 8000 with http protocol and publicly expose it for this guide.
6. Select a GPU deployment plan, and choose Nvidia’s H100 from the GPU dropdown with a count of 2 for high-performance inference.

![gpt-oss-northflank-2.png](https://assets.northflank.com/gpt_oss_northflank_2_dfe873e8ab.png)

1. In advanced options, set a custom command to sleep 1d to start vLLM without loading a default model.
2. Click create service.

### 3. Persist models

Containers on Northflank are ephemeral, so data is lost on redeployment. To avoid re-downloading GPT-OSS:

1. From the service dashboard, go to volumes.
2. Add a volume named vllm-models with 200GB storage.
3. Set the mount path to /root/.cache/huggingface for Hugging Face model downloads.
4. Click create and attach volume.

### 4. Download and serve models

1. Open the shell in a running instance from the service overview or observability dashboard.
2. Download and serve the model with: vllm serve openai/gpt-oss-120b —tensor-parallel-size 2.

To automate this, set the entrypoint to `bash -c` and command to `'export HF_HUB_ENABLE_HF_TRANSFER=1 && pip install --upgrade transformers kernels torch hf-transfer && vllm serve openai/gpt-oss-120b --tensor-parallel-size 2’`

### 5. Configure and test your LLM

1. **Run sample queries**: Test GPT-OSS with coding prompts, adjusting parameters like output style.
2. **Keep iterating**: One of the best parts of self-hosting is you can adapt as quickly as your business demands.

### 6. Interact with models via API

Use the public code.run URL from the service header (e.g., https://api--gpt-oss-vllm--abc123.code.run/v1/models) to check available models. Interact using OpenAI-compatible APIs in Python or other languages. Here’s a Python example:

1. Create a project directory:
    
    ```
    gpt-oss-project/
    ├── .env
    ├── requirements.txt
    └── src/
        └── main.py
    ```
    
2. Add to .env:
    
    ```
    # The API key in your service's runtime variables
    OPENAI_API_KEY=your_api_key_here
    # The URL from your vLLM service's header
    OPENAI_API_BASE="https://your-vllm-instance-url/v1"
    # The model you downloaded and served with vLLM
    MODEL=openai/gpt-oss-120b
    ```
    
3. Add requirements.txt
    
    ```
    openai>=1.65.1
    openapi>=2.0.0
    python-dotenv>=1.0.0
    ```
    
4. Add to main.py:
    
    ```
    import os
    from dotenv import load_dotenv
    from openai import OpenAI
    
    load_dotenv()
    
    client = OpenAI(
        api_key=os.environ.get("OPENAI_API_KEY"),
        base_url=os.environ.get("OPENAI_API_BASE"),
    )
    
    completion = client.completions.create(
        model=os.environ.get("MODEL"),
        prompt="Write a Python function to sort a list"
    )
    
    print("Completion result:", completion)
    
    chat_response = client.chat.completions.create(
        model=os.environ.get("MODEL"),
        messages=[
            {"role": "user", "content": "Explain how to optimize a Python loop"}
        ]
    )
    
    print("Chat response:", chat_response)
    ```
    
5. Run locally with python src/main.py or deploy as a Northflank service.

## Cost of deploying GPT-OSS / ChatGPT open source pricing

Alright, but how much does it cost to self-host gpt-oss?

Running **GPT-OSS-120B** at constant load on **2×H100** GPUs with [vLLM](https://github.com/vllm-project/vllm), here's what you can expect:

**Cost per 1M tokens:**

- **Input tokens:** $0.12
- **Output tokens:** $2.42

**GPU cost on Northflank:**

- **2×H100:** $5.48/hour

Northflank offers some of the **most affordable GPU pricing** available for production workloads. Unlike other platforms that bundle hidden compute or RAM charges, our pricing is transparent and optimized for high-throughput inference. Perfect for serving large Mixture-of-Experts models like GPT-OSS-120B.

## Final thoughts

GPT-OSS marks a major shift in how large models can be used and deployed. OpenAI’s decision to release powerful MoE models under a permissive license gives developers real control for the first time and very importantly, no rate limits. 

Northflank makes it easy to run GPT-OSS securely and efficiently.

Try it out yourself today [here](https://northflank.com/stacks/gpt-oss-120b).]]>
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</item><item>
  <title>How much does an NVIDIA H100 GPU cost?</title>
  <link>https://northflank.com/blog/how-much-does-an-nvidia-h100-gpu-cost</link>
  <pubDate>2025-08-05T15:30:00.000Z</pubDate>
  <description>
    <![CDATA[Compare H100 GPU pricing across top cloud platforms. Learn how Northflank delivers fully bundled, ready-to-run H100 instances with no quotas, fast startup, and a full-stack AI development platform.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/open_source_ai_models_63a310d062.png" alt="How much does an NVIDIA H100 GPU cost?" />The NVIDIA H100 is a high-performance GPU built on the [Hopper architecture](https://northflank.com/blog/h100-vs-a100#h100-built-for-frontier-workloads). It is designed for demanding AI workloads such as large language model training, high-throughput inference, and data-intensive processing. With up to 4.9 TB per second of memory bandwidth and support for FP8 precision, the H100 delivers significant improvements over the previous generation A100.

If you are trying to determine the cost of using an H100, the answer depends on several factors. Costs vary depending on whether you purchase the hardware outright, rent it in the cloud, or pay for just the GPU or an entire system that includes CPU, RAM, and storage.

This guide outlines H100 pricing across major providers and explains how [Northflank](https://northflank.com/) offers one of the most developer-friendly, fully integrated setups.

<InfoBox className='BodyStyle'>

**💭 What is Northflank?**

[Northflank](https://northflank.com/) is a full-stack AI cloud platform that helps teams build, train, and deploy models without infrastructure friction. GPU workloads, APIs, frontends, backends, and databases run together in one place so your stack stays fast, flexible, and production-ready.

[Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how it fits your stack.

> **Need H100 capacity planning?** For teams with consistent H100 requirements or specific availability needs, [request GPU capacity here](https://northflank.com/request/gpu) to explore volume options and reservations.

</InfoBox>

## H100 pricing from NVIDIA

NVIDIA does not publish direct retail pricing, but you can find H100 GPUs through OEMs and authorized resellers. As of mid-2025:

- H100 80GB PCIe typically ranges from $25,000 to $30,000
- H100 80GB SXM ranges from $35,000 to $40,000

> **PCIe vs SXM**
> 
> 
> PCIe is easier to deploy and shows up in more off-the-shelf systems. SXM offers better performance with higher bandwidth and power, often used in tightly coupled multi-GPU servers.
> 

Pricing depends on configuration, cooling, and whether the purchase includes a full server. For example, an eight-GPU DGX H100 system can exceed $300,000. These setups require specialized infrastructure, so many teams opt to rent H100s in the cloud instead.

*If you're curious how the H100 stacks up against the A100, [check out this article](https://northflank.com/blog/h100-vs-a100).*

## H100 cloud pricing comparison

To make sense of H100 pricing, you need to account for more than just the GPU itself. Many platforms list low hourly rates but charge separately for the CPU, RAM, and storage needed to run workloads. Others offer bundled pricing but with tradeoffs in performance or stability.

Here’s how H100 hourly on-demand pricing break down across several popular platforms:

| **Provider** | **H100 SXM (USD/hr)** | **Notes** |
| --- | --- | --- |
| [**Northflank**](https://northflank.com/) | $2.74 | Fully bundled (GPU, CPU, RAM, storage). Fast startup, no quota required, full-stack AI platform. |
| Modal | $3.95 | GPU-only pricing. CPU and RAM billed separately. Serverless model execution. |
| RunPod | $4.18 | GPU only. Setup takes time, and automation is limited |
| Fireworks AI | $5.80 | GPU-only pricing for hosted model serving. No quotas. Fast auto-scaling. |
| Baseten | $6.50 | Fully managed model hosting. Includes CPU, RAM, and NVMe storage. |
| AWS | $7.57 | May Require quota approval. Bundled node (CPU, RAM, disk). Startup takes minutes. |
| GCP | $11.06 | GPU bundled with VM (CPU, RAM, disk). Requires regional GPU quota. |
| Azure | $6.98 | Pricing includes CPU, RAM, and storage. Quotas apply. |
| OCI | $10 | Bare-metal with full machine access (CPU, RAM, NVMe). Quota may be required. |
| Lambda Labs | $3.29 | Bundled pricing. Full-node access (CPU, RAM, storage) |

## Why Northflank is better

Many platforms make it hard to see what you're getting. You often have to manage CPUs, memory, storage, or deal with quota approvals before you can even start. That adds friction and slows you down.

[Northflank](https://northflank.com/) keeps things simple. You get access to H100s with everything included. GPU, CPU, memory, and storage are already set up, so you can focus on running your code, not configuring infrastructure.

Northflank is also more than just GPU hosting. It’s a full-stack platform for AI teams. You can train models, serve APIs, run frontends or backends, and manage databases all in one place. Built-in CI, logs, metrics, and autoscaling help you move faster from idea to production without switching tools or writing extra config.

If you're looking for speed, simplicity, and a complete setup that works, Northflank gives you a better way to build and deploy AI.

## Conclusion

The H100 is a powerful GPU, but cost and usability depend entirely on where you run it. Some platforms bury you in hidden fees or make setup painful. Others seem affordable but fail on reliability.

Northflank gives you fast, consistent access to H100s with clear pricing and no extra complexity. You get everything in one place: GPU, CPU, RAM, and storage, already configured and production-ready.

If you're ready to try it yourself, [sign up and deploy your first H100](https://app.northflank.com/signup). If you want to see how it fits your workflow, [book a quick demo](https://cal.com/team/northflank/northflank-intro).]]>
  </content:encoded>
</item><item>
  <title>The complete guide for your Google Cloud migration</title>
  <link>https://northflank.com/blog/complete-guide-for-google-cloud-gcp-migration</link>
  <pubDate>2025-08-04T22:00:00.000Z</pubDate>
  <description>
    <![CDATA[When searching for &ldquo;Google Cloud migration&ldquo; or exploring cloud migration options, you're likely facing one of two distinct scenarios.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/gcp_cloud_migration_267f331ae9.png" alt="The complete guide for your Google Cloud migration" />When searching for "Google Cloud migration" or exploring cloud migration options, you're likely facing one of two distinct scenarios:

(1) You want to migrate TO Google Cloud from on-prem or another cloud

or

(2) You want to migrate FROM Google Cloud because it's too expensive or complex

Both paths are increasingly common in 2026, and platforms like Northflank can dramatically simplify either journey. This guide covers both Google Cloud migration strategies.

But first…

# What is Google Cloud migration?

Google Cloud migration is the process of moving applications, data, and infrastructure either TO Google Cloud Platform (GCP) or FROM GCP to another platform or on-prem.

**Google Cloud migration**: You're moving from your own servers or another cloud (like AWS) to GCP. Usually because you want better data analytics, AI/ML capabilities, or GCP's sustained use discounts.

**Migrating away from GCP**: You're moving away from GCP to another cloud or back to your own servers. Usually because GCP got too expensive at scale or you need different features. Teams also streamline their reporting by linking [Google Data Studio to PostgreSQL](https://hevodata.com/learn/google-data-studio-postgresql/
), ensuring analytics remain consistent during or after migration.

# **The two Google Cloud migration paths**

## **Path 1: Migrating to Google Cloud**

**Why move to GCP?**

Google Cloud offers compelling advantages. They've got world-class data analytics with BigQuery, AI/ML services that leverage Google's research, and automatic sustained use discounts that can save you 30% without any commitment. The promise is attractive: leverage Google's innovation, get better pricing for predictable workloads.

Yet, to actually use GCP effectively, you need to master:

- **GKE (Google Kubernetes Engine)**: Yes, it's managed Kubernetes, but you still need to configure node pools, set up cluster autoscaling, manage workload identity, and handle VPC-native networking
- **Networking complexity**: VPCs, subnets, firewall rules, Cloud NAT, load balancers
- **IAM confusion**: Service accounts, roles, bindings, conditions
- **Cost surprises**: Egress charges, persistent disk costs, BigQuery slot pricing, GCP billing requires careful monitoring

Most teams spend months just getting a production-ready setup. And that's before you deploy a single application.

### **Enter Northflank**

This is where Northflank comes in. Instead of it being just another GCP abstraction, it's a complete Internal Developer Platform (IDP) that enterprises typically spend years building. Instead of learning GCP, you just use it.

**How it works:**

1. Connect your GCP account to Northflank (5 minutes)
2. Northflank provisions everything: GKE clusters, load balancers, networking, security
2a. Or import your existing GKE clusters. Northflank works with what you already have
3. You deploy your code

**What you get:**

- Production-ready GKE without touching kubectl
- Automatic SSL certificates and load balancing
- Built-in CI/CD from your Git repos
- Database provisioning with one click (Cloud SQL, but simplified)
- Cost controls that actually work
- Full GCP ecosystem integration (Cloud SQL, Cloud Storage, container registries, CDNs)
- Enterprise-grade platform capabilities teams typically spend years building
- Complete control over data residency and deployment regions

## **Path 2: Migrating away from Google Cloud to another cloud or on-prem**

GCP can be expensive at scale, especially for compute-heavy workloads:

- Egress costs add up fast (though Google now offers free data transfer when leaving)
- You're paying for resources 24/7 even with sustained use discounts
- Managed services like Cloud SQL can be pricey compared to self-managed
- Complex pricing for services like BigQuery can surprise you

### The Google Cloud migration challenge

Moving off GCP is daunting because:

1. Your team only knows GCP tools and APIs
2. You've built around GCP-specific services like BigQuery or Firestore
3. Other clouds have different concepts and services
4. Going on-prem means managing infrastructure again

This is where most companies get stuck. They know GCP is getting expensive but can't stomach the migration complexity.

### Northflank, a cloud agnostic platform for your workloads

Northflank solves this by being cloud-agnostic from day one. Here's how migration works:

**To another cloud (AWS/Azure):**

1. Spin up Northflank on AWS/Azure (uses EKS/AKS under the hood)
2. Deploy your apps using the exact same Northflank workflows
3. Migrate data during maintenance window
4. Switch DNS
5. Turn off GCP

**To on-premises:**

1. Get bare metal servers (or VMs)
2. Install k3s or Rancher
3. Connect to Northflank
4. Deploy your apps (same process as GCP)
5. Cancel GCP

## Why Northflank works for both paths

Northflank is not yet another PaaS. It's specifically designed for teams that need cloud flexibility:

- **Multi-cloud native**: Run on GCP, AWS, Azure, Oracle Cloud, Civo, or bare metal
- **No vendor lock-in**: Built on Kubernetes and Docker standards
- **BYOC (Bring Your Own Cloud)**: Your infrastructure, your data, Northflank's simplicity
- **Enterprise-ready**: Build your Internal Developer Platform (IDP) or Application Delivery Platform (ADP) without years of engineering
- **Real company scale**: Teams run 10,000+ containers in production
- **GCP depth**: Supports all GCP instance types across all regions

### **Google Cloud migration TO GCP:**

- Automatic GKE cluster setup and management
- Built-in GCP service integrations (Cloud SQL, Cloud Storage, etc.)
- GCP cost optimization with sustained use discount awareness
- No GCP expertise required

[Check out these docs on how to integrate your Google Cloud account to create and manage clusters using Northflank.](https://northflank.com/docs/v1/application/bring-your-own-cloud/gcp-on-northflank)

### **Google Cloud migration FROM GCP:**

- Cloud-agnostic deployments
- Zero code changes when switching clouds
- Gradual migration support (hybrid cloud)
- Maintain GCP-like developer experience anywhere

### The technical details

Under the hood, Northflank:

- Manages Kubernetes so you don't have to
- Handles ingress, TLS, load balancing automatically
- Provides GitOps workflows without the YAML complexity
- Abstracts cloud differences into a unified API

You write Dockerfiles, push code, and deploy. Whether that runs on GKE, EKS, or your own metal is just a config option.

# Getting started

### Migrating to GCP:

1. Sign up for Northflank
2. Connect your GCP account
    1. Have existing GKE clusters? Import them directly, no need to start from scratch
3. Deploy a test app
4. Migrate your easiest service first
5. Move the rest over weeks, not months

### Migrating from GCP:

1. Pick your target (AWS? Azure? On-prem?)
2. Set up Northflank on the new infrastructure
3. Deploy a non-critical service as a test
4. Plan your data migration strategy (remember: Google offers free egress when leaving)
5. Move services gradually with zero downtime

# Conclusion

Your Google Cloud migration doesn't have to be a multi-year project. Whether you're adopting GCP or escaping it due to costs, the key is abstracting away the complexity while keeping the power.

Northflank does exactly that. Excellent developer experience, any infrastructure. Your choice.

Ready to migrate? Get started [here](https://app.northflank.com/login) or [talk to an engineer](https://cal.com/team/northflank/northflank-intro).]]>
  </content:encoded>
</item><item>
  <title>The complete guide to AWS cloud migration in 2026</title>
  <link>https://northflank.com/blog/aws-cloud-migration-guide</link>
  <pubDate>2025-08-04T22:00:00.000Z</pubDate>
  <description>
    <![CDATA[When searching for &ldquo;AWS migrate&ldquo; or exploring cloud migration options, you're likely facing one of two distinct scenarios.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/aws_cloud_migration_3fe4dd37ee.png" alt="The complete guide to AWS cloud migration in 2026" />When searching for "AWS migrate" or exploring cloud migration options, you're likely facing one of two distinct scenarios: 

(1) You want to migrate TO AWS from on-prem or another cloud

or 

(2) You want to migrate FROM AWS because it's too expensive or complex

Both paths are increasingly common in 2026, and platforms like Northflank can dramatically simplify either journey. This guide covers both AWS cloud migration strategies.

But first…

# What is AWS cloud migration?

AWS cloud migration is the process of moving applications, data, and infrastructure either TO Amazon Web Services (AWS) or FROM AWS to another platform or on-prem.

**AWS cloud migration**: You're moving from your own servers or another cloud (like Google Cloud) to AWS. Usually because you want to scale faster or access AWS's 200+ services.

**Migrating away from AWS**: You're moving away from AWS to another cloud or back to your own servers. Usually because AWS got too expensive or you need more control.

# **The two AWS cloud migration paths**

## **Path 1: Migrating to AWS**

### Why move to AWS?

AWS runs 32% of the global cloud. They've got 200+ services, data centers everywhere, and every enterprise tool you could want. The promise is compelling: scale instantly, access cutting-edge tech.

Yet, to actually use AWS effectively, you need to master:

- **EKS (Elastic Kubernetes Service)**: Sure, it's managed Kubernetes, but you still need to configure node groups, set up cluster autoscaling, manage VPC CNI plugins, and deal with IAM roles for service accounts
- **Networking nightmare**: VPCs, subnets, security groups, NAT gateways, route tables
- **IAM hell**: Policies, roles, instance profiles
- **Cost surprises**: Data transfer charges, NAT gateway fees, EBS volumes you forgot to delete, AWS billing is a full-time job

Most teams spend months just getting a production-ready setup. And that's before you deploy a single application.

### **Enter Northflank: AWS without a learning curve**

This is where Northflank comes in. Instead of it being just another AWS abstraction, it's a complete Internal Developer Platform (IDP) that enterprises typically spend years building. Instead of learning AWS, you just use it.

**How it works:**

1. Connect your AWS account to Northflank (5 minutes)
2. Northflank provisions everything: EKS clusters, load balancers, networking, security
    
    2a. Or import your existing EKS clusters. Northflank works with what you already have
    
3. You deploy your code

**What you get:**

- Production-ready EKS without touching kubectl
- Automatic SSL certificates and load balancing
- Built-in CI/CD from your Git repos
- Database provisioning with one click (RDS, but simplified)
- Cost controls
- Full AWS ecosystem integration (RDS, S3, container registries, CDNs)
- Enterprise-grade platform capabilities teams typically spend years building
- Complete control over data residency and deployment regions

## **Path 2: Migrating away from AWS to another cloud or on-prem**

AWS can be expensive at scale, especially for predictable workloads:

- Bandwidth costs are insane ($0.09/GB adds up fast)
- You're paying 24/7 for peak capacity you use 1% of the time
- Managed services have huge markups (RDS can be 3x the cost of self-managed)
- Every feature is a separate bill

### The AWS cloud migration challenge

Moving off AWS is terrifying because:

1. Your team only knows AWS tools
2. You've built around AWS-specific services
3. Alternative clouds have different APIs and concepts
4. Going on-prem means managing hardware again

This is where most companies get stuck. They know AWS is too expensive but can't stomach the migration complexity.

### Northflank, a cloud agnostic platform for your workloads

Northflank solves this by being cloud-agnostic from day one. Here's how migration works:

**To another cloud (GCP/Azure):**

<aside>

1. Spin up Northflank on GCP/Azure (uses GKE/AKS under the hood)
2. Deploy your apps using the exact same Northflank workflows
3. Migrate data during maintenance window
4. Switch DNS
5. Turn off AWS

</aside>

**To on-premises:**

<aside>

1. Get bare metal servers (or VMs)
2. Install k3s or Rancher
3. Connect to Northflank
4. Deploy your apps (same process as AWS)
5. Cancel AWS

</aside>

## Why Northflank works for both paths

Northflank is not yet another PaaS. It's specifically designed for teams that need cloud flexibility:

- **Multi-cloud native**: Run on AWS, GCP, Azure, Oracle Cloud, Civo, or bare metal
- **No vendor lock-in**: Built on Kubernetes and Docker standards
- **BYOC (Bring Your Own Cloud)**: Your infrastructure, your data, Northflank's simplicity
- **Enterprise-ready**: Build your Internal Developer Platform (IDP) or Application Delivery Platform (ADP) without years of engineering
- **Real company scale**: Teams run 10,000+ containers in production
- **AWS depth**: Supports hundreds of instance types across all major regions

### **For AWS migration TO:**

- Automatic EKS cluster setup and management
- Built-in AWS service integrations (RDS, S3, etc.)
- AWS cost optimization out of the box
- No AWS expertise required

[Check out these docs on how to integrate your AWS account to create and manage clusters using Northflank.](https://northflank.com/docs/v1/application/bring-your-own-cloud/aws-on-northflank)

### **For AWS migration FROM:**

- Cloud-agnostic deployments
- Zero code changes when switching clouds
- Gradual migration support (hybrid cloud)
- Maintain AWS-like developer experience anywhere

### The technical details

Under the hood, Northflank:

- Manages Kubernetes so you don't have to
- Handles ingress, TLS, load balancing automatically
- Provides GitOps workflows without the YAML hell
- Abstracts cloud differences into a unified API

You write Dockerfiles, push code, and deploy. Whether that runs on EKS, GKE, or your own metal is just a config option.

# Getting started

### Migrating to AWS:

1. Sign up for Northflank
2. Connect your AWS account
    1. Have existing EKS clusters? Import them directly, no need to start from scratch
3. Deploy a test app
4. Migrate your easiest service first
5. Move the rest over weeks, not months

### Migrating from AWS:

1. Pick your target (GCP? Azure? On-prem?)
2. Set up Northflank on the new infrastructure
3. Deploy a non-critical service as a test
4. Plan your data migration strategy
5. Move services gradually with zero downtime

# Conclusion

AWS cloud migration doesn't have to be a multi-year project. Whether you're adopting AWS or escaping it, the key is abstracting away the complexity while keeping the power.

Northflank does exactly that. Excellent developer experience, any infrastructure. Your choice.

Ready to migrate? Get started [here](https://app.northflank.com/login) or [talk to an engineer](https://cal.com/team/northflank/northflank-intro).]]>
  </content:encoded>
</item><item>
  <title>How much does an NVIDIA A100 GPU cost?</title>
  <link>https://northflank.com/blog/nvidia-a100-gpu-cost</link>
  <pubDate>2025-08-04T16:30:00.000Z</pubDate>
  <description>
    <![CDATA[Compare A100 GPU cloud pricing across top providers. See why Northflank offers the best all in one value with bundled GPU CPU RAM and storage with no quotas fast startup and full stack support.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/continuous_deployment_blog_post_2_b88b279504.png" alt="How much does an NVIDIA A100 GPU cost?" />The Nvidia A100 is a high-performance GPU used in AI research, large-scale training, inference, and HPC workloads. It's based on the [Ampere architecture](https://northflank.com/blog/h100-vs-a100#a100-optimized-for-largescale-inference), supports multi-instance GPU (MIG), and delivers up to 312 TFLOPs of FP16 compute. You’ll find it in cloud clusters powering LLMs, generative models, and real-time inference engines.

If you're trying to figure out how much it costs to use an A100, the answer isn’t straightforward. Prices vary significantly depending on where you rent it, whether you're getting just the GPU or an entire machine, and whether CPU, RAM, and disk are included.

This guide compares real-world A100 pricing across popular platforms and shows why [Northflank](https://northflank.com/product/gpu-paas) offers one of the most cost-effective setups without compromising performance or reliability.

<InfoBox className='BodyStyle'>

**💭 What is Northflank?**

[Northflank](https://northflank.com/) is a full-stack AI cloud platform that helps teams build, train, and deploy models without infrastructure friction. GPU workloads, APIs, frontends, backends, and databases run together in one place so your stack stays fast, flexible, and production-ready.

[Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how it fits your stack.

> **Planning A100 deployments?** Whether you need on-demand access or reserved capacity, [request GPU capacity here](https://northflank.com/request/gpu) to discuss options that fit your budget and timeline.

</InfoBox>

## A100 pricing from Nvidia

If you’re asking how much it costs to **buy** an A100 outright, Nvidia no longer lists retail pricing directly on its website. That said, the A100 is typically sold through Nvidia partners and OEMs.

Current ballpark pricing:

- **A100 40GB PCIe**: around **$10,000–12,000**
- **A100 80GB PCIe or SXM**: around **$15,000–17,000**

<InfoBox className='BodyStyle'>

**A100 40GB vs A100 80GB** 

Both use the same architecture, but the 80GB model has more memory and higher bandwidth. It's better suited for large models and multi-GPU setups. The 40GB version works well for smaller training runs and inference.

**PCIe vs SXM**

PCIe is easier to deploy and shows up in more off-the-shelf systems. SXM offers better performance with higher bandwidth and power, often used in tightly coupled multi-GPU servers.

</InfoBox>

These prices vary depending on the form factor (PCIe vs SXM), cooling setup, and included server hardware. For example, an 8x A100 node can cost upwards of $150,000 or more when bundled with CPUs, RAM, networking, and chassis.

Because of the high capital cost and power requirements, most teams choose to **rent A100s in the cloud** instead. It’s cheaper, easier to scale, and you only pay for what you use.

That brings us to the next question: how much does it cost to run an A100 per hour?

## A100 cloud pricing comparison

To make sense of A100 pricing, you need to account for more than just the GPU itself. Many platforms list low hourly rates but charge separately for the CPU, RAM, and storage needed to run workloads. Others offer bundled pricing but with tradeoffs in performance or stability.

Here’s how A100 hourly on-demand pricing break down across several popular platforms:

| Provider | A100 40GB (USD/hr) | A100 80GB (USD/hr) | Notes |
| --- | --- | --- | --- |
| [**Northflank**](https://northflank.com/) | $1.42 | $1.76 | Fully bundled (GPU, CPU, RAM, storage). Fast startup, no quota required, full-stack AI platform. |
| Baseten | N/A | $4.00 | Fully managed model hosting. Includes CPU, RAM, and NVMe storage. |
| RunPod | N/A | $2.17 | GPU only. Setup takes time, and automation is limited |
| Fireworks AI | N/A | $2.90 | GPU-only pricing for hosted model serving. No quotas. Fast auto-scaling. |
| Modal | $2.10 | $3.40 | GPU-only pricing. CPU and RAM billed separately. Serverless model execution. |
| AWS | $4.10 | $5.12 | May Require quota approval. Bundled node (CPU, RAM, disk). Startup takes minutes. |
| GCP | $3.67 | $5.12 | GPU bundled with VM (CPU, RAM, disk). Requires regional GPU quota. |
| Azure | $3.40 | $6.00 | Based on ND96 A100 v4 SKUs. Pricing includes CPU, RAM, and storage. Quotas apply. |
| OCI | $3.05 | $4.00 | Bare-metal A100 with full machine access (CPU, RAM, NVMe). Quota may be required. |
| Lambda Labs | $1.29 | $1.79 | Bundled pricing. Full-node access (CPU, RAM, storage) |

## Why Northflank is better

Many platforms make it hard to see what you're really getting. You often have to manage CPUs, memory, storage, or deal with quota approvals before you can even start. That adds friction and slows you down.

[Northflank](https://northflank.com/) keeps things simple. You get access to A100s with everything included. GPU, CPU, memory, and storage are already set up, so you can focus on running your code, not configuring infrastructure.

Northflank is also more than just GPU hosting. It’s a full-stack platform for AI teams. You can train models, serve APIs, run frontends or backends, and manage databases all in one place. Built-in CI, logs, metrics, and autoscaling help you move faster from idea to [production](https://northflank.com/product/deployments) without switching tools or writing extra config.

If you're looking for speed, simplicity, and a complete setup that works, Northflank gives you a better way to build and deploy AI.

## Conclusion

The A100 is a powerful GPU, but cost and usability depend entirely on where you run it. Some platforms bury you in hidden fees or make setup painful. Others seem affordable but fail on reliability.

Northflank gives you fast, consistent access to A100s with clear pricing and no extra complexity. You get everything in one place: GPU, CPU, RAM, and storage, already configured and production-ready.

If you're ready to try it yourself, [sign up and deploy your first A100](https://app.northflank.com/signup). If you want to see how it fits your workflow, [book a quick demo](https://cal.com/team/northflank/northflank-intro).]]>
  </content:encoded>
</item><item>
  <title>How to self-host Qwen3-Coder on Northflank with vLLM </title>
  <link>https://northflank.com/blog/self-host-qwen3-coder-with-vllm</link>
  <pubDate>2025-08-03T22:00:00.000Z</pubDate>
  <description>
    <![CDATA[Qwen3-Coder is Alibaba’s most advanced open-source coding model, designed for agentic code generation, tool use, and long-context reasoning. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/qwen3_coder_2b431b8eda.png" alt="How to self-host Qwen3-Coder on Northflank with vLLM " />## What is Qwen3-Coder?

Qwen3-Coder is Alibaba’s most advanced open-source coding model, designed for agentic code generation, tool use, and long-context reasoning. 

It’s a 480B parameter Mixture-of-Experts model (35B active) with support for 256K–1M token context windows. Benchmarks show it matches or outperforms proprietary models like GPT-4.1 and Claude Sonnet on tasks like code repair, repo-wide reasoning, and tool calling. 

Released under Apache 2.0, it’s free for commercial use and available on Hugging Face and GitHub.

Qwen3-Coder can generate clean, functional code from natural language prompts. 

It’s also great at debugging, offering suggestions to fix errors in code snippets or explaining complex logic. Its agentic capabilities enable it to interact with external tools, like GitHub APIs or IDE plugins, to automate workflows, such as generating pull requests or running tests. 

With its browsing capabilities, Qwen3-Coder can fetch and incorporate up-to-date documentation or code samples from the web, making it a versatile assistant for real-world development. 

## How to self-host Qwen3-Coder?

This guide shows you how to self-host Qwen3-Coder on Northflank using vLLM, a high-performance serving engine with OpenAI-compatible API endpoints. 

With Northflank, you can have Qwen3-Coder running in minutes.

Northflank simplifies GPU resource management, offers easy scaling, and provides integrated monitoring and logging. 

## Why self-host Qwen3-Coder on Northflank?

**1️⃣ Data privacy**: Keep full control over your code, logs, and data in Northflank’s secure cloud.

**2️⃣ High performance**: Use Northflank’s GPU-powered infrastructure for fast, low-latency inference.

**3️⃣ Quick and simple setup**: Get Qwen3-Coder running in under an hour.

**4️⃣ Scalable infrastructure**: Adjust resources easily to match your project’s needs.

**5️⃣ No rate-limits**: Get Claude Sonnet 4 level performance without any rate-limits and API costs.

## Prerequisites

- A Northflank account (which you can create [here](https://app.northflank.com/login))
- Python installed locally (optional, for interacting with the model via API)

# Option 1: One-click deploy

![CleanShot 2025-08-04 at 18.05.52@2x.png](https://assets.northflank.com/Clean_Shot_2025_08_04_at_18_05_52_2x_712639de80.png)

<InfoBox className="BodyStyle">

You can deploy Qwen3-Coder with Open WebUI [using Northflank’s stack template in one click.](https://northflank.com/stacks/deploy-qwen3-30b-coder-32k)

</InfoBox>

### 1. Create your Northflank account

[Sign up](https://app.northflank.com/login) and configure your Northflank account

You can read the [documentation](https://northflank.com/docs/v1/application/getting-started/introduction-to-northflank) to familiarize yourself with the platform.

### 2. Deploy Qwen3-Coder via Northflank

Northflank provides templates to deploy Qwen3-Coder with a few clicks. 

Deploy the stack to save the template in your team, then run it to create a cluster in Northflank’s cloud with a vLLM service for high-performance inference and an Open WebUI for easy interaction. 

For more details on this, see our guide on deploying Qwen3-Coder. 

# Option 2: Deploy Qwen 3-Coder on Northflank manually

Below are the steps to deploy manually.

## 1. Create a GPU-enabled project

![create-gpu-enabled-project.png](https://assets.northflank.com/create_gpu_enabled_project_6cd25e0fd4.png)

1. In your Northflank account, create a new project.
2. Name it, select a GPU-enabled region, and click create.

## 2. Create a vLLM deployment

1. Create a new service in your project and select deployment.
2. Name the service qwen3-coder-vllm and choose external image as the source.
3. Use the image path `vllm/vllm-openai:latest`.
4. Add a runtime variable with the key OPENAI_API_KEY and click the key to generate a random value. Select length 128 or greater and copy this to the environment variable value.
5. In networking, add port 8000 with http protocol and publicly expose it for this guide.
6. Select a GPU deployment plan, and choose Nvidia’s H200 from the GPU dropdown with a count of 8 for high-performance inference.

![vllm-deployment.png](https://assets.northflank.com/vllm_deployment_1e4d2c118d.png)

1. In advanced options, set a custom command to sleep 1d to start vLLM without loading a default model.
2. Click create service.

## 3. Persist models

Containers on Northflank are ephemeral, so data is lost on redeployment. To avoid re-downloading Qwen3-Coder:

1. From the service dashboard, go to volumes.
2. Add a volume named vllm-models with 1000GB storage.
3. Set the mount path to /root/.cache/huggingface for Hugging Face model downloads.
4. Click create and attach volume.

## 4. Download and serve models

1. Open the shell in a running instance from the service overview or observability dashboard.
2. Download and serve the model with: vllm serve Qwen/Qwen3-Coder.

To automate this, set the entrypoint to `bash -c` and command to `export HF_HUB_ENABLE_HF_TRANSFER=1 && pip install hf-transfer && vllm serve Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8 --tensor-parallel-size 8 --quantization fp8 --enable-expert-parallel`.

## 5. Configure and test your LLM

1. **Run sample queries**: Test Qwen3-Coder with coding prompts, adjusting parameters like output style or latency as needed.
2. **Keep iterating**: One of the best parts of self-hosting is you can adapt as quickly as your business demands.

## 6. Interact with models via API

Use the public code.run URL from the service header (e.g., https://api--qwen-coder-vllm--abc123.code.run/v1/models) to check available models. Interact using OpenAI-compatible APIs in Python or other languages. Here’s a Python example:

1. Create a project directory:
    
    ```
    qwen3-project/
    ├── .env
    ├── Dockerfile
    ├── requirements.txt
    └── src/
        └── main.py
    ```
    
2. Add to .env:
    
    ```
    # The API key in your service's runtime variables
    OPENAI_API_KEY=your_api_key_here
    # The URL from your vLLM service's header
    OPENAI_API_BASE="https://your-vllm-instance-url/v1"
    # The model you downloaded and served with vLLM
    MODEL=Qwen/Qwen3-Coder
    ```
    
3. Create Dockerfile:
    
    ```
    FROM python:3.9-slim
    WORKDIR /app
    COPY requirements.txt .
    RUN pip install -r requirements.txt
    COPY . .
    CMD ["python", "src/main.py"]
    ```
    
4. Add requirements.txt:
    
    ```
    openai>=1.65.1
    openapi>=2.0.0
    python-dotenv>=1.0.0
    ```
    
5. Add to main.py:
    
    ```
    import os
    from dotenv import load_dotenv
    from openai import OpenAI
    
    load_dotenv()
    
    client = OpenAI(
        api_key=os.environ.get("OPENAI_API_KEY"),
        base_url=os.environ.get("OPENAI_API_BASE"),
    )
    
    completion = client.completions.create(
        model=os.environ.get("MODEL"),
        prompt="Write a Python function to sort a list"
    )
    
    print("Completion result:", completion)
    
    chat_response = client.chat.completions.create(
        model=os.environ.get("MODEL"),
        messages=[
            {"role": "user", "content": "Explain how to optimize a Python loop"}
        ]
    )
    
    print("Chat response:", chat_response)
    ```
    
6. Run locally with python src/main.py or deploy as a Northflank service.

## 7. Optimize vLLM

Optimize Qwen3-Coder by adjusting vLLM arguments, e.g.:

```
vllm serve Qwen/Qwen3-Coder --tensor-parallel-size 8 --max-model-len 256000 --gpu-memory-utilization 0.85
```

Add debugging environment variables:

```
VLLM_LOGGING_LEVEL="DEBUG"
NCCL_DEBUG="TRACE"
PYTHONUNBUFFERED=1
```

# Option 3: Bring Your Own Cloud (BYOC)

If you want full control over your infrastructure, Northflank supports BYOC. This lets you run Qwen3-Coder inside your own cloud environment (AWS, GCP, or Azure), while still using Northflank’s developer-friendly UI, deployment automation, and observability tooling.

### Why use BYOC?

- **Data residency and compliance**: Keep workloads and data within your private cloud or VPC.
- **Cost optimization**: Leverage your own GPU discounts, reserved instances, or spot fleet.
- **Enterprise-grade control**: Integrate with your existing IAM, networking, and security policies.

### **Step 1: Prepare your cloud provider and Northflank account**

1. **Create/Log in to your cloud provider account**
    - Set up a new project or resource group in [AWS](https://aws.amazon.com/), [GCP](https://cloud.google.com/), or [Azure](https://azure.microsoft.com/en-gb/).
    - Make sure your account has permissions to spin up GPU-based VMs or container instances (e.g., H200s)
2. **Sign Up for Northflank**
    - [Create a Northflank account](https://app.northflank.com/signup), then enable the BYOC functionality by [linking your cloud provider credentials](https://app.northflank.com/s/account/cloud/clusters/integrations/new).
3. **Check your cloud quotas**
    - Before deploying, ensure you have sufficient quota for the GPU resources you intend to use. Spot instances are cheaper but can be reclaimed by the cloud provider, so you'll want to plan for that.

The rest of the process is the same as Step 2 from Option 2.

The only change for BYOC is that instead of choosing “Northflank’s Cloud” in the project creation, you choose Bring Your Own Cloud.

![byoc-choose.png](https://assets.northflank.com/byoc_choose_a437b26acf.png)

# Conclusion

Self-hosting Qwen3-Coder on Northflank with vLLM gives you a powerful coding assistant with minimal setup. 

Northflank’s GPU infrastructure and templates make deployment fast and scalable, so you can focus on coding. Start your Qwen3-Coder project today to boost your development workflow.

Sign up to Northflank, for free, [here](https://app.northflank.com/login).]]>
  </content:encoded>
</item><item>
  <title>B200 vs H200: Best GPU for LLMs, vision models, and scalable training</title>
  <link>https://northflank.com/blog/b200-vs-h200</link>
  <pubDate>2025-08-01T13:00:00.000Z</pubDate>
  <description>
    <![CDATA[Compare NVIDIA's H200 and B200 GPUs for AI workloads. Learn which is best for inference, fine-tuning, or large-scale model training on GPU clusters. Includes pricing, specs, and performance.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/serverless_gpu_4_58f7e081e2.png" alt="B200 vs H200: Best GPU for LLMs, vision models, and scalable training" />The H200 is NVIDIA’s most capable Hopper-based GPU yet. It builds on the H100 by introducing faster memory and better throughput, making it ideal for teams deploying large language models or running high-speed inference at scale.

The B200, on the other hand, represents an entirely new generation. Built on the Blackwell architecture, it is designed from the ground up for training trillion-parameter models, scaling across dense GPU clusters, and supporting next-generation AI systems with higher context and multi-modal complexity.

In this guide, we’ll compare the two GPUs across performance, architecture, and practical deployment to help you choose the right one for your workload.

If you're running or planning large AI workloads in the cloud, [Northflank](https://northflank.com/) gives you fast access to the right GPUs, without long-term commitments, at affordable prices.

## TL;DR: B200 vs H200 at a glance

If you're short on time, here’s how the B200 and H200 compare side by side:

<InfoBox className="BodyStyle">

> **Looking for B200 or H200 capacity?** These GPUs can have limited availability. If you need guaranteed access or have specific capacity requirements, [request GPU capacity here](https://northflank.com/request/gpu).

</InfoBox>

| Feature | H200 | B200 |
| --- | --- | --- |
| Architecture | Hopper | Blackwell |
| Memory Type | HBM3e | HBM3e |
| Tensor Cores | 4th Gen | 5th Gen |
| Transformer Engine | Single | Dual |
| Max Bandwidth | ~4.0 TB/s | ~4.8 TB/s |
| Form Factor | SXM | SXM |
| FP8 Support | Yes | Yes (optimized) |
| NVLink | NVLink 4 | NVLink 5 |
| CUDA Compatibility | CUDA 12.2+ | CUDA 12.4+ |
| Cost on Northflank (Aug 2025) | $3.14/hr | $5.87/hr |
| Ideal For | Inference, fine-tuning, broad compatibility | Large-scale training on B200 clusters, foundation models, multi-node pipelines |

<InfoBox className='BodyStyle'>

**💭 What is Northflank?**

[Northflank](https://northflank.com/) is a full-stack AI cloud platform that helps teams build, train, and deploy models without infrastructure friction. GPU workloads, APIs, frontends, backends, and databases run together in one place so your stack stays fast, flexible, and production-ready. 

[Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how it fits your stack.

</InfoBox>

## B200: Everything you need to know

The B200 is NVIDIA’s most powerful GPU available to developers today. It uses the new [Blackwell architecture](https://resources.nvidia.com/en-us-blackwell-architecture) and is optimized for training the largest models we’ve seen yet. This includes GPT-style models with longer context windows, large batch sizes, and experimental transformer architectures with growing memory and compute demands.

With fifth-generation tensor cores and dual transformer engines, the B200 pushes FP8 performance to a new level. It also ships with 192 GB of HBM3e memory and 6.0 TB/s bandwidth, giving it enough headroom for memory-intensive workloads like vision-language models or retrieval-augmented generation.

What makes B200 especially capable is how well it scales across multi-GPU environments. With NVLink 5, you can achieve faster inter-GPU communication, which is critical for dense clusters and distributed training.

## H200: Everything you need to know

The H200 is the most advanced [Hopper](https://www.nvidia.com/en-us/data-center/technologies/hopper-architecture/) GPU yet. It improves on the H100 by using newer HBM3e memory and increasing total VRAM to 141 GB, which allows for larger batch sizes and higher throughput during inference.

For teams running open-source LLMs, doing model distillation, or deploying AI services with high QPS targets, the H200 offers excellent performance without requiring a new software stack or infrastructure overhaul.

It supports the same fourth-generation tensor cores as H100, and like all Hopper GPUs, it works well with existing CUDA and PyTorch workflows. For teams that already run on H100, moving to H200 is a seamless upgrade that provides meaningful speedups.

## What are the differences between B200 and H200?

Now that we’ve looked at how the B200 and H200 perform on their own, it’s worth breaking down what actually makes them different. The two GPUs aren’t just built for different generations of hardware; they reflect a shift in how teams train and scale deep learning models. Here's how they stack up across architecture, memory, precision, and deployment.

### Architecture and tensor cores

The B200 introduces NVIDIA’s new Blackwell architecture, which features dual transformer engines and fifth-generation tensor cores. These upgrades are especially useful for long-context models and workloads with heavy token parallelism.

The H200 still uses Hopper, which supports one transformer engine per core and slightly older tensor hardware. It remains strong for mainstream training and inference but does not scale as efficiently for newer model classes.

### Memory and bandwidth

Both GPUs use HBM3e, but the B200 includes 192 GB compared to the H200’s 141 GB. It also runs at higher speeds, delivering 6.0 TB/s of bandwidth compared to H200’s 4.8 TB/s. This gives B200 a real advantage when training large models or handling multi-modal inputs.

### NVLink and scaling

The B200 uses NVLink 5 with 1.8 TB/s node-to-node bandwidth. That means it communicates faster with other GPUs and is better suited for large-scale distributed training setups. The H200 uses the same 900 GB/s NVLink as the H100, which still performs well but does not match the B200 at cluster scale.

### Deployment flexibility

Both GPUs ship in SXM format, which is standard for high-end data center deployments. The B200 demands more power and newer infrastructure, while the H200 fits more easily into existing setups that previously ran H100s.

### Software compatibility

The B200 runs best with the latest CUDA releases (12.4 and above) and software like cuDNN 9 or Triton with Blackwell-specific optimizations. The H200 runs on current Hopper-compatible stacks, making it easier to deploy immediately without tooling changes.

## Performance benchmarks

In MLPerf Training v4.1, NVIDIA’s B200 showed clear per-GPU gains over the H200 across large-scale model benchmarks. On tasks like GPT‑3 training and LLaMA fine-tuning, the B200 completed jobs in nearly half the time compared to H100 and H200-based systems.

| Workload | H200 Performance | B200 Performance | Relative Speedup |
| --- | --- | --- | --- |
| GPT‑3 175B Pre-training | Baseline | ~2× faster | 2.0× |
| LLaMA 70B LoRA fine-tuning | Baseline | ~2.2× faster | 2.2× |

These numbers are based on MLPerf’s official v4.1 results. NVIDIA detailed the B200’s performance in their blog coverage of the [Training v4.1 results](https://developer.nvidia.com/blog/nvidia-blackwell-doubles-llm-training-performance-in-mlperf-training-v4-1/) and [Inference v5.0 benchmarks](https://developer.nvidia.com/blog/nvidia-blackwell-delivers-massive-performance-leaps-in-mlperf-inference-v5-0/).

The B200 gains come from newer hardware like fifth-generation tensor cores, faster HBM3e, and dual transformer engines. It also benefits from NVLink 5 for faster GPU-to-GPU communication, which matters in large model training. H200 remains strong for fine-tuning and inference, but doesn't scale as aggressively in multi-node setups.

## How much does B200 and H200 cost?

At [Northflank](https://northflank.com/), you can launch H200 and B200 instances directly in the cloud. We offer flexible pricing with no lock-in, so you can scale up or down depending on your training cycle.

As of August 2025:

- **H200**: $3.14 per hour
- **B200**: $5.87 per hour

H200 gives teams a great middle ground for performance and cost. The B200 costs more but can significantly reduce training time for large models, making it a better fit for heavy experimentation or foundation model teams.

## Which one should you go for?

If you are fine-tuning models, building inference pipelines, or optimizing costs while maintaining speed, the H200 is the most practical choice. It is stable, available, and performs well across most workloads.

If your focus is on pushing model boundaries, scaling training pipelines, or preparing for multi-modal, long-context LLMs, the B200 gives you the horsepower and memory bandwidth to go further, faster.

| Use case | Recommended GPU |
| --- | --- |
| Fine-tuning LLMs | H200 |
| High-throughput inference | H200 |
| Long-context model training | B200 |
| Multi-modal foundation models | B200 |
| Multi-GPU scaling with NVLink | B200 |
| Seamless Hopper upgrades | H200 |

## Wrapping up

The B200 marks a major leap in AI hardware performance. It gives teams building the next generation of models the tools they need to scale training faster and work with more ambitious architectures.

But the H200 still plays a vital role. It balances cost, compatibility, and performance, making it the best option for teams who want to ship quickly without retooling their infrastructure.

At Northflank, we help teams deploy and scale AI workloads with access to cutting-edge GPUs, built-in orchestration, and seamless cloud integrations. You can [launch your first instance in minutes](https://app.northflank.com/signup) or [book a quick demo](https://cal.com/team/northflank/northflank-intro) to see how it fits into your stack.]]>
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</item><item>
  <title>Claude Code: Rate limits, pricing, and alternatives</title>
  <link>https://northflank.com/blog/claude-rate-limits-claude-code-pricing-cost</link>
  <pubDate>2025-07-31T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Claude Code is powerful, but closed models come with two constraints: rate limits and cost ceilings. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/claude_cost_limits_1_45b2c7c93d.png" alt="Claude Code: Rate limits, pricing, and alternatives" /><InfoBox className='BodyStyle'>

Claude Code can get expensive at scale. The alternative is [self-hosting open-source models on Northflank](https://northflank.com/product/gpu-paas). You can get started [here](https://app.northflank.com/signup), by deploying models like [Qwen3](https://northflank.com/stacks/deploy-qwen3-vl-instruct) and [DeepSeek](https://northflank.com/stacks/deepseek-v3-1). 

</InfoBox>

## TL;DR

Claude Code is powerful, but closed models come with two constraints: rate limits and cost ceilings. 

Claude's new pricing and rate limits restrict usage at key moments, especially for high-throughput apps and AI-native products. 

There's a better way: self-host open-source models with Northflank. You get lower cost, no rate limits, and full control over performance.

## What is Claude?

Claude is Anthropic’s family of AI models, known for their conversational ability, large context windows, and safety-oriented alignment. Claude 4 Opus, Sonnet, and Haiku are positioned as general-purpose assistants for reasoning, coding, and text generation.

## What is Claude Code?

Claude Code, launched as part of Anthropic's platform, is an [agentic coding assistant](https://northflank.com/product/sandboxes) capable of reading code, editing files, performing tests, and pushing GitHub commits. It quickly became popular among developers for its ability to handle extended coding sessions and complex development tasks.

Many teams use Claude Code for LLM-generated code, but they’re now running into a wall: **rate limits.**

## Claude pricing and Claude Code pricing

### Chat interface subscriptions

- **Free Plan**: Limited daily messages (varies by demand)
- **Pro Plan**: $20/month - approximately 45 messages every 5 hours
- **Max Plan**: Two tiers at $100/month (5x Pro usage) and $200/month (20x Pro usage)
- **Team Plan**: $30/user/month (minimum 5 users)
- **Enterprise Plan**: Custom pricing starting around $50,000 annually

### API pricing (per million tokens)

- **Claude 4 Opus**: $15.00 input / $75.00 output
- **Claude 4 Sonnet**: $3.00 input / $15.00 output
- **Claude 3.5 Haiku**: $0.80 input / $4.00 output

## Claude Code pricing in practice

Claude Code calls are heavier than chat. They include large system instructions, full file contexts, long outputs, and often multiple steps per user interaction. Which means:

- You burn through tokens faster
- Subscription tiers don’t save you on programmatic use
- You still pay standard per-token API rates on top of any subscription fees

Even at $200/month, you're just buying more throttled access, not control. For teams running agents, IDEs, devtools, or anything programmatic, Claude pricing is both **expensive** and **unstable**.

## What are Claude’s rate limits?

Rate limits are restrictions that API providers implement to control request volume within specific timeframes. 

Anthropic implements these Claude rate limits for several important reasons: 

- Server Resource Management: Preventing any single user from consuming too many computational resources
- Equitable Access: Ensuring fair distribution of API access across all users
- Abuse Prevention: Protecting against malicious activities like scraping or DDoS attacks
- Service Stability: Maintaining overall system performance during peak usage times

### Types of Claude rate limits

Claude API implements several types of rate limits: 

- Requests per minute (RPM) - Limits the number of API calls within a 60-second window
- Tokens per minute (TPM) - Caps the total tokens (both input and output) processed within a minute
- Daily token quota - Restricts the total tokens processed within a 24-hour period

API users also face tier-based restrictions, with higher tiers offering more generous limits after meeting spending thresholds.

## **Claude's new weekly rate limits**

![2345.png](https://assets.northflank.com/2345_961bbd980e.png)

In a post on X, Anthropic announced that it's introducing new weekly rate limits to paid subscribers after it says a small handful of users abused their privileges and, essentially, ruined it for everyone else. 

<InfoBox className="BodyStyle">

🚨 The new limits, effective August 28, 2025, include:

- Weekly caps that reset every seven days
- Separate limits for overall usage and Claude Opus 4 specifically
- Max subscribers can purchase additional usage, beyond what the rate limit provides, at standard API rates

</InfoBox>

![223.png](https://assets.northflank.com/223_058e079e27.png)

### What Claude rate limits mean

Whether you’re using Claude Code through the web interface or via API, the outcome is the same:

- You don’t control the upper bounds
- Throughput varies based on Anthropic’s internal policies
- You might get full-speed access one moment, then dropped or downgraded the next

This creates major friction for:

- **LLM-native devtools** that require fast, repeated completions
- **Autonomous agents** running multi-step reasoning
- **Real-time coding assistants** that rely on low-latency responses
- **Teams scaling prompt workloads** across dozens or hundreds of users

## Why Claude rate limits are a problem

![1222.png](https://assets.northflank.com/1222_4e812e1967.png)

The issue isn’t that Claude is too expensive or too slow. It’s that **you don’t control it**.

Even if you’re paying for Claude Code at enterprise scale, Anthropic can:

- Throttle your usage at unpredictable times
- Impose stricter token limits based on aggregate load
- Prioritize other customers with higher commitments

This makes it difficult to build real-time codegen systems, multi-agent setups, or LLM-native developer tools with guaranteed performance.

More fundamentally, as pointed out [here](https://fourweekmba.com/the-1000-question-why-claudes-rate-limits-signal-a-broader-ai-industry-crisis/?utm_source=rss&utm_medium=rss&utm_campaign=the-1000-question-why-claudes-rate-limits-signal-a-broader-ai-industry-crisis), these rate limits signal a broader issue with the entire **closed model ecosystem**:

> You don’t own the performance. You rent it, and it gets rationed.
> 

Not to mention security implications.

When you send code or context to Claude Code, you're handing sensitive logic, sometimes proprietary codebases or real-time user input, over to a black-box system. You can’t control where the data lands, how long it's retained, or who else might gain access to metadata about your requests. Claude promises privacy, but it's still a third-party API with unknown internal telemetry.

## Host open source models yourself to avoid Claude’s rate limits

![1111.png](https://assets.northflank.com/1111_ef43f15c05.png)

With open models like **[Qwen3](https://northflank.com/stacks/deploy-qwen3-30b-coder-32k)**, **DeepSeek**, **LLaMA**, and **Kimi K2** you can avoid this entire category of limitations.

You choose:

- The exact model you want (code-focused, long-context, parameter count, etc.)
- The GPU it runs on
- The context length
- The batch size
- The latency budget

You can deploy these models using **vLLM, Ollama, or with your own custom code,** and run them on your own infra with **no rate limits and no API surprises**.

## What models can I host on Northflank?

Northflank supports hosting popular open-source AI models that compete directly with closed-source alternatives. Here's what you can run:

**Deepseek Family**

- **Deepseek R1** - Advanced reasoning model (matches OpenAI o1)
- **Deepseek R1 32B** - Smaller reasoning model (matches o1-mini)
- **Deepseek v3** - General-purpose powerhouse (matches Claude Sonnet 3.5)

**Qwen3 Family**

- **Qwen3 Thinking 235B** - Top-tier reasoning (matches Claude Opus 4, GPT-4)
- **Qwen3 32B** - Versatile mid-size model (matches GPT-4.1)
- **[Qwen3 30B](https://northflank.com/stacks/deploy-qwen3-30b-thinking-32k)** - Fast and efficient (matches GPT-4o/Gemini Flash)
- **[Qwen3 Coder](https://northflank.com/stacks/deploy-qwen3-30b-coder-32k)** - Specialized for programming (matches/exceeds Claude for coding)

**Llama 4 Family**

- **Llama 4 Maverick** - Large general model
- **Llama 4 Scout** - Smaller, faster variant

**Coming soon**

- **Kimi K2** - Next-generation model

![CleanShot 2025-08-01 at 14.46.43@2x.png](https://assets.northflank.com/Clean_Shot_2025_08_01_at_14_46_43_2x_7efdacab75.png)

## Cost of self hosting open source models

Let’s take self-hosting Deepseek v3 on Northflank, as an example. 

### **GPU Pricing on Northflank**

- **A100 (80GB)**: $1.76/hour
- **H100 (80GB)**: $2.74/hour
- **H200 (141GB)**: $3.14/hour
- **B200 (180GB)**: $5.87/hour

### **How per-token costs are calculated**

Let’s walk through **Deepseek v3** as an example:

**Step 1: Calculate hourly GPU cost**

- Requires: 8 × H200 GPUs
- Cost: 8 × $3.14 = **$25.12/hour**

**Step 2: Convert to cost per second**

- $25.12 ÷ 3,600 seconds = **$0.006978/second**

**Step 3: Measure processing speeds**

- Input tokens: 7,934 tokens/second
- Output tokens: 993 tokens/second

**Step 4: Calculate per-token costs**

- Input: $0.006978 ÷ 7,934 = $0.0000008795 per token
- Output: $0.006978 ÷ 993 = $0.0000070265 per token

**Step 5: Scale to millions**

- Input: **$0.88 per million tokens**
- Output: **$7.03 per million tokens**

Output tokens are ~8x more expensive because the model generates them much slower (993/second vs 7,934/second). This is why the input/output ratio matters for this use case.

**Compared to Claude Sonnet 3.5** ($3 input, $15 output):

- Deepseek v3 is **3.4x cheaper** for input
- Deepseek v3 is **2.1x cheaper** for output

## How do I get started with open source models?

### Getting started with Northflank

**Step 1: Choose your model**

- **For general use**: Deepseek v3 (matches Claude Sonnet 3.5)
- **For reasoning**: Deepseek R1 or Qwen3 Thinking
- **For coding**: [Qwen3 Coder](https://northflank.com/stacks/deploy-qwen3-30b-coder-32k)
- **For speed**: [Qwen3 30B variants](https://northflank.com/stacks/deploy-qwen3-30b-thinking-32k)

**Step 2: Pick your deployment method**

![claude rate limits northflank template.png](https://assets.northflank.com/claude_rate_limits_northflank_template_0f8c89af17.png)

- **One-click templates**: Deploy popular models with pre-configured settings in minutes
- **Manual setup**: Full control over model parameters and GPU selection
- **[Bring Your Own Cloud](https://northflank.com/product/bring-your-own-cloud)**: Use your existing AWS/GCP/Azure account for maximum control

**Step 3: Select your GPU**

- Small models (< 50GB): 1-2 GPUs
- Medium models (50-100GB): 2-4 GPUs
- Large models (100GB+): 8+ GPUs
- Northflank offers A100, H100, H200, and B200 [options](https://northflank.com/product/gpu-paas)

### Quick start process

![claude rate limits.png](https://assets.northflank.com/claude_rate_limits_3cc738da17.png)

1. **Sign up** for a [Northflank account](https://app.northflank.com/login)
2. **Create a GPU-enabled project** in your preferred region
3. **Deploy from a template** or create a custom vLLM service
4. **Access via API** using OpenAI-compatible endpoints

Most models are serving requests within 30 minutes of starting deployment.

### What you'll need

- A Northflank account (free to create)
- Basic understanding of APIs (for integration)
- Your use case requirements (tokens/month, latency needs)

### Next steps

Ready to cut your AI costs and have complete control over models? [Sign up for Northflank](https://app.northflank.com/login) and deploy your first open source model today. Our templates make it as easy as clicking "deploy," no GPU expertise required.

For detailed guides on specific models, reach out to the Northflank team.]]>
  </content:encoded>
</item><item>
  <title>B100 vs H100: Best GPU for LLMs, vision models, and scalable training</title>
  <link>https://northflank.com/blog/b100-vs-h100</link>
  <pubDate>2025-07-31T13:30:00.000Z</pubDate>
  <description>
    <![CDATA[Compare NVIDIA B100 vs H100 GPUs for AI training and inference. Explore performance, architecture, and pricing and see how to access top-tier GPUs like H100 and B200 with Northflank.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/serverless_gpu_3_b3054290c7.png" alt="B100 vs H100: Best GPU for LLMs, vision models, and scalable training" />The H100 is still one of the most trusted GPUs for building and running large AI models. It’s been the go-to for teams shipping fine-tuned LLMs, scalable inference, and training pipelines that just need to work.

NVIDIA’s new B100 steps things up for teams training larger models or scaling across dense clusters. With faster memory, improved interconnects, and a new architecture, it’s designed for workloads pushing the edge of what’s possible.

This guide breaks down how the two compare across architecture, workloads, and performance so you can decide which one fits your stack. If you're running AI in production or planning to, [Northflank](https://northflank.com/) gives you fast access to the right GPUs, without long-term commitments, at affordable prices.

## TL;DR: B100 vs H100 at a glance

If you're short on time, here’s how the B100 and H100 compare side by side:

<InfoBox className='BodyStyle'>

> **Note on B100 availability:** The B100 has limited availability across most cloud providers. If you have specific requirements for Blackwell architecture GPUs, [request GPU capacity here](https://northflank.com/request/gpu) to explore your options.

</InfoBox>

| Feature | H100 | B100 |
| --- | --- | --- |
| Architecture | Hopper | Blackwell |
| Tensor Cores | 4th Gen + Transformer Engine | 5th Gen + Dual Transformer Engines |
| Memory Type | HBM3 | HBM3e |
| Max Bandwidth | ~3.35 TB/s | ~4.8 TB/s |
| FP8 Support | Yes | Yes (improved) |
| NVLink | 900 GB/s | NVLink 5 (1.8 TB/s node-to-node) |
| Form Factors | PCIe, SXM | SXM (NVLink-focused) |
| Transformer Optimization | Yes | Yes (faster context window support) |
| Target Workloads | LLMs, fine-tuning, training | Foundation model training, ultra-large scale inference |
| Cost on Northflank (July 2025) | 80GB VRAM - $2.74/hr | NA |

<InfoBox className='BodyStyle'>

*💭 **What is Northflank?*** 

[Northflank](https://northflank.com) is a full-stack AI cloud platform that helps teams build, train, and deploy models without infrastructure friction. GPU workloads, APIs, frontends, backends and databases run together in one place so your stack stays fast, flexible, and production-ready.

[Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how it fits your stack.

</InfoBox>


## B100: Everything you need to know

The B100 is onee of NVIDIA’s most powerful GPUs yet. It uses the new [Blackwell architecture](https://resources.nvidia.com/en-us-blackwell-architecture) and was built specifically for frontier-scale workloads. While the H100 pushed AI hardware into the fine-tuning era, the B100 is meant for foundation model training and large-scale inference.

The real upgrades come from the fifth-generation tensor cores and dual transformer engines. This gives it better throughput in FP8 workloads, which are becoming the new standard for model training. Paired with HBM3e memory and NVLink 5, the B100 can scale across nodes faster than anything before it.

This makes it a strong fit for training trillion-parameter models, extending context windows in LLMs, or experimenting with multi-modal architectures that demand more memory and compute.

## H100: Everything you need to know

Most teams today are still shipping with H100, and for good reason. Built on the [Hopper architecture](https://www.nvidia.com/en-us/data-center/technologies/hopper-architecture/), it brought major gains in inference performance, introduced FP8 support, and made training more efficient without changing model code.

The H100 runs well in most cloud environments, thanks to support for both PCIe and SXM form factors. It offers high bandwidth through HBM3 memory and supports the software stacks that teams already use.

For anyone running high-throughput inference, fine-tuning open models, or distributing workloads across many GPUs, the H100 still delivers where it matters. It remains the most accessible GPU for teams that need flexibility and stability.

## What are the differences between B100 and H100?

Now that we’ve looked at how the B100 and H100 perform on their own, it’s worth breaking down what actually makes them different. The two GPUs aren’t just built for different generations of hardware; they reflect a shift in how teams train and scale deep learning models. Here's how they stack up across architecture, memory, precision, and deployment.

### Architecture and Tensor Cores

The H100 introduced transformer engines and FP8 to mainstream AI workflows. The B100 builds on that with dual transformer engines and newer tensor cores, allowing more parallelism across tokens and layers. This matters for models with long context lengths or those doing more compute per step.

### Memory Bandwidth and NVLink

B100 uses faster HBM3e memory, giving it about 40% more bandwidth than the H100. It also uses NVLink 5, which doubles node-to-node communication speeds. For distributed training or multi-GPU setups, this makes B100 significantly more capable at scale.

### FP8 Performance

Both chips support FP8, but B100’s implementation is more efficient. If you're training models from scratch or pushing new architectures, B100 handles mixed-precision workloads with less overhead and better convergence.

### Deployment and Scaling

H100’s dual format (PCIe and SXM) works well across clouds and on-prem environments. B100 is SXM-only and optimized for NVLink-based clusters. If you're running dense workloads with lots of parallelism, B100 fits better. But for most production inference or hybrid cloud use cases, H100 remains more flexible.

### Software Compatibility

B100 requires newer CUDA versions (12.4 and above) and gets the most from updates like cuDNN 9. If your stack is already running H100s, upgrading to B100 might involve more software work, but the performance upside is significant.

## Performance benchmarks (MLPerf v4.1 Proxies)

The best look we have at B100 performance comes from [NVIDIA's MLPerf Training submissions](https://developer.nvidia.com/blog/nvidia-blackwell-doubles-llm-training-performance-in-mlperf-training-v4-1/?utm_source=chatgpt.com) using the HGX B200 platform. Since both B100 and B200 use the Blackwell architecture, these results are a solid proxy.

Compared to H100, Blackwell GPUs delivered major per-GPU speedups in every category. GPT-3 pre-training ran 2 times faster, Llama 2 70B fine-tuning showed a 2.2 times gain, and even workloads like image generation and recommendation systems saw clear improvements.

| Workload | Speedup |
| --- | --- |
| GPT-3 Pre-training | 2.0× |
| Llama 2 70B LoRA fine-tuning | 2.2× |
| Graph neural networks | 2.0× |
| Text-to-image generation | 1.7× |
| Recommenders | 1.6× |
| Object detection | 1.6× |
| BERT training | 1.4× |

These results also came from smaller GPU counts. The GPT-3 benchmark ran with just 64 Blackwell GPUs compared to 256 H100s for similar per-GPU performance.

## How much does B100 and H100 cost?

At [Northflank](https://northflank.com/), we offer access to H100 and other GPUs like the B200. The B100 is not currently supported on our platform, as supply remains constrained industry-wide.

As of July 2025, GPU pricing on Northflank looks like this:

- **A100 40GB:** $1.42/hr
- **A100 80GB:** $1.76/hr
- **H100 80GB:** $2.74/hr
- **H200 141GB**: $3.14/hr
- **B200 180GB**: $5.87/hr

The H100 offers the best value for teams doing fine-tuning or deployment. While it costs more than A100, it can reduce training time and improve model performance, especially on larger tasks.

[Start training on H100 with Northflank](https://app.northflank.com/signup)

## Which one should you go for?

If your workloads are centered on production inference, fine-tuning open-source models, or cost-efficient training, H100 is still the best overall choice. It’s battle-tested, well-supported, and scales cleanly across environments.

The B100 makes sense when you're building for the next generation of AI models, particularly when you need longer context lengths, more tokens per batch, or deeper model architectures. Just keep in mind that availability is limited, and adoption might lag behind other Blackwell GPUs like the B200.

| Use Case | Recommended GPU |
| --- | --- |
| Fine-tuning LLMs | H100 |
| Large context transformers | B100 |
| Cost-optimized inference | H100 |
| Foundation model training | B100 |
| Multi-GPU scaling with NVLink | B100 |
| On-prem & hybrid cloud setups | H100 |

## Wrapping up

The B100 brings a real shift in how frontier-scale models can be trained, but it may not be the right GPU for everyone just yet. It’s faster, more scalable, and built for new model classes, but not yet widely available or fully supported across the ecosystem.

If you're already building with the H100 and need stability, flexibility, and performance, there's no urgency to move. But if you're planning for the next leap in model scale, the B100 is worth watching closely.

At Northflank, we help teams run production-grade AI workloads using hardware that’s available right now. You can [launch GPU instances in minutes](https://app.northflank.com/signup) or [book a quick demo](https://cal.com/team/northflank/northflank-intro) to see how it fits into your stack.]]>
  </content:encoded>
</item><item>
  <title>Top Modal Sandboxes alternatives for secure AI code execution</title>
  <link>https://northflank.com/blog/top-modal-sandboxes-alternatives-for-secure-ai-code-execution</link>
  <pubDate>2025-07-30T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[If you're building AI agents, code interpreters, or platforms that execute untrusted code, Modal Sandboxes might be on your radar. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/modal_sandboxes_59e18e630b.png" alt="Top Modal Sandboxes alternatives for secure AI code execution" />If you're building AI agents, code interpreters, or platforms that execute untrusted code, Modal Sandboxes might be on your radar. But depending on your needs, self-hosting, microVM isolation, flexible deployment, or enterprise features, you may need to explore alternatives.

This guide examines the leading Modal Sandboxes alternatives, comparing isolation technologies, deployment options, pricing models, and production readiness.

We wrote a detailed explanation of container isolation and everything you need to know about it [here](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation). Use it as a primer before going deeper into Modal Sandboxes alternatives.

<InfoBox className='BodyStyle'>

## **TL;DR: Best Modal Sandboxes alternatives**

[**Northflank**](https://northflank.com/) delivers production-proven microVM isolation (Kata Containers/CLH) plus gVisor, accepts any OCI container image, offers unlimited sandbox duration, BYOC deployment, and complete platform capabilities. Handles 2M+ workloads monthly.

- **E2B.dev** uses Firecracker microVMs with excellent AI agent SDKs but limits sessions to 24 hours
- **Modal** provides gVisor containers with persistent storage but requires SDK-defined images, Python-centric platform
- **Daytona.io** offers sub-90ms provisioning for AI workflows, newest in the space
- **Vercel Sandbox** leverages Firecracker for dev environments, 45-minute session limits
- **Cloudflare Workers** uses V8 isolates for instant edge execution, no persistent state
</InfoBox>

## What are Modal Sandboxes?

Modal Sandboxes let you dynamically create containers and execute arbitrary code inside them. Built on gVisor isolation, they support:

- Running code in containers defined through Modal's SDK
- Persistent data across sessions via network filesystems
- Network tunneling and port exposure
- Streaming input/output for interactive processes

While Modal Sandboxes can execute any language, you must use Modal's Python SDK to define custom images, you can't bring arbitrary OCI images. This locks you into their image building process.

## Why consider Modal Sandboxes alternatives?

Modal excels at secure code execution, but teams often need:

- **Any OCI image support**: Use existing containers without SDK requirements
- **Self-hosting or BYOC**: Run in your AWS/GCP/Azure accounts
- **MicroVM isolation**: Hardware-level isolation beyond gVisor
- **Non-Python orchestration**: SDKs in other languages
- **Enterprise features**: audit logs, compliance tools
- **Transparent AND affordable pricing**: Clear cost structure at scale
- **Complete infrastructure**: Databases, APIs, and more beyond sandboxes

## At-a-glance comparison

| Platform | Isolation | Images | Persistence | Deploy options | Best for |
| --- | --- | --- | --- | --- | --- |
| [**Northflank**](https://northflank.com/) | microVM (Kata/CLH) & gVisor | Any OCI image | Unlimited | Managed or BYOC | Complete platform + sandboxes |
| **E2B.dev** | microVM (Firecracker) | Pre-built + custom | 24hr max | Managed only | AI agent tools |
| **Modal** | gVisor | SDK-defined only | Yes (network FS) | Managed only | ML/AI workloads |
| **Daytona.io** | Docker/Kata | Docker images | Limited | Managed only | Quick AI demos |
| **Vercel Sandbox** | microVM (Firecracker) | Limited | 45 min max | Vercel only | Dev previews |
| **Cloudflare Workers** | V8 Isolates | N/A | No | Cloudflare only | Edge functions |

## 1. Northflank - Overall best Modal alternative


![image - 2025-07-25T134747.000.png](https://assets.northflank.com/image_2025_07_25_T134747_000_2e5ac73b94.png)

[Northflank](https://northflank.com/) stands out by offering multiple isolation technologies and deployment flexibility. Since 2021, we’ve processed millions of workloads for companies like Writer and Sentry.

### Key advantages over Modal:

- **Any OCI image**: Bring any container from Docker Hub, GitHub, etc.
- **The most complete isolation**: Kata Containers (microVM) or gVisor per workload
- **True BYOC**: Deploy in your cloud accounts with full control
- **Multi-language SDKs**: Not locked to Python orchestration
- **Complete platform**: Run databases, APIs, cron jobs alongside sandboxes
- **Transparent pricing**: Simple usage-based billing

<InfoBox className='BodyStyle'>


## **🤑 Pricing**

**Northflank**

CPU $0.01667/hr, RAM $0.00833/hr, NVIDIA H100 $2.74 / hour, NVIDIA B200 $5.87 / hour

**Modal Sandboxes Pricing**

CPU $0.0473 / hour, RAM $0.0080 / hour, NVIDIA H100 $3.95 / hour, NVIDIA B200 $6.25 / hour

Take into account that Modal charges for CPU, GPU, and RAM separately when running GPU workloads, with a minimum default reservation of 0.125 CPU cores per function.

For an H100 instance with 26 vCPU, 234GB RAM, and 500GB NVME

**Modal pricing breakdown:**

- H100 GPU: $3.95/hour
- 26 CPU cores: 26 × $0.0473 = $1.23/hour
- 234GB RAM: 234 × $0.0080 = $1.87/hour
- 500 GB storage (charged as additional RAM, 25 GB): 25 × $0.0080 = $0.20/hour
- **Total Modal cost: ~$7.25/hour**

**Northflank: $2.74/hour all-inclusive**

Northflank's GPU pricing is all-inclusive. The $2.74/hour for an H100 includes GPU, CPU, and RAM.

For GPU workloads, Northflank is approximately 62% cheaper than Modal. 

For CPU-only workloads, Northflank's CPU pricing is about 65% less expensive than Modal's.

</InfoBox>


![gpu-prices-northflank.png](https://assets.northflank.com/gpu_prices_northflank_c6dbc88fdb.png)
## 2. E2B.dev

E2B specializes in AI code execution with Firecracker microVMs and polished SDKs. Great for hackathons and demos but lacks production features.

**Pros**: 150ms cold starts, nice SDKs, 24hr persistence

**Cons**: No self-hosting, expensive at scale, sandbox-only

## 3. Daytona.io

Newest player focusing on sub-90ms provisioning for AI workflows. Fast but still maturing.

**Pros**: Blazing fast starts, Docker ecosystem

**Cons**: Limited persistence, young platform

## 4. Vercel Sandbox

Beta offering with Firecracker isolation, tightly integrated with Vercel's platform.

**Pros**: Great DX for Vercel users

**Cons**: 45-min limits, Vercel-only

## 5. Cloudflare Workers

V8 isolates for instant execution at 200+ locations globally.

**Pros**: Zero cold starts, global by default

**Cons**: No persistence, JS/WASM only

## Why teams choose Northflank

### 1. Bring any container

With Modal, you must define images through their Python SDK. Northflank accepts any OCI-compliant image from any registry: Docker Hub, GitHub Container Registry, your private registry, without modifications.

### 2. Stronger isolation options

While Modal uses gVisor only, Northflank gives you access to gVisor and true microVM isolation (Kata Containers) based on your security needs.

### 3. Infrastructure flexibility

- **Your cloud**: Deploy in your AWS/GCP/Azure accounts
- **Compliance**: Keep data in your VPC
- **Hybrid**: Mix Northflank-managed and self-hosted

### 4. Beyond sandboxes

Northflank runs your complete stack, unlikely sandbox-only vertical products:

- Secure code execution
- Backend APIs
- Databases
- Scheduled jobs
- GPU workloads

### 5. Production scale

With 2M+ monthly workloads, Northflank has solved the operational challenges others haven't:

- Multi-tenant isolation
- Resource quotas
- Audit logging
- Enterprise SSO

## Making the right choice

**Choose Modal if**: You're Python-first and comfortable with SDK-defined images

**Choose E2B if**: You need quick AI demos with nice SDKs

**Choose Northflank if**: You want to use any OCI image, need production-grade isolation, deployment flexibility, and a complete platform

## Get started with secure sandboxes

Specialized sandboxing tools have their place, but modern AI applications need more than just isolated code execution. 

Northflank leads because it's the only platform that combines:

- Enterprise-grade microVM isolation (Kata containers using CLH)
- A complete platform for all your workloads
- Production scale (2M+ microVMs monthly)
- True infrastructure flexibility (managed or BYOC)
- Transparent, predictable pricing

Don't settle for a sandbox when you need a platform. 

With Northflank, secure AI execution is just one part of a comprehensive infrastructure solution that grows with your needs.

[Try Northflank today on your own](https://northflank.com/) or [book a demo](https://cal.com/team/northflank/northflank-intro) with a Northflank engineer.


<aside>

## FAQs

### Can I migrate from Modal Sandboxes to Northflank?

Yes. While the APIs differ, the migration is straightforward since Northflank accepts any OCI container image. You can export your existing containers and deploy them directly on Northflank.

### Does Northflank support GPU sandboxes like Modal?

Yes, Northflank supports all major NVIDIA GPUs (H100, A100, etc.) with the same isolation options. Unlike Modal, GPU pricing is all-inclusive and more affordable.

### What's the difference between gVisor and Kata Containers?

gVisor (used by Modal) is a user-space kernel that intercepts syscalls. Kata Containers (available on Northflank) provides true hardware-level isolation using lightweight VMs. Kata offers stronger isolation but gVisor has lower overhead. 

### Is self-hosting available for Modal alternatives?

Only Northflank offers true production-ready BYOC (Bring Your Own Cloud), letting you deploy in your AWS, GCP, or Azure accounts. E2B's self-hosting is experimental, and Modal is managed-only.


</aside>]]>
  </content:encoded>
</item><item>
  <title>Top Edera.dev alternatives for secure AI code execution in 2026</title>
  <link>https://northflank.com/blog/top-edera-dev-alternatives-for-secure-ai-code-execution</link>
  <pubDate>2025-07-29T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[When you're building platforms that execute code from AI models or users, security is everything and don’t let anyone tell you otherwise. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/edera_9a9c9f6617.png" alt="Top Edera.dev alternatives for secure AI code execution in 2026" />When you're building platforms that execute code from AI models or users, security is everything and don’t let anyone tell you otherwise. 

Whether that code comes from GPT-4, Claude, or your customers, one escape could compromise your entire infrastructure.

Traditional containers share kernels. That's a problem. Sophisticated attacks can break out, and when they do, game over.

This guide examines the leading [Edera.dev](http://Edera.dev) alternatives for organizations that need bulletproof code isolation, comparing technologies, features, pricing models, and production readiness.

We wrote a detailed explanation of container isolation and everything you need to know about it [here](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation). Use it as a primer before going deeper into Edera alternatives.

![CleanShot 2025-07-30 at 19.54.38@2x.png](https://assets.northflank.com/Clean_Shot_2025_07_30_at_19_54_38_2x_cd472d7385.png)

## TL;DR

- **[Northflank](https://northflank.com/)** delivers multiple isolation options (Kata Containers with Cloud Hypervisor (CLH) and gVisor, plus complete platform capabilities, BYOC deployment, and handles 2M+ workloads monthly.
- **E2B.dev** specializes in AI sandboxes using Firecracker, offers solid SDKs but caps sessions at 24 hours.
- **Modal** focuses on ML compute with gVisor isolation, Python-centric but enterprise-certified.
- **Vercel Sandbox** provides developer-friendly microVMs limited to 45-minute sessions, currently beta.
- **Cloudflare Workers** leverages V8 isolation for edge compute with zero cold starts, no persistence.
- **Daytona.io** delivers rapid sandbox provisioning for AI workflows, newest player in the space.

## What is Edera.dev?

Edera represents a fundamental rethink of container security. Rather than patch the shared-kernel problem, they eliminated it entirely using Type 1 hypervisors (specifically Xen) to isolate each container with its own kernel.

Launched in 2024 with $20M in funding, their female-founded team built Krata (Rust-based Xen orchestration) to enable 250+ isolated workloads per server. The platform targets enterprise Kubernetes deployments requiring maximum security.

**Edera solves one problem exceptionally well: hypervisor-grade container isolation.**

Organizations needing broader capabilities, general workload orchestration, flexible deployment options, or accessible pricing, should evaluate Edera alternatives.

## Why look for an Edera.dev alternative?

Edera.dev's hypervisor-centric approach excels at security but comes with trade-offs:

- Brand new platform (2024) with limited production deployments
- Enterprise-only pricing model
- Single isolation technology (Xen)
- Focused solely on container security, not full platform needs
- Steep learning curve for hypervisor-based architectures
- Not self-serve, there’s no easy way to try the product

**Above all, limited cloud provider support for Xen creates constraints** 

Edera's reliance on Xen hypervisor significantly limits deployment options across major cloud providers. 

Google Cloud Platform has never supported Xen, exclusively using KVM since inception.

Microsoft Azure runs on Hyper-V and offers no native Xen support, only providing migration tools for legacy Xen workloads. 

While AWS maintains Xen compatibility through its "Xen-on-Nitro" technology for older instance families (M1-M4, C1-C4, etc.), all new instance types since 2017 run exclusively on the Nitro hypervisor. 

This means Edera users on AWS are restricted to legacy instance types, missing out on the performance improvements and cost benefits of modern hardware. 

In contrast, KVM enjoys universal support across all major cloud providers and is the default hypervisor for most Linux distributions. For organizations requiring multi-cloud flexibility or access to the latest cloud infrastructure, this Xen dependency represents a significant limitation that may drive them to explore container isolation alternatives that don't face similar deployment constraints.

## At-a-glance comparison of Edera.dev alternatives

| Platform | Isolation method | Persistent workloads | Deploy anywhere | Primary use case |
| --- | --- | --- | --- | --- |
| [**Northflank**](https://northflank.com/) | **microVM (Kata Containers using CLH) and gVisor** | **Unlimited** | **Yes (BYOC)** | **Full platform + isolation, including AI workloads** |
| E2B.dev | microVM (Firecracker) | 24 hours max | No | AI code interpreters |
| Modal | gVisor | With volumes | No | ML compute jobs |
| Vercel Sandbox | Firecracker | 45 minutes max | No | Dev environments |
| Cloudflare Workers | V8 engine | Stateless only | No | Edge compute |
| Daytona.io | Containers | Unlimited | Not documented | AI development |

# Top [Edera.dev](http://Edera.dev) alternatives, ranked

## 1. Northflank - Most versatile Edera alternative

![northflank-website.png](https://assets.northflank.com/northflank_website_d050074216.png)

[Northflank](https://northflank.com/) stands out by offering four distinct isolation technologies, letting you match security levels to workload requirements. Since 2019, they've processed millions of workloads monthly across their global infrastructure.

**Pros:**

- Different isolation per workload (depending on what’s best): gVisor, Kata, Firecracker, or Cloud Hypervisor
- Complete platform capabilities beyond sandboxing
- Self-service, you can try it without speaking to sales
- Deploy anywhere: managed cloud or your AWS/GCP/Azure/bare-metal
- Proven scale with enterprise customers
- Developer-friendly pricing
- Generous free tier for testing

**Cons:**

- Broader platform might exceed pure sandboxing needs

Northflank's key advantage: you're not buying a sandbox tool, you're getting infrastructure that happens to include world-class isolation. Run your APIs, databases, ML models, and untrusted code on one platform.

## Northflank vs Edera: Platform vs Infrastructure

Edera is building a better container engine using Xen hypervisors. That's important, but it's just one piece of the puzzle.

Northflank delivers the complete platform developers actually need. You get APIs, SDKs, orchestration, monitoring, everything required to run production workloads. Plus, you get access to multiple isolation technologies: Kata, gVisor, Firecracker, or Cloud Hypervisor.

Here's the difference: Edera is building a more secure lock. Northflank gives you the entire security system, plus the house it protects.

For teams that need to ship products today, not tomorrow, Northflank's approach is clear. You get secure workloads running immediately, not promises about future developer tools and integrations. 

## 2. E2B.dev

E2B laser-focuses on AI code execution, building their entire stack around Firecracker microVMs. Czech founders raised $11.5M to create the smoothest developer experience for adding code interpretation to AI apps.

**Pros:**

- Blazing 150ms cold starts
- Purpose-built for AI agents
- Sessions persist up to 24 hours
- Comprehensive SDKs (Python, JS/TS)
- Fully open-source infrastructure

**Cons:**

- Sandbox-only solution
- Production self-hosting not ready
- Hard 24-hour session limit
- No orchestration layer
- Limited language support

Perfect for AI startups needing quick sandbox integration. Less ideal for complex infrastructure requirements.

## 3. Modal

Modal rebuilt cloud computing specifically for ML workloads, using gVisor isolation and memory-safe Rust infrastructure. They've achieved SOC 2 Type 2 certification and support GPUs from consumer to datacenter grade.

**Pros:**

- Near-instant container starts
- Full GPU lineup (T4 through B200)
- Enterprise security certifications
- Generous $30 monthly credits

**Cons:**

- Python ecosystem only
- No infrastructure control
- Serverless-only architecture
- Gets pricey for 24/7 workloads

Ideal for ML teams needing secure, scalable compute without infrastructure management.

## 4. Vercel Sandbox

Vercel brings their frontend expertise to backend isolation, using Firecracker to create lightweight development environments. Still in beta.

**Pros:**

- Lightning-fast boot times (under 125ms)
- Native Node.js and Python
- 5 free CPU hours monthly

**Cons:**

- 45-minute hard timeout
- Beta stability concerns
- Development-only focus
- No production features

Great for preview environments and testing. Not ready for production AI workloads.

## 5. Cloudflare Workers

![CleanShot 2025-07-30 at 10.46.39@2x.png](https://assets.northflank.com/Clean_Shot_2025_07_30_at_10_46_39_2x_74fc2b5bea.png)

Cloudflare's V8-based isolation trades VM-level security for massive scale and geographic distribution. Powers millions of edge functions across 330+ locations.

**Pros:**

- Instant execution (no cold starts)
- Global deployment by default
- 100k daily requests free
- Unbeatable edge latency

**Cons:**

- JavaScript/WASM only
- Ephemeral execution only
- No file system access
- Locked to Cloudflare

Unmatched for edge logic but wrong tool for stateful AI applications.

## 6. Daytona.io

![CleanShot 2025-07-30 at 13.02.33@2x.png](https://assets.northflank.com/Clean_Shot_2025_07_30_at_13_02_33_2x_4a17b41111.png)

Daytona pivoted from dev environments to AI code execution, achieving impressive sub-90ms provisioning. The Codeanywhere team brings IDE expertise to sandbox design.

**Pros:**

- Fastest environment creation
- IDE-grade developer tools
- Full language support
- $200 starter credits

**Cons:**

- Youngest platform (2023)
- Usage-based pricing surprises
- Self-hosting details sparse
- Standard container isolation

Strong potential but needs time to mature. Watch this space.

## Why Northflank is the complete solution

Here's what separates Northflank from single-purpose Edera alternatives: it's infrastructure that scales with your ambitions.

### More than sandboxes

Instead of stitching together services:

- Secure code execution with your choice of isolation
- Production APIs running alongside
- Managed databases with automated operations
- Scheduled jobs and batch processing
- GPU workloads when you need them
- Unified security, monitoring, and deployment

### Battle-tested reliability

Real companies like Sentry and Writer trust Northflank for multi-tenant deployments where security failures aren't an option. Two million workloads monthly prove the platform's stability.

### Deploy your way

Freedom others can't match:

- **Instant start**: Use Northflank's cloud
- **Your cloud**: BYOC to AWS, GCP, Azure
- **Hybrid**: Mix managed and self-hosted
- **Global**: Consistent experience worldwide

### Built for teams

Enterprise features from day one:

- Advanced access controls
- Compliance tooling
- Detailed audit trails
- Professional support

## Summary

Choosing an Edera alternative means balancing security needs against operational reality. Pure sandboxing tools solve one problem. Modern applications need more.

Northflank wins because it delivers:

- Choice of isolation technologies (not locked to one)
- Infrastructure for your entire stack
- Proven reliability at scale
- Deployment flexibility unmatched by competitors
- Pricing that scales from hobby to enterprise

Stop juggling multiple platforms. Get security and scale in one solution.

Northflank turns secure code execution from a special requirement into standard operating procedure.

[Try out Northflank for free. ](https://northflank.com/)]]>
  </content:encoded>
</item><item>
  <title>Top Daytona.io alternatives for running AI code in secure sandboxed environments</title>
  <link>https://northflank.com/blog/top-daytona-io-alternatives-for-running-ai-code-in-secure-sandboxed-environments</link>
  <pubDate>2025-07-29T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[If you're building AI agents, developer tools, or code-executing platforms, at some point you have to run untrusted code: code you didn’t write, can’t predict, and shouldn’t trust.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/daytona_8f96a5b271.png" alt="Top Daytona.io alternatives for running AI code in secure sandboxed environments" />If you're building AI agents, developer tools, or code-executing platforms, at some point you have to run untrusted code: code you didn’t write, can’t predict, and shouldn’t trust.

Maybe it’s LLM-generated. Maybe it’s uploaded by a user. Either way, running it safely and reliably is hard. Especially when you’re doing it at scale.

It requires strong isolation, persistence, orchestration, observability, and infrastructure flexibility. 

This article covers the top alternatives to Daytona.io comparing them across runtime isolation, startup latency, sandbox duration, Git and CI/CD integration, pricing, and real-world use cases.

We wrote a detailed explanation of container isolation and everything you need to know about it [here](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation). Use it as a primer before going deeper into Daytona alternatives.

![CleanShot 2025-07-30 at 19.54.38@2x.png](https://assets.northflank.com/Clean_Shot_2025_07_30_at_19_54_38_2x_cd472d7385.png)

## TL;DR

- **[Northflank](https://northflank.com/)** offers production-proven Kata Containers powered microVMs, and gVisor, with full orchestration, GPU support, long-running jobs, Bring Your Own Cloud (BYOC), and runs your entire infrastructure, not just sandboxes.
- **E2B.dev** uses Firecracker microVMs with great persistence features but no self-hosting in production.
- **Modal** provides fast Python containers with gVisor isolation, ideal for ML workloads but Python-only.
- **Vercel Sandbox** uses Firecracker microVMs for development environments but with session limits.
- **Cloudflare Workers** uses V8 isolates for blazing-fast edge functions but no persistent state.

## What is Daytona.io?

Daytona pivoted in February 2025 from development environments to become infrastructure for running AI-generated code. They provide sandboxes through an SDK that lets AI agents execute code in isolated environments.

Under the hood, Daytona's default configuration uses standard Docker containers, though they support enhanced isolation through Kata Containers and Sysbox when explicitly configured. This tiered approach means security depends heavily on your configuration choices.

**Daytona is built for AI agent workflows, not comprehensive infrastructure.**

If you're trying to run production workloads beyond just code snippets, like databases, long-running services, or GPU jobs, you'll need a more complete platform.

## Why look for a Daytona.io alternative?

Daytona.io focuses specifically on AI agent code execution, which may be too narrow if you need:

- A platform that runs ALL your workloads (not just AI sandboxes)
- Production-proven infrastructure with millions of workloads in the wild
- True multi-tenant isolation without manual configuration
- Support for databases, persistent services, and full applications
- Enterprise features like [Bring Your Own Cloud (BYOC)](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment), compliance, and granular access controls

## At-a-glance comparison of [Daytona.io](http://Daytona.io) alternatives

| Platform | Isolation type | Persistent sandboxes | BYOC / Self-hosting | Best for |
| --- | --- | --- | --- | --- |
| **Northflank** | microVM (Kata Containers using CLH) and  gVisor | Unlimited | Yes | Complete cloud platform + secure AI infra |
| E2B.dev | microVM (Firecracker) | Yes | No | AI agents and codegen tools |
| Modal | Container (gVisor) | Limited | No | Python ML workloads |
| Vercel Sandbox | microVM (Firecracker) | No | No | Dev environment previews |
| Cloudflare workers | V8 Isolates | No | No | Edge functions, API middleware  |

## 1. Northflank (Best overall alternative to Daytona)

![northflank-website.png](https://assets.northflank.com/northflank_website_d050074216.png)

[Northflank](https://northflank.com/) operates over 2 million isolated workloads every month and has been in production since 2019. Unlike platforms built just for AI sandboxes, Northflank is a complete cloud platform that happens to excel at secure code execution.

**Pros:**

- Technologies like Kata Containers with Cloud Hypervisor (CLH) and gVisor, giving you flexibility in your secure compute stack wherever you need it: AWS, GCP, Azure, bare-metal.
- Runs everything: containers, databases, cron jobs, AND secure sandboxes
- Companies like Writer, Sentry, and others have leveraged Northflank's secure runtime to run multi-tenant customer deployments for untrusted code at scale
- Full CI/CD, GitOps, and infrastructure automation built-in
- Transparent, usage-based billing
- True production scale with enterprise features

**Cons:**

- More comprehensive than pure sandbox-only solutions, full platform may be unnecessary if you only need ephemeral sandboxes
- Requires understanding of projects/services model

What sets Northflank apart is that it's not just a sandboxing tool, it's a complete platform. You can run your AI agents, your backend APIs, your databases, and your GPU inference all in one place with consistent security and orchestration.

Building a secure sandboxing platform with Firecracker and Kubernetes isn't a weekend project. It can take a team months or longer, and the complexity doesn't go away, it becomes something you have to operate and maintain every day. Northflank has already solved this at scale.

## 2. E2B.dev

E2B focuses specifically on providing sandboxes for AI applications through Firecracker microVMs. They've built a solid SDK for ephemeral and persistent sandbox management.

**Pros:**

- True microVM isolation via Firecracker
- Excellent persistence features (up to 24hr active, 30 days paused)
- Python, JavaScript/TypeScript, R, Java, Bash support
- Clean SDK design for AI agent integration

**Cons:**

- Limited to sandbox use cases only
- Self-hosting still experimental (not production-ready)
- No transparency on pricing
- Can't run your other infrastructure

E2B is great if you ONLY need sandboxes for AI agents. But if you want to run your complete application stack with the same security guarantees, you'll need additional platforms.

## 3. Modal

![modal-home-page.png](https://assets.northflank.com/modal_home_page_3ef6ad50bc.png)

Modal uses gVisor containers to provide secure Python execution environments. They've optimized heavily for ML/AI workloads with excellent GPU support.

**Pros:**

- Sub-second container starts with custom Rust runtime
- Comprehensive GPU support (T4 to H200)
- Good for batch jobs and model inference
- Container keep-alive and checkpointing features

**Cons:**

- Python-only (no other languages for function definition)
- No BYOC or self-hosting options
- Limited to serverless model (no persistent services)
- Opaque pricing structure

Modal excels at Python ML workloads but isn't suitable if you need multi-language support or want to run persistent services alongside your AI workloads.

## 4. Vercel Sandbox

Vercel's sandbox solution provides Firecracker-based isolation for development environments, leveraging their "Hive" infrastructure that powers millions of builds.

**Pros:**

- Fast microVM provisioning
- Node.js and Python support
- Good for testing and preview environments
- Integrated with Vercel's ecosystem

**Cons:**

- 45-minute maximum runtime
- No persistence between sessions
- Limited to development use cases
- Not designed for production AI workloads

Vercel Sandbox works well for development workflows but isn't built for production AI agent execution at scale.

## 4. Cloudflare Workers

![CleanShot 2025-07-30 at 10.46.39@2x.png](https://assets.northflank.com/Clean_Shot_2025_07_30_at_10_46_39_2x_74fc2b5bea.png)

Cloudflare takes a completely different approach with V8 isolates, the same technology that powers Chrome's tab isolation.

**Pros:**

- Isolates have such a small memory footprint that we, at least, can afford to only bill you while your code is actually executing
- No cold starts, always warm
- 200+ global edge locations
- Excellent for stateless functions

**Cons:**

- JavaScript/WebAssembly only
- No persistent state
- No GPU support
- Not suitable for long-running processes

Workers excel at edge computing but can't handle stateful AI workloads or non-JavaScript languages.

## Why Northflank is the complete solution

The fundamental difference is scope: while other platforms solve pieces of the puzzle, Northflank provides the complete infrastructure layer for modern applications, including secure AI execution.

### Beyond just sandboxes

With Northflank, you're not cobbling together different services:

- Run your AI agents in secure microVMs
- Deploy your backend APIs in the same platform
- Manage databases with automated backups
- Schedule cron jobs for batch processing
- Scale GPU workloads for model inference
- All with consistent security, networking, and observability

### Production-proven at scale

Companies like Writer, Sentry, and others have leveraged Northflank's secure runtime to run multi-tenant customer deployments for untrusted code at scale.

### True flexibility

Unlike single-cloud or hosted-only solutions, Northflank offers:

- **Managed cloud**: Zero setup, just deploy
- **BYOC**: Run in your AWS, GCP, Azure, or bare metal
- **Multi-region**: Deploy globally with consistent experience
- **Any runtime**: Not locked to specific languages or frameworks

### Enterprise-ready from day one

While sandbox-specific tools often lack enterprise features, Northflank includes:

- SSO and directory sync
- Granular RBAC
- Audit logging and compliance tools
- SLAs and dedicated support

## Summary

If you're evaluating platforms for running AI-generated code, the key question isn't just "can it sandbox code?" it's "can it run my entire application securely?"

Specialized sandboxing tools have their place, but modern AI applications need more than just isolated code execution. 

Northflank leads because it's the only platform that combines:

- Enterprise-grade microVM isolation (Kata containers using CLH)
- A complete platform for all your workloads
- Production scale (2M+ microVMs monthly)
- True infrastructure flexibility (managed or BYOC)
- Transparent, predictable pricing

Don't settle for a sandbox when you need a platform. 

With Northflank, secure AI execution is just one part of a comprehensive infrastructure solution that grows with your needs.]]>
  </content:encoded>
</item><item>
  <title>H100 vs A100 comparison: Best GPU for LLMs, vision models, and scalable training</title>
  <link>https://northflank.com/blog/h100-vs-a100</link>
  <pubDate>2025-07-29T15:00:00.000Z</pubDate>
  <description>
    <![CDATA[Compare NVIDIA A100 vs H100 GPUs for deep learning and LLM training. Explore architecture, performance, pricing, and real-world use cases. Choose the right cloud GPU for your AI workload.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/serverless_gpu_1_798a5c5849.png" alt="H100 vs A100 comparison: Best GPU for LLMs, vision models, and scalable training" />When you're training deep learning models at scale, the GPU you choose affects everything from speed to cost. NVIDIA’s A100 and H100 are two of the most capable options available, built for serious workloads but designed for different problems.

The A100 has become the default for large-scale training and inference. It offers stable performance, strong framework support, and efficient throughput for a wide range of models. The H100 is built for what comes next. Its architecture is tuned for transformers, FP8 precision, and high-throughput training at scale.

If you’re running these workloads in the cloud, the difference matters. At [Nothflank](https://northflank.com/), we see teams using A100s to power production inference and others pushing H100s to train massive LLMs. This guide breaks down the differences, starting with a quick side-by-side comparison.

## TL:DR - A100 vs H100 at a glance

If you're short on time, here’s a quick look at how the A100 and H100 compare side by side.

| Feature | A100 | H100 |
| --- | --- | --- |
| Architecture | Ampere | Hopper |
| Process node | 7nm | TSMC 4N |
| Tensor cores | 3rd Gen | 4th Gen + Transformer Engine |
| Memory type | HBM2e | HBM3 |
| Max memory bandwidth | ~2.0 TB/s | ~3.35 TB/s |
| Precision support | FP64, TF32, BF16, FP16, INT8 | FP64, TF32, BF16, FP16, INT8, FP8 |
| Transformer acceleration | No | Yes (Transformer Engine) |
| Multi-instance GPU (MIG) | First Generation (7 instances) | Second Generation (7 improved slices) |
| Hardware video decode | JPEG + NVDEC acceleration | Included but not explicitly highlighted |
| Key use Cases | General AI/ML, CV, inference | LLMs, FP8 training, large-scale HPC |
| Cost on Northflank (July 2025) | 40GB VRAM - $1.42/hr, 80GB VRAM - $1.76/hr | 80GB VRAM - $2.74/hr |

<InfoBox className='BodyStyle'>

**What is Northflank?**

*Northflank is a full-stack AI cloud platform for building, training, and deploying AI applications, with GPU workloads, APIs, frontends, and databases all running in one place.*

[Sign up](https://app.northflank.com/signup) to get started or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how it fits your stack.

</InfoBox>

## A100: Optimized for large-scale inference

The A100 has been a core part of modern deep learning infrastructure since it launched. Built on NVIDIA’s [Ampere architecture](https://www.nvidia.com/en-us/data-center/ampere-architecture/), it powers everything from large-scale training to production inference pipelines. It supports TF32, FP16, and BF16 precision and comes with third-generation Tensor Cores that speed up deep learning operations across a wide range of models.

It uses high-bandwidth HBM2e memory with up to 2 terabytes per second of bandwidth and supports Multi-Instance GPU (MIG), which allows you to split the GPU into isolated slices. This makes it ideal for teams that need flexibility in how they allocate compute resources.

A100s are widely used for deploying inference at scale, and running distributed workloads. If you're working across a range of architectures and need stable, high-throughput compute, the A100 remains a strong, reliable choice.

## H100: Built for frontier workloads

The H100 introduces a new [Hopper architecture](https://www.nvidia.com/en-us/data-center/technologies/hopper-architecture/) designed specifically for the scale and complexity of today’s largest models. With support for FP8 precision and NVIDIA’s Transformer Engine, it enables faster training and better efficiency on LLMs and generative models without requiring major changes to your code.

Its HBM3 memory delivers over 3.3 terabytes per second of bandwidth, which means faster data movement and better performance under heavy workloads. Fourth-generation Tensor Cores and second-generation MIG support give you even more control over how compute is distributed.

If you're fine-tuning large transformers, or pushing batch sizes and sequence lengths beyond what was previously possible, the H100 is built to meet that demand.

## What are the differences between A100 and H100?

Now that we’ve looked at how the A100 and H100 perform on their own, it’s worth breaking down what actually makes them different. The two GPUs aren’t just built for different generations of hardware; they reflect a shift in how teams train and scale deep learning models. Here's how they stack up across architecture, memory, precision, and deployment.

### 1. Architecture and tensor cores

The A100 is based on the Ampere architecture. It uses third-generation tensor cores that support TF32, FP16, and BF16. That setup works well for most deep learning pipelines. The H100 is built on Hopper and introduces fourth-generation tensor cores along with a transformer engine. That engine brings native FP8 support and dynamically mixes precision during training. If you’re working with large transformers or LLMs, this gives H100 a clear edge in speed and efficiency.

### 2. Memory bandwidth and interconnect

A100 uses HBM2e memory and reaches about 2 TB per second of memory bandwidth. H100 upgrades to HBM3, pushing bandwidth beyond 3.3 TB per second. That alone can reduce training time on large batch sizes. H100 also bumps NVLink from 600 GB per second (on A100) to 900 GB per second, which is a major win for multi-GPU training and model parallelism.

### 3. FP8 and precision efficiency

While A100 can handle FP16 and BF16 well, it doesn’t support FP8. H100 does, and that opens up a new level of compute density. It means you can push larger models or use larger batch sizes without running into memory ceilings. And because the transformer engine handles the precision scaling internally, you don’t need to make low-level changes to your code.

### 4. Form factor and deployment options

Both cards are available in PCIe and SXM form factors, but there’s a big difference in how they run. PCIe is what you’ll see in most cloud platforms. SXM is typically for high-density setups and offers higher power limits, better thermal efficiency, and full NVLink support. H100 in SXM form can reach up to 700 watts, while PCIe versions are capped lower.

### 5. Software compatibility

Both GPUs run the standard CUDA stack, but H100’s features rely on the latest versions of CUDA and cuDNN. If you're still on older drivers or toolchains, A100 will likely be more forgiving. To get the most out of H100, especially FP8 support and the transformer engine, you need to be on CUDA 12 and above.

### 6. Real-world usage

A100 is still widely used and highly capable, especially for computer vision models, reinforcement learning, and vision model training. H100 is where teams are going for large-scale language models, diffusion models, or anything that hits a memory or throughput wall on A100. It’s not just about being faster; it unlocks workflows that weren’t practical before.

## Performance Benchmarks

When scaled across multi-GPU clusters, the H100 delivers up to [**30× more performance**](https://developer.nvidia.com/blog/nvidia-hopper-architecture-in-depth/#introducing_the_nvidia_h100_tensor_core_gpu) than the A100 in real-world workloads like GPT-3 training, inference, and genomics.

![image - 2025-07-29T162525.819.png](https://assets.northflank.com/image_2025_07_29_T162525_819_94309ef533.png)*H100 offers up to 30× speedup over A100 ([source](https://developer.nvidia.com/blog/nvidia-hopper-architecture-in-depth/)).*

## How much does A100 and H100 cost?

After comparing each GPU, the next question is usually pricing. not the sticker price, but what it actually costs to run them in the cloud.

[Northflank](https://northflank.com/) offers both A100 and H100 GPUs at affordable prices. no commitments, and you have full control over how you scale. Here's what that looks like today: (July 2025)

- A100 40GB: $1.42/hr
- A100 80GB: $1.76/hr
- H100 80 GB: $2.74/hr

The A100 is still the most cost-efficient option for stable training runs, fine-tuning, and production inference. It’s fast, reliable, and easy to scale across dozens of instances.

The H100 comes in when you’re fine-tuning at the frontier. Large batch sizes, FP8-heavy workloads, and transformer-based models benefit from the extra memory bandwidth and throughput. Even though it costs more per hour, it can bring total training time down, which often means better value in the long run.

## Which one should you go for?

By now, you’ve seen how the A100 handles a broad range of workloads and how the H100 pushes the limits on model size and training speed. Both are powerful, but they serve different needs. The right choice depends on what you're running, how you scale, and where performance matters most. Here’s a breakdown to help you decide.

| Use Case | Recommended GPU |
| --- | --- |
| Training vision models | A100 |
| Fine-tuning transformer LLMs | H100 |
| Mixed precision Inference | A100 |
| FP8 optimized Fine-tuning | H100 |
| Multi-tenant GPU usage | Both (MIG support) |
| Budget-conscious deployments | A100 |
| Maximum performance at scale | H100 |

## Wrapping up

The A100 remains a reliable choice for a wide range of workloads. It’s proven, efficient, and still powers production at scale. But if you're working with transformer-heavy models, large-scale LLMs, or need the speed to shorten training cycles, the H100 brings a different level of performance.

This guide broke down the architectural differences, performance benchmarks, and real-world cost of each GPU. If you're ready to test them in your stack, [Northflank](https://northflank.com/) gives you access to both. You can [launch cloud GPU instances in minutes](https://app.northflank.com/signup) or [book a quick demo](https://cal.com/team/northflank/northflank-intro) to see how it fits your workflow.]]>
  </content:encoded>
</item><item>
  <title>How to migrate from Heroku: A step-by-step guide</title>
  <link>https://northflank.com/blog/how-to-migrate-from-heroku-a-step-by-step-guide</link>
  <pubDate>2025-07-27T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Whether you're looking to migrate Heroku applications due to pricing changes, seeking better performance, or need more flexibility, this guide covers everything you need to successfully migrate from Heroku to Northflank.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/heroku_migrate_82f8bc2c20.png" alt="How to migrate from Heroku: A step-by-step guide" />Whether you're looking to migrate Heroku applications due to pricing changes,
seeking better performance, or need more flexibility, this guide covers everything
you need to successfully migrate from Heroku to Northflank.

## What you'll learn

- How Heroku concepts translate to Northflank's architecture
- Step-by-step migration of your applications and databases
- Best practices for zero-downtime migration
- How to leverage Northflank's advanced features post-migration

## Why migrate to Northflank?

Northflank offers a modern alternative to Heroku with transparent pricing, better performance, and more flexibility:

- **Cost-effective**: Pay only for resources you use with no hidden fees
- **Global deployment**: Deploy to multiple regions worldwide
- **Advanced networking**: Built-in private networking, multiple ports, and protocol support
- **Superior developer experience**: Modern UI, comprehensive API, and CLI tools
- **No vendor lock-in**: Build with Dockerfiles or buildpacks, your choice
- **Better performance**: Migrate Heroku apps to modern infrastructure

## Who should migrate from Heroku?

This Heroku migration guide is perfect for:

- Teams looking to migrate Heroku apps to reduce costs
- Developers needing to migrate Heroku databases with too much fuss
- Companies planning to migrate from Heroku's limited regions to global infrastructure
- Anyone seeking to migrate Heroku worker dynos to a more flexible platform

## Before you begin

### Prerequisites

- A [Northflank account](https://app.northflank.com/signup)
- Access to your Heroku applications and databases
- Your application code in a Git repository (GitHub, GitLab, or Bitbucket)
- Heroku CLI installed for data export

### Concept mapping

Understanding how Heroku concepts map to Northflank will help you plan your migration:

| Heroku | Northflank | Notes |
| --- | --- | --- |
| App | Combined Service or Build + Deployment Service | Single or multi-service architecture |
| Dyno | Instance | Scalable compute units |
| Dyno Types | Resource Plans | Flexible CPU/memory configurations |
| Worker Dyno | Deployment Service (no public ports) | Background processing |
| Scheduler | Cron Job | Time-based task execution |
| Config Vars | Environment Variables / Secret Groups | Enhanced secret management |
| Heroku Postgres | PostgreSQL Addon | Managed database with backups |
| Heroku Redis | Redis Addon | In-memory data store |
| Release Phase | Pre-deploy Command | Database migrations and setup |
| Pipeline | Pipeline with Release Flows | Advanced deployment workflows |
| Review Apps | Preview Environments | Automatic PR deployments |

## Step 1: Analyze your Heroku setup

Before migrating, document your current Heroku configuration:

### List your applications

```bash
heroku apps --team YOUR_TEAM

```

### For each application, note:

1. **Process types** from your Procfile:

```bash
heroku ps --app YOUR_APP_NAME

```

1. **Configuration variables**:

```bash
heroku config --app YOUR_APP_NAME

```

1. **Add-ons and databases**:

```bash
heroku addons --app YOUR_APP_NAME

```

1. **Domains**:

```bash
heroku domains --app YOUR_APP_NAME

```

1. **Current dyno configuration**:

```bash
heroku ps:type --app YOUR_APP_NAME

```

## Step 2: Set up Northflank

### Connect your Git repository

1. Log in to your [Northflank dashboard](https://app.northflank.com/)
2. Navigate to your account settings
3. Connect your Git provider (GitHub, GitLab, or Bitbucket)
4. Authorize Northflank to access your repositories

### Create a project

Projects in Northflank organize your services, databases, and secrets:

1. Click **Create new project** in your dashboard
2. Choose a name and region for your project
3. Select the region closest to your users for optimal performance

## Step 3: Migrate Heroku databases to Northflank

Always migrate databases before applications to ensure your services can connect on first deploy.

### For Heroku Postgres → Northflank PostgreSQL

1. **Create a PostgreSQL addon** in Northflank:
    - Click **Create new** → **Addon**
    - Select **PostgreSQL**
    - Choose your version (match your Heroku version if possible)
    - Select resources based on your current usage
    - Name it descriptively (e.g., `production-db`)
2. **Note the connection details**:
    - After creation, go to the **Connection details** tab
    - Copy the internal connection string for your services

### For Heroku Redis → Northflank Redis

1. **Create a Redis addon**:
    - Click **Create new** → **Addon**
    - Select **Redis**
    - Configure persistence and resources as needed
    - Name it appropriately
2. **Save connection details** for later use

## Step 4: Create your services

The approach depends on your Procfile complexity:

### For single-process applications (web only)

Create a **Combined Service** that handles both building and deployment:

1. Click **Create new** → **Service** → **Combined service**
2. **Configure build settings**:
    - Select your Git repository and branch
    - Choose **Buildpack** as the build type
    - Northflank auto-detects your buildpack, or you can specify custom ones
    - Add any required build arguments
3. **Set runtime configuration**:
    - Add all environment variables from your Heroku config
    - Replace database URLs with Northflank connection strings
    - Configure ports (Northflank auto-detects for buildpacks)
4. **Configure resources**:
    - Select CPU and memory based on your current dyno type
    - Set instance count for horizontal scaling

### For multi-process applications (web + workers)

Use separate **Build** and **Deployment** services for better control:

### Create a build service

1. Click **Create new** → **Service** → **Build service**
2. Configure your repository and buildpack settings
3. Add build-time environment variables if needed

### Create deployment services

For each process type in your Procfile:

1. **Web process**:
    - Create a deployment service
    - Link to your build service
    - Override the start command with your web process command
    - Configure public ports
2. **Worker processes**:
    - Create separate deployment services for each worker
    - Override start commands accordingly
    - Don't add public ports (workers are internal only)
3. **Use Secret Groups** for shared configuration:
    - Create a secret group with common environment variables
    - Link it to all your deployment services
    - Override service-specific variables as needed

## Step 5: Migrate Heroku data - Complete database migration guide

### Preparation

1. **Scale up your Northflank databases** to handle production load
2. **Enable maintenance mode** on Heroku:

```bash
heroku maintenance:on --app YOUR_APP_NAME

```

### Export from Heroku Postgres

1. **Create a backup**:

```bash
heroku pg:backups:capture --app YOUR_APP_NAME

```

1. **Download the backup**:

```bash
heroku pg:backups:download --app YOUR_APP_NAME

```

This creates a `latest.dump` file.

### Import to Northflank PostgreSQL

1. **Get your external connection string** from the Northflank addon's connection details
2. **Restore the backup**:

```bash
pg_restore --verbose --no-acl --no-owner \
  -d "YOUR_NORTHFLANK_EXTERNAL_CONNECTION_STRING" \
  latest.dump

```

For large databases (>20GB), use parallel jobs:

```bash
pg_restore --verbose --no-acl --no-owner --jobs=4 \
  -d "YOUR_NORTHFLANK_EXTERNAL_CONNECTION_STRING" \
  latest.dump

```

### Migrate Redis data

For Redis with persistence enabled:

1. **Export from Heroku** (if using Redis Cloud or similar):
    - Create a backup through your provider's dashboard
    - Download the RDB file
2. **Import to Northflank**:
    - Use `redis-cli` with both connection strings to migrate

## Step 6: Configure advanced features

### Configure health checks

Ensure high availability with health checks:

1. Go to **Health checks** in your service settings
2. Configure HTTP endpoints or TCP checks
3. Set appropriate thresholds and intervals

### Set up autoscaling

Unlike Heroku's limited autoscaling, Northflank offers flexible options:

1. In **Resources & scaling**, enable autoscaling
2. Set min/max instances
3. Configure CPU or memory-based triggers

## Step 7: Update DNS and go live

### Add custom domains

1. In your web service, go to **Networking**
2. Add your custom domain
3. Northflank provides DNS records to configure

### Update DNS records

1. Update your domain's DNS to point to Northflank
2. Northflank automatically provisions TLS certificates
3. Monitor propagation (usually 5-30 minutes)

### Verify and monitor

1. Test your application thoroughly
2. Monitor logs in real-time through the Northflank dashboard
3. Set up alerts for any issues

## Post-migration optimization

### Leverage Northflank features

- **Private networking**: Services communicate internally without internet exposure
- **Multiple ports**: Run multiple services on different ports
- **Persistent volumes**: Attach storage for stateful applications
- **Advanced pipelines**: Create sophisticated deployment workflows

### Cost optimization

- Review resource usage after a few days
- Adjust instance sizes based on actual consumption
- Use Northflank's transparent pricing to optimize costs

### Development workflow

1. **Install Northflank CLI**:

```bash
npm install -g @northflank/cli

```

1. **Set up local development**:
    - Forward services to localhost
    - Execute commands in running containers
    - Manage resources programmatically

## Troubleshooting common issues

### Build failures

- Ensure buildpack compatibility (Northflank uses Heroku-20 stack by default)
- Check build logs for missing dependencies
- Verify environment variables are set correctly

### Database connectivity

- Use internal URLs for service-to-database connections
- Ensure services and databases are in the same region
- Check security settings and connection pooling

### Performance differences

- Northflank containers may have different resource limits
- Adjust memory and CPU allocation as needed
- Enable autoscaling for traffic spikes

## Next steps

- Explore [Northflank's API](https://northflank.com/docs/v1/api) for automation
- Set up [CI/CD pipelines](https://northflank.com/docs/v1/application/cicd/overview)
- Configure [backup schedules](https://northflank.com/docs/v1/application/databases-and-persistence/backup-restore-and-import-data) for databases

## Get help with your Heroku migration

Our team has helped hundreds of companies migrate Heroku apps. We can provide migration support for complex applications. Contact support through your dashboard for assistance with:

- Large database migrations
- Complex application architectures
- Enterprise migration planning
- Performance optimization

<InfoBox className='BodyStyle'>

## 💭 FAQs about Heroku migration

### How long does it take to migrate from Heroku?

Most teams successfully can migrate Heroku applications in 2-4 hours.

### Can I migrate Heroku free tier apps?

Yes, you can easily migrate Heroku free tier applications.

### Do I need to change my code to migrate from Heroku?

No code changes required when you migrate Heroku apps using buildpacks.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>7 Best AI cloud providers for full-stack AI/ML apps</title>
  <link>https://northflank.com/blog/7-best-ai-cloud-providers</link>
  <pubDate>2025-07-25T12:45:00.000Z</pubDate>
  <description>
    <![CDATA[Compare the top AI cloud platforms in 2026 for model deployment, full-stack ML apps, and GPU workloads. See how providers like Northflank, AWS, and GCP stack up for production AI.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/serverless_gpu_7dd4615572.png" alt="7 Best AI cloud providers for full-stack AI/ML apps" />Most AI platforms help you run models. Few help you build products. If you're fine-tuning LLMs, deploying APIs, or launching full-stack ML apps, you need more than access to GPUs. You need a cloud platform that supports the full pipeline from training and inference to CI/CD, staging, and production.

Big providers like AWS and GCP offer the compute, but can slow you down with overhead. Lighter platforms feel fast, but fall short when you need control. That’s where platforms like Northflank come in, offering modern GPU orchestration with real developer workflows built in.

This guide breaks down the top AI cloud providers in 2026 and how they stack up for model deployment, full-stack apps, and production-ready ML infrastructure.

## TL;DR: 7 AI cloud providers for full-stack AI/ML apps

If you're short on time, here are the top picks for 2026. These platforms are optimized for full-stack ML development, model deployment, and LLM app delivery, not just spinning up GPUs.

| Provider | What It Offers | Best For |
| --- | --- | --- |
| **Northflank** | [GPU workloads](https://northflank.com/gpu), APIs, full-stack deployments, CI/CD, secure environments, [Bring your own cloud](https://northflank.com/features/bring-your-own-cloud) | Production-grade platform for deploying AI apps — GPU orchestration, Git-based CI/CD, [Bring your own cloud](https://northflank.com/features/bring-your-own-cloud), secure runtime, multi-service support, preview environments, secret management, and enterprise-ready features. |
| **AWS (SageMaker, Bedrock)** | Managed model training, serverless inference, deep cloud integration | Enterprises, hybrid workflows, scaling LLMs |
| **Google Cloud (Vertex AI)** | MLOps tooling, prebuilt pipelines, TPU + GPU support | Training + deploying with Google-native ML stack |
| **Azure (ML Studio, OpenAI services)** | Model deployment, enterprise security, Microsoft integrations | Regulated workloads, internal Copilots, Office integration |
| **Replicate** | Turn models into APIs, deploy from GitHub repos | Lightweight model hosting, indie devs, community demos |
| **Anyscale** | Built on Ray, runs distributed model jobs at scale | Large-scale fine-tuning, Python ML infra |
| **Modal** | Run functions in the cloud with GPU/CPU autoscaling | Serverless inference, LLM utilities, lightweight compute jobs |

## What makes a good AI cloud provider?

AI cloud providers today are expected to be full platforms, not just infrastructure layers. That means going beyond GPU access to include deployment workflows, secure runtimes, automation, and developer experience. Here’s what matters most:

- **Access to modern accelerators:** H100, L40S, MI300X, and TPUs needs to be available, with fast provisioning and real capacity.
- **Model deployment pipelines:** Support for staging, production, and versioned model endpoints. Deployments should be automated and reproducible.
- **Environment isolation and secrets:** Teams need isolated environments for dev, staging, and prod, with secure secrets and configuration management.
- **CI/CD for ML workflows:** Git-based deploys, preview environments, rollback support, and runtime observability all matter in production AI.
- **Native ML integrations:** Hugging Face, PyTorch, Triton, Jupyter, Weights & Biases, and containerized runtimes should be first-class citizens.
- **Transparent billing and usage tracking:** Per-second GPU usage, fixed pricing tiers, and built-in observability reduce cost surprises.
- **Bring Your Own Cloud:** For teams with compliance or infra preferences, support for hybrid or BYOC setups is essential.

## Top 7 AI cloud providers for full-stack AI/ML apps

This section goes deep on each AI cloud provider in the list. You’ll see what types of GPUs they offer, what they’re optimized for, and how they actually perform in real workloads. Some are ideal for researchers. Others are built for production.

> 💡Note on GPU pricing
> 
> 
> We haven’t included exact pricing here because GPU costs change frequently based on region, demand, and available hardware.
> 
> That said, **Northflank offers some of the most competitive GPU pricing** for production workloads without requiring large upfront commitments.
> 
> For providers like AWS and GCP, competitive rates often require long-term reservations or high minimum spend. In contrast, **Northflank provides flexible, on-demand access** with transparent pricing and real availability, making it a strong option for teams of all sizes.
> 

### 1. **Northflank** – Full-Stack platform for production AI apps

Northflank brings the full DevOps experience to AI. It combines autoscaling GPU workloads with full-stack application support, CI/CD pipelines, environment separation, and infrastructure automation. You can deploy your model, backend, frontend, and database on a managed cloud or your own VPC.

![image - 2025-07-25T134747.000.png](https://assets.northflank.com/image_2025_07_25_T134747_000_2e5ac73b94.png)

**What you can run on Northflank:**

- GPU training, fine-tuning, and inference jobs
- Full-stack LLM products (UI, API, DB)
- Background workers, schedulers, and batch jobs
- Secure multi-env deployment (dev, staging, prod)

**What GPUs does Northflank support?**

Northflank offers access to **18+ GPU types**, including **NVIDIA A100, H100, L4, L40S**, **AMD MI300X**, **TPU v5e**, and **Habana Gaudi**. View the full list [here](https://northflank.com/gpu).

**Where it fits best:**

If you're building production-grade full-stack AI products or internal AI services, Northflank handles both the GPU execution and the surrounding app logic. It’s a strong fit for teams who want Git-based workflows, fast iteration, and zero DevOps overhead.

[**See how Cedana uses Northflank to deploy GPU-heavy workloads with secure microVMs and Kubernetes**](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes)

### 2. **AWS (SageMaker, Bedrock)** – Enterprise AI infrastructure

AWS gives you deep flexibility through SageMaker and Bedrock. You get access to model training pipelines, inference endpoints, fine-tuning tools, and enterprise-scale compute with access to H100 and L40S instances.

![image - 2025-07-25T134749.652.png](https://assets.northflank.com/image_2025_07_25_T134749_652_09ffc64a6a.png)

**What you can run on AWS:**

- Fine-tuning with Hugging Face or JumpStart
- Fully managed inference endpoints
- Enterprise LLM integrations (Anthropic, Meta, Mistral)

**What GPUs does AWS support?**

AWS supports a wide range of GPUs, including ****NVIDIA H100, A100, L40S, and T4. These are available through services like ****EC2, SageMaker, and Bedrock, with support for multi-GPU setups.

**Where it fits best:**

For large companies already in the AWS ecosystem, or teams needing scale with control over infrastructure.

### 3. **Google Cloud (Vertex AI)** – MLOps and TPU-powered training

Vertex AI brings together Google’s AI tooling, including TPUs, prebuilt pipelines, and support for TensorFlow and PyTorch. It supports end-to-end ML workflows, including model registry, training, and deployment.

![image - 2025-07-25T134752.736.png](https://assets.northflank.com/image_2025_07_25_T134752_736_8b4ae16cfc.png)

**What you can run on GCP:**

- Custom model training on GPUs or TPUs
- Pretrained model deployment via AI Studio
- Data pipelines and managed notebooks

**What GPUs does GCP support?**

GCP offers NVIDIA A100 and H100, along with Google’s custom TPU v4 and v5e accelerators. These are integrated with Vertex AI and GKE for optimized ML workflows.

**Where it fits best:**

Ideal for teams that rely on Google-native tools or want integrated MLOps pipelines with TPU acceleration.

### 4. **Azure (ML Studio, OpenAI Services)** – Enterprise-ready model hosting

Azure focuses on integrating OpenAI’s APIs with enterprise systems. You can fine-tune models, deploy endpoints, and integrate with internal systems through the Microsoft stack (Teams, Office, Outlook).

![image - 2025-07-25T134754.988.png](https://assets.northflank.com/image_2025_07_25_T134754_988_982d051edf.png)

**What you can run on Azure:**

- OpenAI GPT endpoints and fine-tuned models
- Internal Copilot agents
- Secure multi-tenant deployments

**What GPUs does Azure support?**

Azure supports **NVIDIA A100, L40S**, and **AMD MI300X**, with enterprise-grade access across multiple regions. These GPUs are tightly integrated with Microsoft’s **AI Copilot** ecosystem.

**Where it fits best:**

For enterprises needing compliance, internal tooling, and secure model deployments within Microsoft environments.

### 5. **Replicate** – Lightweight model hosting from GitHub

Replicate lets developers deploy models from GitHub repos and run them as hosted APIs. It’s ideal for fast iteration and demo apps using community or custom models.

![image - 2025-07-25T134757.005.png](https://assets.northflank.com/image_2025_07_25_T134757_005_af1da58095.png)

**What you can run on Replicate:**

- Hugging Face models as hosted APIs
- Inference endpoints with GPU usage billed per second
- Community demos and shareable LLM tools

**What GPUs does Replicate support?**

Replicate supports a variety of NVIDIA GPUs, including **A100, H100, A40, L40S, RTX A6000, RTX A5000, and more.**

**Where it fits best:**

Best for indie builders and devs looking to turn models into working demos or tools without infrastructure setup.

### 6. **Anyscale** – Ray-native platform for distributed workloads

Anyscale is built on Ray, which means it excels at large-scale Python AI tasks that require parallelism. It abstracts away infrastructure and supports autoscaling jobs.

![image - 2025-07-25T134758.925.png](https://assets.northflank.com/image_2025_07_25_T134758_925_72f577ec4c.png)

**What you can run on Anyscale:**

- Hyperparameter search
- Multi-node training jobs
- Distributed inference or data pipelines

**What GPUs does Anyscale support?**

Replicate supports a variety of NVIDIA GPUs, including **A100, Tesla V100, and more.**

**Where it fits best:**

For ML engineers and researchers building large custom pipelines or distributed AI workloads.

### 7. **Modal** – Serverless compute for model functions

Modal offers a Python-native way to run functions in the cloud with GPU/CPU autoscaling. It’s minimalistic but powerful for AI engineers working on tooling, APIs, and small apps.

![image - 2025-07-25T134800.874.png](https://assets.northflank.com/image_2025_07_25_T134800_874_aa2b55f394.png)

**What you can run on Modal:**

- Inference functions (image, text, audio)
- LLM utilities and embedding pipelines
- GPU batch jobs triggered via API

**What GPUs does Anyscale support?**

Modal supports a variety of NVIDIA GPUs, including the **T4, L4, A10G, A100, H100, and L40S.**

**Where it fits best:**

When you want to run lightweight ML workloads without provisioning infra or containers manually.

## How to choose the best AI cloud provider

There’s no single “best” provider, only what fits your stack, team, and product goals. Here’s a quick guide by use case.

| Use Case | Priorities | Providers to consider |
| --- | --- | --- |
| **End-to-end LLM product deployment** | CI/CD, environment separation, API support, observability, full-stack deployments | Northflank |
| **Model training and fine-tuning** | H100/TPU access, job orchestration, pipeline automation | Northflank, GCP, AWS, Azure, Anyscale |
| **Serverless inference at scale** | Low-latency APIs, autoscaling, per-call billing | Northflank, Modal, Replicate |
| **Enterprise Copilot-style tools** | Compliance, hybrid cloud, Microsoft/OpenAI integrations | Azure, AWS, Northflank |
| **Distributed AI research** | Ray support, multi-node GPU orchestration | Anyscale, GCP, AWS, Northflank |

## Conclusion

Most platforms help you run models. The better ones help you build real products. This guide covered the top AI cloud providers that support everything from training and fine-tuning to deployment, versioning, and full-stack delivery.

If you're building something beyond a single endpoint, such as internal tools, AI-powered products, or multi-service applications, platforms like Northflank offer more than just access to GPUs. You get fast provisioning, strong developer workflows, and the flexibility to deploy across environments without extra overhead.

[Try Northflank](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how it fits your stack.]]>
  </content:encoded>
</item><item>
  <title>The best alternatives to E2B.dev for running untrusted code in secure sandboxes</title>
  <link>https://northflank.com/blog/best-alternatives-to-e2b-dev-for-running-untrusted-code-in-secure-sandboxes</link>
  <pubDate>2025-07-23T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[AI developers are increasingly reaching for platforms that allow them to safely execute arbitrary or user-submitted code, typically generated by agents or LLMs, inside isolated, short-lived environments. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/e2b_03a24f3245.png" alt="The best alternatives to E2B.dev for running untrusted code in secure sandboxes" />AI developers are increasingly reaching for platforms that allow them to safely execute arbitrary or user-submitted code, typically generated by agents or LLMs, inside isolated, short-lived environments. 

E2B.dev is one of the most well-known projects in this space. But it’s far from the only option, and for many teams, not the most suitable.

This article covers:

- What E2B Sandbox is and how it works
- Why secure sandboxing is essential for modern agent workflows
- Where E2B.dev falls short
- How E2B alternatives like Northflank, Modal, Daytona, and Vercel compare, based on latency, flexibility, and developer experience

<InfoBox className='BodyStyle'>

## ⏳ TL;DR

If you’re evaluating secure sandbox platforms for running untrusted or AI-generated code:

- **E2B.dev** is good for experimentation. SDK-based, fast startup, but limited sandbox lifespan, region control, and lacks orchestration.
- **Northflank** offers a more flexible and production-ready option. You can run microVMs in your own VPC, keep sandboxes alive indefinitely, and integrate observability, Git, and custom toolchains. Better for long-running workloads, real apps, and enterprise control.
- **Modal** is fast and well-suited to Python workloads, but doesn’t provide microVM isolation or persistence.
- **Vercel Sandbox** is promising, but still early and limited to short-lived runtimes.
- **Daytona** is fast on cold start, but lacks streaming and long-session stability.
</InfoBox>

## What does running untrusted code in a secure sandbox mean and why is it important?

LLMs frequently generate code, either as part of agents, developer copilots, or internal tools. Executing this code introduces risk: it might be malicious, buggy, or resource-intensive. It might accidentally (or intentionally) read sensitive files, access the network, or overwrite system state.

Secure sandboxes solve this by:

- Isolating code execution in virtualized environments (usually microVMs like Firecracker)
- Restricting access to system resources and networks
- Enforcing execution limits, quotas, and lifespan
- Ensuring multi-tenancy without leakage between user sessions

### This matters when

- Users are uploading or generating scripts
- AI agents are invoking external tools or writing to disk
- You need to support high concurrency and low latency while maintaining safety guarantees

## What is E2B.dev sandbox? Why would you look for an E2B alternative?

E2B.dev is an open-source tool that lets developers spin up isolated Firecracker microVMs to execute AI-generated code. It comes with Python and JavaScript SDKs, supports browser-based and local development, and integrates with tool-calling interfaces like Anthropic’s Claude.

### Pros

- Fast startup (~150ms under ideal conditions)
- Firecracker-based isolation is safer than containers
- SDKs make integration simple

### Cons

- Sandbox sessions are short-lived (5–10 minutes in practice, even on paid plans)
- No orchestration or lifecycle management
- Only PaaS offering is officially supported, BYOC requires deploying and maintaining their infra yourself
- Regional control and infrastructure flexibility are limited
- High costs at scale

### Pricing

- **Free**: $0/month with usage limits (2 vCPU, 512 MB RAM, ~1hr sessions)
- **Pro**: $150/month + usage fees (~$0.000014/vCPU-s)
- **Enterprise**: Custom pricing with higher limits
    
    To run persistent workloads or allow users to return to a sandbox session later, you must upgrade to Pro and build custom keep-alive workarounds.
    

## Common use cases

[E2B.dev](http://E2B.dev) and E2B.dev alternatives are typically used to:

1️⃣ Safely run LLM-generated code via tool-calling

2️⃣ Power developer agents or internal copilots

3️⃣ Launch live developer sandboxes for testing or debugging

4️⃣ Run compute or visualizations in the browser and pass back results (e.g., plotting NVIDIA stock or computing summary stats)

5️⃣ Run MCP servers

If you’re building an AI agent that calls external tools, an IDE-like app for prompt-driven development, or any product where end users run code on shared infrastructure, you need secure sandboxing.

Here are the top E2B alternatives.

### 1. Northflank - #1 [E2B.dev](http://E2B.dev) alternative for secure sandboxing

![CleanShot 2025-07-24 at 11.01.26@2x.png](https://assets.northflank.com/Clean_Shot_2025_07_24_at_11_01_26_2x_386fc5fa6b.png)

[Northflank](https://northflank.com/) is a developer platform that supports running secure microVMs with Firecracker, Kata Containers, Cloud Hypervisor, or gVisor. 

It has been in production since 2019 and executes over 2 million microVMs every month. It supports short-lived jobs, long-running services, BYOC, and multi-tenant sandbox orchestration.

**Strengths:**

- MicroVM isolation with full Kubernetes orchestration
- Sandboxes can persist indefinitely
- Works in your cloud (VPC), with full observability and cost tracking
- Polyglot by design: supports any language, runtime, or framework
- Git and CI/CD integration built-in
- Supports developer agents, untrusted jobs, and long-running AI workloads
- Contributes actively to OSS projects like containerd, Kata, QEMU

**Limitations:**

- Cold-start latency is higher than container-only tools, though tunable
- SDK could be simplified (Northflank is actively working on this)

**Who it's for:**

- Teams building serious agent infrastructure, internal devtools, or production-grade SaaS platforms
- Companies that need enterprise controls or custom cloud environments
- Developers who care about long-lived stateful sandboxes

**Pricing:**

- Starts low (usage-based), with no forced plan tiers
- Supports per-second billing, spot compute, and GPU options
- No minimum fee to access long-lived sessions

A developer using Northflank for spinning up agent sandboxes put it this way:

> "With E2B, a sandbox dies after a few minutes. With Northflank, it stays alive until I kill it. That makes a huge difference when users come back to projects days or weeks later."
> 

| Feature | **Northflank** | **E2B.dev** | **Daytona** | **Modal** | **Vercel Sandbox** |
| --- | --- | --- | --- | --- | --- |
| **Isolation method** | MicroVMs (Firecracker, Kata, gVisor, CLH) | MicroVMs (Firecracker) | Containers (with fast image caching) | Containers | MicroVMs |
| **Sandbox lifespan** | Unlimited (until terminated) | ~5–10 min typical, max 24h on Pro | Varies (minutes–hours), no persistence | Stateless (function-based) | Max ~45 min (beta) |
| **Startup latency** | Varies, can be tuned | Very fast (~150ms) | Extremely fast | Fast | Fast |
| **Persistence** | Yes (stateful, can resume) | No (state loss after session ends) | Limited | No | No |
| **Git & CI/CD Integration** | Built-in | Manual / SDK | Partial (via config) | None | Integrated with Vercel Git flow |
| **Streaming support** | Yes | Yes | No (latency issues) | Yes (for Python stdout) | Yes |
| **Regions & BYOC** | Multi-region, full BYOC/VPC support | Single region PaaS, OSS self-hosting (limited) | Handful of regions, no BYOC | Single region, no BYOC | Vercel platform only |
| **SDK / API** | REST + CLI + SDK (improving) | JS/Python SDKs | REST API | Python API | JS SDK (in beta) |
| **Pricing model** | Usage-based (CPU $0.01667/hr, RAM $0.00833/hr) | $150/mo Pro plan + usage (~4x higher CPU cost) | Paid (undisclosed), no free plan | Usage-based (per function) | Plan-based |
| **Who it's for** | AI infra teams, SaaS platforms, devtools | Hackathons, LLM demos, short-lived agents | Dev env bootstrapping, speed-first use cases | Python-based tooling, bursty compute | Agent UI demos, edge code exec |

### 2. Daytona

Daytona emphasizes developer environments. It’s fast on cold start and supports image-based sandboxing.

**Strengths:**

- Extremely fast initial boot
- Can pull Docker images quickly from Git

**Limitations:**

- Weak on streaming and interactive outputs
- Feels less cohesive, requires custom patching
- Session persistence and developer ergonomics lag behind

**Who it's for:**

- Teams focused on reproducible dev envs with tight image control

**Pricing:**

- Commercial plans, pricing varies

### 3. Vercel Sandbox

Still in beta, Vercel Sandbox provides ephemeral microVMs with a ~45-minute lifespan. It integrates tightly into the Vercel platform, intended for devtools, agent playgrounds, and code demos.

**Strengths:**

- MicroVM isolation
- Simple to get started
- Integrated with Vercel’s broader deployment pipeline

**Limitations:**

- Runtimes capped at ~20–45 minutes
- No orchestration, BYOC, or long-running agent support

**Who it's for:**

- Devtools demos or short-lived interactions
- Vercel-native teams

**Pricing:**

- Bundled into Vercel plans

### 4. Modal Sandboxes

Modal recently added support for Python sandboxes aimed at code execution use cases. It’s very fast to boot, container-based, and designed to scale horizontally via API.

**Strengths:**

- Excellent cold-start speed
- Good for short-lived execution tasks
- Built for Python workloads

**Limitations:**

- No microVM isolation
- Sessions are ephemeral
- No BYOC or VPC support

**Who it's for:**

- Developers running transient Python workloads
- Teams with stateless agent jobs

**Pricing:**

- Pay-as-you-go based on function duration and resource allocation

## Conclusion

E2B.dev helped popularize the concept of secure sandboxes for AI tools, but most teams building real-world agents or developer-facing products will need more control, persistence, and scale than E2B supports today. 

If your use case requires:

- Long-running sandboxes
- Multi-tenant environments
- Running inside your VPC
- Observability, quota controls, and Git integration
- Production-ready support

**Northflank is the better fit when you’re looking for E2B alternatives.**

It combines microVM-level security with full lifecycle orchestration and a mature developer experience. You can spin up workloads in your own cloud, stream output in real-time, and integrate directly with tool-calling flows via SDK or API.

If you're evaluating platforms to run untrusted code safely, Northflank should be on your shortlist.

Beyond Northflank, platforms like Daytona, Modal, and Vercel offer variations of secure sandboxing. 

However, none combine microVM isolation, long-running stateful workloads, polyglot support, BYOC, observability, and enterprise-grade orchestration.]]>
  </content:encoded>
</item><item>
  <title>Best serverless GPU providers in 2026</title>
  <link>https://northflank.com/blog/the-best-serverless-gpu-cloud-providers</link>
  <pubDate>2025-07-23T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[This guide provides a technically grounded comparison of the top serverless GPU platforms in 2025, including detailed breakdowns of their runtimes, orchestration capabilities, and suitability for production systems.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/the_best_serverless_gpu_cloud_providers_2ab2413194.png" alt="Best serverless GPU providers in 2026" />Serverless GPU platforms have matured into serious infrastructure for deploying and scaling AI workloads. 

Teams now expect persistent environments, hybrid cloud flexibility, and full-stack support, not just GPU runtime.

This guide provides a technically grounded comparison of the top serverless GPU platforms in 2025, including detailed breakdowns of their runtimes, orchestration capabilities, and suitability for production systems.

But first, let’s go through the basics.

<InfoBox className="BodyStyle">

> **Serverless GPU capacity questions?** If you're planning production deployments and need to ensure GPU availability, [request GPU capacity here](https://northflank.com/request/gpu).

</InfoBox>

## What are serverless GPUs?

Serverless GPUs let you run GPU-powered workloads without manually provisioning infrastructure. Instead of renting full-time access to GPU machines, you submit a job or deploy a container, and the platform assigns a GPU for just the time you need.

You don’t manage servers, install CUDA drivers, or configure autoscaling. The platform handles that. You only focus on your model, script, or API.

These platforms usually rely on containerized or microVM-based runtimes with access to attached GPUs (e.g. A100s, H100s, L4s). Most charge per second or per job, and many scale automatically based on demand.

## **Why use serverless GPUs?**

Using serverless GPUs solves three key problems:

1. **Provisioning complexity**: No need to manage VMs, AMIs, or GPU quotas across cloud providers.
2. **Cost efficiency**: You don’t pay for idle GPUs. Useful for bursty or on-demand jobs like inference, fine-tuning, or image generation.
3. **Developer velocity**: Most platforms offer simple SDKs, Git integration, and APIs that allow fast iteration and deployment.

You can build:

- Model inference APIs
- Image or video generation pipelines
- Audio transcription endpoints
- Interactive notebooks or agents
- Secure user-submitted code sandboxes

With the right platform, you can handle all of the above with proper orchestration, sandboxing, and observability.

## **Which serverless GPU service is top rated?**

**Northflank** ranks highest among all current platforms. It’s the only one that offers secure microVM isolation, persistent GPU runtimes, CI/CD integration, BYOC support, and full-stack orchestration across services and jobs.

Where most platforms stop at "run a container on a GPU," Northflank gives you a full environment:

- Inference endpoints with autoscaling
- Cron jobs and background processes
- Secure sandboxed runtimes for untrusted code
- Real-time logs, metrics, and alerts

Whether you’re running a Transformer model on H100s or chaining CPU+GPU tasks in a multi-service setup, Northflank delivers both the infrastructure and the operational layer.

## Who offers the best serverless GPU service?

It depends on what you're optimizing for:

- **Best all-around infrastructure**: Northflank
- **Best for Python-only batch jobs**: Modal
- **Best for public model inference**: Replicate
- **Best for model dashboards**: Baseten

If you're shipping production systems that require long-lived APIs, job queues, notebooks, or secure execution, **Northflank is the most robust and future-proof option.**

## **TL;DR: GPU pricing comparison***

| Compute type | Northflank | Modal | Baseten | Replicate | RunPod | Koyeb |
| --- | --- | --- | --- | --- | --- | --- |
| A100 | **$1.42/ h** | $2.50 / h | $4.00 / h  | $5.04 / h | $2.72 / h | $2.0 / h |
| B200 | **$5.87 / h** | $6.25 / h | $9.98 / h | N/A | $5.99 / h | N/A |
| H100 | **$2.74/h** | $3.95 / h | $6.50 / h | $5.49 / h | $4.18 / h | $3.30 / h |
| H200 | **$3.15 / h** | $4.54 / h  | N/A | N/A | $5.58 / h | N/A |
| L4 | Coming soon | $0.80 / h | $0.8484 / h  | N/A | $0.69 / h | $0.70 / h |
| T4 | Coming soon | $0.59 / h | $0.6312 / h | $0.81 / h | N/A | N/A |

*Prices are approximations as of mid-2025 and subject to change

**For Modal and Replicate, you also have to pay for CPU and memory on top of GPUs.

## Northflank - Best service for serverless GPU

![northflank-website.png](https://assets.northflank.com/northflank_website_d050074216.png)

[Northflank](https://northflank.com/) is a full-stack platform built for secure, production-grade GPU orchestration. It combines serverless execution with microVM-based isolation, making it ideal for running inference APIs, fine-tuning pipelines, and GPU-powered agents at scale.

**Pros:**

- Runs GPU workloads inside a secure runtime environment so you can run untrusted AI generated code
- Persistent services with volume support and background jobs
- CI/CD pipelines with GitHub/GitLab integration
- Multi-service orchestration with GPU + CPU coordination
- BYOC and VPC deployment support

**Cons:**

- More infrastructure primitives to learn compared to plug-and-play platforms
- Overhead of configuration may not suit extremely simple demos

**Best for:** Teams deploying AI systems that need orchestration, multi-cloud control, and production-grade observability.

Northflank supports long-running services, scheduled jobs, and CI/CD pipelines, all with GPU-backed compute. Northflank can run LLMs on demand, host fine-tuning pipelines with persistent storage, and execute untrusted code inside isolated microVMs.

It supports multiple isolation types, gVisor, Kata, Firecracker, and lets users deploy across both managed infrastructure and private cloud environments. It integrates natively with GitHub, GitLab, and container registries.

<aside>

💰 Northflank has the most competitive prices on the market, with H100s at $2.74 / hour.

</aside>

## Modal

![modal-home-page.png](https://assets.northflank.com/modal_home_page_3ef6ad50bc.png)

Modal provides a Python-native SDK to run GPU-backed batch jobs and workflows. It prioritizes developer simplicity, letting engineers define functions and run them on A10 or A100 GPUs with minimal boilerplate.

**Pros:**

- Easy to define GPU workflows via Python decorators
- Fast cold start times for stateless jobs
- Good for chaining CPU/GPU steps in data pipelines

**Cons:**

- No persistent services or API endpoints
- No storage volume support or runtime customization
- Limited CI/CD and observability tools

**Best for:** Python engineers experimenting with short-lived inference or data jobs who don’t need orchestration or persistent infrastructure.

Modal focuses on a Python-native interface to GPU jobs. You write Python functions, decorate them with `@stub.function(gpu="A10")`, and Modal handles packaging and execution.

But it lacks long-lived runtimes, persistent volumes, job queues, or background workers. Every job is stateless. That makes it a better fit for model experimentation than for running anything user-facing.

## Baseten

![baseten-homepage.png](https://assets.northflank.com/baseten_homepage_2c66e73096.png)

Baseten is a hosted model serving platform focused on abstracting infrastructure. It lets users upload models and exposes them as HTTP endpoints with built-in autoscaling, dashboards, and alerts.

**Pros:**

- UI-driven deployment with minimal setup
- Automatic scaling and request monitoring
- Supports Hugging Face and PyTorch models

**Cons:**

- No support for background workers or job queues
- Limited visibility into runtime and GPU configuration
- Lacks secure sandboxing or BYOC support

**Best for:** Teams shipping internal model APIs or dashboards that want a simple way to host and monitor inference endpoints without building infra.

Baseten abstracts infrastructure entirely. You upload a model, configure the API, and expose it to users. Baseten manages autoscaling, logging, and basic metrics.

Baseten lacks CI/CD integration, orchestration, or secure isolation. It doesn’t expose underlying container logic, GPU configurations, or billing granularity. It’s a fit for simplicity over flexibility.

## Replicate

![replicate-homepage.png](https://assets.northflank.com/replicate_homepage_38062bccda.png)

Replicate offers instant access to pre-hosted open-source models through public HTTP endpoints. Users don’t deploy code; they simply select a model and call it via API.

**Pros:**

- Zero-config deployment for hundreds of models
- Fast API setup for demos and prototypes

**Cons:**

- No control over execution, hardware, or environment
- No support for custom models or model weights
- Not suitable for secure or scalable workloads

**Best for:** Quick testing of public models, MVP demos, or hobby projects that don’t require customization, privacy, or observability.

Replicate gives developers hosted endpoints for popular models. You don’t manage infrastructure at all. Just pick a public model and call an API.

## RunPod

![runpod-homepage.png](https://assets.northflank.com/runpod_homepage_a696c3aa97.png)

RunPod gives users low-cost access to dedicated GPU machines with optional container orchestration. You can launch notebooks or long-running containers with encrypted volumes.

**Pros:**

- Wide GPU selection including H100, A100, 4090
- Persistent runtimes with attached volumes
- Spot instance pricing and low hourly rates

**Cons:**

- Manual setup required for orchestration and monitoring
- Slow startup latency for bare metal nodes
- Lacks native CI/CD and service autoscaling

**Best for:** ML engineers running training jobs or internal workloads who want flexible, raw GPU compute at a low price point.

RunPod offers raw GPU machines at low prices. You can spin up a container or Jupyter notebook on a 4090, A100, or even H100, paying by the hour. For persistent workloads, it supports encrypted volumes and keeps jobs running across sessions.

Unlike true serverless platforms, you’re responsible for job lifecycle, retries, and cleaning up idle instances. RunPod is cheap, flexible, and infrastructure-heavy.

## Koyeb

Koyeb recently added GPU support to its serverless container platform. It's positioned toward lightweight web services that occasionally require GPU acceleration.

**Pros:**

- Simple deployment model with GitHub integration
- L4 and V100 GPU support in private preview
- Integrated HTTP routing and autoscaling

**Cons:**

- GPU runtime is limited and non-persistent
- No orchestration, volume support, or secure isolation
- Lacks visibility and infra-level control

**Best for:** Web or backend developers deploying basic inference models or GPU-enhanced features in global serverless environments.

Koyeb recently added GPU support as part of its broader serverless platform. Currently in preview, its GPU offering is focused on web apps or inference endpoints needing L4-class performance.

There’s no support yet for long-running training jobs, multi-container orchestration, or microVM isolation. 

## Final thoughts

Choosing the right serverless GPU provider in 2025 depends on what you’re building and how much control you need. The market has grown past basic batch jobs, teams now demand secure execution, orchestration, and flexible GPU runtime environments.

If you need a lightweight way to run public models or quick batch jobs, tools like Replicate and Modal still serve a purpose. But if you’re running anything mission-critical, LLM inference, fine-tuning pipelines, and a platform that takes care of your entire engineering lifecycle, Northflank is the way to go. Especially given its most affordable pricing on GPUs.

No matter what your use case is, whether you're evaluating the top serverless GPU services, comparing pricing across A100 and H100 instances, or searching for the best serverless GPU platforms for inference pipelines, this guide should give you a technically sound starting point.

<InfoBox className='BodyStyle'>

## 💭 FAQs

**What is the cheapest serverless GPU option for A100s or H100s?**

Northflank has, by far, the competitive pricing for A100 and H100 GPUs (and across all types of GPUs). H100s are $2.74/hour and A100s are $1.42/hour.

**Which serverless GPU provider is best for inference APIs?**

Northflank and Baseten both support GPU-backed inference APIs. Northflank is better for long-lived, autoscaling APIs with persistent volumes and job queues. Baseten is simpler but more limited.

**Are there serverless GPU platforms that let me bring my own cloud (BYOC)?**

Northflank supports BYOC and allows you to deploy GPU workloads into your own VPC on AWS, GCP, or Azure. This gives teams full infrastructure control, network visibility, and integration flexibility, all while keeping the developer experience consistent. 

**Do any serverless GPU providers support GPU+CPU coordination for hybrid workloads?**

Only Northflank offers true multi-service orchestration where GPU and CPU containers can work together, which is critical for agentic workflows and multi-modal inference.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>What is AI infrastructure? Key components &amp; how to build your stack</title>
  <link>https://northflank.com/blog/ai-infrastructure</link>
  <pubDate>2025-07-23T15:34:00.000Z</pubDate>
  <description>
    <![CDATA[Learn what AI infrastructure includes, beyond GPUs. We break down the components and how to set up your full stack for deploying and scaling AI workloads.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/ai_infrastructure_blog_post_904a7e479f.png" alt="What is AI infrastructure? Key components &amp; how to build your stack" />## What is AI infrastructure?

AI infrastructure is the full stack of compute, storage, networking, orchestration, and developer tools that support the development, training, and deployment of AI models. It includes everything from GPUs and job schedulers to APIs, databases, and observability tools.

A lot of people hear "AI infrastructure" and immediately think of GPUs, and that’s understandable. Training large models and running inference jobs does require high-performance hardware. The reality is, though, AI teams need more than that. You’re serving more than a model; you’re building an entire product around it.

That means you also need things like secure runtimes (particularly if you’re running code from users or AI agents), vector databases to store embeddings, microservices to expose your models via APIs, and a way to manage deployments across environments. 

On top of that, you need CI/CD, cost tracking, logs, metrics, the standard components in software infrastructure, but adapted for AI workloads.

If you’re building anything more complex than a basic demo, your infrastructure needs to support both the model and the surrounding systems that make it usable, reliable, and secure.

<InfoBox className='BodyStyle'>

💡**Quick note:** Most AI infra platforms today focus on one part of the stack, usually model serving or GPU access. However, AI companies need the full picture: storage, databases, APIs, scheduling, secure environments, and a way to deploy everything reliably.

Platforms like Northflank are built around that idea: supporting your full AI and non-AI workload in one place, with GPUs, background jobs, preview environments, databases, and CI/CD all running side by side.

Try it out [here](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro)

</InfoBox>

## What does AI infrastructure consist of?

If you’re building AI products, you need more than a GPU cluster and a model checkpoint. AI infrastructure brings together multiple layers that work together to support everything from training to deployment to production monitoring.

Let’s see a breakdown of the key components:

**1. Compute (GPUs and CPUs)**

The foundation. GPUs power training and inference workloads, while CPUs handle surrounding tasks like orchestration, background jobs, or API logic. You’ll often need both running together.

**2. Storage**

AI workloads deal with large volumes of data, from raw training sets to embeddings and model artifacts. Object storage, block volumes, and vector databases all play a role here.

**3. Networking**

Fast, secure communication is critical, primarily when models and services are split across nodes or clouds. Your infrastructure should support internal service discovery, public endpoints, and secure API access.

**4. Orchestration**

You need a way to schedule jobs, spin up containers, manage autoscaling, and control the lifecycle of your workloads. Kubernetes is often the backbone here, but it’s the tooling on top that makes it usable.

**5. Developer platform**

This is where many infrastructure platforms fall short. AI teams need APIs, services, preview environments, CI/CD flows, and custom tooling, not only Jupyter notebooks or dashboards.

**6. Security**

Running untrusted or user-generated code is common in AI products (e.g. agents, sandboxes). A secure runtime with tenant isolation, RBAC, secret management, and audit logs is essential, especially at scale.

**7. Observability**

Once your model is live, how do you know it’s behaving as expected? Logs, metrics, usage breakdowns, and cost attribution help you monitor and debug your system in production.

## How to build your AI infrastructure stack

Now that we’ve covered the core components, the next step is figuring out how to put them together into a working stack, one that doesn’t only run a model but helps you ship a complete product.

For most AI companies, particularly those building apps on top of LLMs or training custom models, your infrastructure needs to support a wide mix of workloads. It should handle heavy compute jobs and lightweight services, enable secure collaboration across teams, and give you the flexibility to run across clouds or regions. Additionally, planning for [enterprise data migration](https://xenoss.io/capabilities/data-migration
) ensures that your data moves seamlessly between systems, maintaining performance and security while supporting these diverse workloads.

This kind of stack often needs to support:

**1. Fine-tuning and inference workloads**

Regardless of whether you're using PyTorch, DeepSpeed, or other frameworks, you need the ability to run long-running training jobs and scale inference on demand.

**2. Consistent environments across dev, test, and prod**

No unexpected differences between environments. You should be able to test the same container you plan to deploy.

**3. GPU provisioning and management across providers**

Multi-cloud or hybrid GPU support gives you more flexibility and cost control, particularly when dealing with A100s, H100s, or spot instances. You can provision compute nodes with the latest GPU models across cloud providers on Northflank ([See for yourself](https://northflank.com/gpu)).

**4. APIs, databases, and background jobs**

Most AI products include more than the model. You’ll need Redis for caching, Postgres for storage, and background workers to handle scheduled tasks or async workflows. Northflank lets you deploy services, databases, and background jobs as part of the same project, fully integrated with GPU workloads.

**5. CI/CD tailored to AI and app code**

Pipelines should support both your machine learning logic and the surrounding application code, along with model retraining or evaluation steps. Northflank’s built-in CI/CD system supports both app deployments and custom training pipelines, with native GPU and background job support.

**6. Secure runtime for untrusted workloads**

If your platform lets users submit code (e.g. agents, code interpreters), isolation becomes critical. Your infrastructure should prevent container escapes, cross-tenant access, or unsafe networking. Northflank’s secure runtime was designed to safely run untrusted workloads at scale, supporting multi-tenancy with strong isolation by default.

**7. Cost monitoring, usage tracking, and team access controls**

As usage scales, so does the need for visibility. Track GPU time, container usage, team activity, and costs across environments.

## Why most AI infrastructure platforms fall short

There’s been a wave of new platforms built specifically for AI workloads, and many of them do a great job at solving targeted problems like GPU access or model inference at scale. Tools like Modal, Base10, and Together AI have made it easier for teams to quickly deploy models without managing low-level infrastructure.

The challenge is that these platforms tend to focus on one part of the stack.

If you’re building an AI-powered product, you likely need more than a fast way to serve a model. You also need:

- Databases to store user data, features, and embeddings
- Background jobs to schedule tasks or fine-tune models
- CI/CD pipelines to ship updates across services
- Preview environments to test new features
- APIs to expose your models in production
- Multi-service coordination
- Hybrid cloud or BYOC support to manage GPUs more flexibly

These gaps are understandable, most of these platforms weren’t designed to support full product development workflows. They’re solving for inference, not the complete infrastructure story. [Product management tools](https://airfocus.com/blog/best-product-management-tools-compared/) are often required alongside these platforms to help teams coordinate roadmaps, prioritize features, and align engineering efforts with overall product strategy.

That’s where platforms like Northflank step in: providing the GPU support you’d expect, while also giving AI teams access to the full set of tools they need to build, ship, and scale their entire product.

## Northflank as a full-stack AI infrastructure platform

That full-stack gap is where Northflank comes in.

![new northflank home page.png](https://assets.northflank.com/new_northflank_home_page_70a852adf5.png)

Rather than solving a single slice of the AI pipeline, Northflank is built to support the entire lifecycle of your AI workloads, from training and fine-tuning to deployment, monitoring, and scaling. It’s designed for teams building production-ready products, where model serving is only one part of the system.

1. **Run AI and non-AI workloads side by side**
    
    You can run GPU-intensive jobs like fine-tuning and inference right alongside CPU-based services, notebooks, or background workers. Northflank treats AI workloads like any other container, making it easier to manage them consistently.
    
2. **Deploy your full stack**
If you’re spinning up a Postgres database, deploying a FastAPI service, or running a scheduled job, Northflank supports all of it in one platform. You can launch Redis, RabbitMQ, microservices, Jupyter Notebooks, and more, with CI/CD and preview environments already built in. You can also use 1-click deploy templates to get started quickly with common AI workloads like LLaMA, Jupyter, or model trainers.

3. **Built-in security and scale**
    
    Northflank’s runtime is built for multi-tenant, production-scale usage. With features like RBAC, private networking, audit logs, and SOC 2-aligned practices, you get the security posture required for enterprise and internal AI platforms. Today, it’s already running workloads for over 10,000 developers and processes more than 2 million containers each month.
    
4. **BYOC and hybrid deployments**
    
    You can bring your own [GPUs](https://northflank.com/gpu), across providers, across regions. If you're using A100s, H100s, or mixing on-demand and spot instances, Northflank supports hybrid setups with fast provisioning (under 30 minutes). This gives you more flexibility to manage GPU cost, availability, and failover.
    

<InfoBox className='BodyStyle'>

💡 [Get started for free](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how Northflank can support your entire AI stack, from model training to deployment and everything in between.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>12 Best GPU cloud providers for AI/ML in 2026</title>
  <link>https://northflank.com/blog/12-best-gpu-cloud-providers</link>
  <pubDate>2025-07-23T11:00:00.000Z</pubDate>
  <description>
    <![CDATA[Discover the top 12 GPU cloud platforms for AI/ML in 2026. Compare providers like Northflank, AWS, GCP, and more for training, inference, CI/CD, and full-stack AI deployment.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/kserve_blog_post_668d2be0e4.png" alt="12 Best GPU cloud providers for AI/ML in 2026" />GPU cloud used to mean spinning up a machine and hoping your setup worked. Now it's the backbone of AI infrastructure. The way you train, fine-tune, and deploy models depends on how fast you can access hardware and how much of the surrounding stack is already handled.

Most platforms still make you do too much. You’re expected to manage drivers, configure scaling, wire up CI, and somehow keep everything production-ready. That’s changing fast. Tools like Northflank are cutting through the noise, giving developers a way to ship AI workloads without touching low-level ops.

This isn’t about who has the biggest fleet of GPUs. It’s about who actually helps you build.

Here’s what makes a GPU cloud platform worth using in 2026, and which ones are leading the way.

## TL;DR: 12 Best GPU cloud providers for AI/ML in 2026

If you’re short on time, here’s the full list. These are the platforms that actually work well for AI and ML teams in 2026. Some are built for scale. Some are better for quick jobs or budget runs. A few are trying to rethink the experience entirely. The rest of this article explains how they compare, what they’re good at, and where they fall short.

<InfoBox className="BodyStyle">

> **Need help with GPU capacity planning?** Choosing a provider is just the first step. If you have specific availability requirements or volume needs, [request GPU capacity here](https://northflank.com/request/gpu).

</InfoBox>

| Provider | GPU types available | Key strengths | Ideal use cases |
| --- | --- | --- | --- |
| [**Northflank**](https://northflank.com/) | A100, H100, L4, L40S, TPU v5e, see full list [here](https://northflank.com/gpu). | Full-stack AI: GPUs, APIs, LLMs, frontends, backends, databases, and secure infra | Full-stack production AI products, CI/CD for ML, [bring your own cloud(BYOC)](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes), secure runtime, and many more |
| [**NVIDIA DGX Cloud**](https://www.nvidia.com/en-us/data-center/dgx-cloud/) | H100, A100 | Native access to NVIDIA’s full AI stack | Research labs, large-scale training |
| [**AWS**](https://aws.amazon.com/) | H100, A100, L40S, T4 | Flexible, global, deeply integrated | Enterprise AI, model training |
| [**GCP**](https://cloud.google.com/) | A100, H100, TPU v5e | Optimized for TensorFlow, AI APIs | GenAI workloads, GKE + AI |
| [**Azure**](https://azure.microsoft.com/) | A100, MI300X, L40S | Microsoft Copilot ecosystem integration | AI+enterprise productivity |
| [**TensorDock**](https://tensordock.com/) | A100, H100, RTX 6000 | Low-cost, self-serve, hourly billing | Budget fine-tuning, hobbyists |
| [Oracle Cloud (OCI)](https://www.oracle.com/cloud/) | A100, H100, MI300X | Bare metal with RDMA support | Distributed training, HPC |
| [**DigitalOcean Paperspace**](https://www.paperspace.com/) | A100, RTX 6000 | Developer-friendly notebooks + low ops | ML prototyping, small teams |
| [**CoreWeave**](https://www.coreweave.com/) | A100, H100, L40S | Custom VM scheduling, multi-GPU support | VFX, inference APIs |
| [**Lambda**](https://lambda.ai/) | H100, A100, RTX 6000 | ML-focused infrastructure, model hubs | Deep learning, hosted LLMs |
| [**RunPod**](https://www.runpod.io/) | A100, H100, 3090 | Usage-based billing with secure volumes | LLM inference, edge deployments |
| [**Vast AI**](https://vast.ai/) | Mixed (A100, 3090, 4090) | Peer-to-peer, ultra low-cost | Experimental workloads, one-off jobs |

>While most hyperscalers like GCP and AWS offer strong infrastructure, their pricing is often geared toward enterprises with high minimum spend commitments. For smaller teams or startups, platforms like Northflank offer much more competitive, usage-based pricing without long-term contracts, while still providing access to top-tier GPUs and enterprise-grade features.
>

## What makes a good GPU cloud provider in 2026?

The best GPU platforms in 2026 aren’t just hardware providers. They’re infrastructure layers that let you build, test, and deploy AI products with the same clarity and speed as modern web services. Here’s what actually matters:

- **Access to modern GPUs**
    
    H100s and MI300X are now the standard for large-scale training. L4 and L40S offer strong price-to-performance for inference. Availability still makes or breaks a platform.
    
- **Fast provisioning and autoscaling**
    
    You shouldn’t wait to run jobs. Production-ready platforms offer second-level startup, autoscaling, and GPU scheduling built into the workflow.
    
- **Environment separation**
    
    Support for dedicated dev, staging, and production environments is critical. You should be able to promote models safely, test pipelines in isolation, and debug without affecting live systems.
    
- **CI/CD and Git-based workflows**
    
    Deployments should hook into Git, not require manual scripts. Reproducible builds, container support, and job-based execution are essential for real iteration speed.
    
- **Native ML tooling**
    
    Support for Jupyter, PyTorch, Hugging Face, Triton, and containerized runtimes should be first-class, not something you configure manually.
    
- **Bring your own cloud**
    
    Some teams need to run workloads in their own VPCs or across hybrid setups. Good platforms support this without losing managed features.
    
- **Observability and metrics**
    
    GPU utilization, memory tracking, job logs, and runtime visibility should be built in. If you can't see it, you can't trust it in production.
    
- **Transparent pricing**
    
    Spot pricing and regional complexity often hide real costs. Pricing should be usage-based, predictable, and clear from day one.
    

Platforms like **Northflank** are pushing GPU infrastructure in this direction, one where GPUs feel like part of your application layer, not a separate system to manage.

## Top 12 GPU cloud providers for AI/ML in 2026

> 💡**Note on Pricing:**
We’ve intentionally left out detailed pricing information because costs in the GPU cloud space fluctuate frequently due to supply, demand, and regional availability. Most platforms offer usage-based billing, spot pricing, or discounts for committed use. For the most accurate and up-to-date pricing, we recommend checking each provider’s site directly.
> 

This section goes deep on each platform in the list. You’ll see what types of GPUs they offer, what they’re optimized for, and how they actually perform in real workloads. Some are ideal for researchers. Others are built for production.

### 1. Northflank – Full-stack GPU platform for AI deployment and scaling

[Northflank](https://northflank.com/) abstracts the complexity of running GPU workloads by giving teams a full-stack platform; GPUs, runtimes, deployments, CI/CD, and observability all in one. You don’t have to manage infra, build orchestration logic, or wire up third-party tools. 

Everything from model training to inference APIs can be deployed through a Git-based or templated workflow. It supports [bring-your-own-cloud (AWS, Azure, GCP, and more)](https://northflank.com/features/bring-your-own-cloud), but it works fully managed out of the box.

![image - 2025-07-23T120958.908.png](https://assets.northflank.com/image_2025_07_23_T120958_908_3108c55e82.png)

**What you can run on Northflank:**

- Inference APIs with autoscaling and low-latency startup
- Training or fine-tuning jobs (batch, scheduled, or triggered by CI)
- Multi-service AI apps (LLM + frontend + backend + database)
- Hybrid cloud workloads with GPU access in your own VPC

**What GPUs does Northflank support?**

Northflank offers access to **18+ GPU types**, including **NVIDIA A100, H100, L4, L40S**, **AMD MI300X**, **TPU v5e**, and **Habana Gaudi**. View the full list [here](https://northflank.com/gpu).

**Where it fits best:**

If you're building production-grade AI products or internal AI services, Northflank handles both the GPU execution and the surrounding app logic. Especially strong fit for teams who want Git-based workflows, fast iteration, and zero DevOps overhead.

[**See how Cedana uses Northflank to deploy GPU-heavy workloads with secure microVMs and Kubernetes**](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes)

### 2. NVIDIA DGX Cloud – Research-scale training on H100

[DGX Cloud](https://www.nvidia.com/en-us/data-center/dgx-cloud/) is NVIDIA’s managed stack, giving you direct access to H100/A100-powered infrastructure, optimized libraries, and full enterprise tooling. It’s ideal for labs and teams training large foundation models.

![image - 2025-07-23T121001.310.png](https://assets.northflank.com/image_2025_07_23_T121001_310_05ed9a7995.png)

**What you can run on DGX Cloud:**

- Foundation model training with optimized H100 clusters
- Multimodal workflows using NVIDIA AI Enterprise software
- GPU-based research environments with full-stack support

**What GPUs does NVIDIA DGX Cloud support?**

DGX Cloud provides access to **NVIDIA H100 and A100 GPUs**, delivered in **clustered configurations** optimized for large-scale training and NVIDIA’s AI software stack.

**Where it fits best:**

For teams building new model architectures or training at scale with NVIDIA’s native tools, DGX Cloud offers raw performance with tuned software.

### 3. AWS – Deep GPU catalog for large-scale AI pipelines

[AWS](https://aws.amazon.com/) offers one of the broadest GPU lineups (H100, A100, L40S, T4) and mature infrastructure for managing ML workloads across global regions. It's highly configurable, but usually demands hands-on DevOps.

![image - 2025-07-23T121005.253.png](https://assets.northflank.com/image_2025_07_23_T121005_253_c13797c8b4.png)

**What you can run on AWS:**

- Training pipelines via SageMaker or custom EC2 clusters
- Inference endpoints using ECS, Lambda, or Bedrock
- Multi-GPU workflows with autoscaling and orchestration logic

**What GPUs does AWS support?**

AWS supports a wide range of GPUs, including **NVIDIA H100, A100, L40S, and T4**. These are available through services like **EC2, SageMaker, and Bedrock**, with support for multi-GPU setups.

**Where it fits best:**

If your infra already runs on AWS or you need fine-grained control over scaling and networking, it remains a powerful, albeit heavy, choice.

### 4. GCP – TPU-first with deep TensorFlow integration

[GCP](https://cloud.google.com/) supports H100s and TPUs (v4, v5e) and excels when used with the Google ML ecosystem. Vertex AI, BigQuery ML, and Colab make it easier to prototype and deploy in one flow.

![image - 2025-07-23T121007.454.png](https://assets.northflank.com/image_2025_07_23_T121007_454_783326c800.png)

**What you can run on GCP:**

- LLM training on H100 or TPU v5e
- MLOps pipelines via Vertex AI
- TensorFlow-optimized workloads and model serving

**What GPUs does GCP support?**

GCP offers **NVIDIA A100 and H100**, along with **Google’s custom TPU v4 and v5e** accelerators. These are integrated with **Vertex AI** and GKE for optimized ML workflows.

**Where it fits best:**

If you're building with Google-native tools or need TPUs, GCP offers a streamlined ML experience with tight AI integrations.

### 5. Azure – Enterprise AI with MI300X and Copilot integrations

[Azure](https://azure.microsoft.com/) supports AMD MI300X, H100s, and L40S, and is tightly integrated with Microsoft’s productivity suite. It's great for enterprises deploying AI across regulated or hybrid environments.

![image - 2025-07-23T121010.491.png](https://assets.northflank.com/image_2025_07_23_T121010_491_2596440b51.png)

**What you can run on Azure:**

- AI copilots embedded in enterprise tools
- MI300X-based training jobs
- Secure, compliant AI workloads in hybrid setups

**What GPUs does Azure support?**

Azure supports **NVIDIA A100, L40S**, and **AMD MI300X**, with enterprise-grade access across multiple regions. These GPUs are tightly integrated with Microsoft’s **AI Copilot** ecosystem.

**Where it fits best:**

If you're already deep in Microsoft’s ecosystem or need compliance and data residency support, Azure is a strong enterprise option.

### 6. TensorDock – Low-cost GPUs with instant access

[TensorDock](https://tensordock.com/) is designed for affordability and speed. You get access to A100s, 3090s, and other cards with transparent pricing and self-serve provisioning.

![image - 2025-07-23T121013.373.png](https://assets.northflank.com/image_2025_07_23_T121013_373_9533aad8f8.png)

**What you can run on TensorDock:**

- Fine-tuning or training experiments on a budget
- Short-lived jobs that don’t need orchestration
- LLM hobby projects or community pipelines

**What GPUs does TensorDock support?**

TensorDock provides access to **NVIDIA A100, H100, RTX 6000**, and older-generation GPUs like **3090**, all available on a **self-serve, hourly basis**.

**Where it fits best:**

Perfect for side projects or cost-sensitive workloads that need quick access without cloud overhead.

### 7. Oracle Cloud (OCI) – Bare metal GPUs for distributed workloads

[OCI](https://www.oracle.com/cloud/) offers bare metal GPU access, including H100s and MI300X, with high-speed InfiniBand networking and RDMA support. It’s ideal for distributed training and HPC workloads.

![image - 2025-07-23T121017.159.png](https://assets.northflank.com/image_2025_07_23_T121017_159_14c3c2db19.png)

**What you can run on OCI:**

- Large-scale training jobs using low-latency interconnect
- AI simulations or multi-node fine-tuning workflows
- Workloads needing RDMA performance tuning

**What GPUs does OCI support?**

Oracle Cloud Infrastructure (OCI) offers **NVIDIA A100, H100,** and **AMD MI300X** GPUs on **bare metal** instances with **RDMA and InfiniBand** support for high-speed training.

**Where it fits best:**

If you need high throughput and bare metal control, OCI is uniquely positioned for technical teams building serious infrastructure.

### 8. Paperspace by DigitalOcean – Simple and fast prototyping

[Paperspace](https://www.paperspace.com/) is built for developers who want fast access to GPUs without learning a full cloud platform. It’s great for notebooks, small training runs, and demos.

![image - 2025-07-23T121020.901.png](https://assets.northflank.com/image_2025_07_23_T121020_901_c0fed51110.png)

**What you can run on Paperspace:**

- Jupyter-based model development
- Lightweight training and inference tasks
- Early-stage product demos or MVPs

**What GPUs does Paperspace support?**

Paperspace (by DigitalOcean) offers access to **NVIDIA A100, RTX 6000**, and **3090 GPUs**, ideal for notebooks, prototyping, and smaller ML workloads.

**Where it fits best:**

Great for small teams or solo devs who want to get started quickly with minimal setup.

### 9. CoreWeave – Performance-optimized GPU orchestration

[CoreWeave](https://www.coreweave.com/) offers fractional GPUs, custom job scheduling, and multi-GPU support across A100, H100, and L40S cards. It’s built for speed and flexibility, with strong adoption in AI and VFX.

![image - 2025-07-23T121024.076.png](https://assets.northflank.com/image_2025_07_23_T121024_076_54e32e04d2.png)

**What you can run on CoreWeave:**

- High-throughput inference APIs
- Fractional GPU workloads (low-latency with cost control)
- GPU-heavy media processing pipelines

**What GPUs does CoreWeave support?**

CoreWeave provides **NVIDIA H100, A100, L40S**, and support for **fractional GPU usage**, optimized for inference, media workloads, and dynamic scaling.

**Where it fits best:**

If you need flexible scaling, custom orchestration, or elastic GPU scheduling, CoreWeave delivers performance at every layer.

### 10. Lambda – ML infrastructure with prebuilt clusters and endpoints

[Lambda](https://lambda.ai/) offers GPU cloud clusters optimized for ML, plus model hub integration and support for hosted inference. It’s tailored for teams that want managed training and serving.

![image - 2025-07-23T121027.983.png](https://assets.northflank.com/image_2025_07_23_T121027_983_3220628b99.png)

**What you can run on Lambda:**

- Custom training workflows with Docker containers
- Hosted LLMs and CV models
- Prebuilt inference endpoints for deployment

**What GPUs does Lambda support?**

Lambda supports **NVIDIA H100, A100, RTX 6000**, and 4090 GPUs through **pre-configured ML clusters** and containerized runtimes.

**Where it fits best:**

If you're focused on model lifecycle (train → fine-tune → deploy), Lambda simplifies the process with ML-specific UX.

### 11. RunPod – Secure GPU containers with API-first design

[RunPod](https://www.runpod.io/) gives you isolated GPU environments with job scheduling, encrypted volumes, and custom inference APIs. It’s particularly strong for privacy-sensitive workloads.

![image - 2025-07-23T121032.470.png](https://assets.northflank.com/image_2025_07_23_T121032_470_3d334150c3.png)

**What you can run on RunPod:**

- Inference jobs with custom runtime isolation
- AI deployments with secure storage
- GPU tasks needing fast startup and clean teardown

**What GPUs does RunPod support?**

RunPod offers **NVIDIA H100, A100, and 3090** GPUs, with an emphasis on **secure, containerized environments** and job scheduling.

**Where it fits best:**

If you're running edge deployments or data-sensitive workloads that need more control, RunPod is a lightweight and secure option.

### 12. Vast AI – Decentralized marketplace for budget GPU compute

[Vast AI](http://vast.ai/) aggregates underused GPUs into a peer-to-peer marketplace. Pricing is unmatched, but expect less control over performance and reliability.

![image - 2025-07-23T121035.388.png](https://assets.northflank.com/image_2025_07_23_T121035_388_989cb6958c.png)

**What you can run on Vast AI:**

- Cost-sensitive training or fine-tuning
- Short-term experiments or benchmarking
- Hobby projects with minimal infra requirements

**What GPUs does Vast AI support?**

Vast AI aggregates **NVIDIA A100, 4090, 3090**, and a mix of consumer and datacenter GPUs from providers in its peer-to-peer marketplace. Availability and performance may vary by host.

**Where it fits best:**

If you’re experimenting or need compute on a shoestring, Vast AI provides ultra-low-cost access to a wide variety of GPUs.

## How to choose the best GPU cloud provider

If you've already looked at the list and the core features that matter, this section helps make the call. Different workloads need different strengths, but a few platforms consistently cover more ground than others.

| Use Case | What to Prioritize | Platforms to Consider |
| --- | --- | --- |
| **Full-stack AI delivery** | Git-based workflows, autoscaling, managed deployments, bring your own cloud, and GPU runtime integration. | **Northflank** |
| **Large-scale model training** | H100 or MI300X, multi-GPU support, RDMA, high-bandwidth networking | **Northflank,** NVIDIA DGX Cloud, OCI, AWS |
| **Real-time inference APIs** | Fast provisioning, autoscaling, low-latency runtimes | **Northflank**, CoreWeave, RunPod |
| **Fine-tuning or experiments** | Low cost, flexible billing, quick start | **Northflank,** TensorDock, Paperspace, Vast AI |
| **Production deployment (LLMs)** | CI/CD integration, containerized workloads, runtime stability | **Northflank**, Lambda, GCP |
| **Edge or hybrid AI workloads** | Secure volumes, GPU isolation, regional flexibility | **Northflank,** RunPod, Azure, AWS |

## Conclusion

GPU cloud has become a key part of the AI stack. Choosing the right platform depends on what you’re building, how fast you need to move, and how much of the infrastructure you want to manage yourself. We’ve looked at what makes a great AI platform, broken down the top providers, and mapped out how to pick the right one for your use case.

If you’re looking for a platform that handles the heavy lifting without getting in the way, Northflank is worth trying. It’s built for developers, production-ready, and designed to help you move fast.

[Try Northflank](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how it fits your stack.]]>
  </content:encoded>
</item><item>
  <title>RunPod vs Modal: Which AI infra platform fits your ML workloads in 2026?</title>
  <link>https://northflank.com/blog/runpod-vs-modal</link>
  <pubDate>2025-07-22T14:16:00.000Z</pubDate>
  <description>
    <![CDATA[Comparing RunPod vs Modal for AI and ML deployment? This 2026 guide breaks down GPU access, pricing, orchestration, and full-stack support, plus why more teams are picking unified platforms like Northflank.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/runpod_vs_modal_2ce1544217.png" alt="RunPod vs Modal: Which AI infra platform fits your ML workloads in 2026?" />If your team is deciding between RunPod and Modal, you're most likely building or scaling an AI product and need to move fast.

Both platforms have made GPU access simpler, reducing the amount of complexity that comes with ML infrastructure.

When you go beyond model inference, things become more involved because you now need to:

1. fine-tune models
2. run background jobs
3. serve APIs
4. connect to services like Postgres and Redis

...all while keeping everything secure and cost-efficient.

That’s when the question comes up: *are we only serving models, or are we building full applications that include everything around them?*

In this article, we’ll look at how RunPod and Modal compare, and where Northflank fits in if your team needs a more complete setup for building and running AI workloads.

## RunPod vs Modal vs Northflank: Quick comparison table

Before we break things down further, below is a side-by-side comparison of how RunPod, Modal, and Northflank compare across features your team might care about.

<InfoBox className='BodyStyle'>

💡**Looking to deploy more than just models?** 

Try [Northflank for free](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how we support full AI workloads, including databases, CI/CD, and GPU provisioning, in one place.

> **Need help choosing between platforms?** If you're evaluating GPU platforms and have specific capacity or availability requirements, [request GPU capacity here](https://northflank.com/request/gpu).

</InfoBox>

| **Feature / Capability** | **RunPod** | **Modal** | **Northflank** |
| --- | --- | --- | --- |
| **GPU access** | Spot and on-demand GPUs | Managed and autoscaling GPUs | GPU support with BYOC or on-demand |
| **Inference serving** | Via community templates | Code-defined (Python-first) | Yes – REST/gRPC endpoints, custom APIs |
| **Fine-tuning support** | Manual via container deployments | Yes | Yes – PyTorch, DeepSpeed, custom jobs |
| **Jupyter Notebooks** | Yes (via template) | Yes | Yes – with templates or primitives |
| **Orchestration (jobs, pipelines)** | Yes | Built-in for Python-based flows | Native job scheduling and CI/CD pipelines |
| **Multi-tenant security** | Basic isolation in shared environments; Secure Cloud for sensitive workloads | 	Hosted multi-tenant architecture with shared GPU resource pooling | Secure runtime with isolation and RBAC |
| **CI/CD support** | No built-in pipelines, but can be integrated via API (e.g., GitHub Actions) | No built-in pipelines, but supports CI/CD via GitHub Actions and APIs | Built-in CI/CD with Git-based deploys |
| **Networking control** | Basic container networking | Abstracted networking | Static IPs, custom domains, MTLS |
| **Bring your own cloud (BYOC)** | No | No | Yes – supports hybrid and custom GPU providers |
| **Compliance (SOC 2, etc.)** | 	SOC 2 Type 1 achieved; Type 2 in progress (as of Feb 2025)  | 	SOC 2 Type II compliant (as of Jan 2025) | SOC 2 roadmap, audit logs, SAML, RBAC |
| **Pricing model** | Per-GPU usage | Per-call or per-function pricing | Per-container/minute, with project-based billing |
| **Templates / easy deploy** | Community-made | Code-driven | Northflank templates and GitOps config |

## RunPod: quick GPU access, lower-level control

Now that you've seen the comparison, let’s start with RunPod if your focus is fast GPU access and you prefer handling infrastructure your own way.

![runpod-homepage.png](https://assets.northflank.com/runpod_homepage_a696c3aa97.png)

RunPod is popular among researchers and indie devs for a reason. It gives you spot and on-demand GPU instances with transparent pricing, and you get to run your own containers on top. You control what runs, how it runs, and where your workloads live.

If your team is building custom ML workflows and you're comfortable managing orchestration manually, this setup can work well.

What you get with RunPod:

- Fast access to GPUs at low cost, particularly when using spot instances
- Full control over your containers without being tied to a specific framework
- Community templates for tools like Jupyter, LLaMA, and Stable Diffusion

*You can also see [RunPod alternatives for AI/ML deployment beyond just a container](https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment)*

## Modal: Python-native infrastructure for serving models

If RunPod gives you low-level control, Modal takes the opposite approach. It’s designed to feel like part of your Python workflow, with minimal setup, clean abstractions, and no need to think about containers or orchestration.

![modal-home-page.png](https://assets.northflank.com/modal_home_page_3ef6ad50bc.png)

You write Python functions and register them with a decorator that tells Modal to run the code remotely with autoscaling. It works well for serving inference endpoints without having to manage infrastructure.

What you get with Modal:

- A code-first experience tailored for Python developers
- Autoscaling is built in, so you don’t need to handle resource allocation
- Simple setup for running lightweight model serving workloads

The abstraction is helpful if your use case fits what Modal is built for. If your workload lives entirely in Python and you want to deploy quickly without touching containers, Modal can be a good fit. 

## What to look for beyond GPU access

RunPod gives you low-level control. Modal gives you clean abstractions. Still, if you’re building something that needs to scale, GPU access on its own might not be enough.

A few questions to ask as your stack gets more complex:

- Do you need to run background jobs or long-running processes?
- Are services like vector databases, Postgres, or Redis part of your architecture?
- Do you want built-in CI/CD, logs, metrics, or preview environments to speed up iteration?
- Can you deploy in your own cloud, or do you need to?
- Is secure multi-tenancy important for your team or your users?

If you answered yes to more than one of these, it might be time to think beyond single-purpose tools. That’s where Northflank fits in.

## Where Northflank stands apart for AI teams

If your team is looking to run more than models, including APIs, queues, databases, and jobs, this is where Northflank comes in.

![new-northflank-ai-home-page.png](https://assets.northflank.com/new_northflank_ai_home_page_309df08d0b.png)

Northflank is built as a unified platform, not only for AI workloads but also for everything around them. You can deploy model servers, fine-tuning jobs, Jupyter notebooks, and long-running workers right alongside your database, Redis instance, or Postgres service, all in one place.

What this looks like in practice:

- Run fine-tuning jobs using PyTorch or DeepSpeed
- Host APIs, background workers, and cron jobs together
- Spin up Jupyter notebooks using templates or configure from Git
- Use built-in services like Postgres, Redis, and Mongo without leaving the platform
- Choose between templates for fast deploys or use primitives for full control
- Get GPU provisioning in under 30 minutes across providers
- Deploy in your own cloud with BYOC or run hybrid across multiple regions
- Schedule jobs, manage pipelines, and track builds with built-in CI/CD
- Stay on top of logs, metrics, and audit trails for every workload
- Protect your users with a secure runtime and full multi-tenant isolation

This makes Northflank a good fit when your AI workloads are only part of the stack, and you need consistency across the rest.

<InfoBox className='BodyStyle'>

💡Get started by [deploying your first GPU workload on Northflank for free](https://app.northflank.com/signup), or [book a call with our team](https://cal.com/team/northflank/northflank-intro) to walk through your use case.

</InfoBox>

## Which one should you go for?

At this point, it depends on what you're building and how much control you need.

- Go with RunPod if you want low-level access to GPUs and are managing the rest of the infrastructure yourself. It’s a reliable option for custom setups where cost and flexibility are the priority.
- Go with Modal if you're focused on deploying inference endpoints with minimal setup and your workflow is fully Python-based. It works well for smaller, isolated use cases.
- Go with Northflank if you're running both models and the application logic around them. It gives you a secure, unified environment with GPU provisioning, BYOC, CI/CD, databases, and multi-tenant support, all in one platform.

Each platform serves a different need. The best fit comes down to how much you want to manage and how complete your deployment environment needs to be.]]>
  </content:encoded>
</item><item>
  <title>How to deploy pgvector in 1 minute (using Northflank)</title>
  <link>https://northflank.com/blog/how-to-deploy-pgvector</link>
  <pubDate>2025-07-21T10:15:00.000Z</pubDate>
  <description>
    <![CDATA[Deploy pgvector on Northflank in under a minute. Fully managed PostgreSQL with pgvector preinstalled. Just connect, run CREATE EXTENSION vector, and start building with vector search instantly.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_pgvector_3_f1f1678858.png" alt="How to deploy pgvector in 1 minute (using Northflank)" />If you’re building anything with embeddings, you’ve probably looked into pgvector. It adds vector search capabilities to Postgres, and it’s become the go-to extension for semantic search and AI-related workloads.

But what most guides don’t mention is how much setup it takes just to get started. You’ll often run into tutorials that require you to install `pgvector` manually, build custom Docker images, or tweak local Postgres configurations just to enable the extension.

We wanted to skip all of that.

When you deploy a Postgres database on Northflank, pgvector is already available. You don’t have to install anything. You don’t need a special image. Just run:

```sql
CREATE EXTENSION vector;
```

And you’re good to go.

## How to deploy pgvector on Northflank

1. Go to [Northflank](https://app.northflank.com/signup)
2. Create a [new project](https://app.northflank.com/s/account/projects/new)
3. Create a new [PostgreSQL addon](https://app.northflank.com/s/project/create/addon)
4. Click on “Create Addon”
5. Connect to your [database](https://northflank.com/docs/v1/application/databases-and-persistence/access-a-database#access-a-database-locally)
6. Run `CREATE EXTENSION vector;`

That’s it. You’re now running pgvector on a managed Postgres instance, without touching Docker.

### Example: storing and querying embeddings

```sql
CREATE TABLE items (
  id serial PRIMARY KEY,
  name text,
  embedding vector(3)
);

INSERT INTO items (name, embedding) VALUES
  ('item one', '[1,1,1]'),
  ('item two', '[2,2,2]'),
  ('item three', '[1,1,2]');

SELECT * FROM items
ORDER BY embedding <-> '[1,1,1]'::vector
LIMIT 1;

```

This is how you build fast, SQL-native vector search. It works with anything OpenAI, Cohere, custom models, etc.

## If you want to understand the why

This post is just about getting pgvector running quickly. If you want a full breakdown of what pgvector is, how it works, and how to use it with real-world AI tools, check out our longer guide:

[PostgreSQL Vector Search Guide with pgvector](https://northflank.com/blog/postgresql-vector-search-guide-with-pgvector)

## Get started

If you're ready to stop messing around with setup and start building, head here:

[Deploy pgvector on Northflank](https://app.northflank.com/signup)

No custom builds. No manual install. Just Postgres with vector support, ready when you are.]]>
  </content:encoded>
</item><item>
  <title>7 best TensorFlow alternatives in 2026 for training, fine-tuning, and deploying AI models</title>
  <link>https://northflank.com/blog/tensorflow-alternatives</link>
  <pubDate>2025-07-18T14:55:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for TensorFlow alternatives in 2026? This guide breaks down the best open-source frameworks and full-stack platforms for training, fine-tuning, and deploying modern AI workloads.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/tensorflow_alternatives_6b8ca2777a.png" alt="7 best TensorFlow alternatives in 2026 for training, fine-tuning, and deploying AI models" />> *If you’re in search of TensorFlow alternatives, you're likely comparing PyTorch, JAX, Hugging Face, or even platforms like Modal. However, if you’re looking for something that goes beyond frameworks and can help you train, fine-tune, deploy, and scale your models, then Northflank should be your go-to.*
> 

## Why teams are looking for alternatives to TensorFlow

That moment when your team starts working with production workloads like fine-tuning LLMs, running background jobs, or exposing APIs, and you begin to run into the constraints of TensorFlow.

*Can you relate? What happens when your needs grow beyond what the framework was built for?*

So many teams like yours end up working around things that should already be taken care of.

For example, being locked into a static graph model and spending more time debugging than you should.

Or running into issues when trying to integrate with tools like Hugging Face or DeepSpeed.

And at that point, what’s missing becomes obvious: GPU orchestration, a secure runtime, CI/CD, and a way to deploy models alongside the rest of your application.

*That’s when the question comes up: what are the alternatives?*

You’ve got PyTorch, which gives you more flexibility. JAX, for performance control. Hugging Face, with thousands of ready-to-run models.

> And then there are platforms like Northflank, which let you move past local notebooks and run your entire AI stack on GPUs you control, using the same workflow you’d use for any backend service.
> 

So in this guide, I’ll walk you through the best TensorFlow alternatives in 2026. Some are frameworks. Some are infrastructure platforms. All of them solve problems that TensorFlow alone doesn’t.

## Quick comparison of TensorFlow alternatives

Now that you’ve seen some of the reasons why teams like yours look for alternatives to TensorFlow, I’ll give you a quick comparison to see how the alternatives compare to each other.

Before you look at the table, you should know that some of these tools give you more control at the framework level. Others handle infrastructure, so your team doesn't have to build around the same problems again and again.

<InfoBox className='BodyStyle'>
💡If your team needs infrastructure that supports the full lifecycle from training and fine-tuning to CI/CD and serving API, Northflank is the only option on this list that gives you all of that out of the box.

That said, if your use case is focused purely on experimenting or building with a specific framework, the open-source tools still offer plenty of flexibility; you’ll need to pair them with the right infrastructure layer, like Northflank or something similar, depending on how much control you want over deployment and scaling.

Save your time and [get started for free](https://app.northflank.com/signup) or [book a demo to speak with an expert](https://cal.com/team/northflank/northflank-intro).

</InfoBox>

See the table below:

| Tool | Type | GPU training support | Fine-tuning capabilities | Deployment & Infra support | Open Source availability |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | Platform | Supported across providers | Supported for LLMs and custom models | Full-stack support including CI/CD, jobs, APIs, databases | Not open source |
| **PyTorch** | Framework | Widely supported | Native support for fine-tuning workflows | Requires manual setup for deployment and infra | Fully open source |
| **JAX** | Framework | Supported on GPU and TPU | Requires additional tooling for fine-tuning | Requires custom infra for deployment | Fully open source |
| **Hugging Face Transformers** | Library over PyTorch | Built-in for supported models | Fine-tuning support out of the box | Can run locally or on managed infra | Partially open source (core library is open, some enterprise tools are proprietary) |
| **PyTorch Lightning** | Framework | Built on top of PyTorch | Designed for structured fine-tuning | Deployment available via Lightning AI platform | Fully open source |
| **DeepSpeed** | Training optimizer | Optimized for large models | Advanced fine-tuning capabilities | No built-in deployment, requires separate infra | Fully open source |
| **Modal** | Platform | Supported with serverless jobs | Limited compared to full frameworks | Deployment for Python functions with GPU access | Not open source |

*If you need a detailed comparison, scroll down.*

## What to look out for in TensorFlow alternatives

Once you’ve seen how the different tools compare, the next step is knowing what to prioritize based on how your team works.

*Are you focused on model experimentation, or are you thinking ahead to deployment and compliance?*

Some of the main things teams tend to look for when searching for TensorFlow alternatives:

1. **Dynamic vs. static computation graphs**
    
    Tools like PyTorch and JAX give you more flexibility with dynamic execution, compared to TensorFlow’s static graph approach.
    
2. **Easier debugging and more intuitive APIs**
    
    When you're deep in model development, every hour spent debugging adds up. Frameworks with cleaner, Pythonic interfaces tend to win here.
    
3. **Compatibility with popular tools**
    
    Integrations with libraries like Hugging Face, DeepSpeed, and Ray can speed up your workflow and give you access to thousands of ready-to-use components.
    
4. **Scaling across GPUs and workloads**
    
    If your team is training massive models or running multiple fine-tuning jobs in parallel, you’ll want tools that support horizontal scaling. Northflank handles this directly by letting you run jobs across GPU-powered services without needing to manage infrastructure manually.
    
5. **Support for deployment workflows**
    
    Think CI/CD, secure API endpoints, inference scheduling, and background workers. Northflank supports these natively, while frameworks often require extra tooling to cover these areas.
    
6. **Security, audit logs, and compliance**
    
    For teams running models in production, you’ll need runtime isolation, fine-grained access controls, and visibility into what's running. Northflank includes audit logs, secure environments, and secrets management out of the box.
    
7. **Open-source flexibility vs. full platform coverage**
    
    You’ll need to decide how much infrastructure your team wants to handle. Tools like PyTorch and JAX give you full control. Platforms like Northflank remove that burden so your team can focus on building and shipping.
    

## 7 best TensorFlow alternatives in 2026 (frameworks + platforms)

Now that you’ve seen the comparison table and what to look for, let’s break down each alternative a bit more clearly. We’ll cover what each one is, how it compares to TensorFlow, and when it might be the right fit for your team.

Some of these are open-source frameworks that give you fine-grained control when training and experimenting.

Others are platforms that take care of orchestration, infrastructure, and deployment, so your team can move faster without integrating multiple tools manually.

Depending on what your team is building, you might find yourself combining one or two of these tools. That’s why it helps to know what each one handles well and where you’ll need to plug in additional systems, unless you're going with a platform like Northflank that already includes the infrastructure piece.

### 1. Northflank

[Northflank](https://northflank.com/) is a platform that supports the full lifecycle of AI workloads, from training and fine-tuning to deployment and scaling, while also managing the infrastructure around them, including CI/CD pipelines, GPU provisioning, secure runtimes, background jobs, and built-in databases.

![new-northflank-ai-home-page.png](https://assets.northflank.com/new_northflank_ai_home_page_309df08d0b.png)

You can bring your own GPUs or provision them on-demand across supported providers. The runtime is secure by default, isolating workloads and blocking unsafe container behavior and network access.

It’s built to support tools you're likely already using, like Hugging Face, PyTorch, DeepSpeed, Jupyter, and LLaMA, with templates and building blocks that make them easy to run in production.

You also get infrastructure included, so you don’t have to manage those layers separately:

- CI/CD pipelines and container builds built into every project
- On-demand and bring-your-own GPU support across AWS, GCP, Azure, and more
- Secure-by-default runtime to prevent container escapes and unsafe networking
- Built-in services like Redis, Postgres, object storage, and cron jobs
- First-class support for background jobs, vector DBs, and model APIs
- Templates for common AI tools, including Hugging Face, DeepSpeed, LLaMA, Jupyter

> Go with this if you want to deploy, train, fine-tune, and manage infrastructure in one place without having to connect multiple tools manually.
> 

*See [how Cedana uses Northflank to deploy workloads onto Kubernetes with microVMs and secure runtimes](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes)*

### 2. PyTorch

PyTorch is a widely adopted deep learning framework known for its dynamic computation graph, which makes it easier to experiment, debug, and iterate, particularly compared to TensorFlow's static model.

![pytorch-homepage.png](https://assets.northflank.com/pytorch_homepage_cd05a7d9ff.png)

It’s the default choice for many research teams and production systems because of how flexible and intuitive it is to work with.

You’ll find useful integration with other open-source tools in the ecosystem:

- Hugging Face Transformers run natively on PyTorch, making model deployment and experimentation faster
- DeepSpeed works well with PyTorch for distributed and optimized training
- PyTorch Lightning helps structure and scale complex models while keeping flexibility

> Go with this if your team wants full control over training and fine-tuning, and you're building your own stack from the framework up.
> 

*See this guide on [What is PyTorch? A deep dive for engineers (and how to deploy it)](https://northflank.com/blog/what-is-pytorch)*

### 3. JAX

JAX is a high-performance numerical computing library with a NumPy-like syntax and first-class support for function transformations like `jit`, `vmap`, and `grad`.

It’s designed for high-throughput workloads on GPUs and TPUs, and is widely used in advanced research environments like DeepMind.

![jax-homepage.png](https://assets.northflank.com/jax_homepage_f3801f05a6.png)

While the learning curve is steeper than PyTorch, it gives you detailed control over how computations are structured and run.

Here’s where JAX tends to be a good fit:

- Function-level transformations like `jit` for speed, `vmap` for batching, and `grad` for autograd
- Built-in hardware acceleration with deep integration for TPU and GPU workloads
- Popular in research settings where custom setups and performance tuning are common

> Go with this if you’re building custom training loops or experimenting at research scale and need full control over execution.
> 

### 4. Hugging Face Transformers

Hugging Face Transformers is a popular library that gives you access to thousands of pretrained models across NLP, vision, and audio tasks.

It now focuses entirely on PyTorch, with training, fine-tuning, and serving utilities built around tools like `accelerate`, `optimum`, and DeepSpeed.

![hugging-face-transformers.png](https://assets.northflank.com/hugging_face_transformers_8832c698e8.png)

It’s widely used by teams who want to avoid starting from scratch and get to production faster.

Here’s what makes it useful:

- Thousands of pretrained models covering a wide range of tasks
- Performance integrations with DeepSpeed, `optimum`, and `accelerate`
- Built-in utilities for tokenization, datasets, and fine-tuning workflows

> Go with this if you want fast access to high-performing models and a smoother path to fine-tuning or serving them with PyTorch.
> 

See 7 [best Hugging Face alternatives in 2026: Model serving, fine-tuning & full-stack deployment](https://northflank.com/blog/huggingface-alternatives)

### 5. PyTorch Lightning

PyTorch Lightning is a high-level framework built on top of PyTorch that helps teams structure and scale training code without rewriting core logic.

It’s used by teams that want to run experiments at scale, organize training runs, and eventually turn research code into production workflows.

![pytorch-lighting-homepage.png](https://assets.northflank.com/pytorch_lighting_homepage_961d5d815d.png)

Some of the features that make it practical:

- Clear structure for training loops, model checkpoints, and logging
- Works with Fabric and DeepSpeed for performance optimization
- Built for repeatability, making it easier to share and maintain training code

> Go with this if you're building repeatable training pipelines or turning research projects into production-ready modules.
> 

### 6. DeepSpeed

DeepSpeed is an open-source optimization library developed by Microsoft, designed to support large-scale training and fine-tuning of transformer-based models.

It’s commonly used in LLM workflows and multi-billion parameter model training where performance and memory efficiency are critical.

![deepseed.png](https://assets.northflank.com/deepseed_87c8830470.png)

Some of the features teams rely on:

- ZeRO (Zero Redundancy Optimizer) optimization for sharding model states across devices
- Offloading techniques to move computation and memory to CPU or NVMe
- 3D parallelism support for tensor, pipeline, and data parallel training

> Go with this if you need to fine-tune large models with better GPU memory efficiency and distributed training capabilities.
> 

### 7. Modal

Modal is a closed-source, serverless platform designed for running Python-based GPU jobs without managing infrastructure directly.

It’s often used for lightweight tasks like batch inference or model training in the cloud, especially when speed and simplicity are the goal.

![modal-home-page.png](https://assets.northflank.com/modal_home_page_3ef6ad50bc.png)

What to keep in mind:

- Serverless model abstracts away most infrastructure details
- Built-in support for Python-based GPU workloads
- Missing core features like persistent databases, CI/CD workflows, and secure runtime customization

> Go with this if you want a quick way to run jobs in the cloud and don’t need deeper infrastructure control.
> 

[*See 6 best Modal alternatives for ML, LLMs, and AI app deployment*](https://northflank.com/blog/6-best-modal-alternatives)

## Making the right choice: framework vs full platform

Now that you’ve seen what each alternative offers, the decision often comes down to this:

*Do you need full control at the code level, or are you looking to get your workloads running in production without spending weeks on infrastructure?*

Here’s how to think about it:

- Frameworks like PyTorch, JAX, and DeepSpeed give you deep control over model architecture, training loops, and experimentation. They’re open-source and widely adopted, but they don’t come with deployment, orchestration, or security layers.
- Platforms like Northflank and Modal handle the infrastructure for you. That includes provisioning GPUs, setting up CI/CD, serving models, managing databases, and securing workloads, so your team doesn’t need to build all that from scratch.

In practice, most teams use both:

A framework for training and fine-tuning, and a platform to handle everything else once it’s time to ship.

> Northflank supports both directions, bring your own framework and we’ll take care of the infrastructure, so you can stay focused on building.
> 

## Why AI teams deploy on Northflank

Once your team has chosen a framework that fits your training and experimentation needs, the next step is determining where to run everything reliably, securely, and without compromising your own infrastructure.

That’s where Northflank comes in.

Teams use Northflank to deploy and manage their entire AI stack, including fine-tuning models, serving APIs, and running jobs in production.

Here’s what you get:

- Run fine-tuning jobs, notebooks, and background workers with GPU support
- Built-in observability: logs, metrics, deploy history (no separate tools needed)
- SOC 2 alignment, role-based access control, audit logs, and tenant isolation for production workloads
- Bring Your Own Cloud (BYOC): deploy to AWS, GCP, or spot GPU marketplaces like RunPod and Lambda
- Templates for Hugging Face, LLaMA, Jupyter, Postgres, Redis, and more to get started quickly
- Everything in one place with a clean UI, robust API, and GitOps support for teams that want automation

[See the docs](https://northflank.com/docs) to get started.

## FAQs about TensorFlow alternatives

If you’ve made it this far, you’ve most likely seen how frameworks like PyTorch and JAX differ from TensorFlow, and how platforms like Northflank fit into the bigger picture.

To wrap up, here are some common questions that come up when teams begin evaluating alternatives:

1. **What is the replacement of TensorFlow?**
    
    PyTorch and JAX are two of the most common alternatives today. Many teams also pair these with platforms like Northflank to handle deployment and infrastructure.
    
2. **Which is better: PyTorch or TensorFlow?**
    
    PyTorch is generally preferred for flexibility and dynamic graphs, while TensorFlow may suit teams already deep in its ecosystem. Most newer projects lean toward PyTorch.
    
3. **Is TensorFlow still relevant in 2026?**
    
    Yes, but it’s no longer the default. Many AI teams use PyTorch or JAX and prioritize frameworks that integrate better with modern tooling.
    
4. **Can I deploy Hugging Face models without TensorFlow?**
    
    Absolutely. Hugging Face supports PyTorch and JAX, and works well with platforms like Northflank for model serving.
    
5. **Is Modal open-source?**
    
    No. Modal is a closed-source platform focused on running Python functions with GPU access.
    
6. **Is TensorFlow shutting down?**
    
    No. TensorFlow is still maintained by Google, but its popularity has shifted as more teams move to PyTorch.]]>
  </content:encoded>
</item><item>
  <title>6 Best Dokku alternatives for app deployment in 2026</title>
  <link>https://northflank.com/blog/6-best-dokku-alternatives</link>
  <pubDate>2025-07-18T11:45:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for Dokku alternatives in 2026? Explore the top platforms like Northflank, CapRover, and Railway to scale apps with better CI/CD, multi-cloud support, observability, and automation.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/best_dokku_alternatives_90c3b8ceb5.png" alt="6 Best Dokku alternatives for app deployment in 2026" />Dokku has earned a reputation as a lightweight way to deploy apps without relying on a fully managed platform. It’s open-source, easy to host yourself, and works well for solo developers or small teams who want control without too much overhead.

But as applications and teams mature, Dokku’s simplicity can start to feel limiting. Things like CI/CD, multi-service environments, autoscaling, and built-in observability require custom plugins or manual setup. Managing production infrastructure this way isn’t always sustainable, especially when uptime, security, and collaboration start to matter more.

More teams are moving toward platforms that keep developer control but offer stronger defaults, automation, and scalability, often at a lower total cost than maintaining a patchwork of tools. In this guide, we’ll break down what to look for in a Dokku alternative and how platforms like [Northflank](https://northflank.com/) are helping teams ship faster with less operational stress.

## **TL;DR: Best Dokku alternatives in 2026**

If you’re short on time, here’s a quick look at the top alternatives to Dokku, and why teams are making the switch:

- [**Northflank**](https://northflank.com/) – A flexible, production-ready platform with support for [bring-your-own-cloud (AWS, GCP, Azure, and more)](https://northflank.com/features/bring-your-own-cloud), GPU workloads, LLM deployment, background jobs, databases, advanced CI/CD, preview environments, and multi-service environments. Ideal for growing teams that want full control.
- [**CapRover**](https://caprover.com/) – Self-hosted PaaS built on Docker Swarm, good for small teams needing a lightweight, extensible platform.
- [**Coolify**](https://coolify.io/) – Modern, self-hosted developer platform with UI-based workflows, service orchestration, and secrets management.
- [**Railway**](https://railway.com/) – Fully managed deployment platform focused on simplicity and speed for prototyping and small-to-medium apps.
- [**Fly.io**](http://fly.io/) – Edge-native platform designed for low-latency global deployments and background jobs, with built-in observability.
- [**Render**](https://render.com/) – Managed platform offering support for web services, cron jobs, databases, and auto-scaling with a focus on developer productivity.

## What to look out for in Dokku alternatives

Dokku gives you the basics: git push to deploy, Docker under the hood, and the freedom to self-host. But when you're evaluating alternatives, the goal isn’t to replicate Dokku, it’s to move beyond its limitations. Here are the key areas to consider:

1. **First-class CI/CD and environment workflows**

Look for platforms that support automated builds, preview environments, and seamless promotion across dev, staging, and production. The less you have to script or bolt on, the more reliable your deployments will be.

2. **Multi-service and background job support**

Modern applications rarely consist of a single container. Whether you’re running APIs, workers, cron jobs, or queue consumers, your platform should handle this out of the box with a clear orchestration model.

3. **Built-in scalability and resource controls**

Horizontal scaling, CPU and memory tuning, and workload isolation should be simple to configure. You shouldn’t have to write custom scripts or SSH into a server to adjust capacity.

4. **Monitoring, logging, and alerting**

Logs and metrics are critical for debugging and performance. A good platform gives you visibility into service health, historical usage, and real-time logs without needing to wire up third-party dashboards manually.

5. **Secrets, networking, and access control**

Secrets management, private networking between services, and fine-grained access control (like RBAC) are no longer optional, even for small teams. These should be built-in, not DIY.

6. **Cloud flexibility and portability**

Some teams want a fully managed platform. Others want to host on their own cloud accounts or even across multiple clouds. A good alternative to Dokku should meet you where you are—without locking you in.

## Top 6 Dokku alternatives in 2026

Once you know what you're looking for in an alternative, it becomes easier to filter out platforms that don’t align with your workflow. Here are six strong alternatives to Dokku, each solving different parts of the deployment puzzle.

### 1. Northflank – The leading Dokku alternative for fully managed deployments

[Northflank](https://northflank.com/) is a platform that enables developers to build, deploy, and scale applications, services, databases, GPUs, LLMs, and jobs on any cloud through a self-service approach. For DevOps and platform teams, Northflank provides a powerful abstraction layer over Kubernetes, enabling templated, standardized production releases with intelligent defaults while maintaining necessary configurability.

![Northflank Screenshot](https://assets.northflank.com/pawelzmarlak_2025_07_17_T12_18_55_915_Z_08ec1d06ee.png)

**Key features:**

- Kubernetes-powered, full-stack platform
- Deploy containers, databases, and scheduled jobs
- Private networking, and [Bring your own cloud (AWS, GCP, Azure, etc.)](https://northflank.com/features/bring-your-own-cloud)
- CI/CD integration, real-time logs, with a developer-friendly and consistent experience across UI, CLI, API, and GitOps
- GPU support for AI workloads
- Automatic preview environments and seamless promotion to dev, staging, and production
- Automatic horizontal scaling
- Integrated secrets management

**Why choose Northflank over Dokku?**

- Built-in CI/CD and preview environments, no plugins needed
- First-class support for jobs, services, databases, and GPU/AI workloads
- Scalable Kubernetes-native deployments without managing infrastructure
- Built-in observability, RBAC, and audit trails

**Potential drawbacks:**

- Highly experienced DevOps teams might find it restrictive compared to directly managing raw Kubernetes clusters. It’s a fine balance between ease of use, flexibility, and customization; that line differs for every organization.

*See how [Weights company uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### 2. CapRover – Dokku alternative for simple self-hosted PaaS

[CapRover](https://caprover.com/) is a lightweight, self-hosted platform-as-a-service that uses Docker under the hood. It’s easy to set up on a single VPS and comes with a web-based dashboard and one-command deploys.

![image - 2025-07-18T125518.867.png](https://assets.northflank.com/image_2025_07_18_T125518_867_693d30f80c.png)

**Key features:**

- Deploy with Docker and Git
- Built-in Let's Encrypt SSL and NGINX-based routing
- App templating system for common stacks
- Persistent storage support and volume mounting

**Why choose CapRover over Dokku?**

- Easier initial setup with less manual config
- Built-in UI and one-click app deployments
- Ships with features like HTTPS and load balancing out of the box

**Limitations:**

- No built-in CI/CD or native Kubernetes support
- Not ideal for multi-node clusters or large-scale setups

### 3. Coolify – Open-source alternative with a modern UI

[Coolify](https://coolify.io/) offers a self-hosted PaaS with a polished interface and Git-based deployments. It focuses on ease of use while supporting a wide variety of deployment targets.

![image - 2025-07-18T125514.212.png](https://assets.northflank.com/image_2025_07_18_T125514_212_3d4247dcaf.png)

**Key features:**

- Git-based auto deploys
- Manage services, databases, jobs, and storage
- Built-in support for Docker and Kubernetes
- Real-time logs and UI-driven environment management

**Why choose Coolify over Dokku?**

- Clean UI with better real-time visibility
- Supports both Docker and Kubernetes targets
- Built-in database provisioning and service monitoring

**Limitations:**

- Still evolving and may lack enterprise readiness
- Kubernetes support is limited compared to dedicated platforms

### 4. Railway – Simplified cloud platform with Git integration

[Railway](https://railway.app/) abstracts away much of the infrastructure and lets developers focus on code. It’s designed for teams that want zero-config deploys and a tight Git workflow.

![image - 2025-07-18T125237.207.png](https://assets.northflank.com/image_2025_07_18_T125237_207_ea45eb3a75.png)

**Key features:**

- Git push to deploy
- Automatic environment provisioning
- Real-time logs, metrics, and secrets
- Minimal setup for databases and background workers

**Why choose Railway over Dokku?**

- Extremely fast to deploy from Git
- Simple database and environment management
- Developer-first UI and experience

**Limitations:**

- Less control over infrastructure
- Limited extensibility and scaling for complex workloads

### 5. Fly.io – Global edge hosting with runtime isolation

[Fly.io](https://fly.io/) focuses on deploying applications close to users by running them in isolated micro-VMs across the globe. Ideal for latency-sensitive applications and edge-first architectures.

![image - 2025-07-18T125239.568.png](https://assets.northflank.com/image_2025_07_18_T125239_568_8cb12f15b3.png)

**Key features:**

- Multi-region global app deployment
- Postgres hosting with automated replication
- Built-in metrics and alerts
- CLI-first experience with optional web UI

**Why choose [Fly.io](http://fly.io/) over Dokku?**

- Low-latency app hosting across regions
- Better support for distributed applications
- Built-in Postgres and DNS support

**Limitations:**

- Can require app-level awareness of edge patterns
- Less suited for traditional monolith hosting

### 6. Render – Fully managed cloud platform for web services

[Render](https://render.com/) provides a developer-focused PaaS with built-in infrastructure primitives. It’s designed for teams that want automation without vendor lock-in.

![image - 2025-07-18T125241.690.png](https://assets.northflank.com/image_2025_07_18_T125241_690_bb732711c7.png)

**Key features:**

- Auto-deploy from GitHub or GitLab
- Managed services for databases, cron jobs, and workers
- Built-in HTTPS, DDoS protection, and scaling
- Clear usage-based pricing

**Why choose Render over Dokku?**

- Easy autoscaling and managed infrastructure
- More enterprise-grade features out of the box
- Integrated GitOps workflows and PR previews

**Limitations:**

- Some workloads may need fine-tuning to match pricing model
- Not as customizable as fully self-hosted options

## Making the right choice: Dokku vs other alternatives

| Capability | Dokku | Northflank | CapRover | Coolify | Railway | Fly.io | Render |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Server management | Single VPS | Kubernetes‑based | Swarm cluster | VM or Kubernetes | Fully managed | Fully managed | Fully managed |
| Multi‑cloud / hybrid support | No | Yes | No | Partial | No | Partial | No |
| Built‑in CI/CD | No | Yes | No | Partial | Yes | Yes | Yes |
| GPU and AI‑ML support | No | Yes | No | No | No | Yes | No |
| Autoscaling | No | Yes | Manual | Manual | Yes | Yes | Yes |
| Preview environments | No | Yes | No | No | Yes | Yes | Yes |
| Observability and alerting | Logs only | Full stack APM | Basic metrics | Basic metrics | Basic metrics | Advanced metrics | Advanced metrics |
| Role‑based access control | No | Yes | No | No | Yes | Yes | Yes |

## Why teams deploy on Northflank

Northflank appeals to teams that have outgrown self-hosted platforms like Dokku but don’t want to jump directly into the operational burden of managing Kubernetes. It offers a middle ground: full control, strong abstractions, and developer-first ergonomics.

**Key reasons teams choose Northflank:**

- **Cloud-native without the complexity**
    
    Northflank sits on top of Kubernetes but removes the boilerplate. Developers can deploy services, jobs, and databases without writing Helm charts or managing ingress.
    
- **End-to-end deployment pipeline**
    
    From Git-based CI/CD to automatic preview environments, Northflank integrates the entire development workflow into a single, consistent platform.
    
- **Multi-cloud and portability by design**
    
    Teams can deploy to Northflank’s managed infrastructure or bring their own cloud provider (AWS, GCP, Azure), avoiding vendor lock-in and maintaining data residency.
    
- **Advanced use cases, covered**
    
    Whether running GPU-based workloads for ML inference, asynchronous workers for background jobs, or production-grade APIs with autoscaling and RBAC, Northflank handles it all in one system.
    

## Conclusion

Dokku is still a useful tool for simple apps and teams that prefer to stay close to the infrastructure. But as application complexity increases and production demands grow, many teams find these limitations become bottlenecks to their progress.

Platforms like Northflank offer a more complete solution for 2026: Kubernetes-backed automation, Git-native workflows, advanced observability, and multi-service orchestration—all without the overhead of managing infrastructure manually.

For teams seeking reliability, scalability, and flexibility across cloud providers, [Northflank](https://app.northflank.com/signup) is a natural step forward.

> Ready to explore? Try [Northflank](https://app.northflank.com/signup) or compare platforms head-to-head with your current stack.
>]]>
  </content:encoded>
</item><item>
  <title>7 best KServe alternatives in 2026 for scalable model deployment</title>
  <link>https://northflank.com/blog/kserve-alternatives</link>
  <pubDate>2025-07-17T12:23:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for KServe alternatives? Compare 7 tools for model serving, fine-tuning &amp; AI apps, including Northflank, BentoML, and Modal.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/kserve_alternatives_19ab5461a3.png" alt="7 best KServe alternatives in 2026 for scalable model deployment" />If you’re looking into KServe alternatives, you’re likely building or scaling AI workloads that go beyond basic model serving.

As you might already know, KServe helps deploy models on Kubernetes, but it can get complex once you start dealing with GPU orchestration, secure multi-tenancy, or full-stack infrastructure.

I'll walk you through 7 top alternatives to KServe, with details on what each one does well and how to choose the right setup for your team.

## TL;DR: Best KServe alternatives for model deployment

See a quick look at some of the best platforms to check out if you’re moving beyond KServe:

1. [**Northflank**](https://northflank.com/): Full-stack app and model deployment with GPU support, CI/CD, and secure multi-tenancy
2. [**BentoML**](https://northflank.com/blog/bentoml-alternatives): Python-first framework for packaging and serving ML models as APIs
3. [**Hugging Face Inference Endpoints**](https://northflank.com/blog/huggingface-alternatives): One-click deployment for hosted LLMs and transformers
4. [**Modal**](https://northflank.com/blog/6-best-modal-alternatives): Serverless platform for running Python and ML workloads on GPUs
5. [**Kubeflow**](https://northflank.com/blog/top-7-kubeflow-alternatives): End-to-end MLOps pipelines with built-in support for model serving
6. [**Anyscale**](https://northflank.com/blog/anyscale-alternatives-for-ai-ml-model-deployment): Distributed model serving and agent workloads using Ray Serve
7. [**Replicate**](https://northflank.com/blog/6-best-replicate-alternatives): Hosted APIs for popular models, ideal for testing and lightweight deployment

<InfoBox className='BodyStyle'>

💡Deploy AI workloads on Northflank with GPU, CI/CD, and secure runtime by [getting started for free](https://app.northflank.com/signup) or [booking a demo](https://cal.com/team/northflank/northflank-intro) 

</InfoBox>

## What to look for in a KServe alternative (a must-read!)

I’ll list a few things that you should keep in mind as we walk through each option:

1. **GPU support**
    
    You want to be able to run both inference and fine-tuning jobs. Some platforms only handle serving pre-trained models, while others give you more control over GPU provisioning and scheduling. Platforms like Northflank let you attach GPUs to any workload and choose from different providers to reduce costs. ([See how](https://northflank.com/gpu))
    
2. **Model autoscaling and versioning**
    
    It should be simple to scale models based on traffic and run multiple versions at once. This helps you test safely and avoid service interruptions. Look for platforms like Northflank that make this part of the deployment workflow instead of something you have to manage manually. ([See autoscaling in action](https://northflank.com/docs/v1/application/scale/autoscale-deployments))
    
3. **Bring Your Own Cloud (BYOC)**
    
    If you're using multiple GPU providers or want to avoid vendor lock-in, you need a platform like Northflank that supports BYOC and hybrid deployments. This gives you more flexibility around pricing, availability, and infrastructure control. ([Try deploying in your cloud now](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes))
    
4. **Secure multi-tenancy**
    
    For teams building products with AI agents, sandboxes, or user-submitted code, isolation and runtime security are critical. Northflank, for instance, includes a secure runtime designed to prevent cross-tenant access and container escapes, making it a good fit for environments with lots of users. ([See how to spin up a secure code sandbox & microVM in seconds with Northflank](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh))
    
5. **Built-in services**
    
    Serving a model is only one part of the pipeline. You’ll also need APIs, databases, message queues, or vector stores to support your application. Platforms like Northflank bundle these together, so you can manage everything in one place, rather than combining tools separately.
    
6. **CI/CD or GitOps support**
    
    Deployment should fit naturally into your team’s workflow. Look for tools that support Git-based workflows, pull request previews, and automated pipelines. Northflank supports both UI-based and [GitOps deployment](https://northflank.com/docs/v1/application/infrastructure-as-code/gitops-on-northflank), which helps teams ship faster with less overhead. ([Try automating your builds and deployments with CI/CD](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank))
    

## 7 best KServe alternatives in 2026

Now that you know what to look for, I’ll walk you through some of the top alternatives to KServe that I mentioned earlier.

Each of these platforms takes a different approach to model deployment. Some focus on packaging models into APIs, others handle distributed inference, and a few provide full environments for building, serving, and scaling AI applications.

As you read through them, think about what fits your workflow: Do you need GPU control? Are you deploying more than models? Do you want to run everything in one place?

Let’s break them down one by one.

### 1. Northflank

***Full-stack platform for GPU workloads, model deployment, and app infrastructure***

If you're building more than a model server, such as deploying APIs, managing databases, or running fine-tuning jobs, you'll need something more complete. [Northflank](https://northflank.com/) brings all of that together in one place, built for teams running production AI workloads.

![new-northflank-ai-home-page.png](https://assets.northflank.com/new_northflank_ai_home_page_309df08d0b.png)

What you get:

- GPU support on any service or job so that you can deploy LLMs, training pipelines, or background workers without extra setup ([See this in action](https://northflank.com/gpu))
- Model serving and full-stack app deployment in one platform, including Postgres, Redis, and vector databases
- [Bring your own cloud](https://northflank.com/features/bring-your-own-cloud) or GPU provider to stay flexible with cost and availability
- [Secure multi-tenancy](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale) and runtime isolation, which is important if your users submit code or run AI agents
- [Built-in CI/CD](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) and [GitOps support](https://northflank.com/docs/v1/application/infrastructure-as-code/gitops-on-northflank), so deployments fit into your team’s workflow
- Templates for quick setup, helpful when you're spinning up Jupyter, LLaMA, or DeepSpeed environments

> Choose this option if you want a unified platform for both model serving and app infrastructure.
> 

*See [how Cedana uses Northflank to deploy workloads onto Kubernetes with microVMs and secure runtimes](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes)*

### 2. BentoML

***Python-first framework for packaging and serving ML models as APIs***

If you're working in Python and want full control over how your models are served, BentoML is an option. It lets you package models into containerized REST APIs with minimal overhead and supports popular frameworks like PyTorch, TensorFlow, and scikit-learn.

![bentoml-homepage.png](https://assets.northflank.com/bentoml_homepage_1b6289d1d1.png)

Keep in mind that BentoML focuses on the model serving layer. It doesn’t handle infrastructure, GPU orchestration, or supporting services like databases or CI/CD.

What it’s good for:

- Building custom model servers with full control over API logic
- Serving models locally or inside containers, with clear developer workflows
- Integrating with ML tools like MLflow or Hugging Face for model management
- Running lightweight inference setups in environments where you manage the infrastructure

> Go with this if you want to control how your models are served without relying on a full platform.
> 

*See [6 best BentoML alternatives for self-hosted AI model deployment (2026)](https://northflank.com/blog/bentoml-alternatives)*

### 3. Kubeflow

***End-to-end MLOps platform with pipelines, model training, and serving***

Kubeflow is built for teams already working heavily with Kubernetes. It includes tools for managing the entire ML lifecycle, from data pipelines and training to model versioning and serving. KServe is one of its components, but Kubeflow goes beyond inference to cover the broader MLOps stack.

![kubeflow-homepage.png](https://assets.northflank.com/kubeflow_homepage_81e2ccf028.png)

That said, it can be complex to set up and manage. Most teams that succeed with Kubeflow have a dedicated infrastructure team or significant Kubernetes experience.

What it’s good for:

- Running full ML pipelines on Kubernetes with tight integration between components
- Managing training workflows and metadata, not only inference
- Serving models using KServe alongside other tools in the Kubeflow stack
- Building internal ML platforms where control and customization are priorities

> Go with this if you're already deep into Kubernetes-based MLOps and want full control over your stack.
> 

*See [Top 7 Kubeflow alternatives for deploying AI in production (2026 Guide)](https://northflank.com/blog/top-7-kubeflow-alternatives)*

### 4. Modal

***Serverless platform for running ML workloads on GPUs with minimal setup***

Modal is built for speed. If you're prototyping models or need to run quick inference jobs without setting up infrastructure, it gets you started fast. You can write Python functions, decorate them, and run them on GPUs without touching Kubernetes or worrying about scaling logic.

![modal-home-page.png](https://assets.northflank.com/modal_home_page_2cfbb0b1d2.png)

It works well for isolated tasks but doesn’t support full applications, CI/CD workflows, or bring your own cloud setups.

What it’s good for:

- Running Python code on GPUs quickly, ideal for experiments or demos
- Prototyping LLM or vision models without setting up servers
- Minimal configuration, focused on simplicity over customization
- Lightweight workflows, where you're not deploying full services or pipelines

> Go with this if you want fast GPU access without managing infrastructure.
> 

*See [6 best Modal alternatives for ML, LLMs, and AI app deployment](https://northflank.com/blog/6-best-modal-alternatives)*

### 5. Anyscale (Ray Serve)

***Distributed inference and task execution using Ray clusters***

Anyscale is built on top of Ray, a framework for scaling Python applications across clusters. If you're working with agent-based systems, streaming workloads, or anything that requires distributed scheduling, Ray Serve gives you the control you need for high-throughput inference.

![anyscale-homepage.png](https://assets.northflank.com/anyscale_homepage_0d9cb1948c.png)

Anyscale handles the orchestration, but there's still some complexity involved in managing clusters, especially as your workloads grow.

What it’s good for:

- Running distributed inference at scale using Ray Serve
- Scheduling async workloads, agents, or batch jobs that need parallel execution
- Scaling Python code across machines, without rewriting core logic
- Teams already familiar with Ray, looking for hosted infrastructure

> Go with this if you already use Ray or want distributed scheduling for your models.
> 

*See [Top Anyscale alternatives for AI/ML model deployment](https://northflank.com/blog/anyscale-alternatives-for-ai-ml-model-deployment)*

### 6. Hugging Face Inference Endpoints

***One-click model serving for LLMs and hosted transformers***

If your models are already on Hugging Face, their Inference Endpoints make it easy to deploy them with minimal setup. You can serve popular transformers, fine-tuned models, or even open-weight LLMs with a single click through their UI or API.

![huggingface-inference-endpoints-homepage.png](https://assets.northflank.com/huggingface_inference_endpoints_homepage_b6f442c028.png)

It’s great for getting started quickly, but you’ll run into limitations if you need deeper control over the infrastructure, custom workflows, or cost optimization at higher scale.

What it’s good for:

- Serving Hugging Face-hosted models without managing infrastructure
- Quick deployments for transformers and LLMs**,** directly from your Hugging Face account
- Experimenting with model performance, latency, and cost tradeoffs
- Lightweight use cases, where customization isn’t a priority

> Go with this if you want to serve Hugging Face models quickly and don’t need full control.
> 

*See [7 best Hugging Face alternatives in 2026: Model serving, fine-tuning & full-stack deployment](https://northflank.com/blog/huggingface-alternatives)*

### 7. Replicate

***Hosted GPU inference with auto-generated APIs for ML models***

Replicate is designed for quickly turning machine learning models into public or private APIs. You upload your model, and Replicate handles the infrastructure and exposes it as an endpoint. It’s a great way to share demos or run lightweight inference workloads without having to write serving logic or set up GPUs yourself.

![replicate-homepage.png](https://assets.northflank.com/replicate_homepage_38062bccda.png)

It’s simple to use but limited if you need fine-tuning, scheduling, or integration with a broader application stack.

What it’s good for:

- Exposing models as APIs quickly, with minimal configuration
- Sharing demos or prototypes, especially for vision or generative models
- Running inference on hosted GPUs, without managing servers
- Short-lived or low-traffic workloads, where simplicity matters more than flexibility

> Go with this if you want a quick way to expose your model as an API without managing infrastructure.
> 

*See [6 best Replicate alternatives for ML, LLMs, and AI app deployment](https://northflank.com/blog/6-best-replicate-alternatives)*

## Making the right choice for your AI stack

Now that you’ve seen how each platform compares, you can start to narrow things down based on what you're building and how much control you need.

Use this breakdown to help guide your decision:

1. Do you need full-stack infrastructure and secure GPU workloads → Northflank
2. Do you want a Python API for building custom model servers → BentoML
3. Are you already using Kubernetes and want full MLOps pipelines → Kubeflow
4. Are you serving hosted LLMs with minimal setup → Hugging Face
5. Are you running quick experiments or GPU jobs without infrastructure setup → Modal
6. Are you using Ray or building distributed inference workloads → Anyscale
7. Are you sharing model demos through hosted endpoints → Replicate

<InfoBox className='BodyStyle'>

💡If you're building applications alongside your models and need a platform that supports both with consistent infrastructure, you should take a closer look at how [Northflank](https://northflank.com/) fits into that workflow. 

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Top Flightcontrol alternatives for scalable cloud deployments in 2026</title>
  <link>https://northflank.com/blog/top-flightcontrol-alternatives</link>
  <pubDate>2025-07-17T11:30:00.000Z</pubDate>
  <description>
    <![CDATA[Discover the best Flightcontrol alternatives in 2026. Compare top platforms like Northflank, Qovery, and Porter for multi-cloud support, CI/CD, AI workloads, and scalable, developer-friendly deployments.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/top_flightcontrol_alternatives_1192f1b6ec.png" alt="Top Flightcontrol alternatives for scalable cloud deployments in 2026" />Deploying applications to AWS can be challenging, especially as infrastructure complexity grows. Flightcontrol has helped address this by giving developers a smoother way to ship applications on their own AWS accounts, without managing the raw details of ECS, Lambda, or Terraform. It’s been a strong fit for many teams looking to move fast with less overhead.

But as teams scale and requirements evolve, some begin exploring other options. You might need more flexibility across clouds, better support for background workers or scheduled jobs, or a platform that’s easier to standardize across multiple teams and environments.

In this guide, we’ll explore why some engineering teams consider alternatives to Flightcontrol, what features matter most when evaluating new platforms, and how tools like Northflank are addressing the needs of teams building and scaling modern applications.

## TL;DR – Best Flightcontrol alternatives in 2026

If you’re short on time, here’s a quick look at the top alternatives to Flightcontrol, and why teams are making the switch:

- [**Northflank**](https://northflank.com/) – A flexible, production-ready platform with support for [bring-your-own-cloud (AWS, GCP, Azure, and more)](https://northflank.com/features/bring-your-own-cloud), GPU workloads, LLM deployment, background jobs, databases, advanced CI/CD, preview environments, and multi-service environments. Ideal for growing teams that want full control without Kubernetes overhead.
- [**Qovery**](https://www.qovery.com/) – An internal developer platform with Git-based workflows and managed Kubernetes. Works well for teams focused on automation.
- [**Porter**](https://www.porter.run/) – Kubernetes-native platform with templating support. Best suited for teams already familiar with Kubernetes.
- [**Cloud66**](https://www.cloud66.com/) – Infrastructure-as-code approach to Kubernetes with multi-cloud support. Better for ops-heavy teams with hands-on infrastructure needs.
- [**Portainer**](https://www.portainer.io/) – GUI-based Kubernetes and container management tool. Lightweight, but not a full deployment platform.

## Why consider Flightcontrol alternatives

Flightcontrol helps teams deploy to AWS without managing low-level infrastructure. For teams focused on speed, it’s a strong option. You get control of your own AWS account with a simplified developer experience.

As systems grow and team needs evolve, some limitations can start to show.

Flightcontrol only supports AWS, which can become a challenge if you plan to work across GCP, Azure, or hybrid environments. It also offers less flexibility around GPU-heavy workloads. For teams building more complex systems, such as background workers, scheduled tasks, or machine learning pipelines, it may not be the easiest fit.

Flightcontrol does its job well for teams with simpler needs. But as your infrastructure grows more demanding, it can be worth exploring platforms that give you more flexibility, broader workload support, and room to scale.

## What to look for in a Flightcontrol alternative

Earlier, we looked at why you may consider an alternative to Flightcontrol, and if you're already thinking about switching platforms, the goal isn’t just to fill in what Flightcontrol lacks. It's about choosing a solution that matches where your team is headed. The best deployment platforms give you the flexibility to grow, the tools to move fast, and the visibility to operate with confidence.

Here are the key factors to consider when evaluating your next platform:

### 1. Multi-cloud and deployment flexibility

Can the platform run across AWS, GCP, Azure, or even in self-hosted or air-gapped environments? Even if you’re all-in on one provider now, having multi-cloud support protects you from lock-in and future infrastructure limits.

### 2. Developer experience that scales with your team

A great platform should make life easier for developers, not introduce new layers of complexity. Look for fast feedback loops, clear logs, intuitive UI and CLI workflows, and smooth environment management. Everything should feel consistent, from local development to production deployments.

### 3. Support for modern workloads, including AI and ML

AI and ML applications come with unique requirements. Does the platform support GPU-backed services, model-serving APIs, and background workers? Can it run scheduled tasks and manage resource-heavy jobs without extra infrastructure overhead?

As your product evolves, you’ll likely move beyond just HTTP apps. Your platform should be ready for that.

### 4. Scalability without Kubernetes pain

You shouldn't have to become a Kubernetes expert to scale effectively. The right platform will handle horizontal and vertical scaling, stateless and stateful services, autoscaling, and resource tuning behind the scenes, with room to customize when needed.

### 5. Built-in integrations that reduce glue work

Native support for Git-based workflows, CI/CD pipelines, secrets management, container registries, monitoring tools like Datadog or Prometheus, and infrastructure-as-code frameworks like Terraform or Pulumi can make a huge difference in day-to-day productivity.

### 6. Transparent pricing, you can actually plan around

Look for usage-based pricing with clear breakdowns per environment or service. Bonus points if the platform includes built-in cost visibility and resource optimization tools. You shouldn’t need to guess what your bill will look like next month.

### 7. Security, compliance, and team-level controls

Whether you're an early-stage company or already working in a regulated space, features like role-based access control, audit logging, encryption, and compliance certifications (SOC 2, HIPAA, GDPR) are non-negotiable. These should be easy to enable, not buried behind an enterprise sales call.

### 8. Operational visibility and platform maturity

Your platform is part of your reliability stack. Look for SLAs, built-in monitoring, rollback options, automatic restarts for failing services, and a history of good incident response. You want infrastructure that disappears when things are going well, and shows up fast when something breaks.

## Top 5 alternatives to Flightcontrol

Once you know what you're looking for in an alternative, it becomes easier to filter out platforms that don’t align with your workflow. Here are five strong alternatives to Flightcontrol, each solving different parts of the deployment puzzle.

### 1. Northflank – The leading Flightcontrol alternative for fully managed deployments

[Northflank](https://northflank.com/) is a platform that enables developers to build, deploy, and scale applications, services, databases, GPUs, LLMs, and jobs on any cloud through a self-service approach. For DevOps and platform teams, Northflank provides a powerful abstraction layer over Kubernetes, enabling templated, standardized production releases with intelligent defaults while maintaining necessary configurability.

![pawelzmarlak-2025-07-17T12_18_55.915Z.png](https://assets.northflank.com/pawelzmarlak_2025_07_17_T12_18_55_915_Z_08ec1d06ee.png)

**Key features:**

- Kubernetes-powered, full-stack platform
- Deploy containers, databases, and scheduled jobs
- [Bring your own cloud (AWS, GCP, Azure, etc.)](https://northflank.com/features/bring-your-own-cloud)
- CI/CD integration, real-time logs, with a developer-friendly and consistent experience across UI, CLI, API, and GitOps
- GPU support for AI workloads
- Automatic preview environments and seamless promotion to dev, staging, and production

**Why choose Northflank over Flightcontrol?**

- Greater **flexibility in cloud provider selection**.
- More advanced **automation and CI/CD features**.
- **Enterprise-grade security and monitoring tools**.
- **Lower costs with a transparent pay-as-you-go pricing model**.

**Potential drawbacks:**

- Highly experienced DevOps teams might find it restrictive compared to directly managing raw Kubernetes clusters. It’s a fine balance between ease of use, flexibility, and customization; that line differs for every organization.

*See how [Weights company uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### 2. Qovery

[Qovery](https://www.qovery.com/) is a DevOps automation platform and an internal developer platform (IDP) that simplifies cloud infrastructure management. It allows developers to deploy applications quickly and efficiently without needing deep knowledge of the underlying infrastructure like Kubernetes.

![](https://assets.northflank.com/image_68_3b0184a6a2.png)

**Key Features:**

- Fully managed Kubernetes with deep cloud provider integration
- Built-in CI/CD and GitOps workflows
- Automatic scaling and cost optimization

**Potential Drawbacks:**

- Pricing at scale can be expensive, especially as additional deployment minutes and features are required
- While it supports multiple cloud providers, configuring multi-cloud deployments can be complex

*If you’re looking for alternatives to Qovery, see the* [best Qovery alternatives](https://northflank.com/blog/best-qovery-alternatives)

### 3. Porter

[Porter](https://www.porter.run/) provides a Kubernetes-based platform that aims to simplify container management and deployment.

![](https://assets.northflank.com/image_80_95bebd606d.png)

**Key features**:

- Kubernetes-native approach with simplified UI
- Good template system for common deployments

**Potential considerations**:

- Steeper learning curve for teams without Kubernetes experience
- Less robust CI/CD capabilities compared to Northflank
- Limited enterprise support options

*For a closer look at how Porter compares to other tools, [this article offers a well-rounded analysis.](https://northflank.com/blog/best-porter-alternatives-for-scalable-deployments)*

### 4. Cloud66

[Cloud66](https://www.cloud66.com/) is a **DevOps automation platform** that provides production-ready Kubernetes deployments with **multi-cloud and on-premise support**.

![](https://assets.northflank.com/image_70_4fb7db4ef4.png)

**Key features:**

- Infrastructure as code for Kubernetes clusters
- Multi-cloud compatibility (AWS, GCP, Azure, on-prem)
- Advanced security and compliance features

**Potential drawbacks:**

- Requires more hands-on infrastructure management
- More complex setup compared to fully managed platforms

### 5. Portainer

[Portainer](https://www.portainer.io/) simplifies **container and Kubernetes management**, offering a GUI-based approach for managing deployments across **on-prem, hybrid, and cloud environments**.

![](https://assets.northflank.com/image_71_4bed621467.png)

**Key features:**

- GUI-based Kubernetes management
- Multi-cloud and on-prem deployment support
- Role-based access control for secure operations

**Potential drawbacks:**

- More focused on Kubernetes management rather than full deployment automation
- Lacks built-in CI/CD and GitOps features

*For a closer look at how Portainer compares to other tools, [this article offers a well-rounded analysis.](https://northflank.com/blog/portainer-alternatives)*

## How to choose the best alternative

Selecting the optimal Flightcontrol alternative involves a systematic approach:

1. **Identify your primary challenges**: Pinpoint the specific limitations you're experiencing with Flightcontrol.
2. **Prioritize requirements**: Create a weighted list of features and capabilities most crucial to your workflows.
3. **Consider team expertise**: Evaluate your team's familiarity with the underlying technologies of each platform.
4. **Conduct targeted proof of concept**: Test your most critical workloads on shortlisted platforms.
5. **Evaluate total cost of ownership**: Look beyond base pricing to include potential savings in developer time and infrastructure optimization.
6. **Plan for growth**: Select a platform that can accommodate your projected scaling needs.

Based on these criteria, [Northflank](https://northflank.com/) consistently emerges as the superior choice in 2026, particularly for teams seeking an optimal balance of power, flexibility, and usability. Its comprehensive feature set addresses common Flightcontrol limitations while providing additional capabilities that enhance productivity and control.

## Conclusion

There’s no shortage of platforms trying to simplify cloud deployments, but few strike the right balance between usability and long-term flexibility. Flightcontrol delivers well on its promise, especially for teams that rely heavily on AWS. But not every team stays in that phase for long.

When infrastructure involves multiple services, background workers, AI workloads, or tighter compliance requirements, the platform behind your deployments matters significantly more. That’s when teams start reevaluating what they need, not just today, but six months or a year from now.

The best alternative is one that fits how your team actually works, while providing you with the control and scalability to support what’s coming next.

If you're ready to explore a more flexible approach, [**Northflank**](https://northflank.com/) offers a modern platform built for growing teams and production-grade workloads.

> Ready to explore? Try [Northflank](https://app.northflank.com/signup) or compare platforms head-to-head with your current stack.
>]]>
  </content:encoded>
</item><item>
  <title>Open source LLMs: The complete developer's guide to choosing and deploying LLMs</title>
  <link>https://northflank.com/blog/open-source-llms-the-complete-developers-guide-to-deployment</link>
  <pubDate>2025-07-16T07:45:00.000Z</pubDate>
  <description>
    <![CDATA[This guide shows you exactly how to select, deploy, and scale open source LLMs for production use.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/oss_llms_21018d1b48.png" alt="Open source LLMs: The complete developer's guide to choosing and deploying LLMs" />Running your own language models is all about avoiding API costs and taking control of your AI infrastructure. This guide shows you exactly how to select, deploy, and scale open source LLMs for production use.

<InfoBox className='BodyStyle'>

## 🎯 Quick start guide

- **Open source LLMs** are language models with publicly available weights you can run on your own hardware
- **Top models**: Llama 4, DeepSeek-V3, Qwen 3, and Mistral offer different performance/efficiency tradeoffs
- **Deployment complexity** ranges from simple inference servers to full production stacks with autoscaling
- **Infrastructure is critical**: The right platform can reduce deployment time from weeks to minutes
</InfoBox>

## What is an open source LLM?

An open source Large Language Model (LLM) is a neural network trained for natural language processing tasks whose model weights, architecture specifications, and often training code are freely available for download and use. Unlike proprietary models accessed through APIs (like GPT-4 or Claude), open source LLMs can be:

- **Downloaded and run locally** on your own hardware
- **Modified and fine-tuned** for specific use cases
- **Deployed without usage restrictions** (depending on license)
- **Integrated directly** into your applications without API dependencies

[Read more: Deploy Deepseek R1 on Northflank, in minutes. 
](https://northflank.com/guides/deploy-deepseek-r1-vllm-northflank-ai-llm)

## Why should you use an open source LLM?

**Complete data control**: Your prompts and responses never leave your infrastructure. For industries handling sensitive data (healthcare, finance, legal) this isn't optional.

**Predictable costs**: No surprise bills from token usage spikes. Your costs are tied to infrastructure, not API calls.

**Customization freedom**: Fine-tune models on your domain-specific data. A base model becomes your specialized AI assistant.

**Latency optimization**: Deploy models close to your users. No round trips to external APIs mean faster response times.

**No vendor dependencies**: API changes, rate limits, or service outages won't break your application.

## What is the best open Source LLM? A performance comparison

Choosing the best open source LLM depends on your specific requirements. Here's a comprehensive breakdown of leading models:

### 🏆 Top open source LLMs by category

### Best overall performance: Llama 4 Maverick

- **Parameters**: 17B active (400B total MoE)
- **Strengths**: Multimodal capabilities, excellent reasoning, strong coding
- **Context**: 10M tokens (industry leading)
- **Use case**: Production applications requiring frontier performance

### Best for resource efficiency: Qwen 3 32B

- **Parameters**: 32B
- **Strengths**: Hybrid thinking modes, multilingual support
- **Context**: 32K tokens
- **Use case**: Complex reasoning tasks with moderate hardware

### Best small model: Phi 3 Mini

- **Parameters**: 3.8B
- **Strengths**: Runs on consumer GPUs, fast inference
- **Context**: 4K or 128K variants
- **Use case**: Edge deployment, mobile applications

### Best open alternative to GPT-4: DeepSeek-V3

- **Parameters**: 671B
- **Strengths**: Matches closed-source performance
- **Context**: 128K tokens
- **Use case**: Research and premium applications

### Best for production balance: Mixtral 8x7B

- **Parameters**: 46.7B (MoE architecture)
- **Strengths**: Efficient inference, function calling
- **Context**: 32K tokens
- **Use case**: API services, chatbots

### Performance benchmarks table

| Model | Size | MMLU score | HumanEval | Inference speed | VRAM Required |
| --- | --- | --- | --- | --- | --- |
| Llama 4 Maverick | 400B MoE | 88.6 | 92.8 | Moderate | 80GB+ |
| DeepSeek-V3 | 671B | 87.2 | 89.9 | Slow | 160GB+ |
| Qwen 3 32B | 32B | 82.3 | 81.2 | Fast | 64GB |
| Mixtral 8x7B | 46.7B | 75.3 | 74.4 | Fast | 48GB |
| Phi 3 Mini | 3.8B | 68.8 | 62.2 | Very Fast | 8GB |

## How to run an open source LLM

Running an open source LLM involves several key steps. Here's a practical guide to get you started:

### Step 1: Choose your deployment method

**Local development** (quick testing):

```python
# Using Hugging Face Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "microsoft/Phi-3-mini-4k-instruct"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

```

**Production inference server** (recommended):

```bash
# Using vLLM for optimized serving
docker run --gpus all \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -p 8000:8000 \
  vllm/vllm-openai:latest \
  --model mistralai/Mixtral-8x7B-Instruct-v0.1

```

### Step 2: Optimize for production

**Quantization** reduces model size and speeds up inference:

```python
# 4-bit quantization example
from transformers import BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16
)

```

**Batching** improves throughput:

- Use inference servers like vLLM or TGI
- Configure dynamic batching for optimal GPU utilization
- Monitor queue depths and adjust batch sizes

### Step 3: Build your API layer

Create a production-ready API wrapper:

```python
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class CompletionRequest(BaseModel):
    prompt: str
    max_tokens: int = 100
    temperature: float = 0.7

@app.post("/completions")
async def generate(request: CompletionRequest):
    # Your inference logic here
    return {"completion": generated_text}

```

### Step 4: Handle production challenges

**Memory management**:

- Use model sharding for large models
- Implement proper garbage collection
- Monitor VRAM usage continuously

**Performance optimization**:

- Enable Flash Attention for longer contexts
- Use tensor parallelism for multi-GPU setups
- Implement caching for repeated queries

**Reliability**:

- Add health checks and automatic restarts
- Implement request queuing and backpressure
- Set up proper logging and monitoring

## The infrastructure challenge (why deployment matters)

Here's where most teams hit a wall. You've selected your model, optimized inference, built your API, but production deployment introduces new complexities:

### Common production requirements

**Autoscaling**: Your lunch-hour traffic might be 10x your morning load. Manual scaling isn't sustainable.

**Multi-region deployment**: Users expect low latency. Deploying models globally requires sophisticated orchestration.

**Zero-downtime Updates**: Model improvements shouldn't mean service interruptions.

**Cost optimization**: GPUs are expensive. Inefficient utilization directly impacts your bottom line.

**Observability**: You need visibility into inference latency, error rates, and resource utilization.

### Traditional approach vs. modern solutions

Building this infrastructure from scratch typically requires:

- Kubernetes expertise for orchestration
- Custom CI/CD pipelines for model updates
- Monitoring stack setup and maintenance
- Load balancer configuration
- GPU scheduling optimization

This represents months of engineering work before you can focus on your actual AI application.

## Simplifying LLM deployment with Northflank

Modern platforms eliminate this infrastructure complexity. Here's how a production-ready deployment actually looks:

### Container-based model deployment

Package your LLM with its dependencies:

```
FROM nvidia/cuda:12.1-base
RUN pip install vllm transformers
COPY . /app
CMD ["python", "-m", "vllm.entrypoints.openai.api_server", \
     "--model", "mistralai/Mixtral-8x7B-Instruct-v0.1"]

```

Deploy with automatic GPU provisioning, load balancing, and monitoring, all handled by the platform.

### Real-world scale example

Consider how Weights scaled their AI platform:

- **250+ concurrent GPUs** across multiple clouds
- **500,000+ inference runs daily**
- **Multi-cloud deployment** (AWS, GCP, Azure)
- **Managed by a small team** without dedicated DevOps

This scale would typically require a full infrastructure team. With the right platform, it's achievable by a few engineers focused on product development.

### How Northflank scales with your needs

**Start small, scale smart**: Northflank's platform adapts to your growth:

- Begin with a single GPU for prototyping
- Scale to hundreds of GPUs across regions
- Mix on-demand and spot instances for cost optimization
- No infrastructure rewrite as you grow

**Global GPU availability**: Deploy where your users are:

- GPUs available in 15+ regions worldwide
- Automatic failover between availability zones
- No vendor lock-in or regional contracts
- Same deployment experience everywhere

The combination of BYOC flexibility and managed GPU access means you can optimize for both compliance requirements and cost efficiency without compromise.

## Best practices for open source LLM deployment

### Model selection checklist

- ✅ Verify license compatibility with your use case
- ✅ Benchmark on your specific tasks, not just general benchmarks
- ✅ Consider total cost of ownership, not just model performance
- ✅ Test quantized versions for better efficiency
- ✅ Evaluate ecosystem support and documentation

## Getting started today

The gap between experimenting with open source LLMs and running them in production doesn't have to be insurmountable. Here's your action plan:

1. **Start small**: Deploy Phi 3 Mini for initial testing
2. **Measure everything**: Establish baselines for latency and cost
3. **Scale gradually**: Move to larger models as needed
4. **Optimize continuously**: Monitor usage patterns and adjust

For teams ready to move beyond notebooks, platforms like [Northflank](https://northflank.com/blog/an-engineers-guide-to-open-source-ai-models) provide the infrastructure layer that makes production deployment accessible. No Kubernetes expertise required, just your model and a Dockerfile.

## Conclusion

Open source LLMs represent a fundamental shift in how we build AI applications. The technology is ready; the models rival proprietary alternatives; the tooling ecosystem is maturing rapidly.

The question isn't whether to use open source LLMs, it's how quickly you can move from experimentation to production. With the right approach and infrastructure, that journey is shorter than ever.

Ready to deploy your first open source LLM? Try Northflank today.]]>
  </content:encoded>
</item><item>
  <title>Top 5 Fal.ai alternatives for inference and AI infrastructure</title>
  <link>https://northflank.com/blog/top-5-fal-ai-alternatives-for-inference-and-ai-infrastructure</link>
  <pubDate>2025-07-15T22:00:00.000Z</pubDate>
  <description>
    <![CDATA[Let’s be clear upfront: Fal.ai is excellent at what it’s built for, but in case you might be looking for an alternative, we've got you covered.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/fal_ai_1_e368a67620.png" alt="Top 5 Fal.ai alternatives for inference and AI infrastructure" />Let’s be clear upfront: **Fal.ai is excellent** at what it’s built for. If you’re deploying open-source LLMs and want the lowest possible latency with zero infrastructure overhead, Fal is a strong choice. It’s fast, clean, and highly optimized for running models like LLaMA and Mistral with custom drivers and tuned inference runtimes.

But you’re here looking for Fal.ai alternatives.

Maybe you’ve hit a wall on pricing, flexibility, security, or control. Maybe you’re building more than an inference endpoint and need a platform that supports the rest of your app stack too. Whatever the case, we’ve pulled together the top Fal alternatives, each with different tradeoffs depending on your goals.

## What is Fal.ai?

Fal.ai is a developer platform focused on **low-latency, serverless model inference**. You send a request to an endpoint, and Fal handles the rest: model loading, execution, GPU orchestration, and response. It’s used heavily for serving open-weight LLMs like LLaMA, Mistral, and Mixtral, often via frameworks like llama.cpp or GGUF-based runtimes.

Key features:

- **Inference-optimized backends** with support for quantized weights and model-specific optimizations
- **Cold start minimization** using smart pooling and preloading
- **Simple deployment** via CLI or GitHub integration

Where Fal shines is in raw performance for model inference. But it’s less ideal if:

- You need to host your own stack or run in your own VPC
- You want to run multiple services, not just models
- You’re doing fine-tuning, agentic workloads, or secure internal tooling
- You need more observability, debugging, or enterprise controls

Here are the best platforms to consider depending on what you’re building.

## 1. **Northflank** – Best Fal.ai Alternative for full-stack infrastructure

**[Northflank](https://northflank.com/)** is the best alternative if you're building a real product around LLMs, not just calling a model. It’s designed for teams that need strong infrastructure, real security guarantees, and predictable scale.

Let’s be upfront: Northflank won’t beat Fal on model-specific benchmarks. Fal is optimized at the driver level. But if you're deploying with **vLLM**, **TGI**, or similar inference runtimes, and you combine that with autoscaling and storage optimization, you're going to get within striking distance on performance, *and* gain a ton of flexibility.

**Why teams choose Northflank:**

- **Support for all GPU types:** A100s, H100s, L4s, 4090s, run the same stack across them.
- **Best-in-market GPU pricing:** Northflank offers **enterprise GPU pricing without the commitment**.
- **Spot and reserved instance support:** Optimize for either cost or stability.
- **Secure multi-tenant runtime:** Runs each service in an isolated sandbox with enforced resource boundaries. Perfect for running untrusted or AI-generated code.
- **Bring your own cloud (BYOC):** Deploy directly into your AWS/GCP with full VPC isolation and data control.
- **Built-in autoscaling:** Handle bursty inference workloads or long-running agent chains with clean scale-up/scale-down behavior.
- **Everything else included:** Databases, queues, background jobs, APIs. Northflank handles your full infra stack.
- **Production-grade CI/CD:** Built-in pipelines, preview environments, Git integrations, and automatic rollbacks.

It’s the only platform on this list that can serve LLM endpoints, background agents, a vector DB, and your UI layer *in the same stack*, across dev, staging, and prod environments.

<InfoBox className='BodyStyle'>

Best for: LLM startups, agentic systems, self-hosted eval/test infra, BYOC production deployments. Basically, teams building real LLM products who need CI/CD, full-stack infra, GPU flexibility, and secure deployment.

 **Not for:** Researchers chasing token-per-second benchmarks with custom compiled backends

</InfoBox>

Read more: [Weights uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)

## 2. **RunPod** – Best for low-cost, bare-metal GPU access

**RunPod** gives you cheap, bare-metal access to high-end GPUs with minimal abstraction. You pick your machine, spin up a pod, and run whatever model you want.

**What makes RunPod stand out:**

- **Decentralized compute:** Leverages idle capacity from providers around the world
- **Custom runtimes:** Bring your own Docker container and scripts
- **Community templates:** Ready-to-deploy containers for LLaMA, Mistral, Stable Diffusion, and more

You’re not getting a serverless platform or a pretty UI. But if you want to control exactly how your LLM runs and save on cost, especially for batch workloads or long-running agents, RunPod is a strong option.

**Downsides:** No real observability, no native autoscaling, no full-stack support. You're responsible for securing everything.

<InfoBox className='BodyStyle'>

Best for: Budget-conscious teams running training, evals, or inference-heavy workloads.

 **Not for:** Production teams that need managed infrastructure, logs, or scaling policies
 </InfoBox>

## 3. **Baseten** – Best for UX-focused AI product teams

**Baseten** is designed to make it easy to turn ML models into production-ready APIs, especially for teams who care about UX and product polish.

You bring your model (or pick from a model zoo), and Baseten gives you:

- **Fully managed endpoints** with autoscaling
- **Dashboards and observability** out of the box
- **Built-in rollouts, testing, and versioning**
- **UI-based workflows** for teams that don’t want to touch Terraform

It supports common LLM runtimes like vLLM, Hugging Face Transformers, and custom PyTorch. You can deploy endpoints with a few clicks or through CI/CD.

Where Baseten differs from Fal:

- It’s less about latency extremes, more about **developer experience** and **product lifecycle**
- You can connect models to React frontends, cron jobs, or stream processors
- You don’t get Fal’s tight GPU-level optimization, but you get a lot more flexibility

<InfoBox className='BodyStyle'>

Best for: Startups turning models into real user-facing features with clean ops

**Not for:** Teams who want to run everything in their own cloud or tune infra at the driver level

</InfoBox> 

## 4. **Modal** – Best for serverless LLM pipelines

**Modal** is infrastructure for Python-based ML apps, built around the idea of writing code that defines and deploys your workloads in one place.

Key advantages:

- **Functions-as-a-service** model for both CPUs and GPUs
- **Lightning-fast spin-up times** using container caching
- **Easy parallelism** for LLM inference, eval loops, or post-processing

You can deploy LLM endpoints, batch jobs, or even orchestrated chains, directly from your Python codebase. Modal is great if you want to move fast without worrying about containers and servers.

The downside: it’s not designed for long-lived, stateful services. And BYOC isn’t supported.

<InfoBox className='BodyStyle'>

Best for: ML engineers who want clean code-first infra for serving models or running pipelines

**Not for:** Enterprises or infra-heavy teams needing deeper control, observability, or cloud choice
</InfoBox> 

## 5. **Banana** – Best for lightweight LLM API deployment

**Banana** makes it easy to turn an LLM model into a hosted API with minimal setup. You push a repo, Banana handles the deployment, GPU allocation, and autoscaling.

**Highlights:**

- CLI and GitHub integrations
- Native support for vLLM, Transformers, and Diffusers
- Zero-config deployments for common models
- Pricing optimized for short inference jobs

It’s one of the simplest ways to stand up a hosted LLM API. If you’re a solo dev or small team trying to ship fast, Banana keeps infra out of the way.

Where it falls short:

- Not built for complex workflows or agentic systems
- Limited observability and debugging
- No BYOC, no enterprise-grade isolation

<InfoBox className='BodyStyle'>

Best for: Solo hackers, early MVPs, small AI apps

**Not for:** Teams scaling into production or managing multiple services in one stack

</InfoBox> 

## Final word: Which Fal.ai alternative should you choose?

If you’re chasing benchmark performance on a single model, Fal.ai is still the most optimized option. But if you’re building a real product, something with users, teams, environments, and a roadmap, Northflank is the platform to bet on.

It’s the only alternative that gives you:

- Full-stack deployment: APIs, workers, databases, queues, and model inference, all managed in one place
- Enterprise-grade GPU support with the best pricing on A100s, H100s, L4s, and spot instances
- Real CI/CD, secure multi-tenant runtimes, and BYOC deployment into your own cloud
- Infra that scales with you from early build to enterprise rollout

If you’re serious about shipping and scaling LLM applications, Northflank is the most complete platform on the list.

The rest have their use cases:

- **RunPod** is great for cheap GPU training and batch work, but you’re managing the infra yourself.
- **Baseten** is smooth for iterating on model-backed features, but you’ll hit limits if you need custom infra.
- **Modal** is elegant for Python-based workflows, but not ideal for multi-service deployments.
- **Banana** is great for quick launches, but not built for long-term scale or control.]]>
  </content:encoded>
</item><item>
  <title>7 best Hugging Face alternatives in 2026: Model serving, fine-tuning &amp; full-stack deployment</title>
  <link>https://northflank.com/blog/huggingface-alternatives</link>
  <pubDate>2025-07-15T14:58:00.000Z</pubDate>
  <description>
    <![CDATA[Looking beyond Hugging Face’s hosted models? Compare runtime alternatives like Replicate, BentoML, and Northflank for model serving, fine-tuning, GPU jobs, and full-stack AI deployment.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/huggingface_alternatives_0415de92cb.png" alt="7 best Hugging Face alternatives in 2026: Model serving, fine-tuning &amp; full-stack deployment" />If you're looking for Hugging Face alternatives, maybe for:

- More control over how your models run in production
- Fine-tuning
- Deploying APIs
- Or building full-stack apps around them

I'll walk you through 7 platforms that help you do just that, while still letting you use the models you know and trust.

Okay, let's get into it.

## Quick summary of the 7 top Hugging Face alternatives

If you're in a hurry, I've put together a quick list and summary of the 7 top alternatives to Hugging Face:

1. [**Northflank**](https://northflank.com/) – First on the list because it’s built for teams that want to run Hugging Face models with full control over infrastructure, fine-tune them, deploy APIs, and run supporting services like Postgres or Redis, **all in one place**.
2. [**BentoML**](https://northflank.com/blog/bentoml-alternatives) – Ideal for turning Hugging Face models into self-hosted REST APIs using Python. Lightweight and open-source, with a developer-friendly interface.
3. [**Replicate**](https://northflank.com/blog/6-best-replicate-alternatives) – A hosted way to serve open-source models through inference APIs. Great for testing or integrating models quickly without setting up your own infrastructure.
4. [**Modal**](https://northflank.com/blog/6-best-modal-alternatives) – Useful for running Python functions or GPU jobs in the cloud. Fits well if you're focused on inference or fine-tuning through scheduled tasks.
5. [**Lambda Labs**](https://northflank.com/blog/top-lambda-ai-alternatives) – Provides raw access to GPUs with an SDK and CLI. You manage the environment and orchestration, but you get full control over resources.
6. [**Together AI**](https://northflank.com/blog/together-ai-alternatives-for-ai-ml-model-deployment) – Hosted APIs for open-source LLMs like LLaMA and Mixtral. Simple setup with usage-based pricing, good for building LLM features fast.
7. [**RunPod**](https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment) – Lets you deploy containerized models on GPUs using pre-built templates. Lightweight option for quick inference tasks without managing full apps.

<InfoBox className='BodyStyle'>
💡Make your choice based on how much control you need, what kind of workloads you're running, and how much infrastructure you want to manage.

However, if you're looking for a platform that handles model serving, fine-tuning, full app deployment, and secure multi-tenant environments, **Northflank is your best choice**.

[**Get started for free**](https://app.northflank.com/signup) or [**book a demo**](https://cal.com/team/northflank/northflank-intro) to see for yourself.

</InfoBox>

## What to look for in a Hugging Face alternative (a must-read!)

Before I go into details for each of the alternatives to Hugging Face listed above, it's very important that you know what to look out for.

Because when you're thinking about where to run your models, it's not only about getting access to GPUs. You'll want a platform that can handle the way you build and ship things, particularly if you're working with fine-tuning, scheduling jobs, or deploying full apps around your models.

I'll list a few things you should keep in mind when comparing Hugging Face alternatives below:

1. **GPU job orchestration**
    
    I know you're thinking, “Isn’t every platform doing this already?” Not really. Some tools only let you serve models. Others, like Northflank, let you schedule training jobs, fine-tune with PyTorch or DeepSpeed, and manage long-running [GPU processes](https://northflank.com/product/gpu-paas) as part of your regular workflow. ([See this in action](https://northflank.com/docs/v1/application/gpu-workloads/gpus-on-northflank))
    
2. **Support for your tools**
    
    If you're already using things like Jupyter, PyTorch, DeepSpeed, or Ray, switching platforms shouldn’t break your setup. Northflank, for example, supports these out of the box, so you can run notebooks, distributed training jobs, or containerized workloads without changing your workflow.
    
3. **Bring Your Own Cloud (BYOC)**
    
    Choosing where your GPU workloads run can help you save on cost and avoid being tied to a single provider. Some platforms offer their own GPU pool only. Others, like Northflank, let you bring GPUs from [AWS, GCP, or custom providers](https://northflank.com/product/bring-your-own-cloud) and even mix spot and dedicated resources in hybrid setups. ([See this in action](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes))
    
4. **Security for multi-tenant runtimes**
    
    If you're running anything that involves untrusted code, like your AI agents, notebooks, or [sandboxed environments](https://northflank.com/product/sandboxes), then you need proper isolation. Platforms like Northflank that provide secure runtimes using microVMs and hardened container layers are a safer choice at scale.
    
    You can read more on this in:
    
    - [How to spin up a secure code sandbox & microVM in seconds](https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh)
    - [Secure runtime for codegen tools: microVMs, sandboxing, and execution at scale](https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale)
    - [What is multitenancy?](https://northflank.com/blog/what-is-multitenancy)

1. **App-layer support**
    
    You’re likely deploying more than a model. That might include a Postgres database, a Redis cache, an API backend, background workers, or a CI/CD pipeline. Some platforms don’t account for this at all. Others like Northflank let you deploy the full stack in one place, including services your models depend on.
    
2. **Fast provisioning and developer visibility**
    
    You want to spin things up quickly, scale smoothly, and still get access to logs and metrics when something breaks. Waiting 30 minutes for a GPU to be ready or struggling to trace errors through an unclear dashboard is time lost. On platforms like Northflank, provisioning usually takes under 30 minutes, and services come with built-in metrics and logs.
    

> Not every platform gets it all right. However, if you're building more than a simple model endpoint, these are the things that will make or break your setup.
> If you need expert guidance or consultation to find the best platform that suits your company’s needs, [book a 1:1 call with an expert here](https://cal.com/team/northflank/northflank-intro).


## 7 best Hugging Face alternatives in 2026

Now that you know what to look for, let’s walk through 7 Hugging Face alternatives that give you more control over how your models run, scale, and fit into your full stack.

### 1. Northflank

*Best for running Hugging Face models and your full application stack on your own infrastructure*

![new-northflank-ai-home-page.png](https://assets.northflank.com/new_northflank_ai_home_page_309df08d0b.png)

[Northflank](https://northflank.com/) isn’t a model hub. However, if you're pulling models from Hugging Face and want full control over how they run, this is where it stands out. You can:

- Serve, fine-tune, and deploy models like LLaMA, Mistral, and Whisper
- Treat GPU workloads like any other service (no special handling needed)
- Run APIs, databases, and background workers alongside your models

Northflank supports both **GPU and non-GPU workloads** in the same environment, so you can manage everything in one place.

It’s built for secure multi-tenancy, making it a good fit for AI agents, notebooks, and sandboxed code execution. For example:

- Cedana runs secure workloads with microVMs using Northflank ([see full case study](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes))
- Weights scaled to millions of users without a DevOps team ([see full case study](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s))

You can also [bring your own cloud](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) (BYOC). [See what BYOC means](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment).

It works with A100s, H100s, or spot GPUs across multiple providers using BYOC and hybrid cloud setups.

Also see: [Top AI PaaS platforms](https://northflank.com/blog/top-ai-paas-platforms)

> Go with this if you use Hugging Face for models, but want to run and scale them securely on your own infrastructure, with an all-in-one platform for fine-tuning, inference, background jobs, and full-stack services like databases, APIs, and CI/CD.

[Get started with Northflank for free](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro). See [pricing details](https://northflank.com/pricing).
> 

### 2. BentoML

*Best for turning Hugging Face models into Python services*

![bentoml-homepage.png](https://assets.northflank.com/bentoml_homepage_1b6289d1d1.png)

BentoML is an open-source tool that helps you package and serve machine learning models , including Hugging Face models, as Python APIs. It’s framework-agnostic, so you can work with PyTorch, TensorFlow, and Transformers without extra complexity. You can:

- Build and expose models as REST APIs using FastAPI
- Containerize everything with Docker
- Works well for local testing and cloud deployment

BentoML is a better fit for inference workloads than fine-tuning. It’s ideal if you're comfortable in Python and want to ship model services without managing too much infrastructure.

> Go with this if you want to build and host Hugging Face models as REST APIs locally or on cloud infrastructure.
> 

*If you’re looking for alternatives to BentoML, see [6 best BentoML alternatives](https://northflank.com/blog/bentoml-alternatives)*

### 3. Replicate

*Best for easy-to-use hosted inference endpoints*

![replicate-homepage.png](https://assets.northflank.com/replicate_homepage_38062bccda.png)

Replicate makes it easy to run Hugging Face models through a hosted API, with zero setup or infrastructure management required. You select a model (like Stable Diffusion, LLaMA, or Whisper), send a request, and get a result, including:

- Access trending open-source models through a simple API
- No need to deploy or manage containers
- Usage-based pricing tied to compute time

That simplicity comes with trade-offs. You don’t get much control over the underlying infrastructure, scaling behavior, or runtime limits.

> Go with this if you want to plug in inference APIs without setting up infrastructure.
> 

If you’re looking for alternatives to Replicate, see [6 best Replicate alternatives](https://northflank.com/blog/6-best-replicate-alternatives)

### 4. Modal

*Best for containerized GPU jobs and scheduled Python functions*

![modal-homepage.png](https://assets.northflank.com/modal_homepage_a7380e6d35.png)

Modal is a serverless platform built around running Python code on demand, which makes it a good fit for ML workloads that can be broken into jobs. You can:

- Run inference and fine-tuning jobs at scale
- Works with containers and Python functions
- Supports scheduling and job orchestration out of the box

What you don’t get is support for full-stack applications. There’s no built-in way to run persistent services, databases, or background workers outside of the job model.

> Go with this if your workload is mostly Python-based GPU jobs.
> 

*If you’re looking for Modal alternatives, see [6 best Modal alternatives](https://northflank.com/blog/6-best-modal-alternatives)*

### 5. Lambda Labs

*Best for renting raw GPU compute*

![lambda-homepage.png](https://assets.northflank.com/lambda_homepage_21b6ec7a15.png)

Lambda Labs gives you access to GPUs by the hour, with no opinionated runtime or deployment layer on top. It’s a good option if you want control over everything from the OS up. You can:

- Rent A100, H100, and other NVIDIA GPUs on demand
- Use the CLI or API to spin up instances

You don’t get built-in model serving, job scheduling, or full-stack support; it’s purely GPU infrastructure.

> Go with this if you want direct GPU access and plan to build your own stack.
> 

*If you’re looking for Lambda AI alternatives, see [Top Lambda AI alternatives](https://northflank.com/blog/top-lambda-ai-alternatives)*

### 6. Together AI

*Best for hosted LLM inference using Hugging Face-compatible models*

![togetherai-homepage.png](https://assets.northflank.com/togetherai_homepage_d0e3c7e279.png)

Together AI provides API access to open-source LLMs like LLaMA, Mistral, and Mixtral, many of which are also available on Hugging Face. You send a prompt, and get a response, without worrying about infrastructure. You get:

- Pay-per-token API pricing
- Access to popular Hugging Face-compatible LLMs
- Works well for embedding, summarization, and text generation

You won’t manage your own models, but it’s a fast way to integrate open models into your product.

> Go with this if you need OpenAI-style API access to Hugging Face-hosted LLMs.
> 

*If you’re looking for alternatives to Together AI, see [Top Together AI alternatives](https://northflank.com/blog/together-ai-alternatives-for-ai-ml-model-deployment)*

### 7. RunPod

*Best for spinning up containers on GPU nodes*

![runpod-homepage.png](https://assets.northflank.com/runpod_homepage_a696c3aa97.png)

RunPod gives you a straightforward way to run containers on GPU-backed nodes. It’s more lightweight than a full platform, but works well if you only need compute with minimal overhead. You get:

- Prebuilt templates for Stable Diffusion, Whisper, and other Hugging Face-compatible models
- Bring your own container or use **J**upyter, FastAPI, or Ollama templates

It’s a practical choice for quick experiments or serving models on demand, without the extras.

> Go with this if you want GPU containers with minimal setup and don’t need a full-stack platform.
> 

*If you’re looking for RunPod alternatives, see [RunPod alternatives](https://northflank.com/blog/runpod-alternatives)*

## Comparison table of Hugging Face alternatives

After going through the detailed breakdowns, I’ve put together a side-by-side comparison to help you choose the right alternative to Hugging Face based on your needs.

This table focuses on five core capabilities: fine-tuning, inference, full application support, BYOC (Bring Your Own Cloud), and secure runtime isolation.

| Platform | Fine-tuning support | Inference support | Full app support | BYOC available | Secure runtime isolation |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | Built-in GPU jobs for fine-tuning | Supported with autoscaling | Run APIs, databases, workers together | Supported (run on your own cloud) | MicroVMs and hardened container isolation |
| **BentoML** | Limited support via framework add-ons | Convert models to REST APIs | Limited – mostly API-focused | Enterprise only | Basic container isolation only |
| **Replicate** | Limited (image models only via FLUX) | Hosted APIs for popular models | Not supported | Not supported | Not designed for untrusted workloads |
| **Modal** | Supports batch/scheduled fine-tuning | Works well for Python inference | No support for full-stack applications | Not supported | Limited isolation for containerized jobs |
| **Lambda Labs** | Pre-configured stack available; some manual scripting needed | Manual or bring your own stack | Not included in platform | Not supported | No built-in runtime isolation |
| **Together AI** | Supported (LoRA and full fine-tuning available via API) | Pay-per-token API access | Not supported | Not supported | Not built for secure multi-tenant execution |
| **RunPod** | Possible with setup | GPU containers with templates | No application-layer support | Limited BYOC depending on instance type | Basic sandboxing; no advanced isolation |

## Choosing the right Hugging Face alternative

To wrap up, there’s no single drop-in replacement for Hugging Face. The right choice depends on what you’re building and how much control you need over your infrastructure and workflows. See a quick checklist:

1. Do you want to self-host Hugging Face models, scale them across your own cloud, and run full applications securely? → Use Northflank
2. Are you looking to package models as local APIs using FastAPI or Docker? → Use BentoML
3. Do you need hosted inference with minimal setup? → Use Replicate or Together AI
4. Do you prefer to rent raw GPU compute and build your own orchestration layer? → Use Lambda Labs or RunPod

Each of these tools supports different stages of the ML lifecycle, from serving to fine-tuning to surrounding app infrastructure. The choice comes down to how much of the stack you want to own and how much flexibility your workloads require.

> If you’re looking for a single platform that handles inference, fine-tuning, background jobs, and full-stack app services on your own infrastructure, Northflank stands out.

You can [get started for free](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-intro) to see how it fits your stack.
> 

### Common questions about Hugging Face alternatives

I'll quickly address some of the questions developers often ask about Hugging Face and the tools you might use as alternatives.

1. **What is the best alternative to Hugging Face?**
    
    If you still rely on Hugging Face for models but want more control over how they run, Northflank is the best alternative. For hosted inference APIs, Replicate or Together AI work well.
    
2. **What are the top 5 alternatives to Hugging Face?**
    
    Top alternatives include:
    
    1. **Northflank** - to self-host models and run full applications across your own infrastructure
    2. **BentoML** -  to package models as FastAPI or Docker-based APIs
    3. **Replicate** - to run inference with hosted model APIs
    4. **Modal -** to orchestrate GPU-backed Python functions and model jobs
    5. **Lambda Labs** - to rent raw GPU compute and bring your own orchestration setup
3. **Why is Hugging Face so popular?**
    
    Hugging Face became popular for its Transformers library, which made it easy to access pretrained NLP models. It’s also widely used for its open model hub, datasets, and growing ecosystem around AI research and deployment.
    
4. **Is Hugging Face better than OpenAI?**
    
    They serve different use cases. Hugging Face supports open-source models and community collaboration. OpenAI provides commercial APIs for proprietary models like GPT-4.
    
5. **Is Hugging Face completely free?**
    
    Accessing models and datasets is free, but features like hosted inference endpoints or fine-tuning may require a paid plan, especially at scale.]]>
  </content:encoded>
</item><item>
  <title>Top 7 Kubeflow alternatives for deploying AI in production (2026 Guide)</title>
  <link>https://northflank.com/blog/top-7-kubeflow-alternatives</link>
  <pubDate>2025-07-15T13:45:00.000Z</pubDate>
  <description>
    <![CDATA[Explore top Kubeflow alternatives for ML deployment. Compare platforms like Northflank, MLflow &amp; Metaflow to find faster, scalable, Kubernetes-free MLOps tools for modern AI teams.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/top_ai_paas_blog_post_2_d2263e2748.png" alt="Top 7 Kubeflow alternatives for deploying AI in production (2026 Guide)" />If you’ve spent any time deploying machine learning models in production, you’ve probably come across Kubeflow. It’s powerful, modular, and tightly integrated with Kubernetes, but it’s not for everyone. For teams without deep DevOps expertise, it can feel like overengineering just to get a model into production.

The good news? You have options. If you want simplicity, faster iteration, or just want to avoid managing Kubernetes altogether, there are solid alternatives. Platforms like [Northflank](https://northflank.com/) abstract away the Kubernetes complexity, letting you deploy full-stack AI workloads with minimal DevOps effort.

In this guide, we’ll explore why teams are looking for Kubeflow alternatives, what to look for in a modern ML deployment stack, and which platforms are worth your attention.

## TL;DR – Top Kubeflow alternatives

If you're short on time, here’s a snapshot of the top Kubeflow alternatives. Each tool has its strengths, but they solve different problems, and some are better suited for real-world production than others.

| Platform | Focus Area | Strengths |
| --- | --- | --- |
| Northflank | Full-stack AI apps: APIs, LLMs, GPUs, frontends, backends, databases, and secure infra | Production-grade platform for deploying AI apps, GPU orchestration, Git-based CI/CD, [Bring your own cloud](https://northflank.com/features/bring-your-own-cloud), secure runtime, multi-service support, preview environments, secret management, and enterprise-ready features. Great for teams with complex infrastructure needs. |
| MLflow | Experiment tracking & deployment | Lightweight, model versioning |
| Metaflow | Workflow orchestration | Pythonic, simple DAGs, AWS integration |
| Seldon Core | Model serving | Kubernetes-native, A/B testing, scalable |
| BentoML | Model serving/API creation | Fast model APIs, framework-agnostic |
| Vertex AI | Fully managed ML platform | End-to-end tooling, scalability, GCP native |
| Apache Airflow | Workflow orchestration & scheduling | DAGs, extensible, ecosystem-rich |

## What makes Kubeflow stand out?

Kubeflow isn’t popular by accident. For all its complexity, it solves real problems for teams operating at scale. Here’s where it shines:

1. **Modular by design**: You can pick and choose the components you need. Just want pipelines? Use Pipelines. No need to adopt the full stack.
2. **Kubernetes-native**: Kubeflow is built to run *with* Kubernetes, not alongside it. That means tight integration and full control over scheduling, scaling, and orchestration.
3. **Designed for scale**: Distributed training across GPUs, multi-node clusters, and large datasets is where Kubeflow really earns its keep.
4. **Reproducibility baked in**: From inputs and configs to artifacts and logs, everything is tracked, making it easy to re-run experiments or audit results.
5. **Strong community support**: It has a deep ecosystem and a large user base, which makes troubleshooting and extending the platform a lot more manageable.

## What are the limitations of Kubeflow?

Earlier, we looked at what makes Kubeflow stand out, but let's be honest, it's not all smooth sailing. While Kubeflow solves hard problems, it introduces plenty of its own. Here's where teams often struggle:

1. **Painful setup**: Getting Kubeflow running isn’t a quick task. Between Helm charts, Istio, custom resources, and a maze of YAML, setup can take hours, days, or weeks, depending on your expertise.
2. **High barrier to entry**: It assumes you’re already fluent in Kubernetes. If not, be prepared for a steep learning curve and lots of trial and error.
3. **Heavy resource usage**: Kubeflow isn’t lightweight. Even a minimal install eats up significant CPU and memory, making it a tough fit for smaller teams or projects.
4. **Brittle integrations**: Core components like Pipelines, KFServing, and Katib aren’t always seamless. Keeping them in sync across versions can be frustrating.
5. **Limited multi-tenancy support**: Isolating users or projects cleanly? Not easy. Multi-cloud setups? You’re largely on your own.
6. **Weak CI/CD story**: Kubeflow doesn’t natively support modern CI/CD workflows. Integrating versioning, GitOps, and automated deployments usually means building and maintaining your own glue code.
7. **Not-so-friendly developer experience**: Debugging jobs, monitoring workloads, and managing deployments through Kubeflow often requires dropping down into raw Kubernetes, which is not exactly friendly for fast iteration.

## What to look for in a Kubeflow alternative

If Kubeflow is slowing you down, the goal isn’t just to find something simpler; it’s to find something that actually fits how modern ML teams work. Here’s what you should prioritize in a replacement:

1. **Frictionless developer experience**
    
    You shouldn't need to know Kubernetes internals to deploy a model. Look for platforms with clean CLIs, UIs, or APIs that let you go from prototype to production quickly with minimal config and no boilerplate YAML.
    
2. **Support for the full ML lifecycle**
    
    A deployment tool should go beyond inference. You’ll want support for background jobs, scheduled tasks, data processing, monitoring, and logging all within a single workflow.
    
3. **Scalable, GPU-friendly infrastructure**
    
    Autoscaling, GPU workloads, and efficient job scheduling should be first-class features. Northflank, for instance, makes it easy to run GPU-enabled services without touching infrastructure config.
    
4. **Integrated CI/CD pipelines**
    
    Versioned deployments, automatic builds, and preview environments tied to your Git workflow can save hours. Northflank includes this out of the box, so teams can push code and deploy seamlessly.
    
5. **Security and access control by default**
    
    Features like RBAC, audit logs, SSO, and secrets management should be easy to configure and ready for production use, not bolted on late.
    
6. **Deployment flexibility**
    
    Whether you're in a single cloud, hybrid, on-prem, or air-gapped, your platform should adapt, not force you to rewrite your setup. Multi-region support, isolated environments, and policy-based controls all make a big difference.
    

## Top 6 Kubeflow alternatives for AI/ML deployment

Once you know what you're looking for in an alternative, it becomes easier to filter out tools that don’t align with your workflow. Here are six strong alternatives to Kubeflow, each solving different parts of the AI/ML deployment puzzle.

### 1. Northflank – The leading Kubeflow alternative for production AI

[**Northflank**](https://northflank.com/) isn’t just a model hosting or GPU renting tool; it’s a **production-grade platform for deploying and scaling full-stack AI products**, and it's *built on top of Kubernetes*. But unlike Kubeflow, Northflank abstracts away the operational complexity of K8s, so you get all the power without needing to become an expert in YAML, Helm, or cluster management.

Whether you're serving a fine-tuned LLM, hosting a Jupyter notebook, or deploying a full product with both frontend and backend, Northflank offers broad flexibility without many of the lock-in concerns seen on other platforms.

![image - 2025-06-19T211009.037.png](https://assets.northflank.com/image_2025_06_19_T211009_037_2419b18f99.png)

**Key features:**

- Built on Kubernetes, but with a simplified, developer-first interface
- Bring your own Docker image and full runtime control
- GPU-enabled services with [autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments) and lifecycle management
- Multi-cloud and [Bring Your Own Cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) support
- [Git-based CI/CD](https://northflank.com/docs/v1/application/release/manage-ci-cd), [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), and full-stack deployment
- Secure runtime for untrusted AI workloads
- SOC 2 readiness and enterprise security (RBAC, SAML, audit logs)

**Pros:**

- **Kubernetes under the hood** – full power and portability without the operational pain
- **No platform lock-in** – full container control with BYOC or managed infrastructure
- **Transparent, predictable pricing** – [usage-based](https://northflank.com/pricing) and easy to forecast at scale
- **Great developer experience** – Git-based deploys, CI/CD, preview environments
- **Optimized for latency-sensitive workloads** – fast startup, GPU autoscaling, low-latency networking
- **Supports AI-specific workloads** – Ray, LLMs, Jupyter, fine-tuning, inference APIs
- **Built-in cost management** – real-time usage tracking, budget caps, and optimization tools

**Cons:**

- No special infrastructure tuning for model performance.

**Verdict:**

If you're building production-ready AI products, not just prototypes, Northflank gives you the flexibility to run full-stack apps and get access to affordable GPUs all in one place. With built-in CI/CD, GPU orchestration, and secure multi-cloud support, it's the most direct platform for teams needing both speed and control without vendor lock-in.

[**See how Cedana uses Northflank to deploy GPU-heavy workloads with secure microVMs and Kubernetes**](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes)

### 2. MLflow – Lightweight model tracking and deployment

[**MLflow**](https://mlflow.org/) focuses on experiment tracking, model packaging, and reproducibility. It integrates well with major ML frameworks and allows you to register, version, and deploy models to various environments.

![image - 2025-07-15T145110.678.png](https://assets.northflank.com/image_2025_07_15_T145110_678_b559525615.png)

**Key features:**

- Experiment tracking, artifacts, parameters
- Model registry and versioning
- Works with many frameworks (scikit-learn, PyTorch, TensorFlow)

**Pros:**

- Simple and lightweight
- Open-source and easy to self-host
- Integrates into existing workflows

**Cons:**

- Limited serving capabilities
- No orchestration or full-stack support
- Doesn’t manage infrastructure

**Verdict:**

MLflow is great for teams that already have infrastructure in place and want a clean, open-source way to manage experiments and model versions.

### 3. Metaflow – Human-centric workflow orchestration

[**Metaflow**](https://metaflow.org/) helps data scientists build and manage ML workflows with simple Python scripts. It handles DAGs, versioning, and integrates deeply with AWS for execution, storage, and scalability.

![image - 2025-07-15T145113.388.png](https://assets.northflank.com/image_2025_07_15_T145113_388_20c3ef0d1e.png)

**Key features:**

- Python-native DAGs for ML pipelines
- Local-to-cloud portability
- Strong integration with AWS Step Functions

**Pros:**

- Very intuitive for Python developers
- Good for small teams or fast iterations
- Supports data versioning and retry logic

**Cons:**

- Limited support for GPUs or model serving
- AWS-focused; GCP and Azure users may need extra setup

**Verdict:**

Metaflow is ideal for ML engineers who want simple, Python-based orchestration without the overhead of full-scale platforms like Kubeflow.

### 4. Seldon Core – Kubernetes-native model serving

[**Seldon Core**](https://www.seldon.io/solutions/core/) is an open-source platform focused on serving ML models at scale. It supports advanced use cases like A/B testing, canary rollouts, and model explainability, but it assumes you're comfortable with Kubernetes.

![image - 2025-07-15T145115.695.png](https://assets.northflank.com/image_2025_07_15_T145115_695_eea7cb4fe8.png)

**Key features:**

- Containerized model serving
- A/B testing, canary rollouts, model monitoring
- Native Kubernetes integration

**Pros:**

- High flexibility and customizability
- Great for organizations already using K8s
- Production-grade model serving

**Cons:**

- Requires Kubernetes expertise
- Steep learning curve
- Doesn’t handle model training or experimentation

**Verdict:**

If your team is comfortable with Kubernetes and needs industrial-grade model serving at scale, Seldon Core is a strong fit.

### 5. BentoML – Build and ship ML APIs fast

[**BentoML**](https://www.bentoml.com/) makes it easy to turn trained models into production-ready APIs. It supports multiple ML frameworks and outputs containerized services you can deploy anywhere.

![image - 2025-07-15T145118.162.png](https://assets.northflank.com/image_2025_07_15_T145118_162_9e6660a78a.png)

**Key features:**

- Convert models into REST APIs
- Dockerized deployments
- Support for multiple ML frameworks

**Pros:**

- Very fast to go from model to API
- Lightweight and easy to use
- Great local development experience

**Cons:**

- No pipeline or orchestration features
- Requires extra tools for CI/CD, infra, scaling

**Verdict:**

BentoML is a great choice for quickly turning ML models into production-ready APIs, especially if you already have deployment infrastructure.

*If you’re looking for alternatives to BentoML, see [6 best BentoML alternatives](https://northflank.com/blog/bentoml-alternatives)*

### 6. Vertex AI – Fully managed ML on Google Cloud

[**Vertex AI**](https://cloud.google.com/vertex-ai) is Google Cloud’s end-to-end ML platform. It provides everything from training to deployment, with tight integration into GCP services like BigQuery, AutoML, and Cloud Functions.

![image - 2025-07-15T145119.616.png](https://assets.northflank.com/image_2025_07_15_T145119_616_e7fdce52b7.png)

**Key features:**

- Integrated data prep, training, tuning, and deployment
- Built-in AutoML and LLM support
- Tight integration with GCP services (BigQuery, Dataflow, etc.)

**Pros:**

- End-to-end ML tooling in one place
- Strong scalability and performance
- Great for teams already using GCP

**Cons:**

- Locked into Google Cloud ecosystem
- Cost can grow quickly
- Less flexible for custom workflows

**Verdict:**

Vertex AI is perfect for enterprise teams heavily invested in GCP who want a fully managed, scalable ML platform with minimal setup.

### 7. Apache Airflow – Reliable orchestration for data & ML workflows

[**Airflow**](https://airflow.apache.org/) is a popular workflow orchestrator built for managing complex DAGs. While not ML-specific, its flexibility and extensibility make it a core tool for automating data and ML pipelines at scale.

![image - 2025-07-15T145121.903.png](https://assets.northflank.com/image_2025_07_15_T145121_903_b1764d8f63.png)

**Key features:**

- Python-based DAG definitions for full programmability
- Extensible with custom operators and plugins
- Scalable execution via Celery, Kubernetes, or other executors
- Deep ecosystem of integrations (e.g., GCP, AWS, Docker, Spark)

**Pros:**

- Battle-tested for workflow orchestration
- Flexible scheduling, retries, logging, and dependency management
- Large open-source community and enterprise support options
- Excellent observability and control over jobs

**Cons:**

- Not ML-native — no built-in model tracking, serving, or GPU management
- Requires infrastructure setup and maintenance
- Can become complex for teams unfamiliar with DAG-based workflows

**Verdict:**

Airflow is a strong choice for ML teams with complex pipeline orchestration needs, especially when working with data engineering teams or broader ETL processes. It's not an end-to-end ML platform, but it integrates well with others like MLflow, Vertex AI, or Seldon for a modular stack.

## How to pick the best Kubeflow alternative

Once you understand where Kubeflow falls short and what modern tools offer instead, the next step is figuring out which tool actually fits your workflow. Here’s how to think through the decision:

| Consideration | Recommendation |
| --- | --- |
| Workflow scheduling & orchestration | Choose Northflank, Airflow or Metaflow |
| Team expertise | New to infra? Go with Northflank or Metaflow. |
| Infrastructure model | All-in on GCP? Use Vertex AI. Multi-cloud? Northflank is your friend. |
| Model lifecycle focus | Need full pipeline support? Try Northfank, Metaflow, or Airflow. |
| GPU support | Need GPU inference at scale? Northflank and Vertex AI shine. |
| Reproducibility & tracking | Northflank, MLflow, and Kubeflow Pipelines do this well. |
| Vendor lock-in tolerance | Prefer open tools? Northflank, MLflow, or Airflow will keep things portable. |

## Conclusion

Kubeflow has earned its place in the MLOps community. But it’s not one-size-fits-all. If your team is struggling with the complexity or simply wants something faster and more intuitive, there are solid alternatives ready to meet you where you are.

If you're leaning toward a lightweight tool like MLflow, a cloud-native powerhouse like Vertex AI, or an all-around modern stack like Northflank, your best option comes down to your team’s strengths, workflow needs, and deployment goals.

The MLOps space is constantly growing, and it’s no longer about building the most powerful system; it’s about choosing the right one for the job.

If you're ready to see how Northflank fits into your workflow, you can [sign up for free](https://app.northflank.com/signup) or [book a short demo](https://cal.com/team/northflank/northflank-demo) to explore what it can do.
]]>
  </content:encoded>
</item><item>
  <title>What is a cloud GPU? A guide for AI companies using the cloud</title>
  <link>https://northflank.com/blog/what-is-a-cloud-gpu</link>
  <pubDate>2025-07-11T15:41:00.000Z</pubDate>
  <description>
    <![CDATA[Learn how cloud GPUs work, when to use them over CPUs, and how platforms like Northflank simplify GPU deployment, orchestration, and scaling for AI workloads.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/what_is_cloud_gpu_blog_post_950898ed9f.png" alt="What is a cloud GPU? A guide for AI companies using the cloud" />## What is a cloud GPU?

A cloud GPU is a graphics processing unit that you access remotely through a cloud provider like AWS, Google Cloud, Azure, or a developer platform like Northflank.

Cloud GPUs are designed to handle highly parallel workloads like training machine learning models, generating images, or processing large volumes of data.

For example, let’s assume you’re building a generative AI product and your team needs to fine-tune a model on customer data. So, what do you do?

1. *Do you buy a high-end GPU, set up a local machine, worry about cooling, drivers, and power usage, and hope it scales as your needs grow?*
2. *Or do you spin up a cloud GPU in minutes, run your training job in an isolated environment, and shut it down when you’re done?*

Which option did you go for? I’m “95%” sure you picked the second one because that’s what most teams do. Cloud GPUs give you the flexibility to focus on your model, not your hardware.

And that’s why cloud GPUs have become a core part of modern AI workflows. I mean, you could be:

- Fine-tuning an open-source model like LLaMA
- Deploying a voice generation API
- Or parsing thousands of documents with an LLM

Chances are you’re using a GPU somewhere in that stack.

For most AI companies, running those workloads in the cloud is faster, easier, and far more scalable than trying to do it locally.

<InfoBox className='BodyStyle'>

  **TL;DR:**
  - A cloud GPU gives you remote access to high-performance graphics cards for training, inference, and compute-heavy workloads, without owning the hardware.  

  - AI teams use cloud GPUs to fine-tune models, run LLMs, deploy APIs, and spin up notebooks.  

  - Platforms like [Northflank](https://northflank.com/) let you attach GPUs to any workload, while also running your non-GPU infrastructure like APIs, databases, queues, and CI pipelines in the same place.  

 **Run both GPU and non-GPU workloads in one secure, unified platform:**

  [Get started for free](https://app.northflank.com/signup) or [book a live demo](https://cal.com/team/northflank/northflank-intro)

> **Questions about cloud GPU capacity?** If you're planning cloud GPU deployments with specific availability or capacity requirements, [request GPU capacity here](https://northflank.com/request/gpu).

</InfoBox>

*Next, I’ll walk you through how cloud GPUs work, so that your team can decide how to best run AI workloads without getting stuck in hardware complexity.*

## How cloud GPUs work

Cloud GPUs work a lot like any other cloud resource. See what I mean:

1. You request access to a GPU through a provider
2. You define what you want to run (could be a training script or a container)
3. The provider provisions the GPU, runs your workload, and tears everything down when it’s done.

To make that clearer, see a simple illustration of what that process usually looks like:

![Diagram showing the cloud GPU workflow: a developer pushes code, the cloud provider provisions a GPU, and the container runs with GPU access](https://assets.northflank.com/how_cloud_gpus_work_9a83e38554.png)*From code to cloud GPU: How your container gets GPU access in the cloud.*

Once your container is running, your code has full access to the GPU. That could mean:

- Training a model from scratch
- Fine-tuning a foundation model
- Running inference behind an API
- Or scheduling batch jobs with specific GPU needs

The best part is that you don’t have to touch hardware, install CUDA drivers, or manage infrastructure because the cloud handles all of that. So, your team can focus on building and shipping AI, not troubleshooting machines.

*Next, we’ll talk about why cloud GPUs are used in AI and what makes them so useful for training, inference, and more.*

## Why are cloud GPUs used in AI?

AI workloads are so much compute-intensive and not in the way a typical web server or database is. For instance, training a model like LLaMA or running inference across thousands of prompts requires what we call “parallel computation,” which is something GPUs are designed for.

Okay, so what does that mean?

You could think of it this way: CPUs are built to handle a few tasks at a time, which is great for general-purpose stuff like running a database or serving an API.

Now, GPUs, on the other hand, are built for scale. They’re designed to run thousands of operations in parallel, which is exactly what deep learning tasks like matrix multiplication or backpropagation need.

That’s why Cloud GPUs have become a go-to for AI teams. A few common examples include:

- Fine-tuning open-source models like LLaMA or Mistral
- Deploying inference APIs that respond to real-time requests
- Running background jobs or scheduled training tasks
- Spinning up Jupyter notebooks for quick experimentation

And because it’s all in the cloud, your team can scale up or down on demand, without owning a single server.

<InfoBox className='BodyStyle'>

💡**Note:** Some platforms treat GPU workloads as a separate category, with different setup steps, tools, or workflows. Platforms like [Northflank](https://northflank.com/) don’t. With Northflank, you attach a GPU and run your job the same way you would for a CPU-based service.

That consistency applies to fine-tuning jobs, inference APIs, notebooks, and scheduled tasks, without needing to reconfigure how your team works. Logs, metrics, autoscaling, and secure runtime are already built in.

</InfoBox>

*Next, we’ll break down how GPUs compare to CPUs, so you can understand what each one is really built for.*

## What are the differences between CPU and GPU?

We’ve talked about how GPUs are better suited for AI tasks like training models or running inference, but what really makes them different from CPUs?

Let’s see a quick breakdown:

| Feature | CPU | GPU |
| --- | --- | --- |
| **Cores** | Few cores, optimized for low latency | Thousands of cores, optimized for throughput |
| **Use case** | General-purpose tasks (web, I/O, DBs) | Parallel computation (e.g. ML, deep learning) |
| **Ideal for** | Web servers, databases, CI jobs | Model training, LLM inference, vector search |
| **Architecture** | Complex control logic and branching | Simple cores executing many tasks in parallel |

Let’s also break that down with a few common questions you most likely have:

### 1. How much faster is a GPU compared to a CPU?

It depends on the workload, but for deep learning tasks like matrix multiplication or training with large datasets, a single GPU can be a lot faster than a CPU. That’s because GPUs are built to execute thousands of operations at once, while CPUs handle tasks sequentially or in small batches.

### 2. Do I need both a CPU and a GPU?

Usually yes.

Even when you’re training or fine-tuning a model on a GPU, there’s always a CPU managing orchestration tasks like loading data, handling API requests, or storing checkpoints. So, GPU is like the heavy-lifter, and the CPU is like the coordinator.

### 3. Why are GPUs better than CPUs for deep learning?

Deep learning involves operations like matrix multiplications, convolutions, and activation functions across massive datasets. These are highly parallel operations, something GPUs are made for.

CPUs are better at logic-heavy or branching tasks (like handling web requests), but they can’t match the raw parallelism needed for model training.

<InfoBox className='BodyStyle'>

💡**Note:** In most production setups, teams need to run both types of workloads, web services on CPUs and training or inference on GPUs. Some platforms separate those paths entirely, but others, like [Northflank](https://app.northflank.com/signup), let you run CPU and GPU workloads side by side using the same tools and workflows.

</InfoBox>

*Next, we’ll look at how GPUs made the jump from gaming hardware to powering today’s cloud-based AI workloads.*

## How did GPUs evolve from gaming to the cloud?

GPUs didn’t start in AI; they started in games.

![Infographic showing the evolution of GPUs from gaming to CUDA, general-purpose computing, AI/data centers, and cloud GPUs](https://assets.northflank.com/gpu_evolution_timeline_diagram_8a94bc34d8.png)*Timeline of GPU evolution: from gaming to CUDA, GPGPU, and the cloud*

In the [early 2000s](https://en.wikipedia.org/wiki/Graphics_processing_unit), GPUs were designed almost exclusively for rendering graphics like lighting effects, shading, and 3D transformations in video games. These tasks required processing thousands of pixels in parallel, so hardware vendors like Nvidia built GPUs with hundreds or thousands of tiny cores optimized for this kind of parallel work.

Then something changed.

Researchers and developers realized these same GPU cores could accelerate scientific and data-heavy computations, not only graphics.

*Even in a recent [Reddit thread](https://www.reddit.com/r/MLQuestions/comments/1hwynby/why_did_it_take_until_2013_for_machine_learning/), early adopters reflect on how GPUs were pushed beyond gaming long before it went mainstream.*

![Reddit comment describing early GPU use for non-graphics tasks before mainstream adoption](https://assets.northflank.com/reddit_gpu_early_use_732a8cb0aa.png)*Reddit user explaining how GPUs were first repurposed for scientific computing around 2009–2010*

That’s when the idea of using graphics cards for general-purpose computing called GPGPU started to gain traction.

You might ask:

*“What is GPGPU, and how is it different from traditional GPUs?”*

GPGPU (General-Purpose GPU) refers to using the GPU’s cores for non-graphics tasks like simulations, numerical computing, or training machine learning models. Traditional GPUs were focused solely on rendering, but GPGPU lets you write programs that use the GPU for broader computation.

Now, the turning point came in [2007 when Nvidia launched CUDA](https://forums.developer.nvidia.com/t/cuda-1-0-released/982), a developer framework that made GPGPU programming much more accessible.

![Screenshot of Nvidia’s 2007 forum post announcing CUDA Toolkit 1.0 release](https://assets.northflank.com/nvidia_cuda_1_0_launch_announcement_becf175270.png)*Nvidia’s 2007 forum post announcing CUDA 1.0 - a turning point in GPU programming*

So, rather than hacking graphics APIs to run compute tasks, developers could now write C-like code to run directly on GPUs.

That move made GPU compute mainstream.

Cloud providers began adding GPUs to their infrastructure. AI frameworks like TensorFlow and Pytorch integrated CUDA support. Over time, GPU instances became a standard part of cloud offerings, not only for rendering but also for deep learning, training LLMs, and other high-throughput compute workloads.

You might be wondering:

*”What is GPU compute used for today?”*

Beyond graphics, GPUs now power workloads like:

- Model training
- Real-time inference
- Data science notebooks
- Voice/image generation
- High-speed simulations.

In many AI use cases, they’ve become essential infrastructure.

So, this journey from gaming to general-purpose compute, and then to cloud workloads, laid the foundation for how we use GPUs today in AI.

*Next, we’ll break down the difference between the GPUs powering your gaming laptop and the ones running in AI data centers.*

## What’s the difference between a desktop GPU and a server-grade GPU?

On the surface, both types of GPUs do similar things: they accelerate parallel processing. However, in practice, they’re built for very different environments and workloads.

Let’s see a breakdown of how they compare:

| Feature | Desktop GPU | Server GPU |
| --- | --- | --- |
| **Performance** | Great for burst-heavy tasks like gaming and video rendering | Tuned for high-throughput, 24/7 compute-heavy workloads (e.g. model training, inference) |
| **Memory** | Often uses standard GDDR memory, non-ECC | Includes ECC (Error-Correcting Code) memory to catch and fix data corruption |
| **Form factor & cooling** | Compact, fan-based cooling, consumer PCIe size | Larger, often passively cooled, designed for rack-mounted systems with external airflow |
| **Uptime** | Not designed for continuous full-load operation | Built for non-stop performance in data centers |

### What is a server GPU?

A server GPU (like Nvidia A100, H100, or older Tesla V100 cards) is designed specifically for enterprise workloads like deep learning, large-scale inference, simulations, and cloud-native compute. These GPUs live in data centers and power everything from ChatGPT to autonomous vehicle training.

They usually come with:

- More cores
- More memory (often 40–80 GB+)
- ECC memory
- Support for NVLink or PCIe Gen 4
- Compatibility with multi-GPU clustering

### Why are server GPUs so expensive?

You’re not just paying for raw power; you're paying for reliability, scalability, and specialized features. These GPUs:

- Include ECC memory (adds cost)
- Are validated for long-running jobs
- Have better thermal tolerances
- Often ship with enterprise warranties and driver support

Nvidia also segments pricing based on use case. For example, gaming GPUs may cost $800–$2000, while data center cards often range from $10,000 to $40,000+ per unit.

### Can you use a server GPU for gaming?

Technically? Sometimes. But it’s not practical.

Server GPUs often:

- Lack video outputs (no HDMI/DisplayPort)
- Require different drivers or kernel modules
- Are tuned for sustained workloads, not real-time rendering
- May not support consumer gaming APIs out of the box

So, unless you’re doing machine learning on the side, you’re better off with a high-end desktop GPU for gaming.

Server-grade GPUs aren’t about frame rates; they’re about raw compute, stability, and memory at scale.

<InfoBox className='BodyStyle'>

💡**Run workloads on high-grade GPU hardware without buying it**

With [Northflank](https://northflank.com/), you can access enterprise-grade GPUs like A100s on demand, making it well-suited for model training, inference, or background workers. You don’t have to install a server or purchase the hardware yourself.

</InfoBox>

*Next up, we’ll look at how these GPUs run in the cloud and what makes cloud GPU infrastructure different from running them on-prem.*

## Cloud GPU vs local GPU: which should you use?

Once you understand how GPUs power modern workloads, the next question is: *should you run them locally or in the cloud?*

A local GPU is one that’s physically installed in your own device, like your laptop, desktop, or on-prem server.

A cloud GPU is rented remotely from a provider like AWS, Azure, GCP, or CoreWeave and accessed over the internet.

### What’s the difference between cloud and local GPUs?

Look at the table below:

| Feature | Local GPU | Cloud GPU |
| --- | --- | --- |
| **Location** | On your machine or server | Runs remotely in the cloud |
| **Upfront cost** | High (hardware purchase) | None (pay-as-you-go) |
| **Setup & maintenance** | You manage drivers, cooling, power | Handled by the provider |
| **Performance** | Limited to your hardware | Choose from high-end GPUs on demand |
| **Scalability** | Static (fixed to one machine) | Dynamic (scale up/down based on workload) |

### Why do AI teams prefer cloud GPUs?

For many startups and AI teams, cloud GPUs are the obvious choice when you're training large models, scaling inference workloads, or collaborating across regions. See why:

- **Scalability**: Need 1 GPU today and 16 tomorrow? Easy.
- **Pay-as-you-go pricing**: No need to commit thousands upfront.
- **No hardware maintenance**: You don’t worry about drivers, power supply, or failures.

> Example: Startups building LLMs or fine-tuning models often spin up multiple A100s on demand, then tear them down when done, something you can’t do with local GPUs.
> 

### Are cloud GPUs worth it?

If your work depends on large-scale training or burst workloads, then yes, cloud GPUs let you scale without buying expensive hardware. For tasks like running Jupyter notebooks, small model experiments, or inference at low volume, a local GPU may be enough (and cheaper long-term).

**TL;DR:**

- Training large models or scaling up? Use cloud GPUs.
- Running small experiments or budget-conscious? Local GPU might do the job.

<InfoBox className='BodyStyle'>

💡With platforms like [Northflank](https://northflank.com/), you can deploy cloud GPU workloads without managing infrastructure. Attach GPUs to any job or service, scale them on demand, and run your entire stack, from CPU to GPU workloads, on a unified platform.

→ [Try it out with a free workload](https://app.northflank.com/signup)

</InfoBox>

*Next, we’ll look at how virtual GPUs compare to physical ones, and what that means for performance, cost, and isolation when choosing cloud infrastructure.*

## What is the difference between a virtual GPU and a physical GPU?

If you're looking into cloud GPUs, you'll often come across the terms *physical GPU* and *vGPU*. These aren’t interchangeable; they represent different ways to allocate GPU power.

### Physical GPU vs virtual GPU: what’s the difference?

I’ll define the differences clearly:

1. **Physical GPU**: A dedicated graphics card installed on a server or workstation. When you use it, you get direct, full access to the hardware.
2. **Virtual GPU (vGPU)**: A physical GPU that’s been *virtualized* and shared between multiple users or virtual machines. Platforms like NVIDIA GRID make this possible.

For example:

A physical GPU is like owning a car. A vGPU is like using a ride-sharing service where you share the same resource, but get your own seat.

### What are the pros and cons of a physical GPU and a virtual GPU

Look at the table below:

| Feature | Physical GPU | Virtual GPU (vGPU) |
| --- | --- | --- |
| **Performance** | Highest (full access to GPU cores) | Slightly reduced (shared with other users) |
| **Cost** | Higher (dedicated hardware) | Lower (shared usage) |
| **Isolation** | Full (your workload runs in isolation) | Shared (can impact consistency) |
| **Flexibility** | Less flexible, but predictable | More flexible, but variable performance |

While physical vs virtual GPUs describe how compute is allocated, it’s also important to think about the infrastructure these GPUs run on, such as bare metal or virtual machines.

### Bare metal vs virtual machines

This ties into the broader infrastructure choice:

1. Bare metal means you're using physical hardware directly, which is better for maximum GPU performance and predictable latency.
2. Virtual machines (VMs) run on top of hypervisors and can access vGPUs, which are easier to scale, but with some overhead.

> So, if you’re running latency-sensitive tasks like real-time inference, then physical GPUs on bare metal might be a better fit.
> 
> 
> Then, for experimentation or running many smaller workloads, vGPUs on VMs could be more cost-efficient.
> 

<InfoBox className='BodyStyle'>

💡Provisioning bare metal GPUs is only one piece of the puzzle. To run containers reliably, teams also need orchestration, usually with tools like Kubernetes. However, getting Kubernetes to manage GPU workloads efficiently is complex on its own.

That’s where platforms like [Northflank](https://northflank.com/) help. You get a Kubernetes-based abstraction that removes the setup overhead and gives you GPU-ready orchestration out of the box. It's ideal for deploying AI jobs, inference APIs, and background workers without managing infrastructure complexity.

</InfoBox>

And beyond infrastructure, you’ll want to think about access: should you rent GPUs on-demand or invest in your own?

### Should you rent or buy?

I’ll go straight to the point:

1. Buy if you need consistent access, have stable workloads, or want to avoid ongoing cloud costs.
2. Rent if your usage is spiky, project-based, or if you need access to high-end GPUs you can’t afford outright.

**TL;DR**

- Physical GPUs = full control and consistent performance
- vGPUs = lower cost and greater flexibility, with some trade-offs
- Match your choice to your workload’s scale, duration, and sensitivity

<InfoBox className='BodyStyle'>

💡**Choose the right GPU setup for your workload**

[Northflank](https://northflank.com/) supports physical GPUs and cloud providers offering virtualized GPUs, so you can pick what fits best for cost, performance, and team needs.

</InfoBox>

*Next, we’ll look at what happens when you’re not just choosing between physical or virtual GPUs, but trying to build and run an entire AI workload on top of them.*

## How does Northflank support AI teams using cloud GPUs?

AI teams need more than raw GPU access; they need a platform that lets them run full stacks: notebooks, training jobs, inference APIs, background workers, and everything in between.

That’s where [Northflank](https://northflank.com/) comes in.

![new-northflank-ai-home-page.png](https://assets.northflank.com/new_northflank_ai_home_page_309df08d0b.png)

### What Northflank provides for GPU workloads

Northflank isn’t only about attaching a GPU to a container; it’s about helping you manage the entire lifecycle of your workloads, securely and at scale. See what I mean:

1. **Attach GPUs to any job or service**
    
    You can run GPU-backed training notebooks, inference APIs, or long-running background jobs. GPU support works across all types of workloads.
    
2. **Support for both CPU and GPU workloads**
    
    Run mixed environments in a single deployment. Some jobs can use GPU acceleration while others run on standard CPU nodes.
    
3. **Bring Your Own Cloud (BYOC) and spot GPU marketplace**
    
    Deploy to your own cloud account (like AWS or GCP) or use Northflank’s GPU marketplace to find cost-efficient spot capacity.
    
4. **Secure runtime by default**
    
    GPU workloads are sandboxed, isolated, and protected from unsafe code execution.
    
5. **RBAC, audit logs, and cost tracking**
    
    Control access by team role, track usage across services, and get a clear breakdown of GPU costs.
    

<InfoBox className='BodyStyle'>

💡**Your full AI infrastructure, beyond the GPU**

Northflank runs the services *around* your model too: APIs, queues, databases, workers, and CI/CD, all with GPU support when you need it.

→ [Start building](https://app.northflank.com/signup)

→ [Talk to an engineer](https://cal.com/team/northflank/northflank-intro)

</InfoBox>

*Next, we'll break down when it’s worth using a GPU compared to running your workload on a CPU.*

## Should I use a cloud GPU instead of a CPU?

Not every workload needs a GPU. While cloud GPUs can deliver massive speedups, they’re not always the right fit.

If your workload involves matrix-heavy operations like training or running deep learning models, a GPU can reduce execution time significantly. However, if you're running a standard web server or handling transactional data, sticking to CPUs is more practical.

### When to use a GPU vs a CPU

See the table below:

| Workload type | Use GPU? | Why |
| --- | --- | --- |
| Model training | Yes | Parallel math across large datasets |
| Real-time inference | Yes | Faster response, lower latency |
| Batch data processing | No | Often CPU-bound and linear |
| Web apps or APIs | No | Low parallelism, better on CPU |
| Databases | No | Optimized for CPU |

Let’s make that difference clearer with a simple cost and speed comparison.

### Cost and performance: CPU vs GPU for inference

Let’s say you’re running an AI inference task that returns results from a fine-tuned model. For example:

| Resource | Inference Time | Cost per Hour (est.) |
| --- | --- | --- |
| CPU (8 vCPUs) | ~500ms | $0.35 |
| GPU (A100) | ~50ms | $3.10 |

If you’re handling high traffic and low latency is essential, the GPU pays off. For small-scale or non-latency-critical workloads, CPUs may be more economical.

## Why AI companies are switching to cloud GPUs

To wrap up, the answer is simple:

*Cloud GPUs give AI teams the flexibility to scale, the speed to train and serve models faster, and access to enterprise-grade compute, without being locked into costly on-prem hardware.*

However, the GPU itself is only part of the story.

Modern AI workloads depend on more than raw compute. They need the infrastructure around it: APIs, queues, CI/CD pipelines, job schedulers, and secure runtimes that can handle both GPU and CPU workloads.

That’s one of the areas where platforms like [Northflank](https://northflank.com/) help.

Northflank isn’t only a place to get a GPU. It’s a platform built for real-world AI workloads that gives you full control with BYOC ([Bring your own cloud](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes)), secure execution, team access controls, and flexible deployment workflows across your stack.

Start from here:

→ [Deploy your first GPU workload](https://app.northflank.com/signup)

→ [Or book a demo with our team](https://cal.com/team/northflank/northflank-intro)]]>
  </content:encoded>
</item><item>
  <title>6 best Vast AI alternatives for cloud GPU compute and AI/ML deployment</title>
  <link>https://northflank.com/blog/6-best-vast-ai-alternatives</link>
  <pubDate>2025-07-11T15:30:00.000Z</pubDate>
  <description>
    <![CDATA[Discover the best Vast AI alternatives for GPU hosting and ML workloads. Compare Northflank, RunPod, Baseten, Modal, Vertex AI &amp; SageMaker to choose the right AI infrastructure for your needs.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/top_ai_paas_blog_post_1_b868f7a5bb.png" alt="6 best Vast AI alternatives for cloud GPU compute and AI/ML deployment" />Vast AI is one of the most affordable and flexible ways to access GPU compute. With a global marketplace of providers, container-based deployments, and granular filtering for hardware specs, it’s a strong option for cost-conscious teams running training jobs, experiments, or batch workloads.

But as projects grow, so do the infrastructure demands. You might need better uptime, more consistent performance, or deployment workflows that integrate with your CI/CD stack. At that point, managing raw containers across community hosts can slow you down.

That’s where alternatives like [Nortflank](https://northflank.com/) come in. If you need production-grade deployment, built-in orchestration, or persistent services running alongside GPU jobs, there are platforms that offer more control without adding complexity. In this guide, we’ll compare the top Vast AI alternatives and help you choose the right tool for your workload.

## TL;DR – Top Vast AI alternatives

If you're short on time, here’s a snapshot of the top Vast AI alternatives. Each tool has its strengths, but they solve different problems, and some are better suited for real-world production than others.

| **Platform** | **Best For** | **Why It Stands Out** |
| --- | --- | --- |
| [**Northflank**](https://northflank.com/) | Full-stack AI products: APIs, LLMs, GPUs, frontends, backends, databases, and secure infra | Production-grade platform for deploying AI apps — GPU orchestration, Git-based CI/CD, [Bring your own cloud](https://northflank.com/features/bring-your-own-cloud), secure runtime, multi-service support, preview environments, secret management, and enterprise-ready features. Great for teams with complex infrastructure needs. |
| [**RunPod**](https://www.runpod.io/) | Budget-friendly, flexible GPU compute | Fast setup, competitive pricing, and support for both interactive dev and production inference |
| [**Baseten**](https://www.baseten.co/) | ML APIs and demo frontends | Smooth model deployment with built-in UI tools and public endpoints, no DevOps required |
| [**Modal**](https://modal.com/) | Async Python jobs and batch workflows | Code-first, serverless approach that works well for background processing and lightweight inference |
| [**Vertex AI**](https://cloud.google.com/vertex-ai?hl=en) | GCP-native ML workloads | Good for teams already on GCP, with access to AutoML and integrated pipelines |
| [**SageMaker**](https://aws.amazon.com/sagemaker/) | Enterprise-scale ML systems | Full-featured but heavyweight, better suited for teams deep in the AWS ecosystem |

## What makes Vast AI stand out?

If you've used Vast AI before, you know it appeals to teams who want to access cheap cloud GPUs. Here's why many start with it:

- **Price efficiency**: Vast’s decentralized GPU marketplace allows users to find some of the lowest prices in the market. Bidding on interruptible instances can yield even cheaper rates for non-critical tasks like model training or data preprocessing.
- **Custom container deployments**: You can launch your own containerized workloads without conforming to vendor-specific formats. This flexibility makes Vast especially appealing for ML engineers who need full control over their environment.
- **Granular hardware filtering**: The search interface lets you filter offers based on GPU model, VRAM, system memory, bandwidth, disk size, and trust level. That level of hardware specificity is hard to find elsewhere.
- **Horizontal scaling through liquidity**: With access to thousands of distributed GPUs, Vast can support horizontally scaled training jobs — ideal for deep learning practitioners working on large-scale experiments.
- **Zero commitment and pay-as-you-go**: There’s no account lock-in, credit requirement, or platform-specific configuration overhead. You only pay for the compute you use, with the freedom to spin up and tear down workloads at will.

## What are the limitations of Vast AI?

We have just covered what makes Vast AI a good choice for many teams. But like most tools, it is not perfect, especially for teams looking to deploy full-stack workloads or those seeking a platform with built-in Git and CI/CD integrations.

### 1. No git-connected deploys

Vast AI doesn’t connect to GitHub, GitLab, or any CI/CD provider. There’s no native pipeline, rollback, or tagging. You’re managing builds manually, pushing containers by hand, restarting pods, and hoping nothing breaks.

Platforms like [**Northflank**](https://northflank.com/) connect directly to your Git repos and CI pipelines. Every commit can trigger a build, preview, or deploy automatically. No custom scripts required.

### 2. No environment separation

Everything you launch goes straight to production. There’s no staging, preview branches, or room for safe iteration.

This kills experimentation. There’s nowhere to test model variations or feature branches without risking live traffic.

Platforms like **Northflank** provide full environment separation by default, with staging, previews, and production all isolated and reproducible.

### 3. No metrics, logs, or observability

If your model gets slow or crashes, you’re flying blind. No Prometheus, request tracing, or logs unless you manually SSH and tail them.

There’s no monitoring stack. You can't answer basic questions like: How many requests are failing? How many tokens per second? GPU utilization?

With platforms like **Northflank**, observability is built in. Logs, metrics, traces, everything is streamed, queryable, and tied to the service lifecycle.

### 4. No auto-scaling or scheduling

You can’t scale pods based on demand. There’s no job queue. No scheduled retries. Every container is static. That means overprovisioning and paying for idle GPU time, or building your own orchestration logic.

By default, Northflank supports [autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments), scheduled jobs, and queue-backed workers, making elastic GPU usage feel native.

### 5. No multi-service deployments

Vast AI can run one thing: a container. If you need a frontend, a backend API, a queue, a DB, a cache? You’re cobbling together services across platforms. That fragmentation adds latency, complexity, and risk.

**Northflank** treats multi-service apps as first-class citizens. You can deploy backends, frontends, databases, and cron jobs—fully integrated, securely networked, and observable in one place.

### 6. No secure runtime for untrusted workloads

Vast AI is built for trusted team environments, but it doesn’t offer secure runtime isolation for executing untrusted or third-party code. There’s no built-in sandboxing, syscall filtering, or container-level hardening. If you're running workloads from different tenants or just want extra guarantees around runtime isolation, you’ll need to engineer those protections yourself.

By contrast, **Northflank** containers run in secure, hardened sandboxes with configurable network and resource isolation, making it easier to host untrusted or multitenant workloads out of the box safely.

### 7. No Bring your own cloud (BYOC)

Vast AI runs on its own infrastructure. There’s no option to deploy into your own AWS, GCP, or Azure account. That means: **no VPC peering, private networking, or compliance guarantees** tied to your organization's cloud, and **no control over regions**, availability zones, or IAM policies. If your organization needs to keep workloads within a specific cloud boundary for compliance, cost optimization, or integration reasons, Vast AI becomes a non-starter.

By contrast, platforms like **Northflank support [BYOC](https://northflank.com/features/bring-your-own-cloud)**, letting you deploy services into your own cloud infrastructure while still using their managed control plane.

## What to look for in a Vast AI alternative

Vast AI works if all you need is a GPU and a container.

But production-ready AI products aren’t just containers. They’re distributed systems. They span APIs, workers, queues, databases, model versions, staging environments, and more. That’s where Vast AI starts to fall short.

As soon as you outgrow the demo phase, you’ll need infrastructure that supports:

- **CI/CD with Git integration** – Ship changes confidently, not by SSH.
- **Rollbacks and blue-green deploys** – Avoid downtime, roll back instantly.
- **Health checks and probes** – Know when something’s broken *before* your users do.
- **Versioned APIs and rate limiting** – Manage usage and backward compatibility.
- **Secrets and config management** – Keep credentials out of code.
- **Staging, preview, and production environments** – Test safely before shipping.
- **Scheduled jobs and async queues** – Move beyond synchronous APIs.
- **Observability: logs, metrics, traces** – Understand and debug your system.
- **Multi-region failover** – Stay online even when a zone isn’t.
- **Secure runtimes** – Safely run third-party or multitenant code.
- **Bring Your Own Cloud (BYOC)** – Deploy where *you* control compliance and cost.

You’re not just renting a GPU.

You’re building a platform that's resilient, observable, and secure. You need infrastructure that thinks like that too.

## Top 6 Vast AI alternatives for cloud GPU compute and AI/ML deployment

Once you know what you're looking for in a platform, it becomes a lot easier to evaluate your options. In this section, we break down six of the strongest alternatives to Vast AI each with a different approach to cloud GPU compute, model deployment, infrastructure control, and developer experience.

### 1. Northflank – **The best Vast AI alternative for production AI**

[**Northflank**](https://northflank.com/) isn’t just a model hosting or GPU renting tool; it’s a **production-grade platform for deploying and scaling full-stack AI products**. It combines the flexibility of containerized infrastructure with GPU orchestration, Git-based CI/CD, and full-stack app support.

Whether you're serving a fine-tuned LLM, hosting a Jupyter notebook, or deploying a full product with both frontend and backend, Northflank offers broad flexibility without many of the lock-in concerns seen on other platforms.

![image - 2025-06-19T211009.037.png](https://assets.northflank.com/image_2025_06_19_T211009_037_2419b18f99.png)

**Key features:**

- Bring your own Docker image and full runtime control
- GPU-enabled services with [autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments) and lifecycle management
- Multi-cloud and [Bring Your Own Cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) support
- [Git-based CI/CD](https://northflank.com/docs/v1/application/release/manage-ci-cd), [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), and full-stack deployment
- Secure runtime for untrusted AI workloads
- SOC 2 readiness and enterprise security (RBAC, SAML, audit logs)

**Pros:**

- **No platform lock-in** – full container control with BYOC or managed infrastructure
- **Transparent, predictable pricing** – [usage-based](https://northflank.com/pricing) and easy to forecast at scale
- **Great developer experience** – Git-based deploys, CI/CD, preview environments
- **Optimized for latency-sensitive workloads** – fast startup, GPU autoscaling, low-latency networking
- **Supports AI-specific workloads** – Ray, LLMs, Jupyter, fine-tuning, inference APIs
- **Built-in cost management** – real-time usage tracking, budget caps, and optimization tools

**Cons:**

- No special infrastructure tuning for model performance.

**Verdict:**

If you're building production-ready AI products, not just prototypes, Northflank gives you the flexibility to run full-stack apps and get access to affordable GPUs all in one place. With built-in CI/CD, GPU orchestration, and secure multi-cloud support, it's the most direct platform for teams needing both speed and control without vendor lock-in.

[**See how Cedana uses Northflank to deploy GPU-heavy workloads with secure microVMs and Kubernetes**](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes)

### 2. RunPod - The affordable option for raw GPU compute

[RunPod](https://www.runpod.io/) gives you raw access to GPU compute with full Docker control. Great for cost-sensitive teams running custom inference workloads.

![image - 2025-06-19T211020.974.png](https://assets.northflank.com/image_2025_06_19_T211020_974_7f97807c0a.png)

**Key features:**

- GPU server marketplace
- BYO Docker containers
- REST APIs and volumes
- Real-time and batch options

**Pros:**

- Lowest GPU cost per hour
- Full control of runtime
- Good for experiments or heavy inference

**Cons:**

- No CI/CD or Git integration
- Lacks frontend or full-stack support
- Manual infra setup required

**Verdict:**

Great if you want cheap GPU power and don’t mind handling infra yourself. Not plug-and-play.

*Curious about RunPod? Check out [this article](https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment#what-makes-runpod-stand-out-at-first) to learn more.*

### 3. Baseten – Model serving and UI demos without DevOps

[Baseten](https://www.baseten.co/) helps ML teams serve models as APIs quickly, focusing on ease of deployment and internal demo creation without deep DevOps overhead.

![image - 2025-06-25T171137.699.png](https://assets.northflank.com/image_2025_06_25_T171137_699_acea62b8ab.png)

**Key Features**:

- Python SDK and web UI for model deployment
- Autoscaling GPU-backed inference
- Model versioning, logging, and monitoring
- Integrated app builder for quick UI demos
- Native Hugging Face and PyTorch support

**Pros**:

- Very fast path from model to live API
- Built-in UI support is great for sharing results
- Intuitive interface for solo developers and small teams

**Cons**:

- Geared more toward internal tools and MVPs
- Less flexible for complex backends or full-stack services
- Limited support for multi-service orchestration or CI/CD

**Verdict**:

Baseten is a solid choice for lightweight model deployment and sharing, especially for early-stage teams or prototypes. For production-scale workflows involving more than just inference, like background jobs, databases, or containerized APIs, teams typically pair it with a platform like Northflank for broader infrastructure support.

*Curious about Baseten? Check out [this article](https://northflank.com/blog/baseten-alternatives-for-ai-ml-model-deployment#why-developers-choose-baseten) to learn more.*

### 4. Modal - **Code-first async jobs and Python batch workflows**

Modal makes Python deployment effortless. Just write Python code, and it handles scaling, packaging, and serving — perfect for workflows and batch jobs.

![image - 2025-06-19T211013.585.png](https://assets.northflank.com/image_2025_06_19_T211013_585_7160b4aa37.png)

**Key features:**

- Python-native infrastructure
- Serverless GPU and CPU runtimes
- Auto-scaling and scale-to-zero
- Built-in task orchestration

**Pros:**

- Super simple for Python developers
- Ideal for workflows and jobs
- Fast to iterate and deploy

**Cons:**

- Limited runtime customization
- Not designed for full-stack apps or frontend support
- Pricing grows with always-on usage

**Verdict:**

A great choice for async Python tasks and lightweight inference. Less suited for full production systems.

*Curious about Modal? Check out [this article](https://northflank.com/blog/6-best-modal-alternatives) to learn more.*

### 5. Vertex AI - **GCP-native ML pipelines and AutoML tooling**

Vertex AI is Google Cloud’s managed ML platform for training, tuning, and deploying models at scale.

![image - 2025-06-23T170636.235.png](https://assets.northflank.com/image_2025_06_23_T170636_235_c0b84ecd33.png)

**Key features:**

- AutoML and custom model support
- Built-in pipelines and notebooks
- Tight GCP integration (BigQuery, GCS, etc.)

**Pros:**

- Easy to scale with managed services
- Enterprise security and IAM
- Great for GCP-based teams

**Cons:**

- Locked into the GCP ecosystem
- Pricing can be unpredictable
- Less flexible for hybrid/cloud-native setups

**Verdict:**

Best for GCP users who want a full-featured ML platform without managing infra.

### 6. AWS SageMaker - Enterprise MLOps on the AWS ecosystem

[SageMaker](https://aws.amazon.com/sagemaker/) is Amazon’s heavyweight MLOps platform, covering everything from training to deployment, pipelines, and monitoring.

![image - 2025-06-19T211024.050.png](https://assets.northflank.com/image_2025_06_19_T211024_050_82c4f323dd.png)

**Key features:**

- End-to-end ML lifecycle
- AutoML, tuning, and pipelines
- Deep AWS integration (IAM, VPC, etc.)
- Managed endpoints and batch jobs

**Pros:**

- Enterprise-grade compliance
- Mature ecosystem
- Powerful if you’re already on AWS

**Cons:**

- Complex to set up and manage
- Pricing can spiral
- Heavy DevOps lift

**Verdict:**

Ideal for large orgs with AWS infra and compliance needs. Overkill for smaller teams or solo devs.

## How to pick the best Vast AI alternatives

When evaluating alternatives, consider the scope of your project, team size, infrastructure skills, and long-term needs:

| **If you're...** | **Choose** | **Why** |
| --- | --- | --- |
| Building a full-stack AI product with GPUs, APIs, frontend, models, and app log. | [**Northflank**](https://northflank.com/) | Full-stack deployments with GPU support, CI/CD, autoscaling, secure isolation, and multi-service architecture. Designed for production workloads. |
| Just need raw compute or cheap GPUs fast | **RunPod** | Flexible access to GPU instances with auto-shutdown, templates, and container support. Great for quick experiments or scaling inference. |
| Serving ML models with an opinionated, developer-friendly platform | **Baseten** | Clean developer UX for deploying models with UI frontends, versioning, and logging. Ideal for startups shipping ML products. |
| Running async Python jobs or workflows | **Modal** | Python-first serverless platform. Ideal for batch tasks, background jobs, and function-style workloads. |
| Deep in the GCP ecosystem | **Vertex AI** | Seamlessly integrates with GCP tools like BigQuery and GCS. Good for teams already using Google Cloud services. |
| In an enterprise AWS environment | **SageMaker** | Powerful but complex. Best if you’re already managing infra in AWS and need compliance, IAM, and governance tooling. |

## Conclusion

Choosing the right platform depends on more than just access to GPUs or cheap compute. As you've seen from the alternatives, the real differentiators are in deployment workflows, orchestration features, and how well the platform supports your infrastructure as it scales.

If Vast AI has been working for your training runs or experiments, but you're hitting limits around uptime, scaling, or integration with the rest of your stack, it might be time to look elsewhere. [Northflank](https://northflank.com/) offers a production-grade environment with GPU support, Git-based CI/CD, and the ability to run APIs and services with proper networking, scaling, and monitoring.

If you're ready to see how it fits into your workflow, you can [sign up for free](https://app.northflank.com/signup) or [book a short demo](https://cal.com/team/northflank/northflank-demo) to explore what it can do.]]>
  </content:encoded>
</item><item>
  <title>Secure runtime for codegen tools: microVMs, sandboxing, and execution at scale</title>
  <link>https://northflank.com/blog/secure-runtime-for-codegen-tools-microvms-sandboxing-and-execution-at-scale</link>
  <pubDate>2025-07-10T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Code generation tools are reshaping how developers build software. Instead of writing every line by hand, engineers now use systems that generate code automatically.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/microvms_2_54b348000d.png" alt="Secure runtime for codegen tools: microVMs, sandboxing, and execution at scale" />Code generation tools are reshaping how developers build software. Instead of writing every line by hand, engineers now use systems that generate code automatically, often using large language models (LLMs), to scaffold projects, write functions, and even deploy infrastructure.

But if you’re building a codegen tool, one problem becomes clear fast: you need to **execute untrusted code securely**.

You can’t risk one user breaking into another’s environment, leaking data, or escaping into your backend systems. You need speed, isolation, and safety. That’s where a **secure runtime** comes in, specifically, **sandboxed microVMs** built for ephemeral code execution.

This guide covers:

- What is codegen?
- Which codegen tool is best?
- The infrastructure needed to support safe execution
- Why **secure sandboxing** and **microVMs** matter
- How to use **Northflank** to run untrusted workloads at scale

<InfoBox className='BodyStyle'>

💡 **Northflank runs over 2 million microVMs monthly**, in production since 2021. We contribute to Kata Containers, Cloud Hypervisor, QEMU, and more.

Our platform supports **bring your own cloud** and runs securely in your VPC. Companies like **Writer** and **Sentry** use Northflank to run untrusted, multi-tenant workloads at scale.

**Building secure sandboxing with Firecracker isn’t a weekend project.** We’ve already done it, so you don’t have to. Spin up isolated microVMs in seconds and skip the infrastructure burden.

</InfoBox>

## What is codegen?

At its core, **codegen** (short for *code generation*) automates the production of source code. Early tools included boilerplate generators and compilers. Today’s codegen tools use **LLMs** and embeddings to dynamically generate code from prompts, API specs, full repos, or other inputs.

Modern codegen tools can:

- Translate between languages
- Scaffold components or full apps
- Auto-generate tests, CLI commands, and documentation
- Execute code live and return output in real time

Some run entirely in the browser. Others spin up **sandboxed execution environments** to compile or run code server-side.

That’s where **secure runtimes** come in.

## Which codegen tool is the best?

The codegen landscape is crowded. Most tools fall into two categories:

- **SaaS tools** using proprietary models (e.g. GPT-4, Claude)
- **Open-source agents** using open-weight models (e.g. CodeLlama, DeepSeek-Coder)

Execution is the key differentiator. Most proprietary tools bundle it in; open-source agents require you to bring your own **sandbox runtime**.

Here are the best codegen tools on the market right now.

| Tool / Agent | Core model(s) | Open source | Executes code? | Execution environment | Notes |
| --- | --- | --- | --- | --- | --- |
| **GitHub Copilot** | GPT‑4‑turbo | No | ❌ | None | IDE-only; no runtime |
| **Cursor** | GPT‑4, Claude | No | ✅ | Agent + server-side sandbox | Secure runtime with sandboxed agents |
| **Cody (Sourcegraph)** | Claude + embeddings | Partial | ⚠️ Optional | Local or cloud backend | Execution plug-in optional |
| **Continue** | Configurable OSS LLMs | ✅ | ⚠️ Optional | User‑defined | Backend and sandbox left to user |
| **DeepSeek‑Coder** | DeepSeek‑V3 | ✅ | ❌ | None | Model-only |
| **Replit Ghostwriter** | Proprietary | No | ✅ | Replit-hosted runtime | In-IDE execution |
| **Lovable** | Claude, GPT‑4 | No | ✅ | Browser-based sandbox | Client-side JS sandbox |
| **EngineLabs** | Claude, DeepSeek | No | ✅ | Server-side isolated runners | Secure remote execution |
| **VibeKit** | Codex, Claude Code, Gemini | ✅ | ✅ | Supports Daytona, Modal, E2B | SDK for sandboxed remote execution in secure environments |
| **OpenInterpreter** | GPTs, Claude | ✅ | ✅ | CLI and browser eval | Local inline eval |
| **Ghostwriter CLI** | OSS / Mix | ✅ | ✅ | Local shell backend | CLI agent execution |
| **CodeGeeX** | CodeGeeX2 | ✅ | ❌ | None | Model-only |
| **CodeLlama 70B** | Meta | ✅ | ❌ | None | Foundation model |
| **StarCoder2** | BigCode | ✅ | ❌ | None | Foundation model |
| **Phi‑3 Mini** | Microsoft | ✅ | ❌ | None | Lightweight dev model |

If you want to support real code execution, you’ll need to **build a secure runtime**. That means isolating each user in a **sandbox environment** with resource and network boundaries.

## Code execution is a security risk

It only takes one user to break things. If your codegen tool runs generated Python, JavaScript, or shell commands, especially from arbitrary inputs, you’re opening yourself up to:

- Privilege escalation
- Container escape
- Cross-tenant access
- Denial-of-service

Containers alone don’t cut it. They share the host kernel. A misconfigured capability or kernel exploit can compromise your backend or other users.

To truly isolate untrusted code, you need **VM-level separation,** but traditional VMs are too slow. You don’t want users waiting 10+ seconds to get a response.

That’s why companies like Northflank use **microVMs**.

## What are microVMs? (and what is Firecracker?)

**MicroVMs** are lightweight virtual machines designed for fast-start, short-lived workloads. They combine container-like performance with **VM-grade security isolation**.

### What is Firecracker?

**Firecracker** is a microVM runtime developed by AWS. It powers Lambda and Fargate, offering boot times under 200ms. Other runtimes like **Kata Containers** build on Firecracker to support OCI-compliant containers in VM-isolated environments.

With Firecracker or Kata, each workload runs:

- In a **sandboxed environment** with its own kernel
- Fully separated network + memory namespace
- Strict CPU, memory, disk quotas
- No access to host processes or containers

Perfect for executing **untrusted code** from a user’s LLM prompt.

## How to build a secure codegen tool (without becoming a platform company)

Start with your model. Fine-tuned open-weight LLMs like **CodeLlama**, **StarCoder2**, or **DeepSeek-Coder-V3** can be served using frameworks like **vLLM** on GPUs.

(And can be self-hosted by Northflank, which also offers the most cost efficient GPU on-demand pricing).

But once your codegen tool needs to execute code, you’ll hit the **secure runtime wall**.

Most teams either:

- Build fragile Firecracker orchestration in-house
- Try to bolt Kata onto Kubernetes
- Give up on execution altogether

This is what **Northflank** solves.

## Northflank: Secure runtime for codegen workloads

Northflank lets you spin up **microVM-backed containers** in seconds. It uses **Kata Containers** under the hood, giving you Firecracker-grade security without the ops pain.

Here’s what the setup looks like:

### Step 1: Multi-tenant isolation

Each project runs in a fully separated namespace. You can scope by user, tenant, team, or use case. Choose your region, bring your own cloud (BYOC), or run multi-region. No noisy neighbor risk.

### Step 2: microVM-backed execution

Deploy any container image. Northflank provisions a **secure microVM**, pulls the image, and runs it with full isolation. Every workload gets its own kernel and vNIC.

### Step 3: Optional Docker builds

Use a Dockerfile? Northflank spins up an ephemeral runner, builds your image, and deploys it straight into a microVM-backed service.

You get:

- Strong runtime isolation
- Full CI/CD baked in
- Support for persistent or ephemeral execution
- Automatic cleanup + monitoring

## Why Northflank is the best platform for secure code execution

If you’re building a codegen tool that runs code:

- You need a **secure sandbox**
- You need it to start fast
- You need to scale it without handholding infra

Northflank gives you:

- **Secure runtime execution** using microVMs
- **Firecracker-based isolation** with Kata
- **Autoscaling**, ephemeral or persistent sandboxes
- **Multi-region**, **BYOC**, **GPU support**
- Built-in observability and CI/CD

Whether you’re building the next Copilot or a CLI command generator, **securely executing untrusted code** should not be an afterthought.

## Don’t wait to solve secure execution

Most teams focus on the model, not the infrastructure. But if you run user-submitted code, even briefly, you need a **secure runtime environment** from day one.

Containers aren’t enough. VMs are too slow. MicroVMs are the middle ground, and **Northflank** gives you the easiest way to deploy them at scale.

**Build a safer, faster, more scalable codegen tool, without building your own sandbox platform.**

👉 [Try secure microVMs on Northflank](https://northflank.com/)]]>
  </content:encoded>
</item><item>
  <title>How to spin up a secure code sandbox &amp; microVM in seconds with Northflank</title>
  <link>https://northflank.com/blog/how-to-spin-up-a-secure-code-sandbox-and-microvm-in-seconds-with-northflank-firecracker-gvisor-kata-clh</link>
  <pubDate>2025-07-10T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Spin up microVMs in seconds with Northflank using secure runtimes like Kata Containers, gVisor, Firecracker, and Cloud Hypervisor (clh). Achieve fast, isolated container deployments ideal for multi-tenancy, untrusted code, and production workloads.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/microvms_1_1e95ebd4a2.png" alt="How to spin up a secure code sandbox &amp; microVM in seconds with Northflank" />Running secure, isolated workloads at scale has never been easier. With Northflank, you can spin up **microVM-backed containers** in seconds, combining the performance and compatibility of containers with the strong isolation of virtual machines.

In this guide, we’ll walk you through how to:

- Create a secure, multi-tenant project on Northflank
- Deploy any container image using microVMs
- Build and deploy from Dockerfiles or OCI-compliant containers
- Launch services in seconds with strong network and runtime isolation

<InfoBox className='BodyStyle'>

[Northflank](https://northflank.com/) is a full-stack cloud platform that runs microVM-backed [sandboxes](https://northflank.com/product/sandboxes) using Kata Containers, Firecracker, and gVisor, with isolation applied per workload. It supports managed cloud and BYOC (Bring Your Own Cloud) deployment into your own VPC, alongside databases, GPU workloads, and background jobs in the same control plane. In production since 2021 across startups, public companies, and government deployments.

Building a secure sandboxing platform with Firecracker and Kubernetes isn’t a weekend project. It can take a team months or longer, and the complexity doesn’t go away, it becomes something you have to operate and maintain every day.

Northflank already did the hard part. You can start running secure microVMs in seconds, without any heavy lifting required.

Companies like Writer, Sentry, cto.new, and others have leveraged Northflank’s secure runtime to run multi-tenant customer deployments for untrusted code at scale. 

It’s proven in production and you get to partner closely with the Northflank infrastructure engineering team.

Let’s dive in.

</InfoBox>

## Why use microVMs?

**microVMs** (like those powered by [Kata Containers](https://katacontainers.io/)) offer VM-level isolation with container startup speed. They’re ideal for:

- Running **untrusted code** inside a **secure sandbox**
- Isolating **multi-tenant workloads** in a **secure runtime**
- Securing **AI inference**, **CI jobs**, or **backend functions**
- Minimizing kernel attack surface per container

Northflank natively supports **microVM-backed workloads** with seamless orchestration, startup, and monitoring.

Northflank sandboxes are powered by microVMs or gVisor. Technologies like Firecracker, Kata, gVisor, and Cloud Hypervisor (CLH), giving you flexibility in your secure compute stack wherever you need it: AWS, GCP, Azure, bare-metal.

Whether you're building or want to leverage **Firecracker's minimalist VM design**, **gVisor's syscall interception**, or **CLH's performance**, Northflank delivers strong isolation and orchestration out of the box.

## Step 1: Create a project for multi-tenancy

To enable secure isolation between end-users or workloads, start by creating a dedicated project:

1. Head to your [Northflank dashboard](https://app.northflank.com/signup)
2. Click **“Create Project”**
3. Choose:
    - **Region** for running workloads close to your users
    - **or Bring your own cloud** to run workloads in your own VPC or customers VPC for data privacy and locality

Each project acts as a namespace, giving you strict runtime and network separation, perfect for SaaS platforms, API execution environments, or internal tooling.

Here is a code snippet example of initializing the Northflank SDK and creating a project:

```jsx
import {
  ApiClient,
  ApiClientInMemoryContextProvider,
} from "@northflank/js-client";

const contextProvider = new ApiClientInMemoryContextProvider();
await contextProvider.addContext({
  name: "context",
  token: process.env.NORTHFLANK_TOKEN,
});

const apiClient = new ApiClient(contextProvider, {
  throwErrorOnHttpErrorCode: true,
});

const project = await apiClient.create.project({
  data: {
    name: "sandbox-project",
    region: "europe-west",
  }
});
```

## Step 2: Create a service

Next, create a service backed by microVMs:

1. In your project, click **“Create Service”**
2. Select **“Job or Backend Service”** depending on the use case
3. Choose your container image:
    - Public (e.g. `ubuntu`, `python`, `ghcr.io/...`)
    - Private (via credentials)
4. Choose CPU, memory, disk — your **microVM** will be provisioned accordingly
5. Hit **“Deploy”**

Northflank provisions the **microVM**, pulls your image, and runs it within seconds inside a **secure sandbox**.

Pro tip: Enable persistent or ephemeral disk storage depending on workload needs (e.g. build caching, temporary compute jobs, etc.)

Here is an example of using the Northflank SDK to provision a secure sandbox that will boot in a few seconds:

```jsx
const service = await apiClient.create.service.deployment({
  parameters: {
    projectId: project.data.id,
  },
  data: {
    name: "sandbox-service",
    billing: {
      deploymentPlan: "nf-compute-20", // 2 vCPU, 4GB RAM
    },
    deployment: {
      instances: 1,
      external: {
        imagePath: "alpine:latest",
      },
    },
  },
});
```

## Step 3: (Optional) Build from a Dockerfile

Need to build your image from source?

1. Create a new **Build** in the same project
2. Connect a Git repository or upload a Dockerfile
3. Configure:
    - Build context
    - Dockerfile path
    - Build arguments or secrets
4. Northflank will use fast, ephemeral builds to produce an OCI image
5. Deploy that image to any service — including **microVMs** — with a single click

Builds typically complete in under a minute for small to medium images

## Step 4: Secure by default, scale on demand

Every **microVM** on Northflank runs:

- In its own **secure runtime** with a sandboxed kernel
- With configurable CPU and memory limits
- With no shared namespaces unless explicitly enabled
- Over isolated, per-service networking

The secure sandboxing model prevents container breakout and ensures tenant isolation, even for untrusted workloads.

You can scale vertically (larger instances) or horizontally (replica count) and even autoscale based on resource usage.

## Common use cases

Here are just a few things you can build with Northflank **microVM services**:

| **Use case** | **Benefits** |
| --- | --- |
| **Untrusted code runners** | VM-grade isolation, no risk of container breakout — run in a **secure sandbox** using Northflank secure runtime  |
| **Multi-tenant API hosting** | Isolated execution per customer or request, protected by a Northflank’s hardened **secure runtime** |
| **AI model inference** | Combine with GPUs and secure tenant-level execution using Northflank’s gVisor or microVMs |
| **Serverless backends** | Fast boot times, strong tenancy and network isolation |
| **Build sandboxes** | Run "npm install", "cargo build", or "docker build" safely inside a hardened, using Northflank ephemeral microVMs  |

## Get started with Northflank sandboxes
Use the following resources:

- [Sandboxes on Northflank: overview and concepts](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank): architecture overview and core sandbox concepts
- [Deploy sandboxes on Northflank: step-by-step deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-on-northflank): step-by-step deployment guide
- [Deploy sandboxes in your cloud: BYOC deployment guide](https://northflank.com/docs/v1/application/sandboxes/deploy-sandboxes-in-your-cloud): run sandboxes inside your own VPC
- [Create a sandbox with the SDK: programmatic sandbox creation](https://northflank.com/docs/v1/application/sandboxes/sandboxes-on-northflank#create-sandboxes-with-the-sdk): programmatic sandbox creation via the Northflank JS client

[Get started (self-serve)](https://app.northflank.com/signup), or [book a session with an engineer](https://cal.com/team/northflank/northflank-demo?duration=30) if you have specific infrastructure or compliance requirements.]]>
  </content:encoded>
</item><item>
  <title>Top AI PaaS platforms in 2026 for model deployment, fine-tuning &amp; full-stack apps</title>
  <link>https://northflank.com/blog/top-ai-paas-platforms</link>
  <pubDate>2025-07-09T15:46:00.000Z</pubDate>
  <description>
    <![CDATA[Check out the top AI PaaS platforms in 2026 for deploying, fine-tuning, and scaling machine learning models. Compare options for vector databases, GPU access, model APIs, and full-stack app support.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/ai_paas_platforms_3b6e311f34.png" alt="Top AI PaaS platforms in 2026 for model deployment, fine-tuning &amp; full-stack apps" />AI PaaS (Platform as a Service) is everywhere right now, and if you’re looking for the top AI PaaS to build or scale your stack, you’re in the right place.

I know you’ve been hearing a lot about it lately, and now you’re likely here to figure out which platform can handle your model deployments, fine-tuning jobs, APIs, and everything in between. Don't worry, I've got you.

And you know what? Some teams are starting to realize that GPU access alone isn’t enough. You now need a full-stack infrastructure that supports databases, background workers, secure runtimes, and observability. And what if I told you that you can get all of that in a single platform?

Well, I won't waste your time with the long story. So, I'll cut to the chase and help you find a platform that doesn’t stop at serving models only.

<InfoBox className='BodyStyle'>

### Top AI PaaS platforms to keep on your radar

If you're building or scaling AI apps, these are the platforms you’ll want to check out, some are GPU-focused, others give you full control across your entire stack:

1. [**Northflank**](https://northflank.com) – First on the list because it's a full-stack PaaS with support for model fine-tuning, secure multi-tenancy, APIs, Postgres, Redis, background jobs, and GPU/CPU workloads. Includes BYOC, CI/CD, and fast provisioning for AI workloads in your own cloud or across clouds.

2. [**Lambda AI**](https://northflank.com/blog/top-lambda-ai-alternatives) – GPU cloud platform built for inference workloads. Prioritizes access to high-end GPUs (A100s, H100s), though lacks broader infrastructure features like database hosting or CI/CD.

3. [**RunPod**](https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment) – Lets you run containers on GPU machines for training, inference, or notebooks. Popular for spot pricing and rapid experimentation, but not designed for full app deployments.

4. [**Replicate**](https://northflank.com/blog/6-best-replicate-alternatives) – Lets you deploy and share ML models as hosted APIs. Great for prototyping and public model sharing, but limited control over infrastructure and customization.

5. [**BentoML**](https://northflank.com/blog/bentoml-alternatives) – Framework for packaging and serving models. Ideal if you want to self-host model workloads but need to bring your own infrastructure stack.

6. [**Together AI**](https://northflank.com/blog/together-ai-alternatives-for-ai-ml-model-deployment) – Hosted endpoints for open-source models like LLaMA and Mixtral. Focuses on LLM inference, not broader developer workflows.

7. [**Baseten**](https://northflank.com/blog/baseten-alternatives-for-ai-ml-model-deployment) – Offers a developer-friendly interface and SDK for deploying ML models with observability and scaling. Better suited for model endpoints than multi-service applications.

8. [**Anyscale**](https://northflank.com/blog/anyscale-alternatives-for-ai-ml-model-deployment) – Built around Ray for distributed compute. Useful for large training jobs, especially if you’re already invested in Ray’s ecosystem.

9. [**Paperspace (DigitalOcean)**](https://northflank.com/blog/digitalocean-gpu-paperspace-alternatives) – Entry-level GPU platform with notebooks and endpoints. Helpful for solo devs or lightweight inference tasks but lacks enterprise or multi-service support.

10. [**Hugging Face Inference Endpoints**](https://huggingface.co/inference-endpoints) – Managed API access to pre-trained models. Easy to use, but minimal infra flexibility and no full-stack support.

**[Click here to get started with running secure, production-grade AI workloads with a full-stack AI PaaS](https://northflank.com/)**

</InfoBox>

*Next, let’s talk about what makes a good AI PaaS before we go through each option in detail.*

## What makes a good AI PaaS in 2026?

Can we agree that not all AI PaaS platforms are built the same? I mean, if you're building production-grade AI systems and not prototypes or experiments, there are some non-negotiables you should be looking for.

It's not only about spinning up a model endpoint. You need the kind of platform that can handle production traffic reliably, run fine-tuning jobs, and plug in a vector database.

I’d list some of the things I’d expect from any top AI PaaS today, and yes, platforms like Northflank check all these boxes.

1. **Support for both GPU and CPU workloads:**
    
    AI workloads aren’t limited to model training only. You should be able to run GPU-intensive training jobs and CPU-based background workers side by side on the same platform without complex setups or separate tools.
    
2. **Secure multi-tenancy**:
    
    If your platform runs AI agents or executes generated code, then isolation is important. You should expect a strict separation between users, so that one container can't access or interfere with another.
    
3. **Autoscaling across instance types**:
    
    A good AI PaaS should scale both GPU workloads and CPU-based services automatically. You shouldn’t have to manually intervene to keep costs in check or avoid idle resources.
    
4. **BYOC (Bring Your Own Cloud)**:
    
    You should be able to bring your own cloud account and run workloads across different GPU providers. This gives you more control over pricing, GPU availability, and region-specific deployments.
    
5. **Built-in observability**:
    
    You need full visibility into your workloads. Logs, metrics, and deployment history should all be accessible without having to integrate third-party tools manually.
    
6. **First-class support for databases and APIs**:
    
    Running a model is only part of the story. You’ll also need infrastructure for vector search, session storage, and APIs, which means built-in support for tools like Postgres, Redis, and vector databases.
    
7. **Fine-tuning and inference**:
    
    The platform should support both training custom models and serving them as APIs. You shouldn’t have to switch between multiple tools to cover the full lifecycle.
    
8. Infrastructure primitives and templates:
    
    You might be spinning up a LLaMA model with one click or managing deployments via GitOps. Either way, the platform should support both high-level templates and low-level control.
    
9. **Enterprise features out of the box**:
    
    If you’re deploying at scale, features like RBAC, audit logs, and project-level cost tracking shouldn’t be an afterthought; they should be ready to use from day one.
    

*Now that you know what to look for, let’s go through the top AI PaaS platforms and see what each one supports.*

## Top AI PaaS platforms in 2026 for model deployment & full-stack apps

I’ve broken down the top AI PaaS platforms that teams are using in 2026. You’ll see what each one is built for, where it has limitations, and which types of workloads it's best suited for.

### 1. Northflank – Full-stack AI PaaS with secure multi-tenancy and BYOC

Northflank lets you deploy everything in one place. It goes beyond your model and includes the full application stack around it. That includes your GPU and CPU workloads, along with APIs, databases, background workers, and CI/CD pipelines.

![new-northflank-ai-home-page.png](https://assets.northflank.com/new_northflank_ai_home_page_f28986387e.png)

**What you can run on Northflank**:

- You can deploy both your GPU and CPU workloads in one place, including model fine-tuning jobs, inference endpoints, and background workers.
- You can expose APIs that serve your models or power agent backends using built-in CI/CD and autoscaling.
- You can run supporting infrastructure like Postgres, Redis, and vector databases alongside your applications.
- You can manage long-lived jobs, cron tasks, and ephemeral environments without needing external schedulers.
- You can bring your own cloud (BYOC) and run across providers using spot GPU instances or dedicated clusters.

**Where it fits best**:

- Ideal for teams deploying secure, production-ready AI apps with full-stack infrastructure needs
- Useful if you want to run jobs, APIs, and databases alongside your models without managing separate platforms
- Especially valuable for multi-tenant AI agents, GPU-intensive workloads, and privacy-sensitive deployments

<InfoBox className='BodyStyle'>

Northflank gives you a secure, full-stack foundation for running production-grade AI apps with GPUs, databases, APIs, and jobs all in one place.

Get started with Northflank by [creating an account](https://app.northflank.com/signup) or [booking a demo](https://cal.com/team/northflank/northflank-intro).

</InfoBox>

[*See how Cedana uses Northflank to deploy workloads onto Kubernetes with microVMs and secure runtimes*](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes)

### 2. Lambda AI – GPU-first PaaS for inference workloads

Lambda AI focuses on giving teams access to high-end GPUs like A100s and H100s without layering on too much platform overhead. It’s designed for ML workloads that prioritize raw compute, particularly for training and inference jobs.

You won’t get managed databases, autoscaling APIs, or built-in CI/CD, but if you already have the rest of your stack figured out and need fast, dedicated GPU machines, then Lambda could be a good choice.

![lambda-homepage.png](https://assets.northflank.com/lambda_homepage_21b6ec7a15.png)

**What you can run on Lambda AI**:

- Long-running training jobs on dedicated GPU nodes
- Inference endpoints powered by powerful NVIDIA chips
- Notebooks or research experiments with high memory and compute needs

**Where it fits best**:

- Research teams or ML engineers who want maximum control over compute
- Workloads that depend on specific GPU types (like A100s or H100s)
- Cases where platform simplicity is more important than full-stack features

*See [Top Lambda AI alternatives to consider for GPU workloads and full-stack apps](https://northflank.com/blog/top-lambda-ai-alternatives) if you're comparing options for GPU workloads and full-stack app deployment.*

### 3. RunPod – Simplified container deployments for GPU jobs

RunPod lets you spin up containers on GPU machines quickly, making it a good option for training, inference, or notebook-style development. It's designed for fast experimentation, especially when you don’t need a full application platform around your workloads.

![runpod-homepage.png](https://assets.northflank.com/runpod_homepage_7859326a36.png)

**What you can run on RunPod**:

- Training jobs, fine-tuning tasks, or inference endpoints in isolated containers
- Jupyter notebooks and interactive dev environments
- Custom Docker images with support for GPUs and spot pricing
- Background jobs or one-off tasks with minimal setup

**Where it fits best**:

If you’re running GPU-heavy workloads and want a simple way to experiment or test models, RunPod gives you a quick path. But keep in mind, it’s not built for managing full-stack applications or production deployments at scale.

*See [RunPod alternatives for containerized GPU workloads and full-stack AI apps](https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment) if you're comparing platforms.*

### 4. Replicate – Share and deploy models via hosted APIs

Replicate turns machine learning models into ready-to-use API endpoints with minimal setup. It's a popular choice for sharing open-source models or giving quick access to model outputs without managing your own infrastructure.

![replicate-homepage.png](https://assets.northflank.com/replicate_homepage_38062bccda.png)

**What Replicate is best for:**

- Running public or open-source models as API endpoints
- Sharing models with others via a hosted interface
- Quickly testing models without building full backend services

It’s not built for full-stack applications, fine-tuning workflows, or custom infrastructure, but if your goal is to deploy a model and get a working endpoint in minutes, Replicate makes that easy.

*See [Replicate alternatives for teams that need more infrastructure flexibility](https://northflank.com/blog/6-best-replicate-alternatives) if you're comparing with more customizable platforms.*

### 5. BentoML – Self-hosted model serving framework

BentoML is an open-source framework that helps you turn ML models into production-ready REST APIs. It’s geared toward teams that want full control over how models are packaged, deployed, and served, especially in self-hosted environments.

![bentoml-homepage.png](https://assets.northflank.com/bentoml_homepage_1b6289d1d1.png)

**What you can run with BentoML**:

- Model servers built from frameworks like PyTorch, TensorFlow, and scikit-learn
- REST API endpoints for custom ML models
- Containerized services deployed to Kubernetes or other infrastructure
- Multi-model serving with custom logic and batching

**Where it fits best**:

If you want a framework-first approach to model deployment and prefer to run things in your own environment, BentoML gives you flexibility without forcing a platform. But it does require hands-on infrastructure setup and isn’t designed as a full-stack PaaS out of the box.

*See [6 best BentoML alternatives for self-hosted AI model deployment (2026)](https://northflank.com/blog/bentoml-alternatives) if you're comparing platforms.*

### 6. Together AI – Open model endpoints for LLaMA, Mistral, Mixtral

Together AI gives you hosted access to open-source models like LLaMA, Mistral, and Mixtral through prebuilt inference endpoints. It’s useful for teams that want to evaluate or build on top of popular OSS models without running their own infrastructure.

![togetherai-homepage.png](https://assets.northflank.com/togetherai_homepage_bd07e44d7e.png)

**What you can run on Together AI**:

- Inference calls to OSS models like LLaMA 3, Mistral, and Mixtral
- Prompt-based generation for chat, text, or function-calling agents
- Basic fine-tuning workflows (LoRA, DPO) for supported models
- API integrations with tools like LangChain

**Where it fits best**:

Together AI is best for teams that want fast access to open models via hosted endpoints. It works well for prototyping, evaluation, or agent backends that don’t need custom model weights or self-hosting flexibility.

*See [Top Together AI alternatives for AI/ML model deployment](https://northflank.com/blog/together-ai-alternatives-for-ai-ml-model-deployment) if you're looking for alternative paths to run OSS models.*

### 7. Baseten – Python SDK + UI for model serving and monitoring

Baseten provides a UI-driven platform and Python SDK to help you deploy, monitor, and scale models with minimal infrastructure setup. It’s aimed at data science teams who want to get models into production without managing low-level infrastructure.

![baseten-homepage.png](https://assets.northflank.com/baseten_homepage_2c66e73096.png)

**What you can run on Baseten**:

- Model APIs built from Python, PyTorch, or TensorFlow
- Background workers and fine-tuning jobs
- Dashboards and UI-based workflows for model interaction
- Observability for deployed models (logs, metrics, usage)

**Where it fits best**:

Baseten is an option for teams that want to deploy models with Python and monitor them from a clean UI. It’s useful if you’re focused on fast iteration and want to avoid building your own deployment tools from scratch.

*See [Top Baseten alternatives for AI/ML model deployment](https://northflank.com/blog/baseten-alternatives-for-ai-ml-model-deployment) if you're evaluating other platforms.*

### 8. Anyscale – Ray-based platform for distributed AI workloads

Anyscale is built on top of Ray, making it well-suited for running distributed AI and Python workloads across multiple nodes. It abstracts a lot of the complexity behind Ray while giving you the flexibility to scale large jobs without managing the infrastructure 
manually.

![anyscale-homepage.png](https://assets.northflank.com/anyscale_homepage_0d9cb1948c.png)

**What you can run on Anyscale**:

- Distributed training or hyperparameter tuning with Ray
- Batch inference jobs across GPU and CPU clusters
- Python-based AI agents and pipelines
- Workflows that require autoscaling across many machines

**Where it fits best**:

If you're working on large-scale distributed AI workloads and want a managed Ray environment with autoscaling, Anyscale is an option. It’s relevant for research and production teams building custom training pipelines.

*See [Top Anyscale alternatives for AI/ML model deployment](https://northflank.com/blog/anyscale-alternatives-for-ai-ml-model-deployment) if you’re checking out similar platforms.*

### 9. Paperspace (DigitalOcean) – Entry-level GPU notebooks & endpoints

Paperspace, now under DigitalOcean, gives you an accessible starting point for running Jupyter notebooks, launching GPU-powered VMs, and deploying models via Gradient endpoints. It’s designed more for experimentation than full-stack AI apps, but it’s a familiar entry point for many developers.

![paperspace-homepage.png](https://assets.northflank.com/paperspace_homepage_0a2d3a9357.png)

**What you can run on Paperspace**:

- Jupyter notebooks with access to entry-level or mid-tier GPUs
- Inference endpoints using preconfigured environments (via Gradient)
- Containerized training or fine-tuning jobs with basic orchestration
- Small-scale applications where a single notebook or endpoint is enough

**Where it fits best**:

If you're starting with GPU workloads or want to test models in a notebook environment before scaling up, Paperspace can be a low-barrier option. Just keep in mind that it’s not built for running multi-service, production-grade AI apps.

*See [7 best DigitalOcean GPU & Paperspace alternatives for AI workloads in 2026](https://northflank.com/blog/digitalocean-gpu-paperspace-alternatives) if you're comparing platforms.*

### 10. Hugging Face Inference Endpoints – Hosted APIs for OSS models

Hugging Face provides a quick path to deploy open-source models as production-ready APIs without managing infrastructure. It’s great if your model is already hosted on the Hub or if you’re working with pretrained models from the Hugging Face ecosystem.

![huggingface-inference-endpoints-homepage.png](https://assets.northflank.com/huggingface_inference_endpoints_homepage_c7050f6841.png)

**What you can run with Hugging Face Inference Endpoints**:

- Pretrained models from the Hugging Face Hub (e.g., BERT, LLaMA, Mistral)
- Custom fine-tuned models pushed from your local workflow
- Real-time inference APIs with autoscaling and basic monitoring
- Transformers-based pipelines with simple deployment configs

**Where it fits best**:

This is a good choice if you're focused on deploying public or fine-tuned Hugging Face models as APIs and don’t want to worry about backend setup. It’s less suited for teams building custom infrastructure or multi-service apps around those models.

## Why Northflank leads the next generation of AI PaaS platforms

Most AI PaaS platforms are focused on a narrow slice, usually model inference. However, if you're building production-grade systems, that’s not enough. You need full-stack deployment, workload isolation, flexible cloud choices, and support for the entire lifecycle.

Northflank is designed to run **real-world AI applications**, not only demos or endpoints. It brings together GPU provisioning, secure runtimes, CI/CD pipelines, background jobs, databases, and more on a single platform.

### What Northflank gives you that most AI PaaS platforms don’t:

- **Bring Your Own Cloud (BYOC):** Run across your own cloud accounts with spot or on-demand GPUs from AWS, GCP, Azure, and more
- **Secure runtimes:** Run untrusted agents and generated code in isolated microVMs with MTLS and project boundaries
- **Databases and vector search:** Spin up Redis, Postgres, and Qdrant alongside your services with no external setup needed
- **Multi-service support:** Deploy inference endpoints, APIs, background jobs, and workers in one place
- **Built-in CI/CD:** Trigger builds and deployments from your Git repo with zero-config pipelines
- **Custom templates and infrastructure primitives:** Use prebuilt templates for Jupyter, LLaMA, or fine-tuning, or roll your own setup
- **Enterprise readiness:** Get audit logs, RBAC, billing groups, and cost tracking from day one

<InfoBox className='BodyStyle'>

Get started with Northflank by [signing](https://app.northflank.com/signup) up or booking a [demo](https://cal.com/team/northflank/northflank-intro)

</InfoBox>

## FAQs: Common questions about AI PaaS platforms

**1. What is AI PaaS?**

AI PaaS (Platform as a Service) refers to platforms that let you build, deploy, and scale AI workloads, like model training, inference, and background jobs, without managing the underlying infrastructure. These platforms typically combine compute, APIs, databases, and developer tools in one place. [Here’s a deeper explanation of what AI PaaS means](https://northflank.com/blog/what-is-ai-paas).

**2. What are the top 5 AI PaaS platforms?**

Five notable platforms widely used in 2026 include Northflank (full-stack deployment with secure runtimes, CI/CD, and BYOC), Lambda, RunPod, Replicate, and BentoML.

**3. What makes a good AI PaaS?**

A good AI PaaS should support both GPU and CPU workloads, offer built-in observability and autoscaling, support fine-tuning and inference, and include first-class support for APIs, databases, and secure multi-tenancy. The best ones also let you bring your own cloud (BYOC) and give you templates for managing infrastructure and deployments.

**4. Is there a free AI PaaS?**

Yes, several platforms include free tiers. Northflank has a free plan for CPU workloads and service deployments. Replicate lets you use public models for free with rate limits. RunPod offers occasional credits and affordable pricing for GPU access.

**5. Can I fine-tune my own models on an AI PaaS?**

Some platforms support full fine-tuning workflows while others are built just for inference. Northflank, RunPod, and BentoML support fine-tuning. Replicate and Hugging Face Inference Endpoints are more focused on serving pre-trained models.

**6. Which AI PaaS is best for startups vs enterprise teams?**

Startups might prioritize fast iteration and lower GPU costs, making Lambda or RunPod appealing. Enterprise teams typically need more security, audit trails, cost tracking, and the ability to bring their own cloud, areas where Northflank is designed to meet those requirements.]]>
  </content:encoded>
</item><item>
  <title>6 best Nebius alternatives for AI/ML model deployment in 2026</title>
  <link>https://northflank.com/blog/6-best-nebius-alternatives</link>
  <pubDate>2025-07-09T15:45:00.000Z</pubDate>
  <description>
    <![CDATA[Explore the best Nebius alternatives for AI/ML deployment, with platforms like Northflank offering full-stack support, GPU orchestration, CI/CD, and more control over your infrastructure.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/nebius_alternatives_5d31b0a6b1.png" alt="6 best Nebius alternatives for AI/ML model deployment in 2026" />Nebius is one of the most capable AI platforms available today. With powerful GPU orchestration, integrated tooling for model deployment, and a developer-friendly experience, it’s easy to see why many teams choose it to build and scale ML workloads.

But even with that level of capability, some teams eventually need more flexibility. You might want deeper control over infrastructure, stronger Git-based workflows, or the ability to run full-stack applications alongside inference APIs and background jobs.

That’s where platforms like Northflank come in. For teams that care about CI/CD, runtime control, cost visibility, and multi-service orchestration, there are alternatives that offer more infrastructure ownership without slowing you down. In this guide, we’ll look at the top Nebius alternatives, how they compare, and when it makes sense to switch.

## TL;DR – Top Nebius alternatives

If you're looking to move off Nebius, these platforms offer better flexibility, GPU orchestration, and developer workflows:

| Platform | Best for |
| --- | --- |
| [**Northflank**](https://northflank.com/) | Full-stack AI apps with APIs, LLMs, GPUs, frontends, backends, databases, bring your own cloud, and secure infra. |
| [**RunPod**](https://www.runpod.io/) | Budget-friendly GPU compute for custom ML workloads |
| [**Baseten**](https://www.baseten.co/) | Fast API deployment and demo UIs without DevOps |
| [**AWS SageMaker**](https://aws.amazon.com/sagemaker/) | Enterprise-grade ML pipelines on AWS infra |
| [**Paperspace**](https://www.paperspace.com/) | Accessible GPU cloud for individuals, startups, and education |
| [**Anyscale**](https://www.anyscale.com/) | Scalable Ray workloads and distributed AI systems |

## What to look for in a Nebius alternative

If you're considering a switch from Nebius, you're probably not just chasing new features. Maybe you need more control over your infrastructure, better CI/CD integration, or a platform that can support more than just inference. Before diving into specific tools, it’s worth stepping back to clarify what actually matters for your workload.

This section outlines the key capabilities to look for in a Nebius alternative so you can make a move that fits both your technical requirements and the way your team works.

- **Can it handle full applications?**
    
    If you're building a full-stack application with a frontend, backend, background jobs, and a database, you’ll want a platform that supports all of it together.
    
- **Does it support Git-based workflows?**
    
    Having native CI/CD, Git integration, and preview environments can save hours of setup and glue code. It also makes working with a team a lot smoother.
    
- **How well does it handle GPUs?**
    
    If you're doing ML, LLMs, or anything compute-heavy, check for on-demand GPU access, autoscaling, and reasonable pricing. You want this to be seamless, not a headache.
    
- **What kind of networking and security does it offer?**
    
    Private services, VPC support, custom domains, access control—these things matter a lot once you're shipping to production or dealing with user data.
    
- **Can you bring your own cloud?**
    
    Some platforms let you deploy to your own AWS, Azure or GCP account. This gives you more control over cost, location, and compliance without giving up the developer experience.
    
- **Do you get visibility into costs and usage?**
    
    The best platforms don’t hide billing behind a vague dashboard. You should be able to see exactly what you're using and how much it's costing you.
    
- **Is it flexible enough to grow with you?**
    
    Avoid tools that force you into a very specific pattern or runtime. The best alternatives should give you room to grow without locking you in.
    

## 6 best Nebius alternatives for AI/ML model deployment

Once you know what you're looking for in a platform, it becomes a lot easier to evaluate your options. In this section, we break down six of the strongest alternatives to Nebius each with a different approach to model deployment, infrastructure control, and developer experience.

### 1. Northflank – The best Lambda AI alternative for full-stack AI workloads

[**Northflank**](https://northflank.com/) isn’t just a model hosting or GPU renting tool; it’s a **production-grade platform for deploying and scaling full-stack AI products**. It combines the flexibility of containerized infrastructure with GPU orchestration, Git-based CI/CD, and full-stack app support.

Whether you're serving a fine-tuned LLM, hosting a Jupyter notebook, or deploying a full product with both frontend and backend, Northflank offers broad flexibility without many of the lock-in concerns seen on other platforms.

![image - 2025-06-19T211009.037.png](https://assets.northflank.com/image_2025_06_19_T211009_037_2419b18f99.png)

**Key features:**

- Bring your own Docker image and full runtime control
- GPU-enabled services with [autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments) and lifecycle management
- Multi-cloud and [Bring Your Own Cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) support
- [Git-based CI/CD](https://northflank.com/docs/v1/application/release/manage-ci-cd), [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), and full-stack deployment
- Secure runtime for untrusted AI workloads
- SOC 2 readiness and enterprise security (RBAC, SAML, audit logs)

**Pros:**

- **No platform lock-in** – full container control with BYOC or managed infrastructure
- **Transparent, predictable pricing** – [usage-based](https://northflank.com/pricing) and easy to forecast at scale
- **Great developer experience** – Git-based deploys, CI/CD, preview environments
- **Optimized for latency-sensitive workloads** – fast startup, GPU autoscaling, low-latency networking
- **Supports AI-specific workloads** – Ray, LLMs, Jupyter, fine-tuning, inference APIs
- **Built-in cost management** – real-time usage tracking, budget caps, and optimization tools

**Cons:**

- No special infrastructure tuning for model performance.

**Verdict:**

If you're building production-ready AI products, not just prototypes, Northflank gives you the flexibility to run full-stack apps and get access to affordable GPUs all in one place. With built-in CI/CD, GPU orchestration, and secure multi-cloud support, it's the most direct platform for teams needing both speed and control without vendor lock-in.

[**See how Cedana uses Northflank to deploy GPU-heavy workloads with secure microVMs and Kubernetes**](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes)

### 2. RunPod - The affordable option for raw GPU compute

[RunPod](https://www.runpod.io/) gives you raw access to GPU compute with full Docker control. Great for cost-sensitive teams running custom inference workloads.

![image - 2025-06-19T211020.974.png](https://assets.northflank.com/image_2025_06_19_T211020_974_7f97807c0a.png)

**Key features:**

- GPU server marketplace
- BYO Docker containers
- REST APIs and volumes
- Real-time and batch options

**Pros:**

- Lowest GPU cost per hour
- Full control of runtime
- Good for experiments or heavy inference

**Cons:**

- No CI/CD or Git integration
- Lacks frontend or full-stack support
- Manual infra setup required

**Verdict:**

Great if you want cheap GPU power and don’t mind handling infra yourself. Not plug-and-play.

*Curious about RunPod? Check out [this article](https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment#what-makes-runpod-stand-out-at-first) to learn more.*

### 3. Baseten – Model serving and UI demos without DevOps

[Baseten](https://www.baseten.co/) helps ML teams serve models as APIs quickly, focusing on ease of deployment and internal demo creation without deep DevOps overhead.

![image - 2025-06-25T171137.699.png](https://assets.northflank.com/image_2025_06_25_T171137_699_acea62b8ab.png)

**Key Features**:

- Python SDK and web UI for model deployment
- Autoscaling GPU-backed inference
- Model versioning, logging, and monitoring
- Integrated app builder for quick UI demos
- Native Hugging Face and PyTorch support

**Pros**:

- Very fast path from model to live API
- Built-in UI support is great for sharing results
- Intuitive interface for solo developers and small teams

**Cons**:

- Geared more toward internal tools and MVPs
- Less flexible for complex backends or full-stack services
- Limited support for multi-service orchestration or CI/CD

**Verdict**:

Baseten is a solid choice for lightweight model deployment and sharing, especially for early-stage teams or prototypes. For production-scale workflows involving more than just inference, like background jobs, databases, or containerized APIs, teams typically pair it with a platform like Northflank for broader infrastructure support.

*Curious about Baseten? Check out [this article](https://northflank.com/blog/baseten-alternatives-for-ai-ml-model-deployment#why-developers-choose-baseten) to learn more.*

### 4. AWS SageMaker - Enterprise MLOps on the AWS ecosystem

[SageMaker](https://aws.amazon.com/sagemaker/) is Amazon’s heavyweight MLOps platform, covering everything from training to deployment, pipelines, and monitoring.

![image - 2025-06-19T211024.050.png](https://assets.northflank.com/image_2025_06_19_T211024_050_82c4f323dd.png)

**Key features:**

- End-to-end ML lifecycle
- AutoML, tuning, and pipelines
- Deep AWS integration (IAM, VPC, etc.)
- Managed endpoints and batch jobs

**Pros:**

- Enterprise-grade compliance
- Mature ecosystem
- Powerful if you’re already on AWS

**Cons:**

- Complex to set up and manage
- Pricing can spiral
- Heavy DevOps lift

**Verdict:**

Ideal for large orgs with AWS infra and compliance needs. Overkill for smaller teams or solo devs.

### 5. Paperspace by DigitalOcean – Accessible cloud GPUs for individuals and small teams

[Paperspace](https://www.paperspace.com/) (acquired by DigitalOcean) aims to make cloud GPUs accessible for developers, educators, and startups. With Jupyter support, simple pricing, and a dev-friendly UI, it’s great for prototyping and experimentation.

![image - 2025-07-07T173147.860.png](https://assets.northflank.com/image_2025_07_07_T173147_860_a3723143a3.png)

**Key features:**

- Jupyter notebook support via Gradient
- Pre-configured ML environments
- VM instances with GPU support
- Integration with DigitalOcean services

**Pros:**

- Beginner-friendly UX and onboarding
- Easy to launch and manage GPU instances
- Affordable pricing and credits for education/startups

**Cons:**

- Not suited for complex, multi-service deployments
- Limited Git and CI/CD integrations
- May lack advanced GPU tuning or orchestration features

**Verdict:**

Paperspace is a great way to get started with cloud GPUs or build lightweight ML apps. For larger teams or production use, you'll likely need something more robust.

*Curious about Paperspace? Check out [this article](https://northflank.com/blog/digitalocean-gpu-paperspace-alternatives) to learn more.*

### 6. Anyscale – Best for scalable, distributed AI workloads with Ray

[Anyscale](https://www.anyscale.com/) is a platform built by the creators of Ray, designed to simplify running distributed AI workloads. It’s ideal for teams that need scalable training, tuning, or inference across clusters without managing infrastructure manually.

![anyscale-homepage.png](https://assets.northflank.com/anyscale_homepage_471414362b.png)

**Key features:**

- Native support for Ray-based workloads
- Auto-scaling and serverless infrastructure
- Job and service deployment via CLI and SDK
- Supports distributed training, inference, and tuning

**Pros:**

- Excellent for scaling Ray workloads
- Serverless and infra-light setup
- Good observability and job control

**Cons:**

- Ray-specific; General-purpose app support is limited unless your architecture fits Ray’s distributed model.
- Requires Ray knowledge for complex use cases

**Verdict:**

A great choice if you're already using Ray or building large-scale distributed AI systems. Not meant for full-stack app deployment, but excels at compute-heavy workloads with minimal infra overhead.

*Curious about Anyscale? Check out [this article](https://northflank.com/blog/anyscale-alternatives-for-ai-ml-model-deployment#why-teams-love-anyscale) to learn more.*

## Comparison table: Nebius vs. modern alternatives

By now, you’ve seen how different platforms approach AI and ML deployment—from raw GPU access to full-stack app support and Git-native workflows. But if you're still weighing your options, it helps to see everything side by side.

This table gives you a quick overview of how Nebius compares to the other platforms covered above, so you can map your priorities, whether it's cost control, orchestration, security, or developer experience, to the tool that actually fits your stack.

| Feature | **Nebius** | **Northflank** | **RunPod** | **Baseten** | **SageMaker** | **Paperspace** | **Anyscale** |
| --- | --- | --- | --- | --- | --- | --- | --- |
| **GPU Support** | Inference & Raw GPU access | Auto-scaling GPUs | Raw GPU access | Inference only | Full ML lifecycle | Jupyter, VMs | Ray-native compute |
| **Full-stack App Support** | Limited | Yes | No | No | Yes | No | No |
| **CI/CD and Git Workflows** | No | Yes | No | Limited | Yes | Limited | CLI / SDK |
| **Pricing Transparency** | Hourly rates | Usage-based, clear | Hourly rates | Tiered pricing | Complex | Clear, simple | Usage-based |
| **Bring Your Own Cloud** | Basic | AWS, GCP, Azure and more | Limited | No | AWS only | No | Optional |
| **Security & Compliance** | Basic | SOC readiness, VPC, RBAC | Minimal | Basic | Enterprise-grade | Basic | Limited |
| **Developer Experience** | Mixed | Streamlined, DevOps-ready | Manual setup | Simplified UI | Complex setup | Easy onboarding | Abstracted infra |

## Why Northflank is a production-grade alternative to Nebius

If you're reaching the limits of what Nebius can offer, especially around deployment control, orchestration, or multi-service support, Northflank is worth a serious look.

It’s designed for teams shipping real products, not just running isolated workloads. With built-in GPU orchestration, Git-based CI/CD, preview environments, secret management, and support for background jobs and frontend apps, it covers more of the stack out of the box. You can deploy to Northflank-managed infrastructure or bring your own cloud for more control over cost, compliance, and location.

Northflank also offers a secure runtime for untrusted code, fine-grained access controls, and other enterprise-ready features that make it well-suited for production use at scale.

If your team needs flexibility across services, predictable cost tracking, and infrastructure that can grow with your product, Northflank makes it easier to move fast without giving up control.

## Conclusion

Choosing the right platform depends on more than just raw compute or model hosting. As you’ve seen across the options above, the real difference comes down to how much control you have, how easy it is to manage full applications, and whether the platform can support your workflow as it grows.

If Nebius has worked so far, but you're running into limits around orchestration, CI/CD, or infrastructure flexibility, it might be time to explore alternatives. Northflank gives you a production-grade environment with built-in GPU support, Git-based deployment flows, and the ability to run full-stack apps in your own cloud or on managed infrastructure.

If you’re ready to try it out, you can [sign up for free](https://app.northflank.com/signup) or [book a quick demo](https://cal.com/team/northflank/northflank-demo) to see how it fits into your stack.]]>
  </content:encoded>
</item><item>
  <title>Top Lambda AI alternatives to consider for GPU workloads and full-stack apps</title>
  <link>https://northflank.com/blog/top-lambda-ai-alternatives</link>
  <pubDate>2025-07-07T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[Explore top Lambda AI alternatives like Northflank, RunPod, and CoreWeave for GPU compute, full-stack deployment, CI/CD, and cost-effective ML workflows—tailored to your team’s needs and scale.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/bentoml_alternatives_blog_post_7a71775ccc.png" alt="Top Lambda AI alternatives to consider for GPU workloads and full-stack apps" />Lambda makes it easy to train and deploy AI models on powerful GPUs with minimal setup, and that’s exactly why many startups, researchers, and organizations love it. But if you're exploring other platforms to compare GPU pricing, deploy full-stack apps, or run on your own infrastructure, there are several strong options depending on your needs. Platforms like [Northflank](https://northflank.com/) support full-stack workloads, including GPUs, APIs, backend, frontend, CI/CD, bring your own cloud, and more. This guide walks through the top Lambda AI alternatives, what they excel at, and how to choose the best one for your use case.

## TL;DR – Top Lambda AI alternatives

If you're short on time, here’s a snapshot of the top Lambda AI alternatives. Each tool has its strengths, but they solve different problems, and some are better suited for real-world production than others.

| Provider | Best for | Why it stands out |
| --- | --- | --- |
| [**Northflank**](https://northflank.com/) | Full-stack AI products: APIs, LLMs, GPUs, frontends, backends, databases, and secure infra | Production-grade platform for deploying AI apps — GPU orchestration, Git-based CI/CD, [Bring your own cloud](https://northflank.com/features/bring-your-own-cloud), secure runtime, multi-service support, preview environments, secret management, and enterprise-ready features. Great for teams with complex infrastructure needs. |
| [**RunPod**](https://www.runpod.io/) | Budget-friendly GPU compute for custom ML workloads | Low-cost, flexible GPU hosting with full Docker control. Perfect for DIY inference, model training, or LLM fine-tuning. Offers spot instances for even greater savings. |
| [**Vast.ai**](http://vast.ai/) | Cost-efficient AI compute with a wide range of hardware | Known for its **flexible pricing model**, [Vast.ai](http://vast.ai/) provides access to a **wide variety of GPUs** and **cloud configurations**. Ideal for cost-conscious users who need a mix of performance and flexibility. |
| [**Nebius**](https://nebius.com/) | Managed GPU compute for ML and AI | Nebius offers **easy-to-use managed GPU hosting** with **flexible scaling** and **high availability**. Great for teams who want to offload the complexity of cloud infrastructure while still getting GPU power for ML workflows. |
| [Paperspace by DigitalOcean](https://www.paperspace.com/) | Accessible GPU cloud for individuals, startups, and education | Combines **DigitalOcean’s developer-friendly experience** with **Paperspace’s GPU platform**. Offers Jupyter notebooks, Gradient (a low-code ML suite), and full VM access. Great for prototyping, learning, or deploying small to mid-scale ML applications. |
| [**CoreWeave**](https://www.coreweave.com/) | Enterprise-grade GPU cloud with specialized support | CoreWeave offers **enterprise-level GPU infrastructure** with powerful options for **AI, rendering, and high-performance workloads**. Known for its ability to scale at demand and its excellent customer support for AI-heavy enterprises. |

## What makes Lambda AI stand out?

If you've used Lambda AI before, you know it appeals to teams who want to avoid infrastructure headaches. Here's why many start with it:

- **1‑Click GPU Clusters**: Deploy powerful multi-node GPU clusters, including H100 and B200 instances, with a single click, making it easy to scale up training workflows without managing complex infrastructure.
- **Serverless Inference API**: Run models using Lambda’s serverless endpoints with simplified pricing and no need to manage backend infrastructure. It’s a cost-effective alternative to traditional hyperscalers for hosting and serving models.
- **Hardware Variety**: Offers a wide selection of cutting-edge GPUs (e.g., A100, H100, B200, and older options), giving users flexibility based on budget and performance needs.
- **Integrated Data Science Tools**: Includes tools for Jupyter notebooks, pre-configured deep learning environments, and collaboration features to streamline experimentation and development.
- **Managed & Self-Managed Options**: Choose between a fully managed experience or deploy Lambda’s software stack on your own hardware (on-prem or in other cloud environments), providing maximum flexibility for teams with specific infrastructure preferences.

## What are the limitations of Lambda AI?

We have just covered what makes Lambda AI a good choice for many teams. But like most tools, it is not perfect, especially for teams looking to deploy full-stack workloads or those seeking a platform with built-in Git and CI/CD integrations.

- **Limited Ecosystem Compared to Hyperscalers**: While Lambda excels at providing GPU power, it doesn't offer the extensive set of services and integrations you'd find with larger cloud providers like AWS, Google Cloud, or Azure. For example, you won’t find a wide range of cloud-native services like managed databases, object storage solutions, or real-time analytics.
- **Geographic Availability**: Lambda AI’s infrastructure is more limited in terms of [global data center locations](https://docs.lambda.ai/public-cloud/on-demand/#regions). If you're running workloads in regions outside of the U.S., you may face latency issues or lack region-specific compliance features compared to larger providers with a wider global footprint.
- **No Git-Connected Deployments**: Unlike platforms such as AWS, Azure, or Google Cloud, Lambda AI doesn’t natively support continuous integration/continuous deployment (CI/CD) workflows tied to version control systems like Git. This means you'll need to set up custom workflows or use external tools to handle deployments.
- **No Multi-Service Deployments**: Lambda AI is focused primarily on GPU instances for ML workloads. If your project requires deploying multiple interdependent services (e.g., backend APIs, data pipelines, and databases), Lambda AI may not offer the necessary orchestration tools to handle such complexity. You’ll need to rely on third-party tools for managing a multi-service architecture.
- **No Auto-Scaling or Scheduling**: Lambda AI lacks built-in auto-scaling features, which means you need to manually manage the scaling of GPU instances. Additionally, there is no native job scheduling or orchestration tool requiring you to handle workload management externally.
- **No Metrics, Logs, or Observability**: Lambda AI provides minimal built-in observability tools, such as metrics and logs. While you can integrate third-party monitoring tools, users familiar with more comprehensive cloud platforms may miss these out-of-the-box observability features.
- **No Secure Runtime for Untrusted Workloads**: Unlike some hyperscalers that offer secure enclaves or isolated runtimes for untrusted workloads, Lambda AI doesn’t provide these advanced security features, which may be a concern for sensitive applications.
- **No Bring Your Own Cloud (BYOC)**: Lambda AI doesn’t currently support the “Bring Your Own Cloud” (BYOC) model, which allows you to integrate with existing cloud accounts or hybrid setups. This limits flexibility for teams looking to mix Lambda AI with other cloud providers or on-premise infrastructure.

## What to look for in a Lambda AI alternative

Not every platform is built for the same kind of work. Some are great for cheap GPU access, others are built to run full AI products. Here's what to keep in mind when comparing Lambda AI alternatives:

### 1. Full-stack support

If you're shipping a product, not just training models, you’ll want something that can handle APIs, frontends, backends, and databases. Lambda focuses on GPU compute only. Platforms like Northflank make it easier to manage the full stack in one place.

### 2. GPU flexibility and pricing

Some platforms let you pick from a wide range of GPUs and offer better pricing for spot or community instances. If you're optimizing for budget, RunPod and Vast.ai give you more control over cost.

### 3. CI/CD and Git integration

If your team pushes code regularly, look for built-in CI/CD or Git-based deploys. These help automate releases and reduce the need for extra tooling. Northflank and Nebius support this out of the box.

### 4. Logs, metrics, and observability

When you're in production, you need visibility into how things are running. Lambda is fairly limited here. Northflank and CoreWeave offer better monitoring, metrics, and alerting without extra setup.

### 5. Bring Your Own Cloud

Some teams want to run everything inside their own cloud account for security or compliance. Lambda doesn’t support this model, but Northflank does, so you can deploy using your own AWS, GCP, or Azure account.

## Top Lambda AI alternatives

Below are the top Lambda AI alternatives available today. We'll examine each platform, covering its key features, advantages, and limitations.

### 1. Northflank – The best Lambda AI alternative for full-stack AI workloads

[**Northflank**](https://northflank.com/) isn’t just a model hosting or GPU renting tool; it’s a **production-grade platform for deploying and scaling full-stack AI products**. It combines the flexibility of containerized infrastructure with GPU orchestration, Git-based CI/CD, and full-stack app support.

Whether you're serving a fine-tuned LLM, hosting a Jupyter notebook, or deploying a full product with both frontend and backend, Northflank offers broad flexibility without many of the lock-in concerns seen on other platforms.

![image - 2025-06-19T211009.037.png](https://assets.northflank.com/image_2025_06_19_T211009_037_2419b18f99.png)

**Key features:**

- Bring your own Docker image and full runtime control
- GPU-enabled services with [autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments) and lifecycle management
- Multi-cloud and [Bring Your Own Cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) support
- [Git-based CI/CD](https://northflank.com/docs/v1/application/release/manage-ci-cd), [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), and full-stack deployment
- Secure runtime for untrusted AI workloads
- SOC 2 readiness and enterprise security (RBAC, SAML, audit logs)

**Pros:**

- **No platform lock-in** – full container control with BYOC or managed infrastructure
- **Transparent, predictable pricing** – [usage-based](https://northflank.com/pricing) and easy to forecast at scale
- **Great developer experience** – Git-based deploys, CI/CD, preview environments
- **Optimized for latency-sensitive workloads** – fast startup, GPU autoscaling, low-latency networking
- **Supports AI-specific workloads** – Ray, LLMs, Jupyter, fine-tuning, inference APIs
- **Built-in cost management** – real-time usage tracking, budget caps, and optimization tools

**Cons:**

- No special infrastructure tuning for model performance.

**Verdict:** 

If you're building production-ready AI products, not just prototypes, Northflank gives you the flexibility to run full-stack apps and get access to affordable GPUs all in one place. With built-in CI/CD, GPU orchestration, and secure multi-cloud support, it's the most direct platform for teams needing both speed and control without vendor lock-in.

[**See how Cedana uses Northflank to deploy GPU-heavy workloads with secure microVMs and Kubernetes**](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes)

### 2. RunPod - The affordable option for raw GPU compute

[RunPod](https://www.runpod.io/) gives you raw access to GPU compute with full Docker control. Great for cost-sensitive teams running custom inference workloads.

![image - 2025-06-19T211020.974.png](https://assets.northflank.com/image_2025_06_19_T211020_974_7f97807c0a.png)

**Key features:**

- GPU server marketplace
- BYO Docker containers
- REST APIs and volumes
- Real-time and batch options

**Pros:**

- Lowest GPU cost per hour
- Full control of runtime
- Good for experiments or heavy inference

**Cons:**

- No CI/CD or Git integration
- Lacks frontend or full-stack support
- Manual infra setup required

**Verdict:**

Great if you want cheap GPU power and don’t mind handling infra yourself. Not plug-and-play.

*Curious about RunPod? Check out [this article](https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment#what-makes-runpod-stand-out-at-first) to learn more.*

### 3. [Vast.ai](http://vast.ai/) – Flexible pricing and GPU choice for cost-conscious users

[Vast.ai](http://vast.ai/) offers a unique marketplace model for renting GPUs, letting users choose from a wide variety of hardware configurations at competitive prices. It’s ideal for those who prioritize cost savings and customization over ease of use.

![image - 2025-07-07T173143.908.png](https://assets.northflank.com/image_2025_07_07_T173143_908_0b5e9584fb.png)

**Key features:**

- GPU instance marketplace with transparent pricing
- Wide selection of GPU types and compute providers
- Full Docker environment support
- API access for automation

**Pros:**

- Very cost-efficient, especially with spot-like pricing
- Large selection of GPU models, vendors, and configurations
- Good for experienced ML teams who want control

**Cons:**

- UI and onboarding experience less polished than competitors
- No full-stack or CI/CD support
- Support and SLAs vary across providers

**Verdict:**

Great for cost optimization and flexibility if you know exactly what hardware you need. Best suited for ML engineers who can manage their own environments.

### 4. Nebius – Scalable managed GPU compute with strong availability

[Nebius](https://nebius.ai/) (from the creators of Yandex.Cloud) delivers a polished GPU hosting experience with enterprise features and managed infrastructure. It’s particularly useful for teams seeking reliable performance and less operational complexity.

![image - 2025-07-07T173146.309.png](https://assets.northflank.com/image_2025_07_07_T173146_309_e059d3c7f7.png)

**Key features:**

- Fully managed GPU hosting with predictable performance
- Flexible instance types and scaling
- Kubernetes support
- Access control, logging, and usage analytics

**Pros:**

- Easy setup with managed options
- Good observability (logs, metrics, monitoring)
- High availability and resilience built-in

**Cons:**

- Smaller ecosystem compared to hyperscalers
- Not tailored for full-stack app deployment
- Less developer-focused than alternatives like Northflank

**Verdict:**

If you need stable managed GPU infrastructure and don’t want to manage clusters, Nebius offers a reliable middle ground between raw GPU hosting and fully integrated platforms.

### 5. Paperspace by DigitalOcean – Accessible cloud GPUs for individuals and small teams

[Paperspace](https://www.paperspace.com/) (acquired by DigitalOcean) aims to make cloud GPUs accessible for developers, educators, and startups. With Jupyter support, simple pricing, and a dev-friendly UI, it’s great for prototyping and experimentation.

![image - 2025-07-07T173147.860.png](https://assets.northflank.com/image_2025_07_07_T173147_860_a3723143a3.png)

**Key features:**

- Jupyter notebook support via Gradient
- Pre-configured ML environments
- VM instances with GPU support
- Integration with DigitalOcean services

**Pros:**

- Beginner-friendly UX and onboarding
- Easy to launch and manage GPU instances
- Affordable pricing and credits for education/startups

**Cons:**

- Not suited for complex, multi-service deployments
- Limited Git and CI/CD integrations
- May lack advanced GPU tuning or orchestration features

**Verdict:**

Paperspace is a great way to get started with cloud GPUs or build lightweight ML apps. For larger teams or production use, you'll likely need something more robust.

*Curious about Paperspace? Check out [this article](https://northflank.com/blog/digitalocean-gpu-paperspace-alternatives) to learn more.*

### 6. CoreWeave – Industrial-strength GPU cloud for enterprise AI workloads

[CoreWeave](https://www.coreweave.com/) is a premium GPU cloud provider focused on enterprise AI, rendering, and HPC use cases. If your business requires massive scale, fast GPUs, and white-glove support, CoreWeave delivers.

![image - 2025-07-07T173150.302.png](https://assets.northflank.com/image_2025_07_07_T173150_302_dacbd03ead.png)

**Key features:**

- Access to high-end GPUs (H100, A100, etc.)
- Bare metal and container-based deployments
- SLAs, premium networking, and compliance options
- API access and Kubernetes-native support

**Pros:**

- Built for demanding workloads: inference, fine-tuning, RLHF
- Enterprise-grade performance and security
- Excellent support and customization options

**Cons:**

- Higher cost compared to budget platforms
- Less suitable for solo developers or early-stage startups
- Not focused on full-stack app deployment

**Verdict:**

If you're running enterprise AI at scale and need guaranteed performance, CoreWeave is one of the most capable GPU clouds available. It’s overkill for small projects but essential for high-throughput, mission-critical AI workloads.

## How to pick the best Lambda AI alternatives

When evaluating alternatives, consider the scope of your project, team size, infrastructure skills, and long-term needs:

| Question | Why It Matters |
| --- | --- |
| Are you building a full product or just training a model? | Platforms like **Northflank** offer end-to-end support for APIs, backends, and frontends. Others focus only on compute. |
| Do you want raw GPU access or managed services? | If you want control, **RunPod** or **Vast.ai** work well. For simplicity, look at **Northflank**, **Nebius**, **CoreWeave**, or **Paperspace**. |
| Do you need CI/CD, autoscaling, or Git integration? | These features make a big difference in production. **Northflank** leads here. |
| Is price your biggest concern? | **RunPod, Northflank**, and **Vast.ai** usually offer the best bang for your buck. |
| Do you need advanced security or compliance? | **CoreWeave** and **Northflank** are strongest for enterprise workloads. |

## Conclusion

If you only need access to GPU compute, platforms like RunPod, Vast.ai, and Paperspace are solid options. They're great for training models, running inference, or handling one-off workloads, especially if you're focused on cost or want full control of your environment.

For more managed infrastructure, Nebius and CoreWeave provide scalable GPU performance with stronger availability and support for enterprise workloads.

But if you're building an actual product with a backend, APIs, user-facing frontends, and secure infrastructure, **Northflank** is the most complete platform. It combines GPU orchestration with CI/CD, Git-based workflows, full-stack deployments, secure runtimes, and multi-cloud support.

Northflank is built for teams shipping AI into the real world, not just running experiments.

[**Sign up for free**](https://app.northflank.com/signup) to get started, or [**book a demo**](https://cal.com/team/northflank/northflank-demo) to see how it fits into your workflow.

## Frequently asked questions about Lambda AI alternatives

These common questions come up when teams are checking out Lambda AI and looking at broader deployment options.

**What is Lambda Labs?**

Lambda Labs is a cloud GPU provider offering high-performance machines (like A100 and H100) for training and deploying AI models. It’s popular among researchers, startups, and developers who want raw GPU access without the overhead of traditional cloud providers.

**What is the difference between Lambda Labs and Together AI?**

Lambda gives you infrastructure — GPUs you control. Together AI gives you hosted APIs for open-source models, so you don’t train or run anything yourself.

**Is Lambda Labs worth it?**

Yes, if you’re training models or fine-tuning LLMs and want a cost-effective, no-frills setup with fast GPUs.

**Is Lambda Labs costly?**

It’s cheaper than AWS or GCP, but more expensive than GPU spot marketplaces like Vast.ai. You pay for uptime, so idle instances can rack up cost.

**What is the difference between CoreWeave and Lambda Labs?**

CoreWeave offers large-scale orchestration and autoscaling for enterprises. Lambda focuses on manual, developer-friendly access to individual GPU machines.

**How does Lambda Labs work?**

You log in, spin up a GPU instance, connect via SSH or Jupyter, and train your models. You can also deploy models as serverless endpoints for inference.]]>
  </content:encoded>
</item><item>
  <title>6 best BentoML alternatives for self-hosted AI model deployment (2026)</title>
  <link>https://northflank.com/blog/bentoml-alternatives</link>
  <pubDate>2025-07-07T15:48:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for BentoML alternatives? We compare 6 platforms for GPU scaling, self-hosted model deployment, and full-stack AI workloads, including tools with BYOC, CI/CD, and secure multi-tenancy.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/bentoml_alternatives_d17116517a.png" alt="6 best BentoML alternatives for self-hosted AI model deployment (2026)" />BentoML is a widely used open-source tool for packaging and serving machine learning models. It works well for local development and setting up inference endpoints.

If you’re looking for alternatives to BentoML, say to add autoscaling, get more visibility into your workloads, or support things like APIs, databases, or background jobs, this guide covers several platforms that can help.

We’ll look at platforms like Northflank, Modal, RunPod, and KServe, tools that support a mix of AI and infrastructure needs. For example, Northflank supports both AI and non-AI workloads on one platform. You can deploy model trainers, inference jobs, Postgres, Redis, and schedulers side-by-side, with autoscaling, CI/CD, logs, metrics, and secure runtimes built in.

Let’s look at a breakdown of BentoML alternatives that fit different use cases, from GPU-backed model serving to full-stack deployment.

<InfoBox className='BodyStyle'>

### Quick comparison: BentoML alternatives for AI and infrastructure workloads

If you're looking into other options beyond BentoML, this section covers platforms that support model serving, training, and broader application needs:

1. [**Northflank**](https://northflank.com) – For teams that want to run AI models and full applications side by side on the same platform. Supports model serving, training jobs, APIs, background workers, and databases like Postgres and Redis. Built-in autoscaling, monitoring, and continuous delivery. You can also run GPU workloads in your own cloud using [Bring Your Own Cloud (BYOC)](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes).
2. [**Modal**](https://northflank.com/blog/6-best-modal-alternatives) – Designed for running Python functions and ML inference at scale. Autoscaling is handled for you, with minimal infrastructure setup required.
3. [**RunPod**](https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment) – Lets you run custom containers on GPU machines. Helpful for training and inference workloads, especially when you want access to spot instances or specific GPU types.
4. [**Anyscale**](https://northflank.com/blog/anyscale-alternatives-for-ai-ml-model-deployment) – Built around Ray for distributed compute. Useful for teams that are already using Ray to manage large-scale training jobs or data pipelines.
5. [**Baseten**](https://northflank.com/blog/baseten-alternatives-for-ai-ml-model-deployment) – Offers a low-code UI for deploying models, with autoscaling and basic observability tools included. Good for ML engineers who want to focus on iteration.
6. [**KServe**](https://www.kubeflow.org/docs/external-add-ons/kserve/) – An open-source model serving framework built for Kubernetes. Best suited for infrastructure-savvy teams that prefer OSS and want to run models in-cluster.

**[Deploy models, jobs, and full applications on a single platform](https://northflank.com)**

</InfoBox>

## What to look for in a BentoML alternative

BentoML is useful for serving models, but if you're working toward production or managing multiple services, it helps to step back and ask what else your team might need.

1. **Do you need to serve models only, or are you also training them regularly?**
    
    Some platforms focus on inference, while others let you run full training pipelines, manage datasets, and schedule recurring jobs all in one place.
    
2. **Are you building a single endpoint, or do you want to run supporting services like APIs, schedulers, Redis, or Postgres alongside your model?**
    
    If your application depends on other services, it helps to deploy everything together with shared monitoring, networking, and deployment flows.
    
3. **Do you need autoscaling and monitoring that work outside the BentoML runtime?**
    
    In production, you’ll likely want infrastructure-aware metrics, logs, and autoscaling that adapt to actual usage, not limited to what BentoML provides by default.
    
4. **Is multi-cloud or BYOC (Bring your own cloud) GPU flexibility important to your team?**
    
    Some teams want full control over cloud costs and GPU usage. Bring Your Own Cloud (BYOC) setups let you run on your own infrastructure without giving up developer experience.
    
5. **Do you want to build an internal AI platform, not only an endpoint?**
    
    For teams building long-term ML infrastructure, it's useful to have secure runtimes, RBAC, CI/CD, and the ability to scale across multiple apps and teams.
    

*Next, we’ll look at 6 BentoML alternatives that support some or all of these needs in their own way.*

## 6 best BentoML alternatives for AI/ML model deployment

If you’re looking for a platform that handles more than inference alone, these six alternatives give you different ways to deploy, scale, and manage your AI and ML workloads.

So, choose the best based on your team’s goals, infrastructure setup, and how much flexibility you want in production.

### 1. Northflank – Run your AI models and full applications in one place with autoscaling and support for your own GPUs

If you’re looking for something that supports both model serving and broader application infrastructure, [Northflank](https://northflank.com/) brings it together in one platform. You can deploy your AI workloads alongside your APIs, databases, background jobs, and more, all with built-in autoscaling and monitoring.

![new-northflank-ai-home-page.png](https://assets.northflank.com/new_northflank_ai_home_page_25751c7697.png)

**See some of what you can do with Northflank**:

- Run AI/ML jobs (training, inference) with attached [GPUs](https://northflank.com/gpu)
- Deploy custom Docker images (including Jupyter notebooks, APIs, or background jobs)
- Deploy APIs, workers, [Postgres](https://northflank.com/docs/v1/application/databases-and-persistence/deploy-databases-on-northflank/deploy-postgresql-on-northflank), and [Redis](https://northflank.com/docs/v1/application/databases-and-persistence/deploy-databases-on-northflank/deploy-redis-on-northflank) side-by-side
- Integrate with your existing ML workflows using CI/CD pipelines and custom Docker builds
- [Autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments) for jobs and services
- Built-in [logs](https://northflank.com/docs/v1/application/observe/view-logs), [metrics](https://northflank.com/docs/v1/application/observe/view-metrics), [RBAC](https://northflank.com/docs/v1/application/secure/use-role-based-access-control), and [CI/CD pipelines](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank)
- Run on your own cloud with [BYOC](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) [GPU support](https://www.notion.so/6-best-BentoML-alternatives-for-self-hosted-AI-model-deployment-2025-2296d14c785180979b2ef6efd3a2b559?pvs=21) and fast provisioning

**Pricing highlights:**

- Free plan available for testing and small projects
- Pay-as-you-go with no monthly commitment
- Enterprise pricing available for larger teams and advanced setups

(See full [pricing details](https://northflank.com/pricing))

> Go with Northflank if you want one platform to run both your AI models and full applications, with autoscaling, built-in observability, and support for your own GPUs.
> 

<InfoBox className='BodyStyle'>

💡**See how teams use Northflank in production**:

[**How Cedana deploys GPU-heavy workloads with secure microVMs and Kubernetes**](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes)

*Cedana runs live-migration and snapshot/restore of GPU jobs, using Northflank’s secure runtimes on Kubernetes*

</InfoBox>

### 2. Modal – Python-native model inference with GPU scaling

Modal is built around running Python functions in the cloud, making it easy to serve models or run inference without managing infrastructure. It’s suited for developers who want to write minimal code and quickly scale compute as needed.

![modal-homepage.png](https://assets.northflank.com/modal_homepage_a7380e6d35.png)

**See what you can do with Modal**:

- Run inference functions with GPU support
- Define logic using Python decorators and functions
- Autoscaling handled behind the scenes
- Ideal for short-lived or stateless tasks

**Pricing**: Free tier available. Paid plans are based on compute time and storage usage.

> Go with Modal if you want a Python-first way to deploy and scale inference jobs, with minimal infrastructure setup.
> 

*If you're comparing platforms, you might also want to check out the [6 best Modal alternatives for ML, LLMs, and AI app deployment](https://northflank.com/blog/6-best-modal-alternatives).*

### 3. RunPod – Containerized GPU workloads on demand

RunPod makes it easy to spin up GPU-backed containers for AI training or inference. You can choose from public, secure, or private nodes and run your own Docker containers with access to GPUs.

![runpod-homepage.png](https://assets.northflank.com/runpod_homepage_4d5612b292.png)

**See what you can do with RunPod**:

- Launch GPU containers for training or inference
- Use public nodes or bring your own secure pods
- Run Jupyter notebooks or custom Docker images
- Integrate with your existing ML workflows

**Pricing**: Pay-as-you-go based on GPU type and runtime. No fixed monthly fees.

> Choose RunPod if you want fast, cost-flexible access to GPU containers for ML workloads with minimal setup.
> 

*If you're looking at other platforms that go beyond containerized GPU workloads, [see these RunPod alternatives for AI/ML deployment](https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment).*

### 4. Anyscale – Distributed compute with Ray and managed clusters

If your team is already using Ray or building distributed applications, Anyscale gives you a managed environment to run workloads at scale. It's designed for tasks that benefit from distributed parallelism, like model training, batch jobs, or hyperparameter tuning.

![anyscale-homepage.png](https://assets.northflank.com/anyscale_homepage_471414362b.png)

**What you can do with Anyscale**:

- Launch Ray clusters on AWS in a managed environment
- Run distributed ML workloads and scale out with autoscaling
- Use Ray Serve for model inference and microservice APIs
- Collaborate across users and teams with shared workspaces
- Monitor and track experiments with Ray dashboards

**Pricing**: Free Developer tier available. Paid plans include usage-based billing for compute and cluster management.

> Go with Anyscale if you’re building distributed ML pipelines with Ray and want managed infrastructure built around that ecosystem.
> 

*If you're comparing platforms for distributed model training or inference, [check out these Anyscale alternatives for AI/ML deployment](https://northflank.com/blog/anyscale-alternatives-for-ai-ml-model-deployment).*

### 5. Baseten – Model serving with pre-built templates and observability

Baseten focuses on helping teams deploy and serve ML models quickly using a web-based UI and built-in observability. It’s useful if you’re working with popular open-source models and want minimal infrastructure setup.

![baseten-homepage.png](https://assets.northflank.com/baseten_homepage_a661fb5305.png)

**See what you can do with Baseten**:

- Deploy models from Hugging Face or your own training pipeline
- Use pre-built templates for models like Llama and Whisper
- Built-in monitoring and performance metrics
- Simple interface for deploying REST endpoints

**Pricing**: Free tier available. Paid plans are usage-based, with limits on requests and concurrency.

> Go with Baseten if you want a low-code way to deploy open-source models, with built-in monitoring and templates for fast iteration.
> 

*If you’re comparing Baseten with other tools in this space, [check out these Baseten alternatives for AI/ML model deployment](https://northflank.com/blog/baseten-alternatives-for-ai-ml-model-deployment).*

### 6. KServe (Kubeflow) –OSS serving for teams with infrastructure experience

KServe is an open-source Kubernetes-based model serving tool maintained by the Kubeflow project. It’s best suited for teams that already have Kubernetes expertise and want full control over how models are deployed, scaled, and versioned in production.

![kserve-homepage.png](https://assets.northflank.com/kserve_homepage_4a9ad5c082.png)

**See what you can do with KServe**:

- Serve models using frameworks like TensorFlow, PyTorch, XGBoost, and ONNX
- Manage prediction routing, model canarying, and rollout strategies
- Scale with Kubernetes-native autoscaling and inference graphs
- Deploy multiple model versions with traffic splitting

**Pricing**: Free and open source. You’ll need to run and maintain it yourself on your own Kubernetes infrastructure.

> Go with KServe if you want OSS model serving and already have the Kubernetes knowledge to operate it.
> 

## Comparison table: BentoML vs. modern alternatives

After going through the top alternatives, below is a side-by-side comparison to help you assess how each platform performs in key areas like model serving, GPU support, autoscaling, and broader application deployment.

This table gives you the context you need to bring options back to your team, particularly if you're thinking beyond basic inference.

| Platform | Model serving | GPU scaling | Autoscaling | Deploy non-AI apps | Monitoring & logs | CI/CD integration |
| --- | --- | --- | --- | --- | --- | --- |
| [**Northflank**](https://northflank.com) | Supports both training and inference with custom Docker images or prebuilt templates | Built-in support for attached GPUs and BYOC GPU provisioning | Native autoscaling for both jobs and services | Supports full applications like your APIs, databases, workers, schedulers | Built-in logs and metrics with dashboard access | Built-in CI/CD pipelines with Git-based triggers |
| [**BentoML**](https://www.bentoml.com/) | Inference-focused, container-based model serving | Requires manual setup or third-party tools | Manual configuration only | Limited to model endpoints; no support for broader app deployment | Basic logs, limited observability | No built-in CI/CD support |
| [**Modal**](https://northflank.com/blog/6-best-modal-alternatives) | Python-first inference with minimal setup | GPU support handled internally, no custom provisioning | Autoscaling for functions is abstracted and automatic | Focused on single-function inference workloads | Basic monitoring through internal interface | No native CI/CD, minimal deployment controls |
| [**RunPod**](https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment) | Containerized model serving for training or inference | GPU scaling available per workload, with templates | Limited autoscaling, manual scaling may be required | Basic container support; not designed for full application stacks | Requires external setup for full observability | No built-in CI/CD |
| [**Anyscale**](https://northflank.com/blog/anyscale-alternatives-for-ai-ml-model-deployment) | Distributed model serving and training via Ray | GPU scaling supported within Ray workloads | Limited autoscaling, needs manual Ray setup | Not intended for full app deployment | Ray dashboard provides some observability; limited | CI/CD integration not built in |
| [**Baseten**](https://northflank.com/blog/baseten-alternatives-for-ai-ml-model-deployment) | GUI-based model deployment with prebuilt templates | GPU support available during deployment | Autoscaling support for deployed models | Not designed for deploying broader services or APIs | Built-in observability tools specific to model performance | No full CI/CD, deployment happens via GUI |
| [**KServe**](https://www.kubeflow.org/docs/external-add-ons/kserve/) | Open-source serving for multiple model types | GPU support available with proper configuration | Can autoscale via Kubernetes, but setup is manual | Requires custom YAMLs for services outside ML models | Requires external observability stack like Prometheus/Grafana | CI/CD setup is external and user-managed |

## Why Northflank is a production-grade alternative to BentoML

BentoML is focused on model serving, but when your team needs to go beyond inference, like running APIs, databases, or background jobs alongside your models, that’s where [Northflank](https://northflank.com/) stands out.

Northflank gives you a single platform where you can deploy both your AI workloads and any other workloads, so you’re not restricted to serving models alone. You also get built-in autoscaling, monitoring, and production-grade infrastructure controls.

**What makes Northflank a complete alternative**:

- Deploy models, APIs, Redis, Postgres, and workers in one place
- Run GPU-powered jobs for inference, fine-tuning, and batch processing
- Autoscaling is built-in, no need for manual configuration
- Built-in monitoring, logs, cost tracking, RBAC, and GitOps pipelines
- Supports both AI/ML and traditional web workloads together
- SOC 2-aligned: deploy to private clusters, with secure runtimes and audit logs

> Northflank isn’t limited to inference. You can run your entire AI stack and full application infrastructure together with autoscaling, monitoring, and BYOC GPU support built in.
> 

<InfoBox className='BodyStyle'>

[See how to deploy AI workloads and full apps on Northflank](https://app.northflank.com/signup)

</InfoBox>


## Frequently asked questions about BentoML alternatives

These common questions come up when teams are checking out BentoML and looking at broader deployment options.

**1. What is BentoML used for?**

BentoML is a framework for packaging and serving machine learning models. It helps developers deploy models as REST or gRPC services with Python-friendly tooling.

**2. Is BentoML good?**

Yes, it’s well-suited for teams that want a lightweight, Python-native way to serve models locally or in simple cloud setups. For larger-scale deployments or teams that need autoscaling, monitoring, and full application infrastructure, platforms like [Northflank](https://northflank.com/) are often used.

**3. Is BentoML open-source?**

Yes, BentoML is open-source under the Apache 2.0 license. You can self-host it or use it as part of a larger MLOps toolchain.

**4. What are the advantages of BentoML?**

It’s Python-first, easy to get started with, and integrates well with many ML frameworks. That said, it focuses mainly on model serving. Teams needing to manage full application workloads or deploy in production environments often look to tools like [Northflank](https://northflank.com/) for additional infrastructure control.

**5. What are machine learning ops?**

Machine learning operations (MLOps) is the practice of managing the lifecycle of ML models, from training and deployment to monitoring and retraining. BentoML covers serving, but broader MLOps may involve tools for orchestration, pipelines, observability, and compliance.

## Choosing a BentoML alternative that fits your deployment goals

BentoML is great if you want a Python-based model server that keeps things simple. However, once you need autoscaling, full observability, or the ability to deploy APIs, jobs, and databases alongside your models, you’ll need more than a model server.

That’s where platforms like [Northflank](https://northflank.com/) come in. You can run both AI workloads and full applications on a unified platform, with built-in CI/CD, logs, metrics, and autoscaling, plus support for GPUs and BYOC when needed.

<InfoBox className='BodyStyle'>

[See how to deploy AI workloads and full apps on Northflank](https://app.northflank.com/signup)

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>6 best Replicate alternatives for ML, LLMs, and AI app deployment</title>
  <link>https://northflank.com/blog/6-best-replicate-alternatives</link>
  <pubDate>2025-07-03T17:00:00.000Z</pubDate>
  <description>
    <![CDATA[Replicate makes AI deployment easy, but it lacks scalability. This guide explores the best Replicate alternatives with better infrastructure, GPU support, and CI/CD tools.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/paas_providers_4_c6597fb7f9.png" alt="6 best Replicate alternatives for ML, LLMs, and AI app deployment" />Replicate makes it easy to deploy and run AI models through a simple API, which works well for many teams and use cases. If you're looking at other platforms to compare pricing, deploy full-stack apps, or run on your own infrastructure, there are several options depending on what you need. Platforms like Northflank support broader workloads, including background jobs, APIs, and full control over scaling. This guide highlights some of the top alternatives to Replicate, what they’re best at, and how they might fit into your workflow.

## TL;DR – Top Replicate alternatives

If you're short on time, here’s a snapshot of the top Replicate alternatives. Each tool has its strengths, but they solve different problems, and some are better suited for real-world production than others.

| Platform | Best For | **Why It Stands Out** |
| --- | --- | --- |
| [**Northflank**](https://northflank.com/) | **F**ull-stack AI products: APIs, LLMs, GPUs, frontends, backends, databases, and secure infra | Production-grade platform for deploying AI apps — GPU orchestration, Git-based CI/CD, [Bring your own cloud](https://northflank.com/features/bring-your-own-cloud), secure runtime, multi-service support, preview environments, secret management, and enterprise-ready features |
| **RunPod** | Budget-friendly GPU compute for custom ML workloads | Offers low-cost, flexible GPU hosting with full Docker control — great for DIY inference or LLM fine-tuning |
| **Baseten** | Model API deployment | Great for deploying ML models as APIs with built-in UI builder, logging, and monitoring for quick internal apps |
| **AWS SageMaker** | Enterprise-grade MLOps with AWS integration | Comprehensive ML lifecycle management on AWS — pipelines, model registry, security, and VPC support for large-scale teams |
| **AnyScale** | Scalable Python apps with Ray | Distributed compute, Ray integration |
| **Hugging** | Custom model routing and orchestration | Powerful for advanced routing logic, but requires more infra management |

## What makes Replicate stand out?

If you've used Replicate before, you know it appeals to developers who want to avoid infrastructure headaches. Here's why many start with it:

- **Serverless deployment**: No servers to manage. You call the model via an API, and it just works.
- **Built-in model hub**: Offers a wide variety of open-source models, including Stable Diffusion, Whisper, and LLaMA.
- **Pay-per-inference**: You pay only for the time your model runs.
- **Simple developer experience**: With Cog packaging and REST APIs, it's easy to integrate into apps.
- **Community-powered models**: Developers can share, fork, and remix models in the public registry.

Replicate is a great tool when you want to ship fast and skip the infrastructure rabbit hole. It’s built for developers who want power without complexity. Simple, sharp, and gets out of your way.

## What are the limitations of Replicate?

We just covered what makes Replicate feel smooth and powerful, especially when you're starting out. But like most tools built around simplicity, there's a point where the cracks begin to show. Sometimes it's a missing feature that slows you down. Other times, it's a hard limit that forces you to reconsider your stack. These limitations might not hit immediately. But if you're working on something beyond a quick ML experiment or solo project, you'll likely encounter one or more of the following issues.

- **Inference-only by design**
    
    Replicate is built solely for running inference. If you want to train models or fine-tune them in the same environment, you'll need a different platform or custom workflow.
    
- **No infrastructure control**
    
    You don’t get to choose your compute instance, or configure autoscaling. Everything runs on Replicate’s managed infrastructure with fixed settings, which limits optimization for speed, cost, or memory.
    
- **Opaque pricing at scale**
    
    Replicate charges per second of inference time, which seems simple — until you're making thousands of calls or running heavy models. There’s limited visibility into how compute time translates to cost, making it hard to predict or optimize expenses.
    
- **Model packaging overhead (Cog)**
    
    To deploy your own model, it must be wrapped using [Cog](https://github.com/replicate/cog), Replicate’s custom packaging tool. This adds a step and learning curve, especially if you're coming from a more traditional Docker-based setup.
    
- **No native CI/CD or automation hooks**
    
    There’s no built-in support for continuous deployment, Git-based triggers, or dev/preview environments. Any automation needs to be wired up manually using external tools like GitHub Actions.
    
- **Limited observability and performance tuning**
    
    You get basic logs and outputs, but no fine-grained monitoring, latency tracking, or advanced metrics to help tune model performance or debug production issues.
    
- **Limited networking and isolation controls**
    
    Replicate doesn't support custom VPCs, private endpoints, or service-to-service networking. If you're building internal tools, need access control between services, or require tighter network boundaries, this can be a blocker. It's especially relevant in regulated or enterprise environments where network-level security is essential.
    
- **You can’t bring your own cloud**
    
    Replicate runs entirely on its managed infrastructure. There’s no option to deploy it on your own AWS or GCP account, which limits flexibility and control over cost, region, and compliance.
    

## What to look for in a Replicate alternative

If you’re considering moving away from Replicate, chances are that something has started to feel limiting. Perhaps you've hit a wall with orchestration, need more infrastructure control, or want tighter integration across your entire stack.

Whatever the reason, choosing a new platform isn’t just about feature checklists; it’s about finding a better long-term fit for how you build and scale.

Here are the most important things to consider when evaluating Replicate alternatives:

- **Can it support full application stacks?**
    
    Replicate is great for standalone inference, but if you're building a full product — frontend, backend, queues, schedulers, databases — you’ll want a platform that lets you deploy and connect all of it in one place.
    
- **Does it support Git-based CI/CD?**
    
    Native Git integration, automated deployments, and preview environments make collaboration smoother and reduce time spent wiring up pipelines manually. By default, platforms like **Northflank** are built with Git-based CI/CD at their core.
    
- **How strong is its GPU and compute support?**
    
    Look for platforms with flexible GPU provisioning, queue-based scheduling, autoscaling, and fair usage pricing. Bonus if they support spot GPUs or let you reserve capacity.
    
- **What networking and security features are built in?**
    
    If you're going to production or handling sensitive data, you’ll want private networking (VPCs), service-to-service auth, custom domains, and granular access control. Many platforms skip this entirely, and that’s fine, until it isn’t.
    
- **Can you bring your own cloud?**
    
    Some platforms let you deploy into your own AWS, GCP, or Azure account. This gives you control over regions, security policies, cost visibility, and compliance, without giving up ease of use. By default, platforms like Northflank let you bring your own cloud from the beginning with a fully self-serve setup and no need to talk to sales.
    
- **How transparent is cost and usage tracking?**
    
    Pricing should be predictable, with real-time usage dashboards. If you can’t tell what’s costing what, that’s a red flag — especially when using GPUs or high-volume APIs.
    
- **Is it flexible enough to grow with your product?**
    
    Avoid rigid platforms that lock you into one runtime, deployment model, or API structure. The best tools adapt as your architecture evolves — not the other way around.
    

## Top Replicate alternatives

Below are the top Replicate alternatives available today. We'll examine each platform, covering its key features, advantages, and limitations.

### 1. Northflank – The best Replicate alternative for full-stack AI workloads

[**Northflank**](https://northflank.com/) isn’t just a model hosting or GPU renting tool; it’s a **production-grade platform for deploying and scaling full-stack AI products**. It combines the flexibility of containerized infrastructure with GPU orchestration, Git-based CI/CD, and full-stack app support.

Whether you're serving a fine-tuned LLM, hosting a Jupyter notebook, or deploying a full product with both frontend and backend, Northflank offers broad flexibility without many of the lock-in concerns seen on other platforms.

![image - 2025-06-19T211009.037.png](https://assets.northflank.com/image_2025_06_19_T211009_037_2419b18f99.png)

**Key features:**

- Bring your own Docker image and full runtime control
- GPU-enabled services with [autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments) and lifecycle management
- Multi-cloud and [Bring Your Own Cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) support
- [Git-based CI/CD](https://northflank.com/docs/v1/application/release/manage-ci-cd), [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), and full-stack deployment
- Secure runtime for untrusted AI workloads
- SOC 2 readiness and enterprise security (RBAC, SAML, audit logs)

**Pros:**

- **No platform lock-in** – full container control with BYOC or managed infrastructure
- **Transparent, predictable pricing** – [usage-based](https://northflank.com/pricing) and easy to forecast at scale
- **Great developer experience** – Git-based deploys, CI/CD, preview environments
- **Optimized for latency-sensitive workloads** – fast startup, GPU autoscaling, low-latency networking
- **Supports AI-specific workloads** – Ray, LLMs, Jupyter, fine-tuning, inference APIs
- **Built-in cost management** – real-time usage tracking, budget caps, and optimization tools

**Cons:**

- No special infrastructure tuning for model performance.

**Verdict:** If you're building production-ready AI products, not just prototypes, Northflank gives you the flexibility to run full-stack apps and get access to affordable GPUs all in one place. With built-in CI/CD, GPU orchestration, and secure multi-cloud support, it's the most direct platform for teams needing both speed and control without vendor lock-in.

*See how [Weights uses Northflank to build a GPU-optimized AI platform for millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### 2. RunPod - The affordable option for raw GPU compute

RunPod gives you raw access to GPU compute with full Docker control. Great for cost-sensitive teams running custom inference workloads.

![image - 2025-06-19T211020.974.png](https://assets.northflank.com/image_2025_06_19_T211020_974_7f97807c0a.png)

**Key features:**

- GPU server marketplace
- BYO Docker containers
- REST APIs and volumes
- Real-time and batch options

**Pros:**

- Lowest GPU cost per hour
- Full control of runtime
- Good for experiments or heavy inference

**Cons:**

- No CI/CD or Git integration
- Lacks frontend or full-stack support
- Manual infra setup required

**Verdict:**

Great if you want cheap GPU power and don’t mind handling infra yourself. Not plug-and-play.

*Curious about RunPod? Check out [this article](https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment#what-makes-runpod-stand-out-at-first) to learn more.*

### 3. Baseten – Model serving and UI demos without DevOps

Baseten helps ML teams serve models as APIs quickly, focusing on ease of deployment and internal demo creation without deep DevOps overhead.

![image - 2025-06-25T171137.699.png](https://assets.northflank.com/image_2025_06_25_T171137_699_acea62b8ab.png)

**Key Features**:

- Python SDK and web UI for model deployment
- Autoscaling GPU-backed inference
- Model versioning, logging, and monitoring
- Integrated app builder for quick UI demos
- Native Hugging Face and PyTorch support

**Pros**:

- Very fast path from model to live API
- Built-in UI support is great for sharing results
- Intuitive interface for solo developers and small teams

**Cons**:

- Geared more toward internal tools and MVPs
- Less flexible for complex backends or full-stack services
- Limited support for multi-service orchestration or CI/CD

**Verdict**:

Baseten is a solid choice for lightweight model deployment and sharing, especially for early-stage teams or prototypes. For production-scale workflows involving more than just inference, like background jobs, databases, or containerized APIs, teams typically pair it with a platform like Northflank for broader infrastructure support.

*Curious about Baseten? Check out [this article](https://northflank.com/blog/baseten-alternatives-for-ai-ml-model-deployment#why-developers-choose-baseten) to learn more.*

### 4. AWS SageMaker - Enterprise MLOps on the AWS ecosystem

SageMaker is Amazon’s heavyweight MLOps platform, covering everything from training to deployment, pipelines, and monitoring.

![image - 2025-06-19T211024.050.png](https://assets.northflank.com/image_2025_06_19_T211024_050_82c4f323dd.png)

**Key features:**

- End-to-end ML lifecycle
- AutoML, tuning, and pipelines
- Deep AWS integration (IAM, VPC, etc.)
- Managed endpoints and batch jobs

**Pros:**

- Enterprise-grade compliance
- Mature ecosystem
- Powerful if you’re already on AWS

**Cons:**

- Complex to set up and manage
- Pricing can spiral
- Heavy DevOps lift

**Verdict:**

Ideal for large orgs with AWS infra and compliance needs. Overkill for smaller teams or solo devs.

### 5. Anyscale – Best for scalable, distributed AI workloads with Ray

Anyscale is a platform built by the creators of Ray, designed to simplify running distributed AI workloads. It’s ideal for teams that need scalable training, tuning, or inference across clusters without managing infrastructure manually.

![anyscale-homepage.png](https://assets.northflank.com/anyscale_homepage_471414362b.png)

**Key features:**

- Native support for Ray-based workloads
- Auto-scaling and serverless infrastructure
- Job and service deployment via CLI and SDK
- Supports distributed training, inference, and tuning

**Pros:**

- Excellent for scaling Ray workloads
- Serverless and infra-light setup
- Good observability and job control

**Cons:**

- Ray-specific; General-purpose app support is limited unless your architecture fits Ray’s distributed model.
- Requires Ray knowledge for complex use cases

**Verdict:**

A great choice if you're already using Ray or building large-scale distributed AI systems. Not meant for full-stack app deployment, but excels at compute-heavy workloads with minimal infra overhead.

*Curious about Anyscale? Check out [this article](https://northflank.com/blog/anyscale-alternatives-for-ai-ml-model-deployment#why-teams-love-anyscale) to learn more.*

### 6. Hugging Face - The go-to hub for open-source models and quick prototyping

Hugging Face is the industry’s leading hub for open-source machine learning models, especially in NLP. It offers tools for accessing, training, and lightly deploying transformer-based models.

![image - 2025-06-25T171142.718.png](https://assets.northflank.com/image_2025_06_25_T171142_718_7d54da0df4.png)

**Key Features**:

- Model Hub with 500k+ open-source models
- Inference Endpoints (managed or self-hosted)
- AutoTrain for low-code fine-tuning
- Spaces for demos using Gradio or Streamlit
- Popular `transformer` Python library

**Pros**:

- Best open-source model access and community
- Excellent for experimentation and fine-tuning
- Seamless integration with most ML frameworks

**Cons**:

- Deployment and production support is limited
- Infrastructure often needs to be supplemented (e.g., for autoscaling or CI/CD)
- Not designed for tightly coupled workflows or microservice architectures

**Verdict**:

Hugging Face is a powerhouse for research and prototyping, especially when working with transformers. But when it comes to robust deployment pipelines and full-stack application delivery, it’s often used alongside a platform like Northflank to fill the operational gaps.

## How to pick the best Replicate alternatives

Are you unsure which platform best suits your needs? Here’s a quick guide to the best Replicate alternatives based on what you’re building.

| **Use Case** | **Best Alternative** | **Why It Fits** |
| --- | --- | --- |
| Building a fullstack AI product (frontend, backend, APIs, models) | **Northflank** | Full-stack support, GPU orchestration, CI/CD, secure infra, and no vendor lock-in. Ideal for shipping production-ready AI products fast. |
| Deploying a quick AI/ML prototype or public-facing model demo | **Replicate** | Easiest way to host and share models with an instant REST API. Great for LLMs, diffusion models, and solo projects. |
| Running GPU-heavy workloads on a budget | **RunPod** | Lowest GPU costs with full Docker/runtime control. Perfect for cost-sensitive custom ML training or inference. |
| Turning models or notebooks into internal tools or dashboards | **Baseten** | Built-in UI builder, model autoscaling, and monitoring. Great for MVPs or internal demos without DevOps overhead. |
| Scaling Ray-based training, tuning, or distributed workloads | **Anyscale** | Native support for Ray and distributed compute. Ideal for large-scale parallel workloads or dynamic compute graphs. |
| Operating in a regulated or enterprise-grade environment | **SageMaker** | Enterprise MLOps with IAM, pipelines, VPC, and AWS-native integrations. Suited for teams with compliance and infra constraints. |
| Hosting, fine-tuning, or experimenting with open-source transformer models | **Hugging Face** | Best-in-class model hub and open tooling for NLP and CV. Great for prototyping, research, and open-source collaboration. |

## Conclusion

Replicate made AI deployment dramatically easier. But as your project grows, whether in complexity, scale, or team size, you might find yourself needing more control over infrastructure, deeper integration with your stack, or more predictable performance at scale.

Platforms like RunPod offer raw power and flexible GPU management. Baseten and Hugging Face are great for fast iteration and model hosting. The right choice depends on your direction and the level of control you need along the way.

If you’re looking for a platform that combines developer speed with production-grade flexibility, Northflank stands out.

With full-stack support, GPU orchestration, Git-based CI/CD, and secure deployment options including [Bring Your Own Cloud](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes), Northflank makes it easy to go from prototype to production without rebuilding your stack.

You can [try Northflank for free](https://app.northflank.com/signup) and deploy your first full-stack AI app in minutes, or [book a demo](https://cal.com/team/northflank/northflank-demo) to explore how it fits your team’s needs at scale.]]>
  </content:encoded>
</item><item>
  <title>Best alternatives to Neon and PlanetScale for PostgreSQL hosting (2026)</title>
  <link>https://northflank.com/blog/neon-planetscale-postgres-alternatives</link>
  <pubDate>2025-07-03T16:15:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for a reliable PostgreSQL alternative to Neon or PlanetScale? This 2026 guide compares uptime, VPC deployment, and managed hosting options, including why Northflank might be the better fit.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/neon_planetscale_postgres_alternatives_a3d57aad10.png" alt="Best alternatives to Neon and PlanetScale for PostgreSQL hosting (2026)" />Neon was recently acquired by Databricks, a move that signals how important PostgreSQL has become for modern applications and AI infrastructure. The day after the acquisition was announced, Neon experienced another outage, raising questions among teams about production readiness and long-term reliability.

At the same time, PlanetScale, known for its managed MySQL platform, introduced support for PostgreSQL. It’s a natural step as more developers adopt Postgres for its compatibility, extensibility, and proven track record across SaaS, analytics, and AI workloads.

However, both platforms are focused on databases alone. And in real-world production environments, developers need more than a database. You often need to deploy applications, connect caches, run background jobs, expose APIs, and manage all of these tasks together as part of a single workflow.

PostgreSQL remains a go-to choice for teams that want predictable performance and flexibility, regardless of if they’re running locally in Docker or scaling across Kubernetes. This article breaks down the top alternatives to Neon and PlanetScale for hosting PostgreSQL, with a focus on teams that care about uptime, control, and supporting full-stack workloads.

<InfoBox className='BodyStyle'>

### Quick look: best PostgreSQL alternatives to Neon and PlanetScale (2026)

If you're short on time, here’s a quick overview of platforms that support production-ready PostgreSQL hosting with different levels of control and flexibility:

1. [**Northflank**](https://northflank.com/dbaas/managed-postgresql) – Native Postgres with high availability, backups, logs, and metrics. Deploy to your own cloud (BYOC) or use managed infrastructure. Supports Redis, job runners, APIs, and full-stack app deployment.
2. [**DigitalOcean Managed PostgreSQL**](https://www.digitalocean.com/products/managed-databases) – Affordable managed Postgres with automated backups and high availability. Hosted on DigitalOcean only, with limited observability.
3. [**Render**](https://render.com/docs/postgresql) – Lightweight managed Postgres with app hosting and preview environments. Best for smaller workloads and simple app architectures.
4. [**Supabase**](https://supabase.com) – Postgres bundled with real-time features, auth, and APIs. Ideal for MVPs and small SaaS apps. Limited infrastructure control.
5. [**Aiven**](https://aiven.io/postgresql) – Enterprise-grade Postgres across multiple clouds with private networking, access controls, and compliance features. Infrastructure-focused.
6. [**Heroku Postgres**](https://www.heroku.com/postgres) – Developer-friendly Postgres with easy Heroku integration. Simple to start with, but limited flexibility and costly at scale.

**Northflank gives you Postgres that runs in your own VPC or on managed infrastructure, with support for everything else your app needs, from Redis and workers to APIs and CI/CD pipelines.**

Deploy PostgreSQL with full-stack flexibility → [Try Northflank](https://northflank.com)

</InfoBox>


## Why teams are rethinking Postgres on Neon or PlanetScale

Neon introduced a modern architecture by separating storage and compute, aiming to optimize Postgres for cloud-native workloads. It’s an ambitious approach that has gained traction, particularly among AI app builders and early adopters. However, teams running production systems continue to weigh this design against the platform’s history of outages and growing pains. For production use, reliability often takes priority over architectural experimentation.

PlanetScale, on the other hand, has built a well-regarded reputation for managed MySQL using Vitess. Its recent move to support PostgreSQL reflects growing demand from teams who rely on Postgres for compatibility and extensibility. However, PlanetScale’s PostgreSQL implementation also runs on Vitess, which introduces differences from running native Postgres directly. For some teams, that added abstraction can be a limitation, particularly when working with extensions or aiming for consistent local-to-prod behavior.

Both platforms are focused on managed databases. However, many production teams also need Redis, job schedulers, background workers, APIs, and secure app environments that work together with their database. They’re looking for solutions that support more of the stack, integrate into their cloud environments, and don’t introduce unnecessary complexity when scaling.

## What to look for in modern PostgreSQL hosting

Teams choosing where to run PostgreSQL in production are usually weighing more than performance benchmarks or a simplified setup. Reliability, transparency, and flexibility all influence how well a database solution fits into real-world workflows.

For many teams, including those deploying AI applications, internal platforms, or SaaS products, Postgres needs to integrate cleanly into the environments they already operate in. Regardless of if it's Docker on a laptop, a Kubernetes cluster, or a VPC in AWS, the behavior should remain consistent and predictable.

These are the factors modern teams often prioritize when selecting a PostgreSQL hosting platform:

- **Native Postgres**: No rewrites, no proprietary query layers, and full compatibility with extensions and local development.
- **Bring Your Own Cloud (BYOC)**: The ability to deploy databases inside your own VPC on AWS, GCP, or Azure, without giving up ease of use.
- **High availability**: Zonal redundancy, failover handling, and minimal downtime during upgrades or restarts.
- **Built-in observability**: Access to logs, metrics, usage breakdowns, and operational visibility without relying on external tools.
- **Security and compliance support**: Role-based access control (RBAC), audit logs, and readiness for SOC 2 or GDPR-focused teams.
- **Environment consistency**: PostgreSQL should behave the same in development, staging, and production, regardless of where it's running.
- **Support for full-stack deployments**: Some platforms stop at the database, but many teams also need Redis, background jobs, APIs, and app runtimes deployed alongside it.

## 6 best alternatives to Neon and PlanetScale for PostgreSQL hosting in 2026

There’s no shortage of platforms offering managed PostgreSQL, but the best alternatives to Neon and PlanetScale tend to prioritize stability, full-stack compatibility, and deployment flexibility across cloud environments.

### 1. Northflank – Deploy PostgreSQL alongside your apps, in your cloud or ours

Northflank provides [managed PostgreSQL](https://northflank.com/dbaas/managed-postgresql) that can be deployed in under 60 seconds on its platform or in around 30 minutes within your own cloud using [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) features. It’s designed to work across AWS, GCP, and Azure with predictable behavior across environments.

![northflank-managed postgreSQL.png](https://assets.northflank.com/northflank_managed_postgre_SQL_efdb666dd1.png)

You can host more than just databases because Northflank supports [Redis](https://northflank.com/dbaas/managed-redis), [job runners](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs), [web services](https://northflank.com/cloud/northflank), and full [CI/CD pipelines](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) in one place. PostgreSQL clusters come with high availability, scheduled backups, automated restores, and upgrade workflows.

Teams can also create [preview environments](https://northflank.com/use-cases/preview-environments-backend-for-kubernetes) for every branch, replicating services and databases to support faster testing and reviews, without needing to manage separate infrastructure.

Security and control are built in: [role-based access control (RBAC)](https://northflank.com/docs/v1/application/secure/use-role-based-access-control), [audit logs](https://northflank.com/docs/v1/application/observe/audit-logs), [cost tracking](https://northflank.com/docs/v1/application/billing/monitor-spending), and [secure multi-tenancy](https://northflank.com/blog/what-is-multitenancy#how-northflank-helps-you-manage-multitenant-workloads) are all available by default. Northflank’s [Kubernetes-native secure runtime](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation) is built to support untrusted code and multi-user environments safely.

<InfoBox className='BodyStyle'>

💡Choose Northflank if you want high-availability Postgres, full-stack support (Redis, APIs, CI/CD), and the flexibility to deploy in your cloud or on managed infrastructure.

</InfoBox>

### 2. DigitalOcean Managed PostgreSQL – Simple and affordable

DigitalOcean’s Managed PostgreSQL provides an easy way to deploy Postgres with built-in high availability, daily backups, and automated failover. It’s designed for developers who want to get up and running quickly without managing the underlying infrastructure.

![digitalocean-managed-postgreSQL.png](https://assets.northflank.com/digitalocean_managed_postgre_SQL_5b3fc81d48.png)

The service is tightly integrated with DigitalOcean’s compute, networking, and storage products, making it a natural fit if your workloads are already hosted on the platform. However, it doesn’t support [Bring Your Own Cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) (BYOC), private VPCs outside of DigitalOcean, or advanced runtime isolation. Access to logs and observability tools is also more limited compared to platforms built for larger or regulated teams.

> Choose this if you're already building on DigitalOcean and need a straightforward way to run managed PostgreSQL without extra setup.
> 

<InfoBox className='BodyStyle'>

💡If you're evaluating DigitalOcean for more than databases, these articles might help:

- [Best DigitalOcean Alternatives in 2026](https://northflank.com/blog/best-digitalocean-alternatives-2025)
- [DigitalOcean GPU & Paperspace Alternatives](https://northflank.com/blog/digitalocean-gpu-paperspace-alternatives)

</InfoBox>

### 3. Render – Lightweight Postgres for full-stack apps

Render offers managed PostgreSQL as part of its broader platform-as-a-service, making it easy to spin up databases alongside web services, background workers, cron jobs, and static sites. It’s designed for developers who want to deploy full-stack applications without managing infrastructure directly.

![render-postgres.png](https://assets.northflank.com/render_postgres_f82c318613.png)

Postgres instances on Render include daily backups, persistent storage, and database access controls. That said, it doesn’t support Bring Your Own Cloud (BYOC), and there’s limited control over networking, custom scaling strategies, or advanced database configurations. Observability and metrics are more basic compared to infrastructure-first platforms.

> Choose this if you're deploying simple apps and want PostgreSQL integrated into a streamlined PaaS workflow.
> 

<InfoBox className='BodyStyle'>

💡If you're comparing Render with other platforms, these guides might help:

- [Render vs Heroku](https://northflank.com/blog/render-vs-heroku)
- [Render vs Vercel](https://northflank.com/blog/render-vs-vercel)
- [Render vs Fly.io](https://northflank.com/blog/flyio-vs-render)
- [Top Render Alternatives](https://northflank.com/blog/render-alternatives)

</InfoBox>

### 4. Supabase – Postgres with built-in APIs and auth

Supabase is an open-source platform that provides managed PostgreSQL with built-in authentication, storage, real-time subscriptions, and REST and GraphQL APIs. It’s often described as an open-source Firebase alternative and is well-suited for building MVPs, side projects, and small SaaS applications.

![supabase-homepage.png](https://assets.northflank.com/supabase_homepage_9cecadd065.png)

Supabase handles a lot out of the box, allowing developers to move quickly without needing to combine multiple services. However, it doesn’t support Bring Your Own Cloud (BYOC), and infrastructure-level configuration is limited. Teams looking for more fine-grained control over networking, scaling, or compliance tooling may find it less suited for production at scale.

> Choose this if you're building an MVP or early-stage product and want to move fast with built-in tools and minimal setup.
> 

<InfoBox className='BodyStyle'>

💡You can also [deploy Supabase on Northflank](https://northflank.com/stacks/deploy-supabase) if you want more control over your own Supabase instance, with support for BYOC and integrated full-stack services.

</InfoBox>

### 5. Aiven – Fully managed Postgres across AWS, GCP, and Azure

Aiven provides fully managed PostgreSQL with support for major cloud providers including AWS, GCP, and Azure. It’s designed for infrastructure teams that need flexibility, control, and operational features across different cloud environments.

![aiven-for-postgresql.png](https://assets.northflank.com/aiven_for_postgresql_2323494449.png)

Aiven supports private networking options like VPC peering and PrivateLink, role-based access control, and advanced observability through integrations with monitoring tools like Prometheus and Grafana. It also offers multi-region replication and automated backups.

Unlike platform-as-a-service tools, Aiven doesn’t include app hosting, job runners, or CI/CD tooling; its focus is strictly on managing infrastructure components, such as Postgres, Kafka, Redis, and OpenSearch.

> Choose this if you need managed PostgreSQL with enterprise-grade networking and compliance features across multiple cloud providers.
> 

### 6. Heroku Postgres – Developer-friendly but aging

Heroku Postgres has long been a go-to choice for developers looking to get started quickly with managed databases. It integrates seamlessly with Heroku apps, supports multiple Postgres versions, and includes features like automated backups, point-in-time recovery, and follower databases for read replicas.

![heroku-managed-postgresql.png](https://assets.northflank.com/heroku_managed_postgresql_088bbf42ae.png)

However, as applications grow, teams often find that Heroku Postgres becomes more expensive and restrictive. There’s no support for Bring Your Own Cloud (BYOC), VPC-level networking, or in-depth observability. Access to logs and performance insights is limited compared to newer platforms focused on production scale and compliance.

> Choose this if you're already using Heroku and want minimal setup, but expect to migrate later as your infrastructure needs grow.
> 

<InfoBox className='BodyStyle'>

💡If you’re looking for alternatives to Heroku, checking how it compares to other platforms, wondering about the capabilities, limitations, and alternatives of Heroku Enterprise, or looking for a resource on Heroku pricing comparison and reduction, or looking for how to migrate from Heroku, these guides would help:

- [Top Heroku alternatives in 2026](https://northflank.com/blog/top-heroku-alternatives)
- [Render vs Heroku: Which platform-as-a-service is right for you in 2026?](https://northflank.com/blog/render-vs-heroku)
- [Vercel vs Heroku: Which platform fits your workflow best?](https://northflank.com/blog/vercel-vs-heroku)
- [Heroku Enterprise: capabilities, limitations, and alternatives](https://northflank.com/blog/heroku-enterprise-capabilities-limitations-and-alternatives)
- [Heroku Pricing Comparison & Reduction](https://northflank.com/heroku-pricing-comparison-and-reduction)
- [Heroku outages are getting worse. The best alternative in 2026 with no downtime.](https://northflank.com/blog/heroku-outage-downtime-status)
- [Migrate from Heroku](https://northflank.com/docs/v1/application/migrate-from-heroku)

</InfoBox>

## Why Northflank stands out for full-stack Postgres hosting

[Northflank](https://northflank.com/dbaas/managed-postgresql) runs tens of thousands of production databases across Postgres, MySQL, and Redis for teams building SaaS apps, internal platforms, and AI services. Unlike platforms that stop at database hosting, Northflank is designed to support your entire stack, from your data layer to your application services.

You can deploy and manage:

- **Databases**: Postgres, MySQL, Redis
- **Workloads**: APIs, scheduled jobs, background workers
- **AI infrastructure**: model fine-tuning, notebooks, vector DBs
- **Environment automation**: fast preview environments with cloned services and data

Everything is backed by enterprise-grade features like [role-based access control (RBAC)](https://northflank.com/docs/v1/application/secure/use-role-based-access-control), [audit logs](https://northflank.com/docs/v1/application/observe/audit-logs), and [cost attribution](https://northflank.com/docs/v1/application/billing/monitor-spending) by project. You can run in your own cloud or on Northflank’s infrastructure. Either way, setup is fast, and the experience is consistent across development, staging, and production.

<InfoBox className='BodyStyle'>

[Try Northflank for PostgreSQL hosting](https://app.northflank.com/signup)

Not sure what to do next after signing up? You’ll be guided through creating your first project, database, or full-stack app.
- [Deploy PostgreSQL on Northflank](https://northflank.com/docs/v1/application/databases-and-persistence/deploy-databases-on-northflank/deploy-postgresql-on-northflank)
- [Migrate your existing PostgreSQL database](https://northflank.com/docs/v1/application/databases-and-persistence/migrate-data-to-northflank/migrate-your-postgresql-database-to-northflank)
- [Learn more about Northflank DBaaS](https://northflank.com/dbaas/managed-postgresql)

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>7 best DigitalOcean GPU &amp; Paperspace alternatives for AI workloads in 2026</title>
  <link>https://northflank.com/blog/digitalocean-gpu-paperspace-alternatives</link>
  <pubDate>2025-07-01T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for better GPU cloud platforms than DigitalOcean or Paperspace? Here are 7 alternatives purpose-built for AI teams, from fine-tuning to running LLMs, with full workload support, BYOC, and secure multi-tenancy.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/digitalocean_gpu_paperspace_alternatives_ac41701361.png" alt="7 best DigitalOcean GPU &amp; Paperspace alternatives for AI workloads in 2026" />DigitalOcean GPU Droplets let you attach NVIDIA GPUs like A100s and L40S to cloud instances for training, fine-tuning, and inference. Paperspace, now fully part of DigitalOcean, has been integrated into the same product line as its GPU hosting solution.

This article walks through seven alternatives to DigitalOcean GPU and Paperspace for teams running AI workloads at scale. If you're looking to deploy LLMs, run fine-tuning jobs, schedule batch tasks, or support APIs, databases, and notebooks alongside your models, these platforms offer a broader approach to GPU-powered infrastructure.

<InfoBox className='BodyStyle'>

### Quick look: top alternatives to DigitalOcean GPU & Paperspace in 2026

If you're short on time, here’s a quick breakdown of some of the best platforms for running GPU workloads beyond what DigitalOcean and Paperspace provide:

1. [**Northflank**](https://northflank.com) – Full-stack GPU platform with support for [BYOC](https://northflank.com/features/bring-your-own-cloud) (Bring Your Own Cloud), secure multi-tenant runtime, built-in CI/CD, job scheduling, and full workload orchestration.
2. [**RunPod**](https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment) – Good for fast GPU inference and launching model templates. Limited infrastructure features.
3. [**Lambda Cloud**](https://lambdalabs.com/service/gpu-cloud) – High-performance GPUs for training workloads. No orchestration or platform-level services.
4. [**Modal**](https://northflank.com/blog/6-best-modal-alternatives) – Function-first platform for AI developers. Not built for full-service deployment or persistent workloads.
5. [**Baseten**](https://northflank.com/blog/baseten-alternatives-for-ai-ml-model-deployment) – Inference-focused platform with model autoscaling and chaining. No GPU provisioning or BYOC.
6. [**AnyScale**](https://northflank.com/blog/anyscale-alternatives-for-ai-ml-model-deployment) – Ray-native infrastructure for distributed AI jobs. Suited to advanced orchestration use cases.
7. [**Vast.ai**](https://vast.ai) – Spot GPU marketplace with low pricing. Minimal orchestration and limited isolation for production use.

**Northflank supports AI workloads like LLMs, notebooks, APIs, and fine-tuning jobs - all on your own cloud or with fast GPU provisioning.**

> **Migrating from another platform?** If you're moving GPU workloads and have specific capacity or availability requirements, [request GPU capacity here](https://northflank.com/request/gpu).

</InfoBox>


## When you need more than DigitalOcean GPU and Paperspace

DigitalOcean’s GPU Droplets and Paperspace make it easy to get started with GPU instances. They’re simple to spin up, SSH-friendly, and work well for individual training jobs or quick experiments.

Once you move beyond standalone training jobs and start building production-grade systems, with background tasks, model APIs, or secure, multi-user environments, you’ll likely need capabilities these platforms don’t cover out of the box. Some of them include:

- Manual GPU setup required on DigitalOcean, including drivers and runtime environments
- No shared volume support across multiple Droplets (limits multi-instance coordination)
- Paperspace has limited global presence with only three data center regions
- GPU availability can be restricted during peak usage due to quotas on higher-end models
- No BYOC support or deeper deployment tooling such as full-stack orchestration or built-in CI/CD

If you're deploying complete AI applications rather than running isolated training jobs, these platforms may start to feel restrictive.

That’s where platforms like [Northflank](https://northflank.com/) provide more room to grow:

- Unified support for GPUs, APIs, background jobs, and databases
- [Bring Your Own Cloud](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) (BYOC), spot GPU provisioning, and secure container runtimes
- Built-in CI/CD, job orchestration, and notebook templates
- Enterprise-ready features like RBAC, audit logs, and private networking

Rather than manually piecing everything together, Northflank gives you the tooling and infrastructure needed to ship AI workloads from day one.

## What to look for in a modern GPU cloud platform

GPU access is only one piece of the stack. As AI workloads become increasingly complex, teams like yours need platforms that can support everything around the GPU, from infrastructure orchestration to developer workflows.

Below are a few things to look for when evaluating alternatives to DigitalOcean GPU and Paperspace:

- **Full workload support**: Beyond inference, you’ll likely need to run APIs, background jobs, vector databases, Redis, Postgres, and notebooks in the same environment. Platforms like Northflank support deploying these services alongside your GPU-powered workloads in a single project.
- **Secure multi-tenant runtime**: For teams running AI agents or sandboxed code, security boundaries are vital. Platforms that isolate workloads at the container level, like Northflank, help prevent cross-tenant access and runtime vulnerabilities.
- **Bring Your Own Cloud (BYOC)**: Running GPU workloads across different cloud providers gives you flexibility to manage cost, avoid vendor lock-in, and select the hardware you need (e.g., mixing A100s and H100s). Northflank supports BYOC deployments and hybrid GPU setups with fast provisioning.
- **Support for notebooks, fine-tuning, and long-running jobs**: If you’re running interactive notebooks, fine-tuning models, or GPU-backed workers that need to stay alive for hours, the platform should support both persistent and scheduled workloads. Northflank handles these natively with workload templates and job orchestration.
- **CI/CD and GitOps integration**: To manage complex deployments and collaborate across teams, look for platforms that integrate with your CI pipelines and Git workflows. Northflank supports both push-to-deploy and GitOps-based flows out of the box.
- **Enterprise controls**: Role-based access control (RBAC), audit logs, private networking, and cost attribution are essential when building internal tools or managing multi-user environments. These features are already available on platforms like Northflank and are critical for AI teams working at scale.

## Comparison table: Top DigitalOcean GPU & Paperspace alternatives

If you’re comparing alternatives, here’s a quick breakdown of top platforms. Some are best for cheap GPU access or serverless jobs, while others go beyond raw compute to support full-stack AI applications

| **Platform** | **Best For** | **Why It Stands Out** |
| --- | --- | --- |
| **Northflank** | LLMs, APIs, GPUs, and full-stack AI infrastructure | Unified support for GPU + non-GPU workloads, CI/CD, secure runtimes, BYOC, and databases |
| **RunPod** | Fast inference on diffusion, LLaMA, Whisper | Templates for popular models, spot pricing, quick GPU access |
| **Lambda** | On-demand or dedicated GPU compute | SSH access, strong training performance, flexible pricing |
| **Modal** | Async jobs, Python-first workflows | Function-based compute model, easy to use, great for batch or scheduled jobs |
| **Baseten** | Serving and chaining pre-trained models | Simple deployment and autoscaling, but less infra control |
| **AnyScale** | Ray-based distributed compute | Great for fine-tuning and RLHF workflows at scale |
| **Vast.ai** | Lowest-cost GPU spot pricing | Peer marketplace with great pricing, but no orchestration or secure isolation |

## 7 best alternatives to DigitalOcean GPU & Paperspace in 2026

These are seven platforms that go beyond basic GPU hosting. They provide the infrastructure, orchestration, and flexibility AI teams need to run full workloads at scale.

### 1. Northflank – Unified GPU platform for AI and full-stack workloads

[Northflank](https://northflank.com/) makes it easy to deploy GPU workloads alongside the rest of your stack. There’s no need for separate tooling or isolated environments. You can run notebooks, fine-tuning jobs, APIs, and background workers in the same project.

![new-northflank-ai-home-page.png](https://assets.northflank.com/new_northflank_ai_home_page_25751c7697.png)

**What you can run with GPUs on Northflank:**

- Jupyter notebooks
- Fine-tuning jobs (e.g. PyTorch, DeepSpeed)
- LLM inference endpoints (e.g. LLaMA, Mistral)
- Batch jobs and long-running processes

**Full-stack infrastructure beyond GPU execution:**

- Deploy Postgres, Redis, vector databases, and RabbitMQ alongside your model services
- Run APIs, background workers, and supporting tools in the same environment
- CI/CD pipelines and GitOps support built-in

**Built for flexibility and control:**

- BYOC ([Bring Your Own Cloud](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes)) support to connect your own GPU infrastructure across clouds
- GPU marketplace for fast provisioning
- Secure runtime to isolate untrusted or AI-generated code
- Templates for Jupyter, PyTorch, and LLaMA (deployable via UI or Git)

**Enterprise-ready features:**

- Role-based access control (RBAC)
- Audit logs and cost attribution by project
- Private clusters
- SOC 2 compliance roadmap (with Vanta/SecureFrame integration coming)

> Go with this if you need to deploy full AI applications that include models, APIs, and services, on secure, scalable infrastructure.
> 

### 2. RunPod – GPU compute with easy model deployment

RunPod offers one-click deployment for GPU-powered containers, making it quick to start inference workloads. Templates are available for common AI models like Stable Diffusion, LLaMA, and Whisper, and you can also deploy custom Docker containers with a few clicks.

![runpod-homepage.png](https://assets.northflank.com/runpod_homepage_4d5612b292.png)

**Key features:**

- Prebuilt templates for vision, audio, and LLM workloads (e.g. Stable Diffusion, Whisper, LLaMA)
- Flexible GPU pricing, including on-demand and [spot instances](https://northflank.com/blog/spot-instances#what-are-spot-instances) with potentially up to 60–80% savings
- API access and webhooks for managing containers programmatically

**Limitations:**

- Optimized for inference and experimentation, not full app infrastructure
- No built-in support for deploying supporting services like Postgres, Redis, or background workers
- Lacks integrated CI/CD pipelines and multi-service orchestration

Read more in our guide: [RunPod alternatives for AI/ML deployment](https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment)

> Go with this if you want fast, cost-effective GPU inference without managing additional infrastructure.
> 

### 3. Lambda – High-performance GPU hosting for training

Lambda provides on-demand access to high-end NVIDIA GPUs like H100, A100, and A6000 for teams focused on large-scale model training. The Lambda cloud is designed for speed, raw compute power, and simplicity, making it ideal for training and experimentation without the need for additional orchestration.

![lambda-homepage.png](https://assets.northflank.com/lambda_homepage_3b5c27b40a.png)

**Key highlights:**

- **Multiple GPU types**: Access H100, A100 (SXM or PCIe), A10, GH200, A6000, V100, and more
- **Flexible configurations**: Run single-GPU VMs or multi-GPU clusters with 1, 2, 4, or 8 GPUs
- **Preconfigured environments**: One-click Jupyter and instances with CUDA/cuDNN preinstalled
- **Minute-level billing**: Pay only for what you use, with no egress fees
- **API access**: Manage instances programmatically via Lambda’s API

**Where it’s limited:**

- The Lambda cloud is focused on unmanaged compute only**;** you won’t find built-in support for APIs, databases, CI/CD pipelines, or multi-service orchestration
- No BYOC or hybrid deployment options

> Go with this if your priority is access to raw compute power for training large models, without needing infrastructure or platform services around it.
> 

### 4. Modal – Function-based abstraction for GPU tasks

Modal is a serverless platform that lets you run GPU-powered jobs as Python functions, abstracting away infrastructure management so developers can focus on code. It's designed for simplicity and scalability, especially for batch inference, data processing, and scheduled GPU tasks.

![modal-home-page.png](https://assets.northflank.com/modal_home_page_2cfbb0b1d2.png)

**Key features:**

- **Function-first interface**: Write Python functions and deploy them directly to GPU-enabled containers
- **Auto-scaling execution**: Functions scale based on demand; Modal handles provisioning and scaling
- **Scheduled and event-driven tasks**: Support for cron jobs, webhooks, and API-triggered executions via functions
- **Managed infrastructure**: Handles container orchestration and underlying resource management

**Limitations to be aware of:**

- Does not support long-running or persistent services beyond individual function executions
- Lacks built-in support for databases, APIs, or multi-service orchestration
- No BYOC option; Modal manages all infrastructure

For deeper comparison, see [6 best Modal alternatives for AI/ML deployment](https://northflank.com/blog/6-best-modal-alternatives).

> Go with this if you want a serverless-like GPU workflow for functions or batch tasks, without running full applications or persistent services.
> 

### 5. Baseten – Hosted inference with autoscaling

Baseten is designed to make production-grade AI model deployment frictionless. It focuses on inference, offering managed GPU provisioning, autoscaling, and multi-step orchestration, everything you need to serve ML models at scale without worrying about infrastructure.

![baseten-homepage.png](https://assets.northflank.com/baseten_homepage_7767640804.png)

**What Baseten does well**

- **Model serving with autoscaling:** Automatically scales between zero and your max replica count, letting your model handle spikes in traffic and scale down to save costs.
- **Inference Stack:** Includes low-latency, high-throughput runtimes using optimized kernels like TensorRT; routing, metrics, and performance built in.
- **Chains for mini workflows:** Enables multi-model pipelines and chained logic under a single endpoint.
- **Integration and observability:** Offers API/CLI/SDK interfaces, Prometheus-compatible metrics, and observability out of the box .

**Where it’s limited**

- Baseten handles GPU provisioning and inference orchestration, but it does **not provide BYOC**, nor does it allow deployment of full-stack services (no Postgres/Redis/API containers).
- Custom infrastructure, like private networking or CI/CD pipelines, must be managed externally.

For a deeper comparison, check out our post on [Baseten alternatives for AI/ML model deployment](https://northflank.com/blog/baseten-alternatives-for-ai-ml-model-deployment).

> Go with this if you’re deploying pre-trained or fine-tuned models with autoscaling and workflow chaining, and don’t need infrastructure customization.
> 

### 6. AnyScale – Ray‑native orchestration for distributed AI workloads

AnyScale is a managed platform built around Ray, designed to help you develop, scale, and deploy distributed AI applications with minimal infrastructure overhead.

![anyscale-homepage.png](https://assets.northflank.com/anyscale_homepage_471414362b.png)

**What AnyScale enables:**

- **Ray-native clusters:** Provision and manage clusters effortlessly, supporting Python-first workflows across clouds and accelerators.
- **Auto-scaling & fault tolerance:** Clusters automatically scale based on workload demands, with built-in retries and recovery.
- **RayTurbo performance:** Runs on an enhanced version of Ray designed for maximum GPU utilization and efficiency.
- **Tooling & ecosystem integrations:** Includes hosted development workspaces, monitoring, support for cron-like scheduling, and APIs, integrating natively with frameworks like Tune, RLlib, and Serve.

**Limitations for smaller teams:**

- Steeper learning curve due to Ray’s distributed programming model.
- Not built for managing non-Ray services, e.g., databases, APIs, or microservices.
- No built-in support for BYOC; all infrastructure remains within AnyScale’s managed environment.

See more in [AnyScale alternatives for AI/ML model deployment](https://northflank.com/blog/anyscale-alternatives-for-ai-ml-model-deployment).

> Go with this if your team uses Ray and needs distributed compute with advanced orchestration across GPUs and nodes.
> 

### 7. Vast.ai – Peer GPU marketplace with budget spot pricing

Vast.ai is a decentralized marketplace that connects users with distributed GPU providers, from hobbyists to data centers, offering significantly lower pricing compared to traditional cloud providers.

![vastai-homepage.png](https://assets.northflank.com/vastai_homepage_316e558d18.png)

**What makes Vast.ai stand out:**

- **Spot and on-demand GPUs**: Rent GPUs at up to ~3–5× lower cost via interruptible spot instances or higher-priority on-demand options.
- **Global provider network**: Users can filter by GPU type, performance (via dlperf), RAM, CPU, and security level.
- **Flexible interfaces**: Choose from the web UI, a Python CLI, or REST API to launch containers or VMs with SSH or Jupyter support.

**Trade-offs to consider:**

- Built as a **compute marketplace**, Vast.ai doesn't provide built-in orchestration, secure runtimes, or managed services like APIs, databases, or CI/CD.
- Isolation is handled via unprivileged Docker containers, but there’s no guarantee of cross-tenant security unless you choose trusted providers or enable single-tenant options**.**

> Go with this if cost is your top concern, you’re comfortable managing infrastructure directly, and you can tolerate the limited orchestration and security trade-offs.
> 

## Choosing the right GPU cloud for your AI workloads

Most GPU platforms focus on one thing: serving models. However, if you're building full AI applications, those with APIs, scheduled jobs, databases, or background workers, raw compute isn't enough.

You’ll need a platform that can support:

- Secure multi-tenant environments for generated code
- Job orchestration across notebooks, fine-tuning tasks, and inference
- CI/CD pipelines and Git-based deployments
- Postgres, Redis, and other supporting services

Northflank brings all of this together. You can deploy GPU workloads like LLMs and fine-tuning jobs alongside your full stack, with CI/CD, logs, networking, RBAC, and audit trails already in place.

<InfoBox className='BodyStyle'>
Run secure, production-grade GPU workloads → [Try Northflank](https://app.northflank.com/signup)
</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>6 best Modal alternatives for ML, LLMs, and AI app deployment</title>
  <link>https://northflank.com/blog/6-best-modal-alternatives</link>
  <pubDate>2025-07-01T18:15:00.000Z</pubDate>
  <description>
    <![CDATA[This article reviews Modal's strengths and limitations, then compares top alternatives—like Northflank, Replicate, RunPod, Baseten, and SageMaker—to help teams choose the right platform for scaling AI apps.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/paas_providers_3_4f8f6b33df.png" alt="6 best Modal alternatives for ML, LLMs, and AI app deployment" />In 2022, [Erik Bernhardsson](https://erikbern.com/) introduced the world to [Modal](https://erikbern.com/2022/12/07/what-ive-been-working-on-modal.html?utm_source=northfank.com), a radically simple way to run Python in the cloud without requiring infrastructure. No Dockerfiles, Kubernetes, or ops. Just write code, decorate it, and let Modal handle the rest: scaling, scheduling, GPUs, webhooks, and more.

Since then, Modal has become a go-to tool for machine learning engineers, indie hackers, and teams building fast, without friction. But as with any great tool, it’s not perfect. Eventually, you may find yourself needing more flexibility, better control, or a more production-ready stack. If you’re here, chances are you’ve run into one of those limits and you're looking for what’s next.

This article breaks down exactly where Modal excels, where it struggles, and which alternatives are worth your attention. Whether you're growing past its constraints or just exploring your options, you'll leave with a clear sense of what tool best fits your stack and why.

## TL;DR – Top Modal alternatives

If you're short on time, here’s a snapshot of the top Modal alternatives. Each tool has its strengths, but they solve different problems, and some are better suited for real-world production than others.

| Platform | Best For | Why It Stands Out |
| --- | --- | --- |
| [**Northflank**](https://northflank.com/) | **Full-stack AI products: APIs, frontends, LLMs, GPUs, and secure infra** | **Production-grade platform for deploying AI apps** — GPU orchestration, Git-based CI/CD, BYOC, secure runtime, multi-service support, and enterprise-ready features |
| **Replicate** | Quick model demos and public inference APIs | Easiest way to deploy and share open-source models via REST API with zero infrastructure setup |
| **Anyscale** | Scalable distributed training and Ray-based compute | Ideal for teams building parallel training and inference workflows using Ray, with autoscaling and fault tolerance |
| **RunPod** | Budget-friendly GPU compute for custom ML workloads | Offers low-cost, flexible GPU hosting with full Docker control — great for DIY inference or LLM fine-tuning |
| **Baseten** | Internal tools and fast model API deployment | Great for deploying ML models as APIs with built-in UI builder, logging, and monitoring for quick internal apps |
| **SageMaker** | Enterprise-grade MLOps with AWS integration | Comprehensive ML lifecycle management on AWS — pipelines, model registry, security, and VPC support for large-scale teams |

## What makes Modal stand out?

If you’ve used Modal before, you already know how different it feels. If you’re new, here’s why so many developers love it.

- **No infrastructure setup**
    
    No Dockerfiles, Kubernetes, or YAML. Just write Python, add a decorator, and you're running in the cloud.
    
- **Blazingly fast deploys**
    
    Code runs in the cloud in under a second. It feels like local development, but remote and scalable.
    
- **GPU support when you need it**
    
    Spin up GPU-backed functions with a simple flag. Perfect for ML, inference, and compute-heavy tasks.
    
- **Built-in scheduling and async support**
    
    Easily run cron jobs, background tasks, or batch jobs without extra tooling.
    
- **All in Python**
    
    Everything happens in your Python code—config, deployment, and task definitions. No jumping between files or formats.
    

Modal is great when you want to ship fast and skip the infrastructure rabbit hole. It’s built for developers who want power without complexity. Simple, sharp, and gets out of your way.

## What are the limitations of Modal?

We just covered what makes Modal feel smooth and powerful, especially when you’re starting out. But like most tools built around simplicity, there’s a point where the cracks begin to show. Sometimes it’s a missing feature that slows you down. Other times, it’s a hard limit that forces you to reconsider your stack.

These limitations might not hit right away. But if you're working on something beyond a quick ML experiment or solo project, you'll probably run into one or more of the following.

- **You can't build full applications**
    
    Modal is centered around running isolated Python functions. That works great for inference tasks or background jobs, but if you're building a full product with an API, background workers, frontend, and a database, things can quickly become difficult to manage.. Modal just isn't built for orchestrating multiple services.
    
- **No built-in CI/CD**
    
    There’s no native support for automated testing, deployments from Git, or preview environments. If you're trying to build a proper development pipeline, you’ll need to wire it up yourself with external tools.
    
- **Networking is extremely limited**
    
    You can’t set up private networking, custom VPCs, or define firewall rules. There’s also no first-class support for service-to-service authentication or fine-grained access control, which can be a dealbreaker for secure or internal systems.
    
- **You're tightly coupled to Modal**
    
    Because the platform is so tightly integrated, there’s no easy way to take your code and move it somewhere else. Modal-specific decorators, cloud primitives, and infrastructure assumptions create vendor lock-in over time.
    
- **You can’t bring your own cloud**
    
    Modal runs entirely on its managed infrastructure. There’s no option to deploy it on your own AWS or GCP account, which limits flexibility and control over cost, region, and compliance.
    
- **Not designed for secure or regulated workloads**
    
    Modal doesn’t offer runtime sandboxing or advanced isolation. If you're working in a regulated industry or need strong guarantees around data security or multi-tenant safety, this could be a blocker.
    
- **Costs may scale unpredictably**
    
    Modal's pricing works well for short tasks and small workloads. But for longer jobs, GPU usage, or frequent function calls, costs can rise quickly. And since there's no granular usage dashboard, it can be hard to predict or manage your bill.
    

## What to look for in a Modal alternative

If you’re thinking about moving away from Modal, it’s probably because something started to feel off. Maybe you hit a wall with orchestration, or you need more control over deployment and infrastructure. Whatever the case, switching tools can feel like a big move, so it helps to know what to actually look for.

Here are a few things that really matter when choosing a Modal alternative:

- **Can it handle full applications?**
    
    Modal works well for isolated tasks, but if you're building an actual product with a frontend, backend, background jobs, and a database, you’ll want a platform that supports all of it together.
    
- **Does it support Git-based workflows?**
    
    Having native CI/CD, Git integration, and preview environments can save hours of setup and glue code. It also makes working with a team a lot smoother.
    
- **How well does it handle GPUs?**
    
    If you're doing ML, LLMs, or anything compute-heavy, check for on-demand GPU access, autoscaling, and reasonable pricing. You want this to be seamless, not a headache.
    
- **What kind of networking and security does it offer?**
    
    Private services, VPC support, custom domains, access control—these things matter a lot once you're shipping to production or dealing with user data.
    
- **Can you bring your own cloud?**
    
    Some platforms let you deploy to your own AWS or GCP account. This gives you more control over cost, location, and compliance without giving up the developer experience.
    
- **Do you get visibility into costs and usage?**
    
    The best platforms don’t hide billing behind a vague dashboard. You should be able to see exactly what you're using and how much it's costing you.
    
- **Is it flexible enough to grow with you?**
    
    Avoid tools that force you into a very specific pattern or runtime. The best alternatives should give you room to grow without locking you in.
    

## Top Modal alternatives

Here is a list of the top Modal alternatives you can find. In this section, we talk about each platform in depth, its top features, Pros, and Cons.

### 1. Northflank – The best Modal alternative for GPUs, LLMs, and full-stack AI workloads

[**Northflank**](https://northflank.com/) isn’t just a model hosting or GPU renting tool; it’s a **production-grade platform for deploying and scaling full-stack AI products**. It combines the flexibility of containerized infrastructure with GPU orchestration, Git-based CI/CD, and full-stack app support.

Whether you're serving a fine-tuned LLM, hosting a Jupyter notebook, or deploying a full product with both frontend and backend, Northflank offers broad flexibility without many of the lock-in concerns seen on other platforms.

![image - 2025-06-19T211009.037.png](https://assets.northflank.com/image_2025_06_19_T211009_037_2419b18f99.png)

**Key features:**

- Bring your own Docker image and full runtime control
- GPU-enabled services with [autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments) and lifecycle management
- Multi-cloud and [Bring Your Own Cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) support
- [Git-based CI/CD](https://northflank.com/docs/v1/application/release/manage-ci-cd), [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), and full-stack deployment
- Secure runtime for untrusted AI workloads
- SOC 2 readiness and enterprise security (RBAC, SAML, audit logs)

**Pros:**

- **No platform lock-in** – full container control with BYOC or managed infrastructure
- **Transparent, predictable pricing** – [usage-based](https://northflank.com/pricing) and easy to forecast at scale
- **Great developer experience** – Git-based deploys, CI/CD, preview environments
- **Optimized for latency-sensitive workloads** – fast startup, GPU autoscaling, low-latency networking
- **Supports AI-specific workloads** – Ray, LLMs, Jupyter, fine-tuning, inference APIs
- **Built-in cost management** – real-time usage tracking, budget caps, and optimization tools

**Cons:**

- No special infrastructure tuning for model performance.

**Verdict:** If you're building production-ready AI products, not just prototypes, Northflank gives you the flexibility to run full-stack apps and get access to affordable GPUs all in one place. With built-in CI/CD, GPU orchestration, and secure multi-cloud support, it's the most direct platform for teams needing both speed and control without vendor lock-in.

*See how [Weights uses Northflank to build a GPU-optimized AI platform for millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### 2. Replicate

Replicate is purpose-built for public APIs and demos, especially for generative models. You can host and monetize models in just a few clicks.

![image - 2025-06-19T211017.564.png](https://assets.northflank.com/image_2025_06_19_T211017_564_c7edd8f0e4.png)

**Key features:**

- Model sharing and monetization
- REST API for every model
- Popular with LLMs, diffusion, and vision models
- Built-in versioning

**Pros:**

- Zero setup for public model serving
- Easy to showcase or monetize models
- Community visibility

**Cons:**

- No private infra or BYOC
- No CI/CD or deployment pipelines
- Not built for production-ready apps or internal tooling

**Verdict:**

Great for showcasing generative models, not for teams deploying private, production workloads.

### 3. RunPod

RunPod gives you raw access to GPU compute with full Docker control. Great for cost-sensitive teams running custom inference workloads.

![image - 2025-06-19T211020.974.png](https://assets.northflank.com/image_2025_06_19_T211020_974_7f97807c0a.png)

**Key features:**

- GPU server marketplace
- BYO Docker containers
- REST APIs and volumes
- Real-time and batch options

**Pros:**

- Lowest GPU cost per hour
- Full control of runtime
- Good for experiments or heavy inference

**Cons:**

- No CI/CD or Git integration
- Lacks frontend or full-stack support
- Manual infra setup required

**Verdict:**

Great if you want cheap GPU power and don’t mind handling infra yourself. Not plug-and-play.

### **4. Baseten**

Baseten helps ML teams serve models as APIs quickly, focusing on ease of deployment and internal demo creation without deep DevOps overhead.

![image - 2025-06-25T171137.699.png](https://assets.northflank.com/image_2025_06_25_T171137_699_acea62b8ab.png)

**Key Features**:

- Python SDK and web UI for model deployment
- Autoscaling GPU-backed inference
- Model versioning, logging, and monitoring
- Integrated app builder for quick UI demos
- Native Hugging Face and PyTorch support

**Pros**:

- Very fast path from model to live API
- Built-in UI support is great for sharing results
- Intuitive interface for solo developers and small teams

**Cons**:

- Geared more toward internal tools and MVPs
- Less flexible for complex backends or full-stack services
- Limited support for multi-service orchestration or CI/CD

**Verdict**:

Baseten is a solid choice for lightweight model deployment and sharing, especially for early-stage teams or prototypes. For production-scale workflows involving more than just inference, like background jobs, databases, or containerized APIs, teams typically pair it with a platform like Northflank for broader infrastructure support.

*Curious about Baseten? Check out [this article](https://northflank.com/blog/baseten-alternatives-for-ai-ml-model-deployment#6-ray-serve) to learn more.*

### **5. AWS SageMaker**

SageMaker is Amazon’s heavyweight MLOps platform, covering everything from training to deployment, pipelines, and monitoring.

![image - 2025-06-19T211024.050.png](https://assets.northflank.com/image_2025_06_19_T211024_050_82c4f323dd.png)

**Key features:**

- End-to-end ML lifecycle
- AutoML, tuning, and pipelines
- Deep AWS integration (IAM, VPC, etc.)
- Managed endpoints and batch jobs

**Pros:**

- Enterprise-grade compliance
- Mature ecosystem
- Powerful if you’re already on AWS

**Cons:**

- Complex to set up and manage
- Pricing can spiral
- Heavy DevOps lift

**Verdict:**

Ideal for large orgs with AWS infra and compliance needs. Overkill for smaller teams or solo devs.

## How to pick the best Modal alternatives

Are you unsure which platform best suits your needs? Here’s a quick guide to the best Modal alternatives based on what you’re building.

| **Use Case** | **Best Alternative** | **Why It Fits** |
| --- | --- | --- |
| **Building a fullstack AI product (frontend, backend, APIs, models)** | [**Northflank**](https://northflank.com/) | Full-stack support, GPU orchestration, CI/CD, secure infra, and no vendor lock-in. Ideal for shipping production-ready AI products fast. |
| **Deploying a public-facing ML/AI demo or API** | [**Replicate**](https://replicate.com/) | Easiest way to host and share models with an instant REST API. Great for LLMs, diffusion models, and solo projects. |
| **Running GPU-heavy workloads on a budget** | [**RunPod**](https://www.runpod.io/) | Lowest GPU costs with full Docker/runtime control. Perfect for cost-sensitive custom ML training or inference. |
| **Turning notebooks or models into internal tools quickly** | [**Baseten**](https://www.baseten.co/) | Data scientist–friendly, with built-in UI builder, monitoring, and autoscaling. Fast MVPs without deep DevOps. |
| **Operating in a regulated, enterprise environment** | [**SageMaker**](https://aws.amazon.com/sagemaker/) | End-to-end ML lifecycle with compliance, IAM, and AWS-native services. Best for large orgs with complex infra needs. |

# Conclusion

Modal made cloud development radically accessible. By allowing developers to run Python functions without requiring infrastructure setup, it changed how people experiment, prototype, and deploy ML-powered services. For many, it’s the fastest way to get started, and it deserves credit for that.

However, as your projects evolve, from scripts to products, from demos to production, you may start to feel the constraints: limited orchestration, a lack of CI/CD, networking challenges, or the need for deeper infrastructure control.

That’s where the alternatives we explored come in. Each has its strengths: Replicate for sharing models, RunPod for raw GPU access, Baseten for internal tools, and SageMaker for enterprise pipelines.

**But if you’re looking for a platform that combines developer speed with production-level flexibility, Northflank stands out.**

With full-stack support, GPU orchestration, Git-based CI/CD, and secure deployment options (including [Bring Your Own Cloud](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes)), **Northflank helps you go from prototype to production without rethinking your stack**. It’s built for teams who want to stay fast, without hitting walls later on.

Ready to level up? [Try Northflank for free](https://app.northflank.com/signup) and deploy your first full-stack AI product in minutes, or [book a demo](https://cal.com/team/northflank/northflank-demo) to see how it can support your ML or AI workload at scale.]]>
  </content:encoded>
</item><item>
  <title>Platform June 2025 Release</title>
  <link>https://northflank.com/changelog/platform-june-2025-release</link>
  <pubDate>2025-06-30T21:00:00.000Z</pubDate>
  <description>
    <![CDATA[Added support for ARM, BYOC scalability, build caching, secret security, and more. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_june_changelog_78842491ac.png" alt="Platform June 2025 Release" />We're pleased to introduce ARM support alongside loads of other improvements.

### Infrastructure & compatibility

- Added ARM support across the platform (builds and deploys in BYOC)
- Added support for PostgreSQL 17 and CephFS with erasure coding.
- Improvements to OCI provisioning and GCP static egress IP setup.
- Support for custom launch templates on AWS with validation.

### BYOC (Bring Your Own Cloud)

- Added pod list and pod metrics to BYOC observe page.
- Added logic for setting GCP clusters to use private nodes.
- Improved validation and error feedback across AWS and Azure provisioning flows.
- Displayed provider ID on node pools and added launch template support to the node pool form.
- Added validation for total node count on Azure (< = 400).
- Ensured Ceph-enabled node pools have at least 3 nodes.
- Fixed template creation issues with BYOC clusters and improved default build plan behavior.

### Builds & caching

- Build cache now supports snapshot mode with status tracking
- Improved UI for build service selection and added fallback handling for older build data.
- Added ephemeral storage metrics to builds.

### Template editor

- Search templates by workflow type.
- Added support for random() functions in template args.
- Improved error rendering for node types and secret handling.

### Networking

- Added subdomain lists to domain view page.
- Rich metadata added to multi-project networking selectors.
- Improved error feedback for GitOps triggers with public repos.

### Secrets & security

- Gracefully handled missing global secrets in the UI.

### Addons

- Added custom backup destination support for dump backups.
- Increased restore timeout duration to 12h.
- Introduced runtime multi-arch support for addons.

### UI/UX improvements

- Updated navigation for team/org entities to match projects.
- Improved help/feedback popovers with live support links.
- Added ability to suppress “new version available” banner.
- Made domain verification steps more visually obvious.
- Improved autosuggestion UX in secrets editor.
- Jobs now display custom command overrides more clearly.

### Metrics & observability

- Included build metrics for ephemeral storage and cache volume state.
- UI improvements for node pool metrics and pod logs display.

### Stack templates
 
- New templates: PostHog, Langfuse, Temporal, Jupyter + TensorFlow (BYOC), Growthbook, Outline.
- Added custom arch display under external image inputs.]]>
  </content:encoded>
</item><item>
  <title>What are AWS Spot Instances? Guide to lower cloud costs and avoid downtime</title>
  <link>https://northflank.com/blog/spot-instances</link>
  <pubDate>2025-06-27T17:33:00.000Z</pubDate>
  <description>
    <![CDATA[Learn what Spot Instances are (AWS, GCP, Azure), when to use them, and how Northflank reduces cloud costs without failover scripts or downtime.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/spot_instances_blog_post_9eb8ed3379.png" alt="What are AWS Spot Instances? Guide to lower cloud costs and avoid downtime" />I saw a [Reddit thread](https://www.reddit.com/r/aws/comments/14htvp6/do_people_actually_use_amazon_ec2_spot/) the other day asking if anyone uses AWS Spot Instances, and most of the replies were “yeah, but only for jobs I don’t mind getting interrupted.”

That’s understandable because Spot Instances (on AWS and other clouds) are cheaper than regular ones. AWS claims it is up to [90% cheaper than On-Demand](https://aws.amazon.com/ec2/spot/), and similar pricing models exist on Google Cloud and Azure.

However, the cheap pricing comes with a condition: AWS, for example, can shut them down with just a couple of minutes’ notice whenever the capacity is needed elsewhere.

So for a lot of teams, it ends up feeling more like a gamble than a tool.

The question isn’t if Spot Instances have potential. It’s about using them without breaking things when providers like AWS shut an instance down with little warning.

That’s what this article walks through. You’ll learn:

- What Spot Instances are (in plain terms)
- Why they cost less, and how that affects your setup
- How Spot Instances compare to On-Demand, Reserved Instances, and Savings Plans
- What kinds of workloads are a good fit (and when to avoid them)
- How Spot interruptions work across cloud providers
- How teams run workflows on Spot Instances without writing failover logic
- And a step-by-step guide to running Spot Instances using Northflank, with fallback to On-Demand


<InfoBox className='BodyStyle'>

## TL;DR: Spot Instances at a glance

In case you’re running out of time, here’s a quick summary of what this article covers

- **What are Spot Instances?**
    
    Spot Instances are spare EC2 capacity that AWS sells at up to 90% off, but they can be shut down at any time.
    
    **THE SOLUTION** → P*latforms like [Northflank](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) handle automated fallback from Spot to On-Demand, so your workloads keep running without interruption.*
    
- **Why are they cheaper than On-Demand or Reserved?**
    
    They run on idle AWS infrastructure with no uptime guarantee.
    
- **When should you use Spot Instances?**
    
    They’re great for flexible workloads: ML training, CI/CD pipelines, batch jobs, and rendering.
    
- **How do Spot Instance interruptions work?**
    
    AWS gives a 2-minute warning before reclaiming the instance. Without a fallback strategy, anything running on the instance stops immediately.
    
- **What are the best practices for using Spot safely?**
    
    Use Auto Scaling, Spot Fleets, flexible instance types, and logic to fall back when needed.
    
- **How does Northflank help?**
    
    [Northflank](https://northflank.com/features/platform) lets you assign specific workloads to Spot Instances and automatically falls back to On-Demand when capacity runs out, across clouds and regions, without requiring custom fallback scripts or additional tooling.
    
</InfoBox>

## What are Spot Instances?

Spot Instances are spare EC2 virtual machines that AWS isn’t using right now. They rent them out at a steep discount, sometimes up to 90% off. It’s a clever way for AWS to make use of idle capacity while giving you access to cheaper compute.

So yes, it’s cheap, but at what cost?

AWS can reclaim a Spot Instance whenever it needs that capacity elsewhere. You typically get a 2-minute warning before shutdown, though some providers or setups may give as little as 30 seconds. Either way, the interruption is sudden.

You can picture it like this: it’s like grabbing a table at a restaurant that isn’t fully booked. You’re seated quickly and pay less, but if someone with a reservation shows up, you’re getting bumped.

See the illustration below to get a clearer picture of how Spot Instances fit into the broader EC2 capacity model:

![Diagram showing EC2 capacity split into Reserved Instances (priority), On-Demand Instances (middle priority), and Spot Instances (lowest priority, reclaimable by AWS)](https://assets.northflank.com/ec2_capacity_model_spot_on_demand_reserved_9b0cd476bd.png)*How AWS allocates EC2 capacity across Reserved, On-Demand, and Spot Instances.*

That’s why Spot Instances are best suited for:

- flexible jobs like ML training or batch data processing
- stateless tasks
- short-lived or fault-tolerant workloads

PS: You definitely don’t want to run your main production database on it.

Spot is risky for stateful workloads or critical production services like APIs. If the instance is preempted, your app can go offline with little warning.

## What’s the difference between Spot, On-Demand, and Reserved Instances?

Now that you know how Spot Instances work, it's helpful to compare them with other EC2 pricing models. 

AWS doesn’t change the infrastructure underneath; it's only the cost, availability, and level of commitment that change. 

Choosing the right one comes down to your type of workload, reliability needs, and budget.

Let’s see a breakdown to help you decide:

| **Type** | **Price** | **Uptime guarantee** | **Commitment** | **Instance flexibility** | **Best use cases** |
| --- | --- | --- | --- | --- | --- |
| **Spot Instances** | Up to 90% cheaper than On-Demand | No | None (availability depends on long-term supply and demand) | Limited to instance types and capacity currently available | ML training, large-scale batch processing, CI pipelines, rendering jobs, other fault-tolerant or short-lived workloads |
| **On-Demand** | Standard pay-as-you-go rate | Yes | None (you only pay while the instance is running) | Fully flexible - launch any available instance at any time | APIs, web servers, short-term dev/test, unpredictable usage patterns |
| **Reserved** | ~40–72% cheaper than On-Demand (depending on term/payment) | Yes | 1–3 year term (no change mid-way) | Limited - tied to a specific instance family, region, and OS | Consistent production workloads (e.g. backend services, databases) with stable usage |
| **Savings Plans** | Similar savings to Reserved, but more flexible | Yes | 1–3 year $/hour commitment (flexible usage) | High - applies across EC2, Fargate, and Lambda | Apps with steady usage but less predictable infrastructure setup (e.g. hybrid container + serverless) |

## How Spot Instance pricing and discounts work across AWS, GCP, and Azure

If you're planning to use Spot Instances as part of your cost strategy, it's helpful to understand how pricing and long-term discounts differ across cloud providers.

The table below compares how AWS, GCP, and Azure approach Spot pricing and flexible commitment models.

| **Cloud provider** | **Spot Instance name** | **Discounted commitment option** | **How it works** | **Flexibility** |
| --- | --- | --- | --- | --- |
| **AWS** | Spot Instances | Savings Plans / Reserved Instances | Spot Instances use unused capacity with up to 90% discount. Savings Plans offer cost reduction across EC2, Fargate, and Lambda. Reserved Instances lock in usage for specific instance types and regions for 1–3 years. | Savings Plans are flexible across instance types and services. Reserved Instances are locked to family, region, and OS. |
| **GCP** | Spot VMs (formerly Preemptible VMs) | Committed Use Discounts (CUDs) / FlexCUDs | Spot VMs offer up to ~80% off standard pricing and can be interrupted anytime. FlexCUDs provide 45% off with fewer restrictions than standard CUDs. | FlexCUDs apply across VM types and regions. Standard CUDs are tied to specific SKUs. |
| **Azure** | Spot Virtual Machines | Azure Savings Plans / Reserved Instances | Azure Spot lets you run workloads on unused capacity at a discount. Azure Savings Plans commit to a $/hour spend across services for 1–3 years. Reserved Instances are tied to specific VM types and regions. | Azure Savings Plans offer more flexibility than Reserved VMs, but less than on-demand. |

## When should you use Spot Instances (and when should you avoid them)?

It depends on how much your workload can tolerate interruptions and if it needs to keep running without delays or downtime.

Let’s break it down.

### When Spot Instances are useful

Spot Instances are useful when you’re running:

- **Machine learning training**: These jobs often run in parallel across multiple nodes and can handle interruptions by restarting or picking up from checkpoints. Training large models is compute-heavy, and Spot helps you save significantly on cost.
- **CI/CD pipelines**: Build and test jobs are short-lived and stateless. If a node goes away, the pipeline can rerun or retry the job without major consequences. Spot works well here since you’re not relying on long-term uptime.
- **Rendering**: Tasks like 3D rendering, video encoding, or animation can be broken into smaller, parallel workloads. If one part fails or gets interrupted, it can be requeued without affecting the rest.
- **Batch jobs and analytics**: Large data processing jobs, such as log aggregation, report generation, or ETL pipelines, can be split up and retried. These jobs don’t need to be always-on and are usually tolerant to retries or delays.

### When you should avoid using Spot Instances

Spot isn’t the best fit when you’re running workloads that need guaranteed uptime or can’t afford to be interrupted in the middle of execution. For example:

- **Stateful apps:** If your application relies on session data, caches, or keeps user state in memory, interruptions can break the experience or cause data loss. These apps need consistent availability, which Spot doesn’t guarantee.
- **Databases:** Running a primary database on Spot is risky unless you’ve built in high availability or replication. Losing a database node mid-query or during a transaction can corrupt data or trigger downtime.
- **Customer-facing services without redundancy:** If your app or API is directly serving users and you don’t have replicas or failover strategies, interruptions can lead to visible outages. Spot is too unpredictable unless you’ve built in the layers to recover instantly.

<InfoBox className='BodyStyle'>

#### NOTE
If your workload can fail without causing issues, Spot is an advisable cost-saving move. If not, use On-Demand or pair Spot with a fallback strategy using platforms like [Northflank](https://northflank.com/features/managed-cloud) so you're not caught off guard when AWS reclaims capacity.

</InfoBox>

## How an AI company used Northflank to run Spot Instances reliably (Must-read)

Earlier, we looked at how Spot Instances can work well for flexible workloads, as long as you have a way to recover when AWS takes the capacity back. Without that, teams often end up running into unexpected job failures.

That was the case for an AI voice company called [Weights](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s).

They were running GPU-heavy machine learning jobs that were well-suited for Spot: parallel tasks that didn’t need to run continuously.

The setup made sense on paper, but losing capacity in the middle of a run kept getting in the way.

With [Northflank](https://northflank.com/), they simply tagged jobs to Spot pools, and the platform handled the rest.

When Spot capacity was reclaimed, jobs automatically moved to On-Demand without needing manual configuration or infrastructure code.

It also worked across different cloud providers and zones, so they didn’t need to worry about availability gaps.

### How to use Northflank to run jobs on Spot Instances with automatic fallback (step-by-step)

The Weights team didn’t have to write fallback logic from scratch. They used built-in scheduling features in Northflank to reliably run Spot jobs without maintaining additional scripts or infrastructure tooling.

You can apply the same approach to your own cluster by following the steps below.

<InfoBox className='BodyStyle'>

Northflank makes Spot Instances practical by giving you control over where workloads run and how they respond when Spot capacity becomes unavailable. You can assign cost-sensitive jobs to Spot node pools, rely on automatic fallback to On-Demand, and scale across regions or clouds without writing custom failover scripts.

</InfoBox>

Follow the steps outlined below.

**1. Start by creating a Northflank account**

If you’re new to the platform, go to [app.northflank.com/signup](https://app.northflank.com/signup) to get started.

Once you're signed in, the [Introduction to Northflank guide](https://northflank.com/docs/v1/application/getting-started/introduction-to-northflank) explains how projects, services, jobs, and environments work so you can understand how Northflank fits into your infrastructure setup.

![Northflank signup screen with fields for username, email, and password, and options to sign up with Google, GitHub, or GitLab](https://assets.northflank.com/creating_a_northflank_account_bb133bbcd0.png)*Sign up for a Northflank account to get started with Spot workload scheduling*

**2. Connect your Kubernetes cluster**

To start using Spot Instances, you’ll need to bring your own cluster from AWS, GCP, or another cloud provider.

Northflank guides you through connecting your cluster and installing the required components to enable deployments. Once set up, you can securely deploy builds, jobs, or services to your infrastructure.

Follow the [workload deployment guide](https://northflank.com/docs/v1/application/bring-your-own-cloud/deploy-workloads-to-your-cluster) to:

- Connect your cluster to Northflank
- Set up access to your cloud provider
- Schedule workloads to your nodes

**3. Create separate node pools for Spot and On-Demand**

Inside your cluster settings in the Northflank UI, you can create dedicated node pools.

For the Spot node pool:

- Select Spot-capable instance types
- Enable the **"Use spot instances"** setting
- Optionally restrict the pool to only allow builds or jobs

For the On-Demand node pool:

- Leave the Spot option disabled
- Allow long-running services and fallback jobs to run here

This is what it looks like when setting up a Spot node pool in Northflank:

![Northflank cluster settings screen showing node pool creation with Spot Instances enabled and scheduling options visible](https://assets.northflank.com/create_node_pools_9ef6c89ebd.webp)*Creating a dedicated node pool with Spot Instances enabled in the Northflank UI*

<InfoBox className='BodyStyle'>

You can follow the full [node pool creation guide](https://northflank.com/docs/v1/application/bring-your-own-cloud/deploy-and-scale-node-pools) for more detail.

</InfoBox>

**4. Add labels to your node pools**

To control where workloads run, add **labels** to your node pools during creation. Labels are defined as name–value pairs and help guide workload scheduling decisions. For example:

- `resourceType: highCPU` for compute-optimized pools
- `availabilityZone: 1a` to assign workloads to a specific zone

You can set as many labels as you need by expanding the **Advanced** section when configuring your node pool in the Northflank UI.

Once labels are in place, you can tag your workloads with matching values to influence placement. For example, a job tagged with `resourceType: highCPU` will be scheduled onto a node pool with the same label.

The screenshot below shows how to configure a node pool with a `resourceType: highCPU` label in the Northflank UI:

![Northflank interface showing node pool settings with a label named resourceType and value highCPU](https://assets.northflank.com/node_pool_label_2240fb2cdd.webp)*Label configuration in the Northflank UI for targeting workloads to a specific node pool*

<InfoBox className='BodyStyle'>

You can find more details on how to label node pools and influence workload scheduling in the [full node pool labeling guide](https://northflank.com/docs/v1/application/bring-your-own-cloud/deploy-and-scale-node-pools#add-labels).

</InfoBox>

**5. Tag your workloads to run on Spot pools**

Once your node pools are labeled, head to your workload settings and tag the jobs you want to run on Spot instances.

These [tags](https://northflank.com/docs/v1/application/release/tag-workloads-and-resources#provision-by-tag) will match against your Spot pool’s labels. Northflank then attempts to deploy those jobs to the Spot node pool first. If there’s no available capacity, it will fall back to a matching On-Demand pool.

> [See how to deploy workloads using node pool tags and labels](https://northflank.com/docs/v1/application/bring-your-own-cloud/deploy-workloads-to-your-cluster#deploy-workloads-to-specific-node-pools)
> 


<InfoBox className='BodyStyle'>

Nice Work! If you followed these steps and used the docs to guide setup, your team now has a reliable way to run cost-sensitive jobs on Spot instances with fallback to On-Demand.

Meaning that your team no longer has to worry about managing failover logic or handling unexpected Spot interruptions manually.

</InfoBox>

## Should you start using Spot Instances? (Wrapping up)

If your workloads can handle interruptions, Spot Instances are one of the simplest ways to lower cloud costs.

At the same time, you might still need the stability of On-Demand Instances, particularly for production workflows or time-sensitive jobs.

Platforms like [Northflank](https://northflank.com/features/platform) let you use both Spot and On-Demand. It assigns cost-sensitive workloads to Spot Instances and automatically switches to On-Demand when capacity runs out.

You don’t need to write fallback scripts or set up custom infrastructure, and it works across regions and clouds.

<InfoBox className='BodyStyle'>

[Try it on Northflank](https://app.northflank.com/signup) if you want to run Spot Instances with less manual setup and more reliability.

</InfoBox>

## FAQs about Spot Instances (12 questions answered)

You'll find answers to some of the most commonly asked questions about Spot Instances.

1. **What is a Spot Instance?**
    
    A Spot Instance is a virtual machine that uses unused cloud capacity, most commonly in AWS. It comes at a lower price but can be interrupted at any time when the provider needs that capacity back. Platforms like [Northflank](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) let you use Spot Instances for jobs or builds, with automatic fallback to On-Demand instances if capacity runs out.
    
2. **What is a Spot Instance in simple words?**
    
    It's a cheaper cloud server you can use when there's spare capacity, but the provider can take it back anytime. With platforms like [Northflank](https://northflank.com/features/platform), you don’t need to handle that interruption logic yourself.
    
3. **How much cheaper are Spot Instances?**
    
    They can be up to 90% cheaper than On-Demand instances, depending on the region and instance type. [Northflank](https://northflank.com/features/build) helps you take advantage of this by assigning cost-sensitive workloads to Spot node pools automatically.
    
4. **Are Spot Instances worth it?**
    
    Yes, especially if you're running flexible or retryable workloads like CI pipelines, batch jobs, or machine learning training. [Northflank](https://northflank.com/features/run) supports these kinds of jobs with automatic rescheduling if a Spot instance is reclaimed.
    
5. **How long do Spot Instances last?**
    
    There’s no fixed duration. A Spot Instance might run for hours or get interrupted after a few minutes. Platforms like [Northflank](https://northflank.com/use-cases/production-workloads-deployment-platform) let you schedule jobs with fallback to ensure they still run even if a Spot pool becomes unavailable.
    
6. **Can Spot Instances be stopped?**
    
    Yes. You can stop or terminate them from your side, but the provider (like AWS) can also terminate them with a short warning. With [Northflank](https://northflank.com/features/platform), if this happens, your jobs are automatically retried on an On-Demand pool.
    
7. **What is the difference between Spot Instances and On-Demand Instances?**
    
    On-Demand Instances are reliable and uninterrupted, but come at a higher cost. Spot Instances are cheaper but can be reclaimed at any time. Northflank helps you combine both: use Spot first, then fall back to On-Demand only if needed.
    
8. **What is the difference between Spot Instances and Reserved Instances?**
    
    Reserved Instances are long-term commitments (1–3 years) with guaranteed availability at a discounted rate. Spot Instances are short-term and interruptible, but much cheaper. Platforms like Northflank are better suited for Spot use cases where you want low cost without long commitments.
    
9. **What is the difference between Spot Instances and Savings Plans?**
    
    Savings Plans give you a discount in exchange for a steady usage commitment over time. Spot Instances have no commitment, but pricing and availability can change quickly. Northflank doesn’t require you to lock in a plan; you just choose how to schedule workloads.
    
10. **Are Spot Instances cheaper than Reserved Instances?**
    
    Yes, usually. Spot Instances offer bigger discounts because they’re not guaranteed to run continuously. Reserved Instances are more stable, but cost more than Spot. Northflank lets you choose what’s best for your jobs based on priority and budget.
    
11. **How to set up Spot Instances?**
    
    You can configure them directly through AWS or by using orchestration platforms like Northflank, which lets you assign labels and tags to route workloads to Spot or On-Demand pools with minimal setup.
    
12. **What are the risks of Spot Instances?**
    
    The main risk is that they can be interrupted at any time. Without a fallback mechanism, your jobs could fail or be delayed. Platforms like [Northflank](https://northflank.com/use-cases/disaster-recovery-for-kubernetes) help reduce this risk by automatically shifting jobs to On-Demand pools when Spot capacity isn’t available.]]>
  </content:encoded>
</item><item>
  <title>RunPod alternatives for AI/ML deployment beyond just a container</title>
  <link>https://northflank.com/blog/runpod-alternatives-for-ai-ml-deployment</link>
  <pubDate>2025-06-27T15:45:00.000Z</pubDate>
  <description>
    <![CDATA[RunPod makes it easy to deploy GPU-backed APIs from Docker containers, ideal for quick demos. But it lacks production features like CI/CD, scaling, and observability—where tools like Northflank shine.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/paas_providers_2_5ce15110bf.png" alt="RunPod alternatives for AI/ML deployment beyond just a container" />RunPod offers a compelling proposition: take your model, drop it into a Docker container, and instantly get a GPU-backed API. No infrastructure to manage or cloud headaches. Just raw speed.

And to be fair, that’s exactly what it delivers.

If you’re building in a notebook, testing a new checkpoint, or trying to build a demo for investors quickly, RunPod is a dream. You can get an A100-powered endpoint running in under ten minutes. No AWS, Terraform, or ops team.

But once your demo turns into a production-ready product, the magic starts to fade. You hit the scaling walls. You end up hacking around missing infrastructure, things a production platform would handle natively.

This article breaks down why RunPod falls short once you scale, and walks through the best alternatives depending on what you’re actually building, whether that’s a full-stack AI product, an LLM microservice, a RAG agent, or a managed model API.

## TL;DR – Top Runpod alternatives

If you're short on time, here’s a snapshot of the top RunPod alternatives. Each tool has its strengths, but they solve different problems, and some are better suited for real-world production than others.

| Platform | Best For | Why It Stands Out |
| --- | --- | --- |
| [**Northflank**](https://northflank.com/) | LLMs, APIs, GPUs, full-stack AI infra | GPU containers, Git-based CI/CD, AI workload support, BYOC, secure runtime, and enterprise-ready features |
| **Replicate** | Sharing public ML models easily | Ideal for demos and generative models, with public API hosting |
| **Modal** | Python-first, async jobs, fast iteration | Serverless feel, good for batch workflows |
| **Vertex AI** | GCP-native ML workflows | Great for GCP orgs, less flexible |
| **SageMaker** | Enterprise ML pipelines | Deep AWS integration, but heavyweight |
| **Hugging Face** | Simple LLM APIs from HF-hosted models | Fast setup for popular Hugging Face models, but limited customization |

## What makes RunPod stand out at first?

The genius of RunPod is that it skips everything: infra provisioning, CI/CD, scaling logic, cloud permissions, container registries, load balancers.

All you need is a Docker image and a wallet.

This makes it ideal for:

- Solo builders shipping MVPs
- Tinkering with open-source checkpoints
- GPU benchmarking and quick-turnaround jobs
- Short-term deployments for demos or internal teams

For this use case, it’s nearly perfect. It’s cheaper than Modal, boots faster than SageMaker, and requires zero vendor-specific SDKs.

But if you’re building something production-ready, a customer-facing app, or an inference API that actually needs to stay up and scale under load, RunPod becomes a liability.

## RunPod is not a platform — it’s just a runtime

At its core, RunPod is a way to rent a container on a GPU, and not much else.

It doesn't manage your deployment lifecycle. It doesn’t help you build safe deploy pipelines, expose metrics, store logs, track uptime, handle auto-scaling, or isolate dev vs. prod.

You bring your container. You run it. That’s it.

What feels like "simplicity" at first turns out to be just absence. There's no platform here. Just compute.

## What are the limitations of RunPod?

Once you try to scale, RunPod’s limitations become blockers.

### 1. No git-connected deploys

RunPod doesn’t connect to GitHub, GitLab, or any CI/CD provider. There’s no native pipeline, rollback, or tagging. You’re managing builds manually, pushing containers by hand, restarting pods, and hoping nothing breaks.

Platforms like **[Northflank](https://northflank.com/)** connect directly to your Git repos and CI pipelines. Every commit can trigger a build, preview, or deploy automatically. No custom scripts required.

### 2. No environment separation

Everything you launch goes straight to production. There’s no staging, preview branches, or room for safe iteration.

This kills experimentation. There’s nowhere to test model variations or feature branches without risking live traffic.

Platforms like **Northflank** provide full environment separation by default, with staging, previews, and production all isolated and reproducible.

### 3. No metrics, logs, or observability

If your model gets slow or crashes, you’re flying blind. No Prometheus, request tracing, or logs unless you manually SSH and tail them.

There’s no monitoring stack. You can't answer basic questions like: How many requests are failing? How many tokens per second? GPU utilization?

With platforms like **Northflank**, observability is built in. Logs, metrics, traces, everything is streamed, queryable, and tied to the service lifecycle.

### 4. No auto-scaling or scheduling

You can’t scale pods based on demand. There’s no job queue. No scheduled retries. Every container is static. That means overprovisioning and paying for idle GPU time, or building your own orchestration logic.

By default, Northflank supports [autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments), scheduled jobs, and queue-backed workers, making elastic GPU usage feel native.

### 5. No multi-service deployments

RunPod can run one thing: a container. If you need a frontend, a backend API, a queue, a DB, a cache? You’re cobbling together services across platforms. That fragmentation adds latency, complexity, and risk.

**Northflank** treats multi-service apps as first-class citizens. You can deploy backends, frontends, databases, and cron jobs—fully integrated, securely networked, and observable in one place.

### 6. No secure runtime for untrusted workloads

RunPod is built for trusted team environments, but it doesn’t offer secure runtime isolation for executing untrusted or third-party code. There’s no built-in sandboxing, syscall filtering, or container-level hardening. If you're running workloads from different tenants or just want extra guarantees around runtime isolation, you’ll need to engineer those protections yourself.

By contrast, **Northflank** containers run in secure, hardened sandboxes with configurable network and resource isolation, making it easier to host untrusted or multitenant workloads out of the box safely.

### 7. No Bring your own cloud (BYOC)

RunPod runs on its own infrastructure. There’s no option to deploy into your own AWS, GCP, or Azure account. That means: **no VPC peering, private networking, or compliance guarantees** tied to your organization's cloud, and **no control over regions**, availability zones, or IAM policies. If your organization needs to keep workloads within a specific cloud boundary for compliance, cost optimization, or integration reasons, RunPod becomes a non-starter.

By contrast, platforms like **Northflank support [BYOC](https://northflank.com/features/bring-your-own-cloud)**, letting you deploy services into your own cloud infrastructure while still using their managed control plane.

## What to look for in a Runpod alternative

RunPod works if all you need is a GPU and a container.

But production-ready AI products aren’t just containers. They’re distributed systems. They span APIs, workers, queues, databases, model versions, staging environments, and more. That’s where RunPod starts to fall short.

As soon as you outgrow the demo phase, you’ll need infrastructure that supports:

- **CI/CD with Git integration** – Ship changes confidently, not by SSH.
- **Rollbacks and blue-green deploys** – Avoid downtime, roll back instantly.
- **Health checks and probes** – Know when something’s broken *before* your users do.
- **Versioned APIs and rate limiting** – Manage usage and backward compatibility.
- **Secrets and config management** – Keep credentials out of code.
- **Staging, preview, and production environments** – Test safely before shipping.
- **Scheduled jobs and async queues** – Move beyond synchronous APIs.
- **Observability: logs, metrics, traces** – Understand and debug your system.
- **Multi-region failover** – Stay online even when a zone isn’t.
- **Secure runtimes** – Safely run third-party or multitenant code.
- **Bring Your Own Cloud (BYOC)** – Deploy where *you* control compliance and cost.

You’re not just renting a GPU.

You’re building a platform that's resilient, observable, and secure. You need infrastructure that thinks like that too.

## Top Runpod alternatives

Here is a list of the best Runpod alternatives you can find. In this section, we talk about each platform in depth, its top features, pros, and cons.

### 1. Northflank – The best RunPod alternative for production AI

[**Northflank**](https://northflank.com/) isn’t just a model hosting tool; it’s a **production-grade platform for deploying and scaling production-ready AI products**. It combines the flexibility of containerized infrastructure with GPU orchestration, Git-based CI/CD, and full-stack app support.

Whether you're serving a fine-tuned LLM, hosting a Jupyter notebook, or deploying a full product with both frontend and backend, Northflank gives you everything you need, with none of the platform lock-in.

![image - 2025-06-19T211009.037.png](https://assets.northflank.com/image_2025_06_19_T211009_037_2419b18f99.png)

**Key features:**

- Bring your own Docker image and full runtime control
- GPU-enabled services with [autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments) and lifecycle management
- Multi-cloud and [Bring Your Own Cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) support
- [Git-based CI/CD](https://northflank.com/docs/v1/application/release/manage-ci-cd), [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), and full-stack deployment
- Secure runtime for untrusted AI workloads
- SOC 2 readiness and enterprise security (RBAC, SAML, audit logs)

**Pros:**

- **No platform lock-in** – full container control with BYOC or managed infrastructure
- **Transparent, predictable pricing** – [usage-based](https://northflank.com/pricing) and easy to forecast at scale
- **Great developer experience** – Git-based deployments, CI/CD, preview environments
- **Optimized for latency-sensitive workloads** – fast startup, GPU autoscaling, low-latency networking
- **Supports AI-specific workloads** – Ray, LLMs, Jupyter, fine-tuning, inference APIs
- **Built-in cost management** – real-time usage tracking, budget caps, and optimization tools

**Cons:**

- No special infrastructure tuning for model performance.

**Verdict:** 

If you're building production-ready AI products, not just prototypes, Northflank gives you the flexibility to run anything from Ray clusters to full-stack AI apps in one place. With built-in CI/CD, GPU orchestration, and secure multi-cloud support, it's the only platform designed for teams who need speed *and* control without getting locked in.

*See how [Weights uses Northflank to build a GPU-optimized AI platform for millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### 2. Replicate

Replicate is purpose-built for public APIs and demos, especially for generative models. You can host and monetize models in just a few clicks.

![image - 2025-06-19T211017.564.png](https://assets.northflank.com/image_2025_06_19_T211017_564_c7edd8f0e4.png)

**Key features:**

- Model sharing and monetization
- REST API for every model
- Popular with LLMs, diffusion, and vision models
- Built-in versioning

**Pros:**

- Zero setup for public model serving
- Easy to showcase or monetize models
- Community visibility

**Cons:**

- No private infra or BYOC
- No CI/CD or deployment pipelines
- Not built for production-ready apps or internal tooling

**Verdict:**

Great for showcasing generative models, not for teams deploying private, production workloads.

### 3. Modal

Modal makes Python deployment effortless. Just write Python code, and it handles scaling, packaging, and serving — perfect for workflows and batch jobs.

![image - 2025-06-19T211013.585.png](https://assets.northflank.com/image_2025_06_19_T211013_585_7160b4aa37.png)

**Key features:**

- Python-native infrastructure
- Serverless GPU and CPU runtimes
- Auto-scaling and scale-to-zero
- Built-in task orchestration

**Pros:**

- Super simple for Python developers
- Ideal for workflows and jobs
- Fast to iterate and deploy

**Cons:**

- Limited runtime customization
- Not designed for full-stack apps or frontend support
- Pricing grows with always-on usage

**Verdict:**

A great choice for async Python tasks and lightweight inference. Less suited for full production systems.

### 4. Vertex AI

Vertex AI is Google Cloud’s managed ML platform for training, tuning, and deploying models at scale.

![image - 2025-06-23T170636.235.png](https://assets.northflank.com/image_2025_06_23_T170636_235_c0b84ecd33.png)

**Key features:**

- AutoML and custom model support
- Built-in pipelines and notebooks
- Tight GCP integration (BigQuery, GCS, etc.)

**Pros:**

- Easy to scale with managed services
- Enterprise security and IAM
- Great for GCP-based teams

**Cons:**

- Locked into the GCP ecosystem
- Pricing can be unpredictable
- Less flexible for hybrid/cloud-native setups

**Verdict:**

Best for GCP users who want a full-featured ML platform without managing infra.

### 5. AWS SageMaker

SageMaker is Amazon’s heavyweight MLOps platform, covering everything from training to deployment, pipelines, and monitoring.

![image - 2025-06-19T211024.050.png](https://assets.northflank.com/image_2025_06_19_T211024_050_82c4f323dd.png)

**Key features:**

- End-to-end ML lifecycle
- AutoML, tuning, and pipelines
- Deep AWS integration (IAM, VPC, etc.)
- Managed endpoints and batch jobs

**Pros:**

- Enterprise-grade compliance
- Mature ecosystem
- Powerful if you’re already on AWS

**Cons:**

- Complex to set up and manage
- Pricing can spiral
- Heavy DevOps lift

**Verdict:**

Ideal for large orgs with AWS infra and compliance needs. Overkill for smaller teams or solo devs.

### 6. Hugging Face

Hugging Face is the industry’s leading hub for open-source machine learning models, especially in NLP. It offers tools for accessing, training, and lightly deploying transformer-based models.

![image - 2025-06-25T171142.718.png](https://assets.northflank.com/image_2025_06_25_T171142_718_7d54da0df4.png)

**Key Features**:

- Model Hub with 500k+ open-source models
- Inference Endpoints (managed or self-hosted)
- AutoTrain for low-code fine-tuning
- Spaces for demos using Gradio or Streamlit
- Popular `transformer` Python library

**Pros**:

- Best open-source model access and community
- Excellent for experimentation and fine-tuning
- Seamless integration with most ML frameworks

**Cons**:

- Deployment and production support is limited
- Infrastructure often needs to be supplemented (e.g., for autoscaling or CI/CD)
- Not designed for tightly coupled workflows or microservice architectures

**Verdict**:

Hugging Face is a powerhouse for research and prototyping, especially when working with transformers. But when it comes to robust deployment pipelines and full-stack application delivery, it’s often used alongside a platform like Northflank to fill the operational gaps.

## **How to choose the right Runpod alternative**

| **If you're...** | **Choose** | **Why** |
| --- | --- | --- |
| Building a fullstack AI product with APIs, frontend, models, and app log. | [**Northflank**](https://northflank.com/) | Full-stack deployments with GPU support, CI/CD, autoscaling, secure isolation, and multi-service architecture. Designed for production workloads. |
| Sharing generative models or quick demos publicly | **Replicate** | Easiest way to serve and monetize models publicly with minimal setup. Great for LLMs, diffusion, and vision demos. |
| Running async Python jobs or workflows | **Modal** | Python-first serverless platform. Ideal for batch tasks, background jobs, and function-style workloads. |
| Deep in the GCP ecosystem | **Vertex AI** | Seamlessly integrates with GCP tools like BigQuery and GCS. Good for teams already using Google Cloud services. |
| In an enterprise AWS environment | **SageMaker** | Powerful but complex. Best if you’re already managing infra in AWS and need compliance, IAM, and governance tooling. |
| Experimenting with transformer models or fine-tuning | **Hugging Face** | Excellent for research, pretraining, and community models. Simple inference and fine-tuning, but lacks ops features. |

### Why Northflank should be your default

[Northflank](https://northflank.com/) is the only platform designed to support ML systems end-to-end. It gives you everything RunPod leaves out:

- Git-based CI/CD pipelines
- Autoscaling GPU containers
- Preview environments and safe rollbacks
- Background jobs and async queues
- Logs, traces, and metrics
- Environment separation and secure runtime isolation
- Bring Your Own Cloud or run on managed infrastructure

RunPod is just a runtime. Northflank is infrastructure.

If you're moving beyond a prototype, Northflank should be your default starting point.

## Conclusion

RunPod is optimized for speed and simplicity, not for the complexity of real-world ML systems.

It solves a narrow problem: *“I need a GPU now,”* but it stops short of the bigger challenges: observability, deployment flows, CI/CD, system reliability, cost controls, and runtime security.

And that’s fine, if you’re shipping throwaway demos.

But if you’re building a product? You need more than a GPU with a web URL. You need infrastructure that supports your team, your users, and your roadmap.

**That’s where Northflank comes in.**

Northflank gives you the power of GPUs *and* the platform around them, Git-connected deploys, secure sandboxes, job scheduling, observability, and full system orchestration.

**Ready to build AI products, not just containers?**

[Sign up for free](https://app.northflank.com/signup) or [schedule a demo](https://cal.com/team/northflank/northflank-demo) to see what your infra could look like.]]>
  </content:encoded>
</item><item>
  <title>An engineer’s guide to open source AI models</title>
  <link>https://northflank.com/blog/an-engineers-guide-to-open-source-ai-models</link>
  <pubDate>2025-06-25T21:00:00.000Z</pubDate>
  <description>
    <![CDATA[Open source AI models give you cost-effective alternatives to proprietary solutions with full control over your stack. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/open_source_ai_models_c8b98c5149.png" alt="An engineer’s guide to open source AI models" />Open source AI models give you cost-effective alternatives to proprietary solutions with full control over your stack. 

From Llama 4 for chat to Whisper for speech, these models offer enterprise-grade capabilities without vendor lock-in. 

The challenge? Moving from notebooks to production requires proper infrastructure with autoscaling, APIs, and observability. 

[Northflank](http://northflank.com/) simplifies this with container-based deployment, built-in CI/CD, and GPU support,  letting small teams scale to millions of users without a dedicated DevOps team.

<InfoBox className='BodyStyle'> 

## ⏳ TL;DR

**Open source AI models** are downloadable ML models with open weights and code. You can run, fine-tune, and deploy them on your own infra, no vendor lock-in or per-token pricing.

**Popular types:**

- **LLMs:** Llama 4, DeepSeek-V3, Phi-3 Mini (chat, reasoning)
- **Speech:** Whisper, XTTS-v2 (transcription, voice cloning)
- **Video:** AnimateDiff, CogVideoX (image animation, text-to-video)
- **Multimodal:** Llama 4 Maverick (text + image)

[Here's how to self-host Deepseek.](https://northflank.com/blog/self-host-deepseek-r1-on-aws-gcp-azure-and-k8s-in-three-easy-steps)

**Why use them:** Full control, lower cost, better data privacy, custom tuning.

**What matters:**

- License (check for commercial use)
- Model size vs performance (3B–70B is sweet spot)
- Hardware needs (GPU VRAM)
- Ecosystem (vLLM, HF Transformers)

**Deploying is the hard part.**

You need autoscaling, APIs, GPU orchestration, CI/CD, observability.

**Northflank handles it:**

Deploy open source models in containers with built-in CI/CD, GPU support, BYOC, and full observability. Run LLMs, APIs, schedulers, and vector DB, all on one platform. No infra team required.

</InfoBox>

## What are open source AI models?

Open source AI models are machine learning models whose weights, architecture, and often training code are freely available for use, modification, and distribution. Unlike proprietary models locked behind APIs, these models can be downloaded, fine-tuned, and deployed on your own infrastructure.

**Key benefits of open source AI models:**

- **Cost control**: No per-token pricing or usage limits beyond your infrastructure costs
- **Data sovereignty**: Your data never leaves your systems
- **Customization freedom**: Fine-tune models for your specific use cases
- **No vendor lock-in**: Switch providers or go fully self-hosted anytime
- **Transparency**: Full visibility into model architecture and training procedures

**Types of open source AI models:**

- **Large Language Models (LLMs)**: Text generation, chat, and reasoning
- **Speech models**: Text-to-speech synthesis and speech recognition
- **Vision models**: Image generation, analysis, and processing
- **Video models**: Video generation and editing capabilities
- **Multimodal models**: Combined text, image, and audio understanding

## What to look for when choosing open source AI models

Selecting the right open source model requires evaluating several critical factors beyond just performance benchmarks.

**License considerations** are paramount. Models like Llama 3.3 use custom licenses that allow commercial use but with restrictions for large-scale services. MIT and Apache 2.0 licensed models offer more permissive terms. Always verify license compatibility with your intended use case.

**Hardware requirements** directly impact your deployment costs. A 7B parameter model might run efficiently on consumer GPUs, while 70B+ models require enterprise hardware or multi-GPU setups. Consider memory requirements, inference speed, and whether the model supports optimization techniques like quantization.

**Ecosystem support** determines how quickly you can move from experimentation to production. Models with strong community backing typically offer better documentation, deployment tools, and troubleshooting resources. Integration with frameworks like Hugging Face Transformers, vLLM, or TensorRT can significantly accelerate development.

**Model size versus performance** represents the classic tradeoff in AI deployment. Larger models generally provide better quality but at higher computational and latency costs. The key is finding the smallest model that meets your quality requirements.

## Best open source AI models by category

Below you can find a list of the best open source AI models.

### Large Language Models (LLMs)

**Llama 4 Scout and Maverick** represent the cutting edge of open source multimodal AI. Meta's latest models introduce Scout (17B active parameters with 109B total parameters) and Maverick (17B active parameters with 400B total parameters). Llama 4 Scout dramatically increases the supported context length from 128K in Llama 3 to an industry leading 10 million tokens, while Llama 4 Maverick exceeds comparable models like GPT-4o and Gemini 2.0 on coding, reasoning, multilingual, long-context, and image benchmarks. Both models feature native multimodality with early fusion, seamlessly integrating text and vision capabilities.

**DeepSeek-V3** represents the pinnacle of open source language modeling. DeepSeek-V3 is a 671B-parameter open-source LLM that truly rivals closed-source heavyweights like Sonnet 3.5 and GPT-4o. While resource-intensive, it delivers frontier-level performance for applications requiring maximum capability.

**Phi 3 Mini** excels in resource-constrained environments. Phi 3 Mini is an open source instruct-tuned LLM by Microsoft that achieves state of the art performance for models of its size at just 3.8 billion parameters. Despite its compact size, it offers impressive capabilities with both 4k and 128k context variants.

**Mixtral 8x7B** provides an excellent balance of performance and efficiency through its Mixture of Experts architecture. The model offers strong multilingual capabilities and function calling support while maintaining reasonable resource requirements.

**Qwen 3 32B** delivers advanced reasoning with hybrid thinking modes. Qwen3 models introduce a hybrid approach to problem-solving. They support two modes: Thinking Mode where the model takes time to reason step by step before delivering the final answer for complex problems, and Non-Thinking Mode for quick, near-instant responses suitable for simpler questions. Pre-trained on approximately 36 trillion tokens covering 119 languages and dialects, Qwen3-32B demonstrates competitive performance against larger models while offering flexible reasoning budget control and improved agentic capabilities including enhanced MCP support.

### Speech AI models

**Whisper** remains the gold standard for speech recognition. OpenAI's model offers robust multilingual support and handles various audio conditions effectively, making it ideal for transcription services and voice interfaces.

**XTTS-v2** excels at voice cloning applications. XTTS-v2 is capable of cloning voices into different languages with just a quick 6-second audio sample. This efficiency eliminates the need for extensive training data, making it an attractive solution for voice cloning and multilingual speech generation.

**ChatTTS** focuses on conversational applications. ChatTTS is a voice generation model designed for conversational applications, particularly for dialogue tasks in LLM assistants, offering natural speech synthesis optimized for interactive use cases.

**MeloTTS** provides multilingual capabilities with real-time performance. MeloTTS offers a broad range of languages and accents. A key highlight is the ability of the Chinese speaker to handle mixed Chinese and English speech, making it valuable for international applications.

### Video AI models

**CogVideoX** leads open source video generation with its ability to create high-quality video sequences from text prompts. The model offers various parameter sizes to balance quality and computational requirements.

**Stable Video Diffusion** extends Stability AI's diffusion approach to video generation, providing controllable video synthesis capabilities for creative applications.

**AnimateDiff** specializes in animating static images, offering an accessible entry point for video generation without requiring complex video training data.

## Performance tradeoffs: Size, speed, and accuracy

| Model Category | Model | Parameters | Speed | Quality | GPU memory | Use case |
| --- | --- | --- | --- | --- | --- | --- |
| **LLMs** | Phi 3 Mini | 3.8B | Fast | Good | 8GB | Edge/mobile apps |
|  | Qwen 3 32B | 32B | Fast | Very Good | 64GB | Reasoning/multilingual |
|  | Llama 4 Scout | 17B active (109B total) | Fast | Very Good | 24GB | General chat/long context |
|  | Llama 4 Maverick | 17B active (400B total) | Moderate | Excellent | 80GB+ | Multimodal production |
|  | DeepSeek-V3 | 671B | Slow | Frontier | 80GB+ | Research/premium |
| **Speech** | Whisper Base | 74M | Very Fast | Good | 1GB | Real-time transcription |
|  | Whisper Large | 1.55B | Moderate | Excellent | 6GB | High-quality transcription |
|  | XTTS-v2 | ~2B | Moderate | Very Good | 8GB | Voice cloning |
| **Video** | AnimateDiff | ~860M | Moderate | Good | 12GB | Image animation |
|  | CogVideoX-2B | 2B | Slow | Very Good | 18GB | Text-to-video |

The general pattern shows smaller models offer faster inference and lower resource requirements but sacrifice some quality. The sweet spot for most production applications falls in the 7B-70B range for LLMs, where you get strong performance without requiring specialized infrastructure.

## From notebooks to production: The deployment challenge

Running a model in a Jupyter notebook bears little resemblance to production deployment. Production AI applications require:

**Scalable infrastructure** that can handle varying loads without manual intervention. Your application might see 10 requests per minute during quiet periods and 1,000 requests per minute during peak times.

**Robust APIs** with proper error handling, rate limiting, and monitoring. A simple model inference becomes complex when you add authentication, logging, and health checks.

**Observability and monitoring** to track model performance, latency, and resource utilization. You need visibility into both technical metrics and business outcomes.

**CI/CD pipelines** for updating models and deploying new versions without downtime. Model updates shouldn't require manual server management.

**Resource optimization** including GPU utilization, autoscaling, and cost management. GPUs are expensive, and inefficient usage directly impacts your bottom line.

Most teams underestimate this complexity. What works for experimentation often breaks under production load, leading to weeks of infrastructure work instead of product development.

## Northflank: Production ready AI deployment

This is where Northflank transforms your AI deployment experience. Instead of spending months building infrastructure, you get production-ready deployment in minutes.

**Container-based deployment with GPU support** means you can package your AI models with their dependencies and deploy across multiple cloud providers. Whether you're running Mistral with vLLM or setting up a custom text-generation-webui, Northflank handles the orchestration.

**Built-in CI/CD eliminates deployment friction**. Connect your GitHub repository, configure your Dockerfile, and Northflank automatically builds and deploys your models. Updates become as simple as pushing code.

**Autoscaling responds to demand automatically**. Your AI services scale up during traffic spikes and scale down during quiet periods, optimizing both performance and costs without manual intervention.

**Comprehensive observability** provides insight into your AI workloads. Track inference latency, GPU utilization, and error rates through integrated monitoring and logging.

The [Weights case study](https://www.notion.so/1b86d14c785180d2a570c610d056fba4?pvs=21) demonstrates this in action. JonLuca DeCaro, founder of Weights and former engineer at Citadel and Pinterest, could have built his own infrastructure from scratch. Instead, he used Northflank to scale Weights into a multi-cloud, GPU-optimized AI platform serving millions.

**The results speak for themselves:** With 9 clusters across AWS, GCP, and Azure, 40+ microservices, 250+ concurrent GPUs, 10,000+ AI training jobs and half a million inference runs per day, Weights operates at scale - and does it so seamlessly that most Series B+ startups wish they could be them.

**Practical deployment example**: Deploying Mistral 7B with vLLM on Northflank requires just a Dockerfile and configuration. The platform handles GPU scheduling, load balancing, and scaling automatically. 

> "We cut our model loading time from 7 minutes to 55 seconds with Northflank's multi-read-write cache layer - that's direct savings on our GPU costs."
> 

## Why you should deploy your AI workloads on Northflank

**Bring Your Own Cloud (BYOC)** gives you the flexibility to deploy on AWS, GCP, or Azure while maintaining control over your infrastructure and data. You get the benefits of managed services without vendor lock-in.

**Full workload support** means Northflank isn't just for AI models. You can run your entire application stack - databases, APIs, background jobs, and AI services - on the same platform with unified monitoring and management.

**Stateless and stateful applications side-by-side** let you build complete AI-powered applications. Run your vector databases alongside your embedding models, your chat APIs next to your LLMs, all with consistent deployment and scaling patterns.

As JonLuca puts it: 

> "If we didn't have Northflank managing everything, just keeping track of the Kubernetes clusters, setting up registries, actually running all of it - I think it's three to five people at this point."
> 

For AI teams, this translates to faster time-to-market, lower operational overhead, and the ability to focus on what matters: building AI applications that solve real problems.

**Speed is everything in AI development**. 

> "Now that something like Northflank exists, there's no reason not to use it. It'll let you move faster, figure out what your company is doing, save you money, and save you time."
>

[Deploy your first open source AI model on Northflank today.](https://app.northflank.com/signup)]]>
  </content:encoded>
</item><item>
  <title>12 UK-based AI startups crushing it right now</title>
  <link>https://northflank.com/blog/top-uk-based-ai-startups</link>
  <pubDate>2025-06-25T21:00:00.000Z</pubDate>
  <description>
    <![CDATA[As a UK-based company, we at Northflank are always rooting for other startups in the Land of Shakespeare and Shepherd’s Pie.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/uk_startups_1_70ca61e780.png" alt="12 UK-based AI startups crushing it right now" />As a UK-based company, we at Northflank are always rooting for other startups in the Land of Shakespeare and Shepherd’s Pie. Having spent five years as a VC, startups still catch my eye, inlcuindg UK AI startups. I’ll highlight products we use at Northflank and others that stand out for their ingenuity. 

I’ve tried to highlight what makes these companies unique and why they’ll matter in the long run. 

Keep in mind, this is just an outsider’s perspective, if anything here sparks your curiosity, I encourage you to check them out for yourself.

This isn’t a complete list, but it’s a snapshot of some of the most compelling UK AI startups we’ve seen recently.

## Some of the most interesting UK AI startups right now

We’ve grouped these by company, with a quick snapshot of what each one is building and where they’re based.

| **Company** | **Headquarters**  | **Total funding (USD)** | **Summary** |
| --- | --- | --- | --- |
| [**V7 Labs (V7)**](https://www.v7labs.com/) | London | $43 million | Turning annotated data directly into self-improving AI agents. |
| [**Granola**](https://www.granola.ai/) | London | $67 million | Enabling infinite recall of every conversation you’ve ever had. |
| [**Metaview**](https://www.metaview.ai/) | London | ~$50 million | Transforming interview content into actionable hiring intelligence. |
| [**Attio**](https://attio.com/) | London | ~$64 million | Making CRM powerful yet effortless for end-users. |
| [**VEED**](https://www.veed.io/tools/ai-video) | London | $35 million | Empowering anyone to create professional videos through intuitive text-based editing. |
| [**CuspAI**](https://www.cusp.ai/1) | Cambridge (UK) | $30 million | Turning materials science into an AI-driven search problem. |
| [**Latent Labs**](https://www.latentlabs.com/) | London & San Francisco | $50 million | Accelerating biology by designing protein drugs on-demand. |
| [**Nscale**](https://www.nscale.com/) | London | $155 million | Turning renewable energy into AI factories. |
| [**FluidStack**](https://www.fluidstack.io/) | London | ~$4.5 million | Instantly unlocking GPU capacity through a self-reinforcing marketplace. |
| [**ElevenLabs**](http://elevenlabs.io/) | London & New York | $281 million | Building the definitive marketplace for realistic voice clones. |
| [**Fyxer AI**](https://www.fyxer.com/) | London | $10 million | Delivering AI-powered executive assistance built on real-world EA experience. |
| [**Wordsmith AI**](https://www.wordsmith.ai/) | Edinburgh (UK) | ~$30 million | Automating contract review to accelerate deal velocity. |

### **[V7 Labs](https://www.v7labs.com/)**

![CleanShot 2025-06-26 at 14.01.13@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_26_at_14_01_13_2x_a5e63c8463.png)

Most people know V7 for Darwin, its lightning-fast annotation platform. What’s less obvious is how V7 pairs that creator layer with V7 Go, a no-code “chain-of-thought” engine that turns the freshly-labeled data into multi-step AI agents. 

Darwin’s SAM-2 and Auto-Annotate tools can segment anything from CT lesions to assembly-line parts in minutes, cutting label time by up to 95% and feeding a repository that now spans medical, industrial, and RLHF tasks. A 40k+ expert workforce keeps quality high and continuously expands that dataset across new verticals.

V7 Go breaks knowledge-work into steps that large-language models execute with explicit chain-of-thought reasoning, no API keys or coding required. The same interface pulls in PDFs, images, videos, call recordings, and the labels you just generated. V7 Go breaks knowledge-work into steps that large-language models execute with explicit chain-of-thought reasoning, no API keys or coding required. The same interface pulls in PDFs, images, videos, call recordings, and the labels you just generated.

Customers stay inside one ecosystem: annotate, fine-tune, deploy an agent, then watch that agent identify edge-cases that flow back to Darwin for re-labeling. V7 is not just an annotation tool and not just an LLM orchestration UI; it is a self-reinforcing system that *manufactures* high-quality multimodal data and immediately turns it into working AI agents. That data-to-agent flywheel is the non-obvious reason the company can keep shipping new vertical solutions faster than pure-play labeling platforms or standalone AI-agent startups. Beyond AI platforms like V7, startups looking to turn innovative concepts into functional products can benefit from development partners such as [Anadea](https://anadea.info/services/custom-ai-agent-development), who specialize in creating scalable software, web apps, and tailored digital solutions for ambitious teams.

### **[Granola](http://granola.ai/)**

![CleanShot 2025-06-26 at 14.01.39@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_26_at_14_01_39_2x_d607a3da9b.png)

This is one of those “IYKYK” products that seems like everyone is quietly using. For the uninitiated, Granola is a magical note-taking app that captures notes automatically in the background during meetings. I love that it takes my messy notes as a starting point, tidies them up, and enhances them into a bulleted call summary. It also has a handy Q&A feature where you can quickly ask questions across multiple conversations. This makes it an ideal solution for creating precise [AI Meeting Minutes](https://www.bluedothq.com/tools/ai-meeting-minutes), capturing every important point without the usual manual effort. Granola doesn’t actually joins meetings, running instead silently in the background without storing recordings.

By eliminating the friction of joining meetings, Granola brings us closer to infinite recall of every conversation we’ve ever had. Right now, users initiate that recall. Soon, Granola could proactively deliver action items ahead of recurring meetings, remind you to follow up about a colleague’s recent trip, or surface key insights gathered from multiple customer conversations.

### **[Metaview](http://metaview.ai/)**

![CleanShot 2025-06-26 at 14.02.05@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_26_at_14_02_05_2x_e3bd8bb572.png)

When you’re growing quickly, it’s tough to remember what was said in every candidate interview. Metaview is an AI scribe purpose-built for interviewing, and the product is remarkable. It generates the notes exactly as you’d expect them in an interview context and syncs seamlessly with our ATS. You can run Q&A across multiple interviews with a candidate (”What are their salary expectations? What’s their experience with Kubernetes?”). Its collaboration features help us avoid asking candidates duplicate questions and easily catch up on interviews we didn’t attend ourselves.

Metaview is much more than another AI notetaker. The product automates one of the most laborious and information lossy parts of the recruitment cycle: converting the content of an interview into a collaborative, information dense hiring decision. With LinkedIn commoditizing the resume, Metaview becomes the system of record for the most valuable data: the interview content.

With that foundation, Metaview can support every step of the interview journey. It already helps craft job descriptions and could soon handle targeted candidate outreach, timely follow-ups, and more. If your HRIS is the system of record for life after someone joins, Metaview is the system of record for everything that comes before.

### [Attio](https://attio.com/)

![CleanShot 2025-06-26 at 14.02.19@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_26_at_14_02_19_2x_e6ff757947.png)

Imagine a CRM re-imagined for the modern design era. Let’s be honest: Salesforce (especially) and Hubspot feel bloated and clunky. Attio is the exact opposite. We like this product not just for its customer pipeline and data management, but also because it shifts CRM power features like building automations and dashboards from IT to the users.

You can’t overstate how valuable it is to shift these capabilities away from IT and directly into users’ hands. Attio isn’t necessarily inventing new CRM features, it’s just making them effortless to use. Many powerful features in tools like Salesforce stay unused because enabling them typically involves third-party consultants and extensive planning. Attio stands out not for unique functionality, but because it makes adoption easy.

### **[VEED](https://www.veed.io/)**

![CleanShot 2025-06-26 at 14.02.30@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_26_at_14_02_30_2x_fd3b2da812.png)

I’m a big fan of tools that unlock professional-level capabilities for the masses. Historically, if you ran a small business, creating professional videos was often out of reach due to high costs or complex software. VEED changes this by letting you edit and produce polished videos entirely through intuitive text-based editing and generation. 

Every clip you upload is instantly transcribed, and that transcript becomes the core asset you use for editing. You can cut, reorder, or delete scenes just by editing the text. VEED also lets you give plain-English commands to its AI assistant, such as “remove my filler words” or “tighten the intro to 15 seconds.” 

And then there is the data flywheel: each uploaded video adds paired audio-text-visual data, enriching VEED’s private corpus and improving its models, something few UK AI startups have pulled off at this scale.

### **[CuspAI](https://www.cusp.ai/)**

![CleanShot 2025-06-26 at 14.02.39@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_26_at_14_02_39_2x_147ed7f4a0.png)

This company is making the science fiction we imagined at the dawn of AI a reality. It turns materials science into a search problem powered by AI. Researchers simply describe the properties they need (such as a molecular sponge that captures carbon dioxide), and CuspAI proposes novel structures, reversing the usual “make then test” workflow and cutting discovery timelines.

Most materials-informatics platforms rifle through static databases of known compounds. CuspAI flips this workflow: you start with a property prompt, and then a generative model creates brand-new molecular or crystal structures on the fly and scores them in real time with physics-informed evaluators. The engine returns a ranked short-list that satisfies every constraint. This “inverse-design search engine” means the relevant design space is no longer the ~10 million compounds in public databases, but the uncountable universe of hypothetical materials.

CuspAI isn’t just “AI for materials.” It’s building the first *property-queryable* search engine that spans the **entire hypothetical materials universe**. That ability to generate and vet custom matter on demand rather than sift what already exists is the quiet breakthrough that sets the company apart.

### **[Latent Labs](https://www.latentlabs.com/)**

![CleanShot 2025-06-26 at 14.02.52@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_26_at_14_02_52_2x_75b2d0a31b.png)

What CuspAI is to materials science is what Latent Labs is to biology. A researcher describes a drug target for a particular disease, and the company’s model designs a protein drug that with the desired properties. This approach matters because it cuts down on costly “old school” experimentation and wet lab discovery. 

This speed could translate to faster pandemic response, “on-demand” cancer neoantigen vaccines, or enzyme-replacement therapies tuned to treat inherited diseases. Every design–test cycle feeds new, high-fidelity structure-function data back into the model. Because Latent Labs owns both the generative engine and the experimental feedback, it can compound its moat in a way even AlphaFold never could: each project improves the prior, enabling ever more precise control over molecular behavior.

### **[Nscale](https://www.nscale.com/)**

![CleanShot 2025-06-26 at 14.01.24@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_26_at_14_01_24_2x_deedcd674c.png)

This company positions itself as Europe’s AI hyperscaler. It’s like CoreWeave, but with European sovereignty and a stated commitment to sustainable energy. Nscale provides GPU nodes, training clusters, inference services, and more. I’m a fan of the pure play AI hyperscalers, as the AI focus translates to less convoluted product offerings. 

Don’t be quick to overlook the sustainability angle. Nscale’s flagship site in Glomfjord, Norway sits next to surplus hydro-electric capacity and Arctic air that provides free cooling. The campus runs on 100 % renewable energy and doesn’t tap a stressed national grid. By harvesting power that would otherwise be wasted (“stranded” megawatts) and co-locating liquid-cooled GPU racks right at the source, Nscale can drive electricity and cooling costs down to a level even hyperscalers in temperate climates struggle to match. 

The bottleneck to AI isn’t GPUs, but power. Nscale’s edge is a playbook that turns stranded renewable energy into sovereign, high-density AI factories, a cost, carbon and compliance trifecta that competitors tied to conventional data center grids will find tough to clone.

### **[Fluidstack](https://www.fluidstack.io/)**

![CleanShot 2025-06-26 at 14.01.31@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_26_at_14_01_31_2x_6fdd0f5997.png)

We can’t talk about GPU clouds without mentioning Fluidstack’s single-tenant GPU offerings, which teaches us that liquidity beats scarcity. With creative financing and pre-negotiated GPU supply, Fluidstack can spin up a dedicated cluster in just 48 hours instead of the 8-to-12 week wait you get from the hyperscalers. 

It also runs a public marketplace where companies list idle GPUs for on-demand rentals. As those renters scale, Fluidstack graduates them to high-margin private clusters; when those customers later have excess capacity, they feed it back into the marketplace. The result is a virtuous flywheel that keeps hardware busy and customers happy.

### **[ElevenLabs](https://elevenlabs.io/)**

![CleanShot 2025-06-26 at 14.03.07@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_26_at_14_03_07_2x_4d6d53584d.png)

This might be the least controversial statement in startupland: ElevenLabs is ripping and for good reason. Their voice models are state-of-the-art, spanning text-to-speech, speech-to-text, conversational AI, voice cloning, and more. 

Everyone talks about ElevenLabs’ uncanny realism, but the part most people miss is that the company is quietly building a two-sided “voice marketplace.” Anyone can upload a high-quality clone of their voice and list it for other others to license. Imagine Morgan Freeman narrating your favorite bedtime story or casually chatting with your favorite actor. That’s the future ElevenLabs is building, and it’s incredibly exciting.

### **[Fyxer](https://www.fyxer.com/)**

![CleanShot 2025-06-26 at 14.03.26@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_26_at_14_03_26_2x_5be36e08d7.png)

Imagine having an assistant to organize your calendar and schedule meetings, take meeting notes and draft follow-up items, organize your inbox and draft emails for you. This is Fyxer, and AI-powered assistant. 

Before writing a single line of code, Fyxer spent six-plus years running a fractional-EA agency that handled calendars, inboxes, and board-packet logistics for CEOs and scale-ups. Every permissioned email thread, scheduling wrangle, and follow-up template from that service was logged and labelled, adding up to more than half-a-million hours of “how top EAs actually work” data. That proprietary corpus now fuels the fine-tuning behind Fyxer AI, giving it context-savvy instincts that make it more than a generic copilot. 

### **[Wordsmith](https://www.wordsmith.ai/)**

![CleanShot 2025-06-26 at 14.03.42@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_26_at_14_03_42_2x_8c93f5f895.png)

One of the hats I wear at Northflank is legal, which means I spend way too much time reviewing contracts… often relying on LLMs. I regularly find myself wishing there was a tool that could flag non-standard or overly aggressive clauses, prompt me to quickly review them, and then automate the redlines. I even went so far as writing a PRD to build exactly that on Northflank before stumbling across Wordsmith AI. It’s the ideal tool when bringing in a lawyer feels like overkill. 

Again, we have a powerful data flywheel at play. Wordsmith proposes changes, the user approves them, the counterparty responds with edits, and Wordsmith continuously refines its understanding of market-standard contract terms.

Contract review is a bottleneck to value creation. It’s the opportunities that contracts enable (commerce, partnerships, talent management) that moves economies forward. Speed up contract review, and you speed up economic growth.

## Final thoughts

It’s a good time to be building in the UK. The caliber of talent, availability of capital, and wave of new infra make it a strong environment for ambitious teams. We’ll be watching these UK AI startups closely and probably using more of them ourselves! 

If you’re a UK AI startup and need help with infrastructure, whether that’s setting up autoscaling APIs, managing multi-tenant environments, running fine-tuning jobs on GPUs, or just deploying faster without hiring a DevOps team, let’s chat. We support everything from model training to inference, and can run on your cloud or ours.

[Book a demo today. ](https://northflank.com/)]]>
  </content:encoded>
</item><item>
  <title>Top Together AI alternatives for AI/ML model deployment</title>
  <link>https://northflank.com/blog/together-ai-alternatives-for-ai-ml-model-deployment</link>
  <pubDate>2025-06-25T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[Together AI is great for fast LLM prototyping, but limits emerge with complex use cases. This guide compares top alternatives like Northflank, Baseten, and Modal for scalable, customizable AI deployments.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/paas_providers_1_1b30de17bc.png" alt="Top Together AI alternatives for AI/ML model deployment" />You chose Together AI because you didn’t want to wrangle GPUs, manage model weights, or spin up an ML stack just to run an LLM.

And for a while, it was perfect.

Clean APIs. Fast inference. Instant access to [LLaMA](https://zapier.com/blog/llama-meta/), [Mistral](https://www.freecodecamp.org/news/an-introduction-to-mistral-ai/), [Mixtral](https://www.datacamp.com/tutorial/mixtral-8x22b). No infra setup. No DevOps. No drama.

But then you started to outgrow the defaults.

You wanted to fine-tune with your own data, but had to adapt to their pipeline.

You needed more visibility, but the logs only went so far.

You tried to push beyond basic prompt-response, and the platform pushed back.

Together AI is great for getting started with open-source models. It's fast, simple, and gets you to a working demo in minutes.

But once you start building AI features into your product, things get more complex, more custom, more production-grade, and the walls start closing in.

If you’re at that point, you’re not alone.

This guide walks through the best Together AI alternatives for teams who want to:

- Serve fine-tuned models with more control
- Go beyond text-only inference and rigid APIs
- Debug and monitor their stack like real engineers
- Scale without guesswork around limits or pricing

## TL;DR – Top Together AI alternatives

If you're short on time, here’s a snapshot of the top Together AI alternatives. Each tool has its strengths, but they solve different problems, and some are better suited for real-world production than others.

| Platform | Best for | Notes |
| --- | --- | --- |
| [**Northflank**](https://northflank.com/) | Full-stack ML apps with DevOps-grade flexibility | GPU containers, Git-based CI/CD, AI workload support, BYOC, and enterprise-ready features |
| **Baseten** | Custom model serving with great DX | Full control over Python serving logic, autoscaling, and built-in observability |
| **Modal** | Serverless Python workflows | Great for async-heavy workloads, scales to zero, no infrastructure needed |
| **Replicate** | Sharing public ML models easily | Ideal for demos and generative models, with public API hosting |
| **Hugging Face** | Simple LLM APIs from HF-hosted models | Fast setup for popular Hugging Face models, but limited customization |
| **Ray Serve** | Custom model routing and orchestration | Powerful for advanced routing logic, but requires more infra management |

> ⚡️ **Pro tip:** If you're currently juggling different platforms for GPU and non-GPU workloads, why not simplify? [**Northflank**](https://northflank.com/) is an all-in-one developer platform that supports everything from deploying vector databases to running self-hosted LLMs with secure multi-tenancy, BYOC, and full-stack orchestration across clouds. You can [**try it free**](https://app.northflank.com/signup) or [**book a demo**](https://cal.com/team/northflank/northflank-demo) to see how it fits your stack.
> 

## Why teams love Together AI

Together AI has become a popular choice for teams deploying LLMs without the overhead of running their own infra. It offers a fast path to serving open-source models with solid performance and simple APIs.

Here’s what makes it appealing:

- **Instant access to open models** like Mistral, LLaMA, and Mixtral — no need to manage GPUs, weights, or hosting
- **Simple APIs, fast time to value** — spin up endpoints and see results in minutes
- **Competitive pricing** for base-level inference and prompt-response workloads
- **Hosted fine-tuning and LoRA support** — helpful for domain-specific tweaks without major compute overhead
- **Developer-friendly experience** — solid docs, clean APIs, and a familiar feel for anyone used to OpenAI or Hugging Face

It’s an excellent launchpad, especially for teams that want to move quickly without touching infra. But when your needs go beyond basic inference, it can start to feel limiting.

## What are the key limitations of using Together AI?

Together AI makes it easy to get started with hosted models. But that simplicity starts to work against you once your needs grow. What feels smooth at first can turn into friction fast.

### You're not in control

You don’t control where your models run or how they behave. There’s no infrastructure access, no way to manage latency zones, and limited performance tuning. If runtime matters, you're left hoping everything “just works.”

Platforms like **Northflank** give you deep control over your container environment — even letting you safely run untrusted, AI-generated code using secure runtime isolation. That’s critical for teams deploying fine-tuning jobs, LLMs, or customer-specific workloads.

### Fine-tuning is limited and rigid

Yes, fine-tuning is available, but only through Together's pipeline. You can't bring your own trainer or customize the process. If you already have established workflows or need special training behavior, you’ll hit a hard ceiling.

### Observability is too shallow

You get usage stats and a few basic metrics, but not much else. There's no token-level tracing, no latency breakdowns, and no visibility into GPU activity. When things slow down or costs spike, you're left guessing what happened.

### Weak CI/CD and automation support

There's no built-in support for deployment pipelines, versioned releases, or environment promotion. If you're trying to plug Together AI into a mature MLOps flow, expect to build a lot of scaffolding yourself. Platforms like **Northflank** are built with Git-based CI/CD at their core.

### Pricing can scale quickly and unpredictably

Together AI can be cost-effective at small scale, but prices rise quickly with usage or larger models. Since there are no strong forecasting tools or detailed usage reports, teams often get surprised by their bills.

### Self-hosting requires going through sales

Together AI runs in its own managed cloud by default. They do support Bring Your Own Cloud through Self-hosted and Hybrid deployments, which let you run workloads in your own AWS, GCP, or Azure environment. However, these options are only available on enterprise plans and require working directly with their team. That can be a challenge for teams that want to get started quickly without going through a sales process.

In contrast, Northflank lets you bring your own cloud from the beginning with a fully self-serve setup and no need to talk to sales.

## What to look for in a Together AI alternative

Before switching platforms, it’s important to think beyond checkboxes. What looks simple today can turn into friction tomorrow if you don’t have the right building blocks. Here’s what to seriously evaluate when considering an alternative to Together AI:

### 1. Runtime flexibility

Can you control the serving environment? If your model needs custom dependencies, non-Python services, or GPU-accelerated libs, managed runtimes might not cut it. You’ll want full container-level control — and ideally, the ability to bring your own image.

With platforms like **Northflank**, you can deploy any container, not just models, so your runtime is exactly what your app needs. No workarounds. No black boxes.

### 2. Latency and autoscaling

If you're deploying real-time APIs, latency matters. Cold starts, provisioning lag, and inconsistent scaling can break the user experience, especially for LLMs or vision models.

Look for platforms that let you keep containers warm, scale to zero when idle, and autoscale under load, all with GPU support. **Northflank gives you fine-grained control over autoscaling and lets you keep hot replicas running**, without paying premium prices.

### 3. Ease of deployment

The best deployment workflows match your team’s habits. Whether you’re a solo developer using CLI commands or a larger team pushing to staging via Git, you shouldn’t have to change how you work.

**Git-based deploys, PR previews, CLI tools, and APIs should all be part of the story.** Northflank, for example, supports GitHub-native workflows out of the box, perfect for tight CI/CD pipelines.

### 4. Frontend integration

Not every ML model is just an API. Sometimes you need to ship a product, whether it’s a dashboard, an internal tool, or a fully interactive app. That means deploying both the frontend and backend together.

Many platforms silo inference from everything else. Look for alternatives that support **full-stack deployment**, not just model serving. **Northflank lets you deploy Next.js, React, or any frontend framework alongside your database and APIs,** all from the same repo, on the same platform.

### 5. Cost structure that actually scales

Together AI’s usage-based pricing can spike as you scale, especially with GPU workloads. The right platform should let you control your cost structure, whether that means:

- predictable flat-rate containers
- cost-per-inference
- or autoscaling tuned to your real usage

**Northflank gives you transparent pricing, and because you control your container runtime and scaling, you also control cost.**

### 6. Security and compliance

If you're building for finance, healthcare, or enterprise, compliance isn’t optional. Look for platforms that support SOC 2, HIPAA, GDPR, and secure audit logs, or at the very least, give you the ability to run in your own secure cloud.

Northflank is SOC 2-ready, it supports secure features like RBAC, audit logs, and SAML out of the box, all with multi-tenant isolation and BYOC.

### 7. Bring your own cloud (BYOC)

Many teams don’t want to run models on someone else’s infrastructure. Whether it's for data residency, privacy, or integration with your existing stack, **running in your own cloud can be critical**.

Northflank supports BYOC natively to deploy into your own AWS, GCP, or Azure account without enterprise pricing or sales calls.

### 8. CI/CD and automation support

Manual deploys don’t scale. Look for platforms that treat CI/CD as a first-class feature. Git-based deploys, automated rollbacks, staged environments, and secrets management should be built in, not bolted on.

Northflank was designed with **modern DevOps in mind**, including Git triggers, environment previews, and built-in CI integrations.

## Top Together AI alternatives

Here is a list of the best **Together AI** alternatives you can find. In this section, we talk about each platform in depth, its top features, Pros, and Cons.

### 1. Northflank – The best Together AI alternative for production AI

[**Northflank**](https://northflank.com/) isn’t just a model hosting tool; it’s a **production-grade platform for deploying and scaling real AI products**. It combines the flexibility of containerized infrastructure with GPU orchestration, Git-based CI/CD, and full-stack app support.

Whether you're serving a fine-tuned LLM, hosting a Jupyter notebook, or deploying a full product with both frontend and backend, Northflank gives you everything you need, with none of the platform lock-in.

![image - 2025-06-19T211009.037.png](https://assets.northflank.com/image_2025_06_19_T211009_037_2419b18f99.png)

**Key features:**

- Bring your own Docker image and full runtime control
- GPU-enabled services with autoscaling and lifecycle management
- Multi-cloud and Bring Your Own Cloud (BYOC) support
- Git-based CI/CD, preview environments, and full-stack deployment
- Secure runtime for untrusted AI workloads
- SOC 2 readiness and enterprise security (RBAC, SAML, audit logs)

**Pros:**

- **No platform lock-in** – full container control with BYOC or managed infrastructure
- **Transparent, predictable pricing** – usage-based and easy to forecast at scale
- **Great developer experience** – Git-based deploys, CI/CD, preview environments
- **Optimized for latency-sensitive workloads** – fast startup, GPU autoscaling, low-latency networking
- **Supports AI-specific workloads** – Ray, LLMs, Jupyter, fine-tuning, inference APIs
- **Built-in cost management** – real-time usage tracking, budget caps, and optimization tools

**Cons:**

- No special infrastructure tuning for model performance.

**Verdict:** If you're building production-ready AI products, not just prototypes, Northflank gives you the flexibility to run anything from Ray clusters to full-stack apps in one place. With built-in CI/CD, GPU orchestration, and secure multi-cloud support, it's the only platform designed for teams who need speed *and* control without getting locked in.

*See how [Weights uses Northflank to build a GPU-optimized AI platform for millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### 2. Baseten

Baseten helps ML teams serve models as APIs quickly, focusing on ease of deployment and internal demo creation without deep DevOps overhead.

![image - 2025-06-25T171137.699.png](https://assets.northflank.com/image_2025_06_25_T171137_699_acea62b8ab.png)

**Key Features**:

- Python SDK and web UI for model deployment
- Autoscaling GPU-backed inference
- Model versioning, logging, and monitoring
- Integrated app builder for quick UI demos
- Native Hugging Face and PyTorch support

**Pros**:

- Very fast path from model to live API
- Built-in UI support is great for sharing results
- Intuitive interface for solo developers and small teams

**Cons**:

- Geared more toward internal tools and MVPs
- Less flexible for complex backends or full-stack services
- Limited support for multi-service orchestration or CI/CD

**Verdict**:

Baseten is a solid choice for lightweight model deployment and sharing, especially for early-stage teams or prototypes. For production-scale workflows involving more than just inference, like background jobs, databases, or containerized APIs, teams typically pair it with a platform like Northflank for broader infrastructure support.

*Curious about Baseten? Check out [this article](https://northflank.com/blog/baseten-alternatives-for-ai-ml-model-deployment#6-ray-serve) to learn more.*

### 3. Modal

Modal makes Python deployment effortless. Just write Python code, and it handles scaling, packaging, and serving — perfect for workflows and batch jobs.

![image - 2025-06-19T211013.585.png](https://assets.northflank.com/image_2025_06_19_T211013_585_7160b4aa37.png)

**Key features:**

- Python-native infrastructure
- Serverless GPU and CPU runtimes
- Auto-scaling and scale-to-zero
- Built-in task orchestration

**Pros:**

- Super simple for Python developers
- Ideal for workflows and jobs
- Fast to iterate and deploy

**Cons:**

- Limited runtime customization
- Not designed for full-stack apps or frontend support
- Pricing grows with always-on usage

**Verdict:**

A great choice for async Python tasks and lightweight inference. Less suited for full production systems.

### 4. Replicate

Replicate is purpose-built for public APIs and demos, especially for generative models. You can host and monetize models in just a few clicks.

![image - 2025-06-19T211017.564.png](https://assets.northflank.com/image_2025_06_19_T211017_564_c7edd8f0e4.png)

**Key features:**

- Model sharing and monetization
- REST API for every model
- Popular with LLMs, diffusion, and vision models
- Built-in versioning

**Pros:**

- Zero setup for public model serving
- Easy to showcase or monetize models
- Community visibility

**Cons:**

- No private infra or BYOC
- No CI/CD or deployment pipelines
- Not built for full-stack production-ready AI apps

**Verdict:**

Great for showcasing generative models — not for teams deploying private, production workloads.

### 5. Hugging Face

Hugging Face is the industry’s leading hub for open-source machine learning models, especially in NLP. It offers tools for accessing, training, and lightly deploying transformer-based models.

![image - 2025-06-25T171142.718.png](https://assets.northflank.com/image_2025_06_25_T171142_718_7d54da0df4.png)

**Key Features**:

- Model Hub with 500k+ open-source models
- Inference Endpoints (managed or self-hosted)
- AutoTrain for low-code fine-tuning
- Spaces for demos using Gradio or Streamlit
- Popular `transformer` Python library

**Pros**:

- Best open-source model access and community
- Excellent for experimentation and fine-tuning
- Seamless integration with most ML frameworks

**Cons**:

- Deployment and production support is limited
- Infrastructure often needs to be supplemented (e.g., for autoscaling or CI/CD)
- Not designed for tightly coupled workflows or microservice architectures

**Verdict**:

Hugging Face is a powerhouse for research and prototyping, especially when working with transformers. But when it comes to robust deployment pipelines and full-stack application delivery, it’s often used alongside a platform like Northflank to fill the operational gaps.

### 6. Ray Serve

Ray Serve is part of the Ray ecosystem — built for fine-tuned inference flows, multi-model routing, and real-time workloads.

![image - 2025-06-19T211027.048.png](https://assets.northflank.com/image_2025_06_19_T211027_048_e6fa384429.png)

**Key features:**

- DAG-based inference graphs
- Supports multiple models per API
- Fine-grained autoscaling
- Python-first APIs

**Pros:**

- Powerful for complex inference pipelines
- Good horizontal scaling across nodes
- Open source and flexible

**Cons:**

- Requires orchestration and infra setup
- Not turnkey — steep learning curve
- No built-in frontend or CI/CD

**Verdict:**

Perfect for advanced teams building composable model backends. Just be ready to manage the stack.

## How to choose the right Together AI alternative

Your choice of **Together AI** alternative depends on your priorities:

| Feature / Platform | [Northflank](https://northflank.com/) | Baseten | Modal | Replicate | Hugging Face | Ray Serve |
| --- | --- | --- | --- | --- | --- | --- |
| **Model runtime control** | Full container & runtime flexibility | Python-only | Limited | No custom runtimes | Limited | Full control (manual setup) |
| **GPU support** | First-class support with autoscaling | Available | Serverless GPU jobs | Limited availability | Basic access | Manual provisioning required |
| **Frontend/backend support** | Full-stack apps (Next.js, APIs, databases) | Basic app builder | None | None | Gradio/Spaces only | None |
| **CI/CD & Git deploys** | Git-native CI, preview environments, pipelines | Limited | Manual workflows | No Git integration | Partial | No CI/CD built-in |
| **Bring Your Own Cloud (BYOC)** | Native AWS, GCP, Azure support | No | No | No | Enterprise only | Self-hosted |
| **Observability** | Built-in logs, metrics, usage tracking | Basic monitoring | Minimal | None | Limited | Custom setup needed |
| **Security & compliance** | SOC 2-ready, RBAC, SAML, audit logs | Basic features | Limited | No enterprise security | Varies by tier | No built-in access control |
| **Multi-modal workloads** | Full support (LLMs, vision, custom models) | Text models only | Python-based (text/audio) | Vision and generative models | Hugging Face models only | Supports any model (manual setup) |
| **Pricing model** | Predictable usage-based pricing | Usage-based with potential spikes | Usage-based | Usage-based | Tiered, usage-based | Full control (self-hosted) |
| **Best suited for** | Teams deploying real AI products to prod | Demos and internal tools | Async Python tasks and jobs | Public model endpoints | Research and experimentation | Infra-heavy ML platforms |

## Why Northflank is the best Together AI alternative

Most Together AI alternatives fall into one of two categories:

- Lightweight tools for **demos and prototypes**
- Heavy infrastructure requiring **manual setup or DevOps expertise**

**Northflank is different**:

- Gives you **full runtime control** like Ray or Modal
- Includes **frontend/backend hosting** like Vercel or Railway
- Offers **CI/CD, observability, security, and GPU support** in one platform
- Supports **BYOC** so you can run in your own AWS/GCP/Azure environment
- Ideal for **shipping, scaling, and securing production-grade AI apps**

## Conclusion

Together AI is a great launchpad; it gets you to a working LLM fast, without worrying about infrastructure. But once your needs grow, custom models, full-stack workflows, and tighter control over scaling and cost, the platform can start to feel like a box.

If you're at that point, you don’t need to settle for more limitations.

Platforms like **Northflank** are built for teams that want **freedom without friction,** container-native deployments, GPU orchestration, Git-based CI/CD, full-stack support, and the option to run in *your* cloud, not someone else's.

Whether you're shipping an AI product to real users or just want more control over your stack, **Northflank gives you the tools to build like a real software team.** [**Try Northflank for free**](https://app.northflank.com/signup) and see how fast you can go from model to production. Or [**book a demo**](https://cal.com/team/northflank/northflank-demo) to explore what your stack could look like with Northflank in the loop.]]>
  </content:encoded>
</item><item>
  <title>6 best Aptible alternatives in 2026: Pricing, compliance, and deployment control</title>
  <link>https://northflank.com/blog/aptible-alternatives</link>
  <pubDate>2025-06-24T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[Searching for Aptible alternatives? Compare 6 top platforms based on pricing transparency, HIPAA-grade compliance options, and deployment flexibility, Northflank leads the list.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/aptible_alternatives_d87c7320f0.png" alt="6 best Aptible alternatives in 2026: Pricing, compliance, and deployment control" />> If you’re looking for Aptible alternatives, this comparison breaks down how other platforms compare in terms of pricing flexibility, infrastructure control (including [BYOC](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment)), developer workflows like CI/CD, preview environments, observability, and compliance capabilities.
> 

Aptible has proven to be a reliable choice for healthcare and regulated industries because of its focus on security and HIPAA-compliant hosting.

However, it’s no surprise that teams are beginning to look elsewhere for Aptible alternatives, with production plans starting at a significant amount before any infrastructure costs. And yes, Aptible provides a free Development tier, but it’s limited to non-production workloads.

I’ll break down six alternatives to Aptible that give you more pricing flexibility, container-based workflows, and options to [bring your own cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) or scale with fewer restrictions. You’ll see how they compare across cost, deployment models, scaling, isolation, and support so you can find a platform that fits your workload without overpaying.


<InfoBox className='BodyStyle'>

### Quick look: top Aptible alternatives in 2026

If you're short on time, here’s a quick breakdown of some of the best Aptible alternatives right now:

1. [**Northflank**](https://northflank.com/) – Flexible [usage-based pricing](https://northflank.com/pricing) with no fixed fees. Built-in CI/CD pipelines, preview environments, real-time metrics, logs, job orchestration, and support for both [BYOC](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) and managed infrastructure.
3. [**Heroku**](https://northflank.com/blog/top-heroku-alternatives) – Easy-to-use PaaS with buildpacks and CLI support, but costs add up quickly for production use.
4. [**Google App Engine**](https://northflank.com/blog/app-engine-vs-cloud-run) – Serverless deployment with automatic scaling, instance-level billing, and flexible environment options.
5. [**AWS Elastic Beanstalk**](https://northflank.com/blog/elastic-beanstalk-alternatives) – No platform fee; deploy apps on top of AWS resources like EC2 and RDS with orchestration handled for you.
6. [**DigitalOcean App Platform**](https://northflank.com/blog/best-digitalocean-alternatives-2025) – Clear per-container pricing, fast startup experience, and optional autoscaling for growing projects.

</InfoBox>

## Things to look out for when choosing an Aptible alternative

I’ll quickly go over the important things you should note when comparing the alternatives to Aptible to help you make the right decision for your team.

1. **Cost model and pricing transparency**:
    
    Check if you’re paying a fixed monthly fee, usage-based rates, or simply covering the underlying costs. Some platforms like Northflank give you both managed and BYOC pricing with granular billing and a calculator to estimate how much you’ll be spending up front. For example, you can see the [pricing calculator](https://northflank.com/pricing). 
    
2. **Compute and scaling options**:
    
    You need to understand how the platform handles workloads, for instance, by checking if it’s container-based, VM-driven, or serverless. Platforms like Northflank support [containerized services](https://northflank.com/deploy/run-persistent-and-ephemeral-docker-containers) with vertical and horizontal [scaling](https://northflank.com/docs/v1/application/scale/scale-on-northflank), as well as [autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments) based on usage thresholds.
    
3. **Compliance and isolation**
    
    If you’re building in a regulated industry, check for HIPAA support, network isolation, and bring your own cloud support. Platforms like Northflank give you [dedicated clusters](https://northflank.com/docs/v1/application/bring-your-own-cloud/manage-your-cluster) and static IPs with full [network control](https://northflank.com/features/run) through [BYOC](https://northflank.com/features/bring-your-own-cloud) or [managed infrastructure](https://northflank.com/features/managed-cloud). So, such platforms are a suitable option for teams with strict deployment isolation and compliance-ready architecture.
    
4. **Support and SLAs**
    
    Not all platforms offer 24/7 support or guaranteed response times. So, look for platforms that provide clear support tiers, ticket service-level agreements (SLAs), and the option to negotiate custom agreements, which is particularly important for enterprise teams. For instance, platforms like Northflank provide quality support to all customers via email and dedicated support through a shared Slack channel, as well as contractual obligations and a Service Level Agreement (SLA). You can schedule a demo with Northflank with this [link](https://cal.com/team/northflank/northflank-demo).
    
5. **Ecosystem and integrations**
    
    Check how well the platform integrates with your workflow. Platforms like Northflank integrate with [GitHub](https://northflank.com/docs/v1/application/getting-started/link-your-git-account), [GitLab](https://northflank.com/docs/v1/application/getting-started/link-your-git-account#link-your-gitlab-account), and [Bitbucket](https://northflank.com/docs/v1/application/getting-started/link-your-git-account#link-your-bitbucket-account), support [buildpacks](https://northflank.com/docs/v1/application/build/build-with-buildpacks) and [Dockerfiles](https://northflank.com/docs/v1/application/build/build-with-a-dockerfile), and include [pipelines](https://northflank.com/docs/v1/application/release/create-a-pipeline-and-release-flow), [metrics](https://northflank.com/docs/v1/application/observe/view-metrics), [secrets](https://northflank.com/docs/v1/application/secure/inject-secrets), and [real-time logs](https://northflank.com/docs/v1/application/observe/view-logs) out of the box.
    
6. **Platform reliability and engineering maturity**
    
    Review the platform’s record with uptime and incident transparency. For instance, a platform like Northflank provides [detailed logs](https://northflank.com/docs/v1/application/databases-and-persistence/database-observability-and-monitoring), [usage-based alerts](https://northflank.com/docs/v1/application/observe/set-infrastructure-alerts), and is backed by 24/7 SRE [monitoring](https://northflank.com/docs/v1/application/observe/monitor-containers) on its [managed cloud](https://northflank.com/features/managed-cloud).
    

## Comparison of 6 Aptible alternatives

If you need a high-level breakdown to bring back to your team, I’ll help you with a clear table comparing pricing, infrastructure model, developer experience, observability, and compliance/isolation for all six alternative platforms.

<InfoBox className='BodyStyle'>

It’s safe to note that while Aptible focuses on compliant infrastructure, Northflank combines that with a developer-first experience with [usage-based pricing](https://northflank.com/pricing), [built-in CI/CD](https://northflank.com/docs/v1/application/release/manage-ci-cd), [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), and [real-time observability](https://northflank.com/features/observe) across [logs](https://northflank.com/docs/v1/application/observe/view-logs), [jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs), and [metrics](https://northflank.com/docs/v1/application/observe/view-metrics). You can deploy to [managed infrastructure](https://northflank.com/features/managed-cloud) or [bring your own cloud](https://northflank.com/features/bring-your-own-cloud), all while meeting security and compliance requirements without fixed fees or lock-in.

Try out the platform for [free](https://app.northflank.com/signup) to see how it suits your team or [book a demo](https://cal.com/team/northflank/northflank-intro).

</InfoBox>

See the table below:

| **Platform** | **Pricing model** | **Infrastructure model** | **Developer experience** | **Observability** | **Compliance/Isolation** |
| --- | --- | --- | --- | --- | --- |
| [**Northflank**](https://northflank.com/) | [Usage-based billing](https://northflank.com/pricing) with no fixed fees | [Managed](https://northflank.com/features/managed-cloud) or [BYOC](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) | Built-in CI/CD, preview environments, job orchestration | Real-time logs, metrics, deploy/task visibility | Private networking, shielding, SOC 2 ready, HIPAA, ISO 27001 |
| [Azure App Service](https://northflank.com/blog/azure-alternatives) | Tiered fixed plans with scaling costs | Managed (Azure-only) | DevOps integration, CLI, staging slots | App Insights integration | Supports HIPAA via Azure settings |
| [Heroku](https://northflank.com/blog/heroku-outage-downtime-status) | Fixed plan tiers; add-ons increase cost | Managed (Heroku infra) | Buildpacks, CLI, staging pipelines | Basic logging and metrics via add-ons | Limited isolation, not HIPAA by default |
| [Google App Engine](https://northflank.com/blog/app-engine-vs-cloud-run) | Instance-based billing with autoscaling | Managed (Google Cloud) | Git integration, staging versions | Cloud Monitoring and Logging | VPC support, optional compliance configs |
| [Elastic Beanstalk](https://northflank.com/blog/elastic-beanstalk-alternatives) | No platform fee; pay for underlying AWS usage | Managed on AWS resources | Works with Git, supports blue/green deployments | CloudWatch logs and metrics | HIPAA-eligible with AWS config |
| [DigitalOcean App Platform](https://northflank.com/blog/best-digitalocean-alternatives-2025) | Per-container fixed pricing | Managed (DigitalOcean) | Git-based deploys, staging, limited CI/CD | Basic logs and metrics | VPC networking; no built-in compliance tools |

In the next section, I’ll go into more detail.

## Top 6 Aptible alternatives in 2026

The previous section was a snapshot of the comparison between the 6 Aptible alternatives. In this section, I’ll give you more reasons why you should choose one over the other based on your project’s or team's needs.

### 1. Northflank – #1 recommended Aptible alternative

If your team needs more flexible pricing than Aptible and wants an all-in-one platform with preview environments, the option to deploy in your own cloud (AWS, GCP, or Azure) for compliance and network isolation, or use fully managed infrastructure, then [Northflank](https://northflank.com/) is a perfect fit.

Compared to Aptible’s flat pricing, Northflank charges based on what you actually use and gives you more deployment visibility out of the box.

> *You can either see how it works for your team by checking out the [website](https://northflank.com/) and [getting started for free](https://app.northflank.com/signup) or booking a 1:1 [demo](https://cal.com/team/northflank/northflank-intro).*
> 

![new northflank home page.png](https://assets.northflank.com/new_northflank_home_page_70a852adf5.png)

Some of the features include:

- A free tier and a Pay-as-you-go billing with no fixed monthly fees
- Multi-cloud support via [BYOC](https://northflank.com/features/bring-your-own-cloud) or [managed infrastructure](https://northflank.com/features/managed-cloud)
- Built-in [CI/CD](https://northflank.com/docs/v1/application/release/manage-ci-cd), [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), [cron jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs), and [job orchestration](https://northflank.com/features/run)
- [Private networking](https://northflank.com/docs/v1/application/network/networking-on-northflank#private-networking), [public networking](https://northflank.com/docs/v1/application/network/networking-on-northflank#public-networking), and [audit logs](https://northflank.com/docs/v1/application/observe/audit-logs)
- [Real-time logs](https://northflank.com/docs/v1/application/observe/view-logs), [metrics](https://northflank.com/docs/v1/application/observe/view-metrics), and deployment insights

**Pricing:**

- **Developer sandbox**: Free tier with limited resources (2 services, 2 jobs, 1 BYOC cluster)
- **Pay-as-you-go**: No base fee, you’re billed for compute, memory, disk, bandwidth, etc.
- **Compute examples**:
    - 0.1 vCPU, 256MB: $2.71/mo
    - 1 vCPU, 2GB: $24/mo
    - 8 vCPU, 32GB: $288/mo
    - 20 vCPU, 40GB: $480/mo
- **Other resource costs**:
    - vCPU: $12.00/month
    - Memory: $6.00/GB/month
    - Disk: $0.30/GB/month
    - Network egress: $0.15/GB + $0.50 per 1M requests
    - Logs, metrics, backups: $0.08–$0.50/GB/month
- **Enterprise plans**: Custom pricing for SAML, audit logs, BYOX, white labelling, and self-hosted control planes

> Go with Northflank if you want more control over cost and infrastructure, with modern DevOps features like preview environments, CI/CD pipelines, and the flexibility to deploy in your own cloud, all without committing to a fixed monthly plan.
> 

<aside>

See how [Weights uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)

</aside>

### 2. Microsoft Azure App Service

Azure App Service is a good fit for teams already invested in the Microsoft cloud ecosystem. It provides a fully managed PaaS experience with App Insights integration and enterprise-ready compliance options. Compared to Aptible, Azure gives broader language/runtime support and better enterprise integration but can get expensive without reserved pricing.

![Azure App Service home page.png](https://assets.northflank.com/Azure_App_Service_home_page_8c23eca050.png)

**Features:**

- Fully managed PaaS for .NET, Java, Node.js, Python
- Integrated Azure DevOps, staging slots, and load balancing
- HIPAA support via Azure’s compliance configurations
- Scales vertically with premium tiers and autoscale

**Pricing:**

- **Free Tier (F1)** – Shared CPU (60 CPU-min/day), 1 GB storage, $0/month – ideal for testing.
- **Basic Plan (B1–B3)** – Starts at ~**$54.75/month** (1 vCPU, 1.75 GB RAM); higher tiers up to ~$219/mo.
- **Standard & Premium Plans** – Start at ~$70/month; Premium v3 begins at ~$120/mo (P0 v3), with larger configurations available; Premium v4 in preview from ~$99/mo.
- **Isolated (App Service Environment)** – Dedicated, private environments starting ~**$410/month**.
- **Extras**: bandwidth billed separately; SSL (IP-based) ~$39/mo; custom domains ~$12/year; other add‑ons priced separately.

> Good for Azure-centric enterprises but expensive if cost predictability isn’t managed.
> 


<InfoBox className='BodyStyle'>

If you’re looking for alternatives to Microsoft Azure, see [Top 10 Microsoft Azure alternatives in 2026: Best cloud platforms for your business](https://northflank.com/blog/azure-alternatives)

</InfoBox>

### 3. Heroku

Heroku is known for its developer-friendly experience, ideal for early-stage apps and prototyping. It abstracts away infrastructure complexity with buildpacks and staging pipelines, but it’s less suited for compliance-heavy teams. Compared to Aptible, Heroku has better usability but less isolation and fewer enterprise controls.


![heroku.png](https://assets.northflank.com/heroku_092e1c7f09.png)


**Features:**

- Buildpacks and staging pipelines
- CLI, Git-based deploys, and Heroku Dashboard
- Large ecosystem of third-party add-ons
- Basic metrics and logs via logging add-ons

**Pricing:**

- **Free Tier** – sunset in 2022 (replaced by **Eco** for lightweight, non-commercial use)
- **Eco Plan** – $5/month per dyno (up to 1,000 hours; not prorated; sleeps after 30 mins of inactivity)
- **Basic Plan** – $7/month per dyno (billed ~$0.01/hour, prorated to the second)
- **Standard, Performance, Private, and Shield Plans** – range from ~$25/month to $2,400+/month depending on dyno type and usage
- **Data Services (e.g., Postgres, Redis)** – priced separately; start from $3–$5/month for small projects and scale with usage and performance needs

> Great developer experience, but pricier at scale plus reliability has taken hits lately.
> 


<InfoBox className='BodyStyle'>

If you’re looking for alternatives to Heroku, checking how it compares to other platforms, wondering about the capabilities, limitations, and alternatives of Heroku Enterprise, or looking for a resource on Heroku pricing comparison and reduction, or looking for how to migrate from Heroku, these guides would help:

- [Top Heroku alternatives in 2026](https://northflank.com/blog/top-heroku-alternatives)
- [Render vs Heroku: Which platform-as-a-service is right for you in 2026?](https://northflank.com/blog/render-vs-heroku)
- [Vercel vs Heroku: Which platform fits your workflow best?](https://northflank.com/blog/vercel-vs-heroku)
- [Heroku Enterprise: capabilities, limitations, and alternatives](https://northflank.com/blog/heroku-enterprise-capabilities-limitations-and-alternatives)
- [Heroku Pricing Comparison & Reduction](https://northflank.com/heroku-pricing-comparison-and-reduction)
- [Heroku outages are getting worse. The best alternative in 2026 with no downtime.](https://northflank.com/blog/heroku-outage-downtime-status)
- [Migrate from Heroku](https://northflank.com/docs/v1/application/migrate-from-heroku)
</InfoBox>

### 4. Google App Engine

Google App Engine provides a serverless experience with autoscaling, multiple language runtimes, and deep GCP integration. Compared to Aptible, it’s more usage-based and scalable but can introduce unexpected costs if quotas are exceeded.

![app-engine-home-page.png](https://assets.northflank.com/app_engine_home_page_6f606c62d5.png)

**Features:**

- Supports Python, Go, Java, Node.js, and more
- Autoscaling and staging versions
- App logs and metrics via Google Cloud Monitoring
- Optional compliance configurations via GCP

**Pricing:**

- B1: $0.05/hour
- B2: $0.11/hour
- B8: up to $0.46/hour
- Additional infra costs for network/storage apply in flexible environment

> Ideal for serverless workloads but costs can climb unexpectedly.
> 


<InfoBox className='BodyStyle'>

If you want to see how App Engine compares to Cloud Run, see this article:
[App Engine vs. Cloud Run: A real-world engineering comparison](https://northflank.com/blog/app-engine-vs-cloud-run) 

</InfoBox>

### 5. AWS Elastic Beanstalk

Elastic Beanstalk is a great option if you want AWS’s infrastructure but without the hassle of Kubernetes. It’s PaaS-like, supports blue/green deploys, and integrates with CloudWatch. Compared to Aptible, Beanstalk is more infrastructure-centric and cheaper up front, but requires more AWS familiarity.


![aws elastic beanstalk.png](https://assets.northflank.com/aws_elastic_beanstalk_55fe5f8b0b.png)

**Features:**

- Git-based deploys and CLI integration
- Blue/green deployment support
- Deep AWS service integration (RDS, S3, etc.)
- HIPAA eligibility with proper AWS config

**Pricing:**

- No additional Beanstalk fee
- You pay for EC2, S3, RDS, load balancers, etc.
- Pricing varies widely depending on instance size and region

> Zero additional Beanstalk fee only underlying resource costs, though infra complexity remains.
> 


<InfoBox className='BodyStyle'>

If you’re looking for AWS Elastic Beanstalk alternatives, you should check out this article: [10 best Elastic Beanstalk alternatives in 2026: Deploy apps without the AWS complexity](https://northflank.com/blog/elastic-beanstalk-alternatives)

</InfoBox>

### 6. DigitalOcean App Platform

DigitalOcean App Platform is a simple, cost-transparent solution built for startups and solo developers. It’s great for low-maintenance apps, but lacks some of the advanced isolation, compliance, and job orchestration features found in Aptible.


![Digitalocean app platform's home page.png](https://assets.northflank.com/Digitalocean_app_platform_s_home_page_0f9ea04b7b.png)


**Features:**

- Git-based deploys, autoscaling, and HTTPS out of the box
- Static site hosting, managed DBs, staging environments
- Simple web UI and CLI

**Pricing:**

- **Starter Plan** – Free, but limited to 3 static sites and 512MB RAM
- **Basic Plan** – Starts at $5/month per container for web services

> Easy setup, low cost but fewer advanced infra features.
> 

<InfoBox className='BodyStyle'>

If you’re looking for DigitalOcean alternatives, check out this article: [10 best DigitalOcean alternatives in 2026 for developers and teams](https://northflank.com/blog/best-digitalocean-alternatives-2025)

</InfoBox>

## Choosing the right alternative to Aptible

At the end of the day, the most suitable choice boils down to:

1. How much control do you want over infrastructure, compliance, and cloud providers?
2. Do you need built-in tools like CI/CD, preview environments, and job orchestration?
3. How predictable or flexible do you want your pricing to be?

Among all the platforms, Northflank still stands out if you want full deployment visibility, usage-based billing, and the freedom to run in your own cloud or on fully managed infrastructure, while keeping all the DevOps and compliance features built in.

Each tool in this list brings something different to the table, but if your team is growing or needs more than Aptible’s managed setup, try one of these alternatives to see what fits best.

Or [sign up for free on Northflank](https://app.northflank.com/signup) and see how it works for your team.]]>
  </content:encoded>
</item><item>
  <title>Top Anyscale alternatives for AI/ML model deployment</title>
  <link>https://northflank.com/blog/anyscale-alternatives-for-ai-ml-model-deployment</link>
  <pubDate>2025-06-23T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[Explore the top Anyscale alternatives for scaling LLMs, Ray, and AI inference. Compare platforms like Northflank, Modal, and RunPod for better control, observability, and GPU orchestration.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/paas_providers_2e97a65380.png" alt="Top Anyscale alternatives for AI/ML model deployment" />You chose Anyscale because you wanted to scale Python, not become a distributed systems engineer.

And for a while, it worked.

You could deploy Ray Serve DAGs in minutes, scale actors across GPUs, and skip the Kubernetes rabbit hole entirely.

But now the cracks are showing.

- Want to run a FastAPI service next to your Ray cluster? Too bad.
- Need better logs, metrics, or runtime debugging? Good luck.
- Trying to control cloud costs, reuse GPUs, or add CI/CD? Not built in.

Anyscale is great if your entire stack lives inside Ray. But the moment you step outside that boundary, even slightly, the abstractions start to fight you.

If you’ve hit that wall, you’re not alone.

In this guide, we’ll break down the **best Anyscale alternatives for teams running LLMs, Ray pipelines, and real-time inference at scale** without giving up control, observability, or flexibility.

You’ll learn:

- What Anyscale gets right (and where it breaks down)
- What features truly matter when replacing it
- And which platforms actually support modern AI workloads and not just Ray

## TL;DR – Top Anyscale alternatives

If you're short on time, here’s a snapshot of the top Anyscale alternatives. Each tool has its strengths, but they solve different problems, and some are better suited for real-world production than others.

| Platform | Best For | Notes |
| --- | --- | --- |
| [**Northflank**](https://northflank.com/) | Full-stack ML apps with Ray, APIs, GPUs, and CI/CD | Run Ray clusters, inference jobs, REST APIs, and web services in one platform container-native and GPU-ready |
| **Ray OSS (self-managed)** | Full control of Ray on your infra | Kubernetes, Docker, or VM-based Ray clusters |
| **Modal** | Function-as-a-service with Python and simple parallelism | Doesn’t use Ray, but great for async parallel compute |
| **RunPod** | Cheap, custom GPU workloads with full Docker control | Great for teams that want to run Ray manually at low cost |
| **AWS SageMaker** | End-to-end enterprise ML workflows | Doesn’t support Ray natively, but comparable for some use cases |
| **Vertex AI** | AutoML and pipelines for GCP-native stacks | For teams already on GCP looking to replicate training/inference pipelines |

<InfoBox className='BodyStyle'> 

Looking for a single platform to run **Ray, APIs, CI/CD, and GPU workloads all in one**? [Northflank](https://northflank.com/) is the only one that covers the full stack, not just training scripts.

</InfoBox>

## Why teams love Anyscale

Let's give Anyscale its props. The creators of Ray, which is a distributed computing framework for scaling Python applications, built Anyscale. It’s often the top choice for teams who want the power of Ray without the infrastructure headache. Here’s why it stands out:

### Simplifies Ray cluster management

You don’t need to configure EC2, Kubernetes, or networking. Anyscale handles provisioning, scaling, and the entire cluster lifecycle so teams can stay focused on building.

### Native support for Ray Serve

It makes deploying complex inference pipelines simple. You can route traffic between models, scale components independently, and keep boilerplate code to a minimum.

### Optimized for LLM and RAG workloads

Anyscale supports hot-start LLMs and actor-based serving, making it ideal for use cases like prompt-based inference, retrieval-augmented generation, and agent systems.

### Bring your own cloud (BYOC)

For teams with security, compliance, or data residency requirements, Anyscale supports deployments on your own cloud account, so you can keep control without losing the platform benefits.

### Built for team collaboration

Multiple users can manage jobs, environments, and projects through a shared dashboard that supports real-time coordination.

In short, Anyscale is a great choice for teams that are all-in on Ray and want a smooth path to production without managing infrastructure.

That said, not every team wants to be tightly coupled to Ray or locked into a single platform. Let’s explore some alternatives.

## What are the key limitations of using Anyscale?

Anyscale takes a lot off your plate, but it doesn’t solve everything. As your team matures and your needs grow, some sharp edges start to show.

### 1. Everything has to run on Ray

If it doesn’t fit into the Ray runtime, it doesn’t fit into Anyscale. That means your APIs, frontends, cron jobs, and anything not actor-based need to live somewhere else. You end up stitching together multiple platforms to ship one product.

> Platforms like [Northflank](https://northflank.com/) solve this by running Ray and non-Ray services side by side from APIs and inference pipelines to UIs and batch jobs on the same stack.
> 

### 2. Debugging is harder than it should be

Distributed systems break in weird ways. But when actors silently crash or memory runs out across nodes, the Anyscale UI often leaves you guessing. You spend hours digging through logs just to figure out what went wrong.

It helps to have unified logs, metrics, and tracing across both Ray and non-Ray workloads. **Northflank**, for example, ships this out-of-the-box, so debugging doesn’t require jumping between systems.

### 3. No built-in CI or GitOps workflows

There’s no native support for pull request previews, deploy pipelines, or automatic rollbacks. You have to script everything manually, which defeats the simplicity promise that drew you to the platform in the first place.

By contrast, platforms like **Northflank** offer Git-based workflows, automated deploy previews, and rollback-safe promotions, with no glue code or custom CI needed.

### 4. Costs are hard to predict

You don’t always know how pricing scales with usage. You spin up a few extra nodes, maybe run a large job overnight, and suddenly the bill spikes. Without fine-grained cost visibility, budgeting becomes guesswork.

### 5. No secure runtime for untrusted workloads

Anyscale is built for trusted team environments, but it doesn’t offer secure runtime isolation for executing untrusted or third-party code. There’s no built-in sandboxing, syscall filtering, or container-level hardening. If you're running workloads from different tenants or just want extra guarantees around runtime isolation, you’ll need to engineer those protections yourself.

By contrast, **Northflank** containers run in secure, hardened sandboxes with configurable network and resource isolation, making it easier to host untrusted or multitenant workloads out of the box safely.

## What to look for in an Anyscale alternative

When replacing Anyscale, you're *not* just looking for a generic hosting platform; you need something that:

### Supports distributed Python compute

If you're using Ray, you need control over worker orchestration, memory settings, actor lifecycles, and scaling rules. For example, **Northflank** supports this while giving you flexibility to mix in other runtimes.

### Allows custom environments and base images

You should be able to bring your own Ray version, Torch stack, and Python environment, no one-size-fits-all runtime. Container-native platforms like **Northflank** make this straightforward by letting you build from any Dockerfile or image.

### Handles GPU orchestration and inference workloads

Especially for LLMs, you need GPU affinity, persistent containers, and options for model sharding or batching. Look for platforms that support fine-grained GPU allocation and persistent deployment patterns. **Northflank** includes built-in support for GPU scheduling and scale-to-zero for idle inference endpoints.

### Integrates with your CI/CD pipeline

You want repeatable, automated deploys from GitHub, with promotion flows and rollback. **Northflank** tightly integrates with Git, enabling pull request previews, automatic deploys, and promotion between environments without external CI scripting.

### Plays nicely with non-Ray services

If you're building full-stack AI products with APIs, UIs, and agents, you need to orchestrate more than just Ray clusters. **Northflank** is designed to run sidecar services, background workers, and scheduled jobs alongside Ray, on a unified networking layer.

### Provides a secure runtime for untrusted code

If you’re running AI agents, plugins, or user-submitted code, you need runtime isolation. Look for container sandboxing, syscall filtering, and strict resource boundaries features that **Northflank** includes by default, with hardened multi-tenant security policies.

## Top Anyscale alternatives

Here is a list of the best Anyscale alternatives you can find. In this section, we talk about each platform in depth, its top features, Pros, and Cons.

### 1. Northflank – The best Anyscale alternative for Ray, LLMs, and full-stack AI workloads

[**Northflank**](https://northflank.com/) isn’t just a model hosting tool; it’s a **production-grade platform for deploying and scaling fullstack AI products**. It combines the flexibility of containerized infrastructure with GPU orchestration, Git-based CI/CD, and full-stack app support.

Whether you're serving a fine-tuned LLM, hosting a Jupyter notebook, or deploying a full product with both frontend and backend, Northflank gives you everything you need, with none of the platform lock-in.

![image - 2025-06-19T211009.037.png](https://assets.northflank.com/image_2025_06_19_T211009_037_2419b18f99.png)

**Key features:**

- Bring your own Docker image and full runtime control
- GPU-enabled services with autoscaling and lifecycle management
- Multi-cloud and Bring Your Own Cloud (BYOC) support
- Git-based CI/CD, preview environments, and full-stack deployment
- Secure runtime for untrusted AI workloads
- SOC 2 readiness and enterprise security (RBAC, SAML, audit logs)

**Pros:**

- **No platform lock-in** – full container control with BYOC or managed infrastructure
- **Transparent, predictable pricing** – usage-based and easy to forecast at scale
- **Great developer experience** – Git-based deploys, CI/CD, preview environments
- **Optimized for latency-sensitive workloads** – fast startup, GPU autoscaling, low-latency networking
- **Supports AI-specific workloads** – Ray, LLMs, Jupyter, fine-tuning, inference APIs
- **Built-in cost management** – real-time usage tracking, budget caps, and optimization tools

**Cons:**

- No special infrastructure tuning for model performance.

**Verdict:** If you're building production-ready AI products, not just prototypes, Northflank gives you the flexibility to run anything from Ray clusters to full-stack apps in one place. With built-in CI/CD, GPU orchestration, and secure multi-cloud support, it's the only platform designed for teams who need speed *and* control without getting locked in.

*See how [Weights uses Northflank to build a GPU-optimized AI platform for millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### 2. Ray (Open Source)

Ray OSS gives you full control of the Ray ecosystem without Anyscale. Great for teams that want flexibility and are comfortable managing infra.

![image - 2025-06-23T170651.043.png](https://assets.northflank.com/image_2025_06_23_T170651_043_23c9902861.png)

**Key features:**

- Native support for training, tuning, and serving
- Works on Kubernetes, EC2, Northflank, or bare-metal
- Integrates with MLflow, Prometheus, and W&B

**Pros:**

- Full flexibility and no lock-in
- Scalable and production-capable
- Rich ecosystem of AI tools

**Cons:**

- Infra setup required
- No built-in CI/CD or frontend support
- Steeper learning curve

**Verdict:**

Powerful option for infra-savvy teams. Production-ready, but high effort to maintain.

### 3. Modal

Modal makes Python deployment effortless. Just write Python code, and it handles scaling, packaging, and serving — perfect for workflows and batch jobs.

![image - 2025-06-19T211013.585.png](https://assets.northflank.com/image_2025_06_19_T211013_585_7160b4aa37.png)

**Key features:**

- Python-native infrastructure
- Serverless GPU and CPU runtimes
- Auto-scaling and scale-to-zero
- Built-in task orchestration

**Pros:**

- Super simple for Python developers
- Ideal for workflows and jobs
- Fast to iterate and deploy

**Cons:**

- Limited runtime customization
- Not designed for full-stack apps or frontend support
- Pricing grows with always-on usage

**Verdict:**

A great choice for async Python tasks and lightweight inference. Less suited for full production systems.

### 4. RunPod

RunPod gives you raw access to GPU compute with full Docker control. Great for cost-sensitive teams running custom inference workloads.

![image - 2025-06-19T211020.974.png](https://assets.northflank.com/image_2025_06_19_T211020_974_7f97807c0a.png)

**Key features:**

- GPU server marketplace
- BYO Docker containers
- REST APIs and volumes
- Real-time and batch options

**Pros:**

- Lowest GPU cost per hour
- Full control of runtime
- Good for experiments or heavy inference

**Cons:**

- No CI/CD or Git integration
- Lacks frontend or full-stack support
- Manual infra setup required

**Verdict:**

Great if you want cheap GPU power and don’t mind handling infra yourself. Not plug-and-play.

### 5. AWS SageMaker

SageMaker is Amazon’s heavyweight MLOps platform, covering everything from training to deployment, pipelines, and monitoring.

![image - 2025-06-19T211024.050.png](https://assets.northflank.com/image_2025_06_19_T211024_050_82c4f323dd.png)

**Key features:**

- End-to-end ML lifecycle
- AutoML, tuning, and pipelines
- Deep AWS integration (IAM, VPC, etc.)
- Managed endpoints and batch jobs

**Pros:**

- Enterprise-grade compliance
- Mature ecosystem
- Powerful if you’re already on AWS

**Cons:**

- Complex to set up and manage
- Pricing can spiral
- Heavy DevOps lift

**Verdict:**

Ideal for large orgs with AWS infra and compliance needs. Overkill for smaller teams or solo devs.

### 6. Vertex AI

Vertex AI is Google Cloud’s managed ML platform for training, tuning, and deploying models at scale.

![image - 2025-06-23T170636.235.png](https://assets.northflank.com/image_2025_06_23_T170636_235_c0b84ecd33.png)

**Key features:**

- AutoML and custom model support
- Built-in pipelines and notebooks
- Tight GCP integration (BigQuery, GCS, etc.)

**Pros:**

- Easy to scale with managed services
- Enterprise security and IAM
- Great for GCP-based teams

**Cons:**

- Locked into the GCP ecosystem
- Pricing can be unpredictable
- Less flexible for hybrid/cloud-native setups

**Verdict:**

Best for GCP users who want a full-featured ML platform without managing infra.

## How to choose the right Anyscale alternative

| What You Need | Best Fit | Why It Works |
| --- | --- | --- |
| **Ray + APIs + CI/CD + GPU in one place** | **Northflank** | Run Ray, FastAPI, LLMs, and batch jobs side by side with Git-based deploys |
| **Total control over Ray and infra** | Ray OSS | Full flexibility, but high DevOps overhead |
| **Fastest path to deploy async Python** | Modal | Simple, serverless compute for Python workflows |
| **Raw GPU power on a budget** | RunPod | Cheapest GPUs with full container control |
| **Deep enterprise cloud integration** | SageMaker or Vertex AI | Great if you're already locked into AWS |

### Why Northflank stands out

Most platforms force you into trade-offs. Anyscale locks you into Ray. Modal strips out customization. RunPod leaves you wiring everything together by hand.

**Northflank is different.** It gives you full control without the platform baggage, whether you’re serving LLMs, running Ray jobs, or deploying full-stack apps.

**Only Northflank lets you:**

- Run **Ray and non-Ray workloads together,** inference APIs, async jobs, web apps, and agents in one place
- Use **Git-based CI/CD** with PR previews, auto-deploys, and rollback workflows
- Deploy to **your cloud, your way,** BYOC with full container-level control
- Get built-in **GPU autoscaling and cost tracking** so usage never surprises you
- Move from **prototype to production** without switching platforms or re-architecting

If you’re hitting the limits of Anyscale or stitching together half a dozen tools just to ship, it’s time for a better foundation.

**Northflank is built for production-ready full-stack ML products, not just demos.** [Start for free](https://app.northflank.com/signup) and scale when you're ready.

## Conclusion

Anyscale is a solid choice for teams who are fully bought into the Ray ecosystem, but it’s not the only way to run distributed ML workloads, and for many teams, it’s not the best long-term fit.

Whether you're scaling LLM inference, orchestrating batch jobs, or building full-stack AI products, platforms like **Northflank** offer more control, broader runtime support, and better observability without sacrificing simplicity.

**Modern ML infra should be composable, transparent, and infra-agnostic.**

And it’s finally possible to build that without locking into a single runtime.

Deploy your ML workloads with real CI/CD, BYOC, and GPU auto-scaling on Northflank. [Start free and scale when you're ready.](https://app.northflank.com/signup)

<InfoBox className='BodyStyle'> 

## FAQ

### Is Coiled an alternative to Anyscale?

Not exactly, but it’s a close comparison. [**Coiled**](https://coiled.io/) is the managed cloud platform for **Dask**, just like **Anyscale** is for **Ray**. Both help you scale Python workloads without managing infrastructure, but they support different underlying ecosystems.

### When should I use Dask with Coiled instead of Ray with Anyscale?

- Choose **Dask + Coiled** if your workloads involve dataframes, ETL pipelines, or heavy pandas usage.
- Choose **Ray + Anyscale** for LLM inference, reinforcement learning, or actor-based parallel compute.

Dask is designed around dataframe-like operations. Ray is more task-oriented and better suited for modern AI workloads.

### Can I self-host Ray or Dask?

Yes. Both are open source and fully self-hostable. However, setting up production-grade clusters, handling autoscaling, and integrating CI/CD can be complex. This is where platforms like **Anyscale**, **Coiled**, or **Northflank** add real value.

### What is a vendor-neutral alternative to Anyscale?

**Northflank** is a strong alternative. It lets you run open-source **Ray**, custom APIs, CI/CD pipelines, and GPU workloads in one place without being locked into a single runtime or cloud vendor.

</InfoBox> 
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</item><item>
  <title>Heroku outages are getting worse. The best alternative in 2026 with no downtime.</title>
  <link>https://northflank.com/blog/heroku-outage-downtime-status</link>
  <pubDate>2025-06-20T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Ditch Heroku's outages and limits. Northflank offers better uptime, BYOC, autoscaling, CI/CD, and dev experience—built on Kubernetes, perfect for startups and enterprises.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/heroku_alternatives_f431d11c28.jpg" alt="Heroku outages are getting worse. The best alternative in 2026 with no downtime." />![11.png](https://assets.northflank.com/11_aee490d0af.png)

<InfoBox className='BodyStyle'>

## ⏳ TL;DR

- **Repeated outages**: Heroku’s June 2026 incident left apps down for an entire day. This wasn’t isolated, outages have been increasingly frequent.
- **Stagnant product development**: Basic features like HTTP/2, modern buildpacks, and cloud-native tooling only arrived in recent years.
- **Heroku free tier shutdown**: Heroku ended all free dynos and hobby databases in late 2022, removing a key on-ramp for indie devs and startups.
- **Security breaches**: A serious OAuth token breach in 2022 compromised GitHub tokens and private repo data, with poor communication from Heroku during the incident.
</InfoBox>

### Heroku’s decline and the rise of workload delivery

Heroku was once the go-to PaaS for developers to deploy apps quickly. But over the years, Heroku has suffered multiple major outages and signs of stagnation, leading many teams to seek alternatives. Recent incidents, like a June 2026 outage that caused up to 24 hours of downtime for many customers, have eroded confidence in Heroku’s reliability.

The platform’s stagnation, for example, only starting to adopt modern features like cloud-native buildpacks and HTTP/2 well over a decade into its existence, and the end of its popular free tier have further alienated developers.

![22.png](https://assets.northflank.com/22_ec72518137.png)

Meanwhile, **[Northflank](http://northflank.com/)** has emerged as a modern cloud platform that combines Heroku’s developer-friendly experience with far greater performance, uptime, and flexibility.

### Northflank: Built for performance, uptime, and developer experience

### Modern infrastructure for reliability

- Runs on Kubernetes under the hood
- Deploy across multiple clouds or your own cloud (BYOC)
- Global regional deployment for low-latency, high-availability apps

### Developer experience that rivals (and exceeds) Heroku

- Language and framework agnostic
- Dockerfile and buildpack support
- Automatic TLS, DNS, build & deploy pipelines
- Real-time logs and live metrics

### Transparent communication & support

![33.png](https://assets.northflank.com/33_f9b4893568.png)

- Fast, responsive support even for free-tier users
- Dedicated Slack channels and SLAs for enterprise
- Public changelog and roadmap

<div>  
  <center>  
    <a href="https://app.northflank.com/signup">  
<Button variant={["large", "gradient"]}>Try out Northflank for free</Button>  
    </a>  
  </center>  
</div>

## Northflank vs Heroku: Feature comparison

| **Feature** | **Northflank** | **Heroku** |
| --- | --- | --- |
| **BYOC (Bring Your Own Cloud)** | ✅ Deploy into AWS, GCP, Azure, or your own infra using your own credits | ❌ No BYOC or multi-cloud support |
| **Private Networking** | ✅ Built-in VPC-level isolation, mTLS, internal-only ports | ⚠️ Only via Private Spaces (expensive, limited options) |
| **CI/CD & Preview Environments** | ✅ Pipelines with previews, blue-green, canary, and manual approvals | ⚠️ Basic pipelines, GitHub-only, no advanced flows |
| **Autoscaling & Scale-to-Zero** | ✅ Horizontal/vertical scaling on metrics, supports scale-to-zero | ⚠️ Only on Performance/Loyal dynos; limited control |
| **Full Workload Support** | ✅ Web apps, APIs, workers, cron, databases, GPU workloads, all supported | ❌ Stateless-first; workers and DBs handled via addons |
| **Enterprise-Ready** | ✅ RBAC, audit logs, SSO, custom networking, full infra control | ⚠️ Enterprise features require Shield or Private Spaces, high TCO |
| **Support & Transparency** | ✅ Fast responses, changelogs, direct access to engineers, migration help | ⚠️ Slower support, no transparency around updates |

### 1. **BYOC (Bring Your Own Cloud)**

- Deploy directly into AWS, GCP, Azure, or your own infrastructure
- Use your own cloud credits and optimize for performance
- Heroku offers no multi-cloud or BYOC support

### 2. **Private networking**

- Built-in private networking and VPC-level isolation by default
- mTLS between services, internal-only ports, and custom IP policies
- Heroku offers this only via costly Private Spaces with fewer options

### 3. **CI/CD Pipelines & preview environments**

- Built-in pipelines with automatic previews for PRs
- Advanced flows: blue-green, canary, approvals
- Heroku pipelines are more basic and GitHub-bound

### 4. **Autoscaling & scale-to-zero**

- Horizontal & vertical autoscaling on CPU/mem metrics
- Scale-to-zero for cost savings
- Heroku has limited autoscaling for select dyno types

### 5. **Full workload support**

- Web apps, APIs, workers, cron jobs, databases, GPU workloads
- Run databases natively or bring your own
- Heroku is stateless-first and addon-dependent

### 6. **Enterprise-grade features**

- RBAC, audit logs, SSO, BYOC, custom networking
- Lower TCO than Heroku Private Spaces and Shield
- No vendor lock-in, full control over infra

### 7. **Superior support & transparency**

- Real-time changelogs, fast issue response, direct access to engineers
- Migration guidance from Heroku included

### Northflank Enterprise vs Heroku Enterprise

| Feature | Heroku Enterprise | Northflank Enterprise |
| --- | --- | --- |
| Private Networking | Private Spaces ($1.4/hr) | Included, customizable |
| Multi-cloud / BYOC | No | Yes (AWS, GCP, Azure, on-prem) |
| GPU & custom workloads | No | Yes |
| SSO, RBAC, audit logs | Yes (at cost) | Yes (included) |
| Cost structure | High base cost, inflexible | Pay-for-what-you-use, efficient |
| Custom regions / data control | Limited | 60+ regions + your own cloud |

## It’s time for a change

Heroku pioneered developer-focused PaaS, but in 2026, it’s clear the industry has moved on. Between:

- repeated outages,
- a stagnant roadmap,
- an abandoned free tier,
- and enterprise limitations,

Heroku no longer meets the needs of modern startups or scaling enterprises.

**Northflank is the next generation platform**:

- built on Kubernetes,
- optimized for uptime,
- designed for productivity,
- and ready for both developers and ops teams.

Whether you're a startup founder launching your MVP or an enterprise architect modernizing legacy infrastructure, **Northflank offers the flexibility, performance, and DX Heroku can no longer provide**.

## **Time to migrate?**

Check out our [Heroku migration guide](https://northflank.com/docs/v1/application/migrate-from-heroku) and see how easy it is to make the switch.

**Leave Heroku outages and lock-in behind.** 

[**Deploy with confidence on Northflank.**](https://app.northflank.com/signup)]]>
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</item><item>
  <title>What is the best disaster recovery software in 2026? We’ve got the answer.</title>
  <link>https://northflank.com/blog/what-is-the-best-disaster-recovery-software</link>
  <pubDate>2025-06-20T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Disaster recovery (DR) software helps teams restore systems, data, and services after an outage. This includes things like cloud region failures, database corruption, human error, or ransomware. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Vercel_for_backend_blog_post_1_ec2193f301.png" alt="What is the best disaster recovery software in 2026? We’ve got the answer." />Disaster recovery (DR) software helps teams restore systems, data, and services after an outage. This includes things like cloud region failures, database corruption, human error, or ransomware. The goal is simple: get back online fast, with minimal data loss and operational overhead.

Below is a breakdown of the top disaster recovery tools in 2026, starting with Northflank, which offers the most complete, developer-friendly disaster recovery experience available today.



<InfoBox className='BodyStyle'> 

## ⏳ TL;DR

**Disaster recovery (DR)** is the process of restoring systems, data, and services after a failure, whether it’s a cloud outage, ransomware attack, or human error.

A **disaster recovery plan** defines which systems need to be restored, how quickly (RTO), how much data loss is acceptable (RPO), and who’s responsible for recovery.

**Cloud disaster recovery** uses cloud infrastructure to automate backups, failover, and restores across regions or providers.

### The top choice in 2026: **Northflank**

Northflank is a full disaster recovery system built into a modern workload delivery platform. Schedule backups, trigger restores, fail over to healthy replicas, debug live containers, and run in your own cloud, all in one place.

**Why Northflank stands out:**

- Automated snapshots and native database dumps
- Scheduled backups and point-in-time restores via UI, API, or CLI
- High availability with health-checked multi-replica services
- Built-in logs, metrics, alerts, and shell access
- Supports stateless and stateful workloads
- Works in your own cloud (AWS, GCP, Azure, OCI) or on Northflank’s managed infra
- Secure by default: RBAC, mTLS, and private networking

**Bottom line:** If you're running modern infrastructure and want fast, testable disaster recovery without extra tooling, Northflank is the most complete platform available in 2026.

**[☎️ Book a demo to see how Northflank handles Disaster Recovery.](https://app.northflank.com/signup)**

</InfoBox>

## What is disaster recovery?

Disaster recovery (DR) refers to the process of restoring infrastructure and application functionality after a failure. This typically includes:

- Backups of data and application state
- Automated failover to healthy replicas or regions
- Restore workflows that bring services back online
- Monitoring and alerting to detect issues early

Effective disaster recovery is proactive, testable, and integrated into daily operations.

## What is a disaster recovery plan?

![s1.png](https://assets.northflank.com/s1_3067164345.png)

A disaster recovery plan defines what systems must be restored, how fast, and by whom. 

## What is cloud disaster recovery?

Cloud disaster recovery uses cloud infrastructure to manage backups, failover, and restores. This typically allows for:

- Automated snapshotting and incremental backups
- Region or zone-level redundancy
- Built-in monitoring and alerting
- Fast recovery without manual infrastructure provisioning

Most teams running in the cloud benefit from cloud-native DR tools that integrate directly with their platforms and pipelines.

## Disaster Recovery best practices

Strong disaster recovery doesn’t rely on hope, manual playbooks, or infrequent backups. It’s systematic, automated, and tested. Below are the key practices used by high-performing engineering and DevOps teams to ensure recovery is fast, predictable, and low-risk. 

### 1. Define clear RTO and RPO targets

You need to know how long each system can be offline (RTO) and how much data loss is tolerable (RPO). These thresholds should be set based on the criticality of each workload and reviewed regularly.

- RTO (Recovery Time Objective): how quickly the system must be restored
- RPO (Recovery Point Objective): the maximum age of the data that can be lost

Not all workloads require the same targets. Treat them accordingly.

### 2. Automate backups and test restores

Backups are only useful if they’re recent and restorable. Use automated, scheduled backups that run frequently, ideally using native tooling (e.g. `pg_dump`, snapshots) for each system. Just as important: test your restores. Regularly simulate recovery scenarios in staging to validate your process.

### 3. Treat DR as part of deployment, not separate from it

Disaster recovery is more effective when it’s built into your platform. Backup, failover, observability, and rollback should be integrated into the tools your team already uses. If recovery requires switching to another toolchain or stack, it will be slower and more error-prone.

### 4. Use multi-replica and region-aware deployments

Where possible, run services in multiple zones or regions and maintain at least one healthy replica of each critical component. This allows for fast failover during infrastructure failures without full recovery processes.

- Services: multi-replica with automated health checks and failover
- Databases: use native replication or snapshot-based recovery across regions

### 5. Monitor for failure conditions and act early

Don’t wait for a full outage to know something’s wrong. Use observability tooling that tracks health metrics, logs, and error rates in real time. Trigger alerts early, so you can fail over or recover before customers are impacted.

### 6. Secure your recovery paths

The systems used to perform recovery need to be protected. Make sure backup storage is encrypted, access to DR tooling is scoped through RBAC, and credentials used in restores are rotated and auditable. The ability to recover shouldn’t create new vulnerabilities.

### 7. Write it down (and run drills)

Every organization should have a written, versioned disaster recovery plan. It should list which systems are covered, who is responsible for recovery actions, what tooling is used, and how each type of failure is handled. To ensure these plans remain accessible during a total network outage, you can [merge PDF](https://pdfaid.com/pdf-to-merge) documentation into a single, portable emergency runbook. This allows engineers to keep a standalone, encrypted copy of all recovery procedures on their local devices, making it available even when the primary infrastructure is dark.

Run regular drills. Pick a service, simulate a failure, and walk through recovery. Track time to detect, time to recover, and gaps in documentation or tooling.

- **RTO (Recovery Time Objective)** — how quickly each system must be back online
- **RPO (Recovery Point Objective)** — how much data loss is acceptable
- Step-by-step recovery processes
- Ownership of each part of the response
- Regular testing and simulation

Plans should cover not just infrastructure, but also application dependencies, credentials, and routing configurations.

## #1 🥇 Northflank — Best overall disaster recovery software

![CleanShot 2025-06-21 at 16.02.49@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_21_at_16_02_49_2x_022f137838.png)

**Northflank** is a modern workload delivery platform with disaster recovery built in. It’s designed for teams running services, jobs, and databases on Kubernetes or containers, whether in Northflank’s cloud or their own.

What sets Northflank apart is that DR is not a separate product, it’s part of how the platform works.

### Highlights

- Scheduled and on-demand backups using disk snapshots or native database dumps
- One-click restores via UI, CLI, or API
- Replica failover and health checks for high availability
- Works across services, jobs, databases, and queues
- Live logs, shell access, metrics, and alerts included by default
- Run on Northflank’s managed infrastructure or bring your own cloud (AWS, GCP, Azure, OCI)
- Secure by default: RBAC, mTLS, private networking

Northflank gives you everything you need to handle production outages and service recovery in one place. There’s no extra tooling or manual orchestration required.

### Pricing

- **Free tier**: $0/month plus infrastructure usage
- **Pay-as-you-go**: Based on usage, flat fee per vCPU, memory, and cluster; plus network/egress/storage costs

## Other top disaster recovery tools

### #2 Veeam Backup & Replication

![CleanShot 2025-06-21 at 16.03.37@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_21_at_16_03_37_2x_5ae8a73ad5.png)

**✅ Best for**: Virtual machines, hybrid infrastructure, and traditional IT environments

Veeam is a well-established solution for enterprise backup and DR. It supports VMware, Hyper-V, physical servers, and major cloud providers.

- Instant VM recovery
- Immutable backups
- Application-aware restores (e.g. SQL, Exchange)
- Limited support for modern containerized workloads

Best suited for IT teams with VM-heavy infrastructure.

Pricing: 

- **Virtual machines**: Approx. $10.50–$15.10 per VM or server per month (enterprise pricing)
- **Workstations**: ~$5.80 per unit per month
- **Enterprise license**: $1,815 per 10-instance universal license for 1 year
- **Single-socket license**: ~$938/year

### #3 Zerto (by HPE)

![CleanShot 2025-06-21 at 16.04.34@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_21_at_16_04_34_2x_bfd540150b.png)

**✅ Best for**: Continuous data protection and regulated environments

Zerto specializes in journal-based replication, allowing you to recover systems from seconds before an incident.

- Near-zero RPO and RTO
- Orchestrated failover testing and reporting
- Tight integration with VMware and Azure
- High operational complexity and infrastructure overhead

Good option for industries where downtime is unacceptable.

Pricing:

- **Per-VM license**: ~ $745–$1,142 per VM per year
- Full pricing requires contacting sales; typical enterprise-scale deployments are quote-based

### #4 Acronis Cyber Protect

**✅ Best for**: Small teams that want backup and endpoint protection in one

Acronis combines DR with antivirus, patching, and device protection.

- Backup + security suite
- Cloud and on-premise support
- Simplified setup and admin
- Not suitable for large-scale or complex environments

Solid choice for SMBs, less so for platform teams.

Pricing:

- **Backup Standard (Workstation)**: ~$69/year per machine [l](https://www.capterra.com/p/10010631/Acronis-Backup-12/pricing/?utm_source=chatgpt.com)
- **Backup Standard (Server)**: ~$469/year per server
- **Backup Advanced (Server)**: ~$709/year
- **Virtual Host licenses**: Range from ~$559–$929/year per host
- **DR pricing**: Typically metered per-GB; detailed structure via quoting tools

### #5 Druva Data Resiliency Cloud

**✅ Best for**: Cloud-native SaaS and endpoint recovery

Druva offers disaster recovery as a service (DRaaS), built on AWS.

- Agentless backups for cloud workloads
- Native support for M365, Google Workspace, and EC2
- Centralized policy management
- Less transparent, less flexible for deep infrastructure use

Works well for distributed teams with heavy SaaS adoption.

Pricing:

- **SaaS consumption model**: Pricing varies by use case; enterprise plans may start around $8/user/month for SaaS backup
- **No upfront costs**, tailored pricing based on workloads

### #5 Commvault Cloud

**✅ Best for**: Enterprises with large, diverse infrastructure and compliance needs

Commvault supports hundreds of workload types and platforms, with a focus on enterprise-grade recovery and governance.

- Deep platform integrations
- Granular RBAC, auditing, and encryption
- Custom recovery orchestration
- Steep learning curve and higher cost

Useful for large orgs that need to manage DR across many environments.

Pricing:

- **Per-instance license**: ~ $62.99/month per operating instance
- **Microsoft 365 backup add-on**: $1.70–$3.60 per user per month
- Full enterprise and feature-rich plans require sales consultation

## Final thoughts

Backups are only one part of disaster recovery. What matters is how quickly you can restore full functionality across services, databases, and environments and how much of that process is automated, observable, and tested.

Most tools on the market focus on one part of the stack. Northflank covers the whole thing. It gives you scheduled backups, instant restores, multi-region failover, logging, alerting, and live debugging, all built into the same platform where you deploy and run workloads.

If your infrastructure is modern, containerized, and fast-moving, you need disaster recovery to match. 

Northflank is the most complete option in 2026 for teams that care about uptime, speed, and clean operations.

[Try out Northflank today, for free.](http://northflank.com/)

<InfoBox className='BodyStyle'> 

## 💭 FAQs

### What is disaster recovery software?

Disaster recovery software helps restore systems, applications, and data after an outage or failure. It typically includes automated backups, restore workflows, failover mechanisms, and monitoring tools to minimize downtime and data loss.

### What’s the difference between backup and disaster recovery?

Backups save copies of data. Disaster recovery restores full system functionality, applications, services, data stores, access controls, and routing. Backups are one part of disaster recovery, not the full solution.

### What is RTO vs RPO?

- **RTO (Recovery Time Objective)** is the maximum acceptable downtime for a system.
- **RPO (Recovery Point Objective)** is the maximum acceptable amount of data loss, measured in time.

Both are used to define recovery requirements for different workloads.

### What makes cloud disaster recovery different?

Cloud DR uses cloud-native infrastructure to automate backup, failover, and restore processes. It allows for regional redundancy, faster recovery, and lower operational overhead compared to traditional on-prem DR.

### Which disaster recovery tool is best for Kubernetes?

**Northflank** is the best disaster recovery platform for Kubernetes and containerized workloads. It offers scheduled backups, replica failover, instant restores, logging, alerting, and debugging, all integrated into the deployment platform itself.

### Can I run Northflank in my own cloud?

Yes. Northflank supports Bring Your Own Cloud (BYOC), so you can run in your own AWS, GCP, Azure, OCI, or OpenShift environment. You can also use Northflank’s fully managed cloud infrastructure if preferred.

</InfoBox>]]>
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</item><item>
  <title>7 best CapRover alternatives for Docker &amp; Kubernetes app hosting in 2026</title>
  <link>https://northflank.com/blog/7-best-cap-rover-alternatives-for-docker-and-kubernetes-app-hosting-in-2026</link>
  <pubDate>2025-06-20T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Northflank is the best CapRover alternative for teams needing a modern, production-ready deployment platform that goes beyond Docker and NGINX. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/caprover_alternatives_c9b0a70a20.png" alt="7 best CapRover alternatives for Docker &amp; Kubernetes app hosting in 2026" /><InfoBox className='BodyStyle'> 

## TL;DR

[**Northflank**](http://northflank.com/) is the best CapRover alternative for teams needing a modern, production-ready deployment platform that goes beyond Docker and NGINX. 

[Coolify](https://coolify.io/) is the best open-source self-hosted replacement. 

[Dokku](https://dokku.com/) remains a solid minimal option. 

[Render](https://render.com/) and [Railway](https://railway.com/) offer fully hosted, developer-friendly experiences but lack advanced infra control. 

Choose based on team size, infra preferences, and scaling needs.

</InfoBox>

## Why developers are moving away from CapRover

CapRover made a name for itself as a lightweight, open-source PaaS that abstracts Docker and NGINX. 

But over time, the cracks have started to show:

- Limited activity: GitHub activity has slowed considerably
- Scaling constraints: No built-in CI/CD, minimal observability, single-node by default
- Maintenance burden: You manage the VM, OS updates, backups, TLS renewals
- One-click apps: Many are out of date or unmaintained
- Ecosystem lock-in: It wraps Docker but lacks native Kubernetes support or IaC options

For devs who liked CapRover’s simplicity but want a platform that can scale, these are the top alternatives.

<div>  
  <center>  
    <a href="https://app.northflank.com/signup">  
<Button variant={["large", "gradient"]}>Start deploying with Northflank</Button>  
    </a>  
  </center>  
</div>

## Feature comparison

| Feature | Northflank | Coolify | Dokku | Render | Railway | Porter | Heroku |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Self-hosted support | ✅ BYOC or hosted | ✅ Fully | ✅ Fully | ❌ | ❌ | ✅ Your K8s | ❌ |
| CI/CD built-in | ✅ Advanced | ⚠️ GitHub Only | ❌ Manual | ✅ Basic | ✅ Basic | ✅ GitOps | ✅ Buildpacks |
| Kubernetes native | ✅ Yes | ❌ Docker only | ❌ | ❌ | ❌ | ✅ Yes | ❌ |
| Preview environments | ✅ Per-PR | ✅ Per-branch | ❌ | ✅ Yes | ✅ Yes | ✅ Helm-based | ✅ Review Apps |
| Managed databases | ✅ Built-in | ✅ Limited | ❌ | ✅ Yes | ✅ One-click | ⚠️ External only | ✅ Add-ons |
| Enterprise features | ✅ SAML, RBAC | ❌ | ❌ | ⚠️ Basic Teams | ⚠️ Early Stage | ✅ SSO, RBAC | ✅ Mature |
| Pricing model | Usage-based | Free/Self-hosted | Free/Self-hosted | Per service/user | Per user/usage | Cluster licensing | Per dyno/user |

## 1. Northflank – Best overall CapRover alternative

![Screenshot 2025-05-30 at 2.16.15 PM.png](https://assets.northflank.com/Screenshot_2025_05_30_at_2_16_15_PM_bb3262ce3b.png)

Northflank builds on the simplicity of CapRover but adds serious firepower:

**Key advantages:**

- **Zero-config deployments:** Build and deploy from Git, Dockerfile, or template with full CI/CD support.
- **Full Kubernetes stack:** Run across AWS, GCP, Azure or on Northflank-managed infra.
- **Built-in observability:** Logs, metrics, alerts, live containers, shell access.
- **Secure by default:** TLS, RBAC, mTLS, OIDC auth, private networking.
- **Preview environments:** Auto-deploy per PR with isolated services.
- **Flexible hosting:** Use their managed cloud or bring your own cluster (BYOC).

**Why it's better than CapRover:**

- You don’t manage the server
- Autoscaling, backups, live debugging, observability are built in
- Works for AI, microservices, databases, and cron jobs
- Production-grade out of the box

[Try Northflank](https://northflank.com/) – free tier available.

## 2. Coolify – Best open-source alternative

Coolify is a modern open-source platform that feels like CapRover with active development and better UI.

**What it does well:**

- Self-hostable PaaS using Docker
- Deploy apps, services, and databases
- Simple UI, supports GitHub/GitLab deploys
- Multiple project support, managed volumes

**Trade-offs:**

- No Kubernetes support
- One-man project, roadmap uncertain
- No enterprise-grade features (RBAC, SSO, etc.)

✅ Best for devs who want a free, self-hosted platform with decent UI.

<aside>

📖 Read more: [Top Coolify alternatives.](https://northflank.com/blog/coolify-alternatives-in-2025)

</aside>

## 3. Dokku – Minimalist and reliable

Dokku is often called "Heroku on a VPS" and has been around longer than CapRover.

**Highlights:**

- Git-push to deploy
- Lightweight buildpacks
- Plugin system for databases, storage, etc.

**Downsides:**

- No UI
- Manual server maintenance
- Limited to basic app lifecycle management

✅ Best for devs who want something dead simple and don't need a UI.

## 4. Render – Fully managed Heroku-style PaaS

Render offers hosted deployments with a smoother experience and more features than CapRover.

**Pros:**

- Deploy web apps, APIs, cron jobs, static sites
- One-click Postgres, Redis, cron jobs
- PR preview environments
- Autoscaling and global CDN

**Cons:**

- No self-hosting or infra control
- Pricing climbs fast as usage grows

✅ Best for: Teams that want fully managed infra but don’t need Kubernetes.

<aside>

📖 Read more: Top Render alternatives.

</aside>

## 5. Railway – Fastest time-to-deploy

Railway is optimized for speed and simplicity.

**Strengths:**

- Zero-config deploys from repo
- One-click databases
- Preview deploys on every branch
- Strong UI and DX

**Limitations:**

- Not open-source
- No BYOC or Kubernetes
- Basic CI/CD features only

✅ Best for: Rapid MVP builds and teams that prioritize developer experience.

<aside>

📖 Read more: Top Railway alternatives.

</aside>

## 6. Porter – K8s abstraction layer

Porter provides a CapRover-like UI on top of your existing Kubernetes clusters.

**Features:**

- Connect your EKS/GKE/AKS clusters
- Helm chart GUI
- GitOps-style deploys

**Drawbacks:**

- You manage the clusters
- Limited managed services
- Requires some K8s comfort

✅ Best for: Teams that already use Kubernetes but want an easier deploy experience.

<aside>

📖 Read more: Top Porter alternatives.

</aside>

## 7. Heroku – Legacy, but still viable

Heroku is still relevant for simple apps that don't need infra control.

**Pros:**

- Proven platform
- Mature add-ons
- Review apps

**Cons:**

- Expensive at scale
- Dyno-based, limited customization
- Slower innovation

✅ Best for: Stable teams with predictable apps and budgets.

<aside>

📖 Read more: Top Heroku alternatives.

</aside>

<InfoBox className='BodyStyle'> 

## ⁉️ FAQs

**Q: What’s the best self-hosted CapRover alternative?**

A: Coolify is the closest match with better UI and active development.

**Q: Which CapRover alternative has built-in CI/CD?**

A: Northflank and Render both offer full pipelines. Railway supports basic CI/CD via GitHub.

**Q: Can I use Kubernetes with any of these?**

A: Northflank, Porter, and (to some degree) Heroku Enterprise support K8s. CapRover and Coolify do not.

**Q: Which platform supports BYOC (bring your own cloud)?**

A: Northflank and Porter support BYOC. Others are hosted-only.

**Q: What’s best for deploying microservices?**

A: Northflank and Porter are the most capable. Dokku and Coolify work but aren’t optimized for multi-service apps.

**Q: Cheapest option for hobby projects?**

A: Dokku and Coolify are free if self-hosted. Railway has a $5 tier. Northflank has a generous free tier.

</InfoBox>

## Final thoughts

If you’ve outgrown CapRover or want something with a longer future, the landscape in 2026 offers better options.

- **Northflank**: Best all-around choice for teams that want CapRover simplicity with modern production capabilities
- **Coolify**: Best open-source replacement
- **Dokku**: Best for minimal setups
- **Render/Railway**: Best for hosted convenience
- **Porter**: Best for teams with existing Kubernetes infra

[Explore Northflank](https://northflank.com/) to see how it compares for your use case.]]>
  </content:encoded>
</item><item>
  <title>What is AI Platform as a Service (PaaS) and is it any different than PaaS?</title>
  <link>https://northflank.com/blog/what-is-ai-paas</link>
  <pubDate>2025-06-20T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[AI workloads are everywhere: fine-tuning LLMs, serving embedding models, running inference pipelines. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Vercel_for_backend_blog_post_2_2a5a01e36a.png" alt="What is AI Platform as a Service (PaaS) and is it any different than PaaS?" />AI workloads are everywhere: fine-tuning LLMs, serving embedding models, running inference pipelines. Whether you're building a SaaS feature with OpenAI or deploying custom models on GPUs, at some point you’ll ask: **how do we actually run this in production?**

That’s where **AI Platform as a Service (AI PaaS)** comes in.

<InfoBox className='BodyStyle'> 

## ⏳ TL;DR

- **AI PaaS** is a cloud platform that lets you build, deploy, and scale AI workloads (like LLMs and fine-tuning jobs) without managing infrastructure.
- Good platforms handle GPU autoscaling, job scheduling, observability, and secure runtimes out of the box.
- Common use cases:
    - Serving open-source models (e.g. Mistral, LLaMA)
    - Fine-tuning with [PyTorch](https://northflank.com/blog/what-is-pytorch) on your own cloud
    - Running RAG pipelines with vector databases, inclduing pgvector on PostgresSQL
    - Scheduling batch jobs like transcription or embedding
- Platforms like [**Northflank**](http://northflank.com/) support all of the above, and your other workloads too (APIs, Cron jobs, databases).
- You don’t need a separate stack for AI. You need a platform that treats AI like any other workload.

</InfoBox>

## What is AI PaaS?

**AI PaaS** refers to a category of cloud services that make it easier to build, deploy, and scale AI applications without managing infrastructure directly.

You get:

- [Access to compute (especially GPU)](https://northflank.com/gpu)
- Model hosting or inference endpoints
- Built-in integrations for data pipelines, vector stores, or queues
- Monitoring and autoscaling
- APIs and SDKs to simplify deployment

Some AI PaaS platforms are highly opinionated, designed for a narrow use case (e.g. image generation, chatbot inference). Others are more general-purpose and handle any containerized workload, including AI.

[Northflank](http://northflank.com/) is one example of a platform that supports containerized AI workloads out of the box, including model APIs, batch jobs, and GPU-backed services. You can deploy directly from Git, scale workloads automatically, and access a clean UI, API, and CLI to manage everything in production.

<InfoBox className='BodyStyle'> 

## 🏋️ How Weights runs AI at scale

[Weights](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s) is a GenAI company serving **millions of users**. 

Their two-person engineering team runs everything on Northflank: GPU inference, API services, background queues, and CI/CD, without maintaining their own Kubernetes setup or DevOps tooling.

[Read more →](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s) 

</InfoBox>

## Why would you need an AI PaaS?

Running AI in production is harder than running a demo notebook. You need infrastructure that handles:

- **Scalability**: AI workloads spike and dip. You don’t want to overpay or get throttled.
- **Observability**: You’ll need metrics, logs, and alerts, especially when model performance degrades or latency increases.
- **Resilience**: Inference endpoints need redundancy, health checks, and fast recovery.
- **Security**: Workloads often deal with sensitive user data or proprietary models. You need isolation, role-based access, and private networking (not unlike how VPNs are used to protect data in transit—[what is VPN used for?](https://surfshark.com/blog/how-to-use-a-vpn)). A secure runtime matters if you’re running untrusted code, customer-submitted logic, or fine-tuning third-party models, you want strict container isolation, scoped permissions, and no lateral access.
- **Multi-tenancy and lifecycle management**: One-off experiments are easy. Managing dozens of models across teams is not.

Instead of building all of this from scratch with Kubernetes, CI/CD pipelines, Terraform scripts, and GPU autoscalers, many teams reach for an AI PaaS to get going faster.

## Not all AI PaaS platforms are equal!

There are two main types of AI PaaS offerings:

### 1. **Vertical AI PaaS**

These focus on one thing:

- GPU inference
- Fine-tuning specific foundation models
- Retrieval-augmented generation pipelines (RAG)

Examples: [Baseten](https://northflank.com/blog/baseten-alternatives-for-ai-ml-model-deployment), [Fireworks](https://northflank.com/blog/7-best-fireworks-ai-alternatives-for-inference), Modal

**✅ Pros**

- Faster time to deploy for specific LLM workloads
- Abstract away most ops
- Good for teams building fast experiments or MVPs

**❌ Cons**

- Limited to specific models or runtimes
- Hard to customize
- May not scale with your product needs

### 2. **General-purpose PaaS with GPU support**

These platforms offer broad workload support, services, jobs, CI/CD, databases, with GPU as **one of many** supported runtime environments.

This is where platforms like **Northflank** fits in.

Unlike many vertical AI tools, Northflank doesn’t assume you’re only running ML workloads. It supports everything from microservices to CRON jobs to database services, alongside AI, and integrates with your existing Git repos, Docker images, and secrets.

You can deploy:

- AI inference services on GPU
- Background jobs for batch processing or fine-tuning
- Vector databases and message queues
- Full production apps alongside your AI workloads

Northflank supports:

- BYOC (bring your own cloud) for AWS, GCP, Azure, or OCI
- Autoscaling and high availability out of the box
- Secure, VPC-native deployments
- Unified observability (logs, metrics, alerts)
- Fully managed CI/CD pipelines

### AI workloads vs. other workloads

AI workloads aren’t fundamentally different from other backend workloads. They need CI, they need deployments, they need to scale, and they need to be observable.

You don’t need a separate stack to run AI. You need a stack that supports AI **and** everything else your team is building.

## Common AI PaaS use cases

AI PaaS platforms are useful any time you're trying to move an AI system from prototype to production. Some typical use cases:

### 1. **LLM Inference APIs**

Deploy a containerized service that wraps an open-source or proprietary model (like LLaMA or Mistral). Serve responses via a REST or gRPC endpoint, with autoscaling based on usage.

### 2. **Fine-tuning pipelines**

Run a job to fine-tune foundation models on domain-specific data. You might spin up a GPU workload for a few hours, then shut it down. You need job scheduling, GPU runtime, storage access, and logs.

<InfoBox className='BodyStyle'> 

### 💭 Northflank supports on-demand GPU jobs, which you can run with a single command or trigger from CI. It’s a practical setup for tasks like PyTorch model fine-tuning, where jobs might run for minutes or hours and need access to persistent volumes or cloud buckets. 

[Read more about how you can deploy AI / ML models in production →](https://northflank.com/blog/how-to-deploy-machine-learning-models-step-by-step-guide-to-ml-model-deployment-in-production)

</InfoBox>

### 3. **Batch processing**

Use AI PaaS to run scheduled or event-triggered jobs—e.g. transcribing audio files, labeling images, embedding documents. These jobs can be queue-based and benefit from autoscaling and retries.

### 4. **RAG systems and hybrid search**

Deploy a service that combines LLM inference with vector similarity search. AI PaaS helps you run the LLM component, the embedding generator, the vector store (like Qdrant or Weaviate), and the logic connecting them.

### 5. **Multi-modal applications**

Some teams use AI PaaS to host multi-service apps, e.g. a backend for uploading video, a job that extracts frames, a model that generates captions, and a UI that displays results. AI is part of the stack, not the whole stack.

<aside>


**🧪 Running open-source models on your own cloud?**

Tools like [Deepseek R1](https://northflank.com/blog/self-host-deepseek-r1-on-aws-gcp-azure-and-k8s-in-three-easy-steps) are becoming popular for self-hosted LLM inference.

With Northflank, you can deploy these models in your own AWS, GCP, or Azure environment, on GPU, with autoscaling and secure runtime, **in minutes**, not days.

[See how here.](https://northflank.com/blog/self-host-deepseek-r1-on-aws-gcp-azure-and-k8s-in-three-easy-steps)

</aside>

## How to choose the right AI PaaS

Ask yourself:

- Do you need full control over your environment?
- Are your workloads long-running, bursty, or latency-sensitive?
- Are you fine-tuning or doing inference only?
- Do you already have cloud infra, or are you starting fresh?
- Will your team be deploying more than just AI services?

If you're only deploying one model to test something, a vertical AI PaaS might be fine.

If you're building a product or platform, you need something broader and stronger.

## In other words… AI PaaS is evolving 🙂

The best AI PaaS is often not a dedicated one.

It’s a modern PaaS that treats AI as a first-class citizen **alongside** every other workload you run. That’s what makes platforms like Northflank valuable: they let you run LLM inference, manage your backend services, deploy your frontend, and handle batch jobs, all in one system.

If you're evaluating how to take your AI workloads to production, start with that lens.

Don’t ask “Which AI PaaS should I use?” Ask: **what’s the best platform to run everything, including AI?**

<InfoBox className='BodyStyle'> 

## **Ready to get started?**

Northflank allows you to deploy clusters, code, and databases within minutes. Sign up for a Northflank account and create a free project to get started.

- Create and manage clusters in your AWS, GCP, and Azure accounts
- Deploy Docker containers
- Create your own stateful workloads
- Backup, restore and fork databases
- Observe & monitor with real-time metrics & logs
- Low latency and high performance

[Get started now.
](https://app.northflank.com/signup)

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Can you use Vercel for backend? What works and when to use something else</title>
  <link>https://northflank.com/blog/vercel-backend-limitations</link>
  <pubDate>2025-06-20T19:35:00.000Z</pubDate>
  <description>
    <![CDATA[Vercel can host backend functions, but there are limits. This guide breaks down what Vercel supports, what it doesn't, and when platforms like Northflank are a better fit for backend workflows.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Vercel_for_backend_blog_post_261fddc6a0.png" alt="Can you use Vercel for backend? What works and when to use something else" />> Yes, Vercel can handle backend functions but with limits. If you need background jobs, long-lived services, or more control over your backend setup, a platform like [Northflank](https://northflank.com/) gives your more flexibility without giving up developer experience.
> 

*What do people mean when they search for “Vercel for backend”?*

I’ve seen this question pop up over and over again on Reddit threads, in dev Slack channels, and even during team planning sessions. When someone searches for “Vercel for backend,” they’re not asking if Vercel can render a homepage. What they want to know is:

> *Can I rely on Vercel to run the backend logic of my application?*
> 

Take this Reddit post, for example:

![Screenshot of Reddit post asking for a "Vercel but for backend" alternative](https://assets.northflank.com/Screenshot_of_Reddit_post_asking_for_a_Vercel_but_for_backend_alternative_ed09a5480f.png)*Screenshot of Reddit post asking for a "Vercel but for backend" alternative*

This post nails the sentiment. Developers aren’t only looking for backend capability; they’re looking for the same ease, speed, and workflow that Vercel brings to the frontend, but applied to the backend.

**So, the short answer is “YES”, Vercel can run backend functions, but with limitations,** which I would tell you about shortly.

I’ll not only answer your question, but I’ll break down:

1. What backend functionality Vercel supports, including serverless and edge functions
2. The platform’s architectural limitations for backend services
3. When Vercel is a suitable choice for lightweight backend tasks
4. And when you need a more capable backend platform like [Northflank](https://northflank.com/use-cases/app-platform-for-kubernetes) for long-running services, custom runtimes, or background jobs, or preview environments

<InfoBox className='BodyStyle'>

### TL;DR: Can you use Vercel for backend?

Yes, but only up to a point.

Vercel works fine if you're building lightweight backend functions, that is, APIs tied to your frontend, form handlers, and simple serverless logic. If that’s all you need, then you’re good.

However, if you’re working with background jobs, long-running processes, persistent connections, or custom runtimes, Vercel becomes limiting because you don’t get containers, you don’t get control over your runtime, and you can’t run stateful services.

If your backend has infrastructure needs that go beyond serverless functions, you’re better off using a platform that doesn’t treat backend as an afterthought, like [Northflank](https://northflank.com/use-cases/app-platform-for-kubernetes), which gives you full support for running services, jobs, APIs, and more without giving up the Git-based deploys and fast feedback loop Vercel nails on the frontend.

-> [See how it works in action](https://app.northflank.com/signup)

</InfoBox>

## What backend support does Vercel offer?

I’ll start by explaining how Vercel handles backend workloads, including what it supports, how it works, and where it fits your needs, before we discuss its limitations and the alternative tools you can use. For larger or more complex setups, many teams end up hiring [dedicated backend developers](https://mobilunity.com/tech/hire-dedicated-backend-developers/) to design the right mix of serverless, edge, and containerized services around Vercel.

### 1. API routes in Next.js

If you’re using Next.js, any file under `pages/api` automatically becomes a serverless function on Vercel. You can write handlers using the familiar `req`/ `res` API, and Vercel takes care of deployment, scaling, regional routing, timeouts, and CORS. So, it’s great for small REST endpoints, form submissions, or any backend logic tightly coupled to the frontend.

For example:
Let’s say you want to create a contact form. You’d build a POST endpoint at `pages/api/contact.js`, and write your handler like this:

```jsx
export default async function handler(req, res) {
  const { name, message } = req.body;
  // Save to a database or send email
  res.status(200).json({ success: true });
}
```

As soon as you deploy, Vercel makes this live at `https://your-app.vercel.app/api/contact`.

### 2. Serverless functions (Fluid Compute)

For projects outside Next.js, Vercel supports standalone serverless endpoints in JavaScript, TypeScript, Python, Go, or Ruby placed in an `api/` directory. They introduced their Fluid Compute model in early 2025, which allows functions to handle multiple concurrent requests in the same instance. It is beneficial for I/O-Intensive tasks, such as webhooks or database access, and it reduces cold starts through bytecode caching and idle-task reuse.

So, for example:

You’re building a webhook receiver for Stripe. You create a file like `api/stripe-webhook.js`, and Vercel deploys it as a serverless endpoint:

```jsx
export default async function handler(req, res) {
  const signature = req.headers['stripe-signature'];
  const event = verifySignature(req.body, signature);
  // Process event
  res.status(200).send('Webhook received');
}
```

That’s all you need; you don’t need to manage a server or container.

### 3. Edge functions

If you're using Edge Functions, Vercel runs them in lightweight environments close to the user, using their CDN network. These functions are useful for things like geolocation-based logic, header manipulation, streaming responses, or modifying requests before they hit your app.

They run in a stripped-down runtime powered by V8 isolates. That means you don’t get access to typical Node.js modules like `fs`, `net`, or `tls`, and many npm packages won’t work unless they’re edge-compatible.

**For example:**

Let’s say you want to show a personalized banner to users based on their country. You could write an edge function like this:

```jsx
export const config = { runtime: 'edge' }

export default async function handler(req) {
  const country = req.geo?.country || 'US';
  return new Response(`Welcome, visitor from ${country}`);
}
```

This runs at the edge, so the response is sent faster and closer to the user, before the request reaches the rest of your app.

### 4. Backend templates and community guides

Vercel provides starter guides for deploying frameworks such as Express and FastAPI, as well as boilerplates for Go and Ruby. These streamline the setup of serverless endpoints but still deploy as discrete functions, not as long-running services or containers.

**For example:**

Let’s say you already have a small Express app:

```jsx
const express = require('express');
const app = express();

app.get('/api/ping', (req, res) => {
  res.send('pong');
});
```

You wrap it using `serverless-http`, and export the handler like this:

```jsx
const serverless = require('serverless-http');
module.exports = serverless(app);
```

Then define it in `vercel.json`, and Vercel runs your Express logic as a serverless function.

### 5. Supported runtimes

Vercel natively supports these runtimes for serverless functions:

| Runtime | Status |
| --- | --- |
| Node.js | Full support, streaming enabled, bytecode caching, version selection (LTS: Node 18/20/22) |
| Python | Beta support (v3.12 default, 3.9 via legacy), streaming supported |
| Go | Fully supported |
| Ruby | Fully supported (handler via `Handler` proc/class) |

Community runtimes like PHP or Rust can also be used by specifying a custom runtime in `vercel.json`.

**For example:**

If you're writing a Python handler, you might create `api/hello.py` like this:

```jsx
def handler(request, response):
    return response.send("Hello from Python")
```

Then Vercel deploys it just like any other function. Same deal for Go or Ruby, as long as it follows the expected function signature.

## What Vercel isn’t built for (common backend limitations), and how tools like Northflank can help

So far, we’ve looked at how Vercel handles backend workloads through serverless and edge functions. That model works well for stateless, short-lived tasks. How about once you start needing more control, more runtime flexibility, or anything that has to run beyond a single HTTP request? It becomes limiting.

I'll walk you through some of the common limitations and how platforms like Northflank are designed to handle those cases from the start.

### 1. Cold starts and function timeouts

Vercel runs functions only when they’re needed. So if a function hasn’t been used in a while, the next request waits while it starts up. That’s what people call a “cold start”. On top of that, you’re dealing with time limits like 10 seconds on the free tier and 60 seconds on Pro. That might work for quick responses, but not for tasks that require slower processing, such as generating reports, resizing images, or handling file uploads.

**So, how does Northflank handle it instead?**

![Northflank runs your backend code in always-on containers, no cold starts, no execution timeouts](https://assets.northflank.com/northflank_persistent_containers_vs_vercel_cold_starts_f37d7ee17f.png)*Illustration comparing Vercel’s cold starts and function timeouts with Northflank’s always-on containers for backend workloads*

Now, with Northflank, your code runs in a container that stays up. It’s already running when a request comes in, so there’s nothing to spin up. You’re not limited by a fixed timeout. And even if your handler takes a few minutes, that’s fine. It just keeps going.

Let’s say you're handling image uploads in the background, and each one takes around 90 seconds. With Vercel, you’d have to offload that to another platform or service that stays running in the background. With Northflank, you’d just run it in a background job or long-running container. You’re not rewriting your architecture to make the backend work.

Take a look at what you can do:

- [Deploy straight from GitHub or a Docker registry](https://northflank.com/deploy/run-persistent-and-ephemeral-docker-containers)
- [Run your service continuously or on a schedule](https://northflank.com/docs/v1/application/run/run-an-image-continuously)
- [View logs and debug everything from the dashboard](https://northflank.com/docs/v1/application/observe/view-logs)

This is what your container looks like once it’s up and running:

![Example: A persistent service running on Northflank - always-on, no cold starts, and full visibility into logs and port access](https://assets.northflank.com/northflank_running_container_no_cold_starts_9b59f4f47a.webp)*Northflank dashboard showing a running container with live deployment logs, exposed ports, and container configuration*

### 2. No background workers or long-lived processes

Vercel’s architecture is optimized for short-lived, stateless functions. If your app needs to run something that stays online in the background, like a queue worker, continuously running consumer, or long-running batch job, you’ll eventually run into limitations.

Vercel supports scheduled (cron) jobs, but only as isolated serverless or edge function invocations. You can’t run persistent background processes directly on Vercel. There’s no way to keep a process alive, manage retries, or handle state between runs.

So if you need more than a timed function, like jobs triggered by an event, running longer than 60 seconds, or interacting with persistent storage, you end up managing orchestration across third-party tools like CI runners, external schedulers, or hosted queues to fill in the gaps.

![Diagram showing Northflank running scheduled jobs natively versus Vercel requiring external services like CI runners or cron schedulers](https://assets.northflank.com/northflank_vs_vercel_background_jobs_d9cd3ebfd3.png)*Illustration comparing how Vercel and Northflank handle background jobs*

However, with Northflank, you can run jobs that stay up for as long as you want; it could be every 5 minutes or indefinitely.

So, in place of trying to coordinate multiple tools or services, you schedule your job and let it run in a dedicated container. You can:

- [Set a cron schedule directly in the UI or via API](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs)
- [Choose a concurrency policy (allow, forbid, or replace)](https://northflank.com/docs/v1/application/run/run-an-image-once-or-on-a-schedule#set-the-cron-schedule-and-concurrency-policy)
- Monitor [logs](https://northflank.com/docs/v1/application/observe/view-logs), [metrics](https://northflank.com/docs/v1/application/observe/view-metrics), and [job state](https://northflank.com/docs/v1/application/observe/configure-health-checks) from the dashboard
- [Trigger jobs on commit or image changes using built-in CI/CD](https://northflank.com/docs/v1/application/run/run-an-image-once-or-on-a-schedule)
- Deploy your job from a public image, Git repo, or build service

You get full visibility into job runs, retry attempts, time limits, and resource usage, all without extra setup.

See how it looks when scheduling a cron job in Northflank:

![Northflank dashboard showing a scheduled job with retry limit, time limit, and concurrency policy all configured in the UI](https://assets.northflank.com/cron_job_settings_a78e89d1bf.webp)*Northflank dashboard showing a scheduled job with retry limit, time limit, and concurrency policy all configured in the UI*

With Vercel, you’d have to connect this kind of task to an external runner or CI job but with Northflank, you only have to configure the job, and it runs when scheduled. You don't have to do any workarounds.

### 3. No persistent file system or database layer

Vercel’s serverless model doesn’t support persistent storage. Each function runs in a stateless environment, anything written to disk is gone after the request finishes. So if you need to cache, store session data, or save files between runs, you're on your own.

It also doesn’t come with built-in database support. While you can connect to an external database, it lives outside the Vercel network. That means added latency and no way to run your data layer and services together in a private, low-latency environment.

**How does Northflank approach this differently?**

![Diagram showing Northflank containers with attached storage and internal databases, contrasted with Vercel serverless functions lacking storage and pointing to external services like RDS and S3](https://assets.northflank.com/northflank_vs_vercel_persistent_storage_8867c98921.png)*Illustration comparing how Northflank supports persistent storage and built-in databases, while Vercel relies on external services without native volume support.*

With Northflank, you can attach persistent volumes to your deployments. These volumes use SSDs and let your containers keep data across restarts, ideal for caching layers, intermediate file processing, or any stateful backend.

See how easy it is to attach persistent storage to a service on Northflank with no extra setup or external provider needed:

![Northflank dashboard showing the process of configuring and attaching a persistent volume with mount path /app/data](https://assets.northflank.com/create_persistent_volume_f2b438e9ff.webp)*Creating a persistent volume in Northflank to store and access data across container restarts.*

You can also spin up fully managed databases like PostgreSQL or Redis, right inside the platform. These live on the same internal network as your services, so you get secure, low-latency communication and don’t have to jump between providers:

- [Add a persistent volume to any container](https://northflank.com/docs/v1/application/databases-and-persistence/add-a-volume)
- [Deploy managed databases on Northflank](https://northflank.com/docs/v1/application/production-workloads/persistent-storage-in-production)
- [Connect services and databases using internal DNS](https://northflank.com/docs/v1/application/databases-and-persistence/connect-database-secrets-to-workloads)

So this way, you’re not piecing together a backend stack. You’re running services and stateful data together like it was meant to be.

### 4. WebSocket limitations

Vercel doesn’t support native WebSocket connections, which makes it difficult to run apps that rely on real-time communication or persistent state. If you're building a live dashboard, multiplayer game server, or collaborative editor, you'll have to delegate the WebSocket server to another platform or external service.

Since Vercel’s architecture is based on stateless functions and ephemeral execution, there’s no way to maintain a long-lived connection between a client and server.

**So, how does Northflank help?**

![Side-by-side comparison showing that Vercel does not support WebSockets and requires external services, while Northflank supports WebSockets and gRPC natively with persistent client connections](https://assets.northflank.com/vercel_vs_northflank_websocket_support_cfea20e95f.png)*A visual comparison of WebSocket support: Vercel requires external workarounds, while Northflank supports persistent connections natively.*

On Northflank, you’re not limited to short-lived requests. Your services run in always-on containers with support for multiple protocols and persistent connections, so you can expose WebSocket endpoints directly.

You can:

- Expose any port using HTTP, HTTP/2, gRPC, TCP, or UDP.
- Set whether a port is public or private to control traffic scope.
- Route requests based on subdomain paths (e.g. `/rpc` or `/socket`).
- Serve WebSocket traffic and standard HTTP traffic through the same deployment.

This flexibility makes it easy to host WebSocket-powered apps alongside your web server, without needing extra infrastructure or third-party brokers.

See how Northflank handles protocol-specific routing below. The interface lets you map routes like `/api` or `/rpc` to specific services and ports. This setup supports running a WebSocket server alongside other backend services under one subdomain.

![Custom path-based routing with service-specific ports, ideal for real-time and HTTP traffic separation](https://assets.northflank.com/edit_subdomain_paths_cbc34bb90d.webp)*Northflank UI showing subdomain path routing to separate services on different ports, including visual path-to-service mapping.*

### 5. Previews are limited to the frontend only

Vercel’s preview environments are great for frontend changes. You push a new branch or open a pull request, and you’ll get a unique URL showing how the UI looks in production. But that preview stops there. It doesn’t include your backend logic, jobs, or database changes.

So if your pull request touches anything beyond the UI, like an API endpoint, background worker, or config value, those changes won’t be part of the preview. You’re testing the frontend in isolation, not the full experience.

In practice, this means extra setup just to simulate what the whole app would look like. Some teams spin up staging backends, use mock services, or manually update shared environments. It’s time-consuming and easy to misalign.

**Northflank approaches this differently.**

![Diagram comparing Vercel and Northflank preview environments. Vercel shows frontend-only previews per PR. Northflank includes backend services, databases, background jobs, and secrets, all defined in a visual workflow builder](https://assets.northflank.com/northflank_vs_vercel_preview_environments_fullstack_14d3fe21f3.png)**Comparison of preview environments:** Vercel previews cover frontend changes only, while Northflank generates a full-stack environment per branch, including backend services, databases, and jobs.

Preview environments on Northflank are full-stack. Every branch gets its own environment, including:

- Frontend and backend services
- Databases and persistent volumes
- Scheduled jobs and background workers
- Secrets and shared resources

All connected in a single template, automatically spun up on every pull request.

See how that looks in action:

![Visual preview template builder in Northflank with parallel and sequential steps, including Git triggers, build services, PostgreSQL and Redis addons, and deployment services](https://assets.northflank.com/preview_environment_template_57fa198f34.webp)*Visual workflow builder showing Git triggers, build steps, deployments, secrets, and database addons configured as part of a single preview environment.*

You can define everything in one place, from Git triggers and build flows to environment variables and access rules. Your QA, PMs, and reviewers can visit a single link and interact with the entire working app, rather than a static UI.

And because Northflank gives you control over how long these environments run, you’re not wasting resources because it allows you:

- [Limit previews to working hours or auto-delete them](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment#set-preview-environment-duration-and-creation-times)
- [Inject secrets dynamically per environment](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment#inject-secrets-securely-and-share-environment-resources)
- [Map subdomains for easy access to each service](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment#generate-dynamic-domains-for-preview-environments)

> Go with [Northflank](https://northflank.com/) if your team needs to preview everything: frontend, backend, and all the services in between.
>

## Common questions about using Vercel for backend

I’ll provide some answers to the most frequently asked questions about using Vercel for backend work:

- **Can Vercel be used to host backend?**
    
    Yes, but only with serverless functions. They’re stateless and suited for short-lived backend logic.
    
- **Can I deploy Node.js backend on Vercel?**
    
    You can deploy serverless Node.js functions, but not a traditional long-running Node.js server.
    
- **Is Vercel just for frontend?**
    
    It’s frontend-first and optimized for frameworks like Next.js. Backend support is limited to serverless functions.
    
- **Is Vercel backend free?**
    
    The free plan includes backend functions, but limits apply to execution time, memory, and request volume.
    
- **What are the disadvantages of Vercel?**
    
    No WebSocket support, no persistent storage, cold starts, and backend functions can’t maintain state between requests.
    
- **Do WebSockets work on Vercel?**
    
    No, Vercel does not support WebSockets. You’ll need to use a separate service or platform.
    
- **Can I deploy full-stack app on Vercel?**
    
    Yes, as long as your backend fits within the constraints of serverless functions and external services for state or persistence.
    

### Before you choose Vercel, read these comparisons

If you’re looking for more Vercel comparisons and alternatives, look at the articles below. They can help you make the best decision for your project:

- [**Render vs Vercel (2025): Which platform suits your app architecture better?**](https://northflank.com/blog/render-vs-vercel)
    
    A breakdown of frontend and backend limitations, pricing, and deployment workflows.
    
- [**Vercel vs Netlify: Choosing the right one in 2025 (and what comes next)**](https://northflank.com/blog/vercel-vs-netlify-choosing-the-deployment-platform-in-2025)
    
    Compare developer experience, CDN setup, edge features, and future flexibility.
    
- [**Best Vercel Alternatives for Scalable Deployments**](https://northflank.com/blog/best-vercel-alternatives-for-scalable-deployments)
    
    Ten options if you’ve outgrown Vercel or need more control over backend services.
    
- [**Vercel vs Heroku: Which platform fits your workflow best?**](https://northflank.com/blog/vercel-vs-heroku)
    
    A closer look at build behavior, cold starts, and deployment strategies.]]>
  </content:encoded>
</item><item>
  <title>Top Baseten alternatives for AI/ML model deployment</title>
  <link>https://northflank.com/blog/baseten-alternatives-for-ai-ml-model-deployment</link>
  <pubDate>2025-06-19T20:00:00.000Z</pubDate>
  <description>
    <![CDATA[Explore the top Baseten alternatives in 2026 for deploying ML models at scale. Compare platforms like Northflank, Modal, and RunPod for performance, flexibility, and real-world production use.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/How_to_hire_your_founding_sales_person_019d8c3e2f.png" alt="Top Baseten alternatives for AI/ML model deployment" />You’ve trained the model. It works. Maybe it’s even pushing the state of the art.

Now comes the hard part: getting it into production.

Baseten makes that feel easy. You get model hosting, GPU support, a simple UI, and APIs, no Docker, no Kubernetes.

For a lot of teams, it's a great first step.

But once you try to scale, the cracks start to show. Cold starts. Custom dependencies. Creeping costs. Limited control.

That’s probably why you’re here, looking for something better suited to how you actually build.

The good news? You’ve got options.

In this guide, we’ll break down the best Baseten alternatives in 2026, compare their strengths, and help you figure out which one fits your stack.

This isn’t a sales pitch. It’s a guide for devs building production-ready ML products.

## TL;DR - Top Baseten alternatives

If you're short on time, here’s a snapshot of the top Baseten alternatives. Each tool has its strengths, but they solve different problems, and some are better suited for real-world production than others.

| Platform | Best For | Quick Take |
| --- | --- | --- |
| [**Northflank**](https://northflank.com/) | Full-stack ML apps with DevOps-grade flexibility | GPU containers, Git-based CI/CD, AI workload support, BYOC, and enterprise-ready features  |
| **Modal** | Serverless Python workflows | Great for async-heavy workloads, scales to zero, no infrastructure needed |
| **Replicate** | Sharing public ML models easily | Ideal for demos and generative models, with public API hosting |
| **RunPod** | Cheap on-demand GPU compute | BYO Docker and runtimes, good for experiments and custom inference |
| **SageMaker** | Enterprise MLOps at scale | Deep AWS integration, supports full training and deployment lifecycle |
| **Ray Serve** | Custom model routing and orchestration | Flexible serving, supports DAGs, but requires infra setup |

> ⚡️ Pro tip: If you're building a production-ready product and need a balance between flexibility, performance, and developer experience, [Northflank](https://northflank.com/) offers a modern, production-ready path without the platform lock-in. [Click here to try it free](https://app.northflank.com/signup), or [book a demo here](https://cal.com/team/northflank/northflank-demo).
> 

## Why developers choose Baseten

Baseten has found its sweet spot with Python developers and data scientists who want to deploy models fast, without wrestling with infrastructure.

Here’s what makes it click:

- **Fast, frictionless model deployment**
    
    You can turn a PyTorch, TensorFlow, or scikit-learn model into a live API in minutes. No Dockerfiles, no Kubernetes, no YAML.
    
- **Out-of-the-box UI support**
    
    With built-in Truss tooling and simple UI templates, you can wrap your model in a frontend without writing any JavaScript, perfect for internal tools, demos, or lightweight apps.
    
- **Managed GPU infrastructure**
    
    Baseten handles GPU provisioning, autoscaling, and lifecycle management behind the scenes. You focus on inference, not infra.
    
- **Straightforward pricing**
    
    Transparent, usage-based billing with no upfront commitment. Ideal for prototypes, side projects, or early-stage startups.
    
- **Batteries included**
    
    Background jobs, scheduled tasks, observability tools, and a built-in data store come bundled, so you don’t have to wire up extra services just to get going.
    

For individual developers or small teams without dedicated DevOps support, Baseten offers a smooth path from notebook to API. You can get to production quickly, and the platform mostly stays out of your way.

Of course, the trade-off with any platform like this is control, and that’s where many teams eventually start to feel constrained.

> **Why teams eventually outgrow Baseten**
> 
> 
> Baseten is great for deploying your *first* model. But if you're building a *production-ready* product where latency, cost, security, and full-stack infra matter, you'll start running into limits. That’s where platforms like **Northflank** come in.
> 

## What are the key limitations of using Baseten?

Baseten is a great starting point for shipping models quickly, but as your project grows, it can start to show its limits. Here’s where teams often hit friction:

### 1. You can’t fully customize the runtime

Baseten doesn’t let you bring your own Docker image or control the environment deeply. That’s fine if your model runs on standard packages, but it becomes a headache the moment you need something custom, a private dependency, a system lib, or a non-Python service. Platforms like **Northflank** give you deep control over your container environment, and even let you run untrusted AI-generated code safely via secure runtime isolation, critical for teams deploying fine-tuning jobs, LLMs, or customer-specific code.

### 2. Performance can be unpredictable

What Baseten gives you in convenience, it can cost you in latency. Cold starts and warm-up times can add unexpected delays, especially under load. If you're building something latency-sensitive, say, a real-time API, you may end up optimizing around the platform instead of your product.

### 3. It's closed-source and fully managed

Baseten doesn’t offer a self-hosted option unless you're a big enterprise customer. That means no transparency into the platform, and no way to run it in your own environment. For teams that prioritize ownership, compliance, or long-term control, this can be a significant blocker.

### 4. Pricing scales up quickly

It’s cost-effective at a small scale, but once you start serving real traffic or running heavier models, pricing becomes hard to predict, especially if you're running multiple models with GPU requirements. You might find yourself optimizing for cost instead of user experience.

### 5. CI/CD integration is basic

Baseten has a CLI and some GitHub-friendly workflows, but it lacks the depth of integration that modern teams expect. You can’t fully wire it into your deployment pipeline, test environment, or preview builds. Platforms like **Northflank** are built with Git-based CI/CD at their core.

### 6. Self-hosting requires going through sales

Baseten runs in its own managed cloud by default. They do support Bring Your Own Cloud through Self-hosted and Hybrid deployments, which let you run workloads in your own AWS, GCP, or Azure environment. However, these options are only available on enterprise plans and require working directly with their team. That can be a challenge for teams that want to get started quickly without going through a sales process.

In contrast, Northflank lets you bring your own cloud from the beginning with a fully self-serve setup and no need to talk to sales.

## What to look for in a Baseten alternative

Before switching platforms, it’s important to think beyond checkboxes. What looks simple today can turn into friction tomorrow if you don’t have the right building blocks. Here’s what to seriously evaluate when considering an alternative to Baseten:

### 1. Runtime flexibility

Can you control the serving environment? If your model needs custom dependencies, non-Python services, or GPU-accelerated libs, managed runtimes might not cut it. You’ll want full container-level control — and ideally, the ability to bring your own image.

With platforms like **Northflank**, you can deploy any container, not just models, so your runtime is exactly what your app needs. No workarounds. No black boxes.

### 2. Latency and autoscaling

If you're deploying real-time APIs, latency matters. Cold starts, provisioning lag, and inconsistent scaling can break the user experience, especially for LLMs or vision models.

Look for platforms that let you keep containers warm, scale to zero when idle, and autoscale under load, all with GPU support. **Northflank gives you fine-grained control over autoscaling and lets you keep hot replicas running**, without paying premium prices.

### 3. Ease of deployment

The best deployment workflows match your team’s habits. Whether you’re a solo developer using CLI commands or a larger team pushing to staging via Git, you shouldn’t have to change how you work.

**Git-based deploys, PR previews, CLI tools, and APIs should all be part of the story.** Northflank, for example, supports GitHub-native workflows out of the box, perfect for tight CI/CD pipelines.

### 4. Frontend integration

Not every ML model is just an API. Sometimes you need to ship a product, whether it’s a dashboard, an internal tool, or a fully interactive app. That means deploying both the frontend and backend together.

Many platforms silo inference from everything else. Look for alternatives that support **full-stack deployment**, not just model serving. **Northflank lets you deploy Next.js, React, or any frontend framework alongside your database and APIs,** all from the same repo, on the same platform.

### 5. Cost structure that actually scales

Baseten’s usage-based pricing can spike as you scale, especially with GPU workloads. The right platform should let you control your cost structure, whether that means:

- predictable flat-rate containers
- cost-per-inference
- or autoscaling tuned to your real usage

**Northflank gives you transparent pricing, and because you control your container runtime and scaling, you also control cost.**

### 6. Security and compliance

If you're building for finance, healthcare, or enterprise, compliance isn’t optional. Look for platforms that support SOC 2, HIPAA, GDPR, and secure audit logs, or at the very least, give you the ability to run in your own secure cloud.

Northflank is SOC 2-ready, it supports secure features like RBAC, audit logs, and SAML out of the box, all with multi-tenant isolation and BYOC.

### 7. Bring Your Own Cloud (BYOC)

Many teams don’t want to run models on someone else’s infrastructure. Whether it's for data residency, privacy, or integration with your existing stack, **running in your own cloud can be critical**.

Northflank supports BYOC natively to deploy into your own AWS, GCP, or Azure account without enterprise pricing or sales calls.

### 8. CI/CD and automation support

Manual deploys don’t scale. Look for platforms that treat CI/CD as a first-class feature. Git-based deploys, automated rollbacks, staged environments, and secrets management should be built-in, not bolted on.

Northflank was designed with **modern DevOps in mind**, including Git triggers, environment previews, and built-in CI integrations.

## Top Baseten alternatives

Here is a list of the best Baseten alternatives you can find. In this section, we talk about each platform in depth, its top features, Pros, and Cons.

### 1. Northflank – The best Baseten alternative for production AI

[**Northflank**](https://northflank.com/) isn’t just a model hosting tool; it’s a **production-grade platform for deploying and scaling AI products**. It combines the flexibility of containerized infrastructure with GPU orchestration, Git-based CI/CD, and full-stack app support.

Whether you're serving a fine-tuned LLM, hosting a Jupyter notebook, or deploying a full product with both frontend and backend, Northflank gives you everything you need, with none of the platform lock-in.

![image - 2025-06-19T211009.037.png](https://assets.northflank.com/image_2025_06_19_T211009_037_2419b18f99.png)

**Key features:**

- Bring your own Docker image and full runtime control
- GPU-enabled services with autoscaling and lifecycle management
- Multi-cloud and Bring Your Own Cloud (BYOC) support
- Git-based CI/CD, PR previews, and full-stack deployment
- Secure runtime for untrusted AI workloads
- SOC 2 readiness and enterprise security (RBAC, SAML, audit logs)

**Pros:**

- **No platform lock-in** – full container control with BYOC or managed infrastructure
- **Transparent, predictable pricing** – usage-based and easy to forecast at scale
- **Great developer experience** – Git-based deploys, CI/CD, preview environments
- **Optimized for latency-sensitive workloads** – fast startup, GPU autoscaling, low-latency networking
- **Supports AI-specific workloads** – LLMs, Jupyter, fine-tuning, inference APIs
- **Built-in cost management** – real-time usage tracking, budget caps, and optimization tools

**Cons:**

- No special infrastructure tuning for model performance.

**Verdict:**
If you're building production-ready ML products — not just demos — Northflank gives you full control without platform lock-in. It's the only alternative purpose-built for both AI and traditional apps at scale, with cost efficiency, security, and developer velocity in mind.

*See how [Weights uses Northflank to build a GPU-optimized AI platform for millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### 2. Modal

Modal makes Python deployment effortless. Just write Python code, and it handles scaling, packaging, and serving — perfect for workflows and batch jobs.

![image - 2025-06-19T211013.585.png](https://assets.northflank.com/image_2025_06_19_T211013_585_7160b4aa37.png)

**Key features:**

- Python-native infrastructure
- Serverless GPU and CPU runtimes
- Auto-scaling and scale-to-zero
- Built-in task orchestration

**Pros:**

- Super simple for Python developers
- Ideal for workflows and jobs
- Fast to iterate and deploy

**Cons:**

- Limited runtime customization
- Not designed for full-stack apps or frontend support
- Pricing grows with always-on usage

**Verdict:**

A great choice for async Python tasks and lightweight inference. Less suited for full production systems.

### 3. Replicate

Replicate is purpose-built for public APIs and demos, especially for generative models. You can host and monetize models in just a few clicks.

![image - 2025-06-19T211017.564.png](https://assets.northflank.com/image_2025_06_19_T211017_564_c7edd8f0e4.png)

**Key features:**

- Model sharing and monetization
- REST API for every model
- Popular with LLMs, diffusion, and vision models
- Built-in versioning

**Pros:**

- Zero setup for public model serving
- Easy to showcase or monetize models
- Community visibility

**Cons:**

- No private infra or BYOC
- No CI/CD or deployment pipelines
- Not built for fullstack ML apps all-in-one

**Verdict:**

Great for showcasing generative models — not for teams deploying private, production workloads.

### 4. RunPod

RunPod gives you raw access to GPU compute with full Docker control. Great for cost-sensitive teams running custom inference workloads.

![image - 2025-06-19T211020.974.png](https://assets.northflank.com/image_2025_06_19_T211020_974_7f97807c0a.png)

**Key features:**

- GPU server marketplace
- BYO Docker containers
- REST APIs and volumes
- Real-time and batch options

**Pros:**

- Lowest GPU cost per hour
- Full control of runtime
- Good for experiments or heavy inference

**Cons:**

- No CI/CD or Git integration
- Lacks frontend or full-stack support
- Manual infra setup required

**Verdict:**

Great if you want cheap GPU power and don’t mind handling infra yourself. Not plug-and-play.

### 5. AWS SageMaker

SageMaker is Amazon’s heavyweight MLOps platform, covering everything from training to deployment, pipelines, and monitoring.

![image - 2025-06-19T211024.050.png](https://assets.northflank.com/image_2025_06_19_T211024_050_82c4f323dd.png)

**Key features:**

- End-to-end ML lifecycle
- AutoML, tuning, and pipelines
- Deep AWS integration (IAM, VPC, etc.)
- Managed endpoints and batch jobs

**Pros:**

- Enterprise-grade compliance
- Mature ecosystem
- Powerful if you’re already on AWS

**Cons:**

- Complex to set up and manage
- Pricing can spiral
- Heavy DevOps lift

**Verdict:**

Ideal for large orgs with AWS infra and compliance needs. Overkill for smaller teams or solo devs.

### 6. Ray Serve

Ray Serve is part of the Ray ecosystem — built for fine-tuned inference flows, multi-model routing, and real-time workloads.

![image - 2025-06-19T211027.048.png](https://assets.northflank.com/image_2025_06_19_T211027_048_e6fa384429.png)

**Key features:**

- DAG-based inference graphs
- Supports multiple models per API
- Fine-grained autoscaling
- Python-first APIs

**Pros:**

- Powerful for complex inference pipelines
- Good horizontal scaling across nodes
- Open source and flexible

**Cons:**

- Requires orchestration and infra setup
- Not turnkey — steep learning curve
- No built-in frontend or CI/CD

**Verdict:**

Perfect for advanced teams building composable model backends. Just be ready to manage the stack.

## How to choose the right alternative for your needs

Your choice of Baseten alternative depends on your priorities:

| **Need this** | **Best choice** | **Why it works** |
| --- | --- | --- |
| **Best all-around for production** | [**Northflank**](https://app.northflank.com/signup) | Full Docker control, GPU autoscaling, Git CI/CD, frontend + backend deploys, and BYOC — all without lock-in. |
| **Simple Python workflows** | Modal | Fast serverless jobs, scales to zero, great for prototypes — but limited runtime flexibility. |
| **Cheapest raw GPU compute** | RunPod | BYO Docker with low-cost spot GPUs. Great for training or cheap inference — hands-on infra required. |
| **Public-facing model demos** | Replicate | Ideal for monetizing or sharing generative models — but not built for complex backends. |
| **Enterprise MLOps** | SageMaker | Deep AWS integration and compliance — heavyweight, but robust for large orgs. |
| **Real-time, multi-model orchestration** | Ray Serve | High-perf DAG-based inference — powerful, but complex to operate. |

## Conclusion

Baseten is a great starting point for deploying models without dealing with infrastructure. But once your needs grow, its limits start to show.

If you need more control over runtime, better CI/CD, full-stack support, or flexible GPU deployment, there are stronger options out there. Platforms like Modal and RunPod are great for specific workflows, while SageMaker and Ray Serve suit enterprise and infra-heavy teams.

But if you're building production-ready ML products with real users and care about developer experience, cost control, and full flexibility, Northflank stands out. It gives you control of containers with the ease of modern DevOps.

Deploy your ML workloads with real CI/CD, BYOC, and GPU auto-scaling on Northflank. [Start free and scale when you're ready.](https://app.northflank.com/signup)]]>
  </content:encoded>
</item><item>
  <title>What we learned from hosting our first event in SF</title>
  <link>https://northflank.com/blog/what-we-learned-from-hosting-our-first-event-in-san-francisco</link>
  <pubDate>2025-06-18T23:15:00.000Z</pubDate>
  <description>
    <![CDATA[Earlier this month we hosted our first in-person event in San Francisco. We teamed up with Kindred Ventures and brought together a group of engineers and founders to talk about what it means to build software that lasts.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/1749238907296_deede17e99.jpeg" alt="What we learned from hosting our first event in SF" />Earlier this month we hosted our first in-person event in San Francisco. We teamed up with Kindred Ventures and brought together a group of engineers and founders to talk about what it means to build software that lasts.

It ended up being one of the most energizing nights we’ve had as a team.

![DSC_5321.jpg](https://assets.northflank.com/DSC_5321_b076aeee8e.jpg)

At Northflank we’ve always been remote-first. Our team is forward deployed, globally, and most of our interactions with customers happen online. That works well for building and shipping quickly, but it also means we don’t get many chances to be in the same room as the people we’re building for. So when we do, we take it seriously.

We ran a panel with two of our customers (JonLuca DeCaro @ Weights and Kei Yoshikoshi @ PlayAI) and Diego Rodriguez from Krea AI.

All of them are building AI products, used by millions+ of people. The goal was to have an open conversation about the challenges of building infrastructure and applications that people depend on every day.

Steve Jang from Kindred moderated and kept the discussion sharp and focused.

## **Takeaways from the panel**

A lot of the night focused on something we’ve been hearing more often from teams building in AI: **users form attachments to the output of specific models, especially for audio, image, and video.**

Kei talked about this in the context of PlayAI, where people don’t want their favorite voice models swapped out, even if newer models are technically better. You build something new, improve the performance, and then spend the next week answering messages from people asking why you took their favorite voice away.

Another point that came up is how fast feedback loops have become. Discord is now acting like a real(er)-time PagerDuty. You’ll hear from your community before your alerting system even fires. People care about small changes, and they notice them quickly.

## **Things we learned from hosting our first in-person event**

### 1. You need strong speakers. 

Ideally people who’ve actually been through the hard stuff. We were lucky. All three speakers had clear, real-world experience and weren’t afraid to go deep.

![BWT_7340.jpg](https://assets.northflank.com/BWT_7340_0adb4fd248.jpg)

### 2. You need someone on the mic who knows how to host. 

Steve is a master at this. He asked great questions and let people talk without getting in the way.

![BWT_7053.jpg](https://assets.northflank.com/BWT_7053_f3c6de56c8.jpg)

### 3. You need real food.

Not pizza. We served mini beef wellingtons, beef sliders, fish tacos, chicken skewers, and lots more. Same goes for drinks.

![Brown Beige Aesthetic Photo Collage Instagram Post Portrait.png](https://assets.northflank.com/Brown_Beige_Aesthetic_Photo_Collage_Instagram_Post_Portrait_8d9548f404.png)

### 4. Engineers are social.

If the environment’s right, they’ll talk for hours. You don’t need gimmicks, just people who care about the same problems.

![DSC_5390.jpg](https://assets.northflank.com/DSC_5390_3b43b5e541.jpg)

### 5. Three weeks' notice is the right amount.

It gave us enough time to fill the room with the right people without chasing RSVPs at the last minute. We had 200 people sign up and 100+ on the waitlist. Aim for 50-60% show-up rate.

![BWT_6980.jpg](https://assets.northflank.com/BWT_6980_ccbfe17b21.jpg)

### 6. You need people fully focused on operations.

Someone has to be responsible for the caterers, ice runs, mics, check-in, speaker timing, and anything that can go wrong. This matters more than it sounds like.

![BWT_6594 (1).jpg](https://assets.northflank.com/BWT_6594_1_86124ea00c.jpg)

### 7. It’s worth doing things that don’t scale.

Especially the first time. People remember when something feels well-considered.

(Bonus lesson: engineers love a take-home gift bag. Popcorn for the road, and a Kool Aid, and even something practical [like a branded tumbler ](https://www.qualitylogoproducts.com/blog/tumbler-heat-retention-test/)can leave a lasting impression long after the event ends.)

![DSC_5289.jpg](https://assets.northflank.com/DSC_5289_dcb7ce5151.jpg)

One thing that surprised us was how social the night ended up being. Everyone stayed long after the panel finished. There’s something about being around people who are solving similar problems that naturally pulls conversation out of you. Most people we spoke to had come alone, but by the end of the night, they were deep in discussions about model architecture or deployment tooling or the best way to roll back changes in production.

For us, the biggest takeaway was this: if the theme of the night was “building software that lasts,” the same applies to how we build relationships, and how we show up in person.

![BWT_6943.jpg](https://assets.northflank.com/BWT_6943_efe463eb44.jpg)

We could have run a smaller event, or gone lighter on logistics, but our goal was to leave a lasting impression. Not by being flashy, but by being thoughtful, about who we invited, what we talked about, how the night felt.

That’s how we think about product too. At Northflank, we’re building a workload delivery platform that helps teams deploy and manage GPUs, services, jobs, and databases across production and staging environments. We support bring-your-own-cloud setups and Kubernetes-native infrastructure without requiring teams to write their own platform layer. 

We try to take care of the messy parts of deployment so that teams can focus on shipping product.

Every piece of that, how logs work, how rollback is handled, how autoscaling is tuned, is something we’ve built with care. Because the people who use Northflank are doing serious work, and their expectations are high.

That’s what made this event so rewarding. It reminded us who we’re building for.

Thanks again to everyone who came, and to the speakers and the Kindred team for making it happen. 

We’ll be doing more of these. And next time, we’ll probably order even more food.]]>
  </content:encoded>
</item><item>
  <title>How to hire your founding salesperson</title>
  <link>https://northflank.com/blog/how-to-hire-your-founding-sales-person</link>
  <pubDate>2025-06-18T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[If the founder’s job is to find product-market fit, the first salesperson’s job is to scale it. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/How_to_hire_your_founding_sales_person_5c73a4bf57.png" alt="How to hire your founding salesperson" />If the founder’s job is to find product-market fit, the first salesperson’s job is to scale it. Your first sales hire is one of the most consequential hires you’ll make because they’re the first person entrusted to replicate your “founder sales magic.” Getting this right is a major milestone. It means you’ve successfully transferred your deep understanding of the problem your product solves, why your solution is uniquely positioned, who benefits most, and how to convert their pain into revenue. 

So, who should you hire? Let’s break it down into two parts:

1. **Attributes:** what you’ll evaluate during your initial interview.
2. **Experience:** what you can gauge from their LinkedIn profile.

The attributes are the hardest but most important elements to screen for, so I'll address those first. A bad hire can cost you months, and most founders don’t have that. While you might intuitively understand the experience recommendations, I've shared them here anyways to help you avoid common pitfalls.

## Attributes

Now onto the attributes, which you’ll mostly screen through interviews and references, as the resume won’t necessarily reveal these traits.

### Chefs vs. Cooks

The difference between a chef and a cook is that a chef prioritizes outcomes, while a cook optimizes for process. Chefs idealize making delicious food: recipes, ingredients, and techniques are fungible as long as the final dish is exceptional. Cooks, on the other hand, strictly follow recipes, emphasizing precision and efficiency, sometimes at the expense of asking, *“Is this dish actually delicious?”*

In an early-stage startup, the sales “recipe” is better thought of as a hypothesis, a working guess about your ICP, how to catalyze demand, and the best steps from first contact through closing. Salespeople with a cook mentality will follow the process rigidly, mistaking adherence for success, which becomes problematic when the process inevitably needs iteration. A salesperson with a chef mentality, however, continuously evaluates and refines the process based on what’s effective in the field. They prioritize closing new business, taking ownership of the sales process, and thoughtfully iterating until it consistently works.

At Northflank, our AEs are chefs. We’re selling a big product that solves many unique problems, depending on the size of the engineering team, maturity of the platform function, existing tech stack, and buyer personas involved. This means our AEs have had to experiment with messaging and sales process, thinking critically about what’s working, adapting, and sharing those learnings with the team.

### Calibrating performance and impact

Sales might be the most objectively measurable function in a company, with quota attainment providing a clear, relative indicator of performance. Don’t waste your time on reps who weren’t consistently in the top 10% or at least the top quartile of their team. I struggle to accept excuses like “I had a tough territory” or “a deal slipped” when peers around them were still successful. Clearly, success was achievable.

I emphasize “relative” intentionally: I’d much rather hire a rep who hit only 75% quota on a team where everyone else fell below 50% than someone who achieved 130% alongside peers who performed similarly. Assume it will be challenging for your rep to succeed given your imperfect or nonexistent sales playbook, and therefore, prioritize candidates who’ve demonstrated a consistent pattern of outperforming their peers.

Assessing individual impact, however, is trickier. When hiring from successful companies, you must determine if the company’s success occurred *because of, regardless of,* or *in spite of* the candidate’s contributions. It’s easy to confuse “wow, their previous company grew extremely fast” with “they must be great too.” I won’t spend time on how to weed out the obvious “in spite of” candidates, those tend to be easier to spot, but distinguishing between the other two is tougher.

A quick way to sanity-check this is to look at *when* they were there. If someone joined a company in 2020 that was founded in 2015 and already had 150+ employees, they likely walked into structure, not chaos. They were there when playbooks were in place, growth was steady, and inbound was plenty (yum).

This distinction boils down to their role in shaping the playbook that led to success, similar to the chef versus cook analogy. Was this candidate actively involved in discovering and iterating the sales playbook, attracting new talent, and helping leadership deeply understand the ICP and customer challenges? Or were they simply along for the ride, executing effectively without genuinely driving improvements?

When interviewing candidates, I typically ask about their current role, “What questions do you ask during a discovery call, and what typically takes place on a second call?” I follow-up that up with, “Did this change when you were there?” I don’t actually care about the first question. Instead, I’m focused on their involvement in whatever change might have occurred. A good answer looks something like this:

> *Yeah, it did change. I realized our discovery was broken, so I started asking about X and Y instead. That put us in a better position to do A and B on the second call and drove more urgency in the sales process.*
> 

You can also ask pointed questions about their involvement: Why did they choose their ICP? What mistakes did they make, and how did they adapt afterward? Which signals were they paying attention to, and how did they translate these observations into actionable changes in the sales process? That said, I’m a bigger fan of “questions behind the questions” as they’re harder to game on the spot.

### Self-criticality

Great salespeople are natural storytellers. This trait is invaluable in the sales process, as they help customers see themselves in the narrative of their current, broken processes that can be best remedied by adopting your product. However, this can make hiring tricky, since they're skilled at explaining away bad outcomes. 

Watch out for reps who externalize their losses. I ask every candidate to describe the last deal they lost and why. Poor answers point to factors outside their control, like *a delayed feature, lost budget, their champion leaving, or a prospect going dark.*

Good answers place responsibility on themselves: *I failed to properly multi-thread, misunderstood technical requirements, ran a poorly scoped POC, didn’t engage the budget owner effectively, mistook a “coach” for a “champion,” didn’t utilize internal resources well, etc.*

The best salespeople understand that growth comes from being critical of what's within their control. Your startup environment will inevitably be chaotic and imperfect, giving them plenty of opportunities to place blame elsewhere. But truly self-critical reps rise above these circumstances and find ways to improve, regardless of the challenges around them.

### Immigrant mentality

While the phrase "immigrant mentality" may be controversial, it perfectly captures the spirit of reps who are driven to achieve more and are often fueled by their past challenges. They have something to prove and consistently push through walls to demonstrate their worth. 

Succeeding at a startup demands exactly this level of motivation. Startups are messy but rewarding environments for those who can persevere and build something meaningful. During interviews, I seek to understand candidates' core motivations and look for examples where they've pushed themselves beyond conventional limits to achieve their goals.

I've found this quality in two types of people: those who've overcome genuine hardship and those who've excelled in highly competitive pursuits. The second group includes college athletes, military veterans, and academic competitors. The intense dedication required to succeed at these high levels mirrors the same irrational commitment needed in a startup. 

Personal hardship should be explored with sensitivity through questions like: *Where did you grow up? What did your parents do?* The simple question "Why sales?" often reveals surprising depth. A superficial answer like "I like helping people with their problems" raises red flags. A more compelling response might be "I discovered I had a talent for it and could earn well, which helps me support my family."

I grew up in a small town in Oklahoma, which isn't exactly a launchpad for a tech career. I went to a state school for my undergrad (Boomer Sooner; IYKYK) before making my way to Silicon Valley, where I landed (somewhat serendipitously) at a startup filled with pedigreed people. I have a soft spot for candidates from lesser-known schools. They're consistently humble, deeply appreciative of opportunities, and the fact that they've made it to your interview? Well, that says something.

### Curious, but about the right things

Curiosity is often described as table-stakes for success as a salesperson, as this attribute is essential for developing a strong business case. A great rep cares deeply about discovering their prospects' problems that their products could solve and understanding the negative impact of leaving those problems unaddressed.

But curiosity needs qualification. The research candidates have done before the interview and their corresponding questions provide the best "tell." Do they ask the deeper, second-level questions you'd hope they'd ask in a customer conversation, or are their questions generic and ignorant of your value proposition?

A great question might be: 

> Your documentation explains this set of capabilities, and online reviews suggest customers value them along these dimensions. What product investments might cause this value proposition to expand?"
> 

A poor question would be:

> What's the current valuation of the company?
> 

Sure, valuation matters eventually, but it shouldn't be the first thing a rep asks.

## Experience

The good news is that a lot of what you should screen for is observable on LinkedIn or a resume, meaning you can avoid unnecessary interviews.

### Your stage or bust

Sales support structures vary significantly by company stage. Later-stage companies typically have robust resources like sales playbooks, engineers, marketing teams, and more, while early-stage companies rely mostly on founders and a repurposed fundraising deck. Hiring a salesperson from a more mature organization into your startup will likely cause friction, as they’re used to a higher level of support and structure. At < 10 employees, 70 % of the job is self‑enablement.

Prioritize candidates who have experience as one of the first five hires at a startup. This ensures they can thrive in the inevitably chaotic early-stage environment. I also recommend hiring someone trained in a high-performing sales organization. Unless you, as the founder, have a sales background, bringing in a salesperson without disciplined sales habits guarantees avoidable mistakes, such as insufficient discovery, weak qualification, mistaking enthusiasm for actual need, and investing time with people lacking influence.

### **Buyer persona & domain**

Don’t expect a rep who’s succeeded selling to marketers to automatically replicate that success with engineers. Yes, good reps can learn, but as a founder, you probably won’t have the bandwidth to ramp them up on an entirely new buyer persona and category. 

Your first salesperson needs to hit the ground running and immediately establish credibility with your customers, already understanding their problems, current solutions, tech stacks, and even how they advance in their careers.

### **Category creation vs. Known product categories**

When selling into an existing category, your buyer already knows how to purchase: there’s an established budget owner, allocated funds, designated stakeholders, and clearly defined buying criteria. In these cases, the salesperson’s job is to uncover this buying process, meet the customer’s criteria, handle objections, and close the deal.

Conversely, selling into an emerging category is entirely different: your customers may not even realize a solution exists to their problems. Selling here demands a **missionary** rather than a **mercenary**. While mercenaries excel at running a tight sales process and coordinating resources (sales engineers, executives, channel partners), missionaries help prospects imagine a better future state with your product at the center. They guide prospective buyers to see their problems more clearly and inspire them to champion their solutions internally.

We look for missionaries at Northflank, and, specifically, people who are good storytellers and share our vision for helping software teams focus on what truly matters: creating and running workloads, not managing infrastructure. Our AEs subscribe to a belief that the entire post-commit experience (building, deploying, running, scaling, observing) should feel like *one system*, not a bunch of tools wired together. We’re category creators with the aim of becoming *the* category definer.

### **Demand manufacturers**

You don’t need an account manager, but it’s easy to accidentally hire one. 

Many account executives at mature companies function more like account managers, primarily managing existing business rather than generating new sales. While these reps might identify new business, it takes a completely different skill set to sell into companies who’ve never heard of your product. You need salespeople who’ve repeatedly shown they can prospect into entirely new accounts, source net new opportunities, and convert those opportunities into revenue.

However, simply knowing whether a candidate sold new business or expanded existing accounts isn’t enough. Many account executives rely heavily on BDRs, marketing-generated leads, or channel partners to build their pipeline. 

<InfoBox className='BodyStyle'>

💡 A good rule of thumb is to avoid hiring anyone who didn’t personally generate at least 80% of their pipeline through cold outreach for at least two consecutive years.

</InfoBox>

### Wrapping up

Hiring your first salesperson is a pivotal decision, one that sets the tone for growth and shapes your early sales culture. Applying these filters will help you avoid costly early mishires. If you do hire the wrong person, trust your instincts and act decisively. You can always try again.]]>
  </content:encoded>
</item><item>
  <title>10 best Elastic Beanstalk alternatives in 2026: Deploy apps without the AWS complexity</title>
  <link>https://northflank.com/blog/elastic-beanstalk-alternatives</link>
  <pubDate>2025-06-17T16:51:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for Elastic Beanstalk alternatives? Here are 10 platforms that simplify app deployments, including Northflank, offering better flexibility, pricing, and control, along with guidance on when to use each.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/elastic_beanstalk_alternatives_5d37a76d01.png" alt="10 best Elastic Beanstalk alternatives in 2026: Deploy apps without the AWS complexity" />I remember when AWS Elastic Beanstalk was launched in 2011, how it was positioned as AWS’s “one-click” deployment platform for handling servers, load balancers, and scaling with minimal setup.

Back then, it felt like a big step forward. You didn’t need to configure EC2 instances manually or write infrastructure as code to get a web app running. It abstracted away the heavy lifting for developer teams who wanted to deploy fast without worrying about all the underlying infrastructure.

Fourteen years down the line, the tone has shifted for many.

One developer on the r/aws subreddit called it:

> “a very old service that doesn't really get many updates. You might want to take a look at … for a more modern easy service”
> 
> 
> ~ [reddit](https://www.reddit.com/r/aws/comments/1ff67nj/comment/lmszf8w/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button)
> 

I get why you’re looking for Elastic Beanstalk alternatives. The limited observability, the provider lock-in, and the need for something that matches the way teams ship software today have made it hard to stick with Beanstalk. You might be looking for better debugging support, Git-centric workflows, container or Kubernetes support, and the flexibility to run on any cloud.

I’ll walk you through 10 Elastic Beanstalk alternatives that check those boxes, with clear comparisons and pricing details to help you make the right call for your team.

<InfoBox className='BodyStyle'>

### Quick look: top Elastic Beanstalk alternatives in 2026

If you’re short on time, here’s a quick breakdown of platforms you can use instead of AWS Elastic Beanstalk:

1. [**Northflank**](https://northflank.com/) – All-in-one Kubernetes-based platform with Git-based deploys, background jobs, static IPs, and [BYOC support](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) for deploying to your own cloud.
2. [**Render**](https://northflank.com/blog/render-vs-heroku#render-modernizing-the-developer-experience) – Simple deployment platform with autoscaling, PR previews, and built-in CDN.
3. [**Fly.io**](https://northflank.com/blog/flyio-vs-render#what-its-like-to-build-and-scale-with-flyio) – Run full-stack apps globally with edge deployments and persistent volumes.
4. [**Google Cloud Run**](https://northflank.com/blog/best-google-cloud-run-alternatives-in-2025) – Serverless container hosting with scale-to-zero and HTTP triggers.
5. [**Heroku**](https://northflank.com/blog/render-vs-heroku#heroku-pioneering-simplicity) – CLI-first developer platform with a wide plugin ecosystem and built-in CI.
6. [**Azure App Service**](https://northflank.com/blog/azure-alternatives) – Managed web app hosting with support for .NET, Java, Node.js, and more.
7. [**Netlify**](https://northflank.com/blog/vercel-vs-netlify-choosing-the-deployment-platform-in-2025#netlify) – Best for frontend and Jamstack workflows, with instant rollbacks and deploy previews.
8. [**Vercel**](https://northflank.com/blog/render-vs-vercel#tldr-render-vs-vercel-vs-northflank) – Optimized for frontend teams using React and Next.js, with fast CDN-backed deployments.
9. [**DigitalOcean App Platform**](https://www.digitalocean.com/products/app-platform) – Simple UI for deploying apps with autoscaling and managed infra.
10. [**Cloud Foundry**](https://northflank.com/blog/cloud-foundry-journey-and-alternatives-internal-developer-platform) – Open-source PaaS for enterprises looking to move away from proprietary platforms.
</InfoBox>

## What to look for in Elastic Beanstalk alternatives (*Don’t skip*)

I’ve put together this checklist of things to look out for while evaluating Elastic Beanstalk alternatives, so you don’t waste time and money trying out the wrong platform.

Before anything else, get clear on what’s important to your team right now. You can use this list as a starting point.

![what-to-look-for-in-elastic-beanstalk-alternatives.png](https://assets.northflank.com/what_to_look_for_in_elastic_beanstalk_alternatives_ae80c9913e.png)

**1. Do you want a tool that supports containers or Kubernetes?**

If your team is already using Docker or planning to adopt Kubernetes, the alternative should give you enough flexibility to run container-based workloads without locking you into legacy VM workflows. For instance, platforms like Northflank run on Kubernetes under the hood and give you a higher-level interface that still lets you manage containers directly. ([See how](https://northflank.com/deploy/run-persistent-and-ephemeral-docker-containers))

**2. Do you need better observability?**

Beanstalk makes it hard to understand what’s happening under the hood. If debugging slow deployments or failing apps has been frustrating, you’ll want a platform with centralized logs, real-time metrics, and clearer deployment status. Tools like Northflank provide logs, metrics, and deployment history in one place, so you don’t have to piece it together from different services. ([See how](https://northflank.com/docs/v1/application/observe/view-logs))

**3. Are you trying to avoid provider lock-in?**

Some teams are fine staying inside AWS. Others are actively moving to multi-cloud setups or prefer to run on their own infrastructure. If you want that flexibility, then BYOC ([Bring Your Own Cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment)) support is vital for your team. Northflank, for instance, is one platform that supports BYOC, letting you deploy to your own AWS, GCP, or Azure account. ([See how](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes))

**4. Is your team already using Git-based CI/CD?**

If your deployments are triggered via GitHub or GitLab pipelines, your next platform should integrate tightly with those flows, ideally without you manually configuring webhooks or writing custom scripts. Platforms like Northflank connect directly to your Git provider and auto-build and deploy on every push. ([See how](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank))

**5. Are you managing multiple services or a monorepo?**

Elastic Beanstalk isn’t great at managing multi-service architectures. If your app includes multiple backends, workers, or frontend repos, you’ll want a tool that makes coordinating deployments easier. Tools like Northflank support multi-service applications, jobs, and shared build pipelines out of the box. ([See how](https://northflank.com/docs/v1/application/run/run-containers-and-micro-services))

**6. Do you need transparent and predictable pricing?**

AWS pricing can feel uncertain, especially as your app scales. Look for a platform with transparent pricing models, free tiers for testing, and pay-as-you-go options if you’re not ready for annual contracts. For example, Northflank offers a free tier, predictable monthly pricing, and usage-based billing when needed. ([See pricing details](https://northflank.com/pricing))

## Comparison table: Elastic Beanstalk vs alternatives

After you’ve looked at the checklist, the next thing we’ll do is compare 10 Elastic Beanstalk alternatives side-by-side to help you quickly filter your options based on your team’s priorities.

I’ll compare them based on some of the things I mentioned earlier, like cloud support, background jobs, [BYOC](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) support, pricing, and their best use case.

Note that we’ll go into the details later. So, if you need more information, scroll down to the next section that breaks all of this down.

| Platform | [BYOC Support](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) | Pricing | Best For |
| --- | --- | --- | --- |
| [**Northflank**](https://northflank.com/) | Yes | Free tier + usage-based (pay-as-you-go) | All-in-one platform for full-stack apps, background jobs, CI/CD, and custom cloud deployment,  without managing Kubernetes yourself |
| **Elastic Beanstalk** | No | AWS pay-as-you-go | Simple AWS-native apps that don't need custom orchestration |
| [**Render**](https://northflank.com/blog/render-vs-heroku#render-modernizing-the-developer-experience) | No | Free tier + $19–$29/user/month (plus usage) | Simple full-stack apps with Git deploys and autoscaling |
| [**Fly.io**](https://northflank.com/blog/flyio-vs-render#what-its-like-to-build-and-scale-with-flyio) | No | Free tier + usage-based VMs and bandwidth | Latency-sensitive apps deployed close to users |
| [**Cloud Run**](https://northflank.com/blog/best-google-cloud-run-alternatives-in-2025) | No | Free tier + usage-based (vCPU/GB-seconds) | Stateless container apps that scale to zero |
| [**Heroku**](https://northflank.com/blog/render-vs-heroku#heroku-pioneering-simplicity) | No | $5–$2,400+/month depending on dyno type; add-ons priced separately | Teams who want fast CLI-based deployments without managing infra |
| [**Azure App Service**](https://northflank.com/blog/azure-alternatives) | No | Free tier + $54/month and up; premium & isolated tiers go higher | .NET and enterprise apps needing Azure-native CI/CD |
| [**Netlify**](https://northflank.com/blog/vercel-vs-netlify-choosing-the-deployment-platform-in-2025#netlify) | No | Free tier + $19/user/month (Pro) | Static sites, frontend teams, and Jamstack workflows |
| [**Vercel**](https://northflank.com/blog/render-vs-vercel#tldr-render-vs-vercel-vs-northflank) | No | Free tier + $20/user/month (Pro) | Frontend teams using React/Next.js and edge functions |
| [**DigitalOcean App Platform**](https://northflank.com/blog/best-digitalocean-alternatives-2025) | No | Free tier (static) + $5/month per container | Small projects needing simplified container deployments |
| [**Cloud Foundry**](https://northflank.com/blog/cloud-foundry-journey-and-alternatives-internal-developer-platform) | Partial | Open source (or vendor pricing) | Enterprises modernizing legacy apps across clouds or on-prem |

## Top 10 Elastic Beanstalk alternatives to check out

At this point, you’ve seen the checklist, comparison table, and what to prioritize. Now here’s the detailed breakdown of 10 platforms that can replace Beanstalk, depending on what your team needs right now.

### 1. **Northflank (Most complete platform - Best all-in-one platform)**

Starting with [Northflank](https://northflank.com/). If you need more control over your workloads than Beanstalk gives you, but you don’t want to build and manage Kubernetes yourself, then you should go for a platform like Northflank. Why? Because Northflank is built for teams that wish for container-based deployments, Git-based CI/CD, job orchestration, and cloud flexibility, all in one platform.

Meaning you don’t have to manually combine several platforms since you have everything you need in a single platform.

![new northflank home page.png](https://assets.northflank.com/new_northflank_home_page_9600c53fbb.png)

**So, how does it compare to Elastic Beanstalk?**

Elastic Beanstalk hides most of the infrastructure details, which sounds good until you need to debug or scale something slightly non-standard. Northflank, by contrast, gives you:

- visibility into your services ([see how](https://northflank.com/docs/v1/application/observe/monitor-containers))
- logs ([see how you can view your logs](https://northflank.com/docs/v1/application/observe/view-logs))
- metrics ([see how you can view metrics](https://northflank.com/docs/v1/application/observe/view-metrics))
- job history ([see how](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs))
- deployments ([see how](https://northflank.com/docs/v1/application/getting-started/build-and-deploy-your-code))

… and runs on Kubernetes under the hood, without exposing you to raw manifests.

It also supports multi-service setups, [background jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs) (scheduled or persistent), [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), [secrets management](https://northflank.com/docs/v1/application/secure/manage-secret-groups), and [BYOC](https://northflank.com/features/bring-your-own-cloud) (Bring Your Own Cloud), which lets you deploy into your own AWS, GCP, or Azure account.

**Pricing**

- **Free Tier** – includes 2 services, 2 jobs, 1 addon, and 1 BYOC cluster for testing the platform
- **Pay as you go** – starts at $0/month plus usage, with unlimited projects, BYOC clusters, vCPUs, memory, and global regions
- **Enterprise** – custom pricing for on-prem, bare-metal, BYOX, and org-wide collaboration
- (See full [pricing details](https://northflank.com/pricing) and try the pricing calculator)

> Go with this if you want the **most complete platform**, combining control, flexibility, and visibility, with Kubernetes under the hood, Git-based deployments, background jobs, and [BYOC](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment), all without managing the infrastructure yourself.
> 

### 2. **Render (Best for simple full-stack apps)**

[Render](https://northflank.com/blog/render-vs-heroku#render-modernizing-the-developer-experience) is a good fit if you’re looking for a straightforward alternative to Elastic Beanstalk that doesn’t require much setup. It’s aimed at teams building simple web apps or services that want Git-based deployments, autoscaling, and some background processing without managing infrastructure.

![render's home page.png](https://assets.northflank.com/render_s_home_page_2880e163be.png)

**So, how does it compare to Elastic Beanstalk?**

Elastic Beanstalk expects you to configure environments and piece together various AWS services. Render reduces that overhead. It’s fully managed, handles deployments from Git out of the box, and provides built-in HTTPS, autoscaling, preview environments, and background workers.

> But there are trade-offs. You don’t get deep observability or control over infrastructure, and it doesn’t support BYOC or multi-cloud flexibility, unlike platforms like Northflank. You’re running on Render’s managed AWS infrastructure with limited customization.

**Pricing**

- **Hobby** – starts at $0 per user/month plus compute costs for personal projects
- **Professional** – $19 per user/month for teams building production apps
- **Organization**  – $29 per user/month for higher traffic demands
- **Enterprise** - Custom pricing for enterprises with security

> Go with this if you want a simple hosting platform for small to mid-sized web apps, and don’t need infrastructure control or multi-cloud support.
> 

<InfoBox className='BodyStyle'>
If you're comparing Render with other platforms, these guides might help:
- [Render vs Heroku](https://northflank.com/blog/render-vs-heroku)
- [Render vs Vercel](https://northflank.com/blog/render-vs-vercel)
- [Render vs Fly.io](https://northflank.com/blog/flyio-vs-render)
- [Top Render Alternatives](https://northflank.com/blog/render-alternatives)
</InfoBox>

### 3. **Fly.io (Best for latency-sensitive or edge apps)**

[Fly.io](https://northflank.com/blog/flyio-vs-render#what-its-like-to-build-and-scale-with-flyio) is for teams that care about global performance and want to run their apps close to users without setting up their own edge infrastructure. If you’ve had a hard time dealing with Beanstalk’s limited regional support or had to combine CDN layers for faster response times, then Fly.io might be an option.

![fly.io.png](https://assets.northflank.com/fly_io_87f030b697.png)

**So, how does it compare to Elastic Beanstalk?**

Elastic Beanstalk only runs in AWS regions you configure, and scaling globally usually means more setup, cost, and effort. Fly.io skips that by letting you deploy Dockerized apps directly to edge locations around the world.

> It supports background workers, persistent volumes, and distributed Postgres, but it’s more low-level than platforms like Northflank. You’ll work with a CLI and configuration files to manage apps, and there’s no built-in job orchestration dashboard or multi-service coordination like you’d find elsewhere.

**Pricing**

- **Free plan** – includes 3 shared-CPU VMs and 160GB outbound bandwidth
- **VM pricing** – starts at ~$1.94/month (1 shared CPU + 256MB RAM)
- **Dedicated CPU** – from $31/month (2GB RAM) up to $976/month (128GB RAM)
- **Persistent volumes** – $0.15/GB/month
- **Static IPs** – $0.005/hour
- **Data transfer** – $0.02–$0.12/GB depending on region
- **GPU support** – from $1.50/hour per GPU
- **Reserved VM discounts** – 40% off with annual reservation

> Go with this if you need to run containerized apps closer to users across the globe and are comfortable managing infra with a CLI and config files.
> 

<InfoBox className='BodyStyle'>
If you are looking for more comparison, then check these:
- [Fly.io vs Render](https://northflank.com/blog/flyio-vs-render)
- [Top Fly.io Alternatives](https://northflank.com/blog/flyio-alternatives)
</InfoBox>

### 4. **Google Cloud Run (Best for scale-to-zero serverless containers)**

If you dislike EC2 costs piling up on Beanstalk or want a simpler way to deploy containers without thinking about servers, then [Google Cloud Run](https://northflank.com/blog/best-google-cloud-run-alternatives-in-2025) might be a good option. It’s designed to run stateless containers on demand, scaling them down to zero when idle and back up instantly under load.

It’s ideal for teams that want container-based workflows, but don’t want to manage orchestration, Kubernetes clusters, or VM scaling policies.

![google cloud run home page-min.png](https://assets.northflank.com/google_cloud_run_home_page_min_25317b598a.png)

**So, how does it compare to Elastic Beanstalk?**

Elastic Beanstalk runs on EC2 behind the scenes and charges you even when your app is idle, plus, you still have to configure load balancers, instance types, and scaling rules. Cloud Run skips all that. You deploy a container, set concurrency limits, and Google takes care of the scaling and networking.

It supports HTTP-triggered apps out of the box, integrates with Pub/Sub and Cloud Tasks for background jobs, and plays well with other Google Cloud services like Cloud SQL and Secret Manager.

> But you don’t get fine-grained control over deployment environments or orchestration across services like you would on Northflank.

**Pricing**

- **Free Tier** – 2 million requests/month, 360,000 GB-seconds, and 180,000 vCPU-seconds
- **Paid usage** – starts at ~$0.000024/vCPU-second and ~$0.0000025/GB-second

> Go with this if you want to run stateless containers on demand, only pay when your app is active, and don’t need advanced orchestration or multi-service coordination.
> 

<InfoBox className='BodyStyle'>
If you're interested in seeing other alternatives to Google Cloud Run and how it compares to App Engines, see:
- [Best Google Cloud Run alternatives in 2026](https://northflank.com/blog/best-google-cloud-run-alternatives-in-2025)
- [App Engine vs. Cloud Run: A real-world engineering comparison](https://northflank.com/blog/app-engine-vs-cloud-run)
</InfoBox>

### 5. **Heroku (Best for fast setup and CLI-first workflows)**

If your team loved the simplicity of `git push heroku main`, [Heroku](https://northflank.com/blog/render-vs-heroku#heroku-pioneering-simplicity) still delivers one of the easiest deployment flows around. It’s good for teams that want to focus on writing code instead of setting up CI pipelines, Dockerfiles, or cloud infrastructure.

It solves the same problem Beanstalk tries to solve, which is abstracting away infrastructure, but in a much more developer-friendly manner.

![heroku.png](https://assets.northflank.com/heroku_092e1c7f09.png)

**So, how does it compare to Elastic Beanstalk?**

Elastic Beanstalk gives you basic infrastructure abstraction, but setup still involves AWS IAM, EC2 config, S3 storage, and manual service wiring. Heroku hides nearly all of that. You deploy code directly via CLI or GitHub, and Heroku handles buildpacks, environment variables, add-ons, and scaling via dynos.

> What you don’t get is infra-level control, background job scheduling out of the box, or support for containers unless you bring your own Docker setup. It also lacks multi-cloud or BYOC capabilities, unlike platforms like Northflank, which support all these.

**Pricing**

- **Free Tier** – sunset in 2022 (replaced by **Eco** for lightweight, non-commercial use)
- **Eco Plan** – $5/month per dyno (up to 1,000 hours; not prorated; sleeps after 30 mins of inactivity)
- **Basic Plan** – $7/month per dyno (billed ~$0.01/hour, prorated to the second)
- **Standard, Performance, Private, and Shield Plans** – range from ~$25/month to $2,400+/month depending on dyno type and usage
- **Data Services (e.g., Postgres, Redis)** – priced separately; start from $3–$5/month for small projects and scale with usage and performance needs

> Go with this if you want a platform to deploy web apps fast and don’t need Kubernetes, containers, or background job orchestration.
> 

<InfoBox className='BodyStyle'>
If you're comparing Heroku to other platforms, looking for how to migrate, or it's pricing comparison and reduction these might help:
- [Render vs Heroku](https://northflank.com/blog/render-vs-heroku)
- [Top Heroku alternatives in 2026](https://northflank.com/blog/top-heroku-alternatives)
- [How to migrate from Heroku](https://northflank.com/blog/how-to-migrate-from-heroku-a-step-by-step-guide)
- [Heroku Pricing Comparison & Reduction](https://northflank.com/heroku-pricing-comparison-and-reduction)
- [Vercel vs Heroku: Which platform fits your workflow best?](https://northflank.com/blog/vercel-vs-heroku)
- [Heroku Enterprise: capabilities, limitations, and alternatives](https://northflank.com/blog/heroku-enterprise-capabilities-limitations-and-alternatives)
</InfoBox>

### 6. **Azure App Service (Best for .NET and enterprise-focused apps)**

[Azure App Service](https://northflank.com/blog/azure-alternatives) is ideal if your team is already using Microsoft technologies or needs tight integration with Azure services. It’s fully managed, supports multiple languages, and is especially useful for hosting .NET, Java, Node.js, or PHP apps without managing infrastructure.

It’s built for enterprise-grade workloads, but you’ll need to get familiar with the Azure portal and pricing structure.

![Azure App Service home page.png](https://assets.northflank.com/Azure_App_Service_home_page_8c23eca050.png)

**So, how does it compare to Elastic Beanstalk?**

Elastic Beanstalk can feel fragmented, especially when handling EC2, S3, and CloudWatch separately. Azure App Service wraps deployments, scaling, and monitoring into a unified interface. It also supports GitHub Actions and Azure DevOps pipelines out of the box.

> However, App Service is more opinionated about Azure usage. You’ll need to use Azure CLI or portal workflows, and while it supports Linux containers, it’s not as flexible for multi-service orchestration or background job control as platforms like Northflank.

**Pricing**

- **Free Tier (F1)** – Shared CPU (60 CPU-min/day), 1 GB storage, $0/month – ideal for testing.
- **Basic Plan (B1–B3)** – Starts at ~**$54.75/month** (1 vCPU, 1.75 GB RAM); higher tiers up to ~$219/mo.
- **Standard & Premium Plans** – Start at ~$70/month; Premium v3 begins at ~$120/mo (P0 v3), with larger configurations available; Premium v4 in preview from ~$99/mo.
- **Isolated (App Service Environment)** – Dedicated, private environments starting ~**$410/month**.
- **Extras**: bandwidth billed separately; SSL (IP-based) ~$39/mo; custom domains ~$12/year; other add‑ons priced separately.

> Go with this if you're already in the Azure ecosystem and want managed app hosting with built-in CI/CD and staging environments, especially for .NET-based stacks.
> 

<InfoBox className='BodyStyle'>
You might also want to check out: [Top 10 Microsoft Azure alternatives in 2026: Best cloud platforms for your business](https://northflank.com/blog/azure-alternatives)
</InfoBox>

### 7. **Netlify (Best for frontend and Jamstack workflows)**

[Netlify](https://northflank.com/blog/vercel-vs-netlify-choosing-the-deployment-platform-in-2025#netlify) is a go-to platform for frontend developers and teams working with static sites, headless CMSs, or Jamstack architectures. If you’re building single-page apps or sites powered by frameworks like Next.js, Astro, Hugo, or Eleventy, it gives you everything you need, from Git-based deployments to global CDN and instant rollbacks.

![netlify's home page.png](https://assets.northflank.com/netlify_s_home_page_6929286bb8.png)

**So, how does it compare to Elastic Beanstalk?**

Elastic Beanstalk was never optimized for frontend apps. It’s overkill for static sites and lacks built-in features like atomic deployments or edge caching. Netlify solves that by abstracting away infrastructure entirely, you push to Git, and your site is live globally with HTTPS, CDN, and serverless functions.

> Where it differs: Netlify isn’t suited for complex backend services or multi-service coordination. It supports serverless functions and background tasks, but not long-running containers or job orchestration, like Northflank. And you’re tied to its workflow model.

**Pricing**

- **Free Plan** – Includes 300 build minutes/month and 100GB bandwidth
- **Pro Plan** – Starts at $19/user/month with more team features
- **Business Plan** – Custom pricing for SSO, audit logs, and SLAs
- Additional usage-based pricing for bandwidth, build time, and edge functions ([See pricing](https://www.netlify.com/pricing/))

> Go with this if you’re building frontend apps or static sites and want instant deployments, global CDN, and zero infrastructure overhead.
> 

<InfoBox className='BodyStyle'>
If you're comparing Netlify to other options, check out:
- [Netlify vs Vercel](https://northflank.com/blog/vercel-vs-netlify-choosing-the-deployment-platform-in-2025)
- [7 Netlify alternatives in 2026: Where to go when your app grows up](https://northflank.com/blog/netlify-alternatives)
- [Best Vercel Alternatives for Scalable Deployments](https://northflank.com/blog/best-vercel-alternatives-for-scalable-deployments)
</InfoBox>

### 8. **Vercel (Best for frontend teams using Next.js and React)**

[Vercel](https://northflank.com/blog/render-vs-vercel#tldr-render-vs-vercel-vs-northflank) is the default platform for many teams building with React and Next.js. If your frontend stack is built around server-side rendering, edge functions, and static site generation, Vercel is designed to take care of deployments, previews, scaling, and global delivery.

![vercel-homepage.png](https://assets.northflank.com/vercel_homepage_f09e3a1f3c.png)

**So, how does it compare to Elastic Beanstalk?**

Elastic Beanstalk was built for general-purpose app hosting on AWS, not for frontend frameworks. You’d have to manually manage build pipelines, set up CDN distribution, and handle scaling rules. With Vercel, that’s all automatic, you push your code, and it’s deployed globally with intelligent caching, image optimization, and instant rollbacks.

> But Vercel is specialized. You can’t run background workers, persistent containers, or orchestrate multi-service backends like you can with Northflank. And while you get a seamless experience for frontend delivery, backend logic is limited to serverless and edge functions.

**Pricing**

- **Hobby Plan** – Free for personal use with 100GB bandwidth/month
- **Pro Plan** – $20/user/month with increased limits and team features
- **Enterprise Plan** – Custom pricing with SLAs, advanced controls, and analytics

> Go with this if your team is building on Next.js and wants fast, production-grade frontend deployments without worrying about infrastructure.
> 

<InfoBox className='BodyStyle'>
If you want to see how Vercel compares to other platforms or need more frontend hosting breakdowns, you should look at these:
- [Vercel vs Netlify: Choosing the right one in 2026 (and what comes next)](https://northflank.com/blog/vercel-vs-netlify-choosing-the-deployment-platform-in-2025)
- [Render vs Vercel (2026): Which platform suits your app architecture better?](https://northflank.com/blog/render-vs-vercel)
- [Vercel vs Heroku: Which platform fits your workflow best?](https://northflank.com/blog/vercel-vs-heroku)
- [Best Vercel Alternatives for Scalable Deployments](https://northflank.com/blog/best-vercel-alternatives-for-scalable-deployments)
</InfoBox>

### **9. DigitalOcean App Platform** (Simplest managed option for small apps)

If you’re coming from Elastic Beanstalk and want something far easier to use but still hosted on a known cloud provider, [DigitalOcean App Platform](https://northflank.com/blog/best-digitalocean-alternatives-2025) might be your next step. It’s beneficial for solo developers or small teams who want quick Git-based deployments, auto-scaling, and HTTPS out of the box.

![Digitalocean app platform's home page.png](https://assets.northflank.com/Digitalocean_app_platform_s_home_page_0f9ea04b7b.png)

**So, how does it compare to Elastic Beanstalk?**

Elastic Beanstalk can feel like you’re managing EC2, ELB, S3, and CloudWatch without a clear UI. App Platform strips that down into a clean experience: connect your repo, and it handles builds, deployments, scaling, and certs automatically. However, it doesn’t provide the depth of visibility or orchestration you’d need for complex apps or multi-service backends.

Unlike Northflank, App Platform doesn’t support background jobs or BYOC, and observability is limited compared to more advanced platforms.

**Pricing**

- **Starter Plan** – Free, but limited to 3 static sites and 512MB RAM
- **Basic Plan** – Starts at $5/month per container for web services

> Go with this if you want a simplified app platform for small projects and don’t need background workers, BYOC, or advanced deployment controls.
> 

> You might want to see [10 best DigitalOcean alternatives in 2026 for developers and teams](https://northflank.com/blog/best-digitalocean-alternatives-2025)

### 10. **Cloud Foundry (Best open-source option for large orgs with legacy apps)**

[Cloud Foundry](https://northflank.com/blog/cloud-foundry-journey-and-alternatives-internal-developer-platform) is an open-source PaaS used mostly by large enterprises that want to modernize app delivery without fully rewriting everything for Kubernetes. It’s useful if you’re migrating away from Beanstalk but still need support for monolithic apps, buildpacks, and strict compliance requirements.

![Cloud Foundry home page.png](https://assets.northflank.com/Cloud_Foundry_home_page_3030fcfd63.png)

**So, how does it compare to Elastic Beanstalk?**

Like Beanstalk, Cloud Foundry abstracts a lot of the infrastructure, but it does so in a more standardized, platform-agnostic way. Instead of being locked into AWS, you can run Cloud Foundry on GCP, Azure, AWS, or your own data center. You still get buildpack-based deployments, autoscaling, and service bindings, but without being tied to AWS-specific constructs.

> Compared to modern platforms like Northflank, however, Cloud Foundry is more complex to self-manage and doesn’t give you Git-based deployments or a clean UI out of the box. It’s best suited for teams that already have platform engineering resources or are using VMware Tanzu or other commercial Cloud Foundry distributions.

**Pricing**

- **Open-source** – free to run on your own infrastructure

> Go with this if you need an open-source PaaS with buildpack support, enterprise-grade stability, and the option to run across any cloud or on-prem, and you have the team to manage it.
> 

<InfoBox className='BodyStyle'>
You might want to see [Top Cloud Foundry alternatives in 2026](https://northflank.com/blog/cloud-foundry-journey-and-alternatives-internal-developer-platform)
</InfoBox>

## FAQs: Elastic Beanstalk vs other AWS tools

After looking at the Elastic Beanstalk alternatives, if you still have some questions that other developers frequently ask. I’ll help you quickly answer those questions in a few.

- **Which is better, EC2 or Elastic Beanstalk?**
    
    Beanstalk runs on EC2 and automates provisioning and scaling. Use EC2 for full control; Beanstalk if you want managed environments.
    
- **What’s the difference between Elastic Beanstalk and Kubernetes?**
    
    Kubernetes is more flexible and scalable, but requires more setup. Beanstalk hides orchestration but limits control.
    
- **Elastic Beanstalk vs Fargate — what’s the difference?**
    
    Fargate runs containers without managing servers. Beanstalk wraps EC2-based apps, not container-first.
    
- **What’s the difference between Elastic Beanstalk and Lambda?**
    
    Lambda is for short-lived, event-based functions. Beanstalk is for always-on services like APIs and web apps.
    
- **How does Elastic Beanstalk compare to ECS?**
    
    ECS gives full control over container orchestration. Beanstalk manages deployments with less visibility.
    
- **Is Elastic Beanstalk expensive?**
    
    It has no extra AWS fee, but usage of EC2, storage, and load balancers can become costly over time.
    
- **Elastic Beanstalk vs App Runner — what’s the difference?**
    
    App Runner is newer and focused on containers. Beanstalk supports more app types but is EC2-dependent.
    
- **Can you stop Elastic Beanstalk?**
    
    Not directly, you can terminate environments or scale instances to zero, but there's no built-in pause button.
    

## Not sure which to go for? See a quick way to decide

Ask your team:

> *Do we need* multi-service deployments, Git-based CI/CD, better logs and metrics, background jobs, or want to avoid lock-in and deploy into our own cloud?
> 

If you need any (or all) of these, you’ll want a platform like [Northflank](https://northflank.com/) that gives you everything in one place. It runs on Kubernetes behind the scenes but removes the complexity of setting it up yourself. That means you can scale your apps without relying on several AWS services at once.

See [how Northflank compares to Elastic Beanstalk in action](https://app.northflank.com/signup).]]>
  </content:encoded>
</item><item>
  <title>The complete guide to Kubernetes autoscaling</title>
  <link>https://northflank.com/blog/the-complete-guide-to-kubernetes-autoscaling</link>
  <pubDate>2025-06-16T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Kubernetes autoscaling automatically adjusts compute resources to match application demand. This guide covers everything from basic concepts to advanced patterns, with practical examples for implementing autoscaling in production environments.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/kubernetes_autoscaling_1_1de63c269e.png" alt="The complete guide to Kubernetes autoscaling" />Kubernetes autoscaling automatically adjusts compute resources to match application demand. This guide covers everything from basic concepts to advanced patterns, with practical examples for implementing autoscaling in production environments.

<InfoBox className='BodyStyle'>

## 📖 TL;DR

**Key takeaways:**

- Kubernetes autoscaling includes HPA (horizontal), VPA (vertical), and Cluster Autoscaler
- Works for ALL workloads: web apps, APIs, data processing, GPUs
- Can reduce costs by 50-70% while maintaining performance
- [Northflank](http://northflank.com/) simplifies configuration with visual controls and automated metric collection
- Custom metrics enable business-specific scaling (queue depth, latency, connections)
</InfoBox>

## What is Kubernetes autoscaling?

Kubernetes autoscaling dynamically adjusts resources based on real-time demand. Unlike traditional static provisioning where you guess capacity needs, autoscaling responds to actual usage patterns, scaling up during peaks and down during quiet periods.

| Workload Type | Scaling trigger | Example | Northflank advantage |
| --- | --- | --- | --- |
| **Web Apps** | Request rate | E-commerce during sales | RPS-based scaling |
| **APIs** | Latency/connections | Payment gateways | Custom metrics support |
| **Data Processing** | Queue depth | ETL pipelines | Prometheus integration |
| **ML/GPU** | GPU utilization | Model training | Resource-aware scaling |
| **Microservices** | Service-specific | Order processing | Per-service configs |

### Real-world scenarios where autoscaling is essential

**🛍️ E-commerce flash sales**: An online retailer experiences 20x normal traffic during Black Friday. Without autoscaling, they'd need to provision for peak capacity year-round. With Northflank's autoscaling, their platform automatically scales from 5 to 100 instances during the sale, then back down afterward.

**📹 Social media viral content**: A social platform's video service typically handles 1,000 requests/second. When content goes viral, traffic spikes to 50,000 requests/second within minutes. Autoscaling prevents service degradation by rapidly adding capacity.

**🏦 Financial services batch processing**: A bank processes transactions in nightly batches. Data volume varies from 1GB on weekends to 100GB at month-end. Autoscaling provisions resources only when needed, reducing costs by 80% compared to static provisioning.

## What are the types of autoscaling in Kubernetes?

### Horizontal Pod Autoscaler (HPA)

HPA scales the number of pod replicas based on metrics. It's ideal for stateless applications where adding instances directly increases capacity.

**Basic HPA configuration:**

```yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: webapp-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: webapp
  minReplicas: 2
  maxReplicas: 50
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
```

**Northflank's simplified approach:**
Instead of YAML, Northflank provides toggle controls where you:

- Enable horizontal autoscaling with one click
- Set min/max instances with sliders
- Configure CPU, memory, and RPS thresholds
- View real-time scaling events in the dashboard

### Vertical Pod Autoscaler (VPA)

VPA adjusts resource requests and limits for pods, right-sizing containers based on actual usage.

**VPA configuration example:**

```yaml
apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: webapp-vpa
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: webapp
  updatePolicy:
    updateMode: "Auto"
  resourcePolicy:
    containerPolicies:
    - containerName: webapp
      minAllowed:
        cpu: 100m
        memory: 128Mi
      maxAllowed:
        cpu: 2
        memory: 2Gi
```

### Cluster Autoscaler

Manages the cluster itself by adding or removing nodes based on pod scheduling needs.

**Key behaviors:**

- Adds nodes when pods can't be scheduled
- Removes underutilized nodes after grace period
- Respects pod disruption budgets
- Considers node pools and instance types

### Custom metrics autoscaling with Northflank

Northflank's custom metrics support enables scaling based on application-specific indicators:

```python
# Expose custom metrics in your app
from prometheus_client import Gauge, generate_latest

queue_depth = Gauge('message_queue_depth', 'Pending messages')

@app.route('/metrics')
def metrics():
    queue_depth.set(get_queue_size())
    return Response(generate_latest(), mimetype='text/plain')
```

Then in Northflank:

1. Specify your Prometheus endpoint and port
2. Select metric type (Gauge or Counter)
3. Set threshold values
4. Northflank handles the rest, no adapter configuration needed

## Why Northflank for Kubernetes autoscaling

Northflank transforms complex Kubernetes autoscaling into a straightforward process. Instead of wrestling with YAML configurations and manual metric setup, Northflank provides an intuitive interface that makes autoscaling accessible to teams of all sizes. The platform handles the underlying complexity while giving you powerful features like custom metrics, multi-threshold scaling, and real-time monitoring, all without the operational overhead of managing Kubernetes directly.

## How does autoscaling work in Kubernetes?

### The control loop explained

Every 15 seconds, the autoscaling control loop:

1. **Collects metrics** from all pods in the deployment
2. **Calculates average** utilization across instances
3. **Determines required replicas** using the formula:
    
    ```
    required = ceil[current * (actualMetric / targetMetric)]
    ```
    
4. **Applies scaling decision** based on policies

### Scaling behavior and policies

**Scale-up characteristics:**

- Immediate response to threshold breach
- Can scale multiple instances at once
- No cooldown period by default

**Scale-down characteristics:**

- 5-minute stabilization window
- Gradual reduction to prevent flapping
- Selects highest replica count from window

### Multi-metric coordination

When using multiple metrics (CPU, memory, RPS), the autoscaler:

- Calculates required replicas for each metric independently
- Selects the highest requirement
- Ensures no metric is under-provisioned

Northflank makes this seamless, enable any combination of metrics and the platform coordinates scaling decisions automatically.

## Benefits of autoscaling

### ✅ Cost optimization

Static provisioning wastes resources during low-demand periods. Autoscaling delivers significant savings:

- **Development environments**: 70-80% reduction (scale to zero when unused)
- **Production services**: 50-60% reduction (right-sized for actual load)
- **Batch processing**: 80-90% reduction (resources only when processing)

### ✅ Performance consistency

Autoscaling maintains performance KPIs during demand variations:

- Response times stay consistent during traffic spikes
- Queue processing times remain predictable
- User experience doesn't degrade under load

### ✅ Operational efficiency

Teams save significant time by eliminating manual scaling tasks:

- No midnight interventions for traffic spikes
- Automatic response to seasonal patterns
- Focus on features, not infrastructure

Northflank amplifies these benefits with visual monitoring and one-click configuration changes that would typically require complex YAML editing and kubectl commands.

## Advanced autoscaling patterns

### Predictive scaling

Combine reactive autoscaling with scheduled scaling for predictable patterns:

```yaml
# Morning scale-up for business hours
apiVersion: batch/v1
kind: CronJob
metadata:
  name: morning-scaleup
spec:
  schedule: "0 7 * * MON-FRI"
  jobTemplate:
    spec:
      template:
        spec:
          containers:
          - name: scaler
            image: bitnami/kubectl
            command: ["kubectl", "patch", "hpa", "webapp-hpa",
                     "--patch", '{"spec":{"minReplicas":10}}']
```

### Blue-green deployments with autoscaling

Northflank's deployment strategies work seamlessly with autoscaling:

1. Deploy new version with same autoscaling config
2. Gradually shift traffic while both scale independently
3. Complete cutover with zero downtime

### Geographic scaling patterns

For global applications, implement region-aware autoscaling:

- Scale regions independently based on local demand
- Use Northflank's multi-region support
- Configure different thresholds per region

## Northflank's autoscaling advantages

### Simplified configuration

Traditional Kubernetes autoscaling requires:

- Installing metrics servers
- Configuring RBAC permissions
- Writing YAML manifests
- Setting up Prometheus adapters
- Managing metric aggregation

Northflank provides:

- **Visual configuration**: Sliders and toggles instead of YAML
- **Automatic metric collection**: No manual setup required
- **Integrated monitoring**: See metrics and scaling events together
- **Custom metrics support**: Prometheus endpoints work immediately
- **Multi-metric coordination**: CPU, memory, and RPS in one interface

### How to set up Kubernetes autoscaling

![1.png](https://assets.northflank.com/1_476d79cd92.png)
Here's how a Northflank user configures autoscaling:

1. Navigate to your service's Resources page
2. Expand "Advanced resource options"
3. Toggle "Enable horizontal autoscaling"
4. Set your parameters:
    - Minimum instances: 2
    - Maximum instances: 20
    - CPU threshold: 70%
    - Memory threshold: 80%
    - RPS threshold: 1000

That's it. No YAML, no kubectl, no manual metric server configuration.

### Monitoring and observability


![2.png](https://assets.northflank.com/2_53b22cb9ed.png)

Northflank provides integrated monitoring that shows:

- Current instance count with scaling history
- Real-time metrics for all configured thresholds
- Scaling event logs with reasons
- Cost tracking as instances scale

<InfoBox className='BodyStyle'>


## 💭 FAQs

**Q: Is autoscaling only for GPU/ML workloads?**
A: No! Autoscaling works for any workload: web apps, APIs, batch jobs, microservices. Northflank supports autoscaling for all deployment types.

**Q: How quickly does autoscaling respond?**
A: Metrics are checked every 15 seconds. Scale-up can happen immediately, while scale-down uses a 5-minute window to prevent flapping.

**Q: Can I use custom metrics with Northflank?**
A: Yes! Expose any metric via Prometheus format, and Northflank will use it for scaling decisions. Common examples include queue depth, active connections, or business metrics.

**Q: What happens during deployment updates?**
A: Northflank maintains autoscaling configuration during updates. New pods inherit the same scaling rules, ensuring consistent behavior.

**Q: How do I test autoscaling?**
A: Use load testing tools to simulate traffic. Monitor the Northflank dashboard to see scaling in action. Start with conservative thresholds and adjust based on observations.

**Q: Can I combine different types of autoscaling?**
A: Yes, but carefully. HPA and VPA can conflict if not configured properly. Northflank's platform handles HPA elegantly, and you can use VPA recommendations to set initial resource requests.

</InfoBox>

## Final thoughts

Kubernetes changes transforms how we manage application resources, moving from static provisioning to dynamic, demand-based allocation. While the underlying technology is powerful, complexity has traditionally limited adoption.

Northflank changes this by making enterprise-grade autoscaling accessible to teams of all sizes. Through intuitive interfaces, automatic metric collection, and integrated monitoring, Northflank removes the operational burden while delivering the full benefits of Kubernetes autoscaling.

Whether you're scaling web applications during traffic spikes, optimizing batch processing costs, or managing complex microservices architectures, autoscaling ensures optimal resource utilization. Start with basic CPU-based scaling, monitor real-world behavior through Northflank's dashboards, and gradually introduce custom metrics as your needs evolve.

The future of infrastructure is adaptive, not static. 

With Northflank's approach to Kubernetes autoscaling, that future is accessible today, no infrastructure expertise required.

[Try Northflank for free, today.](https://app.northflank.com/t/cristinabuneas-team)]]>
  </content:encoded>
</item><item>
  <title>We tried the top PaaS providers so you don’t have to</title>
  <link>https://northflank.com/blog/best-paas-providers</link>
  <pubDate>2025-06-15T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Picking a platform is a serious decision, and most of the existing content out there doesn’t give you the full picture. We’ve spent time with the tools on this list. We’ve run into edge cases, scaling issues, pricing traps.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/paas_providers_1_19cfd377a9.png" alt="We tried the top PaaS providers so you don’t have to" />This is the Northflank blog, and we do think Northflank is the best Platform as a Service (PaaS) out there.

But we also know picking a platform is a serious decision, and most of the existing content out there doesn’t give you the full picture. We’ve spent time with the tools on this list. We’ve run into edge cases, scaling issues, pricing traps.

So we put this together to give you a clear, detailed view of what each platform offers and how they compare. We’ll make the case for Northflank as the best PaaS, but we’ll back it up with specifics. If you're evaluating platforms, this should save you hours.

<InfoBox className='BodyStyle'>

## TL;DR

PaaS providers help you deploy, manage, and scale applications without managing the underlying servers or container orchestration. If you want the short list:

- [**Northflank**](http://northflank.com/): Best overall PaaS in 2025. Deep Kubernetes abstraction, fast CI/CD, and full workload control. Works for any workloads (jobs, databases, GPUs, etc)
- [**Heroku**](https://northflank.com/blog/top-heroku-alternatives): Great beginner UX, but limited scale and expensive.
- [**Google App Engine**](https://northflank.com/blog/app-engine-vs-cloud-run): Best for simple services running in GCP.
- [**Azure App Service**](https://northflank.com/blog/azure-alternatives): Strong enterprise tie-in, but heavy.
- [**DigitalOcean App Platform**](https://northflank.com/blog/best-digitalocean-alternatives-2025): Streamlined but rigid.
- [**CapRover**](https://northflank.com/blog/coolify-alternatives-in-2025#3-caprover): Self-hosted freedom, DIY complexity.
- [**Railway**](https://northflank.com/blog/railway-alternatives): Fast prototyping, not production-grade.
- [**Render**](https://northflank.com/blog/render-alternatives): Balanced, but lacks low-level control.

</InfoBox>

## What is PaaS?

Platform-as-a-Service (PaaS) is a deployment environment that abstracts away the infrastructure layer. Instead of configuring servers or writing Kubernetes manifests, developers can push code and have it running in production in seconds.

### What features define a strong PaaS provider?

- **Runtime management**: Automatically handle language runtimes and dependency isolation.
- **Build pipelines**: Trigger builds from Git, support Dockerfiles or buildpacks.
- **Networking**: Auto-provision HTTPS, load balancing, and custom domains.
- **Environment variables & secrets**: Inject at runtime with security controls.
- **Scaling & availability**: Scale horizontally, auto-heal crashed instances.
- **Preview environments**: On-demand clones of your stack for every pull request.
- **Logging & monitoring**: Access logs, metrics, and alerts without configuring Grafana or Loki.

## IaaS vs. PaaS vs. SaaS

- **IaaS (Infrastructure-as-a-Service)**: You manage the VMs, OS, and container runtime. Full control, full responsibility.
- **PaaS (Platform-as-a-Service)**: You focus on your app. The platform runs it, secures it, and scales it.
- **SaaS (Software-as-a-Service)**: You consume the final product. No control, but zero maintenance.

PaaS sits in the middle: fast deployment with enough power under the hood to customize and scale when needed.

## PaaS providers, at a glance

| Provider | CI/CD | Docker support | Preview envs | Scaling | BYOC (Bring Your Own Cloud) | Secrets mgmt | Pricing | Best for |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| [**Northflank**](http://northflank.com/) | Built-in, zero-config | ✅ | ✅ | Auto + manual | ✅ | Encrypted, scoped, at-rest | Free tier, from $5/mo | Fast-moving teams, production-ready |
| [**Heroku**](https://www.heroku.com/) | Git push, buildpacks | ⚠️ Buildpacks | ❌ | Auto (limited) | ❌ | Basic config vars | Starts at $5/mo | Simple apps, prototypes |
| [**Google App Engine**](https://cloud.google.com/appengine) | Cloud Build, CLI | ✅ (Flex env) | ❌ | Fully managed | ❌ | GCP Secrets Manager | Free tier, then PAYG | GCP-native apps |
| [**Azure App Service**](https://azure.microsoft.com/en-us/products/app-service) | GitHub Actions, DevOps | ✅ | ✅ (Staging) | Auto + rules-based | ❌ | Azure Key Vault | Starts free | Enterprise, .NET-heavy teams |
| [**DigitalOcean**](https://www.digitalocean.com/) | Git-based, UI-driven | ✅ | ✅ | Auto | ❌ | Basic env vars | Starts at $5/mo | MVPs, solo devs |
| [**Render**](https://render.com/) | Git-based, Docker | ✅ | ✅ | Auto | ❌ | Encrypted env vars | Free tier, from $7/mo | Heroku replacement |
| [**Railway**](https://railway.com/) | Git + UI, .env based | ⚠️ Limited | ✅ | Abstracted | ❌ | .env-style, basic | Free tier, from $5/mo | Prototypes, quick-start projects |
| [**CapRover**](https://caprover.com/) | Manual, Docker | ✅ | ❌ | Manual only | ✅ (Self-host) | DIY, dashboard or CLI | Free (self-hosted) | Tinkerers, infra-savvy devs |

## 1. Northflank - Best overall PaaS provider

![new northflank home page.png](https://assets.northflank.com/new_northflank_home_page_9600c53fbb.png)

Northflank is a modern PaaS built on Kubernetes, but it abstracts away all the complexity. You never touch YAML unless you want to.

It offers CI/CD automation, microVM-based isolation, fine-grained RBAC, true multi-cloud and BYOC support, and a clean developer experience via both UI and API.

**Key features:**

- **CI/CD:** Per-branch pipelines, artifact storage, zero-config setup. Build every commit with logs and caching.
- **Runtime options:** Use Dockerfiles or buildpacks. Auto-detects runtime if unspecified.
- **Multi-cloud Kubernetes:** [Deploy to Northflank’s infra](https://northflank.com/features/managed-cloud) or [bring your own](https://northflank.com/features/bring-your-own-cloud) (AWS, GCP, Azure). BYOC comes with deep integration and network control.
- **[Security](https://northflank.com/features/platform#security):** MicroVMs for workload isolation, RBAC, encrypted secrets at rest and injection at runtime.
- **Networking:** Auto TLS, traffic splitting, real-time logs, custom domains.
- **[Preview environments](https://northflank.com/features/release):** Full, ephemeral clones per PR.
- **Observability:** Metrics, logs, and tracing baked in, no setup required.

**[Pricing](https://northflank.com/pricing):** Free tier available. Paid starts at ~$5/container/month. Enterprise BYOC plans available on request.

**Best for:** Teams who want the power of Kubernetes without managing it. Especially if you care about enterprise-grade security and deployment speed.

**Pros:**

- Real Kubernetes under the hood, abstracted away
- BYOC with deep integration (networking, IAM, observability)
- Fast CI/CD and instant preview environments
- Secure by default (microVMs, encrypted secrets, RBAC)

**Cons:**

- Requires a bit more upfront understanding than Heroku/Railway
- BYOC setup requires enterprise plan (though it’s robust)

## 2. Heroku

![heroku.png](https://assets.northflank.com/heroku_092e1c7f09.png)

Heroku was the original developer-friendly PaaS. It’s still easy to use, but it’s stuck in 2014. No Docker support, no container-level controls, and cold start delays make it hard to recommend for serious production use.

**Key features:**

- **CI/CD:** Git push to deploy. No support for Docker or CI customization.
- **Scaling:** Scale horizontally/vertically per dyno. No visibility into underlying resources.
- **Add-ons:** Large marketplace for services like Postgres, Redis, and monitoring tools.
- **Limitations:** No support for microservices, multi-region, or BYO cloud.

**Pricing:** No more free tier. Hobby: $5/dyno/month. Standard starts at $25/dyno/month. Enterprise pricing is opaque.

**Best for:** Prototypes, hobby projects, or teaching. Not production-ready for modern apps.

**Pros:**

- Fast to get started
- Large add-on ecosystem
- Simple Git-based deploys

**Cons:**

- No Docker support, no access to infra details
- No real support for microservices or custom networking
- Performance issues (cold starts, noisy neighbors)
- Expensive at scale

<aside>
📖 Read more: [Top Heroku alternatives in 2025](https://northflank.com/blog/top-heroku-alternatives) 

</aside>

## 3. Render

![render's home page.png](https://assets.northflank.com/render_s_home_page_2880e163be.png)

Render takes what made Heroku great and adds modern infrastructure: Docker support, autoscaling, better observability, and no arbitrary limits.

**Key features:**

- **CI/CD:** GitHub/GitLab-based deploys. Supports Docker and buildpacks.
- **Scaling:** Autoscale web services. Background workers and cron jobs supported.
- **Databases:** Built-in PostgreSQL and Redis.
- **Secrets:** Managed securely and injected into runtime.
- **Preview environments:** Available, but gated on higher plans.

**Pricing:** Free tier available. Web services from $7/month. Autoscaling from $20/month. Databases priced separately.

**Best for:** Teams outgrowing Heroku, looking for more flexibility without managing raw Kubernetes.

**Pros:**

- Docker and buildpack support
- Easy autoscaling and preview environments
- Better visibility into performance than Heroku

**Cons:**

- BYOC not supported
- Previews and scaling features can be limited on lower plans
- Databases are basic (no VPC peering, limited control)

<aside>
📖 Read more: [7 Best Render alternatives for simple app hosting in 2025](https://northflank.com/blog/render-alternatives) 

</aside>

## 4. Railway

![railway-min.png](https://assets.northflank.com/railway_min_10957de907.png)

Railway is all about speed. You can go from zero to running app and database in under a minute. But the tradeoff is limited control and no real infrastructure knobs.

**Key features:**

- **CI/CD:** Git-based deploys, preview environments auto-created.
- **Databases:** Easy provisioning for Postgres, MySQL, Redis.
- **Scaling:** Abstracted. You can’t choose CPU/memory settings directly.
- **Secrets:** Basic .env management.
- **Limitations:** No granular runtime control. Preview environments are less configurable.

**Pricing:** Free tier. Paid starts at $5/month (Starter), $12/month (Developer), and scales up from there.

**Best for:** Solo developers, MVPs, and hackathons. Not ready for complex workloads.

**Pros:**

- Extremely fast to spin up projects
- Great onboarding and UI
- Built-in databases and preview envs

**Cons:**

- No infra knobs (can’t control CPU, memory, networking)
- Lacks deep observability and security features
- Hard to use for anything complex or multi-service

<aside>
📖 Read more: [6 best Railway alternatives in 2025: Pricing, flexibility & BYOC](https://northflank.com/blog/railway-alternatives) 

</aside>

## 5. Google App Engine

App Engine is Google Cloud’s original PaaS product. It’s a good fit if you’re already deep in the GCP ecosystem, but lacks flexibility and forces vendor lock-in.

**CI/CD**: Via Cloud Build, Cloud Deploy, or manual deploys.

**Runtime**: Offers "standard" environments (sandboxed, opinionated) and "flexible" environments (more control, higher cost).

**Scaling**: Strong autoscaling and zero-to-one cold start performance.

**Networking**: Tightly integrated with Google-managed load balancers and IAM.

**Observability**: Uses Stackdriver suite for logging, tracing, and monitoring.

Powerful for simple use cases within GCP, but not portable or developer-friendly for broader workloads.

**Pricing:** Free tier. Standard: ~$0.05/hour. Flexible env: more expensive.

**Pros:**

- Tight integration with GCP IAM, VPCs, and Stackdriver
- Strong autoscaling and performance
- Standard and flexible runtimes available

**Cons:**

- Vendor lock-in is severe
- CI/CD and deploy UX is clunky
- Config and runtime limits in “standard” env are painful

## 6. Azure App Service

Microsoft’s App Service is a fully managed platform that integrates tightly with Azure’s enterprise tools. Strong for corporate environments, but the UX and flexibility can frustrate smaller teams.

**CI/CD**: Native GitHub Actions and Azure DevOps support.

**Scaling**: Manual or rule-based autoscaling. Good regional redundancy options.

**Runtime**: Supports .NET, Node, Java, PHP, Python, and custom containers.

**Networking**: Can integrate with VNets, staging slots, and private endpoints.

**Security**: Uses Azure Key Vault, RBAC, and integration with Microsoft Entra ID.

**Pricing:** Free tier. Basic from $13/month. Premium from $55+/month.

Best when you already use Azure. Not ideal for startups or teams moving fast.

**Pros:**

- Excellent for .NET and Microsoft-native teams
- Azure DevOps and GitHub Actions support
- Strong enterprise-grade features (Key Vault, VNets)

**Cons:**

- Poor UX for small teams/startups
- Limited community and docs compared to Heroku-style tools
- Complicated to debug and observe workloads

<aside>
📖 Read more: [Top 10 Microsoft Azure alternatives in 2025: Best cloud platforms for your business](https://northflank.com/blog/azure-alternatives) 

</aside>

## 7. DigitalOcean App Platform

![Digitalocean app platform's home page.png](https://assets.northflank.com/Digitalocean_app_platform_s_home_page_a7b876bb7f.png)

A simplified PaaS offering from DigitalOcean. Easy to use, affordable, and decent for smaller workloads, but lacks advanced control for complex setups.

**CI/CD**: Deploy from GitHub or GitLab. Supports Docker-based deploys.

**Scaling**: Auto-scales vertically or horizontally.

**Networking**: Auto TLS, HTTP/2, custom domains.

**Preview Environments**: Supported, with limitations.

**Secrets**: Basic variable injection via the UI.

**Pricing:** Starter: $5/month. Basic containers: $12/month. Pro scales up.

Great for beginners or cost-conscious teams. Less appealing at scale.

**Pros:**

- Affordable and beginner-friendly
- Docker support out of the box
- Clean UI and simple deploys

**Cons:**

- Lacks advanced autoscaling or BYOC
- Poor for multi-service or high-scale apps
- Observability is shallow compared to others

<aside>
📖 Read more: [10 best DigitalOcean alternatives in 2025 for developers and teams](https://northflank.com/blog/best-digitalocean-alternatives-2025) 

</aside>

## 8. CapRover

CapRover is an open-source, self-hosted PaaS you install on your own server. It’s simple, powerful, and cheap—but everything is on you.

**CI/CD**: Manual deploys or via Docker/webhooks.

**Scaling**: Manual only.

**Add-ons**: One-click app install system.

**Secrets**: Managed via dashboard or CLI.

**Preview Environments**: Not supported out of the box.

**Pricing:** Free. Just pay for your VPS/server.

Perfect for devs who want full control and minimal cost. But no support, no autoscaling, and no managed infra.

**Pros:**

- Full control, zero platform fees
- One-click apps, easy Docker deploys
- Ideal for hobbyists or lean teams with ops skills

**Cons:**

- No autoscaling, preview envs, or monitoring built-in
- No support or managed infra
- Security and uptime are fully on you


<InfoBox className='BodyStyle'>

## 📖 FAQs

(1) **What’s the best PaaS provider for Kubernetes-backed apps?**

Northflank. It gives you all the benefits of Kubernetes—scalability, portability, workload control—without the operational overhead. Plus, you can use your own cloud.

(2) **Which PaaS providers support Docker natively?**

Northflank, Render, DigitalOcean, and CapRover support Docker-based deployments out of the box. Heroku and Railway rely on buildpacks unless you implement workarounds.

(3) **What if I want to use my own cloud account?**

Northflank is the only PaaS on this list that natively supports BYOC (bring your own cloud), letting you run infrastructure in your own AWS, GCP, or Azure account.

(4) **Are preview environments really necessary?**

If your team runs a lot of pull requests, preview environments can save hours. They allow you to spin up exact replicas of your production setup for QA, product, and design reviews. Northflank, Render, and Railway do this well.

(5) **Which PaaS provider is best for side projects and quick MVPs?**

Railway or DigitalOcean App Platform are great low-cost options. If you want a smoother long-term path to scale, start with Northflank.

(6) **CapRover a serious option for production apps?**

Only if you’re comfortable managing your own server and don’t need autoscaling or observability. It’s very capable but you’re on your own.

(7) **Why not just use a raw Kubernetes setup?**

Because 95% of teams don’t need the complexity. Unless you’re building your own internal platform or dealing with highly custom infra, a PaaS like Northflank will get you to production faster and safer.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>10 best continuous deployment tools in 2026 (includes app &amp; automation deployment tools)</title>
  <link>https://northflank.com/blog/continuous-deployment-tools</link>
  <pubDate>2025-06-13T19:48:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for the right continuous deployment tool? Compare 10 top platforms in 2026: CI/CD, app deployment, and automated deployment tools so you can choose what fits your workflow best.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/continuous_deployment_tools_6c656f6bbd.png" alt="10 best continuous deployment tools in 2026 (includes app &amp; automation deployment tools)" />Continuous deployment tools can help your team automatically push code to production once your continuous integration (CI) pipeline has passed all the checks.

I’ve seen how important this is, especially if you’re constantly building containerized apps, using GitOps, or shipping to production regularly. And we both know how important it is to have the right continuous deployment tool if you want to ship faster and reduce risk.

That’s why I’ve put together this list of 10 continuous deployment tools that teams are actively using and trusting in 2026. It includes everything from all-in-one CI/CD platforms to tools focused purely on release and automation.

I also provide you with enough information on what to look out for before choosing a continuous deployment tool.

Let’s get into it.

<InfoBox className='BodyStyle'>

### Quick look: top continuous deployment tools in 2026

If you're short on time, see a fast breakdown of what each tool brings to the table:

1. [**Northflank**](https://northflank.com/docs/v1/application/release/manage-ci-cd) – Built-in CI/CD, container-based workflows, static IPs, GitHub triggers, and BYOC ([Bring Your Own Cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment)) support.

2. [**Octopus Deploy**](https://northflank.com/blog/octopus-deploy-alternatives) – Visual release flows, rollback support, and secrets management.

3. [**Jenkins**](https://northflank.com/blog/jenkins-alternatives-2025) – Widely used open-source CI/CD automation server.

4. [**Bitbucket Pipelines**](https://northflank.com/blog/integrating-with-gitlab-and-bitbucket) – Git-hosted pipelines with integrations.

5. [**GitLab CI/CD**](https://northflank.com/blog/best-gitlab-alternatives) – Full GitOps-compatible CI/CD with Kubernetes support.

6. [**Microsoft Azure DevOps**](https://northflank.com/blog/azure-alternatives) – Enterprise-grade CI/CD, Azure-native.

7. [**AWS CodeDeploy**](https://northflank.com/cloud/aws) – Automates EC2 and Lambda deployments.

8. [**CircleCI**](https://northflank.com/blog/top-circleci-alternatives) – Fast pipelines and reusable configs.

9. [**Argo CD**](https://northflank.com/blog/argo-cd-alternatives-northflank-developer-platform-git-ops-self-service) – GitOps-focused continuous deployment for Kubernetes.

10. [**Flux CD**](https://northflank.com/blog/flux-cd-alternatives) – Lightweight GitOps tool with Helm support.

</InfoBox>

## What are continuous deployment tools?

Continuous deployment tools, or CD tools for short, are the tools that help push your changes to production automatically once your Continuous Integration (CI) pipeline has passed all the required checks.

So, if you had to manually handle all this before, like manual approvals, it saves your team the time to do all that. Meaning your team can move faster and not worry about your workflows being inconsistent.

Now, a lot of people mistake continuous deployment tools with [continuous delivery tools](https://northflank.com/blog/continuous-delivery#tools-that-support-continuous-delivery-and-where-they-fit-best), but they’re different. Ask me how? Continuous delivery tools will prepare your builds for release, but won’t deploy them without someone manually triggering the release. The difference is that continuous deployment tools take out that final manual step. I hope you get the difference now.

Moving forward, you should know that continuous deployment tools are helpful if your team ships often, works with GitOps, or manages containerized services at scale.

<InfoBox className='BodyStyle'>

If you need a more in-depth breakdown of continuous deployment or how it compares to continuous delivery, read these articles on “[What is continuous delivery? Tools, pipelines, and how modern teams are implementing it](https://northflank.com/blog/continuous-delivery)” and “[What is continuous deployment? Why it matters and how to do it right](https://northflank.com/blog/continuous-deployment)”

</InfoBox>

Okay, let’s move forward and talk about the things you should note before choosing a continuous deployment tool.

## What to look for when choosing a continuous deployment tool (*Don’t skip*)

Before you make up your mind on which tool to go for among the many continuous deployment tools out there. You need to be sure that the Continuous deployment tool matches your team’s workflows, infrastructure, and deployment standards. So, let me save you the cost and time.

Ask yourself these questions:

### 1. Does the continuous deployment tool support GitOps workflows?

If you store infrastructure definitions and application configuration (like environment variables, deployment files, or Helm charts) in Git, the tools should be able to sync with Git as the source of truth.

![Diagram showing GitOps syncing with Northflank — Git commits trigger deployments, templates, and preview environments without manual steps](https://assets.northflank.com/northflank_gitops_workflow_6dd2847bd3.png)*How GitOps syncing works on Northflank: Git changes trigger deployments, run templates, and update environments automatically*

For example, platforms like [Northflank](https://northflank.com/docs/v1/application/infrastructure-as-code/gitops-on-northflank) support [GitOps](https://northflank.com/docs/v1/application/infrastructure-as-code/gitops-on-northflank#enable-gitops) through [templates](https://northflank.com/docs/v1/application/infrastructure-as-code/gitops-on-northflank#create-multiple-northflank-templates-from-one-source), [release flows](https://northflank.com/docs/v1/application/infrastructure-as-code/gitops-on-northflank#use-gitops), and [preview environments](https://northflank.com/docs/v1/application/infrastructure-as-code/write-a-template#include-release-flows-and-preview-environment-templates). You can do things like:

- Trigger deployments and scale operations by pushing Git.
- Automatically run templates when a commit updates a Git-tracked file.
- Use Git triggers to control which commits kick off a release or run.
- Reuse templates across environments with argument overrides (e.g., for secrets or region-specific configuration).

So, with such tools like this, you don’t have to do any form of manual syncing or click-through. Once connected, the changes flow from Git to your infrastructure. Easy peasy. 

### 2. Can it handle rollbacks and failed deployments?

Yes, your deployments can fail. I’m sure you’re not surprised. So, make sure that the tool you select can detect issues and help you revert quickly. That might involve redeploying the previous container image, rolling back a Git tag, or using an older artifact.

![Flowchart showing how Northflank supports rollbacks by reverting deployments to previous releases using container images, Git tags, or artifacts](https://assets.northflank.com/rollback_support_northflank_f8a763d06e.png)*Rollback support in Northflank: Easily revert to previous releases using container images, Git tags, or artifacts*

Now, platforms like [Northflank](https://northflank.com/docs/v1/application/release/run-and-manage-releases#roll-back-a-release) can help in these cases because they support automated rollback flows. When a release fails, you can return the pipeline to a previous working state by re-running an older release or restoring a release flow configuration. This includes reverting builds, services, and even custom container registry images. So, it doesn’t require carrying out manual resets.

### 3. Does it support secrets and environment configuration?

You know that your deployments usually need secrets (like API tokens) and configuration values (like feature flags or database URLs). And that means you need to go for a tool that can inject all these securely and separately per environment.

![Diagram showing how Northflank handles secrets and environment configuration across deployments](https://assets.northflank.com/northflank_secrets_env_config_36fa9d07a2.png)*Securely manage secrets, environment variables, and config in Northflank via UI, API, or secret files, then inject at runtime across environments.*

For example, a CD tool like Northflank lets you manage secrets, environment variables, and configuration in the UI or API, then, it injects them at runtime.

To cut the long story short, you can:

- [Set secrets and environment variables](https://northflank.com/docs/v1/application/secure/inject-secrets) per resource and share them across environments using [secret groups](https://northflank.com/docs/v1/application/secure/manage-secret-groups)
- Use the editor to manage values in a table, JSON, or `.env` format
- [Upload secret files](https://northflank.com/docs/v1/application/secure/upload-secret-files) to quickly inject multiple values
- [Link database secrets](https://northflank.com/docs/v1/application/databases-and-persistence/connect-database-secrets-to-workloads) and connection info to deployments
- Inject values as [runtime variables or build-time arguments](https://northflank.com/docs/v1/application/run/inject-runtime-variables), and control how they’re inherited

So it means your team can stay secure while working the way they prefer, either visually or in code.

### 4. Can it manage multiple environments?

If you deploy to more than one environment, like most teams, I mean, if you have preview environments for testing branches, staging for QA, and production for your users, and the deployment tool you're currently using can't help you manage those environments, I understand.

You’ll end up having to copy configuration, repeat secrets, or worse, have to debug production bugs that slipped through because staging wasn't identical.

If you can relate to that, then you should look for a continuous deployment tool that treats environments as a core feature, not something you have to piece together with folders and scripts.

![Diagram showing Northflank managing multiple deployment environments including preview, staging, and production with environment-specific settings](https://assets.northflank.com/northflank_multiple_environments_diagram_68f112f17c.png)*Northflank makes it easy to manage preview, staging, and production environments with scoped secrets, release flows, and automated rules*

Some tools do a decent job here, but [Northflank](https://northflank.com/docs) stands out with native environment support, including:

- [Preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) that spin up automatically from Git branches or PRs, with ephemeral lifecycles
- [Dynamic subdomains and wildcard routing](https://northflank.com/docs/v1/application/domains/wildcard-domains-and-certificates) for easy URL access to each environment
- [Scheduling rules](https://northflank.com/docs/v1/application/build/build-code-from-a-git-repository#build-specific-branches-or-pull-requests) for when preview environments should be created or torn down, so that you don’t waste resources
- [Scoped secrets and config](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment#inject-secrets-securely-and-share-environment-resources) using groups and tags, so that you can reuse values safely across staging and prod
- [Release flows](https://northflank.com/docs/v1/application/release/configure-a-release-flow) that define how changes move through environments, with manual or automated triggers

So, it basically gives you consistency across environments while letting you make any changes you want, like secrets, lifecycle rules, and naming patterns. That’s the sort of tool you should be looking for.

### 5. How steep is the learning curve?

If the continuous deployment tool is difficult to set up or debug, it can cause a blocker and delay your team. That’s why you should look for a continuous deployment tool that has user-friendly documentation, an easy-to-use User Interface (UI), and multiple ways to interact with the platform. For instance, if your team prefers to use the UI, the CLI, or the API, they should be able to.

![A visual summary showing Northflank’s interface options for managing deployments: visual UI, CLI, REST API, JavaScript SDK, and Git workflows](https://assets.northflank.com/northflank_learning_curve_ui_cli_api_2682d935a3.png)*Northflank supports multiple ways to interact with deployments—UI, CLI, API, and Git-based workflows*

A couple of platforms offer this, but one notable one is [Northflank](https://northflank.com/docs), and that’s because you have access to:

- A visual UI for managing release flows, rollback actions, and pipeline statuses
- Git-based workflows via Git triggers, branch rules, and commit filters
- A fully interactive [CLI](https://northflank.com/docs/v1/api/use-the-cli) that prompts you for inputs and supports named contexts for different teams
- A [REST API](https://northflank.com/docs/v1/api/use-the-api) and [JavaScript SDK](https://northflank.com/docs/v1/api/use-the-javascript-client) for programmatically managing services, builds, jobs, and backups
- Support for [resource definitions](https://northflank.com/docs/v1/api/use-the-cli#creating-resources) via JSON/YAML when using the CLI for automation or scripting

So, this means that your team has enough options and can easily get comfortable with using the platform.

### 6. Can you trace what’s running in production?

If something breaks in production and you have no idea what caused it, that’s usually a nightmare, and I can imagine. I’m sure you’ll want logs for every deployment, full visibility into your build steps, and a clear connection between the commit, the container image, and the environment it’s running in.

And that’s the primary reason why you need a continuous deployment tool that can provide traceability without any additional setup. I understand that you need to be able to say precisely what commit is live, how it got there, and what happened along the way.

![Diagram showing how Northflank helps trace what’s running in production through logs, build history, metrics, and container visibility](https://assets.northflank.com/trace_running_containers_northflank_d010c18715.png)*Trace deployments in Northflank, from commit to build to container, with full logs, metrics, and visibility*

Some platforms give you a partial view, but [Northflank](https://northflank.com/docs/v1/application/observe/view-logs) does a good job here because it gives you:

- [Deployment logs](https://northflank.com/docs/v1/application/observe/view-logs) streamed live or viewed historically, right from the UI or CLI
- [Full build history](https://northflank.com/docs/v1/application/production-workloads/production-operations) tied to Git commits so that you can trace every container
- Container logs from all services and jobs, with advanced search and filters
- [Metrics dashboards](https://northflank.com/docs/v1/application/production-workloads/production-operations#view-container-metrics) for interpreting container health and performance
- [Shell access](https://northflank.com/docs/v1/application/production-workloads/production-operations#access-running-containers) to running containers for quick investigation

All this means you don’t have to burn the midnight candle looking for where a bug came from; you can follow the trail from commit to build to container and see what’s happening live.

### **7. Is it self-hosted or cloud-based?**

If your team works in a regulated environment or has strict data policies, you’ll most likely need a self-hosted deployment setup. But if not, cloud platforms are much easier to get started with and maintain because you don’t need to handle cluster provisioning, networking, or ongoing infrastructure maintenance.

![A comparison diagram showing Northflank’s two deployment options: managed cloud and BYOC (Bring Your Own Cloud), with icons for AWS, Azure, GCP, and others](https://assets.northflank.com/northflank_byoc_vs_managed_cloud_d03db9fd0a.png) *Choose between managed cloud or BYOC with Northflank, same UI, CLI, and API across both*

Some platforms make you pick one or the other, but others give you flexibility. For example, Northflank is cloud-native, but also supports [Bring Your Own Cloud](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) (BYOC). So you can:

- Run on [Northflank’s managed cloud](https://northflank.com/cloud/northflank) across global regions like Europe, the US, or Asia
- Or deploy directly into your AWS, Azure, GCP, Civo, or Oracle account using the same UI, CLI, and API

This lets you keep control of cloud spend, data residency, and regional preferences, without giving up the platform experience. You still get auto-upgrades, cost controls, and scalable infrastructure.

Northflank has also been used to self-host popular tools like:

- [Temporal](https://temporal.io/)
- [DeepSeek R1](https://github.com/deepseek-ai/DeepSeek-VL)
- [Reflex](https://reflex.dev/)
- [SuperTokens](https://supertokens.com/)
- [n8n](https://n8n.io/)
- [PostHog](https://northflank.com/guides/how-to-self-host-posthog-on-northflank)
- [vLLM](https://northflank.com/guides/self-host-vllm-in-your-own-cloud-account-with-northflank-byoc)

So if your team wants to run open source tools in your own cloud, that’s one option you can keep in mind.

## Top 10 continuous deployment tools to check out

Now that we’ve looked at what makes a continuous deployment platform flexible, cloud vs self-hosted, CLI vs UI, traceability and environment management, let’s look at some of the tools that support these use cases best.

We’re going to break down what each tool is best for, who it’s built for, and what kind of deployment setups it supports. If you’re choosing your next CD tool, this should help narrow things down.

### 1. Northflank

[Northflank](https://northflank.com/) is a container-native platform with built-in CI/CD, supporting Git-based deploys, static IPs, and BYOC ([Bring Your Own Cloud](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes)). You get full visibility across builds, deployments, environments, and logs without needing third-party plugins or YAML complexity.

**Highlights:**

- Built-in [CI/CD pipelines](https://northflank.com/docs/v1/application/release/manage-ci-cd)
- [Git-based deploys](https://northflank.com/docs/v1/application/build/build-code-from-a-git-repository) with branch rules and commit filters
- Dynamic [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment)
- Static IP [support](https://northflank.com/docs/v1/application/network/expose-your-application)
- [Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud) (BYOC) and [managed cloud](https://northflank.com/features/managed-cloud) hosting
- Built-in [secrets](https://northflank.com/docs/v1/application/secure/inject-secrets), [config groups](https://northflank.com/docs/v1/application/secure/manage-secret-groups), and [scheduling](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs)
- Fully [managed Kubernetes](https://northflank.com/blog/best-managed-kubernetes-platforms) under the hood

**Pricing:** Free tier available, usage-based pricing after that ([See pricing details](https://northflank.com/pricing))

**Use case:** Teams that want deployment, logs, CI/CD, and cloud hosting in one place

<InfoBox className='BodyStyle'>

[See how Clock uses Northflank to scale over 30,000 deployments with 100% uptime and simplify their infrastructure.](https://northflank.com/blog/scaling-30-000-deployments-with-100-uptime-how-clock-uses-northflank-to-simplify-infrastructure)

</InfoBox>

> Go with this if you want CI/CD and deployment in one place, without setting up separate runners, plugins, or infrastructure. You also get BYOC, static IPs, and runtime logs out of the box.
> 

### 2. Octopus Deploy

Octopus Deploy is known for its visual release management and environment-scoped configuration. It’s a good fit for teams that want to automate releases without going deep into code.

**Highlights:**

- Visual release orchestration and approvals
- Scoped variables and secrets
- Manual and automatic triggers
- Built-in support for Kubernetes, VMs, and cloud providers

**Pricing:** Starts at $360 USD / year

**Use case:** Teams looking for GUI-based release automation with structured environments

<InfoBox className='BodyStyle'>

[See 7 Best Octopus Deploy alternatives for modern deployment workflows (2026)](https://northflank.com/blog/octopus-deploy-alternatives)

</InfoBox>

> Go with this if you need GUI-based release automation.
> 

### 3. Jenkins

A classic open-source tool with enormous flexibility, but it comes at the cost of setup and ongoing maintenance. Jenkins gives you full control over your deployment pipeline, but you’ll need to handle scaling, plugins, and updates yourself.

**Highlights:**

- Open-source and self-hosted
- Plugin ecosystem for every use case
- Declarative pipelines with Jenkinsfile
- Wide language and tool integration support

**Pricing:** Free (self-hosted), but infra and plugin costs may apply

**Use case:** Teams that want maximum control and are comfortable managing infrastructure

<InfoBox className='BodyStyle'>

If you are looking for alternatives to Jenkins or want to compare GitHub Actions or CircleCI with Jenkins, see:

- [Jenkins alternatives in 2026: CI/CD tools that won’t frustrate DevOps engineers](https://northflank.com/blog/jenkins-alternatives-2025)
- [GitHub Actions vs Jenkins (2026): Which CI/CD tool is right for you?](https://northflank.com/blog/github-actions-vs-jenkins)
- [CircleCI vs Jenkins: Which one fits your workflow in 2026?](https://northflank.com/blog/circleci-vs-jenkins)

</InfoBox>

> Go with this if you need full control and don’t mind managing infra.
> 

### 4. Bitbucket Pipelines

Bitbucket Pipelines is a great choice if your repos already live on Bitbucket Cloud. It provides lightweight CI/CD integrated directly with the Bitbucket UI and uses YAML-based configuration for builds and deployments.

**Highlights:**

- Integrated with Bitbucket Cloud
- Config-as-code via `bitbucket-pipelines.yml`
- Deployment environments and variables
- Docker support

**Pricing:** Free for up to 5 users (50 minutes per month, $10 per 1,000 additional build minutes, per month & 10 deployment environments)

**Use case:** Bitbucket-centric teams looking for tightly integrated CI/CD

> Go with this if you’re already using Bitbucket Cloud.
> 

### 5. GitLab CI/CD

GitLab offers an end-to-end DevOps suite, encompassing source control, security, and deployments. Its CI/CD capabilities are tightly integrated with GitLab projects, making it ideal for teams following GitOps or DevSecOps practices.

**Highlights:**

- Built-in CI/CD in GitLab
- Supports GitOps and Kubernetes deployments
- Runners for scalable builds
- Auto DevOps support for automation

**Pricing:** Free tier available, premium plans start at $29/user/month

**Use case:** Teams looking for a complete Git-based DevSecOps toolchain

<InfoBox className='BodyStyle'>

See [9 Best GitLab alternatives for CI/CD in 2026](https://northflank.com/blog/best-gitlab-alternatives)

</InfoBox>

> Go with this if you want full DevSecOps in one tool.
> 

### 6. Microsoft Azure DevOps

Azure DevOps supports a full set of tools for planning, building, testing, and deploying applications. It integrates seamlessly with other Microsoft tools and services and is built for large teams in enterprise environments.

**Highlights:**

- Pipelines for CI/CD with YAML or classic editor
- Azure Repos, Boards, Artifacts, and Test Plans
- Built-in environment approvals and gates
- Integration with Microsoft 365 and Azure services

**Pricing:** Free for up to 5 users, paid plans start at $6/user/month

**Use case:** Enterprises working inside the Microsoft ecosystem

<InfoBox className='BodyStyle'>

You might want to check these out:

- [Top 10 Microsoft Azure alternatives in 2026: Best cloud platforms for your business](https://northflank.com/blog/azure-alternatives)
- [Microsoft Azure on Northflank](https://northflank.com/docs/v1/application/bring-your-own-cloud/azure-on-northflank)
- [Azure on Northflank](https://northflank.com/cloud/azure)

</InfoBox>

> Go with this if you’re fully in the Microsoft ecosystem.
> 

### 7. AWS CodeDeploy

AWS CodeDeploy is part of AWS's deployment suite, letting you automate updates to EC2, Lambda, and on-prem servers. It’s tightly integrated with AWS IAM, CloudWatch, and ECS.

**Highlights:**

- Automates deployments to EC2, Lambda, and ECS
- Supports blue/green and in-place deployments
- Integrates with AWS IAM, CodePipeline, CloudWatch
- Fine-grained access control

**Pricing:** Free for AWS Lambda, usage-based pricing for EC2

**Use case:** Teams operating mostly within AWS infrastructure

<InfoBox className='BodyStyle'>

See:

- [Amazon Web Services on Northflank](https://northflank.com/cloud/aws)
- [Documentation showing how you can integrate your AWS account to create and manage clusters using Northflank](https://www.notion.so/Top-HashiCorp-Nomad-alternatives-in-2025-2116d14c78518028b26ac03405d8d832?pvs=21)

</InfoBox>

> Go with this if you’re deep into AWS.
> 

### 8. CircleCI

CircleCI is built for fast, cloud-native CI/CD with smart caching, parallelism, and Docker support. It’s popular for its performance and flexibility, especially for larger teams with complex pipelines.

**Highlights:**

- Docker-native builds
- Caching and parallelism for speed
- GitHub and Bitbucket integration
- Hosted and self-hosted options

**Pricing:** Free for 6,000 build minutes/month; paid plans from $15/month

**Use case:** Teams that want high-performance pipelines with advanced config

<InfoBox className='BodyStyle'>

You might want to see:

- [Top 5 CircleCI alternatives to use in 2026: best CI/CD tools](https://northflank.com/blog/top-circleci-alternatives)
- [CircleCI vs Jenkins: Which one fits your workflow in 2026?](https://northflank.com/blog/circleci-vs-jenkins)
- [CircleCI vs GitHub Actions: Which CI/CD tool is right for your team?](https://northflank.com/blog/circleci-vs-github-actions)

</InfoBox>

> Go with this if you need fast parallelized pipelines.
> 

### 9. Argo CD

Argo CD is a GitOps-style deployment tool for Kubernetes. It continuously monitors Git repositories and syncs the desired state to your Kubernetes clusters. It’s declarative, scalable, and built with Kubernetes in mind.

**Highlights:**

- GitOps-driven deployments
- Visual diffing and sync status
- Role-based access and audit logs
- Supports Helm, Kustomize, and plain YAML

**Pricing:** Free and open-source

**Use case:** Teams managing Kubernetes with Git as the source of truth

<InfoBox className='BodyStyle'>

You might want to see:

- [Argo CD alternatives that don’t give you brain damage and simplify DX for GitOps, clusters & deployments](https://northflank.com/blog/argo-cd-alternatives-northflank-developer-platform-git-ops-self-service)
- [Flux vs Argo CD: Which GitOps tool fits your Kubernetes workflows best?](https://northflank.com/blog/flux-vs-argo-cd)

</InfoBox>

> Go with this if you're managing Kubernetes with GitOps.
> 

### 10. Flux CD

Flux CD is a lightweight GitOps operator that pairs well with Helm and Kustomize. It’s suited for smaller teams or those who want GitOps without extra overhead.

**Highlights:**

- GitOps deployments for Kubernetes
- Native Helm support
- Built-in reconciliation loops
- Lightweight and Kubernetes-native

**Pricing:** Free and open-source

**Use case:** Lean Kubernetes teams looking for lightweight GitOps tooling

<InfoBox className='BodyStyle'>

If you’re looking for alternatives to Flux CD or comparing it to Argo CD, see:

- [7 best Flux CD alternatives in 2026](https://northflank.com/blog/flux-cd-alternatives)
- [Flux vs Argo CD: Which GitOps tool fits your Kubernetes workflows best?](https://northflank.com/blog/flux-vs-argo-cd)

</InfoBox>

> Go with this if you want a lean GitOps setup.
> 

## FAQ: Common questions asked about continuous deployment tools

I’ll help you answer some common questions often asked when it comes to continuous deployment tools:

**1. Which tool is used for continuous deployment?**

Tools like [Northflank](https://northflank.com/), Jenkins, GitLab CI/CD, and Argo CD are widely used for continuous deployment, depending on your stack and infrastructure.

**2. What is the most used CI tool?**

Jenkins remains one of the most used CI tools due to its long history and large plugin ecosystem.

**3. Is Jenkins a continuous deployment tool?**

Yes, Jenkins can be configured for continuous deployment, though it often requires plugins and manual setup.

**4. Which is a CI tool?**

Examples of CI tools include Jenkins, GitHub Actions, CircleCI, GitLab CI/CD, and Bitbucket Pipelines.

**5. Is GitHub a CI CD tool?**

GitHub itself isn’t, but GitHub Actions is a built-in CI/CD service you can use for automating builds and deployments.

**6. Is Kubernetes a CI/CD tool?**

No, Kubernetes is a container orchestration platform. But it’s often paired with CI/CD tools like Argo CD or Flux for deployment.

**7. Is Docker a CI CD tool?**

Docker isn’t a CI/CD tool, it’s used for containerizing applications. But many CI/CD tools use Docker in their pipelines.

**8. What is the difference between Jenkins and GitHub Actions?**

Jenkins is a self-hosted, plugin-driven CI/CD tool, while GitHub Actions is a cloud-based CI/CD system built into GitHub with tighter repository integration.

**9. Can Jenkins do continuous deployment?**

Yes, Jenkins supports continuous deployment, but you’ll need to configure the pipeline and manage the infrastructure yourself.

**10. Is Octopus a CI CD tool?**

Octopus is primarily a CD tool focused on release automation, and it pairs well with CI tools like TeamCity or GitHub Actions.

## How to choose the right continuous deployment tool for your team

I’d like to conclude by saying that the right continuous deployment tool all comes down to your team’s workflow, complexity, and level of control.

If your team prefers a tightly scoped tool that plugs into an existing pipeline, then something like Argo CD or Octopus might work well. But if you’re starting fresh or want to avoid piecing together multiple tools for CI, CD, and runtime, it makes sense to look at platforms that simplify the setup.

You can test a few options side by side. Try running a sample service and see how easy it is to debug a failed deployment. Check how each tool handles secrets, environments, and logs. That hands-on comparison often tells you more than docs alone.

You can also try a CD platform like **Northflank** that gives you CI, CD, and runtime in a single workflow, without needing to manually integrate different tools. As mentioned earlier, you get access to a full suite of deployment features from one dashboard. [Give it a try](https://app.northflank.com/signup) and see how it compares to your current setup.]]>
  </content:encoded>
</item><item>
  <title>Top HashiCorp Nomad alternatives in 2026</title>
  <link>https://northflank.com/blog/hashicorp-nomad-alternatives</link>
  <pubDate>2025-06-13T14:15:00.000Z</pubDate>
  <description>
    <![CDATA[Discover the best alternatives to HashiCorp Nomad, including Northflank, Kubernetes, and Heroku. Compare orchestration tools and developer platforms to scale apps with CI/CD, observability, and ease.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/hashicorp_nomad_alternatives_bb6ee2c887.png" alt="Top HashiCorp Nomad alternatives in 2026" />It often starts with a simple decision.

Your team wants a faster way to deploy services. Kubernetes feels too complex, so you choose something leaner: **HashiCorp Nomad**. It’s simple, flexible, and integrates well with tools you already use.

At first, it’s a good fit.

But over time, your needs grow. You have more services, more engineers, and more deployments. What started out lightweight starts to feel like it’s missing core pieces.

This guide explores what Nomad does well, where it can fall short, and how other platforms, especially developer-first ones like [Northflank](https://northflank.com/), are solving these gaps. Along the way, we’ll compare Nomad to Kubernetes and introduce alternatives for teams that want to ship faster without managing low-level infrastructure.

## TL;DR: Top direct and platform alternatives to HashiCorp Nomad

<InfoBox className='BodyStyle'>

- **[Northflank](https://app.northflank.com/signup):** A developer-first Kubernetes platform with built-in CI/CD, databases, preview environments, and Git-based workflows. Abstracts away Kubernetes complexity without sacrificing control.
- **[Kubernetes](https://app.northflank.com/signup):** Full control, steep learning curve.
- **[Docker Swarm](https://app.northflank.com/signup):** Lightweight and simple, but limited scaling.
- **[Heroku](https://northflank.com/blog/heroku-enterprise-capabilities-limitations-and-alternatives):** Easy Git push deploys, great for small apps.
- **[DigitalOcean App Platform](https://northflank.com/blog/best-digitalocean-alternatives-2025):** Git-based deploys, autoscaling, good balance between simplicity and flexibility.

</InfoBox>

## What is HashiCorp Nomad?

Nomad is a **workload orchestrator**. That means it helps you run applications across servers in a cluster. These apps might be Docker containers, virtual machines, or binaries.

It’s part of the **HashiCorp ecosystem**, so it’s built to work alongside Vault (for secrets), Consul (for service discovery), and Terraform (for infrastructure as code).

Teams often pick Nomad for these reasons:

- It’s **lightweight and fast** to set up
- It’s **easy to understand** compared to Kubernetes
- It **runs non-container workloads**, including Java apps, binaries, and more
- It works well in **hybrid cloud or on-prem setups**

But Nomad doesn’t do everything out of the box. Let’s talk about what happens when teams grow and want more.

## Where teams start running into limits

As teams begin scaling services or adding engineers, Nomad’s simplicity can become a blocker. You get control, but that control comes with overhead.

Here are the most common problems developers and platform teams report:

### Missing CI/CD and preview environments

Nomad doesn’t include a CI/CD pipeline. You’ll need to:

- Choose your own CI tool (like Jenkins, GitHub Actions, or Drone)
- Write and maintain deployment scripts
- Handle secrets and service discovery yourself

There’s also **no support for preview environments** (one-click staging per branch), which many teams now expect during development. This is where platforms like [Northflank](https://northflank.com/) shine; you get all this built in.

### Dependency on other HashiCorp tools

To match the features of other platforms, you usually have to install and configure:

- **Vault** for secret management
- **Consul** for service discovery and health checks
- **Terraform** to define infrastructure

That’s more power but also more to learn, maintain, and secure.

### Lack of built-in observability

Nomad does not ship with dashboards, logs, or metrics. You’ll need to plug in:

- Prometheus or Grafana for monitoring
- Loki or another log aggregator
- Custom alerting pipelines

This might be fine for infrastructure-heavy teams, but it becomes a chore for lean dev teams.

### Smaller community and fewer integrations

Compared to Kubernetes, Nomad has a smaller user base and less third-party tooling. That means:

- Fewer tutorials and examples
- Fewer engineers with Nomad experience
- Less built-in support across CI/CD, observability, and deployment tools

At this point, teams often start asking: “Should we move to something more complete?”

Let’s explore the most common alternatives.

## What teams actually want

Let’s zoom out. When teams move away from Nomad or hesitate to adopt Kubernetes, it’s rarely just about orchestration features. It’s about the day-to-day experience of building, testing, shipping, and scaling software.

In short, developers don’t just want a cluster. They want a platform that removes friction.

Here’s what that looks like in practice:

### A developer experience that feels like magic

Most developers don’t want to spend their time managing infrastructure. They want to write code, push it, and see it live with minimal setup and no complex configuration.

What modern teams expect:

- **Git push = deploy**
    
    Code commits and pull requests should trigger automatic builds and deployments, with no need for custom CI config.
    
- **Preview environments per branch**
    
    Each branch should generate its own live environment with a unique URL for easy testing, collaboration, and QA.
    
- **Built-in CI/CD workflows**
    
    You shouldn’t need to install or configure external tools like Jenkins or ArgoCD. CI/CD should work right out of the box.
    
- **One-click service provisioning**
    
    Adding a database, message queue, or background job should take seconds and require no infrastructure knowledge.
    

Nomad can support some of this, but only if you assemble and maintain all the moving parts yourself. Kubernetes is powerful, but not developer-first by default.

### Scalable infrastructure that just works

As your usage grows, your infrastructure should adapt automatically without needing manual tuning.

Teams are looking for:

- **Automatic scaling based on usage**
    
    Services should scale up during peak times and down when things are quiet.
    
- **Deployments with zero downtime**
    
    New versions should roll out safely, without interruptions or user-visible issues.
    
- **Global infrastructure support**
    
    If your users are spread across the world, your platform should handle distribution and latency optimizations.
    
- **Safe deployment strategies**
    
    Canary releases, blue/green deployments, and easy rollbacks help teams ship confidently without breaking production.
    

While Nomad can be scaled across regions and support advanced strategies, those features require additional tooling and setup. Kubernetes supports them too, but you need to configure everything from scratch.

### Observability and monitoring built in

When something goes wrong, developers need answers fast. But in many traditional setups, observability is an afterthought.

Here’s what developers actually expect:

- **Unified view of logs, metrics, and traces**
    
    A central place to monitor everything, with minimal setup and no need to piece together multiple tools.
    
- **Live service dashboards**
    
    Real-time status and performance data should be easy to access without installing and wiring up Grafana manually.
    
- **Health checks and alerts**
    
    Applications should self-report health issues, auto-restart when needed, and alert the right people.
    
- **Easy debugging workflows**
    
    Whether you need to stream logs, run a shell in a container, or inspect environment variables, it should take seconds.
    

Nomad offers job status and task logs, but for real observability, you’ll need to connect external services like Prometheus or Grafana. Kubernetes gives you flexibility, but the observability stack is usually a separate project on its own.

### Clear and fair cost control

Most teams care about costs, but they don’t want billing to feel like a mystery.

What modern platforms provide:

- **Usage-based pricing that makes sense**
    
    You’re charged based on actual usage, not opaque compute units or underutilized virtual machines.
    
- **Resource limits and quotas**
    
    Prevent runaway costs by setting sensible limits on builds, deploys, and environments.
    
- **Real-time usage visibility**
    
    Developers should be able to see what resources are being used and what they cost, without waiting for an end-of-month bill.
    
- **Free tiers that let teams do real work**
    
    Not just hello-world apps, but actual development and deployment.
    

With Nomad and Kubernetes, cost control is entirely up to you. You’re responsible for managing cloud resources, optimizing usage, and avoiding surprises.

## The best direct HashiCorp Nomad alternatives

When Nomad starts feeling too manual, teams usually explore orchestrators that offer more automation or flexibility. These are drop-in replacements focused on infrastructure, scheduling, and control.

### 1. Kubernetes: for full control and flexibility

[Kubernetes](https://northflank.com/blog/deploy-to-kubernetes-without-writing-yaml) is the most popular orchestrator. It runs nearly everything from microservices to databases and has massive community support.

**Best if your team:**

- Has dedicated DevOps or SRE engineers
- Needs high customization or advanced networking
- Plans to build internal platform tooling

**What you get:**

- Powerful scaling and scheduling features
- Huge ecosystem (Helm, ArgoCD, Prometheus, etc.)
- Works across all major cloud providers

**What to watch out for:**

- High learning curve
- Complex YAML files and APIs
- Needs careful monitoring and cost control

Kubernetes is powerful, but for many teams it’s more than they need—or want to maintain.

### 2. Docker Swarm: for very simple orchestration

[Docker Swarm](https://northflank.com/blog/docker-swarm-vs-kubernetes#what-is-docker-swarm) is a minimal orchestration tool built into Docker itself. It’s quick to start and simple to use.

**Best if your team:**

- Runs a few services
- Wants something lighter than Nomad or Kubernetes
- Is already using Docker Compose

**What you get:**

- Simple CLI and easy local testing
- Built-in service discovery and load balancing
- Low setup time

**Limitations:**

- Limited support for complex apps
- Weak scaling and recovery options
- No built-in CI/CD or metrics

Swarm works for basic needs but has major scaling limitations.

<InfoBox className='BodyStyle'>

Check out how Docker Swarm stacks up against Kubernetes [here](https://northflank.com/blog/docker-swarm-vs-kubernetes).

</InfoBox>

## The best platform HashiCorp Nomad alternatives

These platforms are not just orchestrators. They replace Nomad by giving you a full developer experience on top of infrastructure. Many are built on Kubernetes but abstract away its complexity.

### Northflank: full-stack developer platform built on Kubernetes

[Northflank](https://northflank.com/) is a platform that enables developers to build, deploy, and scale applications, services, databases, jobs, and GPU workloads on any cloud through a self-service approach. For DevOps and platform teams, Northflank provides a powerful abstraction layer over Kubernetes, enabling templated, standardized production releases with intelligent defaults while maintaining necessary configurability.

Northflank advances the legacy of pioneers like Heroku and Pivotal Cloud Foundry. While Heroku perfected the self-service developer experience, it didn't support complex workloads in enterprise cloud accounts. Cloud Foundry offered the right application abstraction to simplify complexity, but its underlying infrastructure proved costly and difficult to implement. Northflank delivers the best of both worlds: support for complex workloads, exceptional developer experience, and appropriate abstractions in your cloud environment—all within minutes and at a reasonable cost.

![image (5).png](https://assets.northflank.com/image_5_fd06403bd1.png)


**Best if your team:**
- Wants to deploy apps without managing infra
- Needs built-in CI/CD and environments per branch
- Cares about developer speed and team productivity

**What you get:**

- GPU support ([A10, A100, H100, H200, and more](https://northflank.com/cloud/gpus))
- Bring your own cloud ([AWS, Azure, GCP, and more](https://northflank.com/features/bring-your-own-cloud)) and connect to existing VPCs
- [CI/CD pipelines](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank), [Dockerfile builds](https://northflank.com/docs/v1/application/build/build-with-a-dockerfile), and [cron jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs).
- [Autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments), including scale-to-zero
- [Built-in logging](https://northflank.com/docs/v1/application/observe/view-logs), [metrics](https://northflank.com/docs/v1/application/observe/view-metrics), and [observability tools](https://northflank.com/docs/v1/application/observe/observability-on-northflank)
- Team collaboration features, including role-based access and environments
- Persistent volumes, managed databases (PostgreSQL, MongoDB, and more), and object storage
- Automatic preview environments and seamless promotion to dev, staging, and production
- Deploy workloads to [6 global regions](https://northflank.com/cloud/northflank/regions) across AWS, GCP, and Azure, with granular control over location and failover strategy

**What to watch out for:**

- Highly experienced DevOps teams might find it restrictive compared to directly managing raw Kubernetes clusters. It’s a fine balance between ease of use, flexibility, and customization; that line differs for every organization.

Northflank gives you the power of Kubernetes but feels like Heroku.

<InfoBox className='BodyStyle'>

*See how [Weights company uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

</InfoBox>

### Heroku

[Heroku](https://northflank.com/blog/heroku-enterprise-capabilities-limitations-and-alternatives) pioneered the idea of Git push to deploy. It’s simple, reliable, and still a popular choice for small teams and early-stage apps.

![image (81).png](https://assets.northflank.com/image_81_ed869cd124.png)

**Best for:**

- Teams that want zero infrastructure
- Startups building SaaS or APIs
- Developers who value simplicity over control

**What you get:**

- Git push deployments and buildpacks
- Easy provisioning of Postgres, Redis, and more
- Built-in metrics, logs, and auto-restarts

**Limitations:**

- Less flexibility for custom workloads
- Higher cost at scale
- Limited control over regions and infra tuning

Heroku is still a great choice for fast-moving teams, but it’s showing its age compared to newer platforms like Northflank.

<InfoBox className='BodyStyle'>

Learn more about Heroku capabilities [here](https://northflank.com/blog/heroku-enterprise-capabilities-limitations-and-alternatives).

</InfoBox>

### DigitalOcean App Platform

[DigitalOcean App Platform](https://northflank.com/blog/best-digitalocean-alternatives-2025) offers a middle ground between full control and ease of use. It’s built on Kubernetes but hides the complexity behind a simple UI and Git-based deploys.

![image (6).png](https://assets.northflank.com/image_6_022540644b.png)

**Best for:**

- Startups and solo devs looking for ease and affordability
- Teams that want autoscaling without managing nodes
- Projects that need more control than Heroku, but less than raw K8s

**What you get:**

- GitHub-based deploys with container support
- Autoscaling, zero-downtime deploys
- Built-in databases, SSL, metrics, and global CDN

**Limitations:**

- Some limits on advanced customizations
- Observability and CI/CD are less advanced than Northflank
- Scaling can be coarse-grained compared to raw Kubernetes

DigitalOcean App Platform is a solid choice if you want simple infrastructure with room to grow.

<InfoBox className='BodyStyle'>

Learn more about DigitalOcean [here](https://northflank.com/blog/best-digitalocean-alternatives-2025)

</InfoBox>

## How does HashiCorp Nomad compare to Kubernetes and Northflank

Here’s a side-by-side look at three major platforms:

| **Feature** | **Nomad** | [**Kubernetes**](https://northflank.com/blog/deploy-to-kubernetes-without-writing-yaml) | [**Northflank**](https://northflank.com/) |
| --- | --- | --- | --- |
| **CI/CD** | Not included (external tools required) | Not included (requires ArgoCD, Tekton, etc.) | Built-in CI/CD with Git-based deploys |
| **Preview environments** | Not supported | Requires manual setup | Automatically created per branch |
| **Observability** | Requires external tools | Requires integration with Prometheus, Grafana | Built-in logs, metrics, dashboards |
| **Secrets management** | Requires Vault | Built-in (Kubernetes secrets) | Built-in via UI and API |
| **Developer experience** | complex | complex | Developer-first, minimal setup |
| **Learning curve** | Medium | High | Low |
| **Self-hosting** | Supported | Supported | Not supported (fully managed) |
| **Pricing** | Free (self-managed) | Varies (cloud or self-managed) | Usage-based with a free tier |

Each has strengths. The right fit depends on your team’s goals, skill sets, and growth plans.

## When and how to try something new

You don’t need to move everything at once.

Many teams start by shifting one or two services to platforms like [Northflank](https://northflank.com/), especially staging and dev environments. From there:

1. Move non-critical workloads first
2. Test preview environments and auto-deploy
3. Monitor performance and scaling
4. Migrate production when the team is confident

This approach keeps risk low and gives your developers immediate quality-of-life improvements.

## Wrapping up

HashiCorp Nomad is lean, fast, and ideal for infrastructure-savvy teams. Kubernetes is powerful, extensible, and widely adopted but also complex and hard to manage for many teams.

**Northflank sits in between.** It’s built on Kubernetes, but designed to feel like a tool for developers. You get all the benefits of proven infrastructure without needing to manage it directly.

So if you’re spending more time wiring together CI/CD pipelines and dashboards than writing code, it might be time for something better.

Try [**Northflank**](https://app.northflank.com/signup). Ship faster. Worry less.]]>
  </content:encoded>
</item><item>
  <title>7 best Kapstan alternatives for Kubernetes deployment in 2026</title>
  <link>https://northflank.com/blog/7-best-kapstan-alternatives-for-kubernetes-deployment</link>
  <pubDate>2025-06-11T23:15:00.000Z</pubDate>
  <description>
    <![CDATA[Northflank is the best Kapstan alternative for teams needing production-grade Kubernetes without YAML complexity. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/What_is_Multitenancy_blog_post_1_01bf86fa07.png" alt="7 best Kapstan alternatives for Kubernetes deployment in 2026" /><InfoBox className='BodyStyle'>

💡 **TL;DR**: **Northflank is the best Kapstan alternative** for teams needing production-grade Kubernetes without YAML complexity. **Railway ranks second** for rapid prototyping, and **Render takes third** for simple web app deployments. Each platform solves specific pain points that Kapstan addressed.

</InfoBox>

Kapstan built its reputation on making Kubernetes deployments dead simple, no YAML wrestling, one-click environment cloning, and GitOps that actually works. With teams spending 20% of their time managing cloud infrastructure instead of building features, finding the right Kapstan alternative becomes critical for maintaining development velocity.

## Why teams are looking for Kapstan alternatives

Engineering teams spend 20% of their time managing cloud infrastructure; time that should otherwise be spent on product development. The core problems Kapstan solved remain universal:

**Kubernetes complexity**: Writing YAML manifests, managing secrets, configuring ingress controllers, and debugging networking issues slow down feature delivery
**Environment management**: Creating consistent dev/staging/prod environments without configuration drift
**DevOps bottlenecks**: Removing dependency on specialized DevOps engineers for routine deployments
**Infrastructure ownership**: Running in your own cloud account while maintaining platform convenience

## Kapstan alternatives comparison table

| Feature | Northflank | Railway | Render | Porter | [Fly.io](http://fly.io/) | Vercel | Heroku |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Multi-cloud | ✅ AWS/GCP/Azure | ❌ Railway only | ❌ Render only | ✅ Your clusters | ❌ Fly network | ❌ Vercel only | ❌ Heroku only |
| BYOC | ✅ Full support | ❌ No | ❌ No | ✅ Your K8s | ❌ No | ❌ No | ❌ No |
| Kubernetes access | ✅ Full API | ❌ Abstracted | ❌ Abstracted | ✅ Native | ❌ MicroVMs | ❌ Serverless | ❌ Dynos |
| Preview environments | ✅ Advanced | ✅ Branch-based | ✅ PR-based | ✅ Helm-based | ✅ App-based | ✅ Branch-based | ✅ Review apps |
| Enterprise auth | ✅ SAML/OIDC | ⚠️ Basic | ⚠️ Teams | ✅ K8s RBAC | ⚠️ Orgs | ✅ SAML/OIDC | ✅ SSO |
| Databases | ✅ Managed | ✅ One-click | ✅ Managed | ⚠️ Via K8 | ⚠️ Limited | ⚠️ 3rd party | ✅ Add-ons |
| Global CDN | ✅ Built-in | ❌ No | ✅ Built-in | ⚠️ Via ingress | ✅ Native  | ✅ Edge network | ⚠️ Via add-ons |

## 1. Northflank - Best overall Kapstan alternative

![new northflank home page.png](https://assets.northflank.com/new_northflank_home_page_9600c53fbb.png)

[Northflank](http://northflank.com/) leverages Kubernetes as an operating system to give you the best of cloud native, without the overhead, exactly like Kapstan did, but with significantly more enterprise features and multi-cloud flexibility.

### Core technical advantages

**True multi-cloud Kubernetes**: Connect your account with Northflank to create and manage Kubernetes clusters in your own cloud account, and gain complete control of your infrastructure, data storage, security, and auditing across GCP, AWS, Azure, and bare metal.

**Zero-config deployments**: With Northflank, I can make 100 commits and 100 deployments in a single day, it keeps up with my pace like nothing else, exactly the developer velocity Kapstan users expect.

**Production-grade security**: Environment variables are encrypted at rest and securely injected at runtime into your containers and builds with fine-grained RBAC and SAML/OIDC integration for enterprises.

**GitOps CI/CD**: Build and deploy every commit you make, or create rules for branches and pull requests with automatic TLS, custom domains, and real-time deployment tracking.

**Preview environments**: Spin up automatic environments per pull request for fast feedback with isolated databases and services - critical for modern development workflows.

### Why it beats Kapstan

- **Better multi-cloud**: Runs natively on any cloud vs Kapstan's more limited options
- **Enterprise ready**: Used by Sentry and other production-scale companies
- **More control**: Full Kubernetes API access when needed
- **[Transparent pricing](https://northflank.com/pricing)**: Running services, jobs, and addons are pro-rated by the second

**Best for**: Teams wanting Kapstan's simplicity with more power and enterprise features


<div>  
  <center>  
    <a href="https://app.northflank.com/signup">  
<Button variant={["large", "gradient"]}>Try Northflank here</Button>  
    </a>  
  </center>  
</div>

## 2. Railway - Best for rapid prototyping

![railway-min.png](https://assets.northflank.com/railway_min_10957de907.png)

Railway delivers the "just deploy it" experience that made Kapstan popular, with even less configuration required.

### Key features

**Zero-config magic**: Railway automatically detects your framework (Node.js, Python, Go, Rust, PHP) and deploys without any setup files or configuration.

**Instant databases**: One-click PostgreSQL, MySQL, MongoDB, Redis with automatic connection strings injected as environment variables.

**Branch deployments**: Every git branch gets its own deployment URL with isolated database - perfect for feature testing.

**Simple pricing**: Predictable monthly costs based on resource usage, not complex per-second billing.

### Limitations vs Kapstan and Northflank

- No bring-your-own-cloud options
- Limited enterprise authentication
- Less control over underlying infrastructure
- No advanced deployment strategies (blue-green, canary)

**Best for**: Startups and small teams prioritizing speed over enterprise features

## 3. Render - Best for web Applications

![render's home page.png](https://assets.northflank.com/render_s_home_page_2880e163be.png)

Render focuses on making web app deployment as simple as Heroku but with modern infrastructure and better performance.

### Core strengths

**Automatic everything**: Static sites, web services, background workers, cron jobs, and databases deploy with minimal configuration.

**Performance focus**: Built-in global CDN, automatic HTTP/2, and SSD-backed databases with connection pooling.

**Preview environments**: Pull request deployments with full service replication including databases.

**Transparent pricing**: No surprise bills with clear per-service pricing and automatic scaling.

### Where it falls short

- Single cloud provider (no multi-cloud)
- Limited Kubernetes access
- Fewer enterprise security features
- No infrastructure-as-code templates

**Best for**: Web-focused teams who want Heroku-style simplicity with better performance

## 4. Porter

![porter homepage.png](https://assets.northflank.com/porter_homepage_fd35ac3c23.png)

If you already have Kubernetes clusters, Porter adds the Kapstan-style developer experience on top.

### Technical approach

**Kubernetes native**: Connects to your existing EKS, GKE, AKS, or self-managed clusters while providing a simple deployment interface.

**Helm integration**: Deploy applications using visual Helm chart configuration instead of writing YAML.

**Full control**: Access to all Kubernetes primitives when needed, unlike platforms that abstract everything away.

**Cost efficiency**: No platform markup - you pay only your cloud provider's infrastructure costs plus Porter's licensing.

### Trade-offs

- Requires existing Kubernetes expertise
- You manage cluster upgrades and maintenance
- More complex than all-in-one platforms
- Limited managed services (databases, queues)

**Best for**: Teams with Kubernetes experience who want better developer UX

## 5. Fly.io

![fly.io-min.png](https://assets.northflank.com/fly_io_min_bfc65ba670.png)

Runs your containers on a global network of physical servers for ultra-low latency worldwide.

### Unique advantages

**Global edge network**: Deploy to 30+ regions with automatic request routing based on user location.

**MicroVM isolation**: Applications run in Firecracker microVMs for better security than containers.

**Global state**: Persistent volumes replicated across regions with automatic failover.

**IPv6 native**: Built-in support for modern networking with Anycast routing.

### Limitations

- Learning curve for distributed systems concepts
- Limited managed services
- No traditional databases (Postgres, MySQL)
- Platform-specific networking requires migration effort

**Best for**: Applications needing global distribution and edge computing

## 6. Vercel + PlanetScale

![vercel-homepage.png](https://assets.northflank.com/vercel_homepage_f09e3a1f3c.png)

Vercel handles frontend and serverless functions while PlanetScale provides branching databases - covering most modern app architectures.

### Combined strengths

**Database branching**: PlanetScale's schema branching works like Git for database changes.

**Edge functions**: Vercel Edge Functions run globally with sub-100ms response times.

**Framework integration**: Deep Next.js, Nuxt, SvelteKit integration with automatic optimization.

**Developer experience**: Git-based workflows for both code and database schema changes.

### Scope limitations

- JavaScript/TypeScript focused
- No support for complex microservices
- Limited background job processing
- Serverless constraints on execution time

**Best for**: Full-stack JavaScript applications and JAMstack sites

## 7. Heroku

![heroku.png](https://assets.northflank.com/heroku_092e1c7f09.png)

Despite being older, Heroku's buildpack system and add-on ecosystem remain compelling for certain use cases.

### Established strengths

**Mature ecosystem**: Hundreds of add-ons for databases, monitoring, logging, and third-party integrations.

**Buildpack system**: Automatic language detection and dependency installation across many frameworks.

**Enterprise features**: Heroku Enterprise provides compliance, SSO, and advanced networking.

**Proven reliability**: Decades of production experience with clear operational practices.

### Limitations

- Expensive at scale compared to alternatives
- Limited container customization
- No modern deployment strategies
- Slower innovation compared to newer platforms

**Best for**: Teams prioritizing stability and ecosystem maturity over cutting-edge features

## Migration guide: Moving from Kapstan

### 1. Application assessment

**Inventory current setup**: Document all services, databases, environment variables, and custom configurations currently deployed through Kapstan.

**Dependency mapping**: Identify service-to-service communication patterns, shared databases, and external integrations.

**Performance requirements**: Note current resource allocations, scaling patterns, and performance SLAs.

### 2. Platform selection

**For most teams (startups and enteprises) that want to move fast**: Choose **Northflank** for comprehensive features and compliance requirements.

**For Startups**: Consider **Railway** for simplicity or **Render** for web-focused applications.

**For Kubernetes teams**: **Porter** provides the smoothest transition if you have K8s expertise.

### 3. Migration strategy

**Parallel deployment**: Run applications on both Kapstan and your chosen alternative during transition.

**Environment-by-environment**: Migrate development first, then staging, finally production.

**Service-by-service**: Move independent services first, then tackle interconnected components.

**Data migration**: Plan database migration carefully with backup and rollback procedures.

## Pricing comparison

### Northflank

- **Starter**: Free for hobby projects
- **Pro**: $20/month per developer with usage-based compute
- **Enterprise**: Custom pricing with SLA and support

### Railway

- **Hobby**: $5/month with $5 usage credit
- **Pro**: $20/month per user with usage-based billing
- **Team**: Custom pricing for organizations

### Render

- **Individual**: Free tier with paid services
- **Team**: $19/month per user
- **Organization**: $29/month per user with advanced features

### Porter

- **Community**: Free for personal use
- **Standard**: $50/month per cluster
- **Enterprise**: Custom pricing with support

<InfoBox className='BodyStyle'>

## 💭 FAQs

### Which Kapstan alternative has the best developer experience?

**Northflank provides the best balance** of simplicity and power, similar to Kapstan's original value proposition.

### Can I migrate from Kapstan without downtime?

Yes, using parallel deployment strategies:

1. Deploy applications to your chosen alternative
2. Set up database replication or backup/restore
3. Use DNS switching to redirect traffic
4. Monitor performance and rollback if needed

**Northflank's BYOC options** make this migration smoother by maintaining familiar infrastructure patterns.

### Which alternative supports the most languages and frameworks?

**Northflank and Porter support any containerized application**, making them the most flexible. Railway and Render have excellent support for popular frameworks but may require additional configuration for custom setups.

### How do costs compare to Kapstan?

**Most alternatives cost less than Kapstan** due to increased competition:

- **Railway**: Often cheaper for small applications
- **Northflank**: Competitive enterprise pricing with more features
- **Render**: Transparent pricing without surprises
- **Porter**: Lowest cost if you manage your own infrastructure

### Which platform scales best for growing companies?

**Northflank scales from startup to enterprise** with comprehensive platform features. Railway works well for small to medium teams, while Porter scales with your Kubernetes expertise.

### Do these alternatives support Infrastructure as Code?

**Yes, with varying approaches**:

- **Northflank**: JSON templates and full API access
- **Porter**: Standard Kubernetes manifests and Helm
- **Railway/Render**: Platform-specific configuration files
- **Fly.io**: fly.toml configuration files

### Which alternative has the best monitoring and observability?

**Northflank provides the most comprehensive observability** with built-in logs, metrics, tracing, and alerting. Other platforms offer basic monitoring with options to integrate external tools.

</InfoBox>

## Final thoughts on choosing your Kapstan alternative

**Go with Northflank** if you want the closest thing to Kapstan's power with better enterprise features and multi-cloud flexibility. It's the safe choice for teams of any size.

**Pick Railway** if you're a startup prioritizing rapid deployment over advanced features. Perfect for MVP development and early-stage products.

**Choose Render** for web applications where you want Heroku-style simplicity with modern performance and reasonable pricing.

**Select Porter** if you have Kubernetes expertise and want to maintain infrastructure control while improving developer experience.

The key is matching your team's technical requirements, budget constraints, and growth trajectory with each platform's strengths. Most teams will find **Northflank offers the best combination** of Kapstan's original benefits with expanded capabilities needed for production-scale applications.

[Try out Northflank today, for free.](https://app.northflank.com/signup)]]>
  </content:encoded>
</item><item>
  <title>How to deploy machine learning models: Step-by-step guide to ML model deployment in production</title>
  <link>https://northflank.com/blog/how-to-deploy-machine-learning-models-step-by-step-guide-to-ml-model-deployment-in-production</link>
  <pubDate>2025-06-11T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Deploying a machine learning model is the last, and hardest, step in the ML lifecycle. You’ve trained your model, tuned your hyperparameters, and now it’s time to move from experimentation to production. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/machine_learning_mdoels_777249fc9f.png" alt="How to deploy machine learning models: Step-by-step guide to ML model deployment in production" />Deploying a machine learning model is the last, and hardest, step in the ML lifecycle. You’ve trained your model, tuned your hyperparameters, and now it’s time to move from experimentation to production. This guide walks through the full process of ML model deployment, including containerization, CI/CD, and infrastructure setup, with examples using Northflank.

<InfoBox className='BodyStyle'>

## **💡 TL;DR (if you’re in a rush)**

Model deployment means taking a trained ML model and making it available in a production environment, usually as an API or part of a larger application. The challenge isn’t the model. It’s everything else: infra, security, CI/CD, observability, latency guarantees, rollout strategies, and update pipelines. 

Platforms like Northflank give you a framework to manage that complexity without giving up control. You still own the model, the logic, the lifecycle, while offloading the infrastructure burden.

</InfoBox>

### What is model deployment in machine learning?

Model deployment is the process of serving your trained machine learning model so it can actually be used, by users, apps, or systems. It usually means:

- Packaging the model (i.e., as a Python app, container, or microservice)
- Deploying it somewhere users or applications can access (i.e., behind an API)
- Making sure it runs consistently and reliably in production

You might deploy the model as a REST API, a batch job, a streaming service, or embed it in an existing product. Either way, deployment is about making the model useful, turning your `.pkl` file into something real.

Then there’s orchestrating:

- Model artifact versioning
- Model-serving logic (loading, preprocessing, inference, postprocessing)
- Runtime dependencies (CUDA, transformers, custom layers)
- Interfaces (HTTP APIs, batch queues, streaming sinks)
- Resource isolation and scaling
- Monitoring and alerting
- Deployment rollback mechanisms

Running inference isn't enough. You need infrastructure that can handle it at scale and under real-world constraints.

### Why is ML model deployment hard in production environments?

Most ML work happens in notebooks, not systems. Your model might depend on a random seed or NumPy version. Your preprocessing code might live in a different repo. Your training pipeline might hardcode file paths to an S3 bucket in your personal AWS account. All of this is invisible until something breaks.

The typical pain points:

- Non-deterministic builds (i.e., package resolution in `pip install` causing subtle shifts in behavior)
- No hash-based versioning for model artifacts
- Fragile dependency trees that assume a local dev setup
- Inference that silently degrades under high load (i.e., batch size changes model output due to floating point artifacts)

And most ML “deployments” are glue scripts running in a VM with no alerting, no retries, no rollback.

### How to deploy a machine learning model step-by-step

1. Train your model and export it (i.e., using `torch.save`).
2. Create an inference script (i.e., FastAPI server).
3. Containerize the inference app using Docker.
4. Set up CI/CD pipelines for versioned deployment.
5. Add monitoring/logging (i.e., request/response logging, latency tracking).
6. Deploy to a cloud environment.
7. Automate testing, validation, and rollback.

This stack is fairly universal, but still leaves you to build out all the infra: Kubernetes manifests, deployment configs, TLS setup, scaling policies, metrics dashboards.

### Best practices for ML model deployment (with examples)

1. **Containerize everything**
    - Wrap your model in a Docker image so it runs the same everywhere.
    
    💡 Northflank builds from your repo and containerizes it automatically.
    
2. **Use CI/CD for ML model deployments**
    - Don’t manually upload models. Use Git pushes to trigger builds.
    
    💡 Northflank handles builds, deploys, and rollbacks from your commits.
    
3. **Expose models behind clean, versioned APIs**
    - Make it callable from other services. Version it. Don’t break consumers.
    
    💡 Northflank gives you automatic TLS, subdomains, and deploy previews.
    
4. **Automate retraining and redeployments**
    - Set up pipelines for data drift detection, retraining, and redeployment.
    
    💡 You can trigger jobs in Northflank via Git or API.
    
5. **Monitor everything**
    - Log inputs and outputs. Track latency, errors, usage.
    
    💡 Northflank comes with built-in logging and metrics dashboards.
    
6. **Secure it properly**
    - Use fine-grained access controls, secret management, and sandboxing.
    
    💡 Northflank has secure environments, encrypted secrets, and microVM support.
    
7. **Don’t glue infra together with duct tape**
    - Avoid bespoke scripts or managing your own Kubernetes for one model.
    
    💡 Northflank abstracts the infra but still gives you full control when you need it.
    

### Step-by-step: Deploying an ML model with Northflank

Here’s how to deploy a machine learning model using Northflank.

**Prerequisites**

- A trained model (we’ll use PyTorch for this example)
- Codebase in GitHub or GitLab
- Dockerfile in the root of your repo
- Basic understanding of containerized apps (FastAPI, Flask, etc.)

<aside>

### 📖 Read more

- [What is Pytorch?](https://northflank.com/blog/what-is-pytorch)
- [How to install Pytorch?](https://northflank.com/blog/how-to-install-pytorch-for-production)
</aside>

**1. Prepare the inference app**
Write a FastAPI app that loads your model and handles POST requests:

```python
from fastapi import FastAPI, Request
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

app = FastAPI()

model = AutoModelForSequenceClassification.from_pretrained("my-bert-model")
tokenizer = AutoTokenizer.from_pretrained("my-bert-model")
model.eval()

@app.post("/predict")
async def predict(request: Request):
    body = await request.json()
    inputs = tokenizer(body["text"], return_tensors="pt", truncation=True)
    with torch.no_grad():
        logits = model(**inputs).logits
        probs = torch.nn.functional.softmax(logits, dim=-1)
    return {"confidence": probs.tolist()}
```

**2. Write a Dockerfile**

```docker
FROM python:3.10-slim

RUN pip install fastapi uvicorn transformers torch python-multipart

COPY . /app
WORKDIR /app

CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8080"]
```

**3. Push code to GitHub**
Make sure your repo is structured and includes all dependencies and configs. Northflank integrates directly with your Git provider.

**4. Create a Northflank service**

![111.gif](https://assets.northflank.com/111_2bfb2212de.gif)

- Log in to Northflank
- Create a new service and connect your Git repo
- Select Dockerfile as the build method
- (Optional: if your app loads weights from disk or uses local caching, set vars like `MODEL_PATH` or `TRANSFORMERS_CACHE`)

**5. Configure builds and deployments**

- Enable auto-deploy on push to `main`
- Use preview environments for PRs
- (Optional: Add a /predict health check with known input to catch silent failures.)

**6. Monitor the deployment**

![Screenshot 2025-06-12 at 17.38.13.png](https://assets.northflank.com/Screenshot_2025_06_12_at_17_38_13_6774895470.png)

- Access logs in real time via the dashboard
- Track CPU/memory usage and request latency
- Use Prometheus-compatible metrics for observability

**7. Roll out new model versions**

- Push a new version with updated weights or code
- Use preview environments to validate behavior
- Promote the new version to production with one click
- Roll back if metrics regress

**8. Schedule batch jobs (optional)**
For batch inference or retraining workflows:

- Create a job service in Northflank
- Trigger on cron schedule or via webhook

### Common mistakes to avoid

❌ Serving models directly from notebooks 

❌ Ignoring dependency management (your `requirements.txt` will betray you)

❌ Hardcoding secrets (they will leak)

❌ Skipping monitoring (“it works” is not a metric)

❌ Building a one-off deployment pipeline you forget how to maintain

<InfoBox className='BodyStyle'>

## 💡 FAQs

### **Machine learning deployment**

1. **What is model deployment in machine learning?**  
It’s the process of making a trained model available for use, typically by wrapping it in an API or embedding it in a product or service.

2. **How do you deploy a machine learning model?**  
Typically, you export the model, wrap it in a server (i.e., FastAPI)  , containerize it (Docker), deploy it to a platform (like Northflank), monitor and maintain it.

3. **What’s the best way to keep environments consistent?**  
Use container builds with pinned dependency versions. Northflank builds from Git so the same image gets tested, previewed, and shipped.

4. **Can I use Northflank for batch inference?**  
Yes. You can run jobs or services depending on your use case.

5. **What about GPUs?**  
You can deploy to GPU-enabled nodes in your own cloud using Northflank’s BYOC model.

6. **How does Northflank compare to managed ML platforms?**  
It gives you infrastructure primitives (builds, deploys, environments) without locking you into an ML-specific abstraction.

</InfoBox>

## **Final thoughts**

ML deployment needs versioned control over code, dependencies, data, and rollout strategy. If you can’t reproduce your model or trace its outputs, it’s not production.

Northflank integrates with your Git repo, builds clean containers, offers isolated deploys, and supports GPU jobs in your own cloud. It’s infrastructure for teams who want to ship machine learning models without reinventing the backend.

[Deploy your first ML model on Northflank today.](https://app.northflank.com/signup)]]>
  </content:encoded>
</item><item>
  <title> What is Multitenancy? Meaning, architecture, benefits &amp; risks</title>
  <link>https://northflank.com/blog/what-is-multitenancy</link>
  <pubDate>2025-06-11T19:36:00.000Z</pubDate>
  <description>
    <![CDATA[Learn exactly what multitenancy means in cloud computing, how it works, its pros and cons, security best practices, and how Northflank’s built-in tenancy support makes managing multitenant workloads easier and safer]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/What_is_Multitenancy_blog_post_1_2ee00ee64a.png" alt=" What is Multitenancy? Meaning, architecture, benefits &amp; risks" />Multitenancy is the way to run shared infrastructure for many customers without giving up isolation or control. It’s not about giving up security, it’s about getting the right architecture so you don’t waste time and resources duplicating what could be shared.

If you’re working in DevOps, you’re thinking about how to isolate workloads while reusing the same resources. As a platform engineer, you’re trying to simplify operations for your team without locking them into a rigid setup. As a software engineer, you’re looking for a way to build apps that can handle growth without running into constant rebuilds or reconfigurations.

That’s what multitenancy is all about. Let’s break down how it works, what makes it useful, and how you can put it to work in your stack.

<InfoBox className='BodyStyle'>

### ⚡️ TL;DR for readers in a hurry

This is the short version if you’re skimming:

- **What it is**: Multitenancy is how you run shared infrastructure for multiple customers while keeping data and workloads separate.  
- **Why you care**: It cuts costs, keeps your deployments fast, and avoids duplicating resources for every new tenant.  
- **Main benefits**: Better resource use, easier updates, and less overhead when you grow.  
- **Challenges**: Risks like resource contention and data isolation, plus the work needed to separate tenants properly.  
- **How can Northflank help?**: [**Northflank**](https://northflank.com/) helps you manage multitenant workloads on Kubernetes without doing it all yourself. You get built-in namespace isolation, RBAC, and a secure foundation for tenant-specific environments.

</InfoBox>

## What is multitenancy?

Multitenancy is an architecture pattern that lets you run a single set of resources, like your application code, databases, or underlying infrastructure, to serve multiple customers at the same time. Each of these customers is a **tenant**. They share the same physical resources, but you keep their data and workloads logically separated.

You can think of each tenant as a user or team that uses your service. They don’t see each other’s data, and they don’t interfere with each other’s performance. That’s the main reason multitenancy works so well when you need to scale without creating new infrastructure for every new tenant.

Each tenant operates in its own isolated environment, even though they all share the same underlying infrastructure. This separation is key to making sure no tenant can access data that isn’t theirs or affect the performance of another tenant’s workload.

A good example is email hosting. When you use a service like Gmail, you’re part of a giant shared system. You share the same backend infrastructure with millions of other users, but your inbox is completely separate. You can’t see anyone else’s data, and they can’t see yours. The application’s logic makes sure your tenant, your inbox, stays isolated.

This shared but isolated setup is at the heart of what makes multitenancy so useful for modern software platforms.

## What is multitenancy in cloud computing?

Now that you know what multitenancy is, let’s see how it fits into cloud environments. In cloud computing, multitenancy means using shared physical infrastructure to run workloads for multiple customers. This might be in a public cloud like AWS or Google Cloud, or on a managed platform you run on your own hardware.

For example, if you’re using a Platform as a Service (PaaS) or Software as a Service (SaaS) platform, you’re already working in a multitenant setup. Platforms like Salesforce and Gmail serve thousands of customers from the same codebase and infrastructure. In these systems, each tenant’s data is separated at the software layer, even though they share the same hardware.

This idea also applies to Infrastructure as a Service (IaaS) and containerized environments. If you’re running workloads in Kubernetes, each namespace can be thought of as a separate tenant. Containers or serverless functions share compute resources, but they often share the same kernel, too, which leaves room for noisy neighbor effects and potential side-channel attacks.

Platforms like [Northflank](https://northflank.com/) address this by using secure runtime isolation based on sandboxing and microVMs. That gives you stronger boundaries between tenants, even for sensitive workloads. I’ll break that down later in the article.

You can see how this looks in practice with the illustration below. It shows how a cloud platform isolates tenants within shared resources:

![Illustration of a cloud-native multitenancy architecture where Tenant A, Tenant B, and Tenant C each have their own namespaces or container groups, while sharing the same pool of compute and storage resources](https://assets.northflank.com/cloud_native_multitenancy_architecture_4ac042d313.png)*How cloud-native multitenancy uses a shared pool of compute and storage while isolating tenants*

This approach is what lets you run workloads for different customers without spinning up separate infrastructure for each one. It saves resources and simplifies updates, while keeping tenants isolated and secure.

Next, I’ll show you the actual architecture of a multi-tenant cloud environment.

## What is a multi-tenant cloud architecture?

We’ve talked about how multitenancy fits into cloud environments. Now, let’s break down the actual architecture behind it. A multi-tenant cloud architecture is how you set up your app, database, and infrastructure to run multiple tenants in the same environment without leaking data or causing performance issues.

To show how these patterns work, see the diagram below that compares shared app instances, shared databases, and isolated tenant environments:

![Diagram comparing four multi-tenant architecture patterns: shared app instances, shared database with tenant IDs, separate schemas for each tenant, and completely separate databases for each tenant, along with their relative isolation](https://assets.northflank.com/multi_tenant_architecture_patterns_e81880c63f.png)*Comparing shared app instances, shared databases, separate schemas, and separate databases in multi-tenant architecture*

As you can see in the illustration above, each pattern handles tenant separation differently. Some use shared resources more heavily, while others prioritize isolation for better security and control.

Let’s break down these patterns:

### 1. Shared app instance

This is where the same application code serves requests for multiple tenants. You see this a lot in SaaS tools where everyone uses the same features and logic. It’s easy to manage at scale because you’re not duplicating deployments, but you still need to handle tenant-specific data separation in your app logic.

### 2. Shared database

All tenants use the same database, but you separate their data with a tenant ID. This pattern saves resources and is easier to manage at first, though it can create challenges as you grow because everything is in one place.

### 3. Separate schemas

Each tenant has its own schema within a shared database. This adds a layer of isolation without needing separate databases. It’s useful when you want better data separation but don’t want the overhead of managing many database instances.

### 4. Separate databases

Each tenant has a completely separate database. This gives you the best isolation and security because tenant data is never stored in the same place. The trade-off is you’ll need to manage and monitor more database instances.

### 5. Isolation mechanisms

No matter which of these patterns you choose, you need to isolate tenants to keep data secure. Containers and VM-level separation are common approaches for compute resources. In Kubernetes, namespaces keep tenant workloads apart. IAM (Identity and Access Management) locks down who can access each tenant’s data and workloads.

These architectural choices shape how you scale, manage, and secure your multi-tenant environment. Next, I’ll walk you through the benefits that make all this work worth it.

## What are the advantages of multitenancy?

Now that you’ve seen the different ways you can set up a multi-tenant architecture, let’s talk about why you’d want to do it in the first place. Multitenancy brings clear advantages when you’re trying to grow your platform without wasting resources or getting slowed down by constant rework.

Let’s look at some of these advantages:

### 1. Cost savings

Because tenants share the same infrastructure, you don’t need to spin up separate environments for every new customer. You’re using your compute, storage, and networking resources more efficiently, which means you’re not paying for unused capacity.

### **2. Scalability**

A well-designed multi-tenant system scales with your workload. You can add tenants without deploying a completely new stack for each one. This flexibility means you can grow faster, and your team can focus on improving the core product instead of managing duplicate environments.

### 3. Centralized updates

When you update your core app or infrastructure, all tenants get the new features or bug fixes at the same time. You’re not managing separate upgrade cycles for each tenant, which cuts down on operational overhead and keeps your platform consistent.

### 4. Better resource utilization

With everyone using the same shared infrastructure, you can balance load across tenants. This helps you avoid wasted resources and makes it easier to tune performance.

These advantages are why multitenancy is so widely used in cloud computing, SaaS, and any platform that needs to serve many customers without sacrificing security or performance.

Next, I’ll walk you through some of the challenges and risks you’ll need to keep in mind as you build out a multi-tenant system.

## What are the disadvantages and risks of multitenancy?

While multitenancy can save you money and make scaling easier, it’s not without its challenges. When you’re running many tenants on the same shared resources, you need to think carefully about how to keep data secure and performance stable.

### 1. Resource contention

When multiple tenants share the same compute, network, or storage resources, one tenant can consume more than their share. This can slow down performance for others and create unpredictable behavior.

### 2. Security risks

You need to make sure no tenant can access data that doesn’t belong to them. If you don’t separate tenants correctly, you open the door to data leaks or targeted attacks. Platforms like [Northflank](https://northflank.com/) address this by combining runtime isolation with features like encrypted environment variables, RBAC, and container sandboxing. These safeguards create stronger boundaries between tenants, even when workloads share the same physical infrastructure.

### 3. Compliance complexity

If you’re working in regulated environments like finance or healthcare, you need to show how you’re keeping tenant data isolated. Proving that separation can be complex, and you’ll need to document exactly how data is kept safe across tenants.

### 4. Limited customization

Because all tenants share the same infrastructure, you can’t always customize the environment for one tenant without affecting others. You need to balance flexibility with stability so that one tenant’s special needs don’t break things for everyone else.

These are the main challenges you’ll face when you move to a multi-tenant setup. Next, I’ll show you how multi-tenancy is implemented in systems and what that looks like in practice.

## How is multi-tenancy achieved?

Let’s get into the technical side of multitenancy. Once you decide to support multiple tenants, you need to figure out how to set it up in your stack. This is where the technical work comes in, and you need to balance security, performance, and maintainability.

Let’s start with a high-level illustration of how multitenancy works across the database, app, and infrastructure layers:

![Illustration of multitenancy architecture with three layers: database models (shared schema, separate schemas, separate databases), app-level patterns with tenant ID isolation, and infrastructure isolation using containers and VMs](https://assets.northflank.com/multitenancy_architecture_diagram_56503ca250.png)*Diagram showing how multitenancy is achieved through database models, app-level patterns, and infrastructure isolation*

To make multitenancy work, you have to think about how you’ll keep tenant data separate and secure at every layer.

### 1. Database models

The first decision is how to store tenant data, and there are a few patterns that teams use to solve this. See a breakdown of the main approaches:

- **Shared schema**: All tenant data sits in the same tables, separated by a tenant ID. This is the simplest and uses the least overhead, but you need to be careful to keep queries scoped to a single tenant.
- **Separate schemas**: Each tenant has its own schema in the same database. This gives you better isolation without having to manage lots of databases.
- **Separate databases**: Each tenant gets its own database instance. This is the most secure and isolated, but it also means more operational overhead.

### 2. App-level patterns

Your application needs to know which tenant is making a request and handle them separately. This might mean using request headers or authentication tokens to identify the tenant. Once you have that tenant ID, your app can load tenant-specific configuration and route requests to the right data.

### 3. Infra-level isolation

Beyond the app and database, you also need to separate tenant workloads in the infrastructure. Containers or virtual machines are the usual tools here, but most platforms stop at standard container boundaries. Platforms like [Northflank](https://northflank.com/) take it a step further by offering out-of-the-box, hardened, sandboxed runtimes and microVM-level isolation, which mitigate privilege escalation risks and improve tenant safety, especially when running untrusted or dynamic code at scale. 

In Kubernetes, namespaces are a natural way to split tenant environments and keep them from interfering with each other. Identity and Access Management (IAM) policies control who can see and do what, so one tenant’s users can’t access another tenant’s data or resources.

All of this together is what makes multitenancy possible: clear boundaries in the database, careful tenant-aware logic in the app, and infrastructure controls to keep workloads separate and secure.

Next, I’ll compare single-tenant and multi-tenant approaches so you can see how they differ in terms of complexity, flexibility, and performance.

## What is the difference between single-tenant and multi-tenant?

Now that you’ve seen how multitenancy is put together, let’s see how it compares with single-tenant systems. If you’re choosing how to build or scale your platform, it’s helpful to see the differences side by side.

This table shows a clear breakdown:

| **Category** | **Single-Tenant** | **Multi-Tenant** |
| --- | --- | --- |
| **Security** | Each customer has a fully isolated environment. | Logical separation is essential to keep tenants’ data secure. |
| **Cost** | Costs more because every customer has their own environment and resources. | Saves costs by sharing resources like compute and storage across tenants. |
| **Flexibility** | Easier to customize for each customer’s specific needs. | Less flexibility for individual customization because changes affect all tenants. |
| **Operational Complexity** | More work to manage separate updates and scaling for each tenant. | Security and isolation need more attention, but updates and scaling are centralized. |

Understanding these differences helps you choose the architecture that works for your team and your platform’s growth.

Next, I’ll give you a concrete example of what multitenancy looks like in practice.

## What is a multi-tenant example?

Let’s see what multitenancy looks like when it’s running in the real world. A good place to start is with SaaS platforms you might already use every day. Platforms like Salesforce and Gmail run on multi-tenant architectures. They serve thousands or even millions of customers from the same core system. Each customer’s data and activity are completely separate, but everything runs on shared infrastructure.

You also see multitenancy in cloud-native systems. In Kubernetes, for example, you can use namespaces to isolate tenants while still running them in the same cluster. Namespaces keep workloads and data apart without duplicating the whole environment for each customer. Serverless deployments work the same way. Functions from different tenants share the same runtime but stay logically separate.

These examples show how multitenancy helps you balance security, scalability, and resource usage in modern systems.

Next, I’ll take you through how security works in multi-tenant environments and what you should keep in mind.

## What is multi-tenant cloud security?

Now that you’ve seen how multitenancy works in practice, let’s talk about the security side of it. Keeping tenant data separate and secure is one of the most important parts of any multi-tenant architecture.

A big piece of this is **Identity and Access Management (IAM)**. IAM lets you control who can access which resources and data. In a multi-tenant system, you need to make sure tenant admins and users can only see their own data and workloads.

You also need to think about **encryption**. Data should be encrypted when it’s stored (at rest) and when it’s moving between systems (in transit). This keeps data safe even if someone gets access to the underlying hardware.

**Network isolation** is another layer of protection. Tenants should be separated at the network level, so one tenant can’t accidentally or intentionally send traffic to another tenant’s environment.

To show how these security layers work together in a typical cloud-native platform, see this illustration:

![Illustration of multi-tenant cloud security with labeled IAM policies, encryption for data at rest and in transit, and network isolation to protect tenant data and workloads](https://assets.northflank.com/multi_tenant_cloud_security_diagram_61b8d01e04.png)*Diagram showing IAM, encryption at rest and in transit, and network segmentation in a multi-tenant cloud environment*

Best practices for multi-tenant security also include clear monitoring, logging, and continuous updates. You need to be able to track who’s doing what, and react quickly if you spot something unusual.

Getting security right in a multi-tenant system is a shared responsibility between the platform and the tenants. The goal is to give each tenant the flexibility to grow without giving up control of their own data.

Next, I’ll show you how Northflank handles multi-tenant workloads so you don’t have to build all of this from scratch.

## How Northflank helps you manage multitenant workloads

When you’re dealing with multitenant workloads, it’s not just about spinning up containers. You need to isolate tenants at the namespace level, manage access to shared resources with precision, and keep an eye on everything without compromising performance or security. That’s where Northflank comes in: it gives you built-in multitenant support that works right out of the box, without relying on separate tools or complex scripts.

Let’s see how.

### 1. Tenant-specific environments in action

Let’s say you’re running a SaaS platform where each customer needs a fully isolated environment to meet security, compliance, and performance needs. With Northflank, you can create a dedicated namespace for each tenant, apply strict RBAC policies, and manage them all from a single control plane. You’re giving each tenant their own environment with clear boundaries. No accidental cross-tenant access and no compromise on performance. It’s multitenancy done right.

### 2. Namespace isolation

Namespace isolation is one of the key building blocks of secure multitenancy. On Northflank, you can restrict access to specific namespaces for your linked Git accounts and services. This means each tenant has a slice of the platform that’s fully theirs, without noise or overlap. 

Under the hood, Northflank reinforces this isolation with features like [gVisor](https://gvisor.dev/) and [Kata Containers](https://katacontainers.io/) for nested virtualization, adding another layer of security around each tenant’s runtime. Network isolation is handled with [Cilium](https://cilium.io/) and Kubernetes NetworkPolicies to prevent cross-namespace or cross-project communication.

If your workloads need trusted communication between services, Northflank’s built-in service mesh with mutual TLS (mTLS) enforces strict authentication and encryption between tenants’ services.

You can learn more about how this works in Northflank’s [documentation on restricting namespaces](https://northflank.com/docs/v1/application/collaborate/manage-git-integrations#restrict-namespaces).

### 3. RBAC for secure environments

Controlling access at scale isn’t optional. Northflank uses role-based access control (RBAC) to give you fine-grained permissioning across your multitenant setup. You can define roles like owner, admin, or default, and assign them to users or groups. This way, you decide exactly who can deploy services, manage secrets, or view activity logs, reducing the risk of accidental or malicious changes.

You can see how easy it is to configure RBAC in Northflank’s [RBAC guide](https://northflank.com/docs/v1/application/secure/use-role-based-access-control).

See how RBAC is implemented in Northflank, showing project-specific restrictions for better control and security.

![Screenshot of Northflank UI showing an admin role with restricted access to specific projects and teams](https://assets.northflank.com/organisation_roles_project_restrictions_409334072d.webp)*A detailed look at Northflank’s RBAC configuration interface, where roles can be restricted to specific projects and teams to maintain secure, multitenant environments.*

### 4. Centralized governance and monitoring

When you’re running multitenant workloads, you need complete visibility. Northflank’s governance and monitoring features give you a single place to manage everything. You get:

- Centralized control over billing, security, and team access
- Audit logs that track activity across teams and projects
- Tools to manage Git integrations and restrict namespaces

It’s all in one place, so you’re not left in the dark about potential issues. See more in the [collaboration and governance docs](https://northflank.com/docs/v1/application/collaborate/collaborate-on-northflank).

### 5. Benefits for platform engineers, DevOps, and software teams

For platform engineers and DevOps teams, this means you spend less time configuring separate tools and more time delivering reliable, secure services. For software teams, you get confidence that every customer environment is secure and isolated, with no accidental leaks or misconfigurations.

Multitenancy can be complex, but it doesn’t have to be a burden. With Northflank’s built-in support for namespace isolation, RBAC, and centralized governance, you can focus on running your workloads at scale, securely, and with complete control.

## FAQ: Common multitenancy questions answered

You might still have questions about how multitenancy works in practice. Let’s clarify some of the most common questions.

**1. What is the meaning of multitenancy?**

Multitenancy means that a single platform or environment serves multiple independent users or teams, called “tenants,” sharing the same underlying resources but staying isolated from each other.

**2. What is multi-tenant with an example?**

A multi-tenant system like [Northflank](https://northflank.com/) allows you to run separate environments for different teams or clients within the same cluster, so each team’s resources are isolated and managed independently.

**3. What are the advantages and disadvantages?**

Multitenancy lowers costs and improves resource usage by sharing infrastructure. However, it can introduce security and data risks if not isolated properly.

**4. What is the difference between single-tenant and multi-tenant?**

Single-tenant systems dedicate an entire environment to one user, while multi-tenant systems share infrastructure across tenants, adding more flexibility and better cost efficiency.

**5. What are the risks of multitenancy?**

The biggest risks are data leakage and resource contention between tenants. That’s why secure role-based access control, strong namespace isolation, and careful governance are critical.

## What’s next for multitenancy in your stack?

We’ve covered how Northflank’s built-in tenancy workflows, namespace isolation, and RBAC-based security can help you manage multitenant workloads with clarity and control. The key takeaway is that multitenancy isn’t just a feature. It’s a way to ensure secure, isolated environments that align with your team’s scaling needs.

If you’d like to continue refining your approach:

- You can check out Northflank’s tenancy workflows in your projects or see detailed guidance in the [Northflank documentation](https://northflank.com/docs).
- For an in-depth look at container isolation and secure workloads, read this [blog post on container isolation and micro-VMs](https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation).
- When you’re ready to set things up, you can [sign up](https://app.northflank.com/signup) and see how tenancy management fits your stack.]]>
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</item><item>
  <title>Koyeb alternatives: Platforms for cloud-native, serverless, and AI workloads</title>
  <link>https://northflank.com/blog/koyeb-alternatives</link>
  <pubDate>2025-06-11T17:15:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for the best Koyeb alternatives? This guide compares top platforms like Northflank, Fly.io, Render, and more, covering GPU support, pricing, DX, and features to help scale your apps smarter.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Koyeb_alternatives_40dd6d23b3.png" alt="Koyeb alternatives: Platforms for cloud-native, serverless, and AI workloads" />Koyeb made waves as a simple, serverless platform for deploying containers and web services, but as teams scale, priorities shift. Maybe you’ve hit a wall with pricing. Maybe you need deeper configurability, better support for background workers, or tighter control over your infrastructure. Or maybe you're just wondering if there’s something faster, cheaper, or more powerful out there.

This guide breaks down the best Koyeb alternatives available today. Whether you're deploying full-stack apps, running scheduled jobs, experimenting with AI workloads, or just want better DX, there's likely a better fit depending on your specific needs.

No fluff. Just real-world options, tradeoffs, and recommendations.

## TL;DR: Top Koyeb alternatives at a glance

If you're short on time or just need a quick overview, here's a curated list of the top alternatives to Koyeb. Each option includes a one-liner on what it's best suited for—whether you're running containerized workloads, deploying ML models, or looking for the best developer experience.

<InfoBox className='BodyStyle'>

- [**Northflank**](https://northflank.com/) – Full-featured platform for containers, cron jobs, and AI workloads. Offers [GPU support](https://northflank.com/gpu), [bring your own cloud(BYOC)](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment), private networking, and strong enterprise features.
- [**Render**](https://northflank.com/blog/railway-vs-render#what-is-render) – Developer-friendly PaaS with support for Docker, background workers, and monorepos. Great for full-stack apps but lacks GPU or BYOC capabilities.
- [Fly.io](https://northflank.com/blog/flyio-vs-render#what-its-like-to-build-and-scale-with-flyio) – Global container platform for latency-sensitive and distributed apps. Offers GPU access and edge deployment, but has a steeper learning curve.
- [**Heroku**](https://northflank.com/blog/vercel-vs-heroku#and-what-about-heroku) – Classic PaaS with simple DX and support for multiple languages. Ideal for MVPs and fast prototyping, but outdated for modern infrastructure needs.
- [**DigitalOcean App Platform**](https://northflank.com/blog/railway-alternatives#4-digitalocean-app-platform--for-teams-that-want-stability-and-better-pricing-clarity) – Easy-to-use option for small teams deploying static sites or simple web apps. Limited support for containers and no GPU capabilities.
- [**Cloudflare Workers**](https://northflank.com/blog/flyio-alternatives#5-cloudflare-workers) – Lightweight, globally distributed platform for serverless functions and APIs. Not container-based, but excellent for edge logic and fast response times.

</InfoBox>

## Why developers choose Koyeb

Koyeb streamlines application deployment by abstracting away infrastructure management. Its key features include:

- Global edge deployments
- Automatic HTTPS and load balancing
- Git-based deployments
- Dockerfile and buildpack support
- Autoscaling, including scale-to-zero
- CLI and web dashboard with developer-friendly UX

These features make it appealing for quickly launching APIs, microservices, or full-stack applications without worrying about Kubernetes or underlying cloud providers.

## Limitations of Koyeb

Despite its polish and ease of use, Koyeb has some constraints that drive teams to explore alternatives:

1. **GPU support** is limited to a single region and is still in early preview. This can be a dealbreaker for production AI workloads.
2. **No BYOC (Bring Your Own Cloud)** or private VPC networking. You are fully tied to Koyeb's infrastructure.
3. **Limited regional control.** While Koyeb uses a global edge network, developers have less granular control over where apps are deployed.
4. **Observability features are basic.** There is no integrated distributed tracing, customizable metrics, or advanced logs beyond the standard output.
5. **Team and security features are minimal.** Larger teams may require audit logs, SSO, fine-grained roles, and other enterprise-grade features not yet supported.
6. **Limited database support.** Only PostgreSQL is natively supported, which may not suit more diverse data workloads.
7. **No support for preview environments.** Useful for testing changes in isolation before merging, but missing from Koyeb’s default toolset.
8. **Infra-as-code dependency for advanced use cases.** While Koyeb offers a CLI and Git-based deployments, some advanced configurations—like secrets management, custom networking, or service discovery—often require additional tooling like Terraform or Pulumi. For teams used to self-service platforms like [Northflank](https://northflank.com/), this can feel like an unnecessary DevOps tax.

## Top Koyeb alternatives: In-depth comparison

Below is a detailed review of leading platforms that can replace or extend what Koyeb offers. These platforms vary in scope, from developer-centric PaaS tools to full infrastructure platforms with greater flexibility.

### 1. Northflank - Best Koyeb alternative.

[Northflank](https://northflank.com/) is a platform that enables developers to build, deploy, and scale applications, services, databases, jobs, and GPU workloads on any cloud through a self-service approach. For DevOps and platform teams, Northflank provides a powerful abstraction layer over Kubernetes, enabling templated, standardized production releases with intelligent defaults while maintaining necessary configurability.

Northflank advances the legacy of pioneers like Heroku and Pivotal Cloud Foundry. While Heroku perfected the self-service developer experience, it didn't support complex workloads in enterprise cloud accounts. Cloud Foundry offered the right application abstraction to simplify complexity, but its underlying infrastructure proved costly and difficult to implement. Northflank delivers the best of both worlds: support for complex workloads, exceptional developer experience, and appropriate abstractions in your cloud environment—all within minutes and at a reasonable cost.

![](https://assets.northflank.com/image_5_fd06403bd1.png)

**Key Features:**

- General Availability GPU support ([A10, A100, H100, H200, and more](https://northflank.com/cloud/gpus))
- Bring your own cloud ([AWS, Azure, GCP, and more](https://northflank.com/features/bring-your-own-cloud)) and connect to existing VPCs
- [CI/CD pipelines](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank), [Dockerfile builds](https://northflank.com/docs/v1/application/build/build-with-a-dockerfile), and [cron jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs).
- [Autoscaling](https://northflank.com/docs/v1/application/scale/autoscale-deployments), including scale-to-zero
- [Built-in logging](https://northflank.com/docs/v1/application/observe/view-logs), [metrics](https://northflank.com/docs/v1/application/observe/view-metrics), and [observability tools](https://northflank.com/docs/v1/application/observe/observability-on-northflank)
- Team collaboration features, including role-based access and environments
- Persistent volumes, managed databases (PostgreSQL, MongoDB, and more), and object storage
- Automatic preview environments and seamless promotion to dev, staging, and production
- Deploy workloads to [6 global regions](https://northflank.com/cloud/northflank/regions) across AWS, GCP, and Azure, with granular control over location and failover strategy

**Pros:**

- GPU support is production-ready and regionally available
- Deep network and deployment customization
- Integrates well with existing infrastructure

**Cons:**

- Learning curve might be slightly higher than Koyeb or Render due to the number of features, but there are enough helpful resources like guides and clear documentation available.

**Pricing**:

- Northflank offers a generous [free tier](https://northflank.com/pricing) that includes deployment of 2 services, 2 jobs, and 1 addon. Users can connect their existing cloud account, with limited resources and plans available. A Pay-as-you-go Pro plan provides additional capabilities.

*See how [Weights company uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### 2. Fly.io

[Fly.io](https://northflank.com/blog/flyio-vs-render#what-its-like-to-build-and-scale-with-flyio) is a globally distributed application platform that positions your code closer to users, delivering exceptional performance without the complexity of traditional infrastructure management.

![](https://assets.northflank.com/image_8_535b905e4b.png)

**Key Features:**

- Deploy to over 30 regions worldwide
- Fine-grained regional placement for services
- Global PostgreSQL with replication support
- Built-in secrets management and wireguard networking
- Direct volume mounting and VM-based container deployment

**Pros:**

- Regional control is excellent for latency-sensitive apps
- PostgreSQL clusters can span multiple regions
- Active and supportive community for open-source stacks

**Cons:**

- No GPU support
- Limited observability and UI tools compared to others
- Troubleshooting and monitoring multi-region setups can be complex

**Pricing**:

- Not publicly listed upfront, pricing is based on usage. [Learn more here](https://fly.io/pricing/).

### 3. Render

[Render](https://northflank.com/blog/railway-vs-render#what-is-render) is a modern cloud platform that streamlines the hosting of web applications, static sites, APIs, and databases, providing automatic SSL certification and CDN integration.

![](https://assets.northflank.com/image_7_04cbeab21d.png)

**Key Features:**

- Automatic deployments from Git
- Background workers, cron jobs, and persistent disks
- Built-in PostgreSQL and Redis
- Autoscaling and PR previews

**Pros:**

- Easy to onboard with minimal DevOps overhead
- Pricing is transparent and competitive
- Great for full-stack teams deploying web apps

**Cons:**

- No GPU support
- No edge or regional deployments
- Limited flexibility compared to platforms like [Fly.io](http://fly.io/) or Northflank

**Pricing**:

- Render provides a [free tier](https://render.com/pricing) for low-traffic applications, with paid plans starting at $19 per user monthly.

### 4. DigitalOcean App Platform

[DigitalOcean App Platform](https://northflank.com/blog/railway-alternatives#4-digitalocean-app-platform--for-teams-that-want-stability-and-better-pricing-clarity) is a PaaS solution built on DigitalOcean's robust infrastructure, striking an optimal balance between simplicity and control for growing applications.

![](https://assets.northflank.com/image_6_022540644b.png)

**Key Features:**

- Automatic builds from GitHub or GitLab
- Built-in autoscaling, HTTP routing, and HTTPS
- Support for web apps, static sites, workers, and databases
- Developer-friendly UI and documentation

**Pros:**

- Simple interface for developers already in the DO ecosystem
- Cheaper for small workloads than enterprise-focused platforms
- Reliable hosting and managed services

**Cons:**

- No GPU or AI-focused features
- Limited regional control
- Less extensible than other platforms

**Pricing**:

- [DigitalOcean App Platform](https://www.digitalocean.com/pricing/app-platform) includes a free tier supporting up to 3 static sites with 1GiB data transfer allowance per app. Paid plans begin at $5 per month with enhanced features.

### 5. Heroku

[Heroku](https://northflank.com/blog/vercel-vs-heroku#and-what-about-heroku) has long been the go-to platform for developers looking for simple, scalable, and easy-to-use cloud hosting. With a large catalog of add-ons and seamless Git-based deployments, Heroku offers a truly developer-friendly experience.

![](https://assets.northflank.com/image_81_ed869cd124.png)

**Key Features:**

- Buildpacks and Git deploys
- Extensive add-on marketplace
- Managed PostgreSQL and Redis
- CI pipelines and review apps

**Pros:**

- Intuitive UX and rapid onboarding
- Large ecosystem and documentation
- Well-tested for hobby and production apps

**Cons:**

- No support for GPUs
- Cold starts on free/low tiers
- Expensive for medium to large-scale workloads

**Pricing:**

- This [article](https://northflank.com/heroku-pricing-comparison-and-reduction) provides a deeper look into Heroku's pricing, breaking it down in an easy-to-understand way.

*For a closer look at how Heroku compares to other tools, this [article](https://northflank.com/blog/heroku-enterprise-capabilities-limitations-and-alternatives) offers a well-rounded analysis.*

### 6. Cloudflare Workers

[Cloudflare Workers](https://northflank.com/blog/flyio-alternatives#5-cloudflare-workers) is a unique serverless platform that allows you to run your app at the edge, anywhere in the world. It’s particularly powerful for serverless functions, APIs, and applications that need extreme global distribution without maintaining traditional infrastructure.

![](https://assets.northflank.com/image_82_916d61ffdc.png)

**Key Features:**

- Instant cold start times (~few ms)
- Global deployment across 300+ locations
- Supports Durable Objects and R2 for state and storage
- JavaScript, TypeScript, Rust via WebAssembly

**Pros:**

- Excellent for stateless workloads
- Fastest cold starts among all platforms
- Easy integration with Cloudflare CDN and DNS

**Cons:**

- Not a general-purpose container platform
- No GPU support
- Limited runtime compatibility (compared to Docker)

**Pricing:**

- The first 100,000 requests each day are free, and paid plans start at just $5/10 million requests.

## Feature comparison table

Choosing the right platform often comes down to how well it handles the features that matter most: seamless deployments, autoscaling, global edge performance, pricing transparency, CI/CD support, and team collaboration.

This table breaks down those capabilities across the top Koyeb alternatives so you can quickly spot trade-offs and strengths. Whether you're migrating off Koyeb or weighing your next deployment platform, this side-by-side snapshot helps you cut through the noise.

| Platform | GPU Support | [BYOC / VPC](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) | Regional Control | CI/CD Integration | Cold Start Behavior | Ideal Use Case |
| --- | --- | --- | --- | --- | --- | --- |
| **Koyeb** | Limited | No | Moderate | Built-in | Minimal | Serverless APIs, small AI workloads |
| [**Northflank**](https://northflank.com/) | Full | Yes | Granular | Built-in | None | AI, hybrid cloud, team collaboration |
| **Fly.io** | No | No | Fine-grained | Optional | Minimal | Latency-sensitive, global DB apps |
| **Render** | No | No | None | Built-in | Noticeable on free | Web apps, MVPs |
| **DigitalOcean** | No | No | Basic | Built-in | Minimal | Small-scale web services |
| **Heroku** | No | No | None | Built-in | High on free tier | Prototypes, internal tools |
| **Cloudflare** | No | No | Full edge | No | Ultra-fast | Edge APIs, global latency-critical scripts |

## How to choose the best Koyeb alternative

Koyeb is a solid starting point for deploying modern apps, but when your workloads get more complex, like running AI inference, handling GPU scheduling, or scaling globally, some of its limitations start to show. Here’s a practical framework to guide your next move:

- **Choose Northflank** if you want the best of both worlds: a Heroku-like developer experience combined with support for advanced use cases like GPU workloads, hybrid or multi-cloud deployments, and fine-grained infra control without touching Kubernetes. It’s built for teams shipping fast while staying future-ready.
- **Choose Fly.io** if your app demands precise regional placement and global latency optimization, especially for edge-heavy or geo-aware services.
- **Choose Render** if you’re looking for Heroku-like simplicity and better pricing, but don’t need more advanced scaling or infra control.
- **Choose DigitalOcean App Platform** if you're already tied into the DigitalOcean ecosystem and need a faster deployment path without re-architecting.
- **Choose Heroku** if you're building small-scale apps or prototypes and want the easiest on-ramp, though it may fall short for serious workloads or scaling.
- **Choose Cloudflare Workers** if your architecture is serverless, stateless, and designed for edge-native performance across the globe.

Ultimately, the best choice depends on how far you want to go. For teams building modern, performance-sensitive apps—especially those using GPUs or AI pipelines—**Northflank** offers a rare blend of power, simplicity, and scalability that’s hard to match.

## Wrapping up

Koyeb offers a solid experience for general-purpose apps, but as workloads become increasingly AI-heavy and GPU-reliant, developers often run into scaling limits, opaque pricing, or a lack of deeper control. For teams building inference APIs, deploying fine-tuned models, or running compute-heavy pipelines, choosing the right platform can make or break the developer experience.

The landscape is evolving. Several alternatives aim to solve different pain points—from granular autoscaling to dedicated GPU support—but very few manage to combine performance, simplicity, and reliability in one place.

That’s where **Northflank** stands out. With built-in CI/CD, seamless autoscaling, native GPU support, and straightforward pricing, it’s built to support modern AI and API workloads without the operational overhead.

[**If you’re scaling AI apps and want more control without sacrificing speed or DX, Northflank is worth a closer look.**](https://app.northflank.com/signup)]]>
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</item><item>
  <title>What is AWS Fargate?</title>
  <link>https://northflank.com/blog/what-is-aws-fargate</link>
  <pubDate>2025-06-09T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[AWS Fargate is a serverless compute engine for containers that eliminates the need to provision and manage servers. But like most AWS products, its simplicity is skin-deep. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/aws_fargate_d9d57b4151.png" alt="What is AWS Fargate?" />AWS Fargate is a serverless compute engine for containers that eliminates the need to provision and manage servers. But like most AWS products, its simplicity is skin-deep. Fargate sits inside a complex web of AWS services, opinions, and limitations. It’s great for what it is, but it’s not the only option. And depending on your use case, Northflank might be a better one.

This guide breaks it all down:

- What is AWS Fargate?
- How does it actually work?
- ECS vs Fargate, what’s the difference?
- Pros and cons (especially for engineering teams that care about performance, cost, and velocity)
- Why platforms like Northflank give you the power of Fargate with fewer trade-offs

Let’s get into it.

## What is AWS Fargate?

AWS Fargate is a container runtime. It’s part of the Amazon ECS (Elastic Container Service) and EKS (Elastic Kubernetes Service) ecosystems. You give Fargate a container image, specify your CPU and memory requirements, and it handles the provisioning of the underlying compute infrastructure.

Think of it as AWS’s answer to “Just run my container.”

### Key features:

- **Serverless execution model**: No need to manage EC2 instances.
- **Per-second billing**: Pay for only what you use.
- **Tight integration with ECS and EKS**: Fargate is not a standalone product; it’s a launch type for ECS or a runtime profile for EKS.
- **Scales automatically**: AWS handles provisioning and scaling of compute resources.

You get a lot of abstraction, but also a lot of AWS lock-in.

## How does AWS Fargate work?

Under the hood, Fargate is orchestrating isolated Firecracker microVMs for each task or pod. Firecracker is a lightweight virtualization technology (also used by Lambda) that spins up secure, fast-booting VMs. Fargate abstracts this completely. You don’t see the VMs, but they’re there.

### The execution lifecycle looks like this:

1. You define a **task definition** in ECS or a **pod spec** in EKS.
2. Specify resource requirements (CPU/memory), networking mode, IAM roles, logging, etc.
3. You run the task or deploy the pod.
4. Fargate pulls the container image and launches it inside a Firecracker microVM.
5. Logs go to CloudWatch. Metrics go to CloudWatch. Secrets and configs come from Parameter Store, Secrets Manager, or injected via task definitions.

The entire lifecycle is invisible, which is great for speed—but opaque for debugging.

## ECS vs Fargate: What’s the difference?

This question confuses a lot of teams. Fargate is **not** an alternative to ECS, it’s a **compute backend** for ECS.

### AWS ECS (Elastic Container Service):

- A container orchestration service.
- Manages how containers are scheduled, run, and scaled.
- You can use ECS with EC2 (your own instances) or Fargate (serverless backend).

### AWS Fargate:

- A **launch type** (in ECS) or **profile** (in EKS).
- Executes containers in isolated microVMs.
- Removes the need to manage EC2 nodes.

### TL;DR:

- **ECS** is the orchestrator.
- **Fargate** is the compute engine.

You can think of it like this:

- ECS + EC2 = You manage the infrastructure.
- ECS + Fargate = AWS manages the infrastructure.

## What are the pros of AWS Fargate?

Fargate makes a lot of sense in specific scenarios, especially when teams want to avoid infrastructure overhead.

### ✅ Simplicity

- No need to configure EC2 instances.
- No autoscaling groups, AMIs, or SSH.
- No capacity planning.

### ✅ Security isolation

- Each container gets its own VM-level isolation.
- More secure than shared-node models.

### ✅ Granular cost control

- Pay per second, per task.
- Fine-tune CPU and memory.

### ✅ Deep AWS integration

- IAM roles for tasks.
- CloudWatch logs and metrics.
- Private networking (VPC native).

### ✅ Good for event-driven workloads

- Great fit for bursty traffic.
- Use with Lambda-style architectures where workloads are short-lived.

## What are the cons of AWS Fargate?

This is where the shine wears off. Fargate has serious limitations, some technical, some practical.

### ❌ Cost

- **Expensive at scale.**
    - Always-on workloads are much cheaper on EC2 or other platforms.
    - You pay for idle time between container startups.
- No instance-based pricing efficiency.

### ❌ Cold starts

- Containers take time to spin up.
- Cold start latency can be several seconds.
- Not ideal for latency-sensitive services.

### ❌ Opaque debugging

- No access to the host.
- Difficult to troubleshoot issues that aren’t in the container logs.
- No SSH or live debugging.

### ❌ Limited customization

- No access to kernel-level configs or advanced networking.
- Can’t run privileged containers or custom runtimes.

### ❌ Vendor lock-in

- Entire deployment model tied to AWS ECS or EKS.
- Migrating off Fargate requires rewriting infrastructure.

## Who should use Fargate (and who shouldn’t)

### Good fit

- Small teams without infra expertise.
- Event-driven systems.
- Periodic batch jobs or scripts.
- Short-lived tasks (e.g. jobs kicked off via API or queue).

### Poor fit

- Latency-sensitive applications.
- High-throughput services with sustained load.
- Large-scale multi-tenant platforms.
- Teams that need deep observability, debugging, or fine-grained control.

## Northflank as an alternative to AWS Fargate

![northflank-container-orchestration.png](https://assets.northflank.com/northflank_container_orchestration_afaed972ac.png)

Fargate solves a narrow set of problems, but leaves you boxed into AWS. Northflank gives you the same “just run my container” experience, but with more control, better observability, and none of the platform sprawl.

### Key advantages of Northflank

### 🚀 Git-based deployments

- Native CI/CD via Git push.
- No need to configure pipelines separately.
- Faster iteration loop for developers.

### 🔍 Built-in observability

- Live logs and metrics without extra setup.
- No jumping between services like CloudWatch, X-Ray, or OpenSearch.

### ⚙️ Real infrastructure control

- Access to container-level configurations.
- Optional use of microVMs (for secure isolation) or standard containers (for speed).

### 🧩 Workload-level abstractions

- Services, jobs, cron, previews, all first-class primitives.
- Not bolted-on like in ECS.

### 🔄 Faster startup times

- No multi-second cold starts.
- Containers boot fast, even with microVM isolation.

### 🛡️ Isolation without complexity

- Choose between container-based or microVM-based isolation.
- Runs hardened by default, no need to manually set seccomp/apparmor profiles.

### 🌍 Multi-cloud and self-host options

- Don’t want to be tied to AWS? You don’t have to be.
- Run in your own VPC, across clouds, or even on-prem.

### 💰 Predictable pricing

- No hidden costs.
- Built for sustained workloads, not spiky jobs.
- Cheaper than Fargate at scale.

## Final thoughts

AWS Fargate is a smart engineering solution built to simplify container infrastructure. For certain jobs, it’s the right tool. But most engineering teams don’t just need “less infrastructure,” they need a *better platform*. One that balances ease of use with deep control. One that doesn't sacrifice observability. One that helps them ship, debug, and scale without wrestling with the guts of AWS.

Want to learn more about how Northflank compares to AWS Fargate, ECS, or EKS? [Talk to us](https://northflank.com/) or spin up your first service in minutes. [It’s free to try](https://app.northflank.com/signup).]]>
  </content:encoded>
</item><item>
  <title>What is PyTorch? A deep dive for engineers (and how to deploy it) </title>
  <link>https://northflank.com/blog/what-is-pytorch</link>
  <pubDate>2025-06-09T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[PyTorch is an open-source machine learning framework developed by Meta’s AI Research lab (FAIR).  Since its release in 2016, it’s become a cornerstone of deep learning research and production-grade AI systems. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/container_orchestration_blog_post_f1bfc0cef4.png" alt="What is PyTorch? A deep dive for engineers (and how to deploy it) " />Wondering what is PyTorch and why it’s become the deep learning framework of choice for modern AI systems? PyTorch is an open-source machine learning framework developed by Meta’s AI Research lab (FAIR).  Since its release in 2016, it’s become a cornerstone of deep learning research and production-grade AI systems. 

Engineers love PyTorch for its dynamic computation graphs, tight Python integration, and broad ecosystem. But most importantly, it lets you get work done without feeling like you’re constantly fighting the framework.

In this post, we’ll go beyond the standard “PyTorch is a deep learning framework” and unpack:

- What is Pytorch and why PyTorch is designed the way it is
- What makes it technically different from other frameworks
- Where it shines (and where it still hurts)
- How to reliably deploy PyTorch models using platforms Northflank, especially with GPU support and autoscaling

To really understand what is PyTorch and why it’s so widely used, we need to look at how it works under the hood.

Let’s dig in.

### The core of what PyTorch is: Dynamic graphs and autograd

At the heart of PyTorch is its dynamic computation graph: a decision that fundamentally changes how you write, debug, and scale models. Unlike TensorFlow 1.x, which required pre-defining a static computation graph before execution, PyTorch lets you build graphs on the fly using regular Python control flow. This makes model development radically more flexible.

Here are examples showing the differences between the two:

**PyTorch: Dynamic computation graph**

```python
import torch

x = torch.tensor(2.0, requires_grad=True)
y = x ** 2 + 3 * x + 1  # Computation happens as you write it

y.backward()  # Triggers autograd
print(x.grad)  # Outputs: tensor(7.)
```

You can use `if` statements, loops, recursion, anything Python allows, because the graph is built dynamically at runtime.

**TensorFlow 1.x: Static computation graph**

```python
import tensorflow as tf

x = tf.placeholder(tf.float32)
y = x**2 + 3*x + 1

grad = tf.gradients(y, x)

with tf.Session() as sess:
    result = sess.run(grad, feed_dict={x: 2.0})
    print(result)  # Outputs: [7.0]
```

You can’t just write a `for` loop or an `if` statement, you have to use TensorFlow's equivalents (`tf.while_loop`, `tf.cond`). Debugging becomes harder because the code you write isn’t the code that executes.

That difference changes how you approach model design altogether.

Want to build a recursive model that adjusts its architecture per input? Go for it. Training with branching logic, stochastic layers, or irregular tensor shapes? No problem. The dynamic graph is redefined on every forward pass, and PyTorch’s `autograd` engine keeps track of operations in real time to compute gradients during backpropagation.

This design choice matters. When you're debugging a model that isn't converging or a loss that's exploding, the last thing you want is to be buried under protobuf files or opaque static graph errors. In PyTorch, you can drop a breakpoint into your forward pass and inspect every tensor just like you would with NumPy. That level of introspection is a game-changer.

Under the hood, PyTorch wraps low-level CUDA/C++ ops in a clean Python interface and ships multiple hardware backends: CUDA for NVIDIA, MPS for Apple Silicon, ROCm for AMD, plus a solid CPU fallback. It handles tensor allocation, memory transfers, and kernel launches for you, but you can still drop down to `torch.cuda.Stream`, `mps_sync()`, manual gradient clipping, or fused kernels when you need to squeeze out more performance. The upshot: you can train models on an M1/M2 GPU today, something TensorFlow still can’t do without workarounds.

The [`autograd`](https://docs.pytorch.org/docs/stable/notes/autograd.html) system is built on a tape-based mechanism: as operations are performed on tensors with `requires_grad=True`, PyTorch records them in a DAG. When you call `.backward()`, it walks the graph in reverse, computing gradients via the chain rule. This means PyTorch doesn’t require symbolic differentiation, just real Python execution.

```python
import torch

# Create tensors with gradient tracking
x = torch.tensor([2.0], requires_grad=True)
y = torch.tensor([3.0], requires_grad=True)

# Forward pass - PyTorch builds the computation graph
z = x * y + x**2  # z = 2*3 + 2^2 = 10

# Backward pass - compute gradients
z.backward()

print(f"dz/dx = {x.grad}")  # Output: dz/dx = tensor([7.])
print(f"dz/dy = {y.grad}")  # Output: dz/dy = tensor([2.])
```

For performance, PyTorch uses custom C++ backends and ATen (its tensor library) along with CuDNN kernels under the hood. As a result, you get performance comparable to low-level CUDA code with a much simpler development experience.

One of the most common follow-up questions to “what is PyTorch” is: how do you move from research to production? That’s where TorchScript comes in.

### Why PyTorch is ubiquitous in research and industry

Any answer to the question “what is PyTorch used for in the real world” should start with where it dominates: research labs and large-scale production systems.

Every major ML lab (OpenAI, DeepMind, Meta, NVIDIA)   uses PyTorch. It powers everything from diffusion models and LLMs to recommender systems and robotics. This widespread adoption isn’t accidental.

- **Ecosystem depth**: PyTorch has best-in-class libraries for vision (TorchVision) and speech (TorchAudio). While TorchText (for NLP) is [no longer in active development](https://docs.pytorch.org/text/stable/index.html), the ecosystem includes many third-party libraries like Transformers (Hugging Face) for language tasks, plus a rich third-party ecosystem including PyTorch Geometric for graph learning.
- **Distributed support**: DDP (Distributed Data Parallel) is tightly integrated and efficient. FSDP (Fully Sharded Data Parallel) allows model parallelism and parameter sharding.
- **Mixed precision**: AMP (Automatic Mixed Precision) helps accelerate training while reducing memory usage, especially on A100s and H100s.
- **Multi-backend support**: PyTorch has granular GPU control across multiple backends including CUDA (NVIDIA), ROCm (AMD), and Metal (Apple Silicon), plus supports custom extensions for specialized hardware.
- **Developer velocity**: You can debug with native Python tools, test hypotheses quickly, and iterate faster than static-graph-based frameworks.

It also doesn’t hurt that Hugging Face’s entire model hub is PyTorch-first. Most pretrained models you want (BERT, GPT, CLIP, Stable Diffusion) come with PyTorch weights and `from_pretrained()` support out of the box.

```python
from transformers import pipeline

# Load a pretrained sentiment analysis model
classifier = pipeline("sentiment-analysis")

# Run inference
result = classifier("PyTorch makes deep learning accessible!")
print(result)  # [{'label': 'POSITIVE', 'score': 0.999}]
```

<InfoBox className='BodyStyle'>
### Check out how to install Pytorch [here](https://northflank.com/blog/how-to-install-pytorch-for-production)
</InfoBox>

### The ugly truth about PyTorch deployment

Let’s say you’ve trained a 300MB model that segments satellite imagery for environmental monitoring. It works brilliantly in your notebook. Now what?

You need to:

- Write a serving layer (FastAPI, Flask)
- Dockerize it
- Configure GPU runtime and drivers
- Set up request queuing and autoscaling
- Deal with CI/CD and versioned rollouts
- Monitor latency, error rates, GPU utilization
- Add secrets, config, security, maybe OAuth

Even worse, you’ll spend days wrestling with IAM roles, container registries, VPCs, ingress controllers, and logs that go nowhere useful. This is no longer ML engineering, it’s DevOps trench warfare.

Most teams duct tape together AWS Lambda, ECS, Terraform, and homegrown CI pipelines. 

The result is fragile infra, painful deploys, and no visibility. PyTorch itself didn’t get you into this mess. But it’s not getting you out of it either.

### How platforms like Northflank help

Northflank turns that deployment mess into something clean, fast, and repeatable. It’s a PaaS built for containerized workloads, with first-class support for GPUs, Git-based CI/CD, secrets management, and autoscaling.

Here’s how PyTorch deployment works on Northflank:

1. Wrap your model in a server (FastAPI, Flask, or… you choose)
2. Write a Dockerfile with your app, model weights, and dependencies
3. Push your code to GitHub
4. Connect it to Northflank via Git integration
5. Enable GPU workload (H100, A100, or CPU-only)
6. Set autoscaling parameters and deploy

<div>  
  <center>  
    <a href="https://app.northflank.com/signup">  
<Button variant={["large", "gradient"]}>Deploy your PyTorch model here</Button>  
    </a>  
  </center>  
</div>

No need for YAML. No need for Helm charts. No need to know what `kubectl get pods` means.

Under the hood, Northflank provisions container workloads with real-time logs, runtime metrics, secrets injection, persistent storage, custom domains, and fine-grained environment configs. 

You can:

- Set per-branch deployment rules (e.g., deploy staging from `develop`, prod from `main`)
- Use service discovery for internal APIs
- Deploy sidecars or job workers
- Roll back instantly if a deploy fails

And yes, you can deploy on GPUs using providers like CoreWeave. If you're running a PyTorch model that needs CUDA, you toggle GPU and you're done. No NVIDIA driver installs. No container hacks.

### A real example: Image classification on FastAPI

Say you’ve trained a ResNet-50 model on ImageNet and want to expose it via an HTTP API.

Your `inference.py` might look like:

```python
from fastapi import FastAPI, UploadFile
import torch
from torchvision import transforms
from torchvision.models import resnet50, ResNet50_Weights
from PIL import Image
import io

model = resnet50(weights=ResNet50_Weights.DEFAULT)
model.eval()

app = FastAPI()
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

@app.post("/predict")
async def predict(file: UploadFile):
    image = Image.open(io.BytesIO(await file.read())).convert("RGB")
    tensor = transform(image).unsqueeze(0)
    with torch.no_grad():
        logits = model(tensor)
        pred = torch.argmax(logits, dim=1).item()
    return {"prediction": pred}
```

Your Dockerfile:

```docker
FROM python:3.12-slim
WORKDIR /app
COPY . .
RUN pip install torch torchvision fastapi uvicorn pillow python-multipart
CMD ["uvicorn", "inference:app", "--host", "0.0.0.0", "--port", "8080"]
```

Push this to GitHub. On Northflank:

- Create a new service
- Connect your repo
- Enable GPU workload
- Set autoscaling and memory limits
- Deploy

Your model is now accessible at a custom URL with real-time logging, GPU metrics, and deploy history. Need to update weights? Push to your branch and Northflank rebuilds automatically.

### Advanced use cases

You can also deploy multi-model APIs, A/B test model versions, and run async job queues with GPU-backed workers. Northflank supports both persistent services and job-type workloads, so you can:

- Batch-process inference jobs
- Schedule retraining pipelines
- Serve multiple models behind one FastAPI interface
- Store model artifacts in object storage and fetch them at runtime

For ML teams operating in hybrid environments (e.g., some models on GPU, others CPU), Northflank gives granular resource control per service.

### Final thoughts

PyTorch is one of the most important tools in modern AI. It made model development intuitive, flexible, and fast. But for all its strengths, it leaves a gaping hole when it comes to production infrastructure.

Northflank is the missing link. It takes everything painful about PyTorch deployment (containers, GPUs, autoscaling, CI/CD) and makes it click.

If you're building serious ML systems and want infra that works with you, not against you, give Northflank a spin. It won’t train your model, but it’ll run the hell out of it.

[Explore Northflank →](https://northflank.com/)]]>
  </content:encoded>
</item><item>
  <title>What is container orchestration? Why it matters and how to choose the best tools for your workloads</title>
  <link>https://northflank.com/blog/container-orchestration</link>
  <pubDate>2025-06-09T18:26:00.000Z</pubDate>
  <description>
    <![CDATA[A technical, in-depth guide to container orchestration for production workloads. See how tools like Kubernetes, Northflank, and Nomad fit in — and how to choose the right one for your team.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/container_orchestration_blog_post_1_72292ab5dc.png" alt="What is container orchestration? Why it matters and how to choose the best tools for your workloads" />> Container orchestration is the automated process of deploying, managing, scaling, and networking containers in production. It’s how you move from running a single container on your laptop to managing thousands of them across different environments.

When you’re working with containers in production, there’s no way around it: you need container orchestration. I’m not talking about some nice-to-have tool, I’m talking about the backbone of how you deploy and run workloads at scale without unnecessary > operational complexity.

You’re managing multiple environments, constant updates, and high availability requirements; container orchestration is what ties it all together. It’s how you make sure containers don’t just run, they run predictably, with built-in failover, scaling, and observability.

One key point to keep in mind: not every container orchestration tool is built for your team’s needs. In this piece, I’ll walk you through what container orchestration means for your stack, how it works, why it matters, and how to choose the tool that will work for you today and keep supporting you as you grow. And, of course, how tools like [Northflank](https://northflank.com/) give you Kubernetes-level control without the usual operational complexity.

Let’s get into it.

<InfoBox className='BodyStyle'>

### ⚡️ TL;DR for readers in a hurry

Here’s the short version if you’re skimming:

- **What it is**: Container orchestration is how you run containers at scale without manual operational complexity.  
- **Why it matters**: Better automation, more resilience, and faster deployments across environments.  
- **Top tools to know about**:  
  1. [**Northflank**](https://northflank.com/) – Built on Kubernetes, it delivers container orchestration with zero-config setup, fully managed and running on your cloud.  
  2. [**Kubernetes**](https://northflank.com/blog/kubernetes-vs-docker#what-is-kubernetes) – The most widely used orchestration tool, built for massive scale and flexibility.  
  3. [**Docker Swarm**](https://northflank.com/blog/docker-swarm-vs-kubernetes#what-is-docker-swarm) – A simpler, native orchestrator for Docker workloads.  
  4. [**OpenShift**](https://northflank.com/blog/openshift-vs-kubernetes#openshift-kubernetes-made-enterpriseready) – Red Hat’s enterprise-ready Kubernetes platform.  
  5. [**Nomad**](https://northflank.com/blog/kubernetes-alternatives-finding-the-right-fit-for-your-team#1hashicorp-nomad) – A lightweight and versatile orchestrator by HashiCorp.  
  6. [**Rancher**](https://northflank.com/blog/rancher-vs-openshift#what-is-rancher) – A management layer that makes working with Kubernetes more accessible.  
- **Where Northflank fits**: [**Northflank**](https://northflank.com/) uses Kubernetes under the hood to give you the power of container orchestration without the DIY burden.

</InfoBox>

## What is container orchestration?

Container orchestration is the automated process of deploying, managing, scaling, and networking containers in production. It’s how you move from running a single container on your laptop to managing thousands of them across different environments.

Let’s break this down a bit.

Running containers locally with [Docker](https://northflank.com/blog/kubernetes-vs-docker#what-is-docker) is easy. I mean, you just run `docker run` and you’re good to go, right?

Okay. Now think about when you have dozens (or thousands) of containers to run. Things get complicated fast. You now need to figure out:

- How do you scale containers up or down based on demand?
- How do you recover from failures automatically?
- How do you route traffic to the right containers?
- How do you update workloads without downtime?

This is exactly where container orchestration comes in. Like I said in the definition above, it automates these tasks so you don’t have to tweak everything yourself manually.

To help you visualize this, take a look at the diagram below. It demonstrates how container orchestration automates scaling, load balancing, and failover across clusters, so you can see the control plane in action:

![A visual diagram showing how an orchestration control plane manages container clusters by automating scaling, load balancing, and failover, with arrows illustrating communication paths](https://assets.northflank.com/container_orchesration_8122c2be81.png)*Container orchestration automates scaling, load balancing, and failover across clusters*

To give you some real-world context, let me show you a few container orchestration tools you might already know. [Kubernetes](https://northflank.com/blog/kubernetes-vs-docker#what-is-kubernetes), for example, is the most widely adopted container orchestrator, handling everything from scheduling pods to rolling out updates automatically at massive scale.

Then there’s [Docker Swarm](https://northflank.com/blog/docker-swarm-vs-kubernetes#what-is-docker-swarm), a simpler orchestration tool integrated directly with Docker. And [OpenShift](https://northflank.com/blog/openshift-vs-kubernetes#openshift-kubernetes-made-enterpriseready) takes Kubernetes and adds security and developer tooling to make it easier for teams to manage workloads.

I’ll go into these tools in more detail later. For now, think of them as different approaches to solving the same core problem, which is managing containers in production so your workloads keep running smoothly, from five containers all the way to fifty thousand.

Let’s keep going. I’ll show you why this orchestration matters and how it changes the way you think about deployments.

## Why do you need container orchestration?

Okay, so we’ve talked about what container orchestration is. Now let’s get to the next important question: why should you care? If you’re working with more than a couple of containers in production, this is what makes sure your stack stays reliable and scalable without creating the complexity of manual tasks.

Let’s look at how container orchestration makes your life easier and why you can’t live without it in production.

### 1. Automation that saves you time (and sanity)

First off, automation. You don’t want to be manually scheduling every container, checking logs for every tiny spike in traffic, or constantly restarting containers that crash. Container orchestration handles these workflows automatically; it’s your control plane that watches everything and responds fast.

![A two-part illustration comparing manual container management (gears and checklists with tangled arrows) to automated container orchestration (central orchestrator managing containers with neat arrows), highlighting the difference in complexity and workflow](https://assets.northflank.com/automated_orchestration_b04ac97bfc.png)*Comparison of manual container management and automated orchestration workflows*

### 2. High availability and failover built-in

Then there’s high availability. When a container fails, orchestration doesn’t ask for permission; it restarts it automatically and redirects traffic so users don’t see an outage. It’s a built-in failover that keeps your services alive, even when things break behind the scenes.

![A diagram on a dark background showing how user traffic is redirected from a failed container to a working container in a container orchestration setup, highlighting built-in high availability and failover](https://assets.northflank.com/high_availability_failover_container_orchestration_465cfd9434.png)*Traffic rerouting in container orchestration when a container fails*

### 3. Better resource utilization

You’re also getting better resource utilization. Without orchestration, it’s easy to have containers sitting idle on some nodes while others are overloaded. Orchestration automatically places containers where resources are available, spreading out workloads to keep your infrastructure balanced.

![A side-by-side diagram comparing container distribution across nodes without orchestration (with one node idle and another overloaded) and with orchestration (evenly balanced nodes), illustrating how container orchestration improves resource utilization](https://assets.northflank.com/better_resource_utilization_container_orchestration_ec4950b49c.png)*Balanced container resource utilization with orchestration vs. without orchestration*

### 4. Faster, predictable deployments

Finally, deployments. Container orchestration makes rolling out new versions smoother. It schedules updates, does rolling updates to prevent downtime, and can roll back if something goes sideways. No more being unsure if your update will take everything down.

So that’s why you need container orchestration. It’s the difference between constant manual troubleshooting and letting your infrastructure work for you.

Next, I’ll show you how container orchestration works in practice, from how containers are scheduled and scaled to how clusters are managed and monitored.

## How does container orchestration work?

Let’s break this down step by step. You already know why container orchestration is critical. Now let’s see how it operates in your infrastructure.

### 1. Scheduling: finding the right place for each container

The orchestrator’s first job is to schedule containers on the most suitable nodes. Each node has its own CPU, memory, and networking resources. The orchestrator decides where to run each container so resources stay balanced.

For example, Kubernetes uses a scheduler to assign pods to nodes automatically:

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: nginx
spec:
  containers:
  - name: nginx
    image: nginx:1.14.2

```

This pod will be placed on a node with enough resources to handle it, no manual placement needed.

### 2. Scaling: adapting to changes in demand

Next up is scaling. When your workload sees a spike in traffic, you need more containers to keep up. The orchestrator adds containers as needed, then scales back down when things quiet down.

Here’s an example of setting the number of replicas in a deployment to 5 for higher traffic:

```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
spec:
  replicas: 5
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
      - name: my-app-container
        image: my-app:latest
```

This tells Kubernetes to keep five pods running across your cluster, balancing the load across available nodes.

### 3. Load balancing: keeping things efficient

Once you have multiple containers (or pods) for the same service, the orchestrator takes care of load balancing. It spreads traffic evenly so no single container gets overwhelmed.

For example, in Kubernetes, you’d expose your app with a Service:

```yaml
apiVersion: v1
kind: Service
metadata:
  name: my-app-service
spec:
  selector:
    app: my-app
  ports:
    - protocol: TCP
      port: 80
      targetPort: 8080
```

The Service load balances traffic to healthy pods automatically.

### 4. Rollbacks: preventing downtime during updates

Updates can fail, you know that. The orchestrator can roll back to the last working version without you having to manually fix things. Kubernetes, for instance, tracks ReplicaSets so it can revert to the previous one if needed.

A simple way to trigger a rollback in Kubernetes:

```bash
kubectl rollout undo deployment/my-app
```

This command brings you back to the last stable deployment.

### 5. Service discovery: making communication seamless

Containers often need to talk to each other. Orchestration tools provide service discovery, so containers find each other without hard-coded IPs. Kubernetes assigns cluster DNS names so pods can communicate with each other dynamically.

For example, pods can access each other via names like:

```bash
my-app-service.default.svc.cluster.local
```

This keeps your architecture dynamic and easier to maintain.

When these building blocks work together, container orchestration keeps your workloads stable and responsive, without you having to manually intervene whenever there’s a spike in traffic, a container failure, or a new deployment.

Next, I’ll show you some of the top container orchestration tools that put all this into practice.

## Best container orchestration tools in 2025

Now that you’ve seen how container orchestration works, let’s look at the top tools that put these concepts into action. I won’t bore you with unnecessary details.

We’ll go through what you need to know about each one: who they’re for, what they’re good at, and why you might choose them for your team.

### 1. Northflank

[Northflank](https://northflank.com/) is a production workload platform that automates container management, streamlining deployment, scaling, and networking across diverse environments. It gives you Kubernetes-level orchestration with a zero-config setup, combining CI/CD, databases, job runners, and more, all fully managed on your cloud or Northflank’s infrastructure.

**Use it if:**

- You want Kubernetes-level orchestration without managing YAML and cluster configurations.
- You’re looking for a platform that combines deployments, databases, and job runners in one.
- You’re a team that wants to focus on shipping software, not infrastructure management.

<InfoBox className='BodyStyle'>
If you’re curious how this works in real-world deployments, take a look at [how Clock scaled 30,000 deployments with 100% uptime using Northflank](https://northflank.com/blog/scaling-30-000-deployments-with-100-uptime-how-clock-uses-northflank-to-simplify-infrastructure).
</InfoBox>

### 2. Kubernetes

[Kubernetes](https://northflank.com/blog/kubernetes-vs-docker#what-is-kubernetes) is the most widely used container orchestration platform, and for good reason. It handles everything: scheduling, scaling, service discovery, and rolling updates. It’s built for complex, production-grade workloads, no matter if you’re running on AWS, GCP, Azure, or your own data center.

**Use it if:**

- You want the broadest ecosystem and community support.
- You need fine-grained control over containerized workloads.
- You’re dealing with complex, microservices-based applications.

<InfoBox className='BodyStyle'>
Yes, Kubernetes is flexible, but managing YAML manifests can be a lot to handle. If you’re curious how to skip writing YAML while still deploying to Kubernetes, check out this [guide on deploying to Kubernetes without YAML](https://northflank.com/blog/deploy-to-kubernetes-without-writing-yaml).
</InfoBox>

### 3. Docker Swarm

[Docker Swarm](https://northflank.com/blog/docker-swarm-vs-kubernetes#what-is-docker-swarm) is Docker’s built-in orchestrator. It’s simpler than Kubernetes and easier to set up if you’re already using Docker. Swarm mode lets you turn a group of Docker nodes into a single virtual host for your containers.

**Use it if:**

- You’re already using Docker and want a lightweight orchestrator.
- You don’t need the full feature set of Kubernetes.
- You’re managing smaller workloads or simpler use cases.

<InfoBox className='BodyStyle'>
If you want to see how Docker Swarm compares to Kubernetes, this [breakdown of Docker Swarm vs. Kubernetes](https://northflank.com/blog/docker-swarm-vs-kubernetes#what-is-docker-swarm) covers the differences and why you might choose one over the other.
</InfoBox>

### 4. OpenShift

[OpenShift](https://northflank.com/blog/openshift-vs-kubernetes#openshift-kubernetes-made-enterpriseready) builds on Kubernetes and adds developer tooling, built-in security features, and enterprise-level support. It’s backed by Red Hat and is widely adopted in enterprises that want to pair Kubernetes with a secure, managed experience.

**Use it if:**

- You need built-in CI/CD tooling and developer workflows.
- Security and compliance are top priorities.
- You’re in an enterprise environment looking for supported, managed Kubernetes.

<InfoBox className='BodyStyle'>
If you want to learn more about OpenShift, check out these related guides:

- [OpenShift vs Kubernetes: What should you use to ship products in 2025?](https://northflank.com/blog/openshift-vs-kubernetes)
- [Best OpenShift alternatives: finding the right Kubernetes platform](https://northflank.com/blog/best-open-shift-alternatives-finding-the-right-kubernetes-platform)
</InfoBox>

### 5. Nomad

[Nomad](https://northflank.com/blog/kubernetes-alternatives-finding-the-right-fit-for-your-team#1hashicorp-nomad) is HashiCorp’s lightweight orchestrator. It can handle containers, VMs, and other workload types all in the same control plane. It’s simpler than Kubernetes and has a smaller footprint, making it a good choice for teams who want flexibility without the overhead of Kubernetes.

**Use it if:**

- You want a single orchestrator for both containers and non-container workloads.
- You’re already using HashiCorp tools like Consul and Vault.
- You value a lightweight, easy-to-manage system.

### 6. Rancher

[Rancher](https://northflank.com/blog/rancher-vs-openshift#what-is-rancher) isn’t an orchestrator itself; it’s a management layer for Kubernetes. It gives you a unified dashboard for managing multiple Kubernetes clusters, with built-in user and access control. Rancher simplifies Kubernetes management and can work across clouds or on-prem.

**Use it if:**

- You’re running multiple Kubernetes clusters.
- You want a single pane of glass for cluster management.
- You want to simplify Kubernetes without giving up its power.

<InfoBox className='BodyStyle'>
If you want to learn more about Rancher’s role in Kubernetes workflows or what alternatives are out there, check out these resources:

- [Rancher vs OpenShift: Which platform fits your Kubernetes workflows best?](https://northflank.com/blog/rancher-vs-openshift#what-is-rancher)
- [7 Best Rancher alternatives in 2025](https://northflank.com/blog/rancher-alternatives)
</InfoBox>

Each of these tools has a specific focus and target audience, so it’s all about matching them to your team’s needs and your infrastructure’s complexity.

Next, let’s talk about how Kubernetes, the orchestrator that powers most of these platforms, fits into the real world and how Northflank leverages it to give you orchestration without the manual work.

## Okay, what about container orchestration with Kubernetes?

Like I mentioned, Kubernetes is the orchestrator behind most of these platforms. It’s the tool that does the actual work of scheduling containers, balancing resources, and recovering from failures. It is the control plane that keeps everything running smoothly.

Kubernetes has become the de facto standard for container orchestration because it handles everything: from scheduling pods across your cluster, to balancing traffic, to rolling out updates without downtime.

For example, when you deploy a new version of your app, Kubernetes doesn’t just replace the old pods all at once. It rolls them out one by one, shifting traffic gradually, so there’s no downtime for your users. This rolling update model is built-in, so no extra tooling is required.

Or think about load balancing. Kubernetes Services handle this out of the box, routing traffic only to healthy pods, so you don’t have to manually configure external load balancers or keep track of every container IP.

All of this makes Kubernetes powerful, but it also means there’s a lot to manage. YAML manifests, cluster resource tuning, and update strategies can add a significant management burden, especially when your focus is just on shipping code.

So, in the next section, I’ll show you how Northflank builds on top of Kubernetes to give you orchestration that works without that management burden.

## Kubernetes‑level control, minus the complexity of container orchestration

Okay, so we’ve covered how Kubernetes gives you fine‑grained control, but like I said, with that comes complexity: YAML manifests, cluster tuning, and update strategies. Let’s talk about how Northflank changes that.

### 1. Zero‑config setup

Northflank handles the container orchestration primitives for you, so you get automated deployments, secure networking, and resource balancing out of the box. No manual YAML, no misconfigured clusters.

See how Northflank’s dashboard gives you everything you need to deploy and manage your workloads at scale, without the effort of writing complex YAML files or managing every detail by hand:

![Screenshot of Northflank’s dashboard showing a container deployment in progress with automated deployments and secure networking](https://assets.northflank.com/northflank_container_orchestration_afaed972ac.png)*Northflank automatically handles container orchestration tasks like scaling, networking, and deployments, so you can focus on writing code, not managing YAML.*

### 2. Self‑service environments

Northflank lets you spin up preview environments on demand, so developers can test changes without waiting for ops. Everything is container‑native, so you’re still working with real container orchestration, just simplified.

Northflank's [self-service environments](https://northflank.com/use-cases/self-service-developer-experience-for-kubernetes) let you deploy preview environments on demand, giving your team a safe place to test and iterate quickly.

### 3. Hosted on your cloud

With Northflank, you can run workloads on AWS, GCP, Azure, or your private data center, all managed through a single control plane. You keep your data and resources where you want them, while Northflank abstracts the orchestration details.

Northflank’s [Bring Your Own Cloud](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) feature gives you a single view of your workloads, no matter where they run.

### 4. Kubernetes-level control, minus the complexity

You still get direct access to Kubernetes primitives if you want them; pods, deployments, services, but they’re surfaced through Northflank’s API, CLI, and UI. No need to manage YAML or remember every `kubectl` command.

Northflank takes the operational complexity out of container orchestration so you can focus on what matters: building and shipping software at scale, without getting lost in the details.

## FAQs: Let’s clear up the confusion

We’ve walked through what container orchestration is, how it works, and how tools like Northflank can take the operational complexity off your plate. Now let’s tackle some of the most common questions I see in the container world, the stuff you’re probably wondering about too.

**1. What is the difference between Docker and container orchestration?**

Docker is a container runtime: it lets you build, run, and manage containers. But when you have dozens or hundreds of containers in production, you need container orchestration to manage how they run together, for scheduling, scaling, and load balancing. Kubernetes (and other orchestrators like Docker Swarm or Nomad) are built to handle that.

**2. What is the most popular container orchestration tool?**

Kubernetes is the most widely used container orchestrator today. It has the largest ecosystem, supports complex deployments, and is backed by huge open-source and commercial communities.

**3. Is Kubernetes an orchestration tool?**

Yes. Kubernetes is a container orchestration system. It manages how containers are scheduled, scaled, and networked across your infrastructure.

**4. What are the alternatives to Kubernetes?**

Some of the main alternatives include Docker Swarm (built into Docker), Nomad by HashiCorp, and OpenShift (Red Hat’s enterprise platform built on top of Kubernetes). Tools like Northflank also simplify Kubernetes for you by managing the control plane and orchestration details. If you’re looking for a more detailed look at Kubernetes alternatives, check out [this guide to finding the right fit for your team](https://northflank.com/blog/kubernetes-alternatives-finding-the-right-fit-for-your-team).

**5. What is Docker Swarm vs Kubernetes?**

Docker Swarm is Docker’s built-in orchestrator. It’s simpler and easier to set up if you’re already using Docker, but less flexible and scalable than Kubernetes. Kubernetes is more powerful, with broader features for complex workloads and larger deployments. If you want to compare them head-to-head, check out [this breakdown of Docker Swarm vs Kubernetes](https://northflank.com/blog/docker-swarm-vs-kubernetes).

**6. What’s the difference between OpenShift and Kubernetes?**

OpenShift is a Kubernetes distribution from Red Hat. It takes the power of Kubernetes and adds built-in security, developer tooling, and enterprise support. It’s still Kubernetes underneath, but with guardrails and pre-packaged integrations. For a closer look at how these two platforms compare, check out [this guide on OpenShift vs Kubernetes in 2025](https://northflank.com/blog/openshift-vs-kubernetes).

## Making the right choice for your team

Okay, let’s bring it all together. Choosing the right container orchestration tool isn’t about hype; it’s about your team’s workflows, your infrastructure, and how you plan to scale.

Here’s what I recommend you look for:

- Does it work with your current cloud setup and CI/CD pipeline?
- Can it scale easily as your workloads grow?
- How much complexity will you need to manage yourself?
- Is there clear observability and control so you’re not flying blind?
- How does it fit your team’s skill set?

The bottom line: you want orchestration that handles the technical details so you can focus on building and shipping. That’s exactly what Northflank solves for, giving you container orchestration with Kubernetes-level control, minus the usual complexity.

> See how it works for your team by [signing up and starting deployments today](https://app.northflank.com/signup).
>]]>
  </content:encoded>
</item><item>
  <title>Best Spectro Cloud alternatives in 2026</title>
  <link>https://northflank.com/blog/spectro-cloud-alternatives</link>
  <pubDate>2025-06-09T14:45:00.000Z</pubDate>
  <description>
    <![CDATA[Explore 6 top Spectro Cloud alternatives in 2026 like Northflank, Platform9, and Portainer for faster, low ops Kubernetes platforms focused on developer velocity, CI/CD, and simplified scaling.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/spectro_cloud_alternatives_00c87219ec.png" alt="Best Spectro Cloud alternatives in 2026" />You adopted Kubernetes because it promised control, consistency, and scale. Then you added Spectro Cloud to tame the complexity. A few months in, your team is spending more time tuning the platform than shipping features.

Your CI pipelines are fragmented. Secrets are duct-taped across environments. YAML is everywhere. And developers? They still need a platform engineer just to deploy a service.

You do not have a Kubernetes problem. You have a platform design problem.

If this sounds familiar, you are not alone. More teams in 2026 are walking away from heavy-handed platform stacks and toward tools that help them move faster without needing a PhD in DevOps.

This guide explores the best Spectro Cloud alternatives for teams that want to build and ship software, not just manage infrastructure. Whether you're a fast-moving startup or a platform team looking to reduce operational load, these seven options are worth your attention.

## TL;DR: 6 Spectro Cloud alternatives to know in 2026

<InfoBox className='BodyStyle'>
- [**Northflank**](https://northflank.com/) – A developer-first Kubernetes platform with built-in CI/CD, databases, preview environments, and Git-based workflows. Abstracts away Kubernetes complexity without sacrificing control.
- **Portainer** – A UI-driven management tool for simplifying Kubernetes and Docker operations
- **Platform9** – Managed Kubernetes for hybrid environments with SaaS convenience
- **OpenShift** – Enterprise-grade container orchestration with deep compliance support
- **KubeSphere** – Full-featured open-source platform for teams that want flexibility and depth
- **Rancher** – Battle-tested multi-cluster management with open-source roots
</InfoBox>

## Why teams are moving beyond Spectro Cloud

Spectro Cloud promises flexibility and control, and it delivers. But for many engineering teams, that control comes at a high cost: time, complexity, and context-switching.

The more you configure, the less you ship.

Product teams find themselves debugging YAML. Platform engineers juggle layers of abstraction. Developers wait for someone else to provision an environment. And the deeper you go into the Spectro stack, the harder it becomes to move fast or adapt.

The tradeoffs are starting to show:

- **High operational overhead**: Palette's power depends on heavy configuration and layered orchestration, meaning more time managing the platform, less time improving the product.
- **Slower developer workflows**: Spinning up a new service or environment often requires ticketing, scripting, and deep platform knowledge.
- **Fragmented delivery pipelines**: CI/CD, secrets, databases, and monitoring are all possible — but rarely native or frictionless.
- **Developer experience is not a priority**: Spectro Cloud was built for platform teams, not for the engineers writing and shipping code.

Teams are realizing that flexibility means little if it comes at the cost of **momentum**.

In 2026, speed is strategy. The platforms that win are the ones that reduce complexity, automate the boring stuff, and let developers focus on what actually moves the business forward — building.

## What makes a strong Spectro Cloud alternative

If Spectro Cloud made Kubernetes manageable, the next wave of platforms is making it **invisible**.

A strong alternative doesn’t just expose more knobs. It rethinks the developer experience from first principles — removing unnecessary steps, automating operational overhead, and making delivery feel seamless.

The best platforms today are judged not by how many features they offer, but by how much **cognitive load they eliminate**.

Here’s what that looks like in practice:

- **Zero-to-deploy simplicity**
    
    Can a developer go from a Git repo to a running service in minutes, without writing YAML or opening a ticket?
    
- **Native delivery workflows**
    
    Does the platform integrate CI/CD, preview environments, rollbacks, and GitOps as first-class citizens, not bolt-ons?
    
- **Built-in infrastructure primitives**
    
    Are databases, background jobs, cron tasks, and secrets all part of the same cohesive experience?
    
- **Unified visibility and control**
    
    Can developers monitor logs, metrics, and resource usage in one place, without bouncing between tools?
    
- **Scalable by design, not effort**
    
    Can a small team scale to production confidently, without wrangling Helm charts or Terraform?
    
- **Flexible deployment targets**
    
    Does it support cloud, on-prem, hybrid, or multi-region setups with minimal friction?
    

Great platforms don’t just help you manage Kubernetes. They help you forget about it.

That’s the benchmark.

## Top 6 Spectro Cloud alternatives in 2026

Looking for a simpler way to run Kubernetes? These alternatives to Spectro Cloud are making waves in 2026, each offering a fresh take on how teams build, deploy, and scale with less overhead.

### **1. Northflank – Kubernetes without the complexity**

[Northflank](https://northflank.com/) is what Spectro Cloud *wants* to be for developers. It gives you all the power of Kubernetes—container orchestration, service discovery, and autoscaling—but wraps it in a developer-first experience.

No need to manage YAML files by hand. With Git-integrated workflows, built-in CI, automatic SSL, and managed databases, Northflank helps you go from code to running service in minutes. It also handles the heavy lifting like horizontal scaling, persistent storage, and background workers with a clean, intuitive UI and APIs.

![](https://assets.northflank.com/image_93_2b254840ee.png)

**Key features:**

- Kubernetes-powered, full-stack platform
- Deploy containers, databases, and scheduled jobs
- [Bring your own cloud (AWS, GCP, Azure, etc.)](https://northflank.com/features/bring-your-own-cloud)
- CI/CD integration, real-time logs, with a developer-friendly and consistent experience across UI, CLI, API, and GitOps
- GPU support for AI workloads
- Automatic preview environments and seamless promotion to dev, staging, and production

**Best for:**

- Dev teams building APIs, microservices, and containerized web apps
- SaaS products needing multi-service architectures
- Teams looking for a fast, clean alternative to older, more rigid platforms like Rancher

**Potential drawbacks:**

- Highly experienced DevOps teams might find it restrictive compared to directly managing raw Kubernetes clusters. It’s a fine balance between ease of use, flexibility, and customization; that line differs for every organization.

*See how [Weights company uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### **2. Portainer**

[Portainer](https://www.portainer.io/) is a **lightweight container management UI** for Docker and Kubernetes environments. It’s not a full-fledged platform like others here, but it offers a user-friendly way to manage container infrastructure visually.

![](https://assets.northflank.com/image_71_4bed621467.png)

**Key features:**

- Simple dashboard for managing containers and clusters.
- Works with both Docker and Kubernetes.
- Role-based access control and team management.
- Minimal resource requirements.

**Best for:**

Self-hosters and small teams that want a visual layer over their existing container infrastructure.

**Potential drawbacks:**

- Lacks deeper DevOps features like CI/CD or GitOps.
- Not intended for large-scale enterprise workloads.

[Read more on Portainer](https://northflank.com/blog/portainer-alternatives)

### 3. Platform9

[Platform9](https://platform9.com/) is a **managed Kubernetes solution** designed for **on-premises, edge, and hybrid cloud environments**. Unlike fully cloud-hosted Kubernetes services, Platform9 allows organizations to run Kubernetes anywhere while benefiting from a **SaaS-based management model**.

![](https://assets.northflank.com/image_40_6281cf93cd.png)

**Key features:**

- **Fully managed Kubernetes** with a 99.9% uptime SLA.
- **Works across on-prem, hybrid, and edge environments**.
- **Zero-touch upgrades and automated operations**.
- **Open-source foundation** with no vendor lock-in.

**Potential drawbacks:**

- Smaller market share compared to OpenShift, which may affect long-term support.
- Reliance on a SaaS-based model may not be suitable for some enterprises.

### 4. OpenShift

[OpenShift](https://www.redhat.com/en/technologies/cloud-computing/openshift) is a **comprehensive Kubernetes platform** developed by Red Hat. It’s designed for hybrid and multi-cloud deployments, offering strong security, compliance features, and enterprise support.

![](https://assets.northflank.com/image_2025_05_01_T201538_690_d20ad45e54.png)

**Key features:**

- Full-stack Kubernetes with integrated developer tools.
- Native CI/CD with Tekton and support for pipelines.
- Robust RBAC, policy enforcement, and compliance capabilities.
- Deep integration with Red Hat Linux and other enterprise tools.

**Best for:**

- Large enterprises already invested in Red Hat infrastructure or needing high-security, compliance-ready Kubernetes environments.

**Potential drawbacks:**

- Complex to set up and maintain without dedicated platform teams.
- Can be resource-intensive and expensive.

[Read more on OpenShift](https://northflank.com/blog/best-open-shift-alternatives-finding-the-right-kubernetes-platform)

### 5. KubeSphere

[KubeSphere](https://kubesphere.io/) is an open-source layer on top of Kubernetes that adds a dashboard and a suite of DevOps tools. It’s modular and flexible, especially for teams that already run their own clusters and want to gradually enhance them with UI and automation.

![](https://assets.northflank.com/image_2025_05_05_T171518_934_726d236200.png)

**Key features:**

- Visual interface for Kubernetes resource management
- Built-in support for CI/CD, observability, and multi-tenancy
- Pluggable architecture: enable only what you need
- Self-hosted and fully open-source

**Best for:**

- Teams already managing their own clusters
- Organizations comfortable maintaining infrastructure but wanting better UX

**Potential drawbacks:**

- Still requires ops knowledge to run and scale effectively
- UI is improving, but the overall experience can feel fragmented
- Lacks the end-to-end polish of a fully integrated platform.

### 6. Rancher

[Rancher](https://www.rancher.com/) is an open-source **Kubernetes management platform** that simplifies deployment and administration, especially in **multi-cluster and multi-cloud environments**. It provides **centralized cluster management**, making it ideal for enterprises running Kubernetes across multiple providers.

![](https://assets.northflank.com/image_39_6cdc97389f.png)

**Key features:**

- **Easy cluster provisioning** and lifecycle management.
- **Built-in security, monitoring, and policy management**.
- Supports **on-premises, hybrid, and multi-cloud environments**.

**Potential drawbacks:**

- Requires some Kubernetes expertise to configure and manage.
- May not have as extensive enterprise support as OpenShift.

[Read more on Rancher](https://northflank.com/blog/rancher-alternatives)

## How to choose the right alternative

Start with your team’s strengths and your goals. Consider these questions:

- Do we want to manage Kubernetes or abstract it away?
- How important is developer velocity?
- Do we need multi-cloud or hybrid support?
- How much customization do we want versus convenience?

If your focus is building software and delivering it fast, [Northflank](https://northflank.com/) is designed exactly for that mission. If your team needs deep control, or is already embedded in a specific ecosystem like Red Hat or Jenkins, one of the other platforms may better fit.

Here is a quick comparison:

| Platform | Best For | Dev Experience | Ops Burden |
| --- | --- | --- | --- |
| [Northflank](https://northflank.com/) | Full-stack app delivery | High | Low |
| Portainer | Visual cluster management | Medium | Medium |
| Platform9 | SaaS-managed hybrid infrastructure | Medium | Low |
| OpenShift | Enterprise compliance | Medium | High |
| KubeSphere | Open-source power users | Medium | High |
| Rancher | Multi-cluster ops | Low | Medium |
| Loft | Self-service for internal teams | Medium | Medium |

## Wrapping up

Spectro Cloud gave teams control. But in 2026, control isn't enough — not when you're shipping fast, scaling fast, and your developers are context-switching between YAML, dashboards, and custom tooling just to deploy a single service.

The best platforms today don’t just manage Kubernetes. They **make it invisible**.

If your team is tired of spending cycles on infra glue, if you're stitching together CI/CD pipelines, provisioning databases manually, or managing secrets through Slack threads, it’s time for a reset.

[**Northflank**](https://northflank.com/) isn’t just a replacement for Spectro Cloud. It's a rethink. A platform designed to let you deploy faster, scale safely, and never touch YAML unless you want to.

Built for speed. Backed by infrastructure. Trusted by teams that ship.

[**Start building on Northflank and stop building your platform from scratch.**](https://app.northflank.com/signup)]]>
  </content:encoded>
</item><item>
  <title>How to install PyTorch and set it up for production</title>
  <link>https://northflank.com/blog/how-to-install-pytorch-for-production</link>
  <pubDate>2025-06-08T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[PyTorch is the toolkit you’ll use to actually build and train your models, but knowing how to install PyTorch correctly is the first step to getting anything working.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/container_orchestration_blog_post_1_92240c4c7e.png" alt="How to install PyTorch and set it up for production" />If you're new to machine learning, the first thing to know is this: PyTorch is the toolkit you’ll use to actually build and train your models, but knowing how to install PyTorch correctly is the first step to getting anything working.

It gives you the ability to do math on big chunks of data (called tensors), use your GPU to speed things up, and write neural networks in Python that can learn from images, text, or just about anything else.

## What is PyTorch, really?

[PyTorch](https://northflank.com/blog/what-is-pytorch) is an open-source machine learning framework built by Meta’s AI Research lab. Think of it like NumPy on steroids: it handles the math you need for deep learning, but it’s GPU-accelerated and supports automatic differentiation (which is how models learn).

Whether you're training a model to recognize cats in photos or building a recommendation engine, PyTorch is the engine under the hood.

We go in more detail in this guide.

Learning how to install PyTorch properly can save you hours of debugging later. Whether you're using a CPU-only machine or a multi-GPU server, the installation process matters.

This guide covers both basic and advanced installs, and gives you the tools to go from local development to GPU-backed production deployment with platforms like Northflank.

## How to install PyTorch

### 1. How to Install PyTorch locally (step-by-step for beginners)

Let’s start simple. If you're a beginner, your best bet is to install PyTorch on your local machine and make sure it runs correctly before worrying about things like Docker or deployment.

### Step 1: Install Python

If you don’t already have Python installed, download version 3.8 or later from [python.org](https://www.python.org/downloads/). Install it like any other app. Make sure to tick the checkbox that says “Add Python to PATH” during installation.

PyTorch requires Python 3.9 or later. Most developers prefer using package managers:

**macOS (Homebrew):**

```bash
brew install python@3.12
```

**Ubuntu/Debian:**

```bash
sudo apt update
sudo apt install python3.12 python3.12-pip
```

**Windows (via Chocolatey):**

```bash
choco install python312
```

**Or download directly:**
If you prefer the official installer, download Python 3.12+ from [python.org](https://python.org/). On Windows, make sure to check "Add Python to PATH" during installation.

**Verify installation:**

```bash
python3 --version# Should show 3.9+
pip3 --version
```

### Step 2: Create a virtual environment

Virtual environments are containers that isolate your Python projects. This avoids conflicts between different libraries. Run the following in your terminal:

```bash
python -m venv myenv
```

Then activate the environment:

- **On macOS/Linux**: `source myenv/bin/activate`
- **On Windows**: `myenv\Scripts\activate`

Once you activate it, your shell prompt should show `(myenv)`.

### Step 3: Install PyTorch

Use PyTorch's official install selector to generate the exact command for your platform.

**CPU-only version:**

```bash
pip install torch torchvision torchaudio
```

**GPU support:**

For NVIDIA GPUs with CUDA 11.8:

```bash
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

```

For Apple Silicon Macs (M1/M2/M3 with MPS acceleration):

```bash
pip install torch torchvision torchaudio
# MPS support is included by default - no special installation needed
```

**Check your system:**

- **NVIDIA GPU**: Run `nvidia-smi` and look at the "CUDA Version" at the top
- **Apple Silicon**: MPS is automatically available on M1/M2/M3 Macs running macOS 12.3+

**Verify GPU acceleration:**

```python
import torch
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"MPS available: {torch.backends.mps.is_available()}")
```

### Step 4: Test your install

Create a Python file or open a Python shell and run:

```python
import torch
print("CUDA available:", torch.cuda.is_available())
if torch.cuda.is_available():
    print("Device:", torch.cuda.get_device_name(0))
```

If everything’s working, you’ll see your GPU name. If not, your setup is likely falling back to CPU.

### 2. What is CUDA (and why it matters)?

CUDA is NVIDIA’s toolkit that allows software like PyTorch to communicate with your GPU. If you want to accelerate model training or inference, you need CUDA. PyTorch comes with CUDA pre-packaged in its installation wheels, **but only if you use the right command.**

If you mismatch your CUDA version with your GPU drivers, your model will run on CPU even if you have a GPU. That’s why `nvidia-smi` is your best friend, it tells you what your GPU supports. Then you match that with the PyTorch install command.

If you're using AMD hardware, things get more complicated. You’ll need the ROCm (Radeon Open Compute) version of PyTorch. Fewer prebuilt packages are available, and compatibility depends on your GPU model and OS. For most beginners: if you’re using NVIDIA, stick to CUDA.

Now that you know how to install PyTorch locally, let’s look at how to containerize it using Docker so it’s portable and production-ready.

### 3. Docker and PyTorch for production

Docker lets you build your PyTorch project once and run it anywhere, with all dependencies pre-installed. This is critical for production or team projects.

Start from a base image that includes CUDA, cuDNN, and PyTorch:

```docker
FROM pytorch/pytorch:2.6.0-cuda11.8-cudnn9-devel
WORKDIR /app
COPY . .
RUN pip install -r requirements.txt
CMD ["python", "inference.py"]
```

This image includes:

- PyTorch 2.6
- CUDA 11.8
- cuDNN 9

These versions must match the capabilities of your target GPU. For example, if you’re deploying on an NVIDIA A100 or H100, CUDA 11.8+ is required. 

You can build and test the image locally with:

```bash
docker build -t my-pytorch-app .
docker run --gpus all my-pytorch-app
```

### 4. Deploying to Northflank (step-by-step)

If your model works locally in Docker, deploying to Northflank is straightforward. It abstracts all the GPU provisioning, networking, and monitoring.

First, push your code to GitHub. Then:

- Log into your Northflank account.
- Create a new service.
- Connect your GitHub repo.
- Select the Dockerfile path.
- Enable GPU and pick your target GPU (e.g. H100, A100).
- Set environment variables and any required secrets (like Hugging Face tokens).
- Configure autoscaling (min/max instances, memory, CPU).
- Deploy.

<div>  
  <center>  
    <a href="https://app.northflank.com/signup">  
<Button variant={["large", "gradient"]}>Deploy your PyTorch model here</Button>  
    </a>  
  </center>  
</div>

Once deployed, you get:

- Live logs and metrics
- GPU and memory usage graphs
- Rollbacks on failed deploys
- Volume mounting (e.g. to cache model files)

Need to serve models behind an API? Wrap your PyTorch inference in FastAPI or Flask, and Northflank can expose it over HTTPS instantly.

<InfoBox className='BodyStyle'>

## [Learn more about GPU workloads on Northflank](https://northflank.com/docs/v1/application/gpu-workloads/gpus-on-northflank)

</InfoBox>

## When things go wrong: debugging common issues

PyTorch not seeing your GPU? First, run this inside your environment:

```python
import torch
print(torch.cuda.is_available())
print(torch.version.cuda)
```

If `cuda.is_available()` is False, it’s likely an issue with how you installed PyTorch, wrong CUDA version, missing GPU drivers, or a CPU-only wheel.

If your container deploys but silently uses CPU:

- Your base image may be CPU-only.
- You may not have enabled GPU on Northflank.
- You forgot to run `.to(device)` in your model code.

If your model crashes during inference with memory errors:

- Increase memory or ephemeral storage on Northflank.
- Mount persistent volumes for models and temp data.
- Split large batches into smaller chunks.

If you're not sure what's going wrong, start small. Run a minimal model, test GPU access explicitly, and incrementally build up.

## Final thoughts

A lot of people think “how to install PyTorch” ends at `pip install`. but if you’re running on GPUs or deploying to production, it’s only the beginning. You need to:

- Match CUDA versions across drivers, wheels, and containers
- Use the right base images
- Validate GPU access
- Prepare for production (persistent storage, scaling, secrets)

Northflank removes a huge amount of overhead. No YAML, no provisioning scripts, no K8s ops. You bring the model, Northflank handles the infra.

[Get started with Northflank →](https://northflank.com/)]]>
  </content:encoded>
</item><item>
  <title>Rancher vs OpenShift: Which platform fits your Kubernetes workflows best?</title>
  <link>https://northflank.com/blog/rancher-vs-openshift</link>
  <pubDate>2025-06-05T17:58:00.000Z</pubDate>
  <description>
    <![CDATA[Comparing Rancher and OpenShift for Kubernetes management? Here’s a detailed, technical breakdown to help DevOps leaders, platform engineers, and developers make an informed choice.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/rancher_vs_openshift_blog_post_fd2d4039b5.png" alt="Rancher vs OpenShift: Which platform fits your Kubernetes workflows best?" />You’re here because you’re figuring out how to manage your Kubernetes workloads without adding unnecessary overhead. Rancher and OpenShift both promise to simplify that, but they take different paths to do it.

I’m going to help you see those differences clearly so you can find the one that matches how your team builds and deploys software.

Let’s break down what each platform does well, where they overlap, and how you can map them to your Kubernetes workflows.

<InfoBox className='BodyStyle'>

    ### Quick look: Rancher vs OpenShift vs Northflank

    Here’s a quick overview of what each platform focuses on:

    1. [**Rancher**](https://rancher.com/) – Manages multiple Kubernetes clusters and provides an open-source orchestration layer that fits a wide range of deployment setups.
    2. [**OpenShift**](https://www.redhat.com/en/technologies/cloud-computing/openshift) – Enterprise-grade Kubernetes distribution with built-in developer tools, security, and compliance features.
    3. [**Northflank**](https://northflank.com/) – A platform built on Kubernetes that combines CI/CD, job runners, databases, and optional [Bring Your Own Cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) for teams who want everything managed in one workflow.

</InfoBox>

### TL;DR: A quick technical comparison for your Kubernetes workflows

If you want to skip the details and see the key differences at a glance, this table has you covered. It’ll help you figure out how each platform approaches cluster management, developer tools, security, and how you’d run your workloads in practice.

| **Feature** | **Rancher** | **OpenShift** | [**Northflank**](https://northflank.com/) |
| --- | --- | --- | --- |
| **Deployment model** | Manages any CNCF-compliant Kubernetes cluster, with optional lightweight cluster setups using RKE or K3s | Enterprise Kubernetes distribution, fully integrated stack with Red Hat Enterprise Linux CoreOS | Managed Kubernetes-based platform with built-in CI/CD, jobs, and database workflows |
| **Cluster management** | Centralized UI for managing multiple clusters across any cloud or on-premises; flexible with BYO clusters | Strictly integrated cluster lifecycle management; built-in support for automated updates and security | Abstracts cluster lifecycle, no manual cluster setup or upgrades to maintain |
| **CI/CD & developer tools** | Connects with external CI/CD tools like Jenkins, GitLab CI; no built-in pipelines | Includes OpenShift Pipelines (Tekton-based), developer-friendly web console, and built-in build tools | Built-in CI/CD, ephemeral preview environments, buildpacks for container image creation |
| **Security & governance** | Flexible RBAC, SSO integration (Keycloak, LDAP), and network policies; open-source foundation for customization | Enterprise security features, integrated image scanning, and compliance-focused governance | Managed secrets, RBAC, and workload isolation; secure by default and customizable |
| **Pricing/licensing** | Fully open-source, no licensing cost, supported by SUSE with optional paid support | Enterprise subscription (with Red Hat support), also has community OKD version | [Usage-based pricing](https://northflank.com/pricing); optional [BYOC](https://northflank.com/features/bring-your-own-cloud) so you can run on your cloud without vendor lock-in |
| **When to use** | Ideal for managing multiple Kubernetes clusters across environments and when you want full control of underlying infrastructure | Best for enterprises needing built-in CI/CD, security compliance, and a consistent dev experience | Suited for teams who want to deploy apps on their own cloud without maintaining the underlying Kubernetes setup |

## What is Rancher?

Let’s break down what Rancher is so you can see how it might fit into your stack. Rancher is an open-source Kubernetes management platform.

See what I mean by “open-source Kubernetes management platform”:

How do you currently manage your clusters? If you’re managing each cluster separately, Rancher steps in to replace that by giving you a single control plane that covers everything, no matter if your clusters are running in the cloud, on bare metal, or at the edge.

So that’s what I mean.

Now, there are a few things that stand out about Rancher. Let’s see some of them:

- **Open-source foundation**: This means you’re not locked into any vendor, and you can customize how you use it.
- **Supports K3s**: K3s is a lightweight Kubernetes distribution that’s great for edge and smaller environments.
- **Multi-cluster management**: You can apply consistent policies, security, and updates across all your clusters from one place.
- **Flexible integrations**: Rancher works well with external tools like Jenkins for CI/CD, Prometheus for monitoring, and Vault for secrets management.
- **Centralized governance**: RBAC, SSO, and security controls are all managed in one spot.

So, who are typically the users of Rancher?

1. Platform engineers who need to run multiple clusters and want a flexible setup without vendor lock-in.
2. DevOps teams who want a single dashboard to manage deployments, updates, and scaling.
3. Companies that need to manage workloads across AWS, GCP, on-premises, and even edge environments.

Take a look at the diagram below to see how Rancher sits on top of your clusters and keeps everything connected:

![Diagram showing Rancher as a control plane managing clusters in AWS, GCP, on-premises, and edge, with integrations for CI/CD and observability](https://assets.northflank.com/what_is_rancher_1b79ebdb26.png)*Rancher architecture showing its central control plane managing multiple Kubernetes clusters across AWS, GCP, on-premises, and edge environments, with CI/CD and observability integrations.*

*See [7 Best Rancher alternatives in 2025](https://northflank.com/blog/rancher-alternatives)*

## What is OpenShift?

Now what about OpenShift? You’ve most likely heard that it’s Red Hat’s Kubernetes platform, but let’s get into what that means for you.

OpenShift is an enterprise-grade Kubernetes distribution that does more than run Kubernetes. It bundles together the security, developer experience, and lifecycle management that you’d otherwise have to build yourself if you were working directly with upstream Kubernetes. (If you’re curious how OpenShift stacks up against Kubernetes, check out [this detailed comparison](https://northflank.com/blog/openshift-vs-kubernetes)).

Let me break this down for you:

- **Integrated CI/CD**: You get OpenShift Pipelines, which is Tekton-based, to run automated build and deploy jobs directly in the platform.
- **Security and compliance**: Built-in image scanning to catch vulnerabilities before they reach production, plus policy controls that enforce security across all workloads.
- **Developer-focused tooling**: There’s a web console that lets you deploy, scale, and monitor your applications without touching `kubectl` every time.
- **Managed cluster lifecycle**: OpenShift handles updates and patching for you. You don’t have to write scripts or workflows to upgrade your clusters, OpenShift takes care of it.

You’ll find OpenShift in places where teams want a complete platform, not just Kubernetes. DevOps leaders lean on it for security and compliance. Platform engineers use it to avoid building their own CI/CD and governance tools. And developers like how it makes it easier to push code into production.

Take a look at the diagram below to see how OpenShift pulls these pieces together:

![Diagram showing OpenShift as a central control plane with built-in CI/CD, security, and developer console, managing multiple Kubernetes clusters and workloads](https://assets.northflank.com/what_is_openshift_0c0bf2aa85.png)*OpenShift architecture showing its built-in CI/CD, security, and developer console, managing clusters and workloads across environments*

If you’re also comparing OpenShift to other Kubernetes-based platforms, this [guide to OpenShift alternatives](https://northflank.com/blog/best-open-shift-alternatives-finding-the-right-kubernetes-platform) might be helpful for you too.

## So, what are the differences you need to know between Rancher and OpenShift?

Now that you’ve seen how Rancher and OpenShift fit into Kubernetes, let’s walk through the differences that will define which one fits your team best.

### 1. Installation approach

Rancher is installed on top of any CNCF-compliant Kubernetes cluster you already run, or it can set up lightweight clusters using RKE or K3s. You decide where and how to deploy it. OpenShift, on the other hand, is a full Kubernetes distribution that replaces vanilla Kubernetes and includes its own installer, configuration, and lifecycle tools.

### 2. Cluster flexibility

Rancher is designed to manage multiple clusters across different environments, including AWS, GCP, on-premises, and edge, and allows you to use any upstream Kubernetes. OpenShift focuses on delivering a consistent Kubernetes environment, typically within a single cloud or on-premises data center, where everything is integrated and controlled by Red Hat’s tooling.

### 3. Built-in features for CI/CD and developers

In Rancher, you’ll integrate with external CI/CD tools like Jenkins or GitLab CI. Rancher doesn’t come with a built-in pipeline system. OpenShift includes OpenShift Pipelines based on Tekton, giving you a native CI/CD experience. It also has a web console for developers to deploy, monitor, and scale apps directly.

### 4. Security controls

Rancher gives you flexibility to connect external tools for security and to customize policies for your clusters. It has RBAC, SSO, and network policies out of the box. OpenShift goes deeper with built-in vulnerability scanning, policy enforcement, and compliance features that are fully integrated and ready to use.

### 5. Licensing and support

Rancher is fully open-source. You can run it without a licensing cost and get commercial support from SUSE if needed. OpenShift has an open-source community version called OKD, but its enterprise version requires a subscription with Red Hat for full support, security updates, and access to Red Hat-certified tooling.

## Where Rancher is often chosen

So after looking at those differences, you might be wondering: when does Rancher make the most sense? Let me walk you through where I see teams choosing Rancher.

You’ll see Rancher in environments where flexibility and open-source control matter more than having a single vendor’s stack. It’s great if you’re running clusters in multiple places, maybe you’ve got some on AWS, some on GCP, and others on bare metal. Rancher makes it easier to manage all of them in one place without dictating how you set up or run your clusters.

Platform engineers like Rancher because it doesn’t tie them to a specific toolchain. You can bring your own CI/CD, secrets management, and observability tools without fighting an opinionated platform. And because it’s fully open-source, there’s no vendor lock-in; if you ever want to swap out Rancher for something else or customize how it works, you can.

For DevOps teams, it’s also helpful because Rancher centralizes your security and access control across all your clusters. You get one control plane for RBAC, SSO, and policies, no matter where your clusters live.

## Where OpenShift is often preferred

Now let’s switch gears and talk about when OpenShift usually stands out as the better fit.

If you’re working in an environment that needs strict governance, security, and built-in compliance features, OpenShift tends to be the go-to. It’s designed for teams that want everything in one place, Kubernetes plus integrated CI/CD, policy enforcement, and lifecycle management, all backed by Red Hat’s enterprise support.

You’ll find OpenShift in large organizations that have to meet regulatory requirements or need to keep everything under tight control. For example, teams in finance, healthcare, and government projects often use OpenShift because it handles security certifications and compliance right out of the box.

From a DevOps perspective, OpenShift’s built-in pipelines and developer tools can speed up getting applications from code to production. Platform engineers like that they don’t have to build a separate pipeline system or integrate a patchwork of third-party tools; it’s already there.

If you’re thinking about running workloads that need a consistent environment with minimal manual setup, OpenShift might be what you’re looking for.

## Okay, let’s talk about pricing and open-source status

We’ve talked about features and use cases, so now let’s cover something that’s always top of mind: what this means for your budget and how open these platforms really are.

Rancher is fully open-source. You can download and use it at no cost, and you’re not locked into any licensing agreements. If you want, you can pay for commercial support from SUSE (who maintain Rancher), but the core platform itself is free to use. That’s why you’ll see Rancher in environments where teams need flexibility and want to avoid vendor lock-in.

OpenShift, on the other hand, is a bit more nuanced. There’s an open-source version called **OKD** that’s free to use and has the same core technology as OpenShift. But if you’re looking for Red Hat’s support, security patches, and access to certified container images, you’re talking about the paid version of OpenShift (OpenShift Container Platform)**,** which requires a subscription. This is where enterprises often lean toward OpenShift because they’re paying not just for the software, but for a tested and supported platform that fits into their compliance requirements.

<InfoBox className='BodyStyle'>
  💡 **Looking for a flexible, usage-based Kubernetes platform?**

  Platforms like [**Northflank**](https://northflank.com/) provide a modern approach to usage-based billing for Kubernetes workloads. You can start for free or use their pay-as-you-go plans, and they support BYOC (Bring Your Own Cloud), so you can run workloads on your own infrastructure or Northflank’s cloud.

  From the transparent pricing page, you’ll see how you can scale resources (like vCPUs, memory, and storage) on demand, and get real-time estimates using the pricing calculator. Northflank also has enterprise-grade support for those who need governance and advanced features — like audit logging and custom SLAs — in regulated environments.
</InfoBox>


So if you’re weighing flexibility and zero-cost adoption, Rancher’s open-source approach might fit you better. If you’re looking for a platform with enterprise-level support and built-in governance, then you can go for OpenShift’s paid version.

## So… how do you decide what’s best for your team?

Alright, so we’ve walked through how Rancher and OpenShift handle clusters, security, and pricing. Let’s talk about how you can decide which one makes the most sense for your setup.

If your team has the internal skillset to manage Kubernetes and you want maximum flexibility to customize your clusters, Rancher is often the better choice. It’s open-source and gives you control over how you integrate your existing tools. Platform engineers who need to manage multiple clusters across different environments, like AWS, GCP, and on-premises, will find Rancher’s multi-cluster approach fits well.

On the other hand, if your team is focused on security, compliance, and a consistent developer experience without having to build everything from scratch, OpenShift is probably the right move. It’s built for enterprises that want a full-stack solution, with built-in CI/CD, security policies, and lifecycle management ready to go. It’s also helpful if you’re working in regulated industries or need to meet specific certifications.

The choice comes down to how much control and customization you need, how much your team wants to maintain themselves, and what kind of environment you’re running in. Both Rancher and OpenShift can work well; it just depends on what you’re building, who will maintain it, and how you want to run your workloads.

<InfoBox className='BodyStyle'>

💡 **Thinking about managed platforms?**  
And if you’re looking for a way to run Kubernetes workloads without maintaining the platform yourself, platforms like [Northflank](https://northflank.com/) can be a smart alternative. Northflank abstracts away the underlying cluster management and adds built-in CI/CD, job runners, and databases so your team can focus on delivering software.

</InfoBox>

## Why some teams choose Northflank for Kubernetes platforms

You might be running into challenges with managing Kubernetes clusters across multiple environments. Rancher and OpenShift are capable tools, but they can become complex to configure and maintain, especially if your team has limited bandwidth or wants to reduce operational burden. [Northflank](https://northflank.com/) provides an alternative path: a managed Kubernetes platform that lets you focus on building software instead of managing infrastructure.

Let’s see how.

### 1. Integrated CI/CD pipelines

Northflank comes with built-in CI/CD pipelines that integrate with your version control systems. When you push a change, Northflank automatically handles everything from container builds to deployments, health checks, and autoscaling. This saves you from orchestrating separate CI/CD tools and manually managing pipeline workflows.

See how Northflank’s CI/CD pipeline automatically tracks commits, deployment logs, and resource usage:

![Northflank Express App dashboard showing CI/CD pipeline logs, deployment status, and active containers](https://assets.northflank.com/combined_service_overview_b920557d85.webp)*Northflank’s built-in CI/CD pipeline overview – from commits to deployments, all in one place.*

[*Learn more about how Northflank CI/CD works*](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank)

### 2. Built-in databases and job runners

If you’re tired of managing database provisioning and background jobs separately, Northflank includes these as first-class features. You can deploy popular databases, like PostgreSQL, MySQL, and MongoDB, directly within your workloads.

See how Northflank makes database provisioning seamless:

![Northflank database provisioning UI with options for Redis, MongoDB, MySQL, and more](https://assets.northflank.com/create_addon_ca6e55b5e4.webp)*Provision databases directly within your project workflows.*

Need cron jobs or background workers? Northflank lets you run those too, with built-in observability and logs.

See how Northflank handles job runners and cron tasks:

![Northflank job runner interface displaying job runs, logs, and triggers](https://assets.northflank.com/jobs_northflank_5a9f2275ef.webp)*Run background jobs and cron tasks with built-in logs and observability.*

*Check how Northflank handles [databases](https://northflank.com/docs/v1/application/databases-and-persistence/deploy-a-database) and [jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs)*

### 3. Advanced observability and monitoring

Northflank gives you detailed logging, metrics, and health checks for every workload. You can view logs in real time, monitor resource usage, and set up alerts for potential issues. This built-in observability means you don’t need to wire up third-party monitoring tools unless you want to.

See live container status and resource usage:

![Northflank database container status dashboard showing real-time container health and resource usage](https://assets.northflank.com/addon_containers_c93db690b9.webp)*Northflank provides real-time observability, with detailed logging and container status for your workloads*

[See how database observability and monitoring works in action](https://northflank.com/docs/v1/application/databases-and-persistence/database-observability-and-monitoring)

### 4. Flexible deployment options with BYOC

Many teams want to deploy on their own cloud for compliance, security, or control reasons. Northflank supports [Bring Your Own Cloud (BYOC)](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) across AWS, GCP, Azure, on-premises, and even bare metal. You get the full Northflank experience in your cloud of choice, with the same deployment workflows and control you’d have if you ran it in Northflank’s managed cloud.

Bring your workloads to your cloud of choice:

![Northflank BYOC overview with cloud providers AWS, GCP, Azure, and more highlighted](https://assets.northflank.com/bring_your_own_cloud_b3a0556452.avif)*Northflank’s Bring Your Own Cloud (BYOC) feature lets you deploy to AWS, GCP, Azure, on-premises, or bare metal with the same consistent experience.*

[See how BYOC works on Northflank](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes)

### 5. Streamlined developer experience

Northflank simplifies Kubernetes so you don’t have to be a platform engineer to get the benefits. The interface abstracts away the complex Kubernetes API interactions, giving you straightforward dashboards and CLI commands to deploy, scale, and observe workloads. This helps your team ship faster and focus on building products, not managing clusters.

See how Northflank simplifies the developer experience:

![Northflank self-service dashboard showing deployment and monitoring features](https://assets.northflank.com/northflank_self_service_416eb918be.png)*Northflank's dashboard abstracts away complex Kubernetes details, making it easier for developers to deploy and manage workloads quickly.*

[Read more about Northflank’s developer experience](https://northflank.com/use-cases/self-service-developer-experience-for-kubernetes)

## FAQ: Common questions asked by teams about Rancher vs OpenShift

When teams start comparing Rancher and OpenShift, they often have questions about how these platforms differ, how they work with Kubernetes, and what alternatives exist. So here’s a technical rundown to help you navigate these differences and figure out what might work best for your team.

### 1. Is OpenShift the same as Rancher?

No, OpenShift and Rancher are not the same. OpenShift is a Kubernetes distribution, Red Hat’s enterprise-grade Kubernetes platform that includes additional developer tools and security features. Rancher is a multi-cluster management platform. It doesn’t replace Kubernetes; it provides a management layer for any Kubernetes cluster, whether it’s OpenShift, vanilla Kubernetes, or something else.

### 2. What is the difference between Kubernetes and Rancher?

Kubernetes is the core container orchestration platform that defines how your containers run and scale. Rancher sits on top of Kubernetes to help you manage multiple clusters, centralize access controls, and provide developers with self-service environments. Think of Rancher as a control plane that simplifies working with Kubernetes at scale.

### 3. What is the difference between OpenShift and Tanzu?

Both OpenShift and Tanzu provide enterprise Kubernetes experiences, but they differ in approach and ecosystem. OpenShift includes Kubernetes and a developer-friendly platform with built-in tools like a CI/CD pipeline, service mesh, and strict security defaults. Tanzu includes Tanzu Kubernetes Grid, Tanzu Mission Control, and integrations with VMware’s infrastructure. If you’re already in the VMware ecosystem, Tanzu can be a better fit, while OpenShift’s Red Hat roots appeal to teams already using RHEL.

### 4. Is OpenShift better than Kubernetes?

OpenShift is not “better” than Kubernetes; it extends Kubernetes with enterprise features, strict security, and developer productivity tools. It’s great if you need those built-in tools and want a supported, integrated stack. But if you’re looking for more flexibility or want to keep things lightweight, plain Kubernetes or another distribution might be a better choice. You can read this article on “[OpenShift vs Kubernetes](https://northflank.com/blog/openshift-vs-kubernetes)".

### 5. Is Rancher free to use?

Yes, Rancher is open source and free to use on your own infrastructure. You can use it to manage as many Kubernetes clusters as you want. Rancher’s commercial offering includes support and managed services for enterprises, but the core platform is free.

### 6. Does Rancher use Kubernetes?

Yes. Rancher doesn’t replace Kubernetes; it manages it. You can use Rancher to deploy new clusters or bring in existing clusters (including OpenShift clusters). It layers on observability, governance, and automation to make Kubernetes easier to work with.

### 7. What is the alternative to OpenShift?

Alternatives to OpenShift include managed Kubernetes services like Amazon EKS, Azure AKS, and Google GKE, as well as other enterprise Kubernetes platforms like VMware Tanzu. If you want to avoid managing Kubernetes altogether, platforms like [Northflank](https://northflank.com/) provide a managed developer experience that abstracts away the cluster management, so you can focus on deploying workloads and scaling applications. You can read this article on “[Best OpenShift alternatives: finding the right Kubernetes platform](https://northflank.com/blog/best-open-shift-alternatives-finding-the-right-kubernetes-platform)”.

## Now it’s time to make your decision

I’ve walked you through the differences: OpenShift’s built-in developer tools and secure defaults, Rancher’s multi-cluster management focus, and Northflank’s self-service developer experience. You know what each platform does, how they work, and where they fit.

You know your team’s needs best. If you’re considering a developer-first Kubernetes experience that doesn’t require managing clusters, you can [check out how Northflank can support your team’s work](https://app.northflank.com/signup).]]>
  </content:encoded>
</item><item>
  <title>Best managed Kubernetes platforms in 2026: What to choose and why It matters.</title>
  <link>https://northflank.com/blog/best-managed-kubernetes-platforms</link>
  <pubDate>2025-06-04T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Discover the best managed Kubernetes platforms in 2026, from Northflank to GKE and OpenShift. Compare features, pricing, and scalability to choose the right Kubernetes solution for your team or enterprise.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/best_managed_kubernetes_platforms_4ee188d440.png" alt="Best managed Kubernetes platforms in 2026: What to choose and why It matters." />If you’ve worked with containers, you already know the power they bring. Fast deployments. Consistent environments. Scalability on demand. But as teams grow and architectures get more complex, raw Kubernetes quickly becomes... not enough. That’s where managed Kubernetes platforms come in.

A great **managed Kubernetes platform** doesn’t just help you run clusters. It simplifies your workflows, adds automation, boosts visibility, and lets you focus on shipping software, not managing infrastructure.

In this guide, we’ll break down what a Kubernetes platform actually is (and what it isn’t), why it matters more than ever in 2026, and how to choose the right one for your team. We'll look at industry leaders like Rancher and OpenShift, and explore how platforms like [**Northflank**](https://northflank.com/) are rethinking the Kubernetes experience from the ground up.

## TL;DR: Best managed Kubernetes platforms in 2026

Looking for the best **managed Kubernetes platform** for your team? Here’s a quick roundup of the top contenders in 2026:

<InfoBox className='BodyStyle'>

- [**Northflank**](https://northflank.com/) – A developer-first Kubernetes platform with built-in CI/CD, databases, preview environments, and Git-based workflows. Abstracts away Kubernetes complexity without sacrificing control.
- **Amazon EKS** – Fully managed Kubernetes service tightly integrated with AWS.
- **Google Kubernetes Engine (GKE)** – Battle-tested platform with strong autoscaling and AI/ML integrations.
- **Azure AKS** – Microsoft’s managed Kubernetes with solid enterprise support and Azure-native features.
- **Red Hat OpenShift** – Enterprise-grade platform focused on compliance, security, and hybrid deployments.
- **Platform9** – SaaS-managed Kubernetes for on-prem, hybrid, and edge environments.
- **Rancher** – Open-source, multi-cluster Kubernetes management for hybrid cloud strategies.
- **VMware Tanzu** – Kubernetes built into VMware’s ecosystem for large enterprises.

</InfoBox>

Keep reading to dive deeper into each platform, compare features, and figure out which one fits your use case.

## What is a managed Kubernetes platform?

A **managed Kubernetes platform** is more than just Kubernetes installed on some servers. It’s an opinionated stack that integrates Kubernetes with networking, security, CI/CD, monitoring, logging, and developer tooling. Think of it as a complete operating system for running cloud-native applications.

Kubernetes itself is powerful, but it was never designed to be developer-friendly. It’s an orchestration engine, not a product. It assumes you have the time, expertise, and tools to stitch everything together. In that sense, Kubernetes is a foundation — a **platform for platforms** — not something most teams should be using raw.

That’s where managed Kubernetes platforms come in. They package Kubernetes with everything you actually need to ship software: workflows, automation, observability, security, and more. Done right, a platform removes the complexity of managing infrastructure and lets you focus on building.

## What actually makes a great managed Kubernetes platform?

Not all managed Kubernetes platforms are created equal. Most of them promise flexibility, power, and scalability — but leave you to wire everything together yourself. A strong platform shouldn’t just host your containers; it should remove friction from your entire workflow.

Here’s what actually matters when you’re evaluating managed Kubernetes platforms in 2026:

- **Fast, intuitive developer experience**
    
    Can your team go from Git push to live deployment without writing YAML or waiting on infra tickets? The best platforms prioritize **developer velocity** — with clean UIs, powerful APIs, GitOps support, and minimal ceremony.
    
    [*(This is where Northflank really stands out.)*](https://northflank.com/)
    
- **Built-in automation and CI/CD**
    
    Look for native CI pipelines, automatic preview environments, and seamless promotion between dev/staging/prod. You shouldn’t have to duct-tape half a dozen tools together just to ship a change.
    
- **Scalability without the stress**
    
    Whether you're running a single app or a platform with hundreds of services, your infrastructure should scale with you — automatically, efficiently, and without surprises.
    
- **Security that doesn’t slow you down**
    
    RBAC, secrets management, network isolation, policy enforcement — all should come baked in. Bonus points if the platform gives you sane defaults out of the box instead of a security checklist you have to manually implement.
    
- **Observability, not opacity**
    
    Logs, metrics, traces — right where you need them. In one place. Not hidden behind a dozen dashboards or paywalled integrations.
    
- **Smart pricing and resource usage**
    
    You want predictable billing, not cloud cost horror stories. Look for platforms that help you use resources efficiently, not just burn compute.
    
- **Real support, real docs**
    
    Platforms should be self-service when you want it, and human when you need it. Great documentation matters. So does knowing someone has your back when things break.
    

## Top managed Kubernetes platforms in 2026

### 1. Northflank **– Kubernetes without the complexity**

[Northflank](https://northflank.com/) gives you all the power of Kubernetes—container orchestration, service discovery, and autoscaling—but wraps it in a developer-first experience.

No need to manage YAML files by hand. With Git-integrated workflows, built-in CI, automatic SSL, and managed databases, [Northflank](https://northflank.com/) helps you go from code to running service in minutes. It also handles the heavy lifting like horizontal scaling, persistent storage, and background workers with a clean, intuitive UI and APIs.

![](https://assets.northflank.com/image_93_2b254840ee.png)

**Key features:**

- Kubernetes-powered, full-stack platform
- Deploy containers, databases, and scheduled jobs
- [Bring your own cloud (AWS, GCP, Azure, etc.)](https://northflank.com/features/bring-your-own-cloud)
- CI/CD integration, real-time logs, with a developer-friendly and consistent experience across UI, CLI, API, and GitOps
- GPU support for AI workloads
- Automatic preview environments and seamless promotion to dev, staging, and production

**Best for:**

- Dev teams building APIs, microservices, and containerized web apps
- SaaS products needing multi-service architectures
- Teams looking for a fast, clean alternative to older, more rigid platforms like Rancher

**Potential drawbacks:**

- Highly experienced DevOps teams might find it restrictive compared to directly managing raw Kubernetes clusters. It’s a fine balance between ease of use, flexibility, and customization; that line differs for every organization.

*See how [Weights company uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### **2. Amazon EKS, Google GKE & Azure AKS**

For teams that prefer **fully managed Kubernetes services**, [Amazon Elastic Kubernetes Service (EKS)](https://aws.amazon.com/eks/), [Google Kubernetes Engine](https://cloud.google.com/kubernetes-engine), and [**Azure Kubernetes Service (AKS)**](https://azure.microsoft.com/en-us/products/kubernetes-service) provide **scalable, managed Kubernetes clusters** with seamless integration into their respective cloud ecosystems.

![](https://assets.northflank.com/image_28_3d89f4ced2.png)

**Key features:**

- **Managed Kubernetes clusters** with automated updates and security patches.
- **Integrated cloud-native services** for storage, networking, and monitoring.
- Reduced **operational overhead** compared to self-managed Kubernetes.

**Potential drawbacks:**

- Deeply tied to their respective cloud ecosystems, making multi-cloud strategies more complex.
- Limited customization compared to self-managed Kubernetes.

### **3. OpenShift**

[OpenShift](https://www.redhat.com/en/technologies/cloud-computing/openshift) is a **comprehensive Kubernetes platform** developed by Red Hat. It’s designed for hybrid and multi-cloud deployments, offering strong security, compliance features, and enterprise support.

![](https://assets.northflank.com/image_2025_05_01_T201538_690_d20ad45e54.png)

**Key features:**

- Full-stack Kubernetes with integrated developer tools.
- Native CI/CD with Tekton and support for pipelines.
- Robust RBAC, policy enforcement, and compliance capabilities.
- Deep integration with Red Hat Linux and other enterprise tools.

**Potential drawbacks:**

- Complex to set up and maintain without dedicated platform teams.
- Can be resource-intensive and expensive.

[Read more on OpenShift](https://northflank.com/blog/best-open-shift-alternatives-finding-the-right-kubernetes-platform)

### **4. Platform9**

[Platform9](https://platform9.com/) is a **managed Kubernetes solution** designed for **on-premises, edge, and hybrid cloud environments**. Unlike fully cloud-hosted Kubernetes services, Platform9 allows organizations to run Kubernetes anywhere while benefiting from a **SaaS-based management model**.

![](https://assets.northflank.com/image_40_6281cf93cd.png)

**Key features:**

- **Fully managed Kubernetes** with a 99.9% uptime SLA.
- **Works across on-prem, hybrid, and edge environments**.
- **Zero-touch upgrades and automated operations**.
- **Open-source foundation** with no vendor lock-in.

**Potential drawbacks:**

- Smaller market share compared to OpenShift, which may affect long-term support.
- Reliance on a SaaS-based model may not be suitable for some enterprises.

### **5. Rancher**

[Rancher](https://www.rancher.com/) is an open-source **Kubernetes management platform** that simplifies deployment and administration, especially in **multi-cluster and multi-cloud environments**. It provides **centralized cluster management**, making it ideal for enterprises running Kubernetes across multiple providers.

![](https://assets.northflank.com/image_39_6cdc97389f.png)

**Key features:**

- **Easy cluster provisioning** and lifecycle management.
- **Built-in security, monitoring, and policy management**.
- Supports **on-premises, hybrid, and multi-cloud environments**.

**Potential drawbacks:**

- Requires some Kubernetes expertise to configure and manage.
- May not have as extensive enterprise support as OpenShift.

### **6. VMware Tanzu**

[VMware Tanzu](https://www.vmware.com/products/app-platform/tanzu) is an enterprise-grade **Kubernetes and application modernization** platform. It offers deep integration with **VMware’s existing infrastructure**, making it a strong choice for companies already using VMware products.

![](https://assets.northflank.com/image_36_0e06e1e049.png)

**Key features:**

- **Enterprise-level security and compliance** controls.
- Seamless **integration with VMware vSphere and other VMware tools**.
- **Multi-cloud Kubernetes support**, including on-premises and cloud deployments.

**Potential drawbacks:**

- Best suited for VMware environments, making it less ideal for teams using other infrastructure solutions.
- Licensing costs may be high for some organizations.

## How to choose the right Kubernetes platform

Not every team has a platform engineering org, and not everyone wants to spend time managing clusters. When choosing a Kubernetes platform, think about what your team actually needs:

- **Are you looking for speed or control?**
    
    Platforms like OpenShift and Rancher offer deep configurability, but require heavy setup and ongoing ops. If you just want to deploy services and scale fast, something simpler might be better.
    
- **How much should your developers need to learn?**
    
    The best platforms let devs ship code without needing to know how Kubernetes works. [Northflank](https://northflank.com/) was built with that in mind — powerful under the hood, but easy on the surface.
    
- **Do you want to be tied to one cloud?**
    
    GKE, EKS, and AKS work well within their clouds. But if you’re thinking multi-cloud, hybrid, or want more flexibility, tools like [Northflank](https://northflank.com/) give you portability without the lock-in.
    
- **Can it scale with your team?**
    
    Northflank is great for small teams and startups, but it is also built to handle serious scale. You can start fast and grow without re-platforming.
    

In short: pick the platform that meets your team where they are — and grows with you. If you want Kubernetes power without the Kubernetes complexity, [Northflank](https://northflank.com/) is one of the few platforms that actually delivers on that promise.

## The future of managed Kubernetes platforms

Kubernetes isn’t going anywhere — but the way we use it is changing fast.

The future isn't about managing clusters. It’s about **not needing to**.

We’re moving toward platforms that **abstract away infrastructure** entirely, while still giving teams the flexibility to scale, secure, and ship with confidence. Think:

- **No YAML. No manual provisioning. No waiting on DevOps.**
- **Security and observability baked in, not bolted on.**
- **Smart defaults, self-healing systems, and environments that just work.**

This is where Northflank is already operating — pushing Kubernetes into the background so teams can focus on building. As AI, edge, and multi-cloud architectures evolve, the winners in this space will be the platforms that **stay invisible** until they’re needed, and **intuitive** when they are.

Northflank isn’t trying to give you "a better dashboard for Kubernetes."

It’s building the platform you’ll wish you had when Kubernetes disappears behind the scenes entirely.

## Conclusion

Kubernetes changed how we build and run software. But managing Kubernetes? That’s still a burden for most teams.

That’s why managed Kubernetes platforms matter. They turn raw orchestration power into something usable, scalable, and developer-friendly. From enterprise-grade setups like OpenShift to cloud-native offerings like EKS and GKE, the ecosystem is full of options, but many still expect you to do too much heavy lifting.

[Northflank](https://northflank.com/) takes a different approach. It delivers the power of Kubernetes without the complexity, giving your team a fast, modern developer experience without sacrificing flexibility or control. From built-in CI/CD and preview environments to autoscaling, background workers, and managed databases, it’s everything you need to ship fast and scale with confidence.

If you're tired of fighting YAML, chasing logs, or waiting on infra tickets, it’s time to try something better. [Try Northflank](https://app.northflank.com/signup) — the fastest way to ship with Kubernetes, minus the Kubernetes pain.

<InfoBox className='BodyStyle'>

## FAQ

**What is a managed Kubernetes platform?**

A managed Kubernetes platform is a complete environment that integrates Kubernetes with CI/CD, observability, security, and developer tooling to streamline app deployment and operations.

**Is Kubernetes a platform or a tool?**

Kubernetes is a tool—an orchestration engine. A managed Kubernetes platform builds on top of it with additional tools, integrations, and automation.

**What are the top managed Kubernetes platforms in 2026?**

Northflank, GKE, EKS, AKS, OpenShift, Rancher, and Platform9.

**How secure is Kubernetes?**

It can be very secure, but it requires proper configuration. Managed platforms often come with best practices pre-applied.

**How does Kubernetes help with scalability?**

Kubernetes automatically scales applications based on CPU/memory usage or custom metrics. It can handle millions of requests with the right setup.

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  <title>Choosing the right enterprise Kubernetes platform in 2026</title>
  <link>https://northflank.com/blog/choosing-the-right-enterprise-kubernetes-platform</link>
  <pubDate>2025-06-04T07:00:00.000Z</pubDate>
  <description>
    <![CDATA[Kubernetes has won. It's the default control plane for container orchestration. But that doesn’t mean it’s usable out of the box, especially not for fast-moving teams who need scale, security, and a platform developers can actually understand.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/continuous_deployment_blog_post_411a09403a.png" alt="Choosing the right enterprise Kubernetes platform in 2026" />Kubernetes has won. It's the default control plane for container orchestration. But that doesn’t mean it’s usable out of the box, especially not for fast-moving teams who need scale, security, and a platform developers can actually understand.

Enter enterprise Kubernetes platforms.

They’re full-blown platforms that standardize deployment, improve security posture, manage multi-cluster sprawl, and cut operational overhead in half (if not more). They help teams do what Kubernetes never tried to: ship software faster, with fewer headaches.

But the landscape is crowded… and confusing.

Do you pick a toolkit like Rancher that lets you manage your own clusters? Go full enterprise with OpenShift? Stick with what your VMware reps tell you? Or ditch the pain entirely and go with a managed platform like Northflank?

If you’re short on time, skip to the TL;DR below.

| Platform | Best for | Self-hosted | Managed | CI/CD | Multi-cluster | Scale-to-zero | Stateful apps |
| --- | --- | --- | --- | --- | --- | --- | --- |
| **🥇 [Northflank](https://northflank.com/)** | Best all-around platform for dev velocity | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| **🥈 [OpenShift](https://www.redhat.com/en/technologies/cloud-computing/openshift)** | Large enterprises with compliance needs | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ |
| **🥉 [Rancher Prime](https://www.rancher.com/products/rancher-platform)** | Multi-cluster, multi-cloud operations | ✅ | ❌ | ⚠️ | ✅ | ❌ | ✅ |
| [**VMware Tanzu**](https://www.vmware.com/products/app-platform/tanzu) | VMware-based infra modernization | ✅ | ✅ | ⚠️ | ✅ | ❌ | ✅ |
| [**Spectro Cloud**](https://www.spectrocloud.com/) | Custom, edge-ready Kubernetes stacks | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ |
| [**Rafay Systems**](https://rafay.co/) | Automation and policy-first ops | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ |

<div>  
  <center>  
    <a href="https://app.northflank.com/signup">  
<Button variant={["large", "gradient"]}>…or just start using Northflank today</Button>  
    </a>  
  </center>  
</div>


## 🧭 How to think about the landscape

Enterprise Kubernetes platforms fall into a few categories. Knowing where each one starts helps clarify what tradeoffs you’re signing up for.

| **Where it starts** | **Key value prop** | **Typical users** | **Examples** |
| --- | --- | --- | --- |
| **Workload-centric / IDP-first** | Push code or define a service; the platform provisions, scales, and heals the infra automatically (often BYOC). | Platform teams who want to eliminate YAML + give developers self-service. | **Northflank** – build/deploy/promote across envs without wiring multiple tools; BYOC or managed. |
| **Dev-experience overlays** | Add supply-chain automation, golden paths, Backstage-style portals on top of any Kubernetes distro. Infra lifecycle handled elsewhere. | Enterprises with existing K8s footprint that need opinionated pipelines. | **TAP** – developer portal + supply chain; taps into Tanzu/K8s underneath. |
| **Hybrid platforms (dev + ops)** | Provide multi-cluster lifecycle **and** self-service app deployments. Often sold to platform-engineering teams. | Mid-large orgs that need guardrails for both ops and dev. | **Rafay Systems**, **Spectro Cloud (Palette)** |
| **Infra-centric cluster managers** | Provision, upgrade, and secure clusters at scale; developer workflow left to other tools or DIY. | Central SRE/infra teams. | **Rancher** |
| **Full-stack distro** | Bundled Kubernetes + CI/CD, service mesh, registry, build pipelines—developers can deploy, ops still manage the stack. | Enterprises preferring an all-in-one SKU. | **OpenShift** |

## 🥇 1. Northflank – Best all-around platform

![new northflank home page.png](https://assets.northflank.com/new_northflank_home_page_9600c53fbb.png)

Northflank gives you the power of Kubernetes with the feel of Heroku. It’s the only platform on this list that’s fully self-service, developer-first, and comes batteries-included.

You get Git-based deployments, autoscaling (including scale-to-zero), secret management, real-time logs, persistent volumes, cron jobs, and a clean UI your team won’t hate. You can run on Northflank’s managed infra, or self-host everything on your own cluster.

### What stands out:

- CI/CD baked in. Build and deploy from Git with configurable pipelines.
- Stateless and stateful support. Databases, persistent volumes, service discovery.
- Multi-cloud support. Deploy to AWS, GCP, Azure, or your own infra.
- Great DX. Fast deploys, easy rollbacks, helpful error messaging, intuitive UI.

### Tradeoffs:

- Less control over raw Kubernetes APIs (by design).
- Fewer ecosystem integrations than OpenShift.

<InfoBox className='BodyStyle'>

**Best for:** Startups, enterprises, product teams, or internal platforms that need to move fast and want a resilient, modern product.

</InfoBox>

## 🥈 2. Red Hat OpenShift

![redhat.png](https://assets.northflank.com/redhat_aa067b7a9c.png)

OpenShift is a heavyweight. Backed by Red Hat (now IBM), it’s the go-to for Fortune 500s with compliance requirements and huge IT orgs. It extends Kubernetes with developer tooling, security controls, and baked-in CI/CD via Tekton.

### What stands out:

- Deep security model. Built-in RBAC, policy engines, image scanning.
- Integrated pipelines. OpenShift Pipelines (Tekton-based) + GitOps support.
- Ecosystem support. Everything from Ansible to Service Mesh.
- Hybrid and multi-cloud ready.

### Tradeoffs:

- Heavy and complex to manage.
- Requires Red Hat subscription and support contracts.
- Limited flexibility if you’re not all-in on the stack.

<InfoBox className='BodyStyle'>

**Best for:** Enterprises who already use Red Hat or need guaranteed support.

</InfoBox>

## 🥉 3. Rancher Prime

![CleanShot 2025-06-04 at 16.26.57@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_04_at_16_26_57_2x_3a5a65e9a9.png)

SUSE Rancher Prime (formerly just Rancher) is designed for teams managing lots of Kubernetes clusters across environments. It doesn’t replace Kubernetes, it gives you a control plane to manage any distro, including EKS, GKE, AKS, and K3s.

### What stands out:

- Vendor-neutral. Works across any certified K8s distribution.
- Multi-cluster management. One dashboard to rule them all.
- CNCF-aligned. Open source roots, strong community.

### Tradeoffs:

- Doesn’t include full developer workflows.
- You still need to set up CI/CD, observability, etc.
- UI can feel dated.

<InfoBox className='BodyStyle'>

**Best for:** Platform teams managing 5+ clusters and not afraid of wiring it all up.

</InfoBox>

## 4. VMware Tanzu

![CleanShot 2025-06-04 at 16.27.43@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_04_at_16_27_43_2x_40413c5f29.png)

Tanzu is VMware’s Kubernetes offering. If your infrastructure is already deep in vSphere or NSX, Tanzu makes sense. It ties Kubernetes into VMware's control plane and offers lifecycle management for clusters and apps.

### What stands out:

- Tight VMware integration.
- Tools for app modernization (Tanzu Build Service, Application Catalog).
- NSX integration for advanced networking.

### Tradeoffs:

- Not particularly developer-friendly.
- Inherits the complexity of VMware’s ecosystem.
- Expensive and slow-moving.

<InfoBox className='BodyStyle'>

**Best for:** Large IT orgs invested in VMware, modernizing slowly.

</InfoBox>

## 5. Spectro Cloud

![CleanShot 2025-06-04 at 16.28.34@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_04_at_16_28_34_2x_428c908045.png)

Spectro Cloud’s Palette platform is for teams that want deep control over their Kubernetes stack, especially in edge or hybrid environments. It lets you define declarative “blueprints” of your clusters and app stacks.

### What stands out:

- Declarative stack management (infra + platform layers).
- Edge-ready architecture.
- Good policy enforcement and security posture.

### Tradeoffs:

- Requires infra maturity to operate.
- No baked-in CI/CD.
- Steeper learning curve.

<InfoBox className='BodyStyle'>

**Best for:** Infrastructure teams managing complex, custom environments.

</InfoBox>

## 6. Rafay Systems

![CleanShot 2025-06-04 at 16.29.09@2x.png](https://assets.northflank.com/Clean_Shot_2025_06_04_at_16_29_09_2x_7220f1f0d3.png)

Rafay positions itself as an operations platform for Kubernetes. It focuses on automation, policy enforcement, and repeatability across enterprise-grade environments.

### What stands out:

- Strong policy engine.
- Cluster blueprints and lifecycle automation.
- Integration with enterprise tools (SSO, audit logging, etc).

### Tradeoffs:

- Less focus on developer workflows.
- Higher complexity.
- Managed-first; less flexible in air-gapped or custom infra setups.

<InfoBox className='BodyStyle'>

**Best for:** Enterprises who want strong guardrails and repeatable infra patterns.

</InfoBox>

## Choosing the right enterprise Kubernetes platform

Here’s the uncomfortable truth: most teams don’t actually want to "do Kubernetes."

They want to ship software reliably, scale on demand, and stop waking up to alerts from clusters they barely understand.

The best platform isn’t the most feature-rich, it’s the one that lets your team stay focused. For some, that’s a full OpenShift stack. For others, it’s a modular Rancher setup. But for most modern product teams, it’s something like Northflank.

Northflank gives you Kubernetes without asking you to *be* Kubernetes. That’s the difference.

## Takeaways 

- **Northflank** is the furthest toward Heroku-style “just declare the workload.” Developers rarely touch cluster primitives. Ops can still run it inside their own cloud via BYOC.
- **Rancher & early Spectro Cloud** were built for managing clusters at scale. DevX is a bolt-on.
- **TAP** assumes you already have a Kubernetes footprint. It adds golden paths and developer portals on top.
- **Rafay & newer Spectro Cloud** now pitch a “platform-as-a-product”: infra lifecycle + service catalog in one.
- **OpenShift** is the full-stack distro. It gives devs push-to-deploy tools, but ops still manage cluster upgrades and platform services.

<InfoBox className='BodyStyle'>

## 💭 FAQs

### 1. What is an enterprise Kubernetes platform?

An enterprise Kubernetes platform is a layer that sits on top of Kubernetes to provide tools for deploying, managing, scaling, and securing applications, often with built-in CI/CD, observability, access controls, and policy enforcement.

### 2. Why not just use vanilla Kubernetes?

Because Kubernetes is a low-level toolkit. It’s powerful, but hard to manage at scale. Enterprise platforms simplify or automate key workflows like deployments, logging, secrets management, and multi-cluster operations.

### 3. What makes Northflank different?

Northflank combines the power of Kubernetes with a great developer experience. It’s fast, easy to use, and supports both managed and self-hosted deployments, plus scale-to-zero, CI/CD, and persistent workloads.

### 4. Can I self-host these platforms?

Some, yes. Northflank, OpenShift, Rancher, and Rafay support self-hosting. Others like Tanzu or Spectro Cloud often come with infrastructure constraints or are managed-first.

### 5. What if I’m on a tight budget?

Start with a managed platform that abstracts most complexity. Northflank’s free tier can handle a lot of early-stage use cases before you scale.

### 6. Do these platforms replace platform engineers?

No, but they give platform teams a head start. Instead of building tooling from scratch, you’re extending a foundation that already works.

</InfoBox>

[Try Northflank for free](https://northflank.com/) today.]]>
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  <title>Kubernetes vs Docker: What you need to know in 2026</title>
  <link>https://northflank.com/blog/kubernetes-vs-docker</link>
  <pubDate>2025-06-03T17:15:00.000Z</pubDate>
  <description>
    <![CDATA[Docker builds containers, Kubernetes runs them at scale. Learn how they work together—and how tools like Northflank simplify both—for faster, scalable, cloud-native app development and deployment.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/kubernetes_vs_docker_7d1d04e2ba.png" alt="Kubernetes vs Docker: What you need to know in 2026" />Imagine spinning up a web service in seconds, scaling it effortlessly, and pushing updates with confidence, all without ever touching a physical server. That is the magic of modern cloud native development. And at the center of it all are two names that every developer knows: Docker and Kubernetes.

But for all the conversations and comparisons, many still wonder: Are Docker and Kubernetes competitors? Are they alternatives or two pieces of the same puzzle? Which one should I use for my next project?

Whether you are a solo developer shipping your first microservice or part of a platform team managing hundreds of workloads, understanding the relationship between Docker and Kubernetes is key to making smarter architecture decisions.

Let’s break it down with clarity, real-world context, and a developer-focused lens.

## TL: DR - Kubernetes vs Docker at a glance

If you are short on time or just want the high-level summary, here is a quick side-by-side comparison of what Docker and Kubernetes do, where they shine, and how they fit into your workflow.

| **Feature** | **Docker** | **Kubernetes** |
| --- | --- | --- |
| What it is | Containerization engine | Container orchestration platform |
| Primary use | Creating and running containers | Managing and scaling containers |
| Complexity | Low | Higher |
| Learning curve | Easy to get started | Steeper learning curve |
| Standalone capability | Yes | No (needs containers to orchestrate) |
| Ideal for | Local development and small apps | Distributed systems and production workloads |
| Popularity in CI/CD | Very high | Very high |
| Requires Docker? | Not necessarily (can use containerd) | No (can use any OCI compliant container runtime) |
| Works well together? | Yes | Yes |
| Simplified by Northflank? | Yes | Yes |

## What is Docker?

Docker is a tool that makes it easier to create, deploy, and run applications using containers. A container is a lightweight, portable, and self-sufficient unit that includes everything needed to run a piece of software, from the code and libraries to system tools and settings.

At its core, Docker solves a problem that has plagued developers for years: “It works on my machine.” With Docker, developers can package applications in a way that guarantees they will run the same, no matter where they are deployed — on your laptop, on a testing server, or in the cloud.

![image - 2025-06-03T181331.565.png](https://assets.northflank.com/image_2025_06_03_T181331_565_887c82dc4d.png)

Docker revolutionized how developers build and ship applications. It replaced bulky virtual machines with fast, consistent containers. It is intuitive to use, has an incredible developer experience, and has become the standard for containerization.

But while Docker makes building and running containers easy, it was never designed to manage them at scale across multiple machines. And that is where Kubernetes enters the picture.

## What is Kubernetes?

Kubernetes, often abbreviated as K8s, is a powerful system for managing containerized applications across a cluster of machines. Originally developed by Google, Kubernetes is now an open-source project maintained by the Cloud Native Computing Foundation.

Kubernetes is not about creating containers — it is about running and scaling them efficiently in production. Imagine you are running dozens of containers across multiple servers. You want to make sure they stay online, can talk to each other, can scale up when traffic spikes, and heal themselves when something breaks. Kubernetes handles all of that and more.

At a high level, Kubernetes provides:

- Scheduling: Places containers on the right nodes
- Load balancing: Routes traffic to the correct services
- Scaling: Adds or removes containers automatically
- Self-healing: Restarts failed containers and maintains the desired state
- Service discovery: Let containers find each other dynamically
- Rollouts: Handles rolling updates and rollbacks

![image - 2025-06-03T181338.038.png](https://assets.northflank.com/image_2025_06_03_T181338_038_7f4014a028.png)

Kubernetes is incredibly powerful but also more complex than Docker alone. That’s because it wasn’t built to be easy — it was built to be flexible. Kubernetes isn’t a developer platform; it’s a platform for building platforms. It gives teams the primitives to run distributed systems, but leaves a lot of the developer experience up to you. Tools like [Northflank](https://northflank.com/) step in to absorb that complexity and make Kubernetes actually usable, especially for teams that want the power without the overhead.

*"Kubernetes can feel overwhelming at first, but it doesn’t have to take years to get decent at it if you’re motivated and stick with it." — Reddit user [source](https://www.reddit.com/r/kubernetes/comments/1hxq62a/overwhelmed_by_docker_and_kubernetes_need_guidance/)*

## What's the difference between Kubernetes and Docker?

The biggest confusion arises because Docker and Kubernetes are often mentioned together, but they solve different problems.

**Docker** is about packaging and running containers. It is the tool that developers use to create a container image and run it locally.

**Kubernetes** is about managing and orchestrating those containers. It does not build images. It schedules and manages them in a production environment.

Think of Docker as the engine that builds and starts the car. Kubernetes is the highway system that coordinates where all the cars go, how they interact, how they scale, and what happens when one breaks down.

*"You use Docker to build the containers, and you use Kubernetes to run them." — Reddit user [source](https://www.reddit.com/r/sysadmin/comments/whrh7o/whats_better_docker_or_kubernetes/)*

Also worth noting: Kubernetes does not actually require Docker to run containers. Under the hood, Kubernetes uses a container runtime like containerd or CRI-O. Docker used to be the default, but Kubernetes moved away from that in favor of lighter runtimes.

Still, Docker and Kubernetes work very well together, especially in development and CI/CD pipelines.

## **Where are Kubernetes and Docker used?**

**Docker is used by:**

- Developers building applications locally
- CI/CD pipelines that need to package apps into containers
- Teams running small services or apps on a single machine or VM
- Anyone who wants portability and consistency across environments

**Kubernetes is used by:**

- Enterprises managing large-scale container deployments
- Teams with distributed microservice architectures
- Cloud providers offering managed container platforms (like GKE, AKS, EKS)
- DevOps teams needing resilience, autoscaling, and rolling deployments

In practice, Docker is often used in tandem with Kubernetes. A common workflow looks like:

1. Developer builds a Docker image locally
2. The image is pushed to a container registry
3. Kubernetes pulls the image and runs it in production

## Key differences: Kubernetes vs Docker

While Docker and Kubernetes often work hand in hand, they serve very different roles in the container ecosystem. Docker is the engine that builds and runs containers, while Kubernetes is the system that manages and orchestrates them at scale. If Docker is the container ship, Kubernetes is the global port logistics network keeping every ship on schedule, rerouted, and operational.

*"Docker and Kubernetes are not mutually exclusive. Docker is used to build and run containers. Kubernetes is used to orchestrate them." — Reddit user [source](https://www.reddit.com/r/docker/comments/f0jld8/whats_the_relation_between_kubernetes_docker/)*

Here is a quick comparison to highlight the key distinctions:

| Category | Docker | Kubernetes |
| --- | --- | --- |
| **Purpose** | Build and run containers | Orchestrate and manage container workloads |
| **Primary Use Case** | Local development, packaging, CI/CD | Production deployment, scaling, cluster management |
| **Scope** | Single container or host | Multi container, multi host environments |
| **Installation** | Lightweight, quick setup | Complex, often requires managed service |
| **Scaling** | Manual | Automatic, based on demand |
| **Networking** | Basic bridge networks | Advanced service discovery and pod networking |
| **Load Balancing** | External tools needed | Built in service load balancing |
| **Resilience** | Manual restart needed | Self healing and automatic restarts |
| **Declarative Config** | Limited (Docker Compose) | Fully declarative YAML configurations |
| **Tooling** | CLI focused (Docker CLI, Docker Compose) | Declarative and API driven (kubectl, Helm) |

This breakdown helps illustrate why both tools are often used together — Docker for building and shipping containers, Kubernetes for running and scaling them across your infrastructure.

## When to use Kubernetes or Docker

If you are just getting started, Docker is your best friend. It is easier to learn, has great tooling, and fits perfectly into development workflows.

Use Docker when:

- You are building and testing apps locally
- You need consistent environments across teams
- You are running a small app or side project
- You want fast feedback loops and minimal overhead

Use Kubernetes when:

- You are managing multiple services across machines
- You need automatic scaling, failover, and self-healing
- You are deploying to production at scale
- You want advanced orchestration features like blue-green deployments or canary rollouts

There is no need to choose one over the other entirely. Most modern teams use both Docker for local development and CI, and Kubernetes for production deployment and orchestration.

## How to choose the right tool

Here are some guiding questions to help you decide what fits your needs:

- **Are you deploying something simple or complex?**
    
    For simple apps or internal tools, Docker alone might be enough. For complex distributed systems, Kubernetes is a better fit.
    
- **Do you need to scale automatically?**
    
    If autoscaling is important, Kubernetes is your friend.
    
- **Are you comfortable with infrastructure?**
    
    Kubernetes has a steeper learning curve. If you prefer to stay focused on code, you might want to start with Docker or use a platform that abstracts Kubernetes.
    
- **What are your team’s DevOps skills?**
    
    If you have strong platform engineering capabilities, Kubernetes offers massive power. If not, a simpler toolchain might serve you better.
    
- **Are you using a managed platform?**
    
    Services like Northflank or AWS Fargate can hide much of the complexity, making Kubernetes approachable even for smaller teams.
    

## How Northflank simplifies Kubernetes and Docker for you

Kubernetes is here to stay. It is powerful, flexible, and production-proven, but it can also be complex, especially for teams that just want to build and ship fast. Docker makes containers accessible, but it does not handle orchestration or high availability on its own.

That is where [Northflank](https://northflank.com/) comes in.

[Northflank](https://northflank.com/) brings together the best of Docker and Kubernetes into a single, streamlined developer platform. It lets you build, deploy, and scale your applications with the power of Kubernetes under the hood, without the operational overhead.

Here is what Northflank handles for you:

| Feature | Without Northflank | With Northflank |
| --- | --- | --- |
| **Kubernetes setup** | Manual cluster provisioning and YAML files | Fully managed infrastructure, no setup needed |
| **Docker container builds** | Handled separately in CI or local dev | Integrated Docker builds from your repo |
| **Deployments** | kubectl or CI scripts required | Git-based auto deployments with preview builds |
| **Scaling and autoscaling** | Requires metrics and configuration | Simple UI or API toggle, autoscaling built in |
| **Health checks** | Custom config in Kubernetes YAML | Built in health checks and service monitoring |
| **CI/CD pipelines** | Separate tooling like Jenkins or GitHub Actions | Built in pipelines with logs and history |
| **High availability** | Requires custom setup | Comes with multi-zone redundancy out of the box |
| **Observability** | Set up with Prometheus, Grafana, etc. | Real time logs, metrics, and service dashboards |

Northflank is designed to remove the barriers between development and deployment. You can work with your favorite tools — Dockerfiles, Git repos, container registries — and let Northflank handle the orchestration layer automatically.

Whether you are a startup shipping fast or an enterprise modernizing your stack, Northflank helps your team stay focused on what matters most: building great software.

*See how [Weights company uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

![Screenshot 2025-05-30 at 2.16.15 PM.png](https://assets.northflank.com/Screenshot_2025_05_30_at_2_16_15_PM_bb3262ce3b.png)

## Wrapping up

Docker and Kubernetes aren’t competitors. They’re complementary tools in the cloud native toolbox.

Docker helps you build containers. Kubernetes helps you run them at scale. Together, they power some of the most reliable systems on the internet today.

The key isn’t choosing between them — it’s knowing how they work together, and how to use that power without slowing yourself down.

And if managing Kubernetes still feels heavy or frustrating, [**Northflank**](https://northflank.com/) is here to change that. It gives you the best of both worlds: the simplicity of Docker, the power of Kubernetes, minus the ops overhead.

No YAML walls. No cluster wrangling. Just clean deployments, built-in CI/CD, and infra that scales with you.

[**Try Northflank today** and see what cloud native feels like when it just works.](https://app.northflank.com/signup)]]>
  </content:encoded>
</item><item>
  <title>How to raise a seed round </title>
  <link>https://northflank.com/blog/how-to-raise-a-seed-round</link>
  <pubDate>2025-06-01T07:00:00.000Z</pubDate>
  <description>
    <![CDATA[Raising a seed round is your first “official” experience securing capital from institutional investors.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/aws_credit_1_8db9301a6d.png" alt="How to raise a seed round " />Raising a seed round is your first “official” experience securing capital from institutional investors. I fully appreciate how intimidating this can feel: you’re pitching your company to professional investors who’ve likely been doing deals for years, while you’re probably doing this for the very first time. Having sat on both sides of the table—as a [former venture capitalist (VC)](https://www.crunchbase.com/organization/vertex-ventures-us), and as someone [who has raised money from VCs](https://northflank.com/blog/northflank-raises-22m-to-make-kubernetes-work-for-your-developers-ship-workloads-not-infrastructure) alongside the founders at [Northflank](https://northflank.com/)—I wrote this post to demystify the seed-round process.

Rather than just offering generic definitions, I’ve tried to provide a clear, opinionated guide on the topics you actually care about: how much money to raise, how to handle board seats, how to construct a compelling narrative, understanding the investor landscape, running the fundraising process, and more. My goal here is to cut through the noise, clearly lay out what matters (and why), and leave you more confident about how to raise a seed round.

## **1. Seed-round fundamentals**

### **1.1 What exactly is a seed round?**

Think of seed funding as the first “real” money you raise after maxing out credit cards, ramen budgets, and maybe a few angel checks. It’s typically the first encounter with institutional investors — the official term for venture capitalists (VCs) who invest in startups for a living. 

| **Region** | **Typical round size** | **Post-money valuation** | **Ownership investors expect** |
| --- | --- | --- | --- |
| US (2025) | $2–4 million | $12–20 million | 10 – 20 % (lead takes ~12 %) |
| Europe (2025) | €1–2.5 million | €8–12 million | 12 – 22 % |

In theory, you should raise enough to reach your next milestone, plus some padding (we’ll talk milestones shortly). In practice, the amount you raise and how much [dilution](https://carta.com/learn/startups/equity-management/share-dilution/) you accept is largely driven by market norms. Whether you raise more or less, or take more or less dilution, depends heavily on factors like category heat ([yes, AI is hot hot hot](https://news.crunchbase.com/venture/global-funding-data-analysis-ai-eoy-2024/)), the category itself (defense or security startups typically have bigger upfront costs), founder backgrounds, and honestly, [what everyone else around you is doing](https://en.wikipedia.org/wiki/Herd_mentality). 

Fortunately, markets are generally efficient enough that you’ll usually get the capital you actually need. This is all a long way of saying that whether you’re raising seed capital in the US or Europe, it’s market forces—more than your spreadsheet—that shape your seed-round dynamics. Speaking of spreadsheets, skip the detailed financial model. At this stage, you’re deep in fairy-dust territory, so don’t burn calories stacking predictions on top of assumptions, on top of guesses. Seed investors won’t rely on detailed forecasts—and neither should you.

Yes, I’m sure you’ve read about the [splashy AI rounds](https://www.businessinsider.com/mira-murati-big-tech-put-thinking-machines-lab-venture-capital-2025-4) where founders raise tens or even hundreds of millions at seed. While these rounds are typical for the news cycle, they're atypical in a fundraising context. 

**Price round vs SAFE/Convertible**

There are two main ways you’ll raise your seed round: a [**priced (equity) round**](https://carta.com/learn/startups/fundraising/priced-rounds/) or a [**convertible round**](https://carta.com/learn/startups/fundraising/convertible-securities/) (SAFEs or convertible notes). Both SAFEs and notes convert into equity at a future financing round, but notes are technically debt instruments with maturity dates and interest, while [SAFEs](https://www.ycombinator.com/documents) are simpler and cleaner—largely thanks to our friends at [Y Combinator](https://www.ycombinator.com/) (aka YC). 

As a rule of thumb: if you’re raising less than $3M, stick with a non-priced round (usually a SAFE). Between $3M and $5M, it’s more of a coin toss, and above $5M, an equity round starts to make sense. 

One quick gotcha: convertible instruments typically have a discount (often around 20%) that kicks in upon conversion. This means your actual dilution can be higher than you’d initially expect. For example, if your SAFE converts at a Series A priced at $20M, a 20% discount means your seed investors convert as if the valuation was only $16M, giving them more ownership than you might have anticipated.

| **Structure** | **Pros** | **Cons** | **“Market” terms (2025)** |
| --- | --- | --- | --- |
| **Price round** (equity) | - Clear ownership & board terms up-front - Gives investors rights they’ll later want (board, pro-rata) | - Legal fees & closing docs (~$15-25 k)- Harder to close quickly | 1× non-participating liquidation pref, no dividends, board seat if >10 % |
| **SAFE / Convertible** | - Cheap & fast (YC post-money SAFE is 5 pages)- Flex on valuation caps | - Cap dilution surprises when notes convert- “Most-favored nation” clauses can spook late investors | Caps: $8-12 m in EU, $12-20 m in US. 20 % discount if uncapped. Interest 2–5 % on convertibles. |

### **1.2 How big should your seed round investment be?**

Yes, I did say your raise size is largely driven by market norms, but that doesn’t mean you shouldn’t have your own hypothesis on how much capital you’ll actually need. Especially as a first-time founder, it’s important to remember that the more you raise, typically the greater dilution you’ll face. 

The market doesn’t have a single fixed price (remember, I shared ranges earlier), and there’s a meaningful difference between raising $3M versus $6M. Your goal at seed is to secure enough funding to reach the next milestone: your Series A, similar to how investors [look for solid fundamentals](https://www.wallstreetzen.com/stock-screener/top-best-stocks-to-buy-now-today) when deciding which stocks to back.

At that next stage, investors want evidence of [product-market fit](https://pmarchive.com/guide_to_startups_part4.html) — that you’re building something people clearly want. The best way to demonstrate this is by showing that customers are willing to pay for your product and invest their time adopting it.

So the real question becomes: **what does it actually take to prove your thesis about market demand for your solution?** 

At the seed stage, nearly all your capital goes toward talent. Separately, keep in mind that momentum matters—a lot. Sure, you could theoretically raise a tiny seed round, hole up alone in your spare bedroom, and spend six years building the perfect product, but investors would quickly raise eyebrows at your lack of momentum. 

Your goal should be meaningful, visible progress within 12–18 months. So, deciding how much to raise boils down to figuring out exactly who you’ll need to hire to ship a compelling product and land your first paying customers within that timeframe. 

Add extra runway for safety, because you definitely don’t want (or need) your bank account balance anywhere close to zero, consider the VC market dynamics for a company like yours, and that’s your raise calculation.

**Milestone-backwards math**

1. Write down the *next proof-point* that unlocks Series A (e.g., “$1M ARR” or “first 10 enterprise logos”).
2. Estimate the burn to get there and add 30% “oops, I gotta pivot” margin.
3. Make sure that buys **18–24 months** of runway so you’re not fundraising in panic mode.

**Dilution sanity check**

- 10 – 20% dilution at seed keeps enough equity for later rounds.
- If investors want >20%, push back: either raise less or find a different investor.

**Three common scenarios**

| **Raise** | **Team Size Now → EoR (End of Runway)** |
| --- | --- |
| $500k “lean seed” | 2 founders → 4-5 people |
| $2M “standard seed” | 3-4 core → 8-10 people |
| $5M “mega seed” | 3-4 core → 10+ people |

**Bottom line:** Raise *just enough* to hit the milestone that makes the next check obvious, keep dilution in the ~15% range, and pick the fundraising instrument that matches the amount of capital you’re raising. If your roadmap doesn’t actually *need* $5M, don’t raise it. Cash is never free—every dollar has dilution and expectation strings attached.

## **2. Team composition & story**

### 2.1  What do investors look for in a founding team?

This could easily be its own blog post, and every investor has their own criteria. Like an orchestra, the quality of the team depends on its composition. Each founder needs to have a role that directly aligns with the company they want to build and the market they aim to dominate. 

[Before joining Northflank](https://chsrbrts.medium.com/softwares-manufacturing-revolution-why-i-m-joining-northflank-33fcda71f074), I was actually one of [its investors](https://vvus.com/). Northflank is a deeply technical product that’s [creating a category where none previously existed](https://northflank.com/blog/build-vs-buy-the-platform-engineers-conundrum). Category creation is uniquely challenging because there aren’t clear reference points for incremental improvement—instead, you’re crafting a new definition of what’s possible. This places significant importance on being product-minded: the ability to clearly identify core problems and translate those insights into innovative solutions that stand out due to their novelty and effectiveness.

Building a technical product aimed at a technical audience demands exceptional expertise. In Northflank’s co-founders, I saw this balance perfectly. [Will Stewart (CEO)](https://www.linkedin.com/in/william-j-stewart/) brings deep intuitions about the problem space, while [Frederik Brix (CTO)](https://www.linkedin.com/in/fbrix/) has the technical acumen to bring these insights to life. While their roles naturally overlap—as both actively shape and develop the product—it was immediately clear to me how well these two complemented each other.

Investors ultimately try to forecast your likelihood of success in a world where most startups fail. They need to believe you’ll validate your hypothesis around a widespread problem that customers desperately need solved. They want proof you can build early momentum. And they need to believe you’ll evolve into the CEO and leaders your company requires at each stage, because running a 5-person startup is vastly different from managing 50 or 500 people. 

While exceptions exist, the sweet spot is typically 2–3 founders (usually two), complemented by at least 2–3 strong early hires. Those early hires signal that you’ve convinced talented people—who could easily opt for safer, more cushy jobs—to risk their livelihoods alongside you. Ultimately, startups are talent acquisition and activation games, and demonstrating you can recruit great talent gives investors confidence you can win these games again and again.

### 2.2  Solo founder? How to address risk perceptions.

Being a solo founder isn’t a deal-breaker. It just raises the bar. You’ll overcome most investor concerns if you demonstrate that you can recruit top talent. Like I mentioned earlier, proving you can win the talent game matters. 

That said, don’t rush into adding a co-founder just because you think it’ll help fundraising. Your responsibility as CEO is to feed your company what it needs, so only bring on a co-founder if you believe doing so meaningfully improves probability of success. 

And don’t overlook the human side: it really is lonely at the top. A co-founder gives you someone to argue with at 1 a.m., celebrate surprise wins, and commiserate over the inevitable “we-just-lost-our-biggest-logo” moments. That emotional ballast alone can be worth 20 points of equity.

### 2.3  Startup advisors

Hot take: most seed-stage “advisory boards” are presentation glitter. Unless you’re working on deep tech (e.g., robotics, semiconductors, biomedical LLMs), a marquee advisor granting you *two* hours a month won’t meaningfully move the needle—or impress VCs who’ve seen the same faces recycled across decks.

If you *do* have a genuine skills gap you can’t hire for yet, then fine—bring in a hands-on advisor with a clear, time-boxed deliverable. Otherwise, pour that energy into hiring or contracting real contributors and let your slide real estate showcase the *team* that’s actually shipping product.

## **3. Crafting the narrative**

### 3.1 Why the narrative matters

Your seed round is almost entirely about narrative. VCs will assess your ability to distill unique insights about a market into a clear, credible plan for capturing value. Doing this effectively requires understanding the fundamental difference between how founders and investors think. 

Founders naturally gravitate toward tactical execution: deciding what to build next, figuring out how to sell it, and navigating real-world constraints. 

Investors, on the other hand, think in broader themes: Which market forces indicate this is a valuable problem to solve? What do these founders see that others overlook? How durable are these trends, and are the founders uniquely positioned to tackle them? 

Your tactics alone don’t form the narrative. Your job is to explain why this category matters and why your team is uniquely poised to dominate it.

A strong narrative clearly answers three questions:

1. **What specific problems exist, and who exactly has them?**
2. **Why are these problems urgent and painful enough that people are desperate to solve them?**
3. **What does winning look like if your company successfully solves these problems?**

Features matter, but they’re implementation details, not the story itself. 

Instead, weave these three elements into a clear, linear narrative. Highlight any observable or impending market shifts that strengthen your case, and avoid vague or empty abstractions.

When I invested in Northflank, the narrative was clear: [Kubernetes](https://kubernetes.io/) is powerful but unusable for most teams. Developer platforms promised solutions but became bloated toolkits and internal science projects. 

Northflank’s insight is that infrastructure shouldn’t be fully abstracted. It should be synthesized with more [powerful and usable primitives](https://northflank.com/use-cases/internal-developer-platform-idp-for-kubernetes). The entire post-commit process—building, deploying, scaling, and monitoring—should feel like one seamless system, not a patchwork of tools. Unlike platforms like Heroku that attempt to remove complexity, Northflank absorbs it. It’s not “Heroku but better,” it’s more like “Kubernetes without tears.”

Most platforms have an expiration date: workloads scale, needs evolve, and teams eventually outgrow them. Northflank is explicitly designed to avoid that graduation. It scales with you, enabling workload complexity without infrastructure complexity. And since it [runs in your own cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) (managed or on-premise), Northflank delivers better economics without added complexity. It’s the anti-graduation platform, the one you’ll never outgrow. 

Notice how it grabs you. The co-founders of Northflank presented this narrative with such clarity that we knew their vision of the future was worth betting on.

![Screenshot 2025-05-30 at 2.16.15 PM.png](https://assets.northflank.com/Screenshot_2025_05_30_at_2_16_15_PM_bb3262ce3b.png)

It’s easy for founders to assume that what’s obvious to them is also clear to investors. But even if investors know your space well, they rarely share your precise vision of the future you’re aiming to build. Sure, some categories feel familiar and straightforward, yet even those require you to demonstrate why your approach creates a unique advantage and why your team is uniquely positioned to capture market share.

Ultimately, your startup represents a bet on the future. Your job is to show investors clearly why that future is worth betting on.

### 3.2  Memo vs Deck – choose your weapon

Start with a narrative and work backwards. Whether you write a memo or create a slide deck should depend entirely on what helps you best tell your story:

- Memo ([Rippling](https://www.rippling.com/resources/series-a-memo), [Amazon style](https://www.larksuite.com/en_us/blog/amazon-6-pager)): when depth beats design
- Slide deck (more common; here’s [Front’s](https://collinmathilde.medium.com/front-series-a-deck-f2e2775a419b) and [Airbnb’s](https://www.alexanderjarvis.com/airbnb-seed-pitch-deck/)): keep it fewer than 10-slides

First things first: your sales deck isn’t your fundraising deck. With that out of the way, let’s put ourselves in a VC’s shoes—they care most about understanding how investing in your company [will generate a meaningful financial return for their fund](https://a16z.com/books/secrets-of-sand-hill-road/). Investors hunt for patterns that signal non-linear growth potential. 

Beyond answering the three questions in the previous section, make sure your narrative also covers *why you, why now, and what’s happening.*

- **Why you?** What makes your company uniquely positioned to tackle this problem?
- **Why now?** What recent changes or developments make it possible to solve this problem now?
- **What’s happening?** Are there macro trends that will accelerate your company’s growth and success?

Your answer to “why you?” should focus on three core elements: your team, your specific approach or product, and the broader context of your market. Explain clearly why existing solutions fall short, why a potentially large market hasn’t yet emerged (but now could), and why previous attempts to solve this problem have failed. Whether you address this across multiple slides, in a detailed memo, or distill it down to a single slide is entirely your call—it’s your story, and telling it convincingly is your job.

In Northflank’s case, they provided both. They wrote a memo AND a deck for their seed round and let investors choose their own adventure. 

## **4. Mapping the investor landscape**

### 4.1  The VC landscape

| **Fund type** | **Typical individual fund size in USD (latest-vintage ranges)** |
| --- | --- |
| **Seed funds** | **$25M – $150M** (top-tier “mega-seed” vehicles can reach $250M–$300M)  |
| **Early-stage funds** (Series A/B specialists) | **$300M – $1B** |
| **Growth funds** | **$1B – $5B** (mega-growth vehicles occasionally exceed $10 B) |
| **Multi-stage platforms** | **$2B – $10B+** for each flagship fund, with multiple parallel vehicles (seed, growth, crossover) often running simultaneously |

You might be wondering: “Isn’t this blog about seed rounds, why mention much larger funds?” Because [big funds regularly invest at seed](https://www.crunchbase.com/funding_round/windsurf-codeium-seed--47f202b1), too. Even funds [managing $40 billion](https://a16z.com/seed/) or more (assets under management, or AUM) happily write seed checks. 

So, how do you choose? The simplest answer is: choose the individual investor rather than the fund. But there’s an important caveat. One valuable “feature” of a VC fund is the brand it lends your company. Certain VC brands act as strong positive signals for future investors, talent, and customers. In my experience, this brand value mostly matters to other VCs (who care a lot), somewhat to talent (who care sometimes), and barely registers with customers (who might hear about your company from your VCs).

But make no mistake: a prestigious VC brand doesn’t guarantee your company’s success. Be careful about assuming that just because a large VC could theoretically fund your company from seed through IPO, they actually will. While these investors will likely participate in future rounds, they often won’t lead them. In the short term, VCs are measured by their markups—valuations that look far more credible when set by other investors. This dynamic creates a [signaling risk](https://bothsidesofthetable.com/understanding-the-risks-of-vc-signaling-37dff617306f): if your seed investor doesn’t invest meaningfully in future rounds (especially their core stage, like growth), other investors will immediately wonder, “What do the insiders know that I don’t?” Although signaling risk isn’t fatal and can be overcome, it’s real enough to factor into your decision-making.

Larger funds [often provide platform teams](https://baincapitalventures.com/hive/)—specialists who can help you with recruiting, developing your sales playbook, PR, executive hiring, customer introductions, M&A, and more. These teams typically include highly talented individuals. But keep in mind, while they can support you, you’re ultimately responsible for doing the work. They can’t build your company for you. In my experience, these platform resources become increasingly valuable from Series A onwards.

### 4.2  Angels 101

**Angel investors** are usually high-net-worth individuals who invest personal capital into early-stage startups. They typically write smaller checks—anywhere from a few thousand up to a few hundred thousand dollars—and invest very early, often before or alongside institutional VCs. 

Broadly speaking, angels come in three flavors:

- **Operator angels**: These are individuals who’ve built, scaled, or exited companies themselves. They’re founders, senior engineers, growth marketers, or product leaders. Operator angels can be genuinely valuable because they’ve been in your shoes recently and can provide tactical advice or introductions based on direct, relevant experience.
- **Celebrity angels**: These angels carry significant name recognition—think professional athletes, entertainers, or famous tech executives. They typically provide less operational support but can lend credibility, generate media buzz, and sometimes unlock access to networks outside typical startup circles. Their value is usually more about signal and PR than day-to-day help.
- **Angel syndicates**: Syndicates are groups of angels who pool their capital together, often led by one or two respected investors. Platforms like AngelList popularized syndicates, enabling angels to write larger combined checks. Syndicates simplify your fundraising by reducing the complexity of managing many individual investors, though you often lose the personal connection you’d have with individual angels.

Ostensibly, angels provide mentorship, industry introductions, or operational advice. But here’s a former investor’s hot take: the main value angels offer is validation. In other words, their decision to invest signals credibility. For example, if you’re building an AI company, an angel check from Yann LeCun, Fei-Fei Li, Demis Hassabis, or Sam Altman acts as a powerful stamp of approval.

Yet for every founder who tells me their angels are indispensable, there are a dozen who barely recall who their angels are. As spicy as it sounds, my advice is usually to skip angel investors and keep your cap table clean. Each additional investor adds overhead—more signatures to chase, more data requests to answer, and greater complexity as your shareholder list expands. While I genuinely respect the pay-it-forward mentality many angels embrace (especially considering most angel investments go to zero), the reality is that most founders don’t effectively activate or engage their angels. Unless you have a clear plan to leverage specific angels, it might be simpler—and smarter—to skip them altogether.

Should you bring angels onto your cap table anyway, here’s how I’d suggest activating each type effectively:

- **Operator angels** – Put them to work helping you hire. For example, if you’ve raised from a well-known CRO, ask them to help interview your first sales hire. If you’ve got a notable engineer, tap them for your first engineering hire. Operator angels know firsthand what great talent looks like, and they can help you avoid costly early mistakes.
- **Celebrity angels** – Leverage their audience and personal brand for distribution. Ask them explicitly to promote your product—ideally on social media, podcasts, or at relevant events. Their primary value is visibility and validation, so make sure you capture and amplify it.
- **Angel syndicates** – Similar to celebrity angels, try to tap their reach and audience. Syndicates often boast large networks, which can help with visibility or introductions. However, in practice, syndicates typically create distance between you and individual members, making direct asks harder to land. You’ll need to rely heavily on the syndicate lead, so set clear expectations upfront about promotion or distribution help.

At Northflank, we opted for operator angels, including [David Cramer](https://www.linkedin.com/in/dmcramer/) (Co-founder & CPO of [Sentry](https://sentry.io/welcome/)), [Scott Johnston](https://www.linkedin.com/in/scottcjohnston/) (former CEO of [Docker](https://www.docker.com/)), [Oskari Saarenmaa](https://www.linkedin.com/in/oskarisaarenmaa/?originalSubdomain=fi) (Co-founder & CEO of [Aiven](https://aiven.io/)), and [Alexis Le-Quoc](https://www.forbes.com/profile/alexis-le-quoc/) (Co-founder & CTO of [Datadog](https://www.datadoghq.com/)). Their experience building companies for technical buyers has made them exceptionally valuable resources. We also have one “celebrity angel,” [Ian Livingstone](https://www.linkedin.com/in/irlivingstone/), who hosts [a podcast](https://www.infrapod.io/) and has notably helped us secure multiple customer introductions.

### 4.3  Accelerators & Pre-Seed programs

**Accelerators and pre-seed programs** are structured cohorts designed to quickly move you from early idea to a business with meaningful traction. They typically bundle mentorship, networking, and resources alongside a modest investment (usually $100k to $500k) in exchange for equity—typically 5–10%. Most accelerators run for about 3–6 months, wrapping up with a “Demo Day” where you pitch your company to a roomful of investors. The big-name programs you’re probably familiar with include [**Y Combinator**](https://www.ycombinator.com/about), [**Project Europe**](https://www.projecteurope.co/), [**Arc**](https://www.sequoiacap.com/arc/), and [**Entrepreneur First**](https://www.joinef.com/).

Should you consider accelerators? Hot-take time: probably not. The main selling point for accelerators is their network—[particularly in the case of Y Combinator](https://www.ycombinator.com/companies). YC genuinely [creates momentum and opens doors](https://www.lennysnewsletter.com/p/pulling-back-the-curtain-on-the-magic) through a stellar alumni network that actively helps each other grow—adopting each other’s products, facilitating warm intros, and generally providing a supportive founder community.

But here are two big reasons why I’d recommend skipping accelerators:

1. **Equity cost**: Giving up 5–10% equity is expensive, especially for a network you’ll likely struggle to fully leverage. Networks can be powerful, but founders often find it harder than expected to meaningfully activate those connections.
2. **Curriculum value**: While accelerator programs generally offer thoughtful, structured guidance, their curriculum won’t be what prevents your company from failing. Realistically, founders succeed or fail based on execution—not classroom-style guidance, however well-intentioned. That’s not to say all accelerators are equal. The good ones won’t push a curriculum on you, they’ll act more like partners. Here’s the cash, we’re here if you want to consult us on anything.

That said, if you’re starting with zero network, no ties to tech hubs, no friends in startups, no investor intros, then a top-tier accelerator can be a legitimate unlock. In those cases, go for prestige. Ones like YC will open far more doors than second-tier programs ever could.

Northflank went through The Family, a Europe-based accelerator, and it made a huge difference early on. Without it, they wouldn’t have raised their $250k angel round or $2M pre-seed, the team at The Family made all the intros. Will and Fred had been building infrastructure since they were teenagers, spinning up game servers and writing custom tooling before most people their age had touched a VPS. But they’d never raised money before. 

The Family didn’t teach them how to build Northflank—they already knew what they wanted to build—but it gave them the early exposure to investors, helped them figure out how much to raise, and made sure they didn’t walk into rookie traps. It made their first round thoughtful and well-supported.

### 4.4 Building an investor list

Like I said earlier, the single most important rule in fundraising is **pick the partner, not the firm**. The partner you choose will represent 90%+ of your interactions with the fund, greatly influence governance—especially if they take a board seat—and play a critical role in future fundraising rounds. VC is an exceptionally networked community, and the first call future investors make when considering your company is usually to the person who led your last round. In other words, you’ll inherit your investor’s reputation—both good and bad—as well as their network.

That said, there are a select few firms whose money you should take regardless of the partner, simply because the firm’s brand is so strong it overrides individual reputation. At the risk of raising eyebrows (and offending my VC friends), I won’t name them publicly here—but drop me a LinkedIn message and I’ll give you my candid take.

So how should you actually build your investor list?

**First**, prioritize investors who specialize in your category. Investors tend to focus on specific sectors like infrastructure, AI, fintech, security, consumer, healthtech, defense, and so on. Working with someone who doesn’t understand your space will just cause unnecessary brain damage.

**Second**, identify investors who can help you level up as a founder and reach the next milestone. If you need help with go-to-market strategy, seek out investors who’ve publicly shared thoughtful content about sales and marketing. Need guidance on scaling management practices? Find investors [who’ve helped founders evolve as leaders](https://www.amazon.com/What-You-Do-Who-Are/dp/0062871331). You get the idea. But tread carefully here: investors won’t (and can’t) build your company for you. Don’t mistake their advice or wisdom for actual execution.

**Lastly**, some people dismiss career investors because they lack direct operating experience. While operating experience can be valuable, I’ll take the opposite view: there are plenty of investors who’ve spent most or all of their careers as VCs who you’d be lucky to have on your cap table. Again, if you want recommendations, just reach out on LinkedIn—I’ll happily share their names.

### 4.5 Investor outreach

The absolute best way to get introduced to a VC is through their network. **The highest-quality intros come from other founders**, followed by talented operators (which is VC-speak for anyone who isn’t a VC), and finally, other investors. When you ask for an intro, keep the request simple and clearly highlight why you’re reaching out specifically to them. For example:

> “Hey [Name], would you mind passing this along to [partner at VC firm]? Looks like they’re actively investing in our space, and I really enjoyed their recent piece about XYZ. I’ve attached a short deck, and included a quick overview of the business and team below.”
> 

A few common outreach tactics that you should absolutely avoid:

- **Automated campaigns:** Even though I left VC over a year ago, I still regularly receive automated pitches through LinkedIn and email. VCs easily spot these mass emails, and almost always ignore them—it’s a signal that you didn’t do your homework.
- **Repeated outreach without response:** Don’t [DDoS](https://en.wikipedia.org/wiki/Denial-of-service_attack) prospective investors. If you got a warm introduction, one gentle follow-up is completely appropriate—messages can easily slip through the cracks. But repeatedly pestering investors sends a clear anti-signal: it instantly makes you look desperate.
- **Fake urgency:** It may seem counterintuitive, but you’re far more likely to get genuine interest from a VC if they believe they’re early in your fundraising process rather than late. If your deal has been on the street for weeks or months, investors will inevitably wonder why their peers passed. Yes, VCs love to say they look for opportunities that are “[non-consensus and right](https://www.youtube.com/watch?v=dBaYsK_62EY),” but in practice, the primary metric VCs are graded on by LPs (their own investors) is getting consistent “up-rounds”(aka “consensus”)—meaning later-stage investors confirm the company’s value every 12–18 months. If your round seems stale, it’s harder for investors to build the necessary conviction.

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💡 Pro tip: Even if you’ve already been raising for weeks, always frame the conversation like this: “We’re just kicking off our fundraising process and are eager to meet with investors we admire before officially starting.” The best investors consistently see the best deals ahead of their peers—that’s exactly how they stay on top. By positioning your company as an early, fresh opportunity, you tap into investors’ desire to feel ahead of the market. Playing into this cycle directly benefits you, creating excitement around your fundraise.

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## **5. Fundraising 101**

### **5.1 What to expect**

Fundraising is a grind—you’ll hear “no” far more often than “yes.” Don’t let that discourage you. The average investor writes one check for every hundred companies they meet, so rejection is the default. That’s fine. Remember, it only takes one “yes.” Realistically, expect to speak with 30+ firms before landing a commitment.

My biggest piece of tactical advice is to keep everyone moving at roughly the same pace. You want investors entering their second and third meetings simultaneously. The absolute worst scenario is being deep into partner meetings with a firm you’re only moderately excited about, while you’re just kicking off with someone you’re genuinely enthusiastic about. Once you’ve built fundraising momentum, it’s nearly impossible to slow things down without raising eyebrows or losing leverage.

### **5.2 Remote vs. in-person meetings**

Unless the investor you’re pitching is a dream pick, do your first meetings remotely. Using a [meeting assistant](https://krisp.ai/ai-meeting-assistant/) can help you capture key points effortlessly and keep your follow-ups on track. Video calls help you stay efficient, establish a consistent rhythm, and quickly figure out who’s genuinely interested. For those investors who show real interest after an initial video call, I’d strongly encourage at least one in-person meeting later in the process. You’re entering a 10+ year relationship with your lead investor, and they’ll have significant influence on your company’s trajectory. You’ll want to be confident you can trust them and collaborate comfortably over the long haul.

That said, if an in-person first meeting is convenient and you think you’ll make a stronger impression that way, absolutely do it. Whether Zoom or face-to-face, remember you only get one first impression—pick whichever format helps you shine brightest.

In Northflank’s case, both institutional rounds were raised entirely remotely, over Zoom. It wasn’t until the Series A that the founders flew out to SF to deepen relationships in person, ultimately leading them to select BCV as their Series A lead investor. You absolutely can build trust over Zoom. I invested in Northflank without ever meeting the founders face-to-face. In fact, my wife met them before I did, but that’s a story for another time.

### **5.3 Which geographies to prioritize**

Short answer: the Bay Area. Period. For all the talk of rising tech ecosystems elsewhere and the endless rebranding of various cities as “Silicon This-or-That,” the Bay Area remains the [undisputed center of gravity for venture capital](https://techcrunch.com/2025/01/07/silicon-valley-is-so-dominant-again-its-startups-devoured-over-half-of-all-global-vc-funding-in-2024/). If you’re a European founder, prioritizing European investors initially is totally fine—but do yourself a favor and sprinkle in a few Bay Area VCs to maximize options down the road.

Of course, that doesn’t mean there aren’t excellent VCs in places like New York or Austin—there certainly are—but their ecosystems still can’t compete with Silicon Valley’s sheer scale and density. And no, [Miami still isn’t really a thing](https://techcrunch.com/2024/09/12/keith-rabois-says-miami-is-still-a-great-place-for-startups-even-as-a16z-leaves/).

### **5.4 Preparing for investor questions**

Before pitching the investors you care most about, I highly recommend starting with a few conversations you’re less excited about. This gives you a chance to refine your pitch, find your rhythm, and reveal the most common questions you’ll face.

Remember: VCs think in “themes” and not “tactics.” For example, if an investor asks, **“Why will buyers choose your product?”** a bad answer would be something tactical like, **“Because we have these features and they work this specific way.”** A much better answer would be:

> “We’re seeing a fundamental shift in buyer expectations, driven by factors X, Y, and Z. We’re building from the ground up to match these new expectations, whereas incumbents are constrained by legacy technologies and business models, making it hard for them to adapt.”
> 

VCs want evidence you’re taking a systematic view of the market landscape around you—how shifting technologies, changing buyer demands, evolving competitive dynamics, and emerging market forces all influence your decisions around building the company. Your ability to thoughtfully frame these forces signals maturity and gives VCs confidence in your judgment.

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💡 Pro tip: It’s perfectly fine not to have every answer. If you’re asked a question like **“How will you price this?”**, resist the temptation to improvise or invent something on the spot. Investors prefer honesty and thoughtful reasoning to false certainty. Instead, respond authentically:

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> “We’re still figuring out pricing. Our hypothesis is that structuring our pricing around XYZ, with target annual contract values in the $X–XXk range, aligns with buyer expectations and lets us build a viable business. Here’s why we think that. But we haven’t tested this yet, so it’s an open question we’ll need to address before bringing on our first sales hire.”
> 

Answers like this reassure investors you’re pragmatic, thoughtful, and aware of the challenges ahead—exactly the signals you want to send during a pitch.

### 5.5 The Fundraising Playbook

Here’s how I’d run a clean fundraising process:

**1. Build your investor list and kick off outreach**

Focus on VCs who specialize in your category and invest at your stage. Figure out warm intros—ideally through other founders or credible operators in your network. The introducer doesn’t have to be the VC’s best friend, but they should at least be a known entity. If you can’t get a warm intro, learn [how to find an email address](https://clearout.io/blog/effective-ways-to-find-email-addresses/) and check if it’s valid to reach out directly.

**2. Track everyone in a Google Sheet or Notion**

You absolutely need a structured way to manage outreach, meeting schedules, and follow-ups. Here’s a [**Notion Template**](https://northflank.notion.site/fundraising-crm-template) we’ve created to help you organize your fundraise effectively.

**3. Book meetings and work your magic**

Make investor meetings conversational rather than transactional. The investor’s questions should help demonstrate (or reveal otherwise) their understanding of your space. Don’t walk into the Zoom call, say hello, and then launch straight into a one-way presentation, which you can make with the help of an [AI pitch deck generators]( https://plusai.com/blog/best-ai-pitch-deck-generators ). Use your deck as a conversation guide, not a script you power through slide by slide. The best meetings are engaging dialogues, not monologues.

**4. Follow up promptly with your deck or memo**

The decision to invest (or lean in) usually happens when you’re not in the room—you’re relying entirely on the investor’s ability to pitch your story internally to their partners. Make their job easier by sending a clear follow-up item (deck or memo) that lays out the core themes of your pitch in a concise, linear way. Make it effortless for them to advocate for you.

**5. You don’t have a term sheet until you have a term sheet**

It might sound obvious, but you’d be surprised how often founders prematurely mention “imminent” term sheets that never materialize. That said, once you actually have a term sheet in hand, it’s completely appropriate—even advantageous—to notify other investors you’ve met with, saying something like:

> “Hey, we’ve really enjoyed getting to know you and could genuinely see ourselves working together. We just received a term sheet and wanted to be transparent about that, but we’d like to keep the door open if you’re still interested. What’s left in your process?”
> 

**6. Term-sheet expiration dates are myths (kind of)**

A savvy VC will typically put an expiration date (usually a week later) on their term sheet to create urgency. A truly confident VC might offer one without a hard expiration, signaling strong conviction in their value as an investor. In my five years as a VC, I never saw a firm pull an active term sheet simply because it hit its expiration date. If they were genuinely excited enough to offer you a term sheet, they won’t change their minds two days after an artificial deadline passes.

That said, the expiration date does matter practically—don’t ignore it or ghost investors. Instead, communicate transparently:

> “Thank you for believing in us—it means a ton. We’re excited about the possibility of partnering with you. However, we’d like to see our process through with a few other investors, which feels like the prudent move as founders. Could we extend your expiration by a few days?”
> 

Transparency here is key. If you go radio silent and return weeks later hat-in-hand, the term sheet will absolutely be gone.

## 6. Term sheets

### 6.1 The term sheet need-to-know hierarchy

Yes, ChatGPT can decode any piece of legal jargon in your term sheet on demand. But there are a handful of terms you need to know cold, without a lifeline—because they drive your dilution, control, and downside protection. These are the ones to commit to memory:

1. **Price mechanics** – Pre/post-money, cap, discount, option-pool expansion.
2. **Investor control** – Board seat, protective provisions.
3. **Downside protection** – Liquidation pref, anti-dilution.
4. **Administrative stuff** – Info rights, pro-rata, ROFR.

**1. Price mechanics**

| **Term** | **What it means** | **Why it matters to you** |
| --- | --- | --- |
| **Pre-Money Valuation** | The company’s value **before** the new capital comes in. | Sets the baseline for dilution. A higher pre-money means you keep more ownership. |
| **Post-Money Valuation** | Pre-money + the new cash. | Investors quote ownership as a % of post-money (= invested capital/post-money). Know both numbers so your dilution math is clear. |
| **Valuation Cap** (SAFE/Note) | The **maximum** valuation at which a SAFE or note converts. | A low cap means more dilution; a high cap is friendlier to founders. |
| **Discount** (SAFE/Note) | % reduction from the next round’s price when the SAFE/note converts (e.g., 20 %). | Functions like an extra haircut on your valuation. |
| **Option-Pool Expansion** | Extra shares carved out **pre-money** for future hires (e.g., 10 %). | In a priced round this dilutes founders, not new investors. Negotiate pool size carefully. |

**2. Investor control**

| **Term** | **What it means** | **Founder watch-outs** |
| --- | --- | --- |
| **Board Seat** | A formal voting seat on your board, usually held by the lead investor. | Adds governance muscle. Make sure the board stays founder-friendly (e.g., 2 founders : 1 investor at seed). |
| **Protective Provisions** | List of actions that require investor consent (e.g., issuing new shares, selling the company). | Standard, but keep the list short so you’re not handcuffed on routine decisions. |

**3. Downside protection**

| **Term** | **What it means** | **Founder watch-outs** |
| --- | --- | --- |
| **Liquidation Preference** | Investor gets paid back before common shareholders in a sale. *1× non-participating* is seed standard. | Push back on anything richer (e.g., participating or >1×). |
| **Anti-Dilution** | Adjusts investor price if you raise a down-round later. Two flavors: **full-ratchet** (bad) and **weighted-average** (more common). | Aim for weighted-average—or none at all—so you’re not crushed in a down round. |

**4. Administrative stuff**

| **Term** | **What it means** | **Practical tip** |
| --- | --- | --- |
| **Information Rights** | Investor right to receive regular financials and KPIs (typically quarterly + annual). | Standard; just align on frequency and format so it isn’t a distraction. |
| **Pro-Rata Right** | Allows investors to maintain their ownership % in future rounds. | Usually standard, but large funds may demand “super” pro-rata (more than their share). Cap it if you can. |
| **ROFR (Right of First Refusal)** | Company (and sometimes investors) can match a third-party offer to buy existing shares. | Protects against unwanted shareholders but can slow secondary sales—know the mechanics. |

### 6.2 To board or not to board

First, it’s important to understand the legal role of a board member. They have a fiduciary duty to act in the company’s best interest—not the founders’ and not even their own VC fund’s. Practically, this means board members oversee governance: approving major decisions (e.g., financings, acquisitions, large expenditures—typically anything above six figures) and holding leadership accountable. Yes, that includes potentially hiring or firing the CEO. But in reality, replacing a CEO at an early-stage startup is extremely rare. If the company’s in trouble, a new CEO typically isn’t going to magically fix things at that stage.

Whether you need a formal board at the seed stage depends largely on your company’s maturity. If you’re still ideating, building an MVP, and have a team of fewer than five, a formal board meeting probably won’t add much value (“Yup, still figuring it out!”). Once you establish a regular operating rhythm—meeting customers, shipping features, hiring consistently—that’s when a board starts to become genuinely useful.

Typically, your board at seed stage consists of the founder(s)—usually two founders, and definitely no more than three—and the partner from your lead investor. The primary benefit of having a board early is that it forces a quarterly pause, pulling you out of daily firefighting and providing space to reflect strategically. Board meetings at this stage are typically informal, collaborative discussions, rather than rigid formalities.

In general, boards can be helpful, and you shouldn’t be concerned if your seed investor wants one. Conversely, if they aren’t pushing for one, there’s no need to demand it. Keep in mind, however, that board members are extremely difficult to remove, and boards quickly become bloated if you add new members every time you raise another round.

While this is unlikely, watch out for clauses requiring early independent board members—this reduces your control prematurely.

### **6.3 Round construction**

“Round construction” is just VC-speak for deciding how many investors to include in your financing round. You’ll inevitably hear from follow-on investors—those who aren’t leading the round but want a piece of it anyway. As someone who’s raised capital and also been an investor, let me say clearly: **less is more**.

First, there absolutely are exceptional follow-on investors out there who can add genuine value. However, remember this: VCs are generally reactive, not proactive. It’s entirely up to you as the founder to activate them and put them to work, such as asking them explicitly for customer introductions or tapping their network for hiring help.

What you definitely don’t want is investors sitting passively on your cap table, only resurfacing at the next financing round to lobby aggressively for their pro rata rights—especially at a time when you’re already stressed about making room for new investors.

A well-structured seed round typically has the lead investor providing roughly 80–90% of the capital, complemented by just one or two carefully selected follow-on investors (or angels) whom you deeply trust and believe you can actively leverage to help your company grow. If you’re not confident they’ll actively contribute, skip these extra investors altogether. Otherwise, you’ll just end up with more paperwork, more signatures to chase, and painful pro rata negotiations down the line.

### 6.4 Governance gotchas

Your lawyers should catch most governance gotchas and lobby to keep terms standard and founder-friendly. That said, if you understand the following key concepts, you can confidently handle many of these negotiations yourself, saving time and legal fees (although, they always find a way). Here’s what you need to know:

| **Term & definition** | **What’s standard at Seed** | **Should you push back?** |
| --- | --- | --- |
| **Excessively broad protective-provision approval rights** – Investor veto over routine matters. | Protective provisions limited to major events (new share classes, sale of company, large debt). | **Definitely** |
| **Class-based voting rights** – A small class of investors can block key decisions. | One overall preferred vote (or board) gate, not multiple class vetos. | **Definitely** |
| **Participating preferred stock** (“double-dip” liquidation) – Investors get 1× back **and** share the rest. | 1× **non-participating** preferred. | **Definitely** |
| **> 1× liquidation preference** – Investors take more than their original investment before commons participate. | 1× non-participating. | **Definitely** |
| **Full-ratchet anti-dilution** – Reprices investor shares to the lowest future price. | Weighted-average, or no anti-dilution at seed. | **Definitely** |
| **Redemption rights** – Investor can demand cash repayment after X years. | None at seed. | **Definitely** |
| **Overly frequent information rights** – Monthly deep-dive reporting. | Quarterly updates + annual financials. | **Potentially** (push for lighter cadence) |
| **Drag-along rights** – Investors can force a sale. | Drag-along requiring board + majority common approval. | **Potentially** (limit scope/thresholds) |
| **ROFR / Co-sale over-reach** – Heavy limits on founder liquidity. | Company + investors get standard ROFR; reasonable co-sale. | **Potentially** (negotiate caps) |
| **Excessively long founder vesting** – > 4 yrs or punitive cliffs. | 4-year vesting with 1-year cliff (re-vesting common). | **Definitely** |
| **Acceleration terms** – Vesting on change of control. | Double-trigger (sale **and** termination). | **Potentially** (try for single-trigger) |
| **Super pro-rata rights** – Investor can buy **more** than their ownership % in later rounds. | Plain pro-rata up to current ownership. | **Definitely** |

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💡 Pro tip: A four-year vesting schedule with a one-year cliff is standard, but watch out for unusually long or overly restrictive terms. Don’t worry if your VC requests that founders re-vest their shares as part of your financing. Candidly, this mechanism primarily protects founders from each other. For example, if one founder gradually becomes less involved or passive, re-vesting prevents other founders from feeling resentment that someone’s benefiting without pulling their weight. It’s simply a way of keeping everyone aligned and motivated over the long haul.

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## 7.1 The “Golden Rules” of fundraising

Here are the core fundraising commandments I’ve seen founders consistently benefit from following:

**#1: Thou shalt not spam investors**

If an investor is interested, they’ll naturally lean in. Bombarding them with repeated follow-ups won’t build genuine excitement and sets a bad tone for what should be a trusting, decade-long partnership. Remember, a forced yes today can quickly become a “why did we do this?” tomorrow.

**#2: Thou shalt raise the optimal amount**

There is a Goldilocks zone for fundraising. Raise too much, and you’ll dilute your ownership unnecessarily. Raise too little, and you’ll find yourself scrambling to hit the milestones needed to unlock the next financing. Your job is to find the sweet spot—enough capital to comfortably reach the next proof-point, but no more than that.

**#3: Thou shalt do investor references**

Always talk to other founders your prospective investor has backed, and ask candid questions:

- How does the investor handle conflict or disagreement?
- Do they show up to meetings prepared and engaged, or do they just dial in and multitask?
- Where have they been most helpful?
- If the founder could change one thing about their investor, what would it be?

Critically, don’t just ask the investor to connect you with a single cherry-picked founder. Instead, request their full portfolio list (or research it yourself) and then independently select who to speak with. You’re trying to uncover potential red flags or hidden frustrations; having investors handpick a reference won’t help you find where the bodies are buried.

**#4: Thou shalt keep your slide decks short**

Long decks dilute your message. Be concise, clearly articulate your vision and core themes, and skip any unnecessary filler. Investors prefer clarity and brevity—make every slide count.

**#5: Thou shalt master your narrative**

This might be the single most important thing you do during fundraising. Your narrative is the engine behind every investor’s decision to invest—it clarifies why your company matters, why it’ll win, and why you’re the ones to make it happen. Investors invest in compelling narratives, not spreadsheets or features. Own your story, practice it repeatedly, and make sure it’s both authentic and memorable.

**#6: Thou shalt not pick an investor solely because they offered the highest valuation**

Yes, valuation matters—ownership is important, and dilution impacts your long-term incentives. However, selecting the investor who proposes the highest valuation, without considering fit, can be counterproductive or even harmful to your company’s future. Prioritize picking the right **person**, not just the firm or the biggest check. Choose someone aligned with your vision, values, and operating style, and negotiate a fair, market-appropriate valuation. The investor you select will shape your journey in profound ways—so don’t trade long-term value for a short-term valuation win.

## **8. About the author**

I started my career on the sales side of two early-stage startups: [**Box**](https://box.com/) ([IPO in 2015](https://techcrunch.com/2015/01/23/box-skyrockets-50-to-more-than-21-per-share-in-first-minutes-as-a-public-company/)) and [**Segment**](https://segment.com/) ([acquired by Twilio in 2020 for $3.2B](https://techcrunch.com/2020/11/02/twilio-wraps-3-2b-purchase-of-segment-after-warp-speed-courtship/)). After those two rides, I spent five years as a venture capitalist, meeting thousands of companies and investing in several along the way.

In May 2024, I joined **Northflank**—a company I originally invested in during their 2022 seed round—as Chief Operating Officer. My venture experience was entirely B2B, so the advice in this post is primarily tuned for B2B founders, though the core principles apply broadly to any founder navigating their early-stage fundraising journey.

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## FAQs

**1. How long does it usually take to raise a seed round?**

If things go exceptionally smoothly, you can wrap up your seed round within a few weeks—but that’s uncommon. In practice, I’d budget roughly two weeks at the fastest end (rare), around six weeks as typical, and potentially up to twelve weeks if the process is dragging. Factors that can influence timing include how prepared you are, your ability to keep investors moving at the same pace, and how quickly investors get excited about your company.

**2. Do I need revenue to raise a seed round?**

Nope, not strictly. But it definitely helps to have at least some early evidence—user traction, customer conversations, pilots—that demonstrates people actually want to adopt or buy what you’re building. Investors mostly want to see that you’re solving a real problem and that someone cares enough to pay or put effort into adoption, even if they haven’t yet.

**3. What equity do founders typically give up in a seed round?** 

Typically around **10–20%**. And keep in mind, if you use SAFEs or convertible notes with valuation caps or discounts, you might face additional dilution down the road when they convert to equity.

**4. Why should I raise a seed round?**

Great question—it’s not always the right move. The fundamental purpose of venture capital is to help your company grow faster than it could by relying solely on cash flow. Most startups spend several years without generating positive cash flow, and venture capital provides the resources to sustain you through that period.

Additionally, many markets—particularly in B2B—tend to consolidate quickly around a small handful of winners. In consumer markets, the consolidation can be even more extreme, often leaving just one dominant player. In either scenario, the ability to move quickly matters a lot. Venture funding can help you accelerate growth and become that category-defining leader before competitors catch up.

If you’re operating in a market where rapid scale doesn’t yield meaningful benefits—or if you can realistically bootstrap your way to becoming a leader without outside capital—then venture funding might not be the right fit.

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  <title>How to build an Internal Developer Platform (and why you might not want to)</title>
  <link>https://northflank.com/blog/how-to-build-an-internal-developer-platform</link>
  <pubDate>2025-05-27T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[An Internal Developer Platform (IDP) is a self-service layer that abstracts away the complexities of infrastructure so that developers can deploy code without touching Kubernetes manifests, Terraform modules, or CI/CD pipelines.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/continuous_delivery_blog_post_1_e533103957.png" alt="How to build an Internal Developer Platform (and why you might not want to)" />## What is an Internal Developer Platform?

An **Internal Developer Platform (IDP)** is a self-service layer that abstracts away the complexities of infrastructure so that developers can deploy code without touching Kubernetes manifests, Terraform modules, or CI/CD pipelines. It's the internal equivalent of platforms like Vercel or Heroku, built specifically for the needs (and constraints) of your company.

It centralizes the workflow for deploying and operating software: provisioning environments, managing secrets, deploying workloads, and monitoring health. Done well, it enables fast, secure, and reliable shipping, without asking every developer to become a DevOps expert.

## Why companies build their own Internal Developer Platforms

On paper, building an IDP sounds strategic. It promises developer autonomy, consistency across environments, and a better security posture. Teams imagine faster deploys, fewer tickets to infra, and more time spent shipping product. But the dream and the reality are rarely aligned.

The platform team sets out to build abstractions over Kubernetes and cloud infrastructure. 

They assemble a stack of open-source tools: Argo for GitOps, Vault for secrets, Prometheus for metrics, Istio or Linkerd for service mesh. They script templates, wire up automations, and create CLIs or dashboards for developers. It starts to look promising.

But what starts as a well-intentioned attempt to reduce friction often turns into a maintenance nightmare. The more features the team builds, the more surface area they commit to supporting. And the further they drift from the real goal: helping developers ship business logic that serves users.

## How to build an Internal Developer Platform (if you still want to)

![giphy.gif](https://assets.northflank.com/giphy_3706b7b368.gif)

### Step 1: Start with developer personas and use cases

Understand who you’re building for. What kind of workloads are being deployed: stateless microservices, cron jobs, stateful databases? Are developers comfortable with GitOps? Do they prefer CLIs or UIs? These questions will shape every layer of your platform.

### Step 2: Assemble your core infrastructure stack

You’ll be building on Kubernetes, but that’s just the beginning. 

A functional IDP needs to handle provisioning, networking, observability, security, and developer interfaces. A typical stack includes:

- Kubernetes for orchestration (EKS, GKE, AKS, or self-managed)
- ArgoCD or Flux for GitOps deployment
- Prometheus, Grafana, Loki, and Tempo for observability
- Vault or Sealed Secrets for secrets management
- Cert-manager for TLS automation
- NGINX or Traefik for ingress
- CSI drivers for persistent storage
- KEDA for workload autoscaling

You’ll also need to set up a service mesh (Istio, Linkerd, or Cilium) if you're managing east-west traffic or want to enforce mTLS.

### Step 3: Build the abstractions

This is where most platform teams stall. The Kubernetes API is too low-level for product engineers. You’ll need to abstract these primitives into composable services:

- Define Helm or Kustomize templates for deploying services
- Create opinionated defaults for environments, secrets, autoscaling, and resource limits
- Build a UI or CLI that lets developers launch services, view logs, roll back deployments, and see metrics
- Implement GitOps flows so developers can commit YAML or JSON manifests and watch their workloads go live
- Integrate with your CI pipelines for push-to-deploy automation

### Step 4: Support multi-tenancy and governance

If you’re serving multiple product teams, you need tenant isolation. Namespaces alone aren’t enough. Implement:

- NetworkPolicies to isolate workloads
- RBAC across Kubernetes and platform UI
- Audit logs for compliance
- Per-team quotas on CPU, memory, and storage

<InfoBox className='BodyStyle'>

💡 Also: think about secrets management from day one. Hardcoded secrets in Helm values files will come back to bite you.

</InfoBox>

### Step 5: Make it observable

A good platform is observable by default. This includes:

- Pre-wired metrics, logs, and traces for every deployed service
- Central dashboards for performance, error rates, and cost
- Alerts on pod restarts, CPU throttling, failed jobs
- Per-deployment views in both UI and CLI

### Step 6: Bake in self-service and security

Developers should be able to:

- Spin up preview environments from PRs
- Restart services
- Trigger rollbacks
- Rotate secrets
- Access logs and metrics

Security should be enforced by the platform, not downstream teams. Run containers as non-root, apply seccomp profiles, disable capabilities, and sandbox workloads when running untrusted or third-party code.

### Step 7: Automate the pain away

IDPs only work if they reduce toil. You’ll need to automate:

- Environment creation
- DNS and TLS setup
- Canary and blue/green deployments
- Image scanning and policy enforcement
- Cost reporting

And finally: documentation. If developers can’t figure out how to use the platform, they’ll go around it.

## The real cost of building an Internal Developer Platform

The hard truth is that most internal platforms degrade over time. They rot. Tooling versions drift. APIs break. Documentation gets stale. And the team that built the platform becomes a bottleneck for every new feature request.

Worse, the adoption often never comes. Developers keep bypassing the platform because it’s too rigid, too slow, or just too confusing. Suddenly, the team has spent 18 months and seven figures on a product no one wants to use.

This is a pattern repeated across the industry: teams build internal Herokus and end up maintaining brittle toolchains. They’re spending more time debugging their factory than baking anything in it.

## So why do it?

There are cases where building your own platform makes sense. If you’re a FAANG-scale company with unique compliance requirements, massive scale, or infrastructure so custom that nothing off-the-shelf fits, then yes. Build it. But be ready to dedicate entire teams to it, like game studios maintaining custom engines.

For everyone else, the rationale starts to fall apart.

Buying a platform used to mean sacrificing control. Early PaaS tools like Heroku and Cloud Foundry were too rigid, too opinionated, and couldn’t support complex enterprise use cases. But that’s no longer true.

## Why buying now makes more sense

Modern platform solutions like Northflank give you the same abstractions: multi-cloud support, workload orchestration, secrets management, observability, but without the multi-year investment. They’re extensible. You can run them in your own cloud. They support GitOps, APIs, CLIs, and UI workflows out of the box.

![unnamed_705cf6938d.avif](https://assets.northflank.com/unnamed_705cf6938d_c8b54c72a2.avif)

These platforms aren’t built in isolation. They benefit from seeing tens of thousands of real-world deployments, absorbing patterns, and adapting to emerging use cases faster than any internal team could.

> *Read more about the "build vs buy" conundrum [here](https://northflank.com/blog/build-vs-buy-the-platform-engineers-conundrum).*
> 

And they work. Companies using Northflank get time back. They avoid wasting quarters on YAML templates and Terraform modules and instead spend that time building actual product.

## From factory to feature

The metaphor is obvious, but worth repeating: you’re not in the business of building factories. You’re in the business of shipping software. The more time you spend optimizing the conveyor belts, the less time you spend delivering what your customers care about.

Your users don’t care about your CI pipeline. They care about whether your product works. Every hour your team spends fine-tuning a deployment script is an hour lost on feature development.

So yes, you *can* build your own Internal Developer Platform. But if your team’s goal is to move fast, stay secure, and ship features that make an impact, it’s probably not worth it.

## Final thoughts

![pipeline-overview.webp](https://assets.northflank.com/pipeline_overview_a452c8a9ce.webp)

Before you sink another month into writing Helm charts or building a UI for ephemeral environments, ask yourself a simple question:

> Are we solving problems unique to our company, or just repeating the same work every other team is doing?
> 

You already know the answer.

**Northflank** helps engineering teams skip the platform tax and go straight to delivery. Git-native. Developer-friendly.

Start [deploying with Northflank](https://northflank.com/) today, for free.

<InfoBox className='BodyStyle'>

## 💭 FAQs

### 1. What’s the difference between a platform and a portal?

A **platform** is the underlying system that automates and abstracts infrastructure—handling deployment, secrets, observability, governance, etc. 

A **portal** is just the interface. It's how developers interact with the platform: a UI, a CLI, a set of APIs.

Building a portal without a robust platform underneath is like building a cockpit without an airplane. It might look slick, but it doesn't actually fly.

### 2. Is GitOps required to build an IDP?

Not technically, but it's the current best practice. GitOps brings auditability, rollback safety, and better alignment between code and infra. If you're not using GitOps, you're probably rebuilding a worse version of it with custom scripts.

### 3. Can’t we just use Backstage?

Backstage is a catalog and portal framework. It’s great for visibility, documentation, and onboarding, but it doesn’t provision environments, handle deployments, or enforce security policies out of the box. You still need to build or plug in the actual platform logic behind it.

### 4. How long does it take to build a real IDP?

If you want something usable by developers, expect 12 months minimum with a dedicated team. Some teams have come to us after trying it for 3+ years. 🙂 And that's just to hit parity with modern out-of-the-box solutions. Keeping it maintained is a permanent cost center.

### 5. What are signs that our internal platform is failing?

- Developers bypass it or complain it’s too rigid
- The platform team becomes a bottleneck for changes
- Onboarding takes longer than it should
- Documentation is outdated or nonexistent
- You're spending time maintaining glue code instead of building product

### 6. What’s the case *for* building, if any?

If your infrastructure or compliance requirements are so specific that no off-the-shelf tool fits (and you have the headcount to support it) building might be justified. But that’s rare. Most teams are better off buying and customizing an existing solution.

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>What is continuous delivery? Tools, pipelines, and how modern teams are implementing it</title>
  <link>https://northflank.com/blog/continuous-delivery</link>
  <pubDate>2025-05-27T17:48:00.000Z</pubDate>
  <description>
    <![CDATA[Understand how continuous delivery works, how it fits into modern CI/CD workflows, and which tools engineers rely on. See how teams use Northflank to simplify their pipelines.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/continuous_deployment_blog_post_2_9993692a0a.png" alt="What is continuous delivery? Tools, pipelines, and how modern teams are implementing it" />> Continuous delivery is the engineering practice of keeping every code change in a deployable state through automated build promotion, release workflows, and environment-specific configuration under version control.

How do you know your team’s continuous delivery setup can push code without breaking something downstream?

I’ll assume you’re either a platform engineer building deployment infrastructure or an engineering lead responsible for delivery standards. You know it’s one thing to get a build passing, and it’s another to move that build through staging and production without manual promotion steps, failing pipelines, or gaps in release ownership.

If you’ve run into issues like:

- Continuous Integration (CI) jobs that don’t pass build artifacts or metadata correctly to the deployment steps
- Manual tagging and promotion across environments
- Limited visibility between staging and production

…then you’ve seen firsthand why continuous delivery (CD) needs to be part of your pipeline strategy.

We’ll cover how teams are approaching continuous delivery in 2025, from where it fits in modern pipelines to how it’s implemented, managed, and scaled in production.

<InfoBox className='BodyStyle'>

### ⚡ TL;DR for readers in a hurry

- Continuous delivery helps your team ship code that’s always in a deployable state.  
- This article breaks down how CD works, where it fits in your pipeline, and how production teams are structuring delivery in 2025.  
- Here are some of the tools teams are using for CD today:

    1. [**Northflank**](https://app.northflank.com/signup) – Unified platform to build, deploy, and promote across environments without scripting or manually integrating tools together.  
    2. [**Argo CD**](https://argoproj.github.io/cd/) – GitOps-based delivery controller for Kubernetes clusters.  
    3. [**Spinnaker**](https://spinnaker.io/) – Multi-cloud delivery platform with detailed deployment strategies.  
    4. [**Harness**](https://www.harness.io/) – CD-as-a-service platform with built-in approval gates and policy controls.  
    5. [**GitHub Actions**](https://github.com/features/actions) – CI-first with flexible delivery workflows using custom YAML.  

Want a faster way to manage CI/CD across environments without manually integrating multiple tools?

**[→ Start building with a unified delivery platform](https://app.northflank.com/signup)**

</InfoBox>



## What is continuous delivery?
Continuous delivery is the engineering practice of keeping every code change in a deployable state through automated build promotion, release workflows, and environment-specific configuration under version control.

Yes, Continuous Integration (CI) handles your build and test automation, but what happens once the code passes the tests? Passing tests doesn’t mean your code is ready to ship. That’s when you’ll start noticing gaps in the delivery process, things like:

- No artifact promotion *(builds stay in CI without being tagged or moved forward)*
- Missing release logic *(no clear steps for packaging, versioning, or promoting builds)*
- Manual steps between environments *(handoffs, approvals, or scripts triggered by hand)*

Continuous delivery (CD) is what fills that gap. It connects a successful build to a production-ready release through automated workflows, environment promotion, and controlled release strategies.

It doesn’t mean every change is deployed to production immediately, but it does mean every change is in a deployable state and can be released at any time.

That’s the key distinction:

- **Continuous integration (CI)** = build and test automation
- **Continuous delivery (CD)** = artifact promotion and release preparation
- **Continuous deployment (CD)** = fully automated deployments to production with no manual approval

CI and CD are often grouped together, but they solve different problems and require different tooling, ownership, and controls.

### Where continuous delivery fits in your pipeline

Now that you have a clearer understanding of what continuous delivery is, you need to see how it fits into the larger CI/CD pipeline.

Take a look at the diagram below, which shows how CD usually fits between CI and deployment in a standard pipeline.

![Flowchart showing the CI to CD to deployment pipeline, with CD steps highlighted in a separate section](https://assets.northflank.com/continuous_delivery_pipeline_diagram_73634e5551.png)*Where continuous delivery fits between CI and deployment*

As you can see in the simple diagram above, the CD layer sits between CI and deployment, taking over once the code is built and tested, and preparing it for release.

> Where CD fits in your pipeline depends on where your team draws the line between building code and releasing it. If you’re responsible for packaging, tagging, and promoting changes through environments, you’re working in the delivery layer, even if you’re not pushing to production directly.
> 

It’s important that you understand where continuous delivery begins and ends, especially as teams scale and start managing multiple environments, where ownership boundaries and release responsibilities become more complex.

Once CD is in place, teams gain faster iteration without compromising control. You can:

- Promote builds without needing to revalidate everything
- Track what went live, when, and through which gate
- Reduce environment inconsistencies between staging and production by aligning release versions
- Handle hotfixes and backports with minimal complexity using tagged, versioned artifacts

Continuous delivery isn’t about moving fast blindly; it’s about removing blockers between code and deployment without giving up precision.

## CI vs CD vs CD (Deployment): What’s the actual difference?

By now, you’ve seen where continuous delivery fits in the pipeline. But when engineers and CI/CD platforms talk about “CD”, they don’t always mean the same thing.

That’s because the term often overlaps between delivery and deployment, and the difference influences how teams define ownership, set automation boundaries, and design platform workflows.

So, for example, if you’re running a platform used by multiple product teams, you might automate everything up to staging but require manual approval before deploying to production. In that case, you’re practicing continuous delivery, not [continuous deployment](https://northflank.com/blog/continuous-deployment). And if that line isn’t clearly defined, it can lead to ownership gaps or unpredictable releases.

Let’s look at how these concepts differ in real-world usage:

| **Term** | **What it handles** | **What it looks like** | **Who typically owns it** |
| --- | --- | --- | --- |
| **CI** (Continuous Integration) | Code health and validation | Builds and runs tests automatically on every commit or pull request | Application or feature teams |
| **CD** (Continuous Delivery) | Release readiness | Packages artifacts, tags releases, and promotes builds to staging or pre-prod | Platform teams or shared DevOps |
| **CD** (Continuous Deployment) | Automated production releases | Pushes changes to production automatically after validation | Often shared, but tightly controlled in regulated teams |

One thing to keep in mind is that **not every team requires full automation all the way to production**.

Let’s say you’re working on a high-compliance system; you might automate everything through staging, but require a manual gate or change review before it goes into production. That’s still continuous delivery, even if you’re not doing continuous deployment.

On the other hand, if you’re shipping a SaaS product with short feedback loops, it might make sense to auto-deploy to production as soon as integration and staging checks pass.

> Now, the key is understanding where automation ends and responsibility begins, and designing your pipeline to match that boundary clearly.
> 

## Inside a continuous delivery pipeline

Now that we’ve clarified how continuous delivery differs from CI and deployment, let’s look inside the delivery layer itself.

What happens once a build passes CI, but isn’t yet in production?

This is where continuous delivery takes over, with a structured automated path that prepares each change for release. These steps aren’t just routine tasks. They’re essential control points that make your delivery process predictable, testable, and traceable.

Before we break it down, take a look at the diagram below. It maps out the core stages of a typical CD pipeline and highlights where automation, promotion, and release logic fit between CI and deployment.

![Flowchart of a continuous delivery pipeline from build to deployment, with CD steps automated](https://assets.northflank.com/continuous_delivery_pipeline_lifecycle_ff1e7f9a4d.png)*Lifecycle of a continuous delivery pipeline, showing where CD fits between CI and deployment*

A standard CD pipeline typically includes:

### 1. Artifact packaging and versioning

After CI, the pipeline packages the build output, such as a Docker image or a binary, and assigns a version tag to it. This ensures every change is traceable and that the same artifact can be promoted safely across environments.

> Example: A versioned build like `web-app:v1.3.5`, tagged at the commit that passed all CI checks and ready for promotion.
> 

### 2. Release workflow execution

The tagged artifact is promoted to staging or test environments. This stage may include integration tests, smoke tests, or load tests. In many teams, this logic is defined declaratively via GitOps or pipeline config files and applied automatically.

### 3. Environment-specific configuration

When promoting builds across environments, your CD systems inject environment-specific values, such as secrets, feature flags, API endpoints, or resource definitions, without modifying the core artifact. This separation enables consistent behavior while adapting to environment context.

### 4. Approval gates and release controls

For teams not practicing continuous deployment, this step inserts a control point. An approval gate might be triggered via GitHub checks, Slack workflows, Jira tickets, or a dashboard toggle, ensuring production criteria are met before release.

### 5. Rollback support and observability

Modern CD systems include visibility and rollback mechanisms. Tagged releases, versioned changelogs, deployment audit trails, and release metadata help teams trace what went live, when and why, and recover quickly if needed.

## Tools that support continuous delivery (and where they fit best)

Now that you’ve seen what a complete CD pipeline looks like, the next question is: how do teams implement it in practice?

There’s no single tool that handles every stage out of the box. Instead, most teams compose their delivery using combinations of CI servers, deployment automation tools, and GitOps controllers, each with its own trade-offs in terms of visibility, flexibility, and operational overhead.

Let’s see where five common platforms fit within the delivery lifecycle:

### 1. Northflank

Modern CI/CD platforms like [Northflank](https://northflank.com/) treat delivery as a core capability. You can go from build to promotion to environment release with full visibility and minimal manual configuration. It’s ideal for teams that want integrated CD workflows without having to orchestrate separate tooling layers.

![northflank home page.png](https://assets.northflank.com/new_northflank_home_page_9600c53fbb.png)

> Best for: Engineering teams that need fast, reliable delivery without the burden of maintaining complex delivery setups.
> 

*See [how Clock scaled 30,000 deployments with 100% uptime using Northflank](https://northflank.com/blog/scaling-30-000-deployments-with-100-uptime-how-clock-uses-northflank-to-simplify-infrastructure).*

### 2. Jenkins

Jenkins gives teams flexibility to script and customize every part of the CD flow, tagging, artifact handling, and environment promotion. But it doesn’t ship with delivery patterns built in. Most setups require maintaining your own infrastructure, plugins, and delivery logic over time.

![jenkins home page](https://assets.northflank.com/jenkins_website_min_455a49acba.png)

> Best for: Teams with existing Jenkins investment and the resources to manage custom CD pipelines.
> 

*See [Jenkins alternatives in 2025: CI/CD tools that won’t frustrate DevOps engineers](https://northflank.com/blog/jenkins-alternatives-2025)*

### 3. GitHub Actions

GitHub Actions can handle parts of the CD process like packaging and promotion steps, especially for teams already using GitHub. But it lacks native support for multi-environment workflows, visibility into deployments, and integrated rollback strategies.

![github actions home page](https://assets.northflank.com/github_actions_home_page_00a6496885.png)

> Best for: Teams working entirely in GitHub that need simple delivery automation.
> 

*See [the best GitHub Actions alternatives for modern CI/CD in 2025](https://northflank.com/blog/github-actions-alternatives)*

### 4. GitLab CI/CD

GitLab pipelines support delivery workflows out of the box, and approvals, promotions, and rollbacks can all be configured in the same place. However, delivery logic is tightly coupled to GitLab’s ecosystem, which can limit extensibility and flexibility across platforms.

![new gitlab cicd home page](https://assets.northflank.com/new_gitlab_cicd_home_page_6db2ffa6b1.png)

> Best for: Teams using GitLab that want to centralize their CI/CD setup within a single platform.
> 

*See [9 Best GitLab alternatives for CI/CD in 2025](https://northflank.com/blog/best-gitlab-alternatives)*

### 5. Argo CD

Argo CD excels at GitOps-based delivery in Kubernetes environments. It tracks application state using Git, promotes changes declaratively, and integrates well with multi-cluster setups. But it relies on external tools for CI and doesn’t manage builds or artifact packaging.

![argocd home page](https://assets.northflank.com/argocd_home_page_59fa1af37c.png)

> Best for: Platform teams focused on GitOps delivery for Kubernetes workloads.
> 

*See [Argo CD alternatives that don’t give you brain damage and simplify DX for GitOps, clusters & deployments](https://northflank.com/blog/argo-cd-alternatives-northflank-developer-platform-git-ops-self-service)*

## What makes continuous delivery hard to get right?

Even with the right tools, getting CD to work at scale isn’t just a config problem; it’s an orchestration challenge.

Teams don’t struggle because they lack pipelines. They struggle because the coordination across build, test, and release often breaks down under real-world complexity.

See where things tend to go wrong:

### Testing bottlenecks slow down release readiness

Your CI may pass, but delivery often depends on integration, performance, and environment-specific tests. If those stages block promotion or require manual coordination, CD loses its value.

### Limited visibility across delivery stages

Especially for platform teams, it’s hard to answer: *What’s in staging? What passed? What’s approved for prod?* Without a clear view across build, release, and environment states, handoffs become a trial-and-error process.

### Environment sprawl introduces inconsistency

Managing delivery across dev, preview, staging, and production environments often results in fragmented workflows, config mismatches, and untracked promotions. CD only works when the environment lifecycle is well-defined and automated.

### Poor tagging and versioning lead to traceability issues

Without proper tagging at release points, it’s hard to know what build made it to which environment. You can’t debug, promote confidently, or recover from failure unless your artifacts are versioned and tied to the code that produced them.

### Failures are hard to debug across multiple systems

When delivery spans CI runners, CD scripts, GitOps config, and external tools, a broken release might require tracing failures across logs in four places. And if ownership is unclear, the fix is delayed even further.

## How Northflank simplifies continuous delivery

If you’ve run into any of the delivery challenges above, like manual promotion steps, unclear release visibility, or inconsistent environments, then you know that fixing CD isn’t about adding another script or tool. It’s about integrating delivery into your platform architecture, not layering it on as an afterthought.

So, in place of running separate CI servers, custom deployment scripts, preview infrastructure, and tagging logic, Northflank provides a unified delivery system that works across environments and adapts to your workflow.

Let’s break down how that addresses the problems we’ve covered:

### 1. Pipeline setup that scales with your workflow

You can build scalable CD pipelines in Northflank through either the visual editor or config files. Define your build, staging, and production stages in one place, then set up release flows that automate tagging, promotion, and deployment, without having to maintain custom pipelines or scripts to connect separate tools.

This means you can:

- Create and manage pipelines visually or via config
- Add release flows to handle build promotion, database backups, deployment, and more
- Configure Git triggers or webhooks to automate releases
- Set environment-specific rules for preview, staging, or production
- Rollback or migrate deployments with a defined, testable workflow

Look at what it looks like when your environments, services, and jobs are aligned in a single visual pipeline, from preview to production:

![Northflank visual pipeline showing preview, development, staging, and production stages with services and jobs](https://assets.northflank.com/pipeline_overview_a452c8a9ce.webp)*Visual pipeline in Northflank showing preview, dev, staging, and production environments, each mapped to services and jobs.*

See “[Create a pipeline and release flow](https://northflank.com/docs/v1/application/release/create-a-pipeline-and-release-flow)” and “[Configure a release flow](https://northflank.com/docs/v1/application/release/configure-a-release-flow)” for setup steps and examples.

### 2. Git-based delivery with tagging and promotion

You can trigger releases from Git branches or tags and promote builds across environments while keeping version tracking intact. Tagging isn’t an afterthought; it’s built directly into the release process.

In Northflank, tags let you:

- Track which version of a service, job, or addon is deployed in each environment
- Promote builds by tag across preview, staging, and production
- Apply environment-specific rules, like node pool selection or secret access
- Control network visibility by tagging resources as `public`, `private`, or `vpc`
- Quickly identify deployments using metadata like `experimental` or `using-deno`

Tags are available across all your projects and teams. Every tagged resource is visible from a central tags overview page, and you can use tags to restrict access, define scheduling rules, or control how workloads behave in your infrastructure.

You can tag services, jobs, and addons to reflect environments like `staging`, `production`, or custom policies like `gpu` or `spot`. These tags stay attached to resources as they’re promoted across environments, giving you clear visibility and control over every workload in your release pipeline.

See how resources are grouped and tracked by tag across services, jobs, and addons:

![Northflank tagged resources list showing services, jobs, and addons under a “production” tag](https://assets.northflank.com/tagged_resources_list_d37ffdda84.webp)*Track which resources are part of each environment by viewing all services, jobs, and addons grouped by tag*

To see how it works, check out [Tag workloads and resources](https://northflank.com/docs/v1/application/release/tag-workloads-and-resources).

### 3. Preview and production environments, side-by-side

You can spin up preview environments automatically from pull requests, giving your team an isolated space to test every change before merging. Each preview runs with its own set of versioned secrets, deploy rules, and resources, so your staging never deviates from production.

In Northflank, environments are fully scoped and declarative. You can define infrastructure and configuration per environment, including:

- Secrets and environment variables
- Build and deploy logic
- Release flows with triggers or manual promotion

This keeps your workflow consistent and reproducible across teams and stages.

See how environments are managed with separate releases and metadata for each deployment:

![Visual representation of a release flow with multiple build, deploy, and backup stages running in sequence and parallel](https://assets.northflank.com/release_run_9b176bf9e4.webp)*A release flow showing sequential and parallel workflows for building, backing up, and deploying across environments*

Each pipeline stage tracks its own release, and preview and production stay isolated but connected through version control.

To set this up, check out [Run and manage releases](https://northflank.com/docs/v1/application/release/run-and-manage-releases).

### 4. Unified platform, no manual orchestration required

You don’t need to manually integrate CI runners, deployment tools, or custom scripts. With Northflank, you build, test, promote, and release from one place, with visibility and control at every stage.

You can configure CI/CD on services, jobs, and builds independently or in combination. Define exactly when a build should run, what it should deploy, and how environments should respond, automatically or through manual triggers.

Set commit filters, path rules, and deployment logic per resource to support different branches or microservices. This keeps your pipeline structured without writing custom automation scripts.

See how a service builds and deploys directly from your Git repo, with CI/CD logic applied automatically:

![Combined service showing build and deploy behavior with CI and CD enabled](https://assets.northflank.com/combined_service_overview_2315c16878.webp)*Combined service showing build and deploy behavior with CI and CD enabled*

You can also configure advanced build rules like skipping specific commits or applying path-based filters.

Northflank gives you the flexibility of a modular CI/CD setup, without having to maintain custom orchestration across multiple systems.

Learn more in the [Manage CI/CD](https://northflank.com/docs/v1/application/release/manage-ci-cd) guide.

## FAQ: Answers to what engineers commonly search

Still have questions about CD? Look at a breakdown of what engineers often ask, and how it applies to real-world delivery workflows.

**1. What is meant by continuous delivery?**

Continuous delivery (CD) is the engineering practice of ensuring every change is always in a deployable state. It uses automated workflows to package builds, promote them across environments, and prepare them for production, without requiring manual rework after CI.

**2. What is the difference between CI and CD?**

CI (Continuous Integration) automates build and test processes when code is committed. CD (Continuous Delivery) handles what happens after: packaging artifacts, tagging versions, running release flows, and managing environment-specific configuration before deployment.

**3. What is the continuous delivery approach?**

It’s a structured way to move validated code through release stages, often including tagging, approvals, and environment-specific setups, while maintaining traceability and automation across the pipeline. It helps teams iterate safely and release without bottlenecks.

**4. Is continuous delivery a good idea?**

Yes, especially for teams managing multiple environments or deploying frequently. It reduces manual steps, gives more visibility into what's shipping, and allows for faster iteration with lower risk, without forcing you into continuous deployment if your org isn’t ready.

**5. What is the purpose of building a CD pipeline?**

A CD pipeline lets you automate the journey from CI to production. It helps standardize promotion flows, version builds, inject environment-specific configuration, and insert control points like approval gates or automated tests along the way.

**6. Is Jenkins a CI or CD tool?**

Jenkins can do both, but it doesn’t come with built-in delivery logic. Most teams using Jenkins for CD have to configure custom pipelines, manage plugins, and maintain release logic themselves. It’s flexible, but not purpose-built for modern delivery workflows.

**7. What is CI/CD in DevOps with an example?**

CI/CD is the backbone of DevOps automation. For example, a developer pushes code to Git. CI tests it, then CD packages and promotes it to staging with an approval gate for production. Northflank, for instance, automates this flow with pipelines tied to Git triggers and release logic.

**8. Is DevOps just CI/CD?**

No. CI/CD is a major part of DevOps, but DevOps also covers infrastructure as code, observability, team culture, incident response, and more. CI/CD is one pillar, delivery automation, but not the whole picture.

**9. What is the continuous delivery lifecycle?**

It starts after CI passes and continues through artifact packaging, tagging, promotion to staging, environment-specific config injection, approval gates, and finally, deployment. The lifecycle ends when the build reaches production, or loops back if rollback is triggered.

## Continuous delivery is a team decision

Now with all you’ve seen, you know that continuous delivery is more than a technical pattern; it’s an architectural decision. It defines how your team moves code from commit to release, how environments stay consistent, and how quickly you can ship with control.

If you’re ready to put those ideas into practice, [try a delivery platform built to handle it end to end](https://app.northflank.com/signup).]]>
  </content:encoded>
</item><item>
  <title>How to deploy to Kubernetes without writing YAML</title>
  <link>https://northflank.com/blog/deploy-to-kubernetes-without-writing-yaml</link>
  <pubDate>2025-05-27T16:15:00.000Z</pubDate>
  <description>
    <![CDATA[Learn how to deploy to Kubernetes without writing YAML or managing infrastructure. Use Northflank to simplify CI/CD, scale apps fast, and get production-ready in minutes.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/deploy_to_k8s_0061051c3d.png" alt="How to deploy to Kubernetes without writing YAML" />If you’ve ever deployed an application to Kubernetes, you know the drill. You start with YAML manifests, configure services and ingress, set up CI/CD, manage secrets, and hope nothing breaks along the way.

Kubernetes is powerful. It gives you fine-grained control over how your applications run at scale. But it also introduces a steep learning curve, especially for developers who just want to deploy and move fast without spending hours on documentation.

What if you could get the benefits of Kubernetes, scalability, resilience, and portability without writing a single YAML file or managing the underlying infrastructure yourself? What if deploying your app felt as straightforward as pushing to a Git repository?

In this guide, we’ll walk through exactly how to make that happen. You’ll learn how to deploy production-ready applications to Kubernetes without touching YAML. And we’ll show you how [Northflank](https://northflank.com/) makes this possible by handling the complexity for you while keeping everything transparent and flexible.

## What is Kubernetes?

At its core, Kubernetes is a container orchestration platform. It was open-sourced by Google back in 2014 and has since become the de facto standard for managing containerized applications in production. Kubernetes is great at handling things like scaling, fault tolerance, and rolling updates, but it assumes a lot of technical knowledge from the people using it.

Kubernetes works by defining desired application states in YAML files. These configurations tell Kubernetes what containers to run, how to connect them, and how to manage their lifecycle. The flexibility and control this offers are powerful, but for many developers, it comes at the cost of simplicity.

## The traditional Kubernetes deployment experience

Let’s paint a picture of what it’s like to deploy an app to Kubernetes the traditional way.

You start by writing multiple YAML files: one for your deployment, another for your service, others for ingress, secrets, config maps, autoscaling, and more. Even a basic app can require 8 to 10 manifests, and that’s just for one environment.

But YAML is just the beginning. You need to choose how to provision your cluster. Run `kubeadm` yourself, or use a managed service like EKS or GKE? Either way, you’ll handle IAM roles, networking, CNI plug-ins like Calico, and upgrade planning. Kubernetes upgrades can’t skip versions, so this becomes a regular task.

Then comes CI/CD. You’ll write scripts to build Docker images, push to a registry, and apply manifests. That requires managing service accounts with the right permissions and keeping your tools in sync. Want zero-downtime deployments? You’ll add your own canary or blue-green logic. Helm or Kustomize can reduce repetition, but add their own complexity with templates and environment files.

Once deployed, the real work begins. You’ll need monitoring, logging, and alerting—usually with Prometheus, Grafana, Fluent Bit, and more. You’re also responsible for cert renewals, vulnerability patching, and disaster recovery. This is not a one-time setup, but an ongoing maintenance load.

Security brings more decisions. Who can access logs? Who can exec into pods? How do you isolate dev from prod? Missteps here lead to risk or friction, and often both.

Even cost and scaling aren’t straightforward. You’ll tune CPU and memory limits, configure autoscaling, and decide when to scale node pools. Miss the mark, and you either overpay or get paged at 2 a.m.

And when something breaks? Readiness probes, CrashLoopBackOffs, and blocked `kubectl exec` can all make troubleshooting painful. Issues rarely affect just one app—they ripple across the cluster.

All of this takes time and expertise. For developers trying to ship features, it’s a major distraction.

## Rethinking Kubernetes deployment with Northflank

Now, imagine a deployment experience where none of that is required. You can deploy your app to Kubernetes without writing or reading a single YAML file. Where CI/CD is already set up, logs and metrics are built in, and infrastructure just works. That’s the idea behind [Northflank](https://northflank.com/).

Northflank is a platform that abstracts away the pain of managing Kubernetes. It’s not a replacement for Kubernetes—it runs on top of it—but it provides a layer that makes deploying and scaling applications dramatically simpler. Think of it as the fast lane for getting your apps from repository to production.

## What you get with Northflank

Before we jump into the steps of deploying to Kubernetes with Northflank, let’s take a look at what Northflank actually brings to the table.

Here are some of the core features that make it stand out:

### Built-in CI/CD

Every service on Northflank comes with its own pipeline. You can build from Dockerfiles, use prebuilt images, and trigger deploys automatically on push or tag. It’s CI/CD without the extra setup.

### Managed cloud

Northflank’s managed cloud gives you the power of the Northflank platform, with no infrastructure setup required. Deploy any project with a hassle-free Kubernetes experience.

### BYOC (Bring Your Own Cloud)

Run Northflank on your own cloud infrastructure if you need more control or want to stay inside your compliance boundaries. Keep your data and workloads close while letting Northflank manage the platform experience.

### GPU and high-performance workloads

Need to run ML models, video processing, or other GPU-intensive tasks? Northflank supports GPU-powered services, and you can scale them just like any other containerized workload.

### Full support for microservices and monorepos

Whether you're deploying a single container or a monorepo with dozens of services, Northflank handles service discovery, health checks, secrets, and shared config with ease.

### Firecracker MicroVM support

For workloads that require stronger isolation and minimal overhead, Northflank can deploy your containers inside MicroVMs using Firecracker. It’s ideal for running untrusted code or spinning up secure, ephemeral jobs.

### Fine-grained access control and audit logs

Collaborate with your team while maintaining control. Northflank offers role-based access controls and detailed audit logs so you always know who did what and when.

### Unified UI, CLI, and API

Whether you prefer point-and-click workflows, scripting everything through a CLI, or building your own automation with the API, Northflank gives you consistent access across all interfaces.

Now that we’ve covered what Northflank is capable of, let’s walk through how to actually deploy an application, without touching YAML.

## How to deploy to Kubernetes with Northflank in 6 Steps

You don’t need to write YAML, run `kubectl`, or configure a CI pipeline manually. Here’s how to get from source code to a production-grade Kubernetes deployment using Northflank — in just six steps.

### Step 1: Sign up and Log In

Start by creating a [Northflank account](https://app.northflank.com/signup). The platform offers a free tier, so you can test things out with no commitment. Once inside, you’re greeted with a clean, developer-friendly dashboard.

### Step 2: Connect your repository

Link your [GitHub, BitBucket or GitLab account to Northflank](https://northflank.com/docs/v1/application/getting-started/link-your-git-account). This lets the platform pull your code, track changes, and trigger deployments automatically. If you're working with prebuilt images, you can also connect to external registries like Docker Hub or GHCR.

### Step 3: Set up your project and service

Create a [project](https://northflank.com/docs/v1/application/getting-started/create-a-project) and [service from your connected repo](https://app.northflank.com/s/project/create/service). Northflank will detect your Dockerfile and build the image automatically. You can configure ports, commands, and runtime settings — all through an intuitive UI or CLI, never by editing raw YAML.

### Step 4: Add environment variables and secrets

Define your environment-specific settings, secrets, and API keys using Northflank’s secure secrets management system. Secrets are encrypted, versioned, and scoped to the exact services that need them, giving you full control without hassle.

### Step 5: Enable built-in CI/CD

[CI/CD is built into the platform](https://northflank.com/docs/v1/application/release/manage-ci-cd). Every code push can trigger a build and deployment pipeline automatically. You can customize build steps, test flows, and even target specific folders in a monorepo. No need for third-party CI setup.

### Step 6: Monitor with logs and metrics

Once your app is live, [you can monitor everything in real time](https://northflank.com/docs/v1/application/observe/view-metrics). Northflank offers detailed logs, performance metrics, and deployment insights — all in one place. Debug faster, track usage, and catch issues before your users do.

## Why developers choose Northflank

Developers aren’t just switching to Northflank for convenience — they’re choosing it because it actually removes friction without giving up control. Here’s what real users are saying about how Northflank changes the game:

### Speed that matches how you work

*“Cycle time is everything. With Northflank, I can make 100 commits and 100 deployments in a single day... I can identify issues and deploy fixes faster than customers can even report them.”*

*— Joshua McKenty, CEO @ Polyguard, Former Field CTO @ Cloud Foundry*

Northflank removes the bottlenecks between code and production. Pipelines are ready out of the box, deploys are automated, and everything is built for iteration speed.

### Simplicity that doesn’t sacrifice power

*“Northflank is way easier than gluing a bunch of tools together... It’s more powerful and flexible than traditional PaaS — all within our VPC.”*

*— David Cramer, Co-Founder @ Sentry*

You don’t need to learn Kubernetes internals or write a single line of YAML. Northflank abstracts the complexity while giving you direct access to logs, metrics, secrets, databases, and more.

### Flexibility for real architectures

*“Northflank is the first batteries-included developer platform that doesn’t suffer from the invisible ceilings that hover over its competitors. We could have built all of Slack with Northflank — and we would have, had it been available.”*

*— Keith Adams, GP @ Pebblebed, Former Chief Architect @ Slack*

Whether you’re running services, jobs, microVMs, or entire monorepos, Northflank supports the way modern teams actually build and scale software.

### Infrastructure that feels invisible

*“This is how Kubernetes should be used.”*

*— Darren Shepherd, CTO @ Acorn Labs, Co-founder @ Rancher*

Under the hood, it’s Kubernetes. But from your perspective, it’s just code in, services out. Northflank gives you all the benefits without dragging you into infrastructure management.

## Real-world example: Cedana

[Cedana](https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes) is a developer platform that helps teams build secure-by-default cloud environments. Their stack requires strong isolation for workloads, support for ephemeral infrastructure, and fine-grained security policies — not something easily achieved with vanilla Kubernetes setups.

Instead of managing Kubernetes directly, Cedana uses Northflank to deploy their workloads with MicroVMs and hardened runtimes. With Northflank, they’re able to spin up isolated services quickly, take advantage of Kubernetes' scalability, and avoid the operational overhead of managing clusters and writing YAML.

The team can deploy secure environments in seconds, manage services through the UI or API, and rely on Northflank’s built-in CI/CD to ship faster. All of this, while running on Kubernetes under the hood.

For Cedana, the real win was the ability to move fast without compromising on security or control — a clear example of how a platform like Northflank can simplify complex infrastructure needs without giving up the power of Kubernetes.

## Comparison: Traditional vs Northflank

Here’s how Northflank stacks up against a traditional Kubernetes experience:

| Feature | Traditional Kubernetes | Northflank |
| --- | --- | --- |
| YAML Required | Yes | No |
| Built-In CI/CD | No | Yes |
| UI and API | No | Yes |
| Easy Monitoring | No | Yes |

## What developers usually ask

Still have questions? Here are a few things developers often want to know when they’re getting started with Northflank or thinking about deploying to Kubernetes without all the overhead.

### What is the easiest way to deploy apps to Kubernetes?

Using a platform like Northflank is one of the simplest ways to deploy to Kubernetes without writing any YAML. It handles builds, deployments, and infrastructure for you.

### Can I deploy microservices to Kubernetes with Northflank?

Yes. Northflank supports deploying multiple services, including background jobs and APIs, making it ideal for microservice architectures.

### Does Northflank support monorepos?

It does. You can set up builds for different directories and deploy multiple services from a single repository.

### Do I need Kubernetes experience to use Northflank?

Not at all. Northflank is designed for developers who want the power of Kubernetes without needing to learn all of its complexities.

## Skip the YAML. Ship your code.

Kubernetes is powerful, but it was never built with simplicity in mind. Most teams spend too much time wrangling YAML, wiring up pipelines, and managing infrastructure just to get their app into production.

But it doesn’t have to be that way.

[Northflank](https://northflank.com/) flips the script. You get the power of Kubernetes with scalability, resilience, and portability, without the busywork. No manifests. No boilerplate. Just code, connected pipelines, and production-ready services in minutes.

Whether you’re launching a side project, scaling a platform, or managing a fleet of microservices, Northflank gives you the speed and control you need without the friction.

[**Try Northflank for free and see what Kubernetes feels like when it gets out of your way.**](https://app.northflank.com/signup)]]>
  </content:encoded>
</item><item>
  <title>Your containers aren’t isolated. Here’s why that’s a problem. microVMs, VMMs and container isolation.</title>
  <link>https://northflank.com/blog/your-containers-arent-isolated-heres-why-thats-a-problem-micro-vms-vmms-and-container-isolation</link>
  <pubDate>2025-05-26T17:45:00.000Z</pubDate>
  <description>
    <![CDATA[Securely run untrusted AI-generated code and agents using Kubernetes-native microVMs and gVisor—prevent container escapes, protect API keys, and isolate runtime environments with Northflank.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/secure_runtime_alternatives_281a3e4a3f.png" alt="Your containers aren’t isolated. Here’s why that’s a problem. microVMs, VMMs and container isolation." />Everyone wants to build faster now. AI agents write code, generate tasks, even deploy apps. Package managers like npm let you install 12,000 transitive dependencies with a single command. 

This means you can ship fast. It also means you might be inviting an attacker to run code in your infrastructure… and you'd barely notice.

This isn't theoretical. Malicious packages get published. Supply chain attacks spread laterally. Developers run random scripts from GitHub just to get something working. And now we're asking AI agents to decide what packages to install, what commands to run, and what services to spin up.

If you're deploying this stuff into the same environment that runs your customer workloads, you're playing with fire.

## The illusion of isolation

Containers are often treated like security boundaries. They're not. Namespacing, seccomp, AppArmor, disabling kernel capabilities, running as non-root, all of these help, but none of them prevent container escapes. `runc`, the default container runtime for Docker and Kubernetes, shares the host kernel. If an attacker breaks out of a container, they get access to the host. And if that host is shared between tenants? Game over.

A successful container escape lets someone:

* Steal environment variables (your Stripe key, your DB password)

* Access the node's IAM permissions (hello, S3 bucket exfiltration)

* Laterally move into other workloads (multi-tenant compromise)

Now imagine one of those variables is your OpenAI key, your Cloudflare API token, or access to your Kubernetes cluster, because an AI agent "needed it" to run. 

That's not a rare edge case. It's the whole point of multi-tenancy. If you're not isolating untrusted code at the kernel level or below, you're leaving yourself and your customers exposed.

## You need container sandboxing

This is where secure runtimes come in. You need a way to execute code that *cannot* affect anything outside its box, no matter how malicious it gets.

Two leading approaches stand out:

### gVisor

gVisor is a user-space kernel developed by Google. It intercepts syscalls and simulates a Linux kernel, acting as a kind of syscall proxy. This gives you a strong sandbox: code thinks it's talking to Linux, but it's actually talking to gVisor. The result? A drastically reduced attack surface.

gVisor supports multiple execution modes:

1. PTrace uses ptrace to intercept syscalls. Very secure, very slow, no longer maintained

2. KVM: uses virtualization for syscall isolation. Fastest option for bare-metal.

3. Systrap: uses seccomp to intercept syscalls. Better performance than ptrace.

Storage modes include:

* Directfs: Securely exposes file descriptors to the sandbox. Replaces gofer RPCs for more performant filesystem operations

* Overlayfs: provides a union filesystem with isolation.

It’s not a silver bullet. gVisor doesn’t support every syscall, which means not every workload can run inside it. But for AI agents, scripts, and anything you wouldn’t run on prod bare metal, it’s a strong contender.

![3.png](https://assets.northflank.com/3_ea4634ed44.png)

### KVM \+ microVMs

Kernel-based Virtual Machines (KVM) take a different approach. Instead of simulating a kernel, you boot an actual VM, fast and minimal. Technologies like Firecracker (used by AWS Lambda), Cloud Hypervisor, and Kata Containers let you run these VMs with overhead that’s just a few percent above containers.

Kata Containers integrates with Kubernetes and CRI. Under the hood, it spins up lightweight VMs with full kernel isolation, giving you the best of both worlds: container workflows with VM-level security.

Northflank uses Kata Containers and Cloud Hypervisor in production. We run over 2 million microVMs per month inside Kubernetes. It works. It scales. And it keeps your workloads isolated even in high-density, noisy, multi-tenant environments.

## When should you care about container isolation?

If you're:

* Running code you didn’t write (AI-generated, open source, customer-submitted, AI agents you didn’t write)  
* Providing an execution sandbox for others (ML workloads, agents, plugins)  
* Offering multi-tenant services on shared infrastructure  
* Just paranoid enough to want true production isolation  
* Or using code gen tools that generate and deploy code in real-time without human review

Then yes, you should care. And you should probably be running secure runtimes.

## It’s not just about compute isolation

Isolation isn't just about sandboxing code. You need to defend across layers:

### Networking security in Kubernetes

* Use **service mesh \+ mTLS** to enforce trusted service-to-service communication.  
* Use **Cilium** and Kubernetes **NetworkPolicies** to block cross-namespace or cross-project traffic.

### Workload permissions

* Enforce strict **PodSecurityPolicies** or **PodSecurityAdmission**.  
* Remove default capabilities, deny host access, and disable privileged mode.  
* Use **RBAC** to deny Kubernetes API access from within pods.

### Resource abuse (noisy neighbors)

* Rate-limit DNS, disk, and CPU usage.  
* Apply **cgroups** and **ephemeral storage limits** to sandbox abuse.

### Running secure isolation is an ongoing operational grind

gVisor and Kata Containers are evolving constantly. New kernel versions break things. Security patches deprecate syscalls. Performance regressions creep in. 

One week, a runtime works flawlessly; the next, it silently fails under load. You’re juggling compatibility between container runtimes, kernel modules, CRI implementations, and virtualization backends. 

Nested virtualization might stop working on a new GCP instance type. A gVisor update might block a syscall your workload suddenly needs. You can’t set it and forget it. Maintaining secure isolation means continuous testing, fast iteration, and deep understanding of the stack at every level, from Kubernetes to KVM. 

## Most teams don’t have the time or muscle to do that well. We do.        

### How to get started DIY

You don’t need to rebuild your entire stack to get secure runtimes.

### How to install and setup gVisor

Install via [runsc](https://gvisor.dev/docs/user_guide/quick_start/):

```shell
sudo apt-get update && \
sudo apt-get install -y \
    apt-transport-https \
    ca-certificates \
    curl \
    gnupg

sudo apt-get update && sudo apt-get install -y runsc



```

In Kubernetes, configure your container runtime class and use `runsc`.

### How to install and setup Kata Containers (on Kubernetes)

Follow the [official guide](https://quay.io/repository/kata-containers/kata-deploy):

```shell
# Deploy the needed RBAC for running the kata-deploy daemonset
kubectl apply -f https://raw.githubusercontent.com/kata-containers/kata-containers/main/tools/packaging/kata-deploy/kata-rbac/base/kata-rbac.yaml

# Deploy the kata-deploy daemonset
kubectl apply -f https://raw.githubusercontent.com/kata-containers/kata-containers/main/tools/packaging/kata-deploy/kata-deploy/base/kata-deploy.yaml

# Ensure kata-deploy is ready
kubectl -n kube-system wait --timeout=10m --for=condition=Ready -l name=kata-deploy pod

# Deploy the runtimeClasses
kubectl apply -f https://raw.githubusercontent.com/kata-containers/kata-containers/main/tools/packaging/kata-deploy/runtimeclasses/kata-runtimeClasses.yaml

# And run an example workload using Kata Containers and Cloud Hypervisor
kubectl apply -f https://raw.githubusercontent.com/kata-containers/kata-containers/main/tools/packaging/kata-deploy/examples/test-deploy-kata-clh.yaml
```

Or, skip the setup.

## What we do at Northflank

![2.png](https://assets.northflank.com/2_8c7193e4cd.png)

Northflank offers a secure runtime environment by default. Whether you're running production workloads or sandboxed AI agents, every container is isolated in the way that makes sense for your workload:

* On environments where nested virtualisation is unavailable? We use **gVisor**.  
* On infra that supports nested virtualisation? We run **Kata \+ Cloud Hypervisor**.

You get VM-grade security, container-grade workflows, and Kubernetes-native orchestration, on any cloud, in 30 minutes with Bring your own Cloud or in seconds on Northflank PaaS.

That matters when you're working with AI tools like MCPs or self/hosted agents, especially when those services demand your API tokens or environment variables to work. They might need your Cloudflare auth token, your Stripe secret key, your Postgres access. You're giving them access to run against your infra. Without proper isolation, you're giving them the ability to *become* your infra.

If you're using code gen tools, you better know where the source code is being generated (and where it's being used) because your most precious asset (your source code) could be under attack.

Secure runtime is the thing that prevents someone else's AI from running your infrastructure into the ground. You should wrap it, because otherwise it'll ruin the rest of your stack.

If you're building infrastructure for the next generation of apps (AI-driven, plugin-based, user-customizable) you need more than just a container. You need a sandbox that doesn’t break when someone does something stupid, malicious, or both.

That’s what we’ve built. If you want help running it, come talk to us.

To deploy a secure microVM on Northflank via the API is two requests, all you need to do is the following:

1. Create a project to provide tenant, network and namespace isolation  


    ```javascript
    await apiClient.create.project({
      data: {
        "name": "New Project",
        "description": "This is a new project.",
        "region": "europe-west",
      }    
    });
    ```

2. Create a Northflank service that deploys an existing container image from a registry. This will spawn a container on a Kubernetes cluster and isolate it in a microVM, also known as a VMM.

    ```javascript
    await apiClient.create.service.deployment({
      data: {
        "name": "alpine-linux",
        "infrastructure": {
          "architecture": "x86"
        },
        "billing": {
          "deploymentPlan": "nf-compute-50"
        },
        "deployment": {
          "type": "deployment",
          "instances": 1,
          "docker": {
            "configType": "customCommand",
            "customCommand": "sleep 5000"
          },
          "external": {
            "imagePath": "alpine:latest"
          },
        },
      }
    });
    ```
3. If you need advanced networking, custom DNS, secrets injection, white-labelled Git based CI/CD, certificate management, autoscaling this all comes built in  
]]>
  </content:encoded>
</item><item>
  <title>What is release management? A practical guide (with fewer acronyms and more advice)</title>
  <link>https://northflank.com/blog/what-is-release-management</link>
  <pubDate>2025-05-25T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Release management is the process of planning, scheduling, coordinating, and deploying software releases. It covers everything from preparing builds to rolling them out into production. The goal is simple: ship new code fast, without breaking things.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/k_native_alternatives_1_1df2e935c7.png" alt="What is release management? A practical guide (with fewer acronyms and more advice)" />If you've ever had a Friday deployment go sideways and spent your weekend untangling a postmortem doc longer than your last vacation itinerary, congratulations: you've experienced the joys of release management.

For those who haven't, imagine trying to coordinate a multi-team product launch across different environments, with last-minute hotfixes flying in like rogue fireworks, and a noisy Slack channel in the background.

Release management is what keeps all of that from spiraling into chaos. 

In this guide, we’re going to answer two things:

1. What is release management, really?
2. What makes good release management tools worth the trouble?

We’ll also show how platforms like Northflank are making this easier for developers

<InfoBox className='BodyStyle'>

### 📌 TL;DR: What is release management?

- Release management is how you plan, coordinate, and ship software without chaos.
- It covers CI/CD, environment consistency, deployment strategies, observability, and rollbacks.
- As systems scale, the risk of broken deploys, slow rollouts, and confused teams grows.
- You can reduce risk by shipping smaller changes more often, standardizing environments, and automating everything, especially rollbacks.
- The right tools make all the difference. Northflank unifies CI/CD, environment management, and release orchestration into one resilient, developer-friendly platform.
</InfoBox>

## **What is release management? (in depth)**

![image.png](https://assets.northflank.com/image_81b0b3310d.png)

Release management is the process of planning, scheduling, coordinating, and deploying software releases. It covers everything from preparing builds to rolling them out into production. The goal is simple: ship new code fast, without breaking things.

In practice, it’s a lot messier. Releases are often complex, involving:

- Coordinating across engineering, QA, product, and DevOps
- Managing multiple environments (dev, staging, prod)

> *Wondering what those are? There’s also a guide for that [here](https://northflank.com/blog/what-are-dev-qa-preview-test-staging-and-production-environments).*
> 
- Dealing with compliance, approvals, and rollback plans
- Ensuring monitoring, alerting, and observability are in place

Done well, release management increases deployment frequency, reduces the risk of incidents, and improves developer productivity. Done poorly, it becomes a bottleneck, or worse, a source of outages.

## A brief history of release management (or: how we got here)

In the old days (read: 2000s), release management meant scheduled releases every few months, with enormous spreadsheets, fragile Jenkins jobs, and anxiety-inducing deployment windows.

Then came agile. CI/CD. Microservices. "Move fast and break things."

Now teams are expected to ship multiple times a day. With distributed systems and dozens of environments, keeping releases safe, fast, and understandable has become an existential challenge. 

That’s why the release management space (and the tools built for it) have had to evolve

## **Key components of release management**

### 1. **Version control and build pipelines**

Release management starts with your source control and CI pipelines. You can’t release what you haven’t built. Tools like GitHub Actions, CircleCI, or GitLab CI help automate builds, run tests, and ensure each commit doesn’t blow up the app.

### 2. **Artifact management**

You need a way to store and version your build outputs: container images, binaries, whatever your system runs on. Artifact registries (like ECR or JFrog) help here. This part is usually underappreciated until something breaks in production and you can't roll back because no one saved the last working image.

### 3. **Environment management**

![pipeline-overview.png](https://assets.northflank.com/pipeline_overview_e6d71d50ae.png)

You can’t just deploy to production. You’re testing in staging, running experiments in dev, and probably have a pre-prod ghost town someone forgot about. Managing consistent environments, with the same configs, secrets, and deployment logic, is critical. This is also where things start to break at scale.

### 4. **Release orchestration**

This is the heart of it: defining what gets released, where, and when. Do you use feature flags? Progressive delivery? Blue/green or canary deployments? A solid orchestration layer lets you model this and run it safely, and ideally, not manually.

### 5. **Monitoring, alerting, and rollbacks**

![readiness-probe.png](https://assets.northflank.com/readiness_probe_4827e88e0b.png)

You shipped. Now what? 

Good release management includes observability: metrics, logs, traces, alerts. And rollback strategies that aren’t “call Steve, he knows where the script is.”

## **Why release management gets complicated**

![1n4iuWZFnTeN6qvdpD.gif](https://assets.northflank.com/1n4iu_WZ_Fn_Te_N6qvdp_D_073870a7a7.gif)

Because software gets complicated. Especially when:

- You’re running a microservices architecture
- Different teams own different parts of the system
- Compliance and audit trails are required
- Your CI/CD process is duct-taped together with bash scripts

Release management no longer means just pushing code. You have to manage complexity and risk in a world where everything is distributed, and expectations are high.

## How to make release management less painful

Release management isn’t supposed to be thrilling, it’s supposed to be reliable. 

If it does, something’s wrong… and fixable. Here’s how teams can make release management less chaotic and more routine 

**Embrace smaller, more frequent releases**

Deploying massive chunks of code once a quarter is asking for pain. Smaller, more frequent releases reduce the blast radius when something goes wrong and help teams build confidence in the process. It also gives you more opportunities to validate in production.

**Standardize environments and configs**

Use infrastructure as code and treat environment setup as a first-class citizen. Everything should be versioned, repeatable, and consistent.

**Automate everything you can**

Manual steps are where things break. Automate builds, tests, deploys, rollbacks, and health checks. The less you have to remember or document, the less you’ll forget. Bonus: it makes onboarding easier, too.

**Add observability to every stage**

Shipping blindly is reckless. Add monitoring, logging, and tracing at every stage of the release pipeline, not just in prod. That way, when something goes wrong, you’re not stuck guessing or grepping through logs from six different services.

**Don’t wait for a crisis to test your rollbacks**

If your rollback plan only exists in theory, it’s not a real plan. Test it. Practice it. Make sure it works even when Steve is on PTO.

**Share the load AND the knowledge**

Release management shouldn’t live in one person’s head. Document the process, share context across teams, and make sure there are backups. You want a system that survives vacation schedules and surprise outages.

**Use the right release management tools**

You can follow all the best practices in the world, but if your tools fight you at every step, you’re still in for a rough time. The best thing you can do for your team is to use a platform that treats release management as a first-class concern.

That’s where tools like Northflank come in. It combines CI/CD, environment management, and release orchestration into one platform, so you don’t have to stitch it all together yourself. You get built-in support for blue/green and canary deployments, visibility across environments, and safety features like automated rollbacks and health checks.

The result: a team that doesn’t dread release day.

### What makes a great release management tool?

There are a million release management tools out there. The best ones do the following:

**✅ Unify the deployment process**

Great tools give you a clear, centralized view of what’s being deployed, where, and by whom. They remove ambiguity. 

**✅ Support flexible release strategies**

Blue/green, canary, feature flags, A/B testing, you should be able to define and control your rollout strategy without writing custom scripts every time.

**✅ Treat environments the right way**

Managing infrastructure shouldn’t be a separate concern. The best release management tools integrate environment provisioning and configuration directly into the release process.

**✅ Automate rollbacks and health checks**

You need to ship fast AND safe. Smart automation should handle health checks and roll back automatically when something breaks.

**✅ Work for both platform engineers and developers**

If it only makes the life of one team easier, it’s not a real solution. A good release tool is intuitive for developers *and* powerful for platform teams.

<div>  
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    <a href="https://app.northflank.com/signup">  
<Button variant={["large", "gradient"]}>Deploy with Northflank. Sleep through the night.</Button>  
    </a>  
  </center>  
</div>                      

## **So… How does Northflank help with release management?**

Northflank wasn’t built to be a CI/CD tool. 

CI/CD is just one piece of the puzzle, and Northflank goes far beyond it. It’s a full workload delivery platform, designed to meet the reality of how modern teams ship software today.

Instead of stitching together a fragile mix of CI tools, environment scripts, deployment templates, and homegrown dashboards, Northflank gives you a single, unified platform that just works. 

Every step of your release process, from builds to environments to rollout strategies, lives in one place and speaks the same language.

You get:

- CI/CD pipelines integrated with deployment environments, not bolted on
- Environment configurations that stay consistent across staging, production, and everything in between
- Progressive delivery strategies baked in: blue/green, canary, manual gates
- Clear visibility into what’s shipping, where, and by whom
- Audit logs, observability, and safety nets built into the core, not as afterthoughts

Northflank was built with care, for teams who want to move fast, but refuse to compromise on quality. It's engineered to be resilient under pressure, clear in its intent, and easy to trust.

### Final thoughts

Release management is what separates teams who ship with confidence from teams who ship and hope.

The right process, backed by the right tooling, can turn shipping from a fire drill into a boring, predictable habit. And boring is good when it comes to production.

So the next time someone asks, "What is release management?" you can tell them:

"It’s how we ship fast, stay sane, and stop waking up to PagerDuty alerts. Also, we use Northflank."

[Start for free with Northflank today.](https://app.northflank.com/signup)]]>
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  <title>Top 6 Knative alternatives for when you don’t want to build a PaaS</title>
  <link>https://northflank.com/blog/top-knative-alternatives</link>
  <pubDate>2025-05-24T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Knative is a Kubernetes-based framework for building serverless platforms. It gives platform engineers the core primitives to deploy and scale HTTP workloads, respond to events, and build serverless pipelines.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/k_native_alternatives_37833d23c5.png" alt="Top 6 Knative alternatives for when you don’t want to build a PaaS" />Knative is great, if you want to build your own PaaS, but most teams don’t. In this article, we'll walk you through the top 6 Knative alternatives that avoid that.

Knative is a Kubernetes-based framework for building serverless platforms. It gives platform engineers the core primitives to deploy and scale HTTP workloads, respond to events, and build serverless pipelines.

At a high level, Knative has two main components:

- **Knative serving** – Lets you deploy containerized applications that scale based on traffic, including scale-to-zero.
- **Knative eventing** – Provides event sources, brokers, and triggers to build loosely coupled, event-driven systems.

Knative is used behind the scenes in systems like Google Cloud Run, but on its own, it’s *not* a plug-and-play developer experience. It’s a platform toolkit, not a full platform.

## Why teams are looking for Knative alternatives

While Knative gives a lot of control, it comes with serious overhead:

- **Operational complexity**: You need to manage Istio/Kourier, configure autoscaling, provision TLS, and monitor custom resources across your clusters.
- **No batteries included**: There’s no built-in CI/CD, secret management, observability, or UI.
- **Requires platform expertise**: You’re effectively building your own PaaS. Most teams aren’t staffed (or interested) enough to take that on.

![CleanShot 2025-05-23 at 17.55.53@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_23_at_17_55_53_2x_325695c8bb.png)

For many teams, the goal isn’t to build a platform. It’s to ship resilient, scalable software fast. That’s where **Knative alternatives** come in, offering similar functionality, but without the heavy lifting.

> For a deeper breakdown of this tradeoff, check out [Build vs buy: The platform engineer’s conundrum](https://northflank.com/blog/build-vs-buy-the-platform-engineers-conundrum), which explores why even well-funded teams are starting to move away from building in-house platforms.
> 

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<Button variant={["large", "gradient"]}>…or just use Northflank and skip the platform tax</Button>  
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<InfoBox className='BodyStyle'>

## ⏱️ Short on time? Quick recap of top Knative alternatives

If you don’t want to build your own PaaS, here are the top **Knative alternatives** worth considering:

- **[Northflank](https://northflank.com/)** – Best all-around option. Full platform with autoscaling, CI/CD, and support for both stateless and stateful services. Managed or self-hosted.
- **[OpenFaaS](https://www.openfaas.com/)** – Lightweight, open-source toolkit for function-based deploys. Requires more setup.
- **[Render](https://render.com/)** – Simple, managed PaaS with autoscaling. Less flexible, better for small apps.
- **[Fission](https://fission.io/)** – Kubernetes-native FaaS with extensibility. No built-in CI/CD or UI.
- **[Google Cloud Run](https://cloud.google.com/run)** – Managed Knative. GCP-native, solid for stateless apps.
- **[Koyeb](https://www.koyeb.com/)** – Fast global deploys for stateless apps. Not suitable for persistent workloads.
</InfoBox>

## Choosing the right platform

The best **Knative alternative** depends on your team's goals, constraints, and maturity. 

Key factors to consider:

- **Self-hosted vs. managed**: Do you want full control over infrastructure, or would you rather offload it?
- **Support for scale-to-zero**: Not all platforms do this natively.
- **Support for stateful services**: Some tools focus only on stateless functions.
- **CI/CD integration**: Is deployment tied to Git pushes? Can you customize build pipelines?
- **Customizability vs. simplicity**: How much infra plumbing are you willing to manage?

Below are six platforms that serve as strong Knative alternatives, each with different strengths. One stands out as the best all-around option for modern teams.

### **1. Northflank** – The top Knative alternative overall

![CleanShot 2025-05-22 at 16.39.03@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_22_at_16_39_03_2x_a1219941f0.png)

**Northflank** gives you the power of Kubernetes and Knative’s dynamic scaling, but without needing to build and glue together the whole system yourself.

### Key features

- Auto-scaling (including scale-to-zero) for services, jobs, and cron
- Built-in CI/CD from Git, with custom build pipelines
- First-class support for databases, persistent volumes, service discovery
- Deploy to Northflank’s managed infra or your own cluster (self-hosted)
- Rich UI + API + CLI support

### Technical edge

- Uses Kubernetes under the hood, but abstracts it behind clean primitives
- Autoscaling is configurable at the service level: concurrency, min/max replicas, resource limits
- Networking, TLS, and DNS management are handled out-of-the-box

Northflank removes the need for a platform team. You get the benefits of Knative (scale-to-zero, container-first workflows, modern infra) without wiring everything up from scratch.

✅ **Best for**: Full-stack teams, enterprises, startups, or product orgs who want a developer platform that scales without needing to build one.

### **2. OpenFaaS** – Lightweight serverless toolkit

![CleanShot 2025-05-26 at 09.48.10@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_26_at_09_48_10_2x_09e786a5a8.png)

OpenFaaS is an open-source framework for running functions as a service. It's a lighter-weight alternative to Knative that supports Kubernetes, Docker Swarm, or bare metal.

### Key features

- Function-based deploys with scale-to-zero
- Runs on top of your existing Kubernetes or container runtime
- CLI + UI for deploys

### ⚠️ Limitations

- Requires you to manage your own ingress, secrets, observability
- Best suited for stateless workloads, not full-service apps

✅ **Best for**: DevOps teams comfortable managing infra who want a simpler serverless engine than Knative.

### **3. Render** – Developer-friendly PaaS

![render's home page.png](https://assets.northflank.com/render_s_home_page_2880e163be.png)

Render offers a managed platform for apps, background workers, and static sites. It abstracts away Kubernetes entirely and provides an opinionated developer experience.

### Key features

- Autoscaling, cron jobs, HTTP and background workers
- Built-in Postgres and Redis support
- Git-based deploys

### ⚠️ Limitations

- Scale-to-zero only available on higher-tier plans
- Less configurable than Knative/Northflank (no fine-tuned resource or traffic management)
- Not ideal for multi-region or enterprise use cases

✅ **Best for**: Small teams who want a Heroku-style experience with some container flexibility.

### **4. Fission** – Kubernetes-native FaaS

![CleanShot 2025-05-26 at 09.49.56@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_26_at_09_49_56_2x_25e8358c4c.png)

Fission is another open-source framework built specifically for Kubernetes-native functions. It’s less heavy than Knative and focuses entirely on serverless execution.

### Key features

- Deploy functions directly using source code or containers
- Supports HTTP triggers, message queues, and timers
- Written in Go and highly extensible

### ⚠️ Limitations

- Like Knative, requires a managed K8s cluster
- No built-in CI/CD or observability
- Not designed for long-running services

✅ **Best for**: Kubernetes-heavy teams looking for simple, extensible function execution without the Knative footprint.

### **5. Google Cloud Run** – Knative with a UX layer

![cloudrun home page.png](https://assets.northflank.com/cloudrun_home_page_a1ce4d09f3.png)

Cloud Run is effectively a hosted Knative instance, abstracted for developer friendliness. It’s good for HTTP-based services that need scale-to-zero and strong GCP integration.

### Key features

- Container-based deploys from Git or Artifact Registry
- Managed scaling and load balancing
- Built-in identity, IAM, and monitoring via Google Cloud

### ⚠️ Limitations

- Limited control over runtime environment
- Tightly coupled to GCP (vendor lock-in)
- Not ideal for persistent or hybrid workloads

✅ **Best for**: GCP-centric teams deploying stateless web services.

### **6. Koyeb** – Global-first serverless PaaS

![CleanShot 2025-05-26 at 09.51.13@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_26_at_09_51_13_2x_fa28c4a75e.png)

Koyeb focuses on fast global deployment of stateless applications. It runs containers on a global edge network with support for auto-scaling and GitOps-style deploys.

### Key features

- Instant HTTP APIs with automatic TLS
- Fast cold start times via Firecracker microVMs
- Global routing and regional failover

### ⚠️ Limitations

- No support for persistent data or stateful apps
- Runtime customization is limited
- Not self-hostable

✅ **Best for**: Simple APIs and frontend backends where speed and global reach matter most.

## A comparison of Knative alternatives

| Platform | Scale-to-zero | CI/CD built-in | Stateful support | Self-hosted option | Best for |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes | Product teams, startups, full-stack apps |
| **OpenFaaS** | ✅ Yes | ❌ No | ❌ No | ✅ Yes | Infra-savvy teams, simple FaaS needs |
| **Render** | ⚠️ Partial* | ✅ Yes | ✅ Yes | ❌ No | Small teams, quick deploys |
| **Fission** | ✅ Yes | ❌ No | ❌ No | ✅ Yes | Kubernetes-heavy teams, extensibility |
| **Cloud Run** | ✅ Yes | ⚠️ Partial* | ⚠️ Limited | ❌ No | GCP users, stateless web services |
| **Koyeb** | ✅ Yes | ✅ Yes | ❌ No | ❌ No | APIs, global-first stateless apps |

*Render’s scale-to-zero and Cloud Run’s CI/CD depend on plan or setup.

## Final thoughts

Knative is a great framework, if your goal is to build a PaaS. But most teams don’t want to maintain their own platform; they want to ship features, handle traffic, and stay reliable.

That’s why **Knative alternatives** are gaining traction, especially those that strike a better balance between power and simplicity.

**Northflank** stands out because it offers:

- A developer-friendly experience
- Full support for container-based apps and jobs
- Native autoscaling (including scale-to-zero)
- Managed and self-hosted options
- Everything you’d need to build a PaaS—already built

If your team is tired of stitching together infrastructure and just wants to deploy and scale with confidence, **Northflank is the most complete and pragmatic Knative alternative** on the market.

Start deploying with Northflank, for free, [here](https://app.northflank.com/signup).]]>
  </content:encoded>
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  <title>10 best preview environment platforms in 2026 (frontend, backend &amp; GitOps)</title>
  <link>https://northflank.com/blog/preview-environment-platforms</link>
  <pubDate>2025-05-23T16:16:00.000Z</pubDate>
  <description>
    <![CDATA[Check out 10 platforms offering preview environments in 2026, from frontend-focused solutions like Vercel to full-stack GitOps platforms like Northflank.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/preview_environment_platforms_055ae261c8.png" alt="10 best preview environment platforms in 2026 (frontend, backend &amp; GitOps)" />Preview environments are now a core part of modern delivery workflows, but most setups still fall short. Teams often set up previews manually using CI workflows, rely on long-lived staging branches, or manage temporary environments inconsistently across services.

That approach doesn’t scale in 2026.

If you're deploying backend services, frontend apps, or full-stack systems, you need a way to provision isolated environments for every pull request, including databases, secrets, and background jobs.

In this article, we compare 10 platforms that provide preview environments across a range of needs: GitOps-driven infrastructure, frontend deployment pipelines, and automated full-stack previews with teardown support.

*New to preview environments? Jump to the FAQ section to get up to speed.*

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		<a href="https://app.northflank.com/signup">
			<Button variant={["large", "gradient"]}>Start building preview environments without the overhead >>></Button>
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<InfoBox className='BodyStyle'>

### Quick overview: best preview environment platforms in 2026

If you're short on time, here's a snapshot of the top platforms helping teams create reliable preview environments per PR or branch:

1. [**Northflank**](https://northflank.com/) – Full-stack previews with DB forks, teardown schedules, secrets, and BYOC support.

2. [**Render**](https://render.com/) – Simple deploy previews for web apps; ideal for frontend workloads.

3. [**Qovery**](https://www.qovery.com/) – Infrastructure-backed platform with PR previews, though setup complexity varies.

4. [**Codefresh**](https://codefresh.io/) – GitOps-native previews using ArgoCD and ApplicationSets.

5. [**Bunnyshell**](https://www.bunnyshell.com/) – Multi-service testing environments you can reuse across teams.

6. [**Shipyard**](https://www.shipyard.build/) – No-code test environments and workflow previews.

7. [**Porter**](https://www.porter.run/) – Kubernetes-based PaaS with environment provisioning via Git.

8. [**Okteto**](https://www.okteto.com/) – Automatic Kubernetes previews for dev branches and services.

9. [**Vercel**](https://vercel.com/) – Frontend-focused platform with instant Git-based previews and Next.js support.

10. [**Netlify**](https://www.netlify.com/) – JAMstack-friendly, branch-based previews for static and frontend projects.

</InfoBox>

Let’s break down what each platform does well and which one fits your team’s workflow best.

## What teams need to understand before choosing a preview environment platform

Before you choose a platform, it's worth stepping back to think about how your team works and what your preview environments need to support. Not every platform handles the same types of workloads or infrastructure assumptions.

Let’s look at a few questions to help you evaluate what fits:

![preview-environment-checklist.png](https://assets.northflank.com/preview_environment_checklist_0e9d0dff97.png)

### 1. What kind of workloads are you previewing? Frontend-only? API + DB?

Are you deploying a single-page app or a system with services, databases, and background jobs? Some platforms are optimized for frontend UIs, while others are built for full-stack orchestration.

### 2. Do you need stateful previews (databases, secrets, jobs)?

If your PR environments need seeded databases, secret injection, or persistent storage, rule out platforms that only support ephemeral frontend containers.

### 3. How are previews triggered: Git events, CI steps, or deploy buttons?

Some platforms hook directly into your Git provider. Others depend on CI pipelines or manual actions. Look for native Git-based flows if automation matters to your team.

### 4. Do you want GitOps-native control or a managed UI-based system?

Teams already using ArgoCD or Kubernetes might prefer full GitOps control. Others may want a managed dashboard to avoid maintaining YAML or custom pipelines.

### 5. Are you deploying in your cloud (BYOC) or using managed infra?

Some platforms let you bring your own cloud and manage your infra, others offer everything pre-hosted. Decide based on your control requirements, security posture, and existing architecture.

## Technical comparison: 10 best preview environment platforms in 2026

If you’ve gone through the decision checklist above, you should have a clearer idea of what kind of previews your team needs. Now let’s look at how 10 different platforms handle preview environments, from frontend-only branches to full-stack setups with Git-based triggers, teardown logic, and secret injection.

### #1. Northflank – Full-stack, Git-based automation, BYOC or managed

Northflank is built for engineering teams that want automated, full-stack previews triggered by Git pushes, pull requests, or manual deploys, without needing to manage separate tools.

![full-stack-preview-environments.png](https://assets.northflank.com/full_stack_preview_environments_a4ccb7bd37.png)

**Why it stands out:**

- Previews support jobs, services, and databases out of the box.
- [GitOps-friendly workflows](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment#manage-your-preview-environment-template-with-gitops) without needing to write or maintain [ArgoCD configurations](https://northflank.com/blog/argo-cd-alternatives-northflank-developer-platform-git-ops-self-service).
- Integrated [secret injection](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment#inject-secrets-securely-and-share-environment-resources), teardown scheduling, and database cloning.
- Works across frontend, API, and background workers in the same pipeline.
- You can deploy on managed infrastructure or bring your own cloud (BYOC).

> Go with this if you want Git-triggered full-stack previews without configuring separate CI pipelines, ArgoCD setups, teardown logic, and secret injection. Northflank handles all of it out of the box, on managed infrastructure or your own cloud (GCP, AWS, or Azure).
> 

*See [how to set up a preview environment](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment)*

### #2. Render – Automatic previews for web apps, frontend-focused

Render automatically deploys preview environments for every pull request, primarily supporting frontend projects like static sites, React, and Node.js apps.

![render's home page.png](https://assets.northflank.com/render_s_home_page_2880e163be.png)

**Preview environment capabilities:**

- GitHub-based auto-deployments for each pull request.
- Preview environments are only available on Professional workspace plans ****or higher.
- Public share links with unique subdomains for team reviews.
- Supports databases and secrets via manual setup, but does not auto-clone production data or inject secrets automatically.
- Teardown is limited to PR lifecycle; no scheduling or persistent preview state like DB snapshots or volumes.

> Go with this if your team only needs frontend previews with minimal setup and you’re already on a paid Render plan.
> 

*See [7 Best Render alternatives for simple app hosting in 2026](https://northflank.com/blog/render-alternatives)*

### #3. Qovery – Full-stack support, PaaS feel but less Git-native control

Qovery provides full-stack previews with a developer-friendly PaaS interface, but deeper customization still depends on understanding deployment pipelines and infrastructure behavior.

![qovery home page.png](https://assets.northflank.com/qovery_home_page_2333881965.png)

**Highlights:**

- Deploys full applications across services and containers.
- Works with GitHub/GitLab to trigger previews from pull requests.
- Preview environments can be customized but require advanced setup.
- Abstracts infra setup, but advanced teams may still configure cloud/Kubernetes settings manually.

> Go with this if your team wants full-stack Git-triggered previews with a PaaS interface, but is also comfortable tweaking infra behind the scenes.
> 

See [Best Qovery alternatives in 2026](https://northflank.com/blog/best-qovery-alternatives)

### 4. Codefresh – GitOps-based previews via ArgoCD + ApplicationSets

Codefresh allows you to define preview environments using GitOps principles and ArgoCD’s ApplicationSets.

![codefresh home page.png](https://assets.northflank.com/codefresh_home_page_4f83c49bd0.png)

**Technically, it supports:**

- Git-based automation using ArgoCD and Helm charts.
- Fine-grained control of deployments and rollbacks.
- Requires ArgoCD, either self-hosted or managed through Codefresh’s zero-maintenance Hosted GitOps.
- Includes a visual UI and optional managed GitOps runtime.

> Go with this if you’re comfortable with GitOps and want previews managed entirely through ArgoCD definitions.
> 

*See [7 best Codefresh alternatives in 2026](https://northflank.com/blog/codefresh-alternatives) & [Argo CD alternatives that don’t give you brain damage](https://northflank.com/blog/argo-cd-alternatives-northflank-developer-platform-git-ops-self-service)*

### 5. Bunnyshell – Reusable environments for dev, test, and previews

Bunnyshell provides ephemeral environments for every pull request, built to replicate production setups and simplify testing, debugging, and QA. It also emphasizes template-driven reuse, making it easier for teams to spin up consistent environments.

![bunnyshell home page.png](https://assets.northflank.com/bunnyshell_home_page_e9d0b68577.png)

**Capabilities:**

- Automatically create environments for each PR and destroy them post-merge.
- Use reusable templates to define services, databases, and infrastructure.
- Sync environments with CI/CD and version control tools like GitHub.
- Built-in remote development support and secret management.

> Go with this if you want consistent dev/test environments tied to Git workflows and don’t want to manage infrastructure manually.
> 

### 6. Shipyard – No-code workflows and ephemeral environments for testing

Shipyard helps QA teams and non-developers deploy previews using drag-and-drop flows.

![shipyard-homepage.png](https://assets.northflank.com/shipyard_homepage_427324b4b2.png)

**Features:**

- Visual editor to create test workflows and preview triggers.
- Good fit for QA automation and staging previews.
- Limited control over underlying infrastructure and custom workflows.

> Go with this if you want previews tied to QA workflows with no YAML or infra management required.
> 

### 7. Porter – Kubernetes-based platform with preview deploys + secrets

Porter uses Helm under the hood and supports ephemeral environments via preview deploys.

![porter homepage.png](https://assets.northflank.com/porter_homepage_fd35ac3c23.png)

**Technical details:**

- Supports secrets, PR-triggered previews, and one-click deploys.
- Preview lifecycle and secrets management via UI.
- Limited teardown automation unless configured manually.

> Go with this if you already work with Kubernetes and want a simplified layer for managing previews.
> 

*See [Best Porter alternatives for scalable deployments](https://northflank.com/blog/best-porter-alternatives-for-scalable-deployments)*

### 8. Okteto – Kubernetes developer environments with auto-previews per branch

Okteto sets up per-branch preview environments using Kubernetes namespaces, ideal for teams building with microservices.

![okteto home page.png](https://assets.northflank.com/okteto_home_page_85abf5b004.png)

**What it provides:**

- Per-branch deployments for apps, databases, and services.
- Namespace isolation for each preview.
- Requires your own Kubernetes cluster.

> Go with this if you want isolated Kubernetes previews for each branch and manage your own infra.
> 

### 9. Vercel – Git-based frontend previews, tight Next.js integration

Vercel provides one of the smoothest preview experiences for frontend teams working with React, Next.js, or static sites.

![vercel-homepage.png](https://assets.northflank.com/vercel_homepage_f09e3a1f3c.png)

**Best for:**

- Frontend-only deployments tied to PRs.
- Built-in domain previews with GitHub and GitLab.
- No built-in support for backend or DB services.

> Go with this if you’re building React or static sites and want automated frontend previews out of the box.
> 

*See [Best Vercel Alternatives for Scalable Deployments](https://northflank.com/blog/best-vercel-alternatives-for-scalable-deployments)*

### 10. Netlify – JAMstack-focused, branch-based previews, best for static sites

Netlify supports deploy previews on pull request branches, similar to Vercel, but focused on JAMstack architectures.

![netlify's home page.png](https://assets.northflank.com/netlify_s_home_page_fc0a46230b.png)

**Good for:**

- Static site generators like Hugo, Gatsby, and Jekyll.
- Git-based automatic previews.
- Not intended for backend previews or multi-service apps.

> Go with this if your stack is static-first and you want clean branch previews with minimal setup.
> 

See [7 Netlify alternatives in 2026: Where to go when your app grows up](https://northflank.com/blog/netlify-alternatives)

## Why Northflank works well for full-stack previews

So, we’ve seen how different platforms handle preview environments, but Northflank takes it further. It doesn’t treat them as an add-on. The entire platform is built to support automated, production-like previews across services, jobs, and databases, making full-stack testing feel like a native part of your workflow.

Let's break down what makes that possible. 

### 1. Full-stack scope without the complexity

Many platforms stop at spinning up a frontend or a single service. Northflank goes further: each preview environment can include microservices, background jobs, and persistent services like Postgres or Redis, all orchestrated together. You can even share databases across environments when needed or isolate them completely.

See what a full-stack preview environment on Northflank looks like, complete with Postgres, Redis, background jobs, and linked secrets, all managed in a single template:

![Northflank visual editor showing a preview environment template with Postgres, Redis, background job, and linked secrets](https://assets.northflank.com/preview_environment_template_15e725d011.webp)*A full preview environment with multiple services, jobs, and shared resources configured visually in Northflank.*

### 2. Git-triggered automation that scales

Previews are created automatically based on Git triggers, like pull requests or specific branches. This fits naturally into existing workflows without needing to touch CI YAML files. You can define granular rules for what triggers a preview, and Northflank handles the rest.

You can define Git triggers directly from the UI. There’s no need to write or maintain custom CI logic. Just select your repository, set the branch or PR rules, and Northflank takes care of provisioning the environment. See the screenshot below:

![Northflank interface showing Git trigger configuration for preview environments, including repository selection and pull request rules](https://assets.northflank.com/create_preview_template_form_6cff6c5f18.webp)*Git triggers defined visually (no CI YAML required)*

### 3. Preview lifecycle control

Each environment can be given a defined duration, auto-cleanup window, and active hours. This prevents idle previews from running endlessly and consuming resources, perfect for teams managing dozens or even hundreds of environments.

Northflank gives you full control over how long preview environments live. You can set active hours for weekdays, define default durations, and even reset timers automatically on environment updates. See the screenshot below:

![Northflank UI showing configuration for preview environment duration and weekday active hours](https://assets.northflank.com/preview_duration_and_active_hours_178b134489.webp)*Define active hours and durations to reduce idle previews and save on resources*

### 4. Everything-as-code, if you want it

Prefer Infrastructure as Code (IaC)? Northflank supports defining preview environments with templates and GitOps. This lets teams manage changes in source control while still benefiting from the rich UI and audit history.

Templates in Northflank act as code-defined blueprints for previews. You can version them, trigger them from Git, and reuse them across projects, all while keeping full visibility in the UI.
See the screenshot below:

![Northflank UI showing a configured preview environment template with Git triggers and naming conventions](https://assets.northflank.com/create_preview_template_form_c3b3853ce7.webp)*Create reusable preview templates with Git triggers and structured naming*

### 5. Built-in security, no extra tooling

Secrets are automatically injected at runtime. There’s no need to manually manage extra tools or vault setup because Northflank provides encrypted secret storage out of the box, so you can safely manage credentials across environments.

[Secret injection](https://northflank.com/docs/v1/application/secure/inject-secrets) in Northflank is built into the platform. You can define environment variables and secrets per preview, and they’re automatically encrypted and scoped without needing an external vault.

See the screenshot below:

![Northflank preview environment template showing secrets and runtime environment variable configuration UI](https://assets.northflank.com/preview_arguments_a8b173056d.webp)*Define secrets and environment variables directly in the preview configuration.*

## Common questions about preview environments

So far, we’ve walked through what preview environments are capable of and how different platforms approach them. If you're new to the concept or just need clarity on how it all works, here are answers to the most common questions teams ask when evaluating or implementing preview environments.

### **What are preview environments?**

Preview environments are temporary, automatically provisioned copies of your application, usually spun up for each pull request or feature branch. They let developers, QA, or stakeholders test code changes in isolation before merging to main.

Think of them as disposable, per-branch staging setups. If you need more detailed explanation, read this article on “[Dev, QA, preview, test, staging, and production environments. What's the deal?](https://northflank.com/blog/what-are-dev-qa-preview-test-staging-and-production-environments)” and “[The what and why of ephemeral preview environments on Kubernetes](https://northflank.com/blog/the-what-and-why-of-ephemeral-preview-environments-on-kubernetes-sandbox-testing)”.

### **What is preview deployment?**

A preview deployment is the actual process of launching that temporary environment based on your code changes. It’s triggered by Git events like pull requests and gives you a live URL to test or review the feature in a real-world setup.

### **What are the three different testing environments?**

Most teams use three main types of testing environments:

- **Development** – local or shared, for day-to-day engineering.
- **Preview** – per-branch or per-PR, for early feedback and QA.
- **Staging** – a production-like replica used just before releasing to users.

Preview sits right between dev and staging, offering fast feedback without affecting shared test setups.

### **What is the preview process in CI/CD?**

In a typical CI/CD pipeline, the preview process works like this:

1. A pull request is opened.
2. The CI pipeline builds the app and runs tests.
3. A preview environment is deployed automatically.
4. Reviewers test the app via a temporary URL.
5. The environment is destroyed after the PR is merged or closed.

Some platforms require custom scripts for this. Others, like Northflank, handle it out of the box with Git triggers and teardown logic. See [how Northflank handles it](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment). 

## Choosing the right platform for your stack

We’ve looked at how 10 platforms approach preview environments, from frontend-only setups to full-stack systems with Git-triggered automation. Now comes the part that can get overwhelming: figuring out which one fits your team’s stack and workflow.

A few decision points to keep in mind:

- **What needs to run in your previews?** Just a UI, or also APIs, databases, and background workers?
- **How much control do you want?** Do you need full GitOps workflows, or would a managed UI-based system save time?
- **Who manages the infra?** Are you bringing your own cloud or expecting the platform to handle it?
- **Do you need lifecycle automation?** Like teardown after merge, or previews that expire on a schedule?

If you’re building across services and want Git-based automation without maintaining ArgoCD setups or integrating multiple tools manually, Northflank is worth a look.

**[Deploy your first full-stack preview environment today by signing up](https://app.northflank.com/signup).**]]>
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  <title>App Engine vs. Cloud Run: A real-world engineering comparison</title>
  <link>https://northflank.com/blog/app-engine-vs-cloud-run</link>
  <pubDate>2025-05-23T15:00:00.000Z</pubDate>
  <description>
    <![CDATA[Compare Google Cloud's App Engine vs. Cloud Run: understand serverless tradeoffs, scaling, pricing, and control. Learn which platform fits your app, team, and growth goals best.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/fly_io_vs_render_blog_post_74ccb06c56.png" alt="App Engine vs. Cloud Run: A real-world engineering comparison" />Choosing between Google App Engine and Google Cloud Run is a surprisingly tough decision. At first glance, they seem to promise the same thing: serverless simplicity, automated scaling, and less infrastructure to worry about. But as you dig deeper, it becomes clear these platforms are built on very different philosophies, and those differences matter.

This is not just about which tool has more features. It’s about how each one fits into your team’s workflow, your architecture decisions, and your long-term growth. Should you prioritize speed of deployment or flexibility in runtime? Is ease of use more important than full control? What about pricing surprises once traffic scales?

This article aims to answer those questions honestly. We’ll walk through the technical capabilities, developer experience, and operational tradeoffs of App Engine and Cloud Run in a way that reflects how engineering teams actually make decisions. Not just what’s possible, but what works in the real world.

If you're in the middle of choosing a serverless platform, migrating off App Engine, or just trying to avoid future lock-in, you’re in the right place.

## TL;DR – What you should know upfront

**This table is your quick-start guide.** If you're just trying to get something working quickly, pick the column that matches your project and dive in. If your team is planning for growth, dealing with traffic spikes, or building more than a basic app, keep reading. The real differences become clear as complexity increases.

| **Category** | **App Engine** | **Cloud Run** |
| --- | --- | --- |
| **Philosophy** | Classic PaaS – Google manages almost everything | Container-first – you bring the code, Google runs it |
| **Developer experience** | Simple CLI deploys, great for fast MVPs and small teams | Requires Docker knowledge, better for experienced or polyglot teams |
| **Flexibility** | Limited runtime access and customization | Full control over runtime, language, and binaries |
| **Performance & scaling** | Fast for steady workloads, but cold starts and sandbox limits apply | Great for spiky traffic, configurable concurrency and scaling |
| **Pricing model** | Charged per instance-hour; steady traffic is cheaper | Charged per request + resource usage; bursty traffic is more cost-efficient |
| **Security & networking** | Secure by default, but limited VPC/networking options | Deeper networking control, supports private/internal services |
| **Best for** | Rapid prototyping, early-stage apps, low-maintenance deployments | Teams needing control, microservices, or multi-language/runtime apps |
| **Limitations** | Locked to GCP, limited customizability, sandbox constraints | Some complexity upfront, Docker knowledge needed |

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    <a href="https://app.northflank.com/signup">  
<Button variant={["large", "gradient"]}>Try Northflank – Deploy on any cloud in minutes >>></Button>  
    </a>  
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## App Engine vs Cloud Run: Two different philosophies

**Google App Engine** was built on the classic Platform-as-a-Service (PaaS) model. You write your app in one of the supported languages such as Python, Java, Go, or Node.js, then deploy with a simple command. App Engine handles everything else: scaling, patching, networking, and even integration with Google Cloud’s internal services like Datastore or Task Queues.

**Google Cloud Run** takes a different approach. It’s based on containers. You bring your own Docker image (or use Cloud Build to create one), and Cloud Run runs it in a serverless fashion. You’re not limited by language or runtime, and you can configure things more granularly. It’s technically closer to Kubernetes and Knative than traditional PaaS, but Google’s abstraction makes it feel almost as easy to use as App Engine.

In short, App Engine abstracts everything. Cloud Run offers just enough control without overwhelming you.

## Developer experience: Who gets to move faster?

For developers, App Engine can feel like a dream, at least early on. It’s designed for speed. You deploy with a single command, there’s no need to manage Dockerfiles, and the configuration is minimal. It’s particularly friendly to small teams or projects that need to get something working quickly.

But that simplicity comes at a cost. App Engine restricts what you can do. In the Standard environment, your app runs in a sandbox. You can’t access the local filesystem or run arbitrary binaries. Background threads are limited. If your use case is straightforward, such as a basic web API, it works well. But if you need more flexibility, you’ll quickly hit limits.

Cloud Run has a higher barrier to entry. You need to understand containers and how to structure an app for stateless, ephemeral environments. But in return, you get full control. You can use any language, run custom binaries, and design your app however you want. If your team is already using Docker, the learning curve isn’t steep. If not, there’s some ramp-up, but the flexibility is worth it.

### **DX verdict**:

- For beginner-to-intermediate teams who want “it just works,” App Engine wins.
- For polyglot teams or those already using Docker, Cloud Run offers unmatched control.

## Technical constraints and power

This is often where the real decision gets made. Under the surface, how much control do you actually have?

App Engine Standard operates within a fairly restrictive sandbox. You don’t get access to the underlying OS, can't write to the local filesystem, and long-running background tasks are discouraged or outright disallowed. Runtime versions are pinned, and updates are controlled by Google. This keeps your environment secure and stable but limits your options, such as running custom binaries or persistent socket connections.

App Engine Flexible loosens these restrictions. It runs on Docker containers behind the scenes, allowing more custom setups. But this comes with slower deployments, longer startup times, and higher minimum resource usage—which means you’re paying even when nothing is happening.

Cloud Run gives you full control over the runtime because you define the container. You can use any language, framework, or binary, and you’re not stuck with a specific API or SDK version. Want to run Rust or bundle FFMPEG into your service? No problem. This flexibility makes it ideal for workloads that don’t fit into App Engine’s model, such as data processing tasks, streaming services, or specialized microservices.

The tradeoff is complexity. With great power comes the need to write Dockerfiles. But for many teams, especially those integrating with CI/CD systems, it’s a welcome trade.

### **Technical verdict**:

- App Engine is great for conventional web apps and APIs.
- Cloud Run is better for complex, container-native workloads.

## Scalability and performance: Both impressive, but different

Both platforms scale automatically, though in different ways.

App Engine Standard can scale to zero and ramp up quickly, which is cost-efficient. But its instance-based model means cold starts can be noticeable. It tries to keep some warm instances running, but during quiet periods, the spin-up delay is felt. The Flexible environment provides more power, but it scales more slowly and has a higher baseline cost.

Cloud Run also scales to zero and spins up instances per request. It’s fast, though still container-based, so cold starts do happen, especially with large or unoptimized containers. You can fine-tune concurrency and memory, which helps balance performance and cost. Under spiky load, Cloud Run generally handles traffic more predictably, especially if you use pre-warmed instances or set minimum instance values.

If latency is critical and you can live within App Engine’s constraints, it might feel snappier. If flexibility is more important, Cloud Run’s model gives you more control.

### **Scaling verdict**:

- App Engine: smoother scaling for steady workloads.
- Cloud Run: better for spiky or highly variable traffic.

## Pricing: Where things get real

Both App Engine and Cloud Run offer generous free tiers, but their pricing models differ.

App Engine Standard charges per instance-hour. With light, steady traffic, the free tier may cover your needs. But if your app must stay online during off-hours, you’ll pay for idle time. The Flexible environment uses VMs, resulting in higher costs and less granular billing.

Cloud Run charges per request, vCPU-second, and memory-second. You only pay when your code is running. For sporadic traffic, this is often cheaper. It’s particularly appealing for APIs, webhook handlers, or event-driven services.

However, at high sustained volumes, Cloud Run can become expensive if you’re not careful with memory and concurrency settings. App Engine’s flat hourly rate may sometimes be more predictable.

A good rule of thumb: if your traffic is spiky or you want scale-to-zero, choose Cloud Run. For steady traffic, App Engine may be cheaper or easier to estimate.

### **Pricing verdict**:

- App Engine is cost-predictable but potentially wasteful.
- Cloud Run is cost-efficient, especially for low-traffic or bursty workloads.

## Security and networking: Both solid, slightly different strengths

Both platforms integrate with Google Cloud IAM, use HTTPS by default, and support custom domains. But Cloud Run gives you more control over networking.

Cloud Run supports VPC connectors, allows static IP addresses (through Serverless VPC Access), and gives more fine-grained access controls. It also supports private services that aren’t accessible from the internet, which is useful for internal APIs or microservices.

App Engine’s networking is a bit more rigid. You don’t get static IPs easily, and its VPC integration is limited. However, it benefits from tight integration with App Engine-specific services, which can simplify certain setups.

### **Security verdict**:

- App Engine: simple, secure defaults.
- Cloud Run: deeper control and enterprise-friendly features.

## What if App Engine and Cloud Run don’t cover everything you need?

Even with all their strengths, App Engine and Cloud Run can still leave certain teams feeling boxed in. Maybe it's not obvious on day one. But as your stack grows, your infrastructure becomes more ambitious, and your product starts pushing boundaries, the cracks start to show.

You might find yourself needing GPU support for ML workloads or image processing, but neither platform handles that natively. Or maybe your team wants to self-host a growing set of open-source tools, things like n8n, Temporal, Meilisearch, or GrowthBook, and the idea of writing Terraform or wiring up Kubernetes just to get started feels like overkill.

Or maybe it’s something more fundamental. Maybe you want to deploy across **multiple cloud providers**, not because it’s trendy, but because it makes business sense, redundancy, latency, cost arbitrage, or data sovereignty. With App Engine and Cloud Run, you're deeply tied to Google Cloud. There's no easy off-ramp. That’s fine until it’s not.

That’s where platforms like [**Northflank**](https://northflank.com/) start to look less like “alternatives” and more like enablers.

[Northflank](https://northflank.com/) blends the simplicity of modern PaaS with the flexibility most teams don’t realize they need until it’s too late. It's [**BYOC (Bring Your Own Cloud)**](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) model means you can deploy across AWS, GCP, or Azure without rewriting your stack. It’s serverless without being server-bound.

![byoc. 2-min.png](https://assets.northflank.com/byoc_2_min_09d9c7300d.png)

Need GPU support? [Northflank](https://northflank.com/) handles it natively. Want to spin up a full-stack template for your app, complete with a database, caching layer, and background jobs? **Stack Templates** let you deploy entire systems ready to go, production-configured, with just a few clicks. No DevOps rabbit hole. No “clone this repo and pray it works.”
> For example, [Weights](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s) an AI company, scaled to millions of users running AI workloads and complex backends without hiring a single DevOps engineer, all on Northflank.
> 

![gpu-workloads-northflank.webp](https://assets.northflank.com/gpu_workloads_northflank_9bf76ec92d.webp)

It’s not trying to be a replacement for App Engine or Cloud Run. It’s just designed for teams that have already started asking, “What happens next?” What happens when your needs go beyond HTTP endpoints and autoscaling APIs?

App Engine is great for getting started. Cloud Run is excellent when you need flexibility inside the Google Cloud world. But Northflank is what you reach for when your platform needs to grow with your product, not hold it back.

## So, which should you choose?

If your goal is to ship quickly with minimal setup, and your app fits within the boundaries of what Google thinks a web app should look like, **App Engine** is still a strong contender. It’s great for small teams, prototypes, and apps that don’t require much customization under the hood.

If you’re comfortable with Docker and want more control over your runtime, deployment structure, and language choice while still enjoying a serverless experience, **Cloud Run** is a powerful and flexible choice. It fits especially well for container-native teams that are already thinking in microservices or want to integrate tightly with other Google Cloud services.

But if you’re the kind of team that’s thinking beyond a single app, building platforms, orchestrating tools, or simply looking for a way to self-host with minimal friction, [**Northflank**](https://northflank.com/) offers a compelling alternative. It fills the space that traditional serverless often leaves behind: GPU workloads, cross-cloud deployment, production-grade infrastructure in a few clicks, and a modern developer experience that doesn’t require becoming a DevOps expert.

## Wrapping up

At the end of the day, choosing a platform isn’t just a technical decision, it’s a bet on how you want to build.

App Engine feels like training wheels for the cloud: fast, safe, and great for early momentum. Cloud Run meets you halfway, offering flexibility without dragging you into DevOps quicksand. Both are solid. Both can take you pretty far.

But if you’ve ever wished you could just launch the stack you need, on the cloud you prefer, with real control and no second-guessing, that’s when you start looking for something more.

That’s when [Northflank](https://northflank.com/) quietly starts to make sense. It’s not louder. It’s just broader. GPU support, real multi-cloud, and production-ready infrastructure that doesn’t take a weekend to set up. Not a bet on one cloud, but a platform that bends to the way *you* work.

If you're building something serious, it might be worth building it somewhere that grows the way you do.

[Take a look at the quickstart guide](https://northflank.com/docs/v1/application/getting-started/introduction-to-northflank), or [create a free account](https://app.northflank.com/signup) to try it for yourself.]]>
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  <title>7 best Fireworks AI alternatives for inference in 2026</title>
  <link>https://northflank.com/blog/7-best-fireworks-ai-alternatives-for-inference</link>
  <pubDate>2025-05-21T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[If you’re searching for alternatives to Fireworks AI, chances are you’re not just chasing lower latency.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/best_fireworks_ai_alternatives_for_inference_efa442beec.png" alt="7 best Fireworks AI alternatives for inference in 2026" />If you’re searching for alternatives to Fireworks AI, chances are you’re not just chasing lower latency; you’re running into walls. Fireworks gets you from zero to hosted LLM in minutes, but when your use case becomes more complex than calling an endpoint, you need infrastructure that doesn’t vanish behind an API. You need tools that are opinionated enough to help, but flexible enough to stay out of your way.

This guide breaks down the top Fireworks AI alternatives based on how these platforms behave in the hands of engineers shipping real products. We'll look at control, extensibility, stack integration, and the tradeoffs that come with each choice.

## **Why you might be looking for a Fireworks AI alternative**

Fireworks AI does one thing well: serve optimized open models fast. But once you need to fine-tune, deploy in your own cloud, or run anything adjacent to the model, it becomes clear what Fireworks isn’t trying to solve.

Reasons teams look elsewhere for Fireworks AI alternatives:

- **You need infra control** – Bring Your Own Cloud (BYOC) isn’t supported unless you’re a major enterprise customer.

<aside>

The need for [BYOC (Bring Your Own Cloud)](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) sneaks up on you. At first, it’s easy to just use whatever the inference provider gives you… fast, managed, simple. But over time, it starts to bite: costs get harder to predict, data ends up siloed, and you have no real control over where or how your workloads run. 

When you can deploy into your own cloud, everything changes. 

You get to use your existing infrastructure, stay compliant, manage costs more tightly, and integrate cleanly with the rest of your stack. As AI workloads get heavier and more critical, this stuff stops being nice-to-have. It becomes necessary. BYOC gives you the knobs you’ll wish you had sooner.

</aside>

> Read more: [Why smart enterprises are insisting on BYOC for AI tools](https://northflank.com/blog/why-smart-enterprises-are-insisting-on-byoc-for-ai-tools)
> 
- **You need to orchestrate more than inference** – No support for APIs, queues, jobs, or database-backed workflows.
- **You care about compliance or cost transparency** – Fireworks’ fully-managed setup hides both optimization opportunities and data residency levers.
- **You want better debugging and monitoring** – Logs and metrics are thin. There’s no way to trace performance regressions or cost anomalies meaningfully.

What you need next is a platform that treats inference as a component, not the product.

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    <a href="https://app.northflank.com/signup">  
<Button variant={["large", "gradient"]}>Deploy your AI on Northflank today</Button>  
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## **What to look for in a better inference platform**

- **Inference throughput**: Can it handle batch and real-time use cases without falling over?
- **Model flexibility**: Can you bring your own weights, customize pipelines, or use niche architectures?
- **Infra surface area**: Are you allowed to deploy in your cloud, or is it a black box?
- **System-level integration**: Can you run APIs, cron jobs, vector stores, and other components in the same stack?
- **Observability**: Logs, metrics, tracing, tools for real debugging, not just dashboards.
- **CI/CD maturity**: Git-driven deploys, rollbacks, staging environments, templated infra, all signal long-term viability.

<InfoBox className='BodyStyle'>

## ⏱️ Quick ranking: Fireworks AI alternatives

1. [**Northflank**](https://northflank.com/) — Infrastructure for real software, not just inference.
2. [**Amazon SageMaker**](https://aws.amazon.com/sagemaker/) — Enterprise-grade and deeply integrated with AWS, but clunky and complex.
3. [**Google Vertex AI**](https://cloud.google.com/vertex-ai) — Excellent for Google-native NLP, less great for OSS models or custom infra.
4. [**Together AI**](https://together.ai/) — Great performance, but hosted-only and tightly scoped.
5. [**BaseTen**](https://baseten.com/) — Good if you want managed inference + observability, and don’t need stack control.
6. [**Modal**](https://modal.com/) — Serverless flexibility, but you’ll build everything yourself.
7. [**Replicate**](https://replicate.com/) — For prototypes and solo builders—not for prod.

</InfoBox>

## 1. Northflank: Infrastructure for real AI systems

![CleanShot 2025-05-22 at 16.39.03@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_22_at_16_39_03_2x_a1219941f0.png)

**Northflank isn’t a model API. It’s a platform for deploying GPU-backed workloads and full systems into your own cloud or theirs.** You get control over the compute layer *and* the app layer (models, APIs, queues, databases, cron jobs), all deployable in a single stack.

### What makes it different

- True **BYOC** support for AWS, GCP, Azure, or on-prem Kubernetes
- **GPU-native scheduling** with spot/preemptible node support
- Co-locate model inference with APIs, job queues, and stateful services (Postgres, Redis)
- Git-based CI/CD with rollback, health checks, autoscaling, and environment promotion
- Declarative JSON templates for reproducible multi-service architectures

### Limitations

- No built-in model catalog (but they’re working on it, and templates come close). You must containerize or use templates
- Requires some infrastructure familiarity if using BYOC in production

Northflank wraps Kubernetes with a high-level developer experience. Under the hood, each workload runs in its own namespace, with support for GPU resource requests, autoscaling policies, per-environment secrets/configs, and managed service-to-service networking. GPU services can use node selectors or taints to run on dedicated pools. You can define GPU-backed containers that autoscale with load or stay warm across replicas.

## 2. Amazon SageMaker: Flexible, powerful, but operationally heavy

![CleanShot 2025-05-21 at 14.02.40@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_21_at_14_02_40_2x_14769d3381.png)

SageMaker is the inference backbone for many large enterprises. It gives you detailed control over compute, autoscaling, and security, and plugs seamlessly into the broader AWS ecosystem.

SageMaker lets you deploy models using containers, Python SDKs, or prebuilt endpoints via JumpStart. It supports asynchronous inference, streaming, and multi-model endpoints on a single instance. You can use model registries, versioning, and pipelines to handle full MLOps workflows. Inference is tightly coupled with IAM, VPC config, and other AWS primitives, giving strong governance but requiring deep AWS knowledge.

### What makes it different

- Supports multi-model endpoints, GPU/CPU variants, spot pricing
- Deep IAM integration, encryption, and network control
- Good tooling for A/B testing, shadow deploys, and autoscaling

### Limitations

- Steep learning curve; the UX feels fragmented
- Overhead is high for small teams or MVP use cases
- Pricing gets complex quickly if not carefully managed

## 3. Google Vertex AI: Great for Google-native ML, but not OSS-first

![CleanShot 2025-05-21 at 14.03.08@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_21_at_14_03_08_2x_f79aac111f.png)

Vertex AI offers fully managed inference and training with tight integration into GCP. Ideal if you’re using PaLM 2, Gemini, or embedding NLP into an app built on Google's stack.

Vertex AI provides managed endpoints for models trained on AutoML or via custom training pipelines. It supports Tensor Processing Units (TPUs) for inference and connects directly to services like BigQuery, Cloud Storage, and Firebase. You can fine-tune foundation models like PaLM 2 or deploy your own TensorFlow, PyTorch, or XGBoost models. However, deployment of general OSS models like LLaMA requires extra configuration and isn’t as streamlined.

### What makes it different

- TPU-backed inference for Google’s foundation models
- Unified interface for training, tuning, and deploying
- Strong support for semantic search and document AI

### Limitations

- Limited flexibility for OSS model hosting
- Requires deep GCP adoption to get full value
- Not BYOC; usage stays within Google’s control plane

> 🔎 Note: SageMaker and Vertex AI are full-stack ML platforms, designed to cover everything from data prep to training, tuning, and deployment. That makes them powerful, but also heavyweight. If your goal is just to serve models as part of a broader application system, not build an entire MLOps pipeline, they can feel overbuilt. You get a lot of knobs, but not always the ones you actually need for real-time, product-facing inference.
> 

## 4. Together AI: High-throughput OSS inference alternative to Fireworks AI

![CleanShot 2025-05-21 at 14.03.22@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_21_at_14_03_22_2x_54da716a8f.png)


Together AI is a fast, reliable option for hosted model inference across a large library of open-source models. It shines when you want plug-and-play APIs and are okay with living in their cloud.

Together’s platform abstracts the infrastructure entirely. You can rent dedicated GPU endpoints (with token-based pricing) or use serverless endpoints for bursty workloads. They also support LoRA fine-tuning and quantized models out of the box. Their infra is optimized for inference throughput, but there’s no way to colocate your own business logic or services.

### What makes it different

- Access to a massive model catalog, including LLaMA 3, Mistral, Mixtral, Falcon
- Supports long-context (128K) inference, LoRA-based fine-tuning
- OpenAI-style APIs, high throughput on dedicated endpoints

### Limitations

- No BYOC; all workloads must run on Together’s infrastructure
- No support for deploying additional services or systems alongside the model
- Pricing can spike for high-throughput or long-context use cases

## 5. Baseten: Observability and managed inference

![CleanShot 2025-05-21 at 14.03.44@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_21_at_14_03_44_2x_5adbae7f83.png)

Baseten focuses on the experience of running inference in production: monitoring, model packaging, and deployment workflows. If you’re an ML team with limited infra capacity, this feels polished.

Each deployment in Baseten is a containerized Truss bundle: Python model + hooks + dependencies. Baseten provisions the infra, adds monitoring (request timing, error rates, throughput), and surfaces usage metrics. But you can’t run custom services or databases. It’s inference-focused.

### What makes it different

- Ships with Truss: a model packaging tool with pre/post-processing hooks
- Built-in dashboards, A/B testing, and rollback support
- Integrates with common cloud storage and CI tools

### Limitations

- No BYOC or self-hosted deployment options
- Limited extensibility, can’t deploy full-stack systems
- Customization tied to Truss; harder to swap in custom pipelines

## 6. Modal: Serverless infra for arbitrary ML workflows

![CleanShot 2025-05-21 at 14.04.05@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_21_at_14_04_05_2x_6f8e540a7d.png)

Modal is a flexible compute platform for Python code. You can use it to serve models, batch process documents, or run training jobs, with minimal infra boilerplate.

Modal treats functions like cloud-native microservices. You define @modal.functions with container environments, resource limits (GPU/CPU), and caching directives. Modal handles provisioning, scaling, and invocation via API. But you build everything yourself—no built-in routing, observability, or stack scaffolding.

### What makes it different

- Code-first development with native Python decorators
- Scale-to-zero compute with GPU and CPU instance types
- Mount remote storage and load models dynamically

### Limitations

- No BYOC or on-prem execution support
- No prebuilt stack scaffolding, you build everything
- Observability and routing require external setup or tooling

## 7. Replicate: Fast OSS model hosting for prototypes

![CleanShot 2025-05-21 at 14.04.21@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_21_at_14_04_21_2x_bc3eaf78f9.png)

Replicate is the fastest way to deploy and test community models. Great for demos, hackathons, or testing niche models.

Replicate uses Dockerized environments (via Cog) to package models with entrypoint scripts. Jobs run on shared infra with optional GPU use. It’s minimal but effective. Not intended for high-scale production, but great for fast iteration.

### What makes it different

- Vast model library with community-maintained endpoints
- One-command deploys using Cog (their CLI + runtime)
- Built-in API keys and per-second billing

### Limitations

- Not designed for sustained production traffic
- Limited visibility into system-level performance
- You’re limited to what Cog + the UI offers, no orchestration

## Fireworks AI alternatives at a glance

| Provider | BYOC | Full stack support | Model catalog | GPU support | Pricing model |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | ✅ Yes | ✅ Yes | ❌ No | ✅ Yes | Free tier, usage-based, or custom enterprise (BYOC or managed) |
| **SageMaker** | 🟡 Partial (AWS only) | 🟡 Partial | ✅ Yes | ✅ Yes | Usage-based + infra costs |
| **Vertex AI** | ❌ No | 🟡 Partial | ✅ Yes | ✅ (TPU too) | GCP-native pricing (TPU optional) |
| **Together AI** | ❌ No | ❌ No | ✅ Yes | ✅ Yes | Token-based or dedicated endpoint pricing |
| **BaseTen** | ❌ No | ❌ No | ✅ Yes | ✅ Yes | Usage-based |
| **Modal** | ❌ No | 🟡 Partial | ❌ No | ✅ Yes | Per-call and storage compute billing |
| **Replicate** | ❌ No | ❌ No | ✅ Yes | ✅ Yes | Per-second usage billing |

## Final thoughts

![image.png](https://assets.northflank.com/image_f090560183.png)
Fireworks AI is a great way to serve open models fast. But if you’re building a real product, one that includes inference, APIs, data pipelines, and custom infra, you need a system.

Northflank is the only Fireworks AI alternative on this list that:

- Supports BYOC with full-stack deployment
- Offers GPU-native orchestration with cost control
- Integrates inference with real production infrastructure

If the model is part of your stack, not your whole product, Northflank is the only one that gets it.

Try it out [here](https://app.northflank.com/signup).]]>
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  <title>San Francisco meetup for CTOs and engineers. You're invited!</title>
  <link>https://northflank.com/blog/northflank-panel-krea-weights-play-san-francisco</link>
  <pubDate>2025-05-20T20:45:00.000Z</pubDate>
  <description>
    <![CDATA[Northflank is hosting a get-together for technical leaders who care about building software that holds up at scale, under pressure, and over time.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Group_2_f4e6beb65a.png" alt="San Francisco meetup for CTOs and engineers. You're invited!" />## Join us in SF 🌉 Building software that lasts. A panel with Krea.ai, Weights, and PlayAI 

Northflank is hosting a get-together for technical leaders who care about building software that holds up at scale, under pressure, and over time.

Big thank you to Kindred Ventures (early backers of Coinbase, Uber, Perplexity, and Northflank!) for lending us their office in Jackson Square.

There’ll be actual dinner, not finger food, so bring an appetite. 

You’ll hear from:

- Diego Rodriguez, CTO @ Krea.ai
- JonLuca DeCaro, CTO @ Weights
- Kei Yoshikoshi, Head of Product & Eng @ PlayAI

We’ll be talking about what durability looks like in practice, especially in GenAI.

📍 June 5, 6pm | Jackson Square, SF

Limited spots. Engineers only.

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  src="https://lu.ma/embed/event/evt-yS7hNpyp8p6bbhE/simple"
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  </content:encoded>
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  <title>7 Coolify alternatives in 2026 (ranked &amp; compared)</title>
  <link>https://northflank.com/blog/coolify-alternatives-in-2026</link>
  <pubDate>2025-05-19T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Coolify is an open-source platform that lets you deploy apps on your own infrastructure – essentially a DIY alternative to Heroku, Netlify, or Vercel. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/coolify_alternatives_f96b361271.png" alt="7 Coolify alternatives in 2026 (ranked &amp; compared)" />[Coolify](https://www.coolify.io/) is an open-source platform that lets you deploy apps on your own infrastructure – essentially a DIY alternative to Heroku, Netlify, or Vercel. 

It’s great for developers who want full control, but not every team can or wants to manage their own servers. Startups and enterprises often need more **production-ready**, fully managed solutions with robust CI/CD, scalability, and support. If you’re looking for powerful app deployment platforms like Coolify, but with hosted options or advanced features, then this guide is for you.

Below, we rank six of the best Coolify alternatives in 2026. We’ll cover what makes each platform unique, their ideal use cases, key features, pros and cons, and who should choose which. 

<InfoBox className='BodyStyle'>

### ⏱️ Quick look: Top Coolify alternatives in 2026

Short on time? Here’s a quick breakdown of the best Coolify alternatives and what they’re known for:

1. **Northflank** – Kubernetes-native self-service developer platform that gives you everything in one place: built-in CI/CD pipelines, container image builds, preview environments, and Kubernetes-native deployments. 
2. **Heroku** – Pioneering PaaS with simple git-push deployments and a rich add-on ecosystem, though costs rise at scale without a free tier
3. **CapRover** – Self-hosted, open-source PaaS that brings Heroku-like simplicity to your own server or VPS; great for devs who want full control, but lacks built-in CI/CD or support
4. **Vercel** – Frontend-focused cloud for Next.js and modern web frameworks, offering serverless functions and a global edge network (great DX, limited backend capabilities)
5. **Netlify** – Jamstack deployment platform with Git-based CI/CD, built-in CDN, and serverless functions; ideal for static sites and frontend apps, but not built for complex backends
6. **Render** – All-in-one cloud host for web apps, APIs, and databases, featuring auto-deploy from Git, background workers, and more affordable pricing than legacy PaaS
7. **Railway** – Developer-friendly cloud with instant deployments and one-click databases; very easy for prototypes and MVPs, though usage-based free credits and fewer enterprise features limit long-term use

</InfoBox>

### #1 Northflank – Best overall Coolify alternative

![image.png](https://assets.northflank.com/image_83a7f944a9.png)

**Northflank** is a modern self-service developer platform that gives you everything in one place: built-in CI/CD pipelines, container image builds, preview environments, and Kubernetes-native deployments. 

It provides a powerful abstraction layer over Kubernetes, so development teams get the benefits of containerized infrastructure **without** the usual complexity. 

Northflank supports [**Bring Your Own Cloud (BYOC)**](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment): you can run it fully hosted or deploy it into your own AWS, GCP, or Azure account for more control[.](https://northflank.com/blog/codefresh-alternatives#:~:text=Image%3A%20northflank%27s%20home%20page) 

**Ideal use cases:** Deploying complex **microservices** or API backends, running full-stack SaaS applications in production, and any team that wants a cloud-agnostic platform. 

**Key features:**

- End-to-end [CI/CD automation](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank)
- [Preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) per branch
- Kubernetes under the hood
- Managed databases & add-ons
- Bring Your Own Cloud support

**Pros:**

- **All-in-one platform:** Combines continuous integration, continuous delivery, container orchestration, and monitoring in one service, so you don’t need to stitch together multiple tools.
- **Cloud flexibility:** Offers a fully managed cloud service or deployment into your own infrastructure.
- **Microservices-ready and scalable:** Built on a container/Kubernetes foundation, Northflank easily handles multi-service architectures, APIs, and background jobs. It’s designed for “Docker-first” projects and can scale to [hundreds of services](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s) or even GPU workloads.
- **Transparent pricing (with free tier):** Northflank has a generous free tier (deploy 2 services, 2 jobs, 1 database addon) and straightforward [usage-based pricing](https://northflank.com/pricing) beyond that.
- **Robust feature set:** Includes features often only found in enterprise setups, like private network support (VPC peering), integrated secrets management, rollbacks, and fine-grained access control.

**Cons:**

- Learning curve for advanced features
- Smaller add-on ecosystem
- Not fully open-source

**Who it’s best for:** Development teams (from startups to mid-size enterprises) that want a **production-grade, full-stack platform** without managing Kubernetes themselves. If you’re building a SaaS product with multiple services or are practicing GitOps/CI/CD workflows, Northflank provides an ideal balance of power and ease-of-use. 

## Other Coolify alternatives you should consider

### #2 Heroku

![111111.png](https://assets.northflank.com/111111_fefaa0671a.png)

[**Heroku**](https://www.heroku.com/) is the original PaaS. It made deployment simple: push your code with Git, and it just worked. But it hasn’t kept up. As of late 2022, there’s no free tier, and scaling can get expensive fast.

**Best for:** Teams that need quick, reliable deploys and don’t mind paying more for simplicity.

**Key features:**

- Git-based deployments using buildpacks
- Wide selection of add-ons (Postgres, Redis, monitoring, etc.)
- Pipeline support for dev/staging/prod environments
- Review apps on pull requests
- CLI and dashboard for managing apps

**Pros:**

- Simple Git-based deploys
- Huge add-on marketplace
- Great for small teams and prototyping

**Cons:**

- No free tier anymore
- Expensive at scale
- Limited infra control and customizability
- Vendor lock-in risk

> *Read more: [Top Heroku alternatives in 2026](https://northflank.com/blog/top-heroku-alternatives)*
> 

### #3 CapRover

![3232.png](https://assets.northflank.com/3232_770a9bca0e.png)

[**CapRover**](https://caprover.com/) is an open-source PaaS that runs on your own infrastructure. It’s popular with devs who want Heroku-like simplicity without giving up control. You deploy it to a VPS, install apps with one click (or Docker), and manage it via a slick GUI. But it’s entirely your responsibility to manage uptime, scaling, and updates.

**Key features:**

- Self-hosted PaaS you deploy on your own server or VPS
- One-click app deployments with built-in SSL and Docker support
- Built-in support for databases and custom webhooks
- Web GUI and CLI for managing services
- Supports custom domains and scaling with Traefik or NGINX

**Pros:**

- Free and open source
- Docker-native
- Easy to set up on any cloud VPS

**Cons:**

- Fully self-managed — you own infra, updates, security
- No built-in CI/CD or preview envs
- Not suitable for teams needing support or compliance

### #4 Vercel

![424.png](https://assets.northflank.com/424_a7fce97597.png)

[**Vercel**](https://vercel.com/) is the go-to for frontend teams, especially those using Next.js. It’s fast, sleek, and handles preview environments and edge deployments out of the box. But it’s not built for heavy backend logic.

**Best for:** Frontend-heavy apps, marketing sites, and teams who want best-in-class DX for modern web frameworks.

**Key features:**

- Native support for Next.js and modern frontend frameworks
- Automatic deployments from Git with preview URLs
- Serverless and Edge Function support
- Global CDN with smart caching
- Built-in analytics and performance monitoring

**Pros:**

- Sharp developer workflow
- Fast global CDN
- Native support for Next.js, Edge Functions

**Cons:**

- Backend support is limited
- Usage-based pricing can get expensive fast
- Free tier can’t be used commercially

### #5 Netlify

![555.png](https://assets.northflank.com/555_2494bab128.png)

[**Netlify**](http://netlify.com/) is similar to Vercel but more static-site focused. It’s a solid choice for marketing sites and frontend apps, with nice extras like built-in forms and identity.

**Best for:** Jamstack apps, static sites, and content-heavy frontends.

**Key features:**

- Git-based CI/CD with deploy previews
- Global CDN for static content
- Built-in serverless functions
- Form handling and identity/auth tools
- Edge Functions and split testing support

**Pros:**

- Easy CI/CD pipeline
- Built-in forms, auth, edge functions
- Free tier allows commercial use

**Cons:**

- Not designed for backend apps
- Some features (e.g. identity, functions) cost extra at scale
- Less flexibility for dynamic workloads

> *Read more: [Top Netlify alternatives in 2026](https://northflank.com/blog/netlify-alternatives)*
> 

### #6 Render

![1234.png](https://assets.northflank.com/1234_1ab0833154.png)

[**Render**](https://render.com/) is a modern PaaS with a Heroku feel but better pricing and more features. It supports everything from web services to background workers and databases.

**Best for:** Full-stack teams looking for a balance between simplicity and backend power.

**Key features:**

- Auto-deploy from Git with zero-downtime deploys
- Managed databases (Postgres, Redis, etc.)
- Support for Docker and custom build environments
- Background workers and cron jobs
- Free HTTPS and global CDN for static sites

**Pros:**

- Supports Docker, workers, and cron jobs
- Predictable pricing
- Great for production apps

**Cons:**

- No self-hosted option
- Fewer integrations than Heroku
- Not as customizable as raw cloud infra

> *Read more: [Top Render alternatives in 2026](https://northflank.com/blog/render-alternatives)*
> 

### #7 Railway

![34332.png](https://assets.northflank.com/34332_06698971ba.png)

[**Railway**](https://railway.com/) is designed for speed and simplicity. It’s great for MVPs, personal projects, and demos, but lacks depth for scaling.

**Best for:** Hackathons, indie hackers, and early-stage startups.

**Key features:**

- One-click app and database templates
- Automatic environment provisioning
- Unified project view (services + DBs)
- Auto-generated environment variables
- Usage-based pricing with $5 credit for new projects

**Pros:**

- Super easy onboarding
- Clean UI with built-in database support
- Free to start with pay-as-you-go pricing

**Cons:**

- Credit-based free tier (apps shut down when used up)
- Not great for complex apps or long-term prod use
- Limited enterprise or infra-level controls

## Coolify alternatives, at a glance

| Platform | Best for | Key features | Self-hosted option | Free tier | Backend support | Pricing model |
| --- | --- | --- | --- | --- | --- | --- |
| **Northflank** | Full-stack apps, microservices, BYOC | Full CI/CD, preview envs, Kubernetes, BYOC, GPU — most complete platform | ✅ Yes (BYOC) | ✅ Yes | Full support (persistent services, jobs, workers, databases, BYOC) | Usage-based |
| **Heroku** | Quick, reliable deploys for small teams | Git deploys, add-ons, pipelines, review apps | ❌ No | ❌ No | Good support (persistent web/worker dynos, but limited control/customization) | Dyno-based |
| **CapRover** | Self-managed Heroku-style deployments | Docker-native, one-click apps, custom domains, open source | ✅ Yes | ✅ Yes | Docker-native (manual infra, no CI/CD, self-managed only) | Free (self-hosted) |
| **Vercel** | Frontend-heavy apps using Next.js | Next.js support, edge CDN, serverless functions | ❌ No | ✅ Yes (non-commercial only) | Serverless-only (no support for persistent or long-running backends) | Usage-based |
| **Netlify** | Jamstack sites, static frontends | CI/CD, CDN, forms, auth, edge functions | ❌ No | ✅ Yes (commercial OK) | Serverless-only (limited backend flexibility, no persistent processes) | Usage-based |
| **Render** | Full-stack apps, APIs, background workers | Git auto-deploy, Docker, managed DBs, cron jobs | ❌ No | ✅ Yes | Full support (Docker, web services, workers, cron jobs, managed DBs) | Usage-based |
| **Railway** | MVPs, hackathons, small-scale apps | 1-click deploys, DB templates, usage-based pricing | ❌ No | ✅ Yes ($5 credit) | Basic support (easy web + DB deploys, no advanced infra or scaling options) | Credit + usage |

## Conclusion

Choosing the right deployment platform depends on your project’s needs and your team’s priorities. **Coolify** itself offers a self-hosted solution for those who want full control, but the alternatives above provide options ranging from fully managed PaaS to hybrid models. 

For front-end-centric projects, platforms like **Vercel** and **Netlify** excel at delivering speed and simplicity, though you may outgrow them as your product and team mature.

**Heroku** remains a solid choice for its ease-of-use and ecosystem, but be mindful of cost and the recent removal of free plans. **Railway** and **Render** represent the new wave of developer-friendly cloud platforms; Railway for getting started quickly on a small scale, and Render for balancing simplicity with more production features as you grow.

Among these, **Northflank stands out as the #1 Coolify alternative** for startups and enterprises in 2026. It gives you the convenient, all-in-one experience (CI, CD, hosting, databases) of a PaaS while embracing modern, cloud-native technology under the hood. With support for microservices, Kubernetes, and even deployment to your own cloud infrastructure, Northflank offers a level of power and flexibility that’s hard to match. 

[Try out Northflank today, for free.](https://app.northflank.com/signup)]]>
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</item><item>
  <title>Fly.io vs Render: How they handle jobs, scaling, and production workloads in 2026</title>
  <link>https://northflank.com/blog/flyio-vs-render</link>
  <pubDate>2025-05-16T14:54:00.000Z</pubDate>
  <description>
    <![CDATA[Compare how Fly.io and Render handle background jobs, scaling, and production apps in 2026. See the key trade-offs, performance differences, and an alternative built for modern teams.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/flyio_vs_render_82ae78f134.png" alt="Fly.io vs Render: How they handle jobs, scaling, and production workloads in 2026" />> “*Fly seems incredibly cheap in the calculator, but in our experience a Fly shared-cpu-1x machine is far less capable than other shared 1vCPU setups like a Render Standard instance or a Heroku Std-1x dyno.*” ~ Someone on [Reddit](https://www.reddit.com/r/webdev/comments/1j8vace/comment/mhjr5kw/) comparing Fly.io vs Render
> 

That’s one of the many performance differences developers have called out when working with these platforms.

Have you had to compare throughput or reliability across [Fly.io](http://Fly.io) and [Render](https://render.com/)? It doesn’t take long to see that, beyond onboarding speed, they take very different approaches to how applications are deployed, scaled, and managed in production.

This comparison focuses on how each platform handles production workloads, specifically in areas like:

- How job execution is structured and isolated
- How predictable resource access is across different service types
- How much configuration is needed to keep apps running over time
- And how much control you’re given when you need to scale or optimize

I reviewed both platforms from a technical perspective, verified their latest updates, and focused entirely on how they operate in production environments.

Let’s look at the differences that show up once your service is deployed, to help you decide which platform is a better fit for how you build and run applications.

<div>  
  <center>  
    <a href="https://app.northflank.com/signup">  
<Button variant={["large", "gradient"]}>Try a production-ready alternative to Fly and Render >>></Button>  
    </a>  
  </center>  
</div>

## What to know before choosing between Fly.io vs Render

Let’s start with a technical comparison of how Fly.io and Render handle the basics once your app is up and running.

If you're deploying production workloads and care about things like job orchestration, regional scaling, or how services behave when they reach usage limits, this breakdown gives you a side-by-side view of what to expect from each platform.

| **Feature** | **Fly.io** | **Render** |
| --- | --- | --- |
| **App lifecycle** | Apps can scale to zero by stopping or suspending Machines using `fly.toml` settings like `auto_stop_machines` and `min_machines_running`.<br></br><br></br>You control scale and region through CLI or API.<br></br><br></br>There’s no enforced hard limit on app concurrency, but tuning soft and hard concurrency thresholds is critical for managing load and latency. | Free-tier services are suspended after 750 instance hours per month unless upgraded.<br></br><br></br>Services on paid plans support scaling across instances, but Render does not scale services to zero automatically.<br></br><br></br>Free services don’t support autoscaling or persistent disk and may be restarted by Render at any time. |
| **Worker and cron support** | You define background workers and cron jobs as separate process groups in your `fly.toml` file.<br></br><br></br>Each group runs in its own VM, so you can scale job workloads independently.<br></br><br></br>Cron jobs typically use a containerized scheduler like `supercronic`, and queue workers require explicit commands.<br></br><br></br>There’s no built-in scheduler, the flexibility is there, but you handle setup, scaling, and deployment. | First-class support for background workers and cron jobs.<br></br><br></br>You can create both directly in the dashboard, no need for custom process groups or container logic.<br></br><br></br>Cron jobs support schedule expressions, commands, environment variables, and manual triggering.<br></br><br></br>Background workers run continuously and are ideal for queue-based workloads. |
| **Pricing behavior** | Usage-based billing for compute (per second), storage (per hour), and bandwidth.<br></br><br></br>Prices vary by region and instance type. Reserved blocks offer 40% discounts.<br></br><br></br>Data egress and cross-region transfer are billed separately, with granular rates for newer orgs. GPU, static IPs, and SSL certs have separate costs. | Fixed monthly render hosting pricing per user, plus compute costs.<br></br><br></br>Each plan includes a bandwidth allowance (e.g., 500 GB for Pro, 1 TB for Org).<br></br><br></br>You pay for provisioned resources with transparent per-second billing. Staying within plan limits keeps costs predictable, extra usage may require upgrading to a higher tier. |
| **Databases** | [Fly.io](http://fly.io/) provides Postgres clusters via Fly Postgres, which is not a managed service.<br></br><br></br>You control replication, failover, and HA setups across regions. Supports multi-region read replicas and rerouting writes using `fly-replay`.<br></br><br></br>Also offers Upstash for Redis, fully managed with global read replicas and fixed or usage-based pricing. You manage both through the Fly CLI. | Fully managed Postgres with high availability, read replicas, and predictable pricing.<br></br><br></br>Render provides managed PostgreSQL with high availability, daily backups, read replicas, and private networking.<br></br><br></br>You can monitor metrics, scale vertically, and restrict access by IP. Storage can be increased without downtime, and each instance can host multiple databases. Starts at $6/month with a free tier. |
| **Scaling options** | Supports both metric-based autoscaling and Fly Proxy’s autostart/autostop, which spins Machines up and down based on traffic.<br></br><br></br>You can scale on custom metrics like queue depth or Temporal workflows.<br></br><br></br>Built-in support for multi-region deployments lets you run apps close to users and replay writes to the primary region. | Supports manual and autoscaling based on CPU or memory usage.<br></br><br></br>Autoscaling is available on paid plans only.<br></br><br></br>No native support for multi-region services or global load balancing, each service is tied to a single region. |
| **Team collaboration** | Team management is handled at the organization level.<br></br><br></br>There's no per-app access control, all members of an org have access to all apps in that org.<br></br><br></br>For more granular permissions, you’ll need to create and manage separate orgs. | Role-based access per service.<br></br><br></br>Teams can manage permissions at the workspace level, with audit logs and user roles available on higher-tier plans.<br></br><br></br>Admins can invite users, manage billing, and assign roles.<br></br><br></br>Developers have limited access to protected environments. Hobby workspaces don’t support team members. |

Here’s how many developers think about the choice:

> “*Some developers choose Fly.io for fine-grained control over VM types, region placement, and custom autoscaling. Others prefer Render because it abstracts away infrastructure, handling things like build pipelines, background workers, and TLS automatically.*”
> 

## What it’s like to build and scale with Fly.io

The table above shows how Fly.io and Render compare on the surface, from autoscaling behavior to team roles. But beyond the feature list, Fly.io takes a different approach to developer control.

![fly.io home page](https://assets.northflank.com/fly_io_min_bfc65ba670.png)

While Render gives you a more guided path with built-in defaults, Fly.io appeals to teams that want to choose where their services run, manage deployments as code, and scale apps globally on their own terms. If you care about regional performance or want low-latency setups at the edge, Fly.io gives you more flexibility, as long as you’re prepared to manage it.

### How Fly.io works and who it’s built for

If you’ve ever searched for “what is Fly.io”, it’s a platform that lets you run containers globally without managing infrastructure directly. It’s built for teams that want precise control over regions, scaling, and services, often writing configuration instead of clicking through dashboards.

- You define services in a `fly.toml` file, including scaling behavior, regions, ports, and process groups.
- Deployments are container-based but run inside Firecracker VMs. You’re not limited to pre-set types like "Worker" or "Cron Job", you define them manually.
- Fly.io is popular in the Elixir and Phoenix ecosystem, where fast boot times, distributed messaging, and low-latency edge setups matter.
- Billing is usage-based: compute per second, volumes per hour, and bandwidth per region.
- Built-in PostgreSQL hosting is available, with support for global replication and regional read replicas.
- You can deploy apps close to users in over 20 global regions, with better latency than US-only platforms.

### Where Fly.io gives you more control

Now that you’ve seen how Fly.io is structured and who it’s built for, let’s look at what that control gives you in practice.

Fly.io exposes nearly every layer of your app’s runtime, which is useful if you want infrastructure that adapts to how your app works.

- You can deploy to specific regions and assign different process groups (web, worker, etc.) to different locations.
- Scaling is manual unless you configure autoscaling, either based on traffic via Fly Proxy or using metrics like CPU or queue depth.
- Apps can scale to zero, which helps reduce cost for staging environments or internal tools.
- You configure volumes, networking (including private WireGuard networks), and service discovery through the CLI or API.
- Deployments behave like lightweight VM orchestration, and nearly everything is scriptable.
- Developers often choose Fly.io for low-latency routing and global deployment control, especially in communities like Elixir, Rails, and DevOps.

### Where Fly.io expects more from you

Once you step beyond initial setup, Fly.io assumes you’re comfortable owning the details. The platform exposes a lot, but that also means doing more configuration yourself.

- Background jobs and cron tasks aren’t native features. You define them as separate `processes` in your `fly.toml` and use tools like `supercronic` or custom schedulers to manage execution.
- Trial environments are limited. The $5 free credit typically covers only minimal usage (e.g. a single shared-CPU VM), and services are halted once the credit runs out unless billing is added.
- You’ll handle VM orchestration manually: configuring volume mounts, setting concurrency thresholds, tuning health checks, and managing failover or region affinity via CLI or API.
- There’s no managed Redis, MongoDB, or external database marketplace. You either self-host these inside your org as containers or connect to third-party services.
- Multi-region setups introduce cost variables, like cross-region volume replication or latency-driven traffic spikes, that require monitoring to avoid unexpected charges.
- Fly.io pricing is based on granular usage, per-second compute, per-hour storage, and bandwidth per region. That level of control works well for tuned setups, but costs can spike if services aren’t tightly managed.

> *Read more: [Top 6 Fly.io alternatives in 2026](https://northflank.com/blog/flyio-alternatives)*
> 

## What it’s like to launch and run apps on Render

If Fly.io gives you full control over how services run, Render takes the opposite path, it handles most of the infrastructure decisions for you.

![render's home page](https://assets.northflank.com/render_s_home_page_min_23e582c5c1.png)

You don’t need to configure regions, process groups, or schedulers. Instead, you define your app in the dashboard or via Git, and Render takes care of provisioning services like web apps, background workers, cron jobs, and databases using built-in defaults.

It’s a good fit if you’d rather spend less time managing containers or infrastructure logic and more time shipping. Tasks like setting up deployments, logs, health checks, and access control are already handled, with no need to write config files or define service behavior manually.

### Why Render works well if you don’t want to manage infrastructure

What is Render? It’s a PaaS designed to abstract the infrastructure layer while still supporting full-stack applications. You define your services, web apps, background workers, cron jobs, and databases, and Render provisions and connects them automatically.

- You can deploy from Git without writing a Dockerfile. Custom Docker builds are supported if your setup requires them.
- Background workers and cron jobs are defined as first-class service types. You don’t need to create separate containers or define process groups manually.
- Built-in CI/CD, deployment logs, health checks, and environment-level access controls are included (no additional setup needed).
- Pricing is based on plan and instance type. There’s no per-second or per-region billing to monitor.
- If your team doesn’t want to manage Docker, YAML, or cloud regions, Render lets you skip those layers and focus on application logic.

### Where Render takes care of the setup for you

Render provides built-in abstractions for jobs, logging, and monitoring, so you don’t have to configure them yourself. Once you define a service, Render handles provisioning, deployment, and runtime behavior using defaults that are consistent across environments.

- You define cron jobs and background workers directly in the dashboard or via a `render.yaml` file. No need to manage schedulers or run additional containers.
- Each service includes real-time logs, deploy history, and basic failure alerts by default.
- Free-tier services support always-on behavior (up to 750 instance hours/month), with autosuspend only when usage limits are exceeded.
- Billing is tied to instance size and plan. Render pricing rules are clearly visible in the UI and docs.
- This setup works well if you’re building production apps without a dedicated infrastructure team, and want services to be preconfigured with sane defaults.

### Where Render can start to feel limiting

The structured environment that makes Render easy to start with also introduces constraints as your infrastructure needs grow. If you need region-level control, usage-based cost scaling, or database flexibility, these limitations can affect how far you can scale on the platform.

- Pricing is flat per instance and user, regardless of traffic. This works well at steady usage but provides no cost advantage for idle or low-traffic services.
- Services are pinned to one of five fixed regions. There’s no support for global load balancing or deploying the same service across multiple regions.
- Only PostgreSQL and Redis are available as managed databases. You’ll need to self-host other options like MongoDB or MySQL.
- Team billing is based on seat count, not usage. Adding developers increases monthly costs even if resource usage stays constant.
- Infrastructure-level controls like per-region autoscaling, private networking, or traffic shaping are not exposed, making it harder to support complex or multi-region setups.

> *If these limits are blockers for you, check out [7 Best Render alternatives for simple app hosting in 2026](https://northflank.com/blog/render-alternatives)*
> 

## What if Fly.io vs Render doesn’t cover all that you need?

Fly.io gives you low-level control, but expects you to manage most of the stack yourself. Render handles setup and deployment for you, but trades off flexibility, especially if you need fine-grained control over regions, workloads, or team structures.

If you're comparing Render vs Fly.io and noticing limitations in how they support production workloads, that’s a common experience for teams building beyond basic deployments.

If you're looking for a platform that includes built-in job orchestration, production-ready defaults, and support for advanced workloads, without locking you into a single region or asking you to build everything from scratch, [Northflank](https://northflank.com/) fills that gap.

Let’s quickly see what that looks like in practice:

### Run in your own cloud with BYOC

Northflank supports [Bring Your Own Cloud (BYOC)](https://northflank.com/docs/v1/application/bring-your-own-cloud/deploy-workloads-to-your-cluster), so you can run services inside your own AWS, GCP, or Azure accounts while managing deployments through Northflank’s dashboard, API, or CI/CD integrations. You keep full control over where your infrastructure runs, whether that’s for compliance, cost management, or data residency.

Here's how Northflank integrates with your own cloud, while keeping control in your hands through the dashboard, CLI, or API:

![**Deploy in your own cloud with full control** (use Northflank’s UI, CLI, or API to manage services across AWS, GCP, Azure, and more)](https://assets.northflank.com/byoc_2_min_09d9c7300d.png) *Deploy in your own cloud with full control (use Northflank’s UI, CLI, or API to manage services across AWS, GCP, Azure, and more)*

### Native support for jobs and scheduled tasks

Background workers and cron tasks are [first-class service types](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs) in Northflank. You don’t need to spin up extra containers or rely on external schedulers, you can define, run, and scale jobs from the UI or API.

See how scheduled jobs are managed directly in the Northflank dashboard, with visibility into cron schedules, job history, and associated commits:

![Screenshot of a scheduled cron job in Northflank showing the job status, cron expression, recent job runs, related commits, and build history in the project UI](https://assets.northflank.com/cron_jobs_northflank_bb7543f527.webp)*Managing a scheduled cron job in Northflank’s UI (with commit history, recent job runs, and job metadata all visible in one place)*

### Built-in features for production workloads

Northflank includes [structured logs](https://northflank.com/docs/v1/application/observe/configure-log-sinks), [audit trails](https://northflank.com/docs/v1/application/observe/audit-logs), [secret management](https://northflank.com/docs/v1/application/secure/security-on-northflank), [health checks](https://northflank.com/docs/v1/application/observe/configure-health-checks), and [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) as part of its core platform. You get the production-ready defaults most teams need, without having to configure each one from scratch.

Here’s how a preview environment template is defined in Northflank, from Git triggers to automated lifecycle settings:

![Screenshot of the Northflank UI showing a preview environment template configuration with Git trigger and naming rules](https://assets.northflank.com/create_preview_template_form_3cd679b3f1.webp)*Create a structured preview environment with Git triggers, naming rules, and lifecycle automation.*

### Designed for teams (from startup to enterprise)

You get [role-based access control](https://northflank.com/docs/v1/application/secure/use-role-based-access-control), [billing visibility](https://northflank.com/docs/v1/application/billing/view-invoices), [usage analytics](https://northflank.com/docs/v1/application/observe/view-metrics), and [project-level isolation](https://northflank.com/docs/v1/application/getting-started/create-a-project), so each team or client can manage their services independently. From growing startups to enterprises with strict controls, Northflank supports [multi-user collaboration](https://northflank.com/docs/v1/application/collaborate/collaborate-on-northflank) and [access governance](https://northflank.com/docs/v1/application/secure/security-on-northflank) out of the box.

Here’s what it looks like to configure access roles and restrict permissions by project and team in Northflank:

![Northflank RBAC interface showing granular role settings with project and team restrictions](https://assets.northflank.com/organisation_roles_project_restrictions_f57cc936fd.webp)*Set custom access roles across teams and projects with Northflank’s role-based access control (RBAC)*

### One-click deployment for common stacks

With [stack templates](https://northflank.com/stacks), you can deploy services like GrowthBook, PostHog, Temporal, or vLLM in a few clicks, with sensible defaults already configured for networking, storage, and scaling.

Here’s how Northflank helps you skip boilerplate setup with production-ready stack templates:

![Northflank stack templates dashboard showing categories and preconfigured apps like n8n, Outline, GrowthBook, and Temporal.](https://assets.northflank.com/northflank_stack_templates_6c7af07965.png)*Deploy GrowthBook, Temporal, and other tools with one click using Northflank’s built-in stack templates*

### Support for GPU workloads

Northflank supports [GPU-based workloads](https://northflank.com/gpu), so you can run services like vLLM or TGI on infrastructure with NVIDIA GPUs. You can choose from a range of models (including H100, A100, T4, and others), and deploy in your own cloud or on Northflank-managed clusters.

- You can provision GPU nodes on AWS, GCP, Azure, Oracle, or Civo.
- Support is available for time slicing and NVIDIA MIG, so resources can be partitioned across workloads.
- Multiple GPU types are supported, including both NVIDIA and AMD models.
- Access is self-service but requires a short onboarding step, you define your use case and preferred provider [here](https://northflank.com/gpu).

Here’s a view of how GPU node pools are configured directly in the Northflank UI:

![Screenshot of GPU node pool configuration in Northflank showing autoscaling and NVIDIA GPU options like T4, A100, and H100 across multiple zones](https://assets.northflank.com/gpu_workloads_northflank_9bf76ec92d.webp)*Provision GPU node pools with autoscaling and support for time slicing (shown here with NVIDIA T4, A100, and H100 across AWS zones)*

### Full API, CLI, and UI parity

You can do everything programmatically with the Northflank [API](https://northflank.com/docs/v1/api/use-the-api) and [CLI](https://northflank.com/docs/v1/api/use-the-cli), from creating projects and deployments to managing secrets, databases, builds, and more. All core functionality available in the UI can also be handled via API or command line, so you’re free to automate workflows or build your own platform interface.

Northflank’s CLI and REST API both support full context switching, Git integration, granular permissions, and resource creation with structured definitions, which are ideal for infrastructure-as-code and CI/CD pipelines.

Set up access and permissions through the UI or script the same process via API or CLI, it’s the same flow underneath:

![create-api-role.webp](https://assets.northflank.com/create_api_role_c24759bb0c.webp)*Create API roles in the UI with scoped permissions and project restrictions (the same structure applies when defining roles programmatically)*

## Decide what fits your app and team structure

Fly.io gives you low-level control with manual setup for regions, scaling, and job orchestration. Render handles those layers for you, but limits flexibility across regions and service types.

If you need support for background jobs, CI/CD, GPU workloads, or infrastructure-as-code, without giving up UI visibility or control over where your apps run, [Northflank](https://northflank.com/) gives you that balance.

[Try a production-ready alternative to Fly and Render](https://app.northflank.com/signup)]]>
  </content:encoded>
</item><item>
  <title>How Cedana uses Northflank to deploy workloads onto Kubernetes with microVMs and secure runtimes</title>
  <link>https://northflank.com/blog/how-cedana-uses-northflank-to-deploy-workloads-onto-kubernetes-with-microvms-and-secure-runtimes</link>
  <pubDate>2025-05-15T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Cedana is building live migration and snapshot/restore infrastructure for GPU-heavy workloads, with applications ranging from resilient cloud infrastructure to on-prem clusters plagued by high GPU failure rates. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_weightsgg_works_casestudy_4_38732927e6.png" alt="How Cedana uses Northflank to deploy workloads onto Kubernetes with microVMs and secure runtimes" /><InfoBox className='BodyStyle'>

### TL;DR

[Cedana](https://cedana.ai/) is building live migration and snapshot/restore infrastructure for GPU-heavy workloads, with applications ranging from resilient cloud infrastructure to on-prem clusters plagued by high GPU failure rates. 

Founded by [Niranjan Ravichandra](https://www.linkedin.com/in/niranjanravichandra/) and [Neel Master](https://www.linkedin.com/in/neelmaster1/), who come from aerospace and robotics backgrounds, the startup emerged out of YC in 2023 and is deeply technical, fully remote, and almost entirely made up of engineers.

They chose Northflank to avoid the burden of managing infra manually, run production deployments for customers, and spin up complex Kubernetes environments with microVMs and secure runtime (including Kata Containers and Cloud Hypervisor), which Northflank supports out of the box for enterprise customers.

As a result, they now deploy customer environments in one click and test secure runtime workloads, avoid vendor lock-in from PubSub and RDS, and ship infrastructure tools faster.

</InfoBox>

## The problem

### Infrastructure-level resilience in a GPU-constrained world

"We started back in 2023, with the idea of making systems more resilient," said Niranjan, CTO and co-founder of Cedana. "I come from an aerospace background and Neel comes from a long robotics background as well."

Both founders had firsthand experience with large, mission-critical systems and the rigorous standards of uptime and fault tolerance required in those domains. The software world, especially in cloud and AI infrastructure, didn’t reflect that same level of rigor.

"Given the high failure rate that we've both experienced in our careers, we thought it’d be great to take some of those learnings and bring them down to earth. Literally."

They initially envisioned a resilience platform, but it quickly evolved: "What we're building now is effectively live migration as a service."

Live migration is notoriously difficult, especially for GPUs. Cedana targets two customer segments:

1. **Infrastructure providers**: cloud platforms or "neo-clouds" looking for dynamic compute migration
2. **On-prem users**: orgs running their own clusters who need ways to minimize downtime and manage hardware failure, especially GPU-related

"GPUs fail at a higher rate than anything else. And as we increase with successive generations, the failure rate just gets harder and harder."

The pace of hardware churn also makes operationalization hard:

> "Coupled with the pressures of trying to get a new fleet of GPUs in every year or so, it makes it very difficult for smaller organizations that are building on-prem clusters to manage their GPUs efficiently."
> 

GPU snapshot and restore offers an alternative to full live migration. In practice, Cedana supports both.

> "We become a proxy to the weights on the GPU itself. So companies are using us to circumvent the need to manage weights themselves. And because we capture all the runtime state, the cold start time is like two to 10 times faster."
> 

They're also testing secure compute use cases:

> "We've also been dipping our toes into the world of Kata and Cloud Hypervisor for confidential and secure computing as well."
> 

Live migration, snapshot restore, and confidential computing is a potent stack. But getting there requires a platform that doesn’t fight you.

![CleanShot 2025-05-16 at 15.15.45.gif](https://assets.northflank.com/Clean_Shot_2025_05_16_at_15_15_45_26ab49daf6.gif)

<aside>
💡 [See how Weights, another one of our customer, uses Northflank to scale to millions of users without a DevOps team.](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)
</aside>

## The solution

### From internal prototyping to full customer environments

Cedana discovered Northflank while trying to avoid reinventing the internal tooling they'd once had at larger companies.

> "At my last company, which was acquired by Shopify, we had a lot of nice internal tooling that abstracted away some of the complexities of the cloud. We were a GCP shop, and we just didn’t have to worry about it."
> 

Northflank gives teams a self-serve, first-class platform they don’t have to build from scratch or maintain. It covers what internal tools usually do, but without the overhead. Most teams, startups or enterprises, are better off focusing their engineers on the product, not on infrastructure toil.

Hiring engineers at Cedana meant Niranjan needed a way to offer a frictionless development environment:

> "Stumbled upon Northflank, and it just kind of smoothed over a lot of the rough edges I was anticipating with working in the cloud with the team."
> 

The Cedana team uses Northflank in two key ways:

### 1. Testing custom infrastructure components

One engineer is using Northflank for testing Kata-based workloads:

> "One of our engineers, for example, makes use of the fact that you can deploy Northflank with Kata containers in GCP, and is just using that for our Kata cloud hypervisor checkpoint restore testing."
> 

Thanks to cluster lifecycle automation:

> "Northflank manages the cluster, creates it on our behalf, and then we can either choose to deploy things via Northflank or just kubectl apply on our end."
> 

They also SSH into nodes directly to test secure runtimes:

> "The cluster's been created with Kata and Cloud Hypervisor already working on it, and [an engineer] just SSHs into the nodes and messes with them directly."
> 

### 2. Deploying customer-facing infrastructure

Cedana uses Northflank to host production environments, not just staging or test:

> "We do serve production customers through Northflank. So we have a template that we deploy for every customer, effectively, that we define in Northflank with a couple of microservices, a Postgres database... incredibly easy to set up."
> 

With each new pilot or POC:

> "All I have to do is just reapply that template."
> 

They also use Northflank's add-ons for RabbitMQ:

> "Previously, we were playing around with using Google PubSub... a couple of customers came to us and asked us for a self-hosting solution. So we decided to kind of rip that out and switch to RabbitMQ."
> 

This was made possible because of Northflank's flexibility:

> "A nice benefit of all of this is that Northflank is not prescriptive in how they want you to deploy stuff. You can just run any container you want."
> 

> "If I want to just helm install my Helm chart onto a cluster, I can. At the end of the day, it's just Kubernetes."
> 

## The results

### Production-grade infrastructure without a platform team

- **No support overhead**: "I've almost never had to contact support... And on the occasions that I did, super responsive."
- **Multi-cloud and on-prem portability**: "Northflank isn’t just avoiding cloud lock-in, it's avoiding service lock-in."
- **Isolated, production-grade clusters by default**: Cedana runs secure Kubernetes workloads in microVMs with full sandboxing and runtime isolation. Provisioning, scaling, and teardown are fully managed, no custom scripting required.
- **Enterprise-readiness out of the box**: "Things that you would want that feel like defaults and should be given, they're just there. Like MFA support, things like that SOC 2 requires."
- **SOC 2 compliance with minimal lift**: Cedana successfully completed their SOC 2 Type I audit using Northflank and are now deep in their Type II process. "We needed things like audit logs and MFA support—Northflank already had them built-in. I was literally just taking screenshots of the platform for our auditors."

Even their customer delivery model is evolving around Northflank:

> "The next step... is kind of take this model that Northflank has let us build out, package that into a couple of Helm charts, and just give that to customers. It will look like they have a Northflank deployment inside their own clusters."
> 

## Final thoughts

> "I never hit a strange guardrail. In a video game where it’s fake open world, you'll run into a wall. It's kind of similar with a lot of other platforms. But here, it just works."
> 

Most platforms promise flexibility, but often hit you with invisible walls once you try anything remotely advanced (like [deploying your own Helm charts](https://northflank.com/docs/v1/application/databases-and-persistence/create-a-custom-addon-type), or manually configuring a GPU runtime). Cedana didn’t want a “pretend open world” where you’re nudged back to the path the platform thinks you should take.

With Northflank, they didn’t encounter these walls. Whether it’s launching a GPU-enabled Kubernetes cluster, SSH-ing into nodes, or testing Cloud Hypervisor inside a VM, everything worked as expected.

Northflank acts like a game engine with great defaults and a modding API. You can click-to-deploy and get up and running fast, but if you want to go deep and build your own systems, it’s all there too. That’s why Cedana can go from proof-of-concept to production, without rewriting how they work.]]>
  </content:encoded>
</item><item>
  <title>Vercel vs Heroku: Which platform fits your workflow best?</title>
  <link>https://northflank.com/blog/vercel-vs-heroku</link>
  <pubDate>2025-05-15T16:15:00.000Z</pubDate>
  <description>
    <![CDATA[Vercel or Heroku? This guide breaks down which platform fits your app’s needs whether you're building a frontend in Next.js or deploying a full backend API with databases and background jobs.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/railway_vs_render_blog_post_5474b67f51.png" alt="Vercel vs Heroku: Which platform fits your workflow best?" />
You’ve probably heard this question before, or maybe even asked it yourself: Should I deploy my app on Vercel or Heroku?

It’s a common fork in the road for many developers. Vercel is sleek, fast, and clearly built for frontend workflows, especially if you’re in the Next.js world. Heroku, on the other hand, has been around for ages and is often the go-to for quick backend deployments.

But as I found out after juggling both on several projects, neither is perfect for *everything*. And that’s where [Northflank](https://northflank.com/) enters the chat.

Let’s break it all down so you can make the right call based on *your* project’s needs.

## TL;DR: What you need to know upfront

This table's your cheat sheet. If you're just trying to get your app out there and don’t want to overthink infrastructure right now, pick the column that matches your stack the closest and go. But if you're juggling a frontend-heavy Next.js app, a separate API, and maybe even a database, you'll want to read on.

| Feature | Vercel | Heroku |
| --- | --- | --- |
| Best for | Frontend-heavy apps (Next.js) | Full stack apps (backends-heavy) |
| Deployment style | Git-based, serverless functions | Git-based, dynos (containers) |
| Databases included | No (external integrations) | Yes (Postgres, etc.) |
| Cold starts? | Sometimes, with serverless APIs | Rare |
| Free tier | Generous for frontend apps | Limited, recently downgraded |
| UI/UX | Polished, simple | Classic, but a bit dated |

## Heroku vs Vercel: What's the core difference?

At a high level, both Heroku and Vercel help you ship code fast, but they’re optimized for different kinds of workflows.

- **Vercel** is hyper-focused on frontend developers. Especially if you’re building with Next.js, it’s hard to beat the DX. You get zero-config deploys, edge rendering, CDN caching, and PR preview environments out of the box.
- **Heroku**, on the other hand, is more of a **general-purpose platform-as-a-service**. It doesn’t care whether you’re deploying a frontend, an API, a background worker, or even a full monolith—you just push your code, and it runs. That flexibility is powerful, especially for teams that want a simple way to deploy full-stack apps without getting into infra complexity.

So while developers often use Heroku to host their backends (because it pairs well with relational DBs and server-based frameworks), it’s not *only* for backends. You can absolutely serve frontend apps there too—it just lacks some of the **special features** Vercel brings for frontend-specific tooling like edge functions or static optimization.

## So, when should you use Vercel?

Let’s say you're building a sleek frontend app in Next.js. Maybe a dashboard, maybe a content-heavy site. You want blazing-fast performance, and you don’t want to mess with Dockerfiles or EC2s. You just want to code, push, and ship.

That’s where Vercel wins.

You connect your GitHub repo, push to `main`, and boom—it’s live. Previews for every PR, built-in edge caching, and a CDN you don’t even have to think about. It’s optimized for the frontend experience.

But it’s not a backend platform. Yes, you can spin up serverless functions with `api/`, but that comes with some tradeoffs (cold starts, execution limits, statelessness).

![image-10 (1).png](https://assets.northflank.com/image_10_1_3f01f81712.png)

### Vercel pros:

- **DX built for speed**: The platform deploys directly from Git without extra setup or configuration.
- **Next.js native**: Features like **ISR (Incremental Static Regeneration)** for updating static pages after build, **SSR (Server-Side Rendering)** for dynamic content at request time, and **middleware** for handling logic like redirects or auth at the edge are all tightly integrated.
- **Edge network**: Your users get content fast, no matter where they are.
- **Preview deploys**: Fantastic for collaboration and testing before going live.

### Vercel cons:

- **Cold starts**: Serverless means your API might take a few seconds to warm up. Not great for user-facing endpoints.
- **No native DBs**: You’ll need to use third-party DBs like Supabase or Railway.
- **Not ideal for heavy backend logic**: Think queues, cron jobs, file storage—these need external services.
- **Vendor lock-in**: If you rely on edge functions, ISR, or other proprietary features, replatforming later may require re-architecting parts of your app.
- **Pricing can escalate**: Serverless pricing may look cheap initially, but at scale, costs for function invocations and bandwidth add up, especially if you rely on third-party DBs with their own bills.

[Check out this in-depth article on best Vercel alternatives](https://northflank.com/blog/top-heroku-alternatives)

## And what about Heroku?

You’re building an Express API, or a full-on backend service with authentication, background jobs, and a database. You don’t care about edge rendering; you just want to deploy and run. That’s Heroku territory.

It feels like Platform-as-a-Service before that became a buzzword. You run a command or connect a repo, and your app is up and running with a URL. Add-ons like Postgres and Redis are a click away.

But… it’s showing its age. UI hasn’t changed much in years, the pricing model got tougher, and scaling options are more limited than modern alternatives like [Northflank](https://northflank.com/).

![image-11 (1).png](https://assets.northflank.com/image_11_1_b0135788cd.png)

### Heroku pros:

- **Super easy backend deploys**: Push your code, and it just works.
- **Add-on ecosystem**: One-click setup for DBs, queues, logs, etc.
- **Docs and community**: Tons of Stack Overflow posts and tried-and-true tutorials.

### Heroku cons:

- **Old-school UI**: Gets the job done, but it doesn’t feel modern or intuitive.
- **The free tier was removed**: There will be no more free dynos for long-running services.
- **Vendor lock-in**: While more portable than Vercel (e.g. using Docker or buildpacks), Heroku-specific features like Add-ons or Dyno metrics don’t always migrate cleanly.
- **Pricing surprises**: Dynos are easy to spin up, but as you scale (especially with workers or staging apps), you can hit pricing walls faster than expected.

[Check out this in-depth article on best Heroku alternatives.](https://northflank.com/blog/top-heroku-alternatives)

## Why do developers use Heroku for backend and Vercel for frontend?

Because, frankly, it works.

A common setup: deploy the frontend to Vercel (especially if it's built with Next.js) and deploy the backend API to Heroku. Why?

Because each platform plays to its strengths:

- Vercel gives you lightning-fast frontend performance with static generation, edge rendering, and preview deploys.
- Heroku gives you a flexible, hassle-free way to run APIs, workers, and services—and spin up managed databases like Postgres with just a few clicks.

You *can* run full-stack apps on either platform, technically. But splitting them this way lets each part of your app live in an environment built for it.

That said, juggling two dashboards, syncing environment variables across systems, and managing deploy flows separately can get painful quickly. That’s exactly the problem [Northflank](https://northflank.com/) is trying to solve.

## Vercel too frontend? Heroku too restrictive? Northflank might be just right

If you’ve ever tried splitting your stack frontend on Vercel, backend on Heroku, you know the struggle:

- Two dashboards
- Inconsistent deployment flows
- Manually syncing environment variables
- Disconnected logs and metrics
- Complicated CI/CD setups to glue it all together

It works until your app grows, your team scales, or you need consistent behavior across staging and production. That’s where [**Northflank**](https://northflank.com/) shines.

### One platform. Full stack. No DevOps drama.

[Northflank](https://northflank.com/) is a **Kubernetes-powered PaaS** that abstracts the infrastructure complexity while giving you the power to:

- Deploy frontend, backend, databases, and cron jobs—all in one place
- Use **Git-based CI/CD** with [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) and one-click promotions
- Manage real-time **logs, metrics, and environment variables** across your entire stack
- Scale horizontally with ease, or vertically with [**GPU support**](https://northflank.com/gpu) for AI workloads
- Use **Northflank’s managed cloud** or [bring your own cloud](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) (AWS, GCP, Azure)
- Enjoy a **consistent DX** across CLI, UI, API, and GitOps

![](https://assets.northflank.com/image_5_fd06403bd1.png)

Unlike **Vercel**, which is heavily tailored to frontend workflows, and **Heroku**, which is showing its age in scalability and modern team workflows, **Northflank is purpose-built for full-stack development** with production-grade flexibility and a very good user experience.

> For example, [Weights](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s) an AI company, scaled to millions of users running AI workloads and complex backends without hiring a single DevOps engineer, all on Northflank.
> 

### How Northflank compares to Vercel and Heroku

| Feature | Vercel | Heroku | **Northflank** |
| --- | --- | --- | --- |
| Frontend support | Best for Next.js | Basic static hosting | Full support, preview envs |
| Backend support | Serverless only | Dyno-based | Full container support |
| Database support | External only | Add-ons | Built-in, fully managed |
| Background jobs | Not supported | With worker dynos | Native cron + job support |
| Preview environments | Frontend only | None | Full-stack previews |
| Unified platform | No | No | Yes |
| Vendor lock-in risk | High (ISR, Edge) | Moderate (add-ons) | Low (portable containers) |
| Cost predictability | Usage-based | Dyno-per-resource | Usage-based & Transparent pricing |
| Bring your own cloud | No | No | Yes ([AWS, Azure, GCP and many more.](https://northflank.com/features/bring-your-own-cloud)) |

## Answers to what developers are already asking

You’ve most likely seen these questions pop up in forums, Discord threads, or while comparing docs. See a quick rundown of the most common ones.

### Can I run a full-stack app on Vercel?

Sort of. You can use serverless functions for your backend, but you’ll hit limits for long-running processes or large-scale logic.

### Is Heroku still a good option in 2026?

Yes, especially for backends. Just be mindful of pricing and where it’s lagging behind in modern features.

### Does Northflank support databases?

Yes, Northflank offers fully managed Postgres, MySQL, and more. Everything runs on a single platform, eliminating the need to configure third-party databases.

### Which platform is best for a team?

- **Frontend team**? Go with Vercel.
- **Backend team** or solo dev? Heroku’s simplicity still wins.
- **Fullstack team** or startup? [Northflank](https://northflank.com/) keeps everything under one roof.

## Wrapping up: choose what matches your stack

If you’re building mostly frontend apps and want a fast, seamless deploy experience, **Vercel** will probably fit right in with your workflow.

If you need simple backend deployments with a familiar ecosystem and don’t mind managing some add-ons yourself, **Heroku** remains a solid choice.

Both platforms have their strengths. It really comes down to what you prioritize.

But if you want a single platform to run your full stack, including frontend, backend, databases, and jobs with unified tooling and consistent environments, [Northflank](https://northflank.com/) might be the better fit.

[Sign up for free](https://app.northflank.com/signup) and see how it handles your stack without the typical multi-platform headaches.]]>
  </content:encoded>
</item><item>
  <title>Why smart enterprises are insisting on BYOC for AI tools</title>
  <link>https://northflank.com/blog/why-smart-enterprises-are-insisting-on-byoc-for-ai-tools</link>
  <pubDate>2025-05-14T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Let’s say you’ve built an AI product that enterprises want to buy. Great. Now comes the part that trips up a lot of vendors: deployment. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/byoc_2_059babeef4.png" alt="Why smart enterprises are insisting on BYOC for AI tools" />If you’re evaluating AI tools for your company, you’ve probably noticed a pattern: most vendors expect you to use their hosted setup: vendor cloud, vendor region, vendor controls. 

It’s SaaS all the way down.

But for a growing number of enterprises, that assumption doesn’t fly anymore.Instead, they’re asking for BYOC: Bring Your Own Cloud. Run the software inside *your* infrastructure, on *your* terms.

At [Northflank](https://northflank.com/), we talk to engineering and platform teams every day. The message is consistent: if the product touches sensitive data, affects performance, or plugs into core systems, it’s not getting deployed unless it runs inside the company’s own cloud.

Here’s why BYOC is becoming the default:

## 1/ **You already have systems that work**

You don’t need a vendor’s opinionated monitoring stack. You already have metrics, logging, dashboards, alerting, and incident workflows wired up across your infra.

[ITSM tools](https://zenduty.com/blog/top-itsm-tools/) further support this by helping manage service requests, incidents, and changes without disrupting established processes.

Running software in a vendor-controlled environment just breaks that flow. Now you’ve got to maintain two sets of tools and patch over the disconnect.BYOC avoids all that. Run the software in your own cloud account and everything integrates out of the box: same CI/CD, same observability, same playbooks.

## 2/ **AI SaaS gets expensive fast**

LLM inference and vector search aren’t cheap, and running them behind a vendor SaaS paywall often means paying for their infra *plus* their markup.With BYOC, you can:

- Run workloads on your own committed cloud spend
- Share GPU resources across internal teams
- Co-locate compute with your data
- Fine-tune performance and cost

You’re paying for redundancy. BYOC gives you the efficiency and control to avoid that.

## 3/ Security and compliance kill deals

If you’re in healthcare, finance, or any regulated industry, sending sensitive data to a third-party vendor environment can trigger weeks of review, or an outright no. 

BYOC keeps everything inside your perimeter. No data egress, no mystery zones, no special exceptions to get legal sign-off. You control where the software runs and how it connects.

For many teams, that’s not a nice-to-have. It’s the only path to production.

## 4/ Performance depends on proximity

AI workloads are often latency-sensitive. If inference happens 200ms away in someone else’s region, you feel it. You also don’t get a say in the hardware, you get whatever the vendor picked.BYOC lets you control all of it:

- Deploy in the same region as your app
- Choose hardware that fits your footprint
- Optimize for cost, speed, or both

You own the performance envelope.

## 5/ Kubernetes makes this possible (but not painless)

Most modern AI tools can technically run anywhere. They’re built on containers, Kubernetes, Helm charts. That’s what makes BYOC feasible.

But “feasible” isn’t the same as fun.

Most teams don’t want to wrestle with Helm values, secret management, networking edge cases, and YAML sprawl just to get an app live. They want flexibility without the operational tax.

## 6/ Where Northflank helps

Deploying vendor software into your own cloud shouldn’t feel like assembling furniture with missing parts. Northflank turns that mess into a clean, automated workflow.

- One-click installs into your AWS or GCP account
- No need to write your own Terraform or manage Helm charts
- Secure, auditable, and production-ready out of the box

Whether you’re evaluating third-party tools or building your own internal platform, Northflank gives you a first-class deployment model that meets enterprise standards.

BYOC isn’t a niche ask anymore. It’s what smart teams are demanding because it puts them back in control of cost, performance, and security.If your vendor doesn’t support it, the conversation is already over.]]>
  </content:encoded>
</item><item>
  <title>7 Helm alternatives to simplify Kubernetes deployments</title>
  <link>https://northflank.com/blog/7-helm-alternatives-to-simplify-kubernetes-deployments</link>
  <pubDate>2025-05-13T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Helm has long been the go-to tool for managing Kubernetes applications, often described as the “package manager” for Kubernetes. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/helm_alternatives_b9e4a844c4.png" alt="7 Helm alternatives to simplify Kubernetes deployments" />[Helm](https://helm.sh/) has long been the go-to tool for managing Kubernetes applications, often described as the “package manager” for Kubernetes. It allows you to bundle up Kubernetes YAML manifests into reusable charts, templatize them, and deploy them with a single command. 

In fact, Helm is the beloved package manager for Kubernetes – it uses charts (collections of YAML templates) to define even the most complex apps. By filling in a values file and running `helm install`, teams can deploy applications ranging from a simple web service to an entire WordPress stack with a database. It’s no surprise Helm became a cornerstone of the Kubernetes ecosystem, simplifying manifest creation and handling tasks like upgrades (even database migrations) that would be tedious to script by hand.

But despite its popularity, many developers have a love-hate relationship with Helm. Its powerful templating comes at the cost of complexity and a steep learning curve.

This article explores 7 Helm alternatives that can simplify Kubernetes deployments.

<aside>
💡

**Quick summary: Best Helm alternatives in 2026**

Need a simpler option than Helm? Here’s the fastest way to find the best tool for Kubernetes deployments:

- **🥇 [Northflank](https://northflank.com/)** – Fully integrated Kubernetes platform; zero YAML, built-in CI/CD, and easy multi-cluster management
- **🥈 [Kustomize](https://kustomize.io/)** – Lightweight YAML patching and overlay tool for simpler config management
- **🥉 [Skaffold](https://skaffold.dev/)** – Speeds up Kubernetes dev loops; automates builds, tests, and deployments
- **[Argo CD](https://argo-cd.readthedocs.io/)** – Powerful GitOps automation and multi-cluster sync from Git
- **[Jsonnet/Tanka](https://tanka.dev/tutorial/jsonnet/)** – Code-based Kubernetes configuration for maximum flexibility
- **[Kapitan](https://kapitan.dev/)** – Flexible inventory-driven templating; powerful for large-scale deployments
- **[CDK8s](https://cdk8s.io/)** – Write Kubernetes configs in familiar programming languages like TypeScript or Python
</aside>

## What Helm does

Before you ditch Helm, it’s worth understanding what it actually does. Helm packages Kubernetes manifests into *charts*—directories of YAML templates (using Go templating) plus a `values.yaml` for customization.

Think of a chart as a recipe with placeholders for things like image tags and replica counts. You fill them in via the values file or CLI flags. Helm renders these into standard YAML, applies them to the cluster, and tracks the result as a **release**. That release metadata enables upgrades, rollbacks, and deletes.

Helm took off because it simplified deploying complex apps. Instead of juggling 20 YAML files, you run `helm install`. Upgrades are easy too—just run `helm upgrade` and it applies the diffs. Many charts also include logic for migrations, CRDs, and setup scripts, making ops less painful.

## Where things break

If Helm is so great, why do people seek Helm alternatives? 

Well, **Helm is not all sunshine and rainbows,** especially when you’re the one writing or maintaining the charts. Here are some of the pain points that often come up:

### **Complexity & learning curve**

Helm’s templating language is based on Go templates embedded in YAML. This can get confusing fast. You end up with double-braces and sprinkles of logic right inside your YAML files. 

![CleanShot 2025-05-12 at 09.22.19@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_12_at_09_22_19_2x_f6a9882c78.png)

There’s truth in that tongue-in-cheek complaint above – Helm templates can be hard to read and debug. Newcomers face not only Kubernetes’ learning curve but **Helm’s own syntax and quirks**. If you peek into a complex chart’s templates, it can feel like deciphering hieroglyphs (lots of `{{ if ... }}` and template includes). Small mistakes can lead to big deployment issues, and it’s not always obvious which value controls what without extensive chart docs.

### **Opaque release management**

Helm tracks each deployment as a release by storing state in Kubernetes secrets. This enables rollbacks and upgrades, but can cause issues if something fails mid-upgrade—you might need to manually rollback or delete the release secret. Helm also blocks reuse of a release name, which complicates CI/CD for ephemeral environments. Large releases can even hit Kubernetes size limits. Managing releases across multiple environments or clusters adds more overhead—many teams use tools like Helmfile just to keep it all in sync.

### **Debugging and diffing changes**

With plain Kubernetes manifests or Kustomize, you can often see exactly what YAML you’re applying. With Helm, what you apply is generated from templates + values, which means you often **don’t see the final YAML until Helm renders and deploys it**. 

![CleanShot 2025-05-12 at 09.28.30@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_12_at_09_28_30_2x_fcb05e5f2a.png)

A tiny tweak in values might enable some hidden part of the chart (i.e, turn on an ingress you didn’t expect), and suddenly you’ve deployed something surprising. The advice to run `helm template --dry-run` and check the output diff is common, but it’s an extra manual step to avoid unpredictable results. 

### **Chart quality and security**

Helm charts depend heavily on their maintainers. Some are solid, others are buggy or misconfigured—missing RBAC rules, lax security settings, etc. You often end up forking or patching them yourself. Helm 3 removed Tiller (which had broad cluster access), but supply chain concerns remain: pulling a random chart and deploying it to your cluster requires trust. In multi-tenant setups, blindly installing third-party charts is risky—Helm will render and apply whatever YAML is inside.

### **Overkill for simple apps**

For very simple deployments (say you have a couple of Deployments and Services), introducing Helm might add more complexity than it removes. If you don’t need reusability or dynamic templating, plain manifests or a lighter-weight tool might suffice. Using Helm in CI/CD for a simple app can feel like using a chainsaw to cut butter – lots of sharp edges, little benefit. 

Given these pain points, it’s no surprise that Kubernetes users have explored many *Helm alternatives*.

![CleanShot 2025-05-12 at 09.32.25@2x.png](https://assets.northflank.com/Clean_Shot_2025_05_12_at_09_32_25_2x_e6fdd1df28.png)

This might be extreme, but it encapsulates a real frustration that exists in the DevOps community. Not everyone hates Helm – but many have found simpler or more specialized solutions to specific problems Helm has.

# The top 7 Helm alternatives, ranked

## #1 🥇 Northflank: The most complete Helm alternative

![image.png](https://assets.northflank.com/image_90cb168803.png)

[**Northflank**](https://northflank.com/) is a modern platform that can be seen as an alternative approach to using Helm – instead of templating YAML at all, you deploy your apps via Northflank’s interface or API and let *it* handle the Kubernetes under the hood. Northflank essentially provides a PaaS-like layer on Kubernetes (it’s been described as “Heroku for your own clusters”), so developers get the benefits of K8s without having to write Helm charts or even raw manifests. The idea is to take away the **“YAML hell” (and Helm)** and replace it with a smoother deployment experience.

How does Northflank simplify things? A few key points:

### **No YAML or templating required**

With Northflank, you don’t write Kubernetes YAML directly, nor do you need a templating language. You define your services, jobs, databases, etc., through Northflank’s UI or configuration, and it handles generating and applying the Kubernetes resources. One might say Northflank gives you **the power of Kubernetes without needing to write any YAML or Helm charts**. Instead of wrestling with Chart.yaml and values, you push your code or container image and let the platform take care of deployment definitions.

### **Built-in configuration management**

Northflank allows you to manage environment variables, secrets, and other config per environment through its dashboard or API. Need different settings for dev vs prod? That’s handled with straightforward configuration profiles, not separate values files and template logic. This addresses the multi-environment issue by providing a cleaner interface to customize settings for each deployment stage without duplicating config in many places.

### **Integrated CI/CD and GitOps**

Northflank can automatically build your code (integrating with your Git repo) and deploy on merge – basically CI/CD out-of-the-box. It sets up “golden path” pipelines so that every commit can go through tests, builds, and end up in a preview or production environment. This removes the need to script `helm upgrade` in your CI; instead, Northflank is continuously deploying your app based on Git events. For teams, this means no manual helm commands or custom Argo workflows – it’s baked in. (Northflank even supports a GitOps mode if you prefer, syncing from your Git like ArgoCD would.)

### **Multi-cluster made easy**

Northflank supports **BYOC (Bring Your Own Cloud)**, allowing you to attach multiple Kubernetes clusters (on AWS, GCP, Azure, on-prem, etc.) to the platform. It presents a unified interface to deploy across them. Want to deploy Service A to cluster X and Y? It’s a matter of selecting the target in Northflank, not manually configuring two Helm contexts. This is great for enterprises running in multiple regions or cloud providers – you get one pane of glass. Under the hood, Northflank’s control plane orchestrates the workloads on your clusters, handling all the Helm/manifest grunt work.

### **Helm compatibility when needed**

Northflank doesn’t shun Helm entirely – it actually can **consume Helm charts** for certain use cases. For example, Northflank’s “bring your own addon” feature allows you to deploy a Helm chart (say for a third-party service like Redis) inside Northflank if it’s not natively supported. Northflank will run that Helm chart for you. This means you can still leverage the Helm ecosystem for things like databases, but you **don’t have to manage Helm** yourself; Northflank acts as the operator. In essence, Northflank cherry-picks the benefits of Helm (reuse of community charts) without exposing you to Helm’s complexity directly.

### **Developer Experience**

The Northflank approach is aimed at improving developer experience. All a developer needs is a Docker image (or source repo). You point Northflank to it, and it deploys it. All your developers need is a Dockerfile or Buildpack. Northflank picks up changes from Git and can automatically kick off builds. This is a far cry from requiring every developer to understand Kubernetes internals or Helm. It’s easier to onboard new projects and team members since there’s less bespoke YAML to learn.

In summary, **Northflank replaces Helm by abstracting away Kubernetes deployment configuration entirely**. It trades some flexibility (you’re using the platform’s way of doing things) in exchange for **massive simplicity**. 

No more Helm client, no `values.yaml`, no debugging failed template renderings. 

One could joke that Northflank’s alternative to Helm is: *don’t even give developers a chance to write Kubernetes YAML.* 😄

If your goal is to **deploy apps with minimal ops headache**, Northflank’s all-in-one approach might be the #1 Helm alternative to consider.

## Helm alternatives, at a glance

| Tool | Learning Curve | Flexibility (templating power) | Multi-cluster/Env support | CI/CD integration |
| --- | --- | --- | --- | --- |
| **Helm** | 🔴 High – must learn Helm syntax and chart structure. | 🔴 High – Go templating (loops, conditionals), many community charts with built-in configurability. | 🟠 Moderate – can deploy to any cluster, but managing many releases/environments requires extra tooling (Helmfile, etc.). | 🟠 Medium – works in CI but requires scripting; GitOps tools support Helm charts natively. |
| **🥇 Northflank** | 🟢 Low – use UI or simple config; no Kubernetes knowledge needed. | 🟢 High – covers most app configs (services, jobs, addons) | 🟢 High – built-in multi-cluster and multi-env management via a unified platform. | 🟢 High – built-in pipelines and Git integrations for automated build/deploy (CI/CD as a first-class feature). |
| **🥈 Kustomize** | 🟢 Low – just YAML with a few extra files (kustomization.yaml). | 🟠 Medium – can patch/overlay any field, but no arbitrary logic or packaging. | 🟠 Medium – great for multi-env overlays; for multi-cluster, usually used with GitOps or scripts (not automatic across clusters by itself). | 🟠 Medium – easily used in GitOps (just commit YAML); in CI, just build kustomize and apply. |
| **🥉 Skaffold** | 🟢 Low – simple YAML config for pipeline; mostly convention-driven. | N/A – not a config tool (relies on manifests or Helm charts). | 🔴 Low – focused on single-cluster dev workflow (for multi-cluster, you’d run separate Skaffold or use other CD tools). | 🟢 High – purpose-built for CI/CD and dev loops; great integration with local dev and CI pipelines. |
| **Argo CD** | 🟠 Medium – need to learn GitOps concepts and Argo specifics (Application CRs, etc.). | N/A – no templating (uses whatever manifests you give it). | 🟢 High – can manage deployments to multiple clusters/environments easily via Git sources. | 🟢 High – it *is* a CD tool; integrates with Git repos for automated deployment (supports Helm, Kustomize, etc.). |
| **Jsonnet** | 🔴 High – new language to learn; requires JSON mindset. | 🟢 Very High – essentially a programming language for config (conditions, loops, modularization all possible). | 🟠 Medium – can generate configs for any env/cluster, but you must handle applying to clusters (often used with other tools). | 🟠 Medium – usually used with companion tool (like Tanka or CI scripts) to integrate into deployment process. |
| **Kapitan** | 🔴 High – complex tool with multiple features and options to learn (inventory, Jsonnet/Jinja, etc.). | 🟢 Very High – supports Jsonnet, Jinja2, and more; can template across multiple systems (K8s, Terraform, etc.). | 🟢 Very High – explicitly designed to manage many environments/clusters with a single inventory (solves multi-env thoroughly). | 🟠 Medium – you run Kapitan compile in CI and then apply; no built-in CD, but can be paired with GitOps or CI scripts. |
| **CDK8s** | 🟠 Medium – must know a general-purpose language (TypeScript/Python) and CDK8s API. | 🟢 High – full programming capabilities in familiar languages; extremely flexible patterns. | 🟠 Medium – can write code to target multiple envs, but output is static manifests (multi-cluster handled via separate configs or git branches). | 🟠 Medium – integrates via code build steps; use with GitOps or manual apply; no native controller (which some consider a pro). |

## #2 🥈 Kustomize


![kustomize.png](https://assets.northflank.com/kustomize_168c165780.png)
Kustomize is often the first word out of anyone’s mouth when discussing Helm alternatives. It’s built into `kubectl` (`kubectl apply -k <directory>`), emphasizing a **“patch and overlay”** approach rather than templating. With Kustomize, you write your Kubernetes YAML as usual (called a base) and then define *overlays* that modify that base for different contexts (like different envs or clusters). Overlays can add or override fields using strategic merge patches or JSON patches. For example, your base Deployment might set replicas: 1, and your production overlay patch changes replicas to 3 – when you build with Kustomize, the prod YAML has 3 replicas. 

**Pros:**

- Built into `kubectl` (`kubectl apply -k`)
- Easy to learn—no templating language, just YAML
- Great for multi-environment setups via overlays
- Keeps configs DRY and easy to reason about

**Cons:**

- No packaging/reuse mechanism like Helm charts
- No dynamic logic or value injection (limited to patches)
- No versioning or chart repos—everything lives in Git
- Often used with other tools to handle more complex needs

## #3 🥉 Skaffold


![skaffold.png](https://assets.northflank.com/skaffold_017233dd99.png)
Google’s **Skaffold** is another tool that often enters the conversation, though it serves a different purpose. Skaffold is all about **streamlining the development and CI/CD workflow** for Kubernetes applications. It automates the build-push-deploy cycle so you don’t have to run a bunch of commands manually. Importantly, Skaffold can work with Helm *or* with raw manifests (or Kustomize). So it’s not an “either/or” replacement at the configuration level, but it can reduce your reliance on Helm’s CLI for deployment.

**Pros:**

- Automates image builds, tagging, pushing, and deployment
- Supports Helm, Kustomize, or plain YAML out of the box
- Tightens dev feedback loop with `skaffold dev` (auto-redeploy on code changes)
- Reduces CI scripting by handling the full deployment pipeline
- Great for local development and simple CI setups

**Cons:**

- Not a templating or config management tool
- Doesn’t replace Helm charts—just automates their usage
- Not ideal for multi-cluster or GitOps workflows
- Extra tooling developers need to install and learn

## #4 Argo CD


![argocd.png](https://assets.northflank.com/argocd_06c9c3305a.png)
Next up is **Argo CD**, a popular GitOps continuous 
delivery tool. Argo CD’s motto could be *“stop running `helm upgrade` and let Git drive your deployments.”* It watches a Git repository containing your desired Kubernetes manifests and ensures your cluster state matches it, automatically applying any changes. Now, importantly, Argo CD is **tool-agnostic about the manifests** – you can store plain YAML, Kustomize overlays, or even Helm charts in Git and ArgoCD will apply them. So you can use Argo CD *with* Helm (many do!), but you can also use Argo CD as a Helm replacement by switching to Kustomize or plain manifests.

**Pros:**

- Git is the source of truth; deployments happen via `git push`
- Great for managing multi-env and multi-cluster setups
- Automatic syncing, rollback support, and diffing built-in
- Supports Helm, Kustomize, and more without requiring CLI tools
- Works well with teams adopting GitOps workflows

**Cons:**

- Requires installing and operating Argo CD in your cluster
- Has its own learning curve (Application CRs, syncing behavior)
- Not a templating tool—still relies on Helm/Kustomize/etc. for config generation

## #5 Jsonnet (and Tanka)


![jsonnet.png](https://assets.northflank.com/jsonnet_5451cc6e21.png)
Moving further from Helm, we get into territory of treating Kubernetes manifests not as Helm templates or YAML patches, but as code in a programming language. **Jsonnet** is a JSON templating language (created by Google) that has been embraced by some Kubernetes folks as a more robust way to generate manifests. Jsonnet isn’t Kubernetes-specific, but you can use libraries (like `kube-libsonnet`) to easily produce Kubernetes objects. Tools like **Grafana Tanka** build on Jsonnet to provide a CLI and structure for Kubernetes config. 

**Pros:**

- Full programming power: conditions, loops, functions, inheritance
- Ideal for DRY, reusable config and complex abstractions
- Safer than raw YAML (type-checked, structured)
- Tanka provides a solid workflow for applying and diffing changes
- Great for teams with engineering-heavy infra practices

**Cons:**

- Steep learning curve; you need to learn a new language
- Smaller ecosystem and community support vs. Helm/Kustomize
- Adds a build step (Jsonnet → YAML), not kubectl-native
- Can be overkill for simple apps or small teams

## #6 Kapitan


![kapitan.png](https://assets.northflank.com/kapitan_68e3f6e60f.png)

Kapitan is a lesser-known but very interesting tool that can serve as a Helm alternative. It was developed at DeepMind for managing configuration at scale. Kapitan is often described as a **“configuration orchestration tool”** or an “infrastructure compiler.” It supports multiple templating engines under one roof: you can use Jsonnet, Jinja2, or even pure Python (Kadet) to generate YAML. It introduces the concept of an **inventory** – a hierarchical set of parameters (think of it like a tree of values for different environments, clusters, etc.). Kapitan compiles your templates using the inventory, and can output configs for Kubernetes, Terraform, or anything else. 

**Pros:**

- Supports multiple templating languages: Jsonnet, Jinja2, Python
- Inventory model makes multi-env/cluster config clean and DRY
- Built-in secrets management (encrypt/decrypt at compile time)
- Can generate configs for more than just Kubernetes

**Cons:**

- Steep learning curve; not beginner-friendly
- Heavy for small teams or simple projects
- Smaller community and ecosystem compared to Helm or Kustomize
- Requires learning Kapitan’s compilation model and inventory structure

## #7 CDK8s


![cdk8.png](https://assets.northflank.com/cdk8_93d9b27712.png)Last but not least, **CDK8s (Cloud Development Kit for Kubernetes)** offers a fresh, developer-friendly take on Kubernetes configuration. Inspired by the AWS CDK, CDK8s lets you write your Kubernetes manifests using familiar programming languages (TypeScript, Python, Java, etc.) and then synthesize those into YAML for application. 

**Pros:**

- Write manifests in code (TypeScript, Python, etc.) instead of YAML
- Supports full programming features like loops, functions, and classes
- Great IDE support and testability
- Easy to reuse and abstract config patterns
- No controllers needed—just generates YAML you can apply or commit to Git

**Cons:**

- Steeper learning curve for teams unfamiliar with code-based IaC
- Not ideal for ops teams who prefer YAML or GUI workflows
- Smaller community and ecosystem compared to Helm
- Risk of inconsistent patterns if not well-structured across teams

## Choosing the right tool for y*our* team

Helm can be useful, but it also adds a lot of overhead. The templating is hard to read, managing releases is clunky, and it’s easy to lose track of what’s actually being deployed. That’s why so many teams start looking for alternatives.

Some tools are better for specific use cases:

- **Kustomize** works well if you want to keep things in plain YAML
- **Skaffold** is great for speeding up development and CI
- **Argo CD** makes Git-based deployment easier to manage
- **Jsonnet** or **CDK8s** give you more control if you prefer writing config in code
- **Kapitan** is built for large setups with lots of environments

But if your main goal is to make Kubernetes deployment less painful and you're not interested in mastering Helm's templating quirks, **Northflank is the way to go.**

It handles configuration, CI/CD, and multi-cluster deployment in one platform. You don’t need to wire together half a dozen tools just to ship a container. You define your services, jobs, and environments the way normal people do, through clean config or a UI that doesn’t make you feel like you’re defusing a bomb.

Yes, we’re biased. You’re on the Northflank blog. But there’s a reason we built it this way: because we were tired of fighting the same tooling battles over and over again. Helm made things easier, for a while. Then it made them harder. We wanted a system that didn’t break under the weight of its own abstractions.

[Sign up for free today](https://app.northflank.com/signup).]]>
  </content:encoded>
</item><item>
  <title>Railway vs Render (2026): Which cloud platform fits your workflow better</title>
  <link>https://northflank.com/blog/railway-vs-render</link>
  <pubDate>2025-05-13T18:24:00.000Z</pubDate>
  <description>
    <![CDATA[Detailed breakdown of Railway vs Render in 2026: pricing, features, developer experience, and deployment workflows. Understand the differences before choosing your next cloud platform]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/railway_vs_render_7a4f9a69cd.png" alt="Railway vs Render (2026): Which cloud platform fits your workflow better" />Having to choose between platforms like Railway or Render might seem straightforward at first, until you start thinking about things like:

- app uptime
- background workers
- scheduled tasks
- pricing predictability
- Or how much control you have once your app is deployed.

This comparison exists because those questions don't show up on the homepage. I've gone through both platforms and seen where they perform well and where they introduce limitations depending on your workload.

If you're trying to avoid unexpected shutdowns, manual workarounds for cron jobs, or unclear billing once your app scales, and you want to know which platform constraints are reasonable depending on what you're building, this article clearly explains them.

It's also worth noting that as of mid-2026, Railway has had a recurring pattern of outages and degraded performance, including a December 2025 incident that paused builds across all plan tiers in their EU West region. If reliability is a factor in your decision, that context matters. Northflank has historically maintained 99.99% uptime, contractually guaranteed under enterprise SLAs.

Let’s get into it.

<div>
	<center>
		<a href="https://app.northflank.com/signup">
<Button variant={["large", "gradient"]}>Find the right platform for your next project >>></Button>
		</a>
	</center>
</div>


## Railway vs Render comparison at a glance

Before we go into what it’s like to build on each platform, let’s do a quick side-by-side comparison of what developers usually care about upfront.

These things tend to impact your setup and production readiness early, like how each platform handles cron jobs, what happens when free limits are reached, or how easily you can keep services online without extra configuration.

*If you're trying to make a fast decision or need a high-level overview to guide your research, this table should help you spot the differences quickly.*

| **Feature** | **Railway** | **Render** |
| --- | --- | --- |
| **Free tier** | $5 in usage credits (one-time). After that, you need to upgrade to keep services running. | Always-on free tier for certain services (static sites, web services with limits). No one-time credit model. |
| **App sleep behavior** | Apps stop running when usage credits are used up, even on Hobby plan. Requires paid plan to stay online. | Free-tier services may spin down on inactivity, but don’t shut down due to credit exhaustion. Paid plans stay always-on. |
| **Background jobs** | No first-class support. You can configure background workers using separate services or workarounds. | Built-in support for background workers as a service type. Easy to configure and monitor. |
| **Cron jobs** | Recently added support. You can schedule jobs natively, but the feature is still maturing. | Cron jobs are natively supported and well-integrated. Available even on free tier. |
| **Supported databases** | Built-in support for PostgreSQL, MySQL, Redis, and MongoDB. Provisioned directly from the UI. | Native support for PostgreSQL and Redis. Other databases require external setup or custom containers. |
| **Deployment flow** | Git-based deploys with Railpack (Railway’s fast, zero-config build system). | Git-based deploys with support for Docker and custom build commands. |
| **Pricing model** | Paid plans start at $5/month (Hobby) and $20/month (Pro), each including usage credit. Additional charges apply based on RAM, CPU, and storage consumption. | Tiered pricing with fixed plans (Free, Pro, Team). Pro plan includes predictable build minutes and resource limits. |
| **Team support** | Realtime collaboration in the dashboard. Pricing per teammate on Pro plan ($20/user/month). | Team features available on Team plan ($29/user/month). Includes access controls and audit logs. |
| **Regions** | 8 regions across US, EU, and Southeast Asia. Includes multiple zones like Oregon, Virginia, and Amsterdam. | 5 regions: Oregon, Ohio, Virginia (USA), Frankfurt (Germany), and Singapore. |
| **Private networking** | Internal communication is only supported within the same environment. No confirmed support for cross-environment or cross-region networking. | Private networking is region-specific. Services in different regions cannot communicate privately. Must use public networking for cross-region traffic. |

## Let’s look at Railway first

If you looked at the table and thought, “*Hey! I just want to deploy something fast without setting up too much.*” Then, Railway might have stood out to you.

Let’s break down what it does well and what you need to watch out for if you plan to stick with it beyond a quick prototype.

### What is Railway?

[Railway](https://railway.com/) is a developer platform built to simplify infrastructure setup and speed up deployments. It’s especially popular among solo developers, early-stage teams, and hackathon projects, and basically anywhere, speed and convenience matter more than long-term infrastructure control.

![railway.png](https://assets.northflank.com/railway_min_10957de907.png)

You connect your GitHub repo and select a template if you want one. Railway handles build, deploy, and provisioning behind the scenes. Their UI gives you a real-time view of your logs and service status, and you can work alongside teammates in the same environment without refreshing or guessing what’s happening.

It does not aim to replace full DevOps pipelines; it aims to reduce the setup steps between writing code and seeing it live.

### So, where does Railway stand out?

Now that you know what Railway is aiming for, let’s look at the areas where it delivers well, especially if you’re focused on fast shipping, minimal configuration, or short-lived projects.

These features tend to click with developers looking for less setup effort without giving up flexibility.

Let’s take a look at them:

**1. Fast deploys with Railpack**

Railway now uses [Railpack](https://railpack.com/), their custom build system, instead of relying on [Nixpacks](https://nixpacks.com/docs/getting-started). It’s designed to automatically detect common languages and frameworks and build without extra configuration.

![railpack.png](https://assets.northflank.com/railpack_396fe25941.png)*Railpack*

**2. Real-time collaboration in the UI**

Multiple teammates can view logs and deployment state live in the UI. It’s one of the few platforms that actually feels like pair programming at the infrastructure level. Updates are reflected instantly without needing to reload or manually sync.

![Railway deploy panel with real-time logs and build tabs](https://assets.northflank.com/railway_deploy_logs_36c9c441da.png) *Railway deploy panel with real-time logs and build tabs - Source: Railway docs*

**3. Templates and ease of starting new projects**

Railway’s template marketplace makes it easy to spin up services with PostgreSQL, Redis, or full-stack frameworks like Next.js. It’s especially useful when you want to get something running quickly without setting everything up from scratch.

> If you're looking for something more tailored to production use cases, [Northflank’s stack templates](https://northflank.com/stacks) take a different approach. They include one click deploy templates like [GrowthBook](https://northflank.com/stacks/deploy-growthbook), [PostHog](https://northflank.com/stacks/deploy-posthog), and others that are built for production-level workloads.
> 

**4. Usage-based billing that favors occasional workloads**

Railway charges based on actual usage: RAM hours, CPU hours, and storage. If your service doesn’t run 24/7 or only spikes occasionally, this model can be more cost-effective than flat pricing, especially for staging or internal tools.

### But, where can things get complicated with Railway?

Everything above makes Railway a practical option if you need speed, flexibility, and a quick way to get your app online. Once you move past the initial setup phase or start running workloads that need to stay up longer term, a few limitations become more noticeable.

This section covers the areas that might cause problems depending on how you use the platform. They are not dealbreakers for everyone, but they are important to understand before you commit.

**1. Services stop when you exhaust trial credits**

Railway gives new users a one-time $5 trial credit. Once that’s used up, your services stop running until you upgrade to a paid plan. This is confirmed in their documentation and applies even if the app was previously live.

![Railway documentation showing one-time $5 free trial credit](https://assets.northflank.com/railway_free_trial_credits_5f43a9c7da.png) *Screenshot from Railway Docs showing free trial credit policy*

**2. No native worker model**

There’s no dedicated background worker type in Railway. If your app needs async processing, background queues, or scheduled tasks running independently, you’ll need to manually set those up as standalone services. This works, but it requires more setup and ongoing management.

**3. Cron support is functional but has some limitations**

Railway’s updated cron experience avoids full redeploys for every job and makes scheduling faster, but it still comes with limits. You can’t pass dynamic parameters into jobs, and there’s no native support for things like variable input or environment-aware execution.

If your cron tasks are simple, it’ll get the job done, but for anything more flexible or state-dependent, you’ll need workarounds like custom variables or external schedulers.

See a user explaining why they built a custom scheduler instead of using Railway's cron feature ([Source](https://station.railway.com/feedback/new-cron-experience-a74e2afa#0gdz)):

![railway-user-complaint.png](https://assets.northflank.com/railway_user_complaint_01afa46828.png)

**4. Logs and metrics are built in, but deeper observability takes work**

Railway gives you built-in logs and basic metrics (CPU, memory, and network usage) per service, all visible in the dashboard. That’s usually enough for early-stage projects or debugging simple issues. But if you need distributed tracing or full observability pipelines, you’ll need to deploy your own stack.

Railway provides a template to set up the OpenTelemetry Collector with Prometheus and Grafana, but it’s opt-in and takes extra setup compared to platforms where these tools are integrated by default.

*See [6 best Railway alternatives in 2026: Pricing, flexibility & BYOC](https://northflank.com/blog/railway-alternatives)*

## Now let’s talk about Render

If Railway is about speed and simplicity, Render is the platform that leans more into structure, predictability, and production readiness. It’s often mentioned as a Heroku-style alternative, with built-in features that make it easier to run apps continuously without much manual setup.

![render vs heroku.png](https://assets.northflank.com/render_vs_heroku_a111e24ca4.png)*Source: Render docs*

Where Railway is great for fast iteration, Render tends to click more with developers who want reliable defaults, background workers, and straightforward pricing from day one.

### What is Render?

[Render](https://render.com/) is a cloud application platform that supports everything from static sites to background workers, cron jobs, and persistent services with production-ready defaults.

You still get the convenience of Git-based deploys and prebuilt templates, but with more guardrails in place for uptime, pricing, and task separation.

![Render’s home page](https://assets.northflank.com/render_s_home_page_min_23e582c5c1.png)*Render*

There’s no usage credit system or unexpected shutdowns. Your services stay online as long as your plan is active, and most of the operational setup (like workers or jobs) is treated as a first-class concept in the platform.

### Where Render gets things right from the start

Once you start working with Render, the difference in defaults becomes clear. It’s built for teams and solo developers who care more about keeping things online than managing low-level infrastructure, and many of the things you’d need to configure manually elsewhere are built in here by default.

You start to notice it in places like these:

**1. Background workers and cron jobs are built in**

You can spin up a worker process or schedule a cron job natively. So, no need for manual service duplication or hacks. It’s a clean model, especially if your app depends on background queues, async processing, or regular scheduled tasks.

**2. Apps don’t get shut down unexpectedly**

There’s no credit-based threshold. Once your service is deployed under a plan, it stays up unless you stop it manually or reach a clearly defined usage limit. This makes it much easier to build and host projects that need 24/7 uptime without unexpected downtime.

**3. Straightforward pricing model**

Render uses flat monthly pricing tiers based on service type and resource size. So, no granular tracking of minutes or CPU time. You know what you’ll pay upfront, which helps avoid unexpected costs if you’re running multiple services long-term.

![render pricing](https://assets.northflank.com/render_pricing_f95d39c250.png) *Render’s pricing*

**4. Logs and debugging are well thought out**

You get per-service logs, events, deploy output, and a UI for checking failures or reviewing runtime behavior. Basic metrics like memory usage and request logs are also built into the dashboard.

![Render’s log explorer](https://assets.northflank.com/log_explorer_render_f763a31009.webp)*Render’s log explorer - Source (Render’s docs)*

**5. Better suited for always-on apps**

If you’re building something that should stay online and reliable, like a production API, a background worker, or a user-facing dashboard, then Render’s model is easier to manage than a system that depends on monitoring credit usage.

### So, where do the limits show on Render?

For many production use cases, Render covers a lot out of the box, but like any platform, it comes with some limitations. These aren’t necessarily blockers, but they’re the kinds of things you’ll notice more as your team grows or your usage becomes more demanding.

Let’s take a look at some of them:

**1. Per-user pricing adds up on teams**

Each additional team member incurs an extra cost, regardless of their individual resource usage. While this is manageable for small teams, expenses can escalate significantly as your team expands.

![Render pricing per user](https://assets.northflank.com/render_per_user_7d25c5072d.png)*Render’s pricing per user*

**2. Monthly build minute quotas can be limiting**

Render sets monthly limits on build minutes - 500 per month on the Hobby plan, and 500 per member on Professional workspaces (shared across the team). If you deploy often or run multiple CI workflows, you might run through those minutes quickly, especially in active development cycles.

![Render docs showing monthly build minute limits by plan](https://assets.northflank.com/build_minute_quotas_render_673c75eb99.png)*Render docs showing monthly build minute limits by plan*

**3. Not ideal for spinning up quick experiments**

Render charges a flat per-user fee on paid plans ($19/month for Professional), even if you're just testing or deploying something short-term. It’s not usage-triggered billing, so unless you’re on the free Hobby tier, you’re paying for the full month regardless of how lightly you use it.

**4. No built-in MongoDB or wide database provisioning**

Render supports PostgreSQL and Redis natively, but other databases like MongoDB require a custom setup using their private service feature. While you can deploy MongoDB via one-click or manual configuration, it’s more hands-on compared to fully managed databases and may introduce additional setup time if your stack depends on broader out-of-the-box support.

![Render supports PostgreSQL and Redis; other databases require manual setup](https://assets.northflank.com/render_supported_databases_1b0a286abf.png)*Screenshot from Render docs showing built-in support for PostgreSQL and Redis*

*See [7 Best Render alternatives for simple app hosting in 2026](https://northflank.com/blog/render-alternatives)*

## Help me choose: which one fits my workflow?

At this point, you’ve seen what each platform handles well and where some constraints start to become relevant. If you’re still undecided, this section breaks it down based on what kind of work you’re doing and how you like to deploy.

Think of it as a quick filter before you commit to setup.

![Side-by-side checklist showing when to use Railway vs. Render based on billing, jobs, uptime, and team features](https://assets.northflank.com/Railway_vs_Render_37e9b60d68.png)*Railway vs. Render — what fits your workflow better?*

### Railway might be the better choice if:

- You’re fine with 24/7 uptime as long as billing is usage-based and you can monitor consumption
- You’re okay with configuring workers and observability manually if needed
- You value real-time visibility and collaboration in a shared dashboard
- You prefer fast deploys and flexible starter templates over rigid service configuration or fixed pipelines

### Render tends to be a better fit if:

- You’re deploying services that need to stay online without manual credit monitoring
- You want built-in support for background workers and scheduled jobs without extra setup
- You prefer fixed monthly pricing with clearly defined resource limits
- You need role-based access control, audit logs, and team-level service management
- You’re running production workloads where uptime, isolation, and operational defaults matter

## Not sold on either? See what Northflank does differently

If neither Railway nor Render fully meets your needs, you might be looking for something with more built-in control, simplified infrastructure management, or more comprehensive support for production workloads. That’s where [Northflank](https://northflank.com/) comes in.

Let’s look at what it handles differently:

![northflank's home page](https://assets.northflank.com/northflank_s_home_page_min_28b6d64579.png)

Northflank is a container-based platform designed for production environments. It supports background jobs, scheduled tasks, [bring your own cloud](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) (BYOC), [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), and team-level workflows, all built around fast deployments and clear observability.

Let’s quickly take a look at where Northflank fits in and what it handles differently from the start.

### 1. Deploy in your own cloud (BYOC)

You can run your workloads inside your own AWS, GCP, or Azure account while still using Northflank’s UI, CI integrations, and job system. This gives you more control over network boundaries, cost management, and compliance requirements.

*See [What is BYOC and why it matters](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment), and [Northflank's BYOC feature details](https://northflank.com/features/bring-your-own-cloud)*

![Diagram showing Northflank’s BYOC architecture, where the control plane (UI, API, CI tools) connects to workloads running on AWS, GCP, Azure, or other providers in the user’s own cloud environment](https://assets.northflank.com/byoc_2_min_09d9c7300d.png) *Northflank’s architecture for running workloads in your own cloud with full access to its platform features*

### 2. Built-in support for background jobs and cron tasks

You can define jobs as standalone services and schedule them directly in the platform without needing extra services or scripting. It supports both recurring schedules and ad-hoc job runs.

*See [how to create and schedule jobs in Northflank](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs).*

![Northflank UI displaying a cron job configuration screen, including schedule details, recent job runs, build history, and associated git commits for a background job](https://assets.northflank.com/northflank_Cron_job_setup_d14216ec3c.webp)*Cron job setup and run history in Northflank’s job dashboard*

### 3. Structured logs and real-time visibility

Each deployment, job, and service comes with its own structured logging panel. You can view output in real time, filter by level or time, and inspect logs across services without additional setup.

*See [how log visibility works in Northflank](https://northflank.com/docs/v1/application/observe/view-logs) for real-time tailing, filtering, and cross-service inspection.*

![Northflank UI showing structured logs from a container with live tailing and filtering options](https://assets.northflank.com/build_logs_northflank_099eeec92f.webp) *Real-time log tailing and filtering in Northflank's deployment view*

### 4. Production-level configuration and team features

Northflank includes [access controls](https://northflank.com/docs/v1/application/secure/use-role-based-access-control), [environment-specific secrets](https://northflank.com/docs/v1/application/secure/inject-secrets), per-service [build](https://northflank.com/docs/v1/application/build/build-with-buildpacks) and runtime settings, [audit trails](https://northflank.com/docs/v1/application/observe/audit-logs), and integrated build pipelines, all accessible through both the UI and API.

You also get team-focused features like account-level permissions, team-level billing, and centralized settings. You can [create and manage teams](https://northflank.com/docs/v1/application/collaborate/create-a-team) directly from your dashboard, with full control over configuration and member access.

![Screenshot of the Northflank dashboard showing the interface for creating and managing a team, including settings, integrations, and billing overview](https://assets.northflank.com/team_dashboard_northflank_e0d8a720c8.webp) *Team creation and management interface in the Northflank dashboard.*

### 5. Stack templates to simplify full app deployment

Northflank’s stack templates help you get up and running faster by providing pre-configured setups for complete applications, frameworks, and infrastructure tools.

These include analytics platforms like PostHog, feature flagging tools like GrowthBook, authentication providers like SuperTokens, language frameworks like Flask and Next.js, and even AI tools like vLLM and DeepSeek.

Each template comes with build settings, health checks, and deployment options pre-filled, saving time and helping you follow best practices from the start.

You can browse the full list of templates at [northflank.com/stacks](https://northflank.com/stacks).

![Screenshot of the Northflank stack templates page showing a search bar, filter categories like DevOps and AI, and templates such as n8n, Outline, GrowthBook, and Temporal](https://assets.northflank.com/northflank_stack_templates_fe4e477323.png)

The stack templates library on Northflank, featuring one-click deployment options for tools across AI, DevOps, CMS, and more.

## Answers to what developers are already asking

You’ve most likely seen these questions pop up in forums, Discord threads, or while comparing docs. See a quick rundown of the most common ones.

### Is Railway better than Render?

It depends on what you're building. Railway is great for fast iteration and usage-based billing, while Render is better suited for long-running services with fixed pricing and built-in job types. Scroll up to the comparison chart to see which fits your workflow.

### Can I use Railway for hobby projects?

Yes, but keep in mind that the free tier is a one-time $5 usage credit. After that, your services will shut down unless you upgrade to a paid plan.

### Does Railway have a free tier?

Not in the traditional sense. It gives you $5 in usage credits once. Render’s free tier, on the other hand, includes always-on web services (with limits) and doesn’t rely on credit-based metering.

### What are Render’s limitations?

Monthly build minute quotas, per-user pricing on paid plans, and limited native database support outside PostgreSQL and Redis. You can deploy other databases, but setup is manual.

### Is Railway or Render cheaper in the long run?

Railway can be cheaper for low-usage projects because the billing scales with consumption. Render’s fixed pricing is easier to predict for long-running services or teams with stable workloads.

### Can I use MongoDB or Redis?

Railway has built-in support for both. Render supports Redis and PostgreSQL natively, but MongoDB needs to be deployed as a private service or hosted externally.

### Are either good Heroku replacements?

Yes, but with tradeoffs. Railway focuses on speed and simplicity, while Render covers more of Heroku’s original feature set, such as workers, jobs, and tiered pricing. [Northflank](https://northflank.com/) is also worth investigating for teams looking for production-level defaults or private networking.

## Before you choose: let’s wrap this up without wasting your time

If you prefer flexible billing, fast deploys, and don’t mind setting up jobs or workers yourself, Railway will likely match your workflow. If you want built-in background workers, predictable pricing, and structured defaults for production, Render might be the safer bet.

Both platforms can work well. It just depends on what you’re optimizing for.

But if you’ve tried both and still feel like something’s missing, like more control over networking, built-in job orchestration, or the ability to run everything inside your own cloud, [Northflank](https://northflank.com/) might be a better fit.

You can [sign up for free](https://app.northflank.com/signup) and see how it handles your stack.]]>
  </content:encoded>
</item><item>
  <title>CircleCI vs GitHub Actions: Which CI/CD tool is right for your team?</title>
  <link>https://northflank.com/blog/circleci-vs-github-actions</link>
  <pubDate>2025-05-13T11:00:00.000Z</pubDate>
  <description>
    <![CDATA[Compare CircleCI vs GitHub Actions for CI/CD. Learn about key features, pricing, scalability, and pros/cons to find the best tool for your team’s needs. Get insights on automation and integration.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/circleci_vs_github_actions_92ce02aca2.png" alt="CircleCI vs GitHub Actions: Which CI/CD tool is right for your team?" />Let’s be honest, CI/CD is not exactly the most exciting part of building software. But choosing the right tool can save you hours of confusion and frustration later.

If you have ever bounced between GitHub Actions and CircleCI, wondering which one actually fits better with your project and team, you are not alone.

Both promise fast builds and smooth automation, but they feel very different once you start using them for real work. What works for a solo project might be a nightmare at scale.

In this guide, we break down what makes each tool tick using real feedback from developers on Reddit, G2, and other platforms. And if you are thinking, *“I just want something that works without duct-taping tools together,”* we will show you how platforms like [Northflank](https://northflank.com/) are offering a more modern way to do CI/CD.

## Quick comparison

If you’re in a hurry and want a clear view of the differences, this table highlights how CircleCI and GitHub Actions compare across the most relevant areas for teams choosing a CI/CD tool in 2026.

| Feature | CircleCI | GitHub Actions |
| --- | --- | --- |
| **Repository support** | GitHub, GitLab & Bitbucket | GitHub only |
| **Hosting** | Cloud or self-hosted servers | GitHub-hosted runners (cloud) or self-hosted runners |
| **Setup & UI** | Powerful dashboard, detailed logs, allows SSH into jobs | Integrated into GitHub UI; easy to configure via web UI |
| **Configuration** | Single YAML file (`.circleci/config.yml`), supports one Docker image per job; uses reusable *orbs* for common tasks | Multiple workflow YAML files; actions (reusable tasks) from GitHub Marketplace |
| **Container support** | Native Docker support; also Linux VM, macOS, and Windows runners | Docker-support via runner containers; recently added Linux, Windows, macOS runners |
| **Parallelism & scaling** | Built-in job parallelism, powerful caching, auto-scaling machines | Limited concurrency on free tier; can self-host runners or pay for more parallel jobs |
| **Built-in features** | CI/CD pipelines only; needs external tools for hosting/DB | CI/CD plus general workflow automation (issues, releases, etc.) |
| **Pricing model** | Free tier (6,000 build minutes), paid plans use credits | Free for public repos, paid minutes beyond free tier (2,000 min/mo for private repos) |
| **Best for** | Teams needing advanced CI features and multi-repo support | GitHub-centric teams and open-source projects |

## What is CircleCI?

CircleCI is a CI/CD platform that runs your builds, tests, and deployments whenever you push code. It’s fast, flexible, and works seamlessly with GitHub and Bitbucket. You can run it in the cloud or self-host it if you need more control.

Getting started is simple: you drop a `.circleci/config.yml` file into your repo. That’s your pipeline — a YAML file that defines what happens when you push. CircleCI reads it and kicks off your jobs in Docker containers or virtual machines.

Here’s a quick example for a Node.js project:

```yaml
version: 2.1

jobs:
  build:
    docker:
      - image: cimg/node:14.17
    steps:
      - checkout
      - run: npm install
      - run: npm test

workflows:
  version: 2
  build_and_test:
    jobs:
      - build

```

With this, CircleCI installs dependencies and runs tests automatically. It supports parallel jobs, test splitting, SSH access for debugging, and orbs — reusable config snippets for common tasks.

It’s built for CI/CD only, so you’ll still need other tools for infrastructure or hosting, but if you want pipelines that are quick to set up and easy to scale, it’s a solid pick.

## What is GitHub Actions?

GitHub Actions is GitHub’s built-in automation system. It lets you run workflows whenever something happens in your repo, like pushing code, opening a pull request, or creating a new release.

Since it’s built into GitHub, setup is dead simple. No extra tools, no third-party setup. You just create a `.github/workflows` folder and drop in a YAML file to define your pipeline.

Here’s an example workflow that runs tests on a Node.js project:

```yaml
name: Run Tests

on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-node@v3
        with:
          node-version: 14
      - run: npm install
      - run: npm test

```

GitHub Actions can handle more than just CI/CD. You can automate labeling issues, sending notifications, deploying apps, or even running scripts on a schedule.

For many teams, it’s a go-to because it’s right there in your repo, easy to use, and backed by a huge marketplace of prebuilt actions. And if you’re building open source, the free tier is pretty generous.

## What developers like and don’t like about CircleCI

### What developers like about CircleCI

- **It’s built for speed**
    
    CircleCI shines when it comes to performance. You can run jobs in parallel, split tests across containers, and use smart caching to cut build times. For larger projects, that adds up fast — a lot of devs say it’s one of CircleCI’s biggest wins.
    
- **You have full control over pipelines**
    
    The `config.yml` lets you define every step of your pipeline. You choose the environment, resources, job types (Docker or VM), and more. If your team likes fine-tuning CI, CircleCI gives you room to do it.
    
- **It fits into different setups**
    
    Whether your code lives on GitHub, GitLab, or Bitbucket, CircleCI connects easily. It also integrates with major cloud providers like AWS, Azure, and GCP.
    
- **You can self-host if needed**
    
    Want full control or have compliance needs? CircleCI lets you run on your own infrastructure too.
    
- **Debugging is a breeze**
    
    CircleCI’s UI shows detailed logs and makes it easy to jump into failed jobs. You can even SSH into a running build to troubleshoot, which is super helpful when something breaks.
    
- **Free credits are generous**
    
    The free plan comes with 6,000 build minutes monthly, which is usually plenty for small teams or side projects.
    

### What developers don’t like about CircleCI

- **The setup curve is steeper**
    
    Compared to something like GitHub Actions, CircleCI’s YAML syntax and job structure can take a bit of learning. Once you get it, it's powerful — but there's a bit more ramp-up.
    
- **Pricing can surprise you**
    
    CircleCI uses credits to bill for compute time. Some features, like Docker layer caching or parallel jobs, can burn through those quickly, and a few teams mention unexpected usage spikes.
    
- **It’s not an all-in-one**
    
    CircleCI only handles CI/CD. You’ll still need to plug in your own infrastructure, hosting, and deployment logic — it’s not trying to manage everything for you.
    
- **Support can be hit or miss.**
    
    Some users reported great help, while others said support felt slow or limited. It seems to depend on your plan and urgency.
    

## What developers like and don’t like about GitHub Actions

### What developers like about GitHub Actions

- **It’s already on GitHub**
    
    If your code lives on GitHub, using Actions is a no-brainer. No setup, no new accounts — just drop in a workflow file and you’re up and running.
    
- **You can get started fast**
    
    The UI is clean, and GitHub gives you starter templates to build from. Many developers say they had pipelines running in minutes with almost no friction.
    
- **It’s not just CI/CD**
    
    Actions can automate everything from labeling issues to publishing releases. It’s like having a built-in task runner for your whole repo.
    
- **Free for open-source**
    
    Public repos get unlimited free minutes. Even private repos get 2,000 minutes/month for free, which is great for small teams.
    
- **Huge marketplace of reusable actions**
    
    Need to deploy to AWS? Set up Python? Run a linter? The GitHub Marketplace has thousands of plug-and-play actions ready to go.
    

### What developers don’t like about GitHub Actions

- **You’re locked into GitHub**
    
    It only works if your code is hosted on GitHub. If you use GitLab, Bitbucket, or host your own Git server, GitHub Actions isn’t an option.
    
- **Scaling can hit limits**
    
    For small projects, GitHub-hosted runners are fine. But bigger teams sometimes hit queuing delays or need to pay for faster machines or self-hosted runners to keep up.
    
- **Complex workflows can get messy**
    
    Basic CI is easy, but once you’re dealing with multiple services, environments, or lots of secrets, the YAML gets harder to manage. It can feel limiting for advanced setups.
    
- **Not ideal if you’re not all-in on GitHub**
    
    If your workflow spans across other tools or platforms, GitHub Actions starts to feel more restrictive. It works best when everything is already inside the GitHub ecosystem.
    

### GitHub Actions vs CircleCI: Key Differences

| **Category** | **CircleCI** | **GitHub Actions** |
| --- | --- | --- |
| **Repo support** | Works with GitHub, GitLab and Bitbucket (cloud or self-hosted) | Only works with GitHub repositories |
| **Platform integration** | Standalone service focused on CI/CD — requires integration with GitHub/GitLab/Bitbucket | Fully built into GitHub — CI, code, and automation in one place |
| **UI & setup** | Separate dashboard with detailed insights, SSH access, and fine-grained control | Simple GitHub UI, starter templates, logs in the Actions tab |
| **Workflow structure** | One `config.yml` per project (free tier limited to one Docker image per job). Uses “orbs” for reusable logic | Multiple `.yml` files per repo, each triggered by events. Uses reusable community “actions” |
| **Performance & scaling** | Built-in parallelism and autoscaling, better suited to high-concurrency workloads | GitHub-hosted runners have limits unless you pay or self-host |
| **Pricing** | Credit-based pricing (with a generous free tier). Can become costly for heavy parallel builds or advanced features | Free tier includes 2,000 minutes/month for private repos (unlimited for public), usage-based beyond that |

## Tired of the limits of CircleCI and GitHub Actions?

If GitHub Actions feels too limited and CircleCI too heavy to maintain, you’re not out of options.

[**Northflank**](https://northflank.com/) gives you built-in CI/CD with the flexibility of a self-managed platform, without the usual overhead. You don’t have to set up runners, manage agents, or wire together a bunch of services. Just connect your repo, define a build method like a Dockerfile or buildpack, and your service is ready to go.

You can deploy on Northflank-managed infrastructure or [bring your own cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) and run everything on your own Kubernetes cluster.

![](https://assets.northflank.com/northflank_pipeline_overview_a4eac26a83.png)

Northflank includes CI/CD that runs on every push, plus logs, metrics, secrets, and container builds — all from one interface. It supports background jobs, cron tasks, and preview environments without needing extra tools or plugins.

You’re not locked into GitHub. You can use any Git provider, including self-hosted.

Northflank also supports GPU workloads without the friction. Unlike CircleCI, you don’t need a high-tier plan or custom setup.

![](https://assets.northflank.com/northflank_gpu_c83f42ee25.png)

If you want more control than GitHub Actions but less maintenance than CircleCI, Northflank might be the right balance. It’s fast to get started and is designed to stay out of your way.

[See how it works](https://northflank.com/docs/v1/application/getting-started/introduction-to-northflank) or [try it free](https://app.northflank.com/signup).

## How to choose the right CI/CD tool

The right tool depends on where your code lives, how your team works, and what you’re building.

- **Fully on GitHub?**
    
    GitHub Actions is built in — simple, clean, and great for teams that want minimal setup.
    
- **Using Bitbucket or multiple repos?**
    
    CircleCI works well across platforms and gives you more control over pipelines.
    
- **Small project or prototype?**
    
    Actions’ free tier and ease of use are hard to beat. CircleCI’s parallelism and speed can really help larger projects with long test suites.
    
- **Need more than just CI/CD?**
    
    CircleCI is focused on pipelines, and GitHub Actions is flexible across GitHub workflows.
    
    Want both CI/CD *and* built-in app hosting? Northflank gives you everything — pipelines, deployments, environments, and more in one seamless platform.
    
- **Trying to stay on budget?**
    
    GitHub Actions is free for public repos and light usage. CircleCI’s free tier is generous, but costs can add up at scale. Northflank has predictable pricing with built-in infrastructure, so you get fewer surprises.
    
- **Team experience?**
    
    GitHub Actions is great for beginners, while CircleCI is more suited to experienced teams that need fine-tuned control. Northflank sits comfortably in between — powerful yet developer-friendly.
    

> You don’t need the most popular tool. Just the one that fits your code, your team, and what you’re building.
> 

## Wrapping up

GitHub Actions makes it easy to get started. It’s built right into GitHub, so you can add a workflow file and go. For many teams, that’s more than enough, especially for lightweight CI and simple automation.

CircleCI gives you more control. You can fine-tune your pipelines, run jobs in parallel, choose your execution environment, and manage caching more precisely. But with that flexibility comes a bit more complexity in setup and maintenance.

What if you need both?

You might want fast setup, but without giving up visibility or customization. Or maybe you want built-in CI/CD that works across clouds, without managing runners or patching things together with plugins.

That’s where [**Northflank**](https://northflank.com/) comes in.

It combines the simplicity of Git-based automation with the power of container-native workflows — no extra tools, no custom infrastructure. Everything from builds and deployments to logs, metrics, secrets, and environments lives in one place.

If GitHub Actions feels too limited, and CircleCI starts to feel like a project of its own, Northflank could be the middle ground that just works.

[Take a look at the quickstart guide](https://northflank.com/docs/v1/application/getting-started/introduction-to-northflank), or [create a free account](https://app.northflank.com/signup) to try it for yourself.]]>
  </content:encoded>
</item><item>
  <title>CircleCI vs Jenkins: Which one fits your workflow in 2026?</title>
  <link>https://northflank.com/blog/circleci-vs-jenkins</link>
  <pubDate>2025-05-08T16:56:00.000Z</pubDate>
  <description>
    <![CDATA[Compare CircleCI and Jenkins in 2026 - setup, performance, customization, and which CI/CD tool makes sense for your team today.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/circleci_vs_jenkins_f6b97d3133.png" alt="CircleCI vs Jenkins: Which one fits your workflow in 2026?" />> Been in a DevOps thread lately? The CircleCI vs Jenkins talk still pops up.
> 

Some developers say Jenkins gives you all the control, but it comes with maintenance costs, outdated UI, manual setup, and constant plugin updates. Others like CircleCI’s simplicity but mention that credits can run out quickly or that the pricing becomes hard to manage as workloads increase.

So, which one’s the right fit for your team in 2026? That’s what you’ll find out.

I went through recent user reviews, Reddit threads, and hands-on comparisons to break it down clearly. If you're deciding between hosted pipelines and fully self-managed CI, this guide should help you figure out what fits your setup best.

<div><center><a href="https://app.northflank.com/signup"><Button variant={["large", "gradient"]}>Find the right platform for your next project >>> </Button></a></center></div>


## Quick comparison of CircleCI vs Jenkins

If you’re in a hurry and want a clear view of the differences, this table highlights how CircleCI and Jenkins compare across the most relevant areas for teams choosing a CI/CD tool in 2026.

| **Feature** | **CircleCI** | **Jenkins** |
| --- | --- | --- |
| **Setup** | Cloud-hosted by default. Just connect your repo and add a config file. | Self-hosted by default. Requires manual installation, server setup, and agent management. |
| **Hosting** | Runs on CircleCI’s managed infrastructure. You can also use self-hosted runners. | Typically runs on your own servers (on-prem or cloud). Full control, but more setup. |
| **Config style** | Uses YAML (`.circleci/config.yml`). Supports orbs (reusable config packages). | Uses Groovy-based `Jenkinsfile` or UI-based job configs. Supports freestyle and pipeline jobs. |
| **Performance** | Shared runners by default. Supports test splitting, parallelism, and caching. Performance and cost are tied to usage credits. | Depends entirely on your infrastructure. You manage scaling, agents, and job concurrency. |
| **Pricing** | Free tier with 30,000 monthly credits. Paid plans scale with usage. Some teams say costs increase quickly with parallel jobs. | Free and open-source. Costs are tied to infrastructure and internal maintenance effort. |
| **User interface** | Clean, modern UI with visual pipeline views and job insights. Easier to onboard. | Outdated interface. Relies heavily on plugins for visibility and usability improvements. |
| **Great fit for** | Teams that want fast setup, no infrastructure burden, and GitHub/GitLab integration. | Teams that need deep customization, plugin support, or full control over CI/CD workflows. |

## Overview of Jenkins

Let’s talk about the tool that’s been powering CI/CD pipelines long before most of today’s platforms existed: [Jenkins](https://www.jenkins.io/).

![jenkins x home page.png](https://assets.northflank.com/jenkins_x_home_page_ea832a2d5d.png)

It’s been around for over a decade, and even in 2026, you’ll still find it running inside banks, governments, enterprise IT departments, and large engineering orgs with complex compliance needs.

Unlike CircleCI, Jenkins doesn’t run in the cloud unless you put it there. You install it yourself, manage your agents, and configure your builds from scratch. In return, you get full control over how everything runs.

And that’s what’s kept Jenkins around for so long: total flexibility, a massive [plugin ecosystem](https://plugins.jenkins.io/), and a level of customization that newer tools don’t always give you.

### So, what is Jenkins?

Jenkins is an open-source automation server you install and run on your own infrastructure. It was originally a fork of a project called Hudson and has been maintained by a large open-source community since 2011.

It lets you define and automate everything from build steps to test stages, deployment flows, and approval gates. It’s designed for teams that need custom pipelines, care about environment-level control, or have legacy systems that don’t work with newer, hosted CI tools.

You can build pipelines using Groovy in a `Jenkinsfile`, or configure jobs directly in the UI. But either way, you're in charge of the setup.

![jenkins dashboard.png](https://assets.northflank.com/jenkins_dashboard_8a83a7c232.png)

### But what does a basic Jenkins pipeline look like?

Let’s see what a basic pipeline in Jenkins looks like using a `Jenkinsfile`. This one installs dependencies and runs tests for a Node.js project:

```groovy
pipeline {
  agent any
  stages {
    stage('Install') {
      steps {
        sh 'npm install'
      }
    }
    stage('Test') {
      steps {
        sh 'npm test'
      }
    }
  }
}
```

Each `stage` defines a step in the process, and `agent any` tells Jenkins to run it on any available worker. You can customize this further by specifying node labels, environment variables, post-build conditions, and more.

### So, why are teams still using Jenkins in 2026?

With all the newer CI/CD platforms out there, you’d think Jenkins would have faded by now, but it hasn’t. And there are a few major reasons why.

- **You can run it anywhere**. Teams in air-gapped environments or behind firewalls still rely on Jenkins because cloud-based platforms aren’t an option.
- **It’s deeply customizable**. Jenkins supports over [1,800 plugins](https://plugins.jenkins.io/), and that flexibility is hard to match. You can integrate with just about anything.
- **It handles complex workflows**. If your pipeline spans multiple services, environments, and approval flows, Jenkins can model that, as long as you’re willing to configure it.

A developer on Reddit put it like this:

> “Jenkins, albeit great, has been gradually fading. But it still works well for teams that need full control.”
> 
> 
> ~ [u/mparigas](https://www.reddit.com/r/devops/comments/1hir6a5/comment/m30vm4m)
> 

So while Jenkins may not be the default choice for new projects, it’s still deeply entrenched in teams with long-lived systems and strict infrastructure requirements.

### What are the limitations of Jenkins?

All that control and flexibility comes with a cost, and for many teams, it’s a steep one.

- **Manual maintenance**. You’re in charge of installing updates, managing agents, and resolving issues when plugins break.
- **Outdated interface**. The UI hasn’t changed much in years, and even seasoned users say it feels clunky.
- **Steep learning curve**. If you're not already familiar with Groovy, Jenkinsfiles, or plugin configurations, onboarding takes time.

Recent G2 reviews reflect this clearly:

> “Initial setup takes a lot of time and effort... a dedicated team is necessary.”
> 
> 
> — [G2 reviewer, April 23, 2025]
> 

![circleci vs jenkins review 1.png](https://assets.northflank.com/circleci_vs_jenkins_review_1_220c368e99.png)

> “The UI hasn’t changed in a long time and needs improvement.”
> 
> 
> — [G2 reviewer, January 29, 2025]
> 

![circleci vs jenkins review 2.png](https://assets.northflank.com/circleci_vs_jenkins_review_2_caed4a5066.png)

These aren’t edge cases; they’re the experience of many teams still managing Jenkins in 2026.

*See more: [Jenkins alternatives in 2026: CI/CD tools that won’t frustrate DevOps engineers](https://northflank.com/blog/jenkins-alternatives-2025)*

## Overview of CircleCI

So, Jenkins gives you control, but what if your team doesn’t want to manage CI infrastructure at all?

That’s where [CircleCI](https://circleci.com/) comes in. Unlike Jenkins, CircleCI runs in the cloud by default, and most teams can start building in minutes without installing anything. You connect your GitHub or GitLab repo, add a `.circleci/config.yml` file, and your pipelines kick off.

![circleci home page.png](https://assets.northflank.com/circleci_home_page_5010422a55.png)

CircleCI provides [hosted runners](https://circleci.com/docs/runner-scaling/), usage-based pricing, and a more modern developer experience out of the box. For teams that prioritize speed over flexibility, it’s often the simpler choice.

### What is CircleCI?

CircleCI is a CI/CD platform that automates your builds, tests, and deployments, with no servers to install or agents to manage if you're using the default cloud-hosted setup.

Let’s say a developer pushes code to a repo. CircleCI picks that up automatically, runs tests in a clean containerized environment, and deploys if everything passes. Everything is defined in a YAML file located in `.circleci/config.yml`.

![circleci dashboard.png](https://assets.northflank.com/circleci_dashboard_ad512f848a.png)

Unlike Jenkins, which often needs plugins to do anything useful, CircleCI has built-in support for things like Docker, caching, test splitting, and secrets management, all surfaced through its dashboard and APIs.

### What does a basic CircleCI config look like?

Let’s see a minimal setup that installs dependencies and runs tests in a Node.js app:

```yaml
version: 2.1

jobs:
  build:
    docker:
      - image: cimg/node:14.17
    steps:
      - checkout
      - run: npm install
      - run: npm test

workflows:
  version: 2
  build_and_test:
    jobs:
      - build

```

This file lives in `.circleci/config.yml`. You don’t need to set up agents or servers. CircleCI handles that through its cloud environment unless you’ve configured self-hosted runners.

### So, why are teams switching to CircleCI?

A big reason is the setup time. Jenkins may give you flexibility, but CircleCI gets you running faster with less infrastructure overhead. That’s especially true for small teams or startups that want to avoid managing a Jenkins stack.

One developer on [Dev.to](https://dev.to/farrukhkhalid/why-developers-are-ditching-jenkins-for-circleci--216g) said:

> “We switched from Jenkins to CircleCI and cut our CI maintenance time by 80%.”
> 

You also get:

- Hosted runners with automatic scaling
- Parallelism and test splitting out of the box
- Orbs (reusable, versioned packages of pipeline logic)

All without touching a plugin directory.

### Now, where can CircleCI feel limiting?

For all its ease, CircleCI isn't perfect. There are some tradeoffs:

- **It’s YAML-only.** You don’t get multiple config formats like Jenkins (e.g. UI jobs or Groovy scripts).
- **Complex workflows get hard to model.** Managing multi-service builds or conditionals can require nested YAML that’s hard to manage.
- **Pricing scales quickly.** CircleCI uses a credit-based system. Each build minute costs credits based on the compute class.
- **Some Docker and Kubernetes features are gated behind paid plans.** Self-hosted runners, Kubernetes support, and resource-intensive builds often require a Performance or Scale plan.
- GPU support exists, but only on Scale or Custom plans, or through self-hosted runners that you set up and manage.

So while CircleCI is a great default for teams that want simplicity, it’s not always the best choice if you need detailed control or are operating at large scale.

See [Top CircleCI alternatives in 2026](https://northflank.com/blog/top-circleci-alternatives)

## When should you use Jenkins? When is CircleCI better?

After seeing how both tools work in practice, the main question is: which one fits your setup?

Jenkins and CircleCI are built for very different environments. One isn’t “better” than the other across the board, but depending on your team, one is likely a better fit.

### Use Jenkins if:

**1. You need plugin-level control**

Jenkins supports over 1,800 plugins, and you can wire up almost any tool, service, or condition into your pipeline. That level of flexibility is hard to match, especially if you're modeling a workflow with non-standard approval gates, custom test setups, or multiple internal systems.

**2. You run in air-gapped or regulated environments**

Jenkins is one of the few CI tools that works well on isolated networks, without any cloud dependencies. It’s still the default for teams behind strict firewalls, compliance layers, or on-prem-only setups.

**3. You already have Jenkins set up across teams**

If your org already runs Jenkins reliably with working pipelines, moving off it just for the sake of modernization may not be worth the effort.

### Use CircleCI if:

**1. You want to move fast with minimal setup**

CircleCI’s hosted environment means no agents, no infrastructure to manage, and builds start running as soon as you commit a config file. Most new teams are up and running in under an hour.

**2. You prefer a SaaS platform**

CircleCI is fully managed, with built-in scaling, dashboards, metrics, and secrets. You don’t need a DevOps team to keep it online.

**3. You don’t need deep custom plugin workflows**

If your pipelines are straightforward, run tests, deploy code, maybe cache a few things, CircleCI handles all of that without the complexity of Jenkins.

## Need more than hosted pipelines or plugins?

If CircleCI feels too limited and Jenkins is too much work to maintain… there’s another path.

[Northflank](https://northflank.com/) gives you the flexibility of a self-managed CI/CD platform without requiring you to configure agents or manage plugins.

You don’t need to install runners, set up a VM cluster, or wire together multiple tools just to get a working pipeline. Every service on Northflank comes with built-in CI/CD that’s tied to your Git commits, and you can deploy using [Dockerfiles](https://northflank.com/docs/v1/application/build/build-with-a-dockerfile), [buildpacks](https://northflank.com/docs/v1/application/build/build-with-buildpacks), or [prebuilt containers](https://northflank.com/deploy/run-persistent-and-ephemeral-docker-containers).

It also works in both directions:

- You can run everything on **Northflank-managed infrastructure**
- Or [bring your own cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) (BYOC) and deploy to your own Kubernetes cluster

![northflank pipeline overview.png](https://assets.northflank.com/northflank_pipeline_overview_a4eac26a83.png)

Northflank brings together:

- [Git-based CI/CD](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) that triggers on push (no extra integration logic required)
- [Secrets](https://northflank.com/docs/v1/application/secure/manage-secret-groups), [logs](https://northflank.com/docs/v1/application/observe/view-logs), [metrics](https://northflank.com/docs/v1/application/observe/view-metrics), and [container builds](https://northflank.com/features/build) all in the same interface
- Support for [background jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs), [cron tasks](https://northflank.com/docs/v1/application/run/run-an-image-once-or-on-a-schedule), and [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) without extra setup

You’re not locked into GitHub, and you’re not left managing Jenkins.

Also, Northflank supports GPU workloads out of the box, unlike CircleCI which requires a high-tier plan or manual runner setup. [See how it works](https://northflank.com/gpu).

![northflank gpu.png](https://assets.northflank.com/northflank_gpu_c83f42ee25.png)

So if your team wants more control than CircleCI allows, but less overhead than Jenkins demands, this might be the middle ground that works.

## Questions developers ask about CircleCI and Jenkins

Still deciding? These are some of the questions developers regularly ask when comparing CircleCI and Jenkins, answered with the facts that matter.

### **Is CircleCI better than Jenkins?**

It depends on what you're optimizing for. CircleCI is faster to start with and handles infrastructure for you. Jenkins gives you more control and customization, especially for edge-case workflows or on-prem setups. If your team needs to run pipelines in an isolated network or with complex plugin logic, Jenkins still makes sense. For most teams starting fresh, CircleCI is easier to adopt.

### **Why is Jenkins considered outdated?**

Jenkins hasn’t changed much in terms of UI or core architecture. It still relies heavily on plugins (some of which are no longer maintained), and configuration is often handled through Groovy or XML. For developers used to modern platforms with built-in dashboards and YAML pipelines, Jenkins can feel slow and maintenance-heavy. That said, it’s still widely used in enterprise environments because of its flexibility.

### **What are the limitations of CircleCI?**

- Configuration is YAML-only and can become complex in large workflows
- Some features like Kubernetes runners or premium Docker resources are locked behind paid plans
- Cost scales with usage, which can be unpredictable for high-frequency builds
- No built-in dashboard for managing multiple repos or projects together

### **Does Jenkins use Kubernetes?**

Not directly out of the box, but there are plugins that allow Jenkins to run agents inside Kubernetes clusters. You’re responsible for setting that up and maintaining it. Jenkins itself doesn’t have native support for container orchestration; it relies on how you configure your infrastructure.

### **What languages does CircleCI support?**

CircleCI supports any language that runs in a Docker image. That includes Node.js, Python, Java, Go, Ruby, PHP, Rust, C#, and more. You can use [official CircleCI images](https://circleci.com/developer/images) or bring your own. There are no hard language limitations as long as the image contains the right runtime.

### **Which CI/CD tool is best in 2026?**

There’s no single best. It depends on your environment and team. CircleCI is great for cloud-based teams that want to move fast and don’t want to manage infra. Jenkins is still the right call for teams that need full control, are working in regulated setups, or already have Jenkins in place. And if you’re looking for something in between, with Git-based automation and built-in CI/CD, Northflank could be a better fit.

## Choosing between setup speed and full control

If you’ve made it this far, you’ve seen what both CircleCI and Jenkins bring to the table.

CircleCI makes it easy to get started. You connect your repo, push a config file, and let the platform handle the rest. It’s fit primarily for teams that want to skip infrastructure management and don’t need deep customization.

Jenkins, on the other hand, is all about flexibility. If your pipelines need to run on specific environments, use uncommon plugins, or live entirely behind a firewall, it’s still one of the most capable tools out there (as long as you're ready to manage it).

What if neither tool suits your needs?

You may want something self-hostable but with less maintenance burden than Jenkins. Or you may want a platform that gives you Git-based automation, container builds, and log visibility without locking you into a specific cloud provider or requiring dozens of plugins.

That’s where [Northflank](https://northflank.com/) comes in.

You can see how it works in this [quickstart guide](https://northflank.com/docs/v1/application/getting-started/introduction-to-northflank) or spin up your own service in minutes. Start by [creating a free account](https://app.northflank.com/signup).]]>
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</item><item>
  <title>Top 5 CircleCI alternatives to use in 2026: best CI/CD tools</title>
  <link>https://northflank.com/blog/top-circleci-alternatives</link>
  <pubDate>2025-05-08T12:45:00.000Z</pubDate>
  <description>
    <![CDATA[CircleCI still works, but 2026 DevOps needs more. Explore 5 CircleCI alternatives offering faster, scalable, Kubernetes-native CI/CD with better DX, transparency, and container-first workflows.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/openshift_alts_2_a9ca91eef0.png" alt="Top 5 CircleCI alternatives to use in 2026: best CI/CD tools" />CircleCI still gets the job done — fast builds, YAML pipelines, solid Git integrations. But in 2026, "good enough" isn't cutting it anymore.

Engineering teams are moving fast, scaling up, and shifting toward container-native, GitOps-driven workflows. They're juggling microservices, Kubernetes clusters, and infrastructure-as-code — and they need CI/CD platforms that keep up without slowing them down.

The cracks are starting to show. Maybe you’ve hit the limits of YAML. Maybe billing feels opaque or deploy visibility is lacking. You’re not alone.

Whether you're looking for something more developer-friendly, Kubernetes-native, or just easier to scale, there are better options now.

Here’s a fresh look at 5 **CircleCI alternatives worth trying in 2026** — platforms that offer modern DevOps workflows, less setup pain, and a better developer experience from commit to production.

## TL;DR: 7 CircleCI alternatives to watch in 2026

Just want the list? Here are 5 CircleCI alternatives developers are turning to in 2026:

| Tool | Best For | Strength Highlight |
| --- | --- | --- |
| [**Northflank**](https://northflank.com/) | Dev-first and Cloud-native CI/CD + hosting | Simple, K8s-native, Docker-native, easy to integrate, Built-in preview envs. |
| [**Jenkins**](https://www.jenkins.io/) | Highly customizable CI/CD workflows | Open-source flexibility with a large plugin ecosystem |
| [**GitLab CI/CD**](https://docs.gitlab.com/ci/) | All-in-one DevOps platform | Integrated repo, CI/CD, and security pipelines |
| [**GitHub Actions**](https://docs.github.com/en/actions) | Small teams & automation fans | Seamless GitHub integration, fast setup |
| [**AWS CodePipeline**](https://aws.amazon.com/codepipeline/) | AWS-native workflows | End-to-end automation across AWS services |

## What are developers saying about CircleCI?

CircleCI remains a powerful CI/CD tool, especially valued for its speed and seamless integration with Git-based workflows. Developers appreciate its flexibility and performance:

> “What I like the best about CircleCI is its speed and flexibility, and automation of testing and user-friendly interface.”
> 
> 
> — *Kenneth Joy M., Developer (G2, Jan 2025)*
> 

> “Since we have many versions of our app running for different clients, CircleCI allows us to deploy them all in a speedy and timely fashion.”
> 
> 
> — *Remmelt K., Senior iOS Developer (G2, Nov 2024)*
> 

But the praise is often tempered by real frustrations. Developers have flagged growing pain points that are hard to ignore:

**Opaque Billing & Limited Visibility**:

> “Billing is a mess and nightmare! There is no transparency on how many build minutes and credits I spent each day... Support engineers do not thoroughly read and understand issues.”
> 
> 
> — *Kok How T., Verified User (G2, Nov 2023)*
> 

**YAML Sprawl and Learning Curve**:

> “One of the downsides of CircleCI is the complexity of configuration for newbies or beginners, especially with advanced features.”
> 
> 
> — *Kenneth Joy M., Developer (G2, Jan 2025)*
> 

**Scaling Costs for Larger Teams**:

> “It’s not as well suited for larger teams as the cost quickly scales up. It’s also not well suited for more complicated builds because the configuration process is pretty arcane.”
> 
> 
> — *Dillon Welch, Director of Engineering (TrustRadius)*
> 

*If you’d like to dive deeper into the feedback, you can check out the full reviews [here](https://www.g2.com/products/circleci/reviews#reviews)*

## What to look for in CircleCI alternatives

Choosing the right CI/CD platform goes beyond just faster builds. It's about finding the right fit for your team's needs today. Here’s what you should keep in mind when considering alternatives:

### Developer experience (DX)

Does the platform simplify your workflow or add friction? The best CI/CD tools offer an intuitive, easy-to-use experience, which leads to quicker onboarding and fewer roadblocks for your developers.

### Speed & reliability

CI/CD should feel invisible. Look for tools with fast build times, smart caching, and consistent performance. You don’t want to be waiting for your pipeline to run or dealing with flaky results.

### First-class container & Kubernetes support

If you're working with containers or Kubernetes, make sure the platform natively supports these technologies. Whether it’s Docker or Helm charts, your CI/CD should integrate seamlessly with your containerized workflows.

### Built-in deployment & hosting options

Some platforms now offer more than just CI/CD — they handle deployment, hosting, and even environment management. If you want a one-stop solution, look for a tool like [Northflank](https://northflank.com/) that can manage your environments and spin up live previews automatically.

### Scalability without pain

As your team and services grow, the CI/CD platform should scale effortlessly. It should support multiple teams and repos without dramatically increasing costs or complexity. Make sure it can handle your growth without the growing pains.

### Integrations that just work

Your CI/CD tool should integrate smoothly with your existing stack — whether that's GitHub, Slack, Terraform, or whatever tools your team already uses. Plus, consider built-in support for infra-as-code and secrets management.

### Pricing & transparency

CI/CD costs can spiral quickly. Look for a pricing model that’s transparent, with no hidden costs. You’ll want something that scales fairly with your team and doesn’t penalize you as you grow.

### GPU and AI workload support

For teams running machine learning, AI, or GPU-accelerated jobs, support for GPU-enabled runners is a game-changer. Some newer platforms now offer native GPU support to handle these increasingly common workflows.

### Data residency & regional control-plane options

With growing compliance needs (like GDPR), it's worth checking whether your CI/CD provider offers control-plane hosting or data residency in specific regions — especially if you're operating in the EU or other regulated environments.

## 5 CircleCI alternatives to watch in 2026

CircleCI works, but if you're looking for something that fits modern workflows better, here are 5 alternatives to check out in 2026.

### 1. Northflank — The cloud-native CI/CD devs actually enjoy using

[Northflank](https://northflank.com/) is a CI/CD platform that’s optimized for Kubernetes-native and containerized applications. It’s designed to simplify the deployment pipeline, offering powerful automation while maintaining a focus on scalability, flexibility, and ease of use.

Northflank stands out with its deep integration into cloud-native technologies, particularly Kubernetes, making it ideal for modern, containerized workloads. Unlike some traditional CI/CD platforms, it doesn’t require complex configuration or manual scaling, allowing teams to quickly set up, scale, and automate their deployment processes without hassle.

![](https://assets.northflank.com/image_73_4960b1b179.png)

**Pros:**

- Seamless Kubernetes integration
- Automatic horizontal scaling
- Built-in CI/CD and logging
- Native GPU workload support for AI/ML pipelines
- EU-based control-plane option for data residency
- Transparent pricing with no hidden costs

**Cons:**

- Smaller community compared to CircleCI

**Why Choose Northflank Over CircleCI?**

- More advanced automation and CI/CD features.
- Greater flexibility in cloud provider selection.
- Kubernetes-native with built-in auto-scaling.
- Lower costs with transparent pricing models.
- Enterprise-grade security and compliance tools.

### 2. Jenkins

[Jenkins](https://www.jenkins.io/) remains one of the most popular CI/CD tools due to its flexibility and extensive plugin ecosystem. While it requires manual setup and maintenance, it’s a great option for teams that want full control over their pipelines.

![](https://assets.northflank.com/image_74_46326a2940.png)

**Pros:**

- Highly customizable with thousands of plugins
- Free and open-source
- Supports self-hosted and cloud deployments

**Cons:**

- Requires manual scaling
- Steeper learning curve for beginners

[Read more on Jenkins.](https://northflank.com/blog/jenkins-alternatives-2025)

### 3. GitLab CI/CD

[GitLab CI/CD](https://docs.gitlab.com/ci/) is a natural choice for teams already using GitLab. It offers built-in CI/CD functionality, making it easy to integrate with repositories.

![](https://assets.northflank.com/image_75_1f6b5d553a.png)

**Pros:**

- Seamlessly integrated with GitLab repositories
- Built-in security scanning and compliance tools
- Supports both cloud and self-hosted deployment

**Cons:**

- Can be resource-intensive for large projects
- Limited customization compared to Jenkins

[Read more on GitLab CI/CD.](https://northflank.com/blog/best-gitlab-alternatives)

### 4. AWS CodePipeline

[AWS CodePipeline](https://aws.amazon.com/codepipeline/) is a fully managed CI/CD service that integrates seamlessly with other AWS services, making it ideal for teams deeply embedded in the AWS ecosystem.

![](https://assets.northflank.com/image_79_08ab9bfc6d.png)

**Pros:**

- Deep integration with AWS services
- Fully managed with automatic scaling
- Pay-as-you-go pricing

**Cons:**

- Limited flexibility outside AWS
- Can be complex to configure for non-AWS users

[Read more on GitHub Actions.](https://northflank.com/blog/github-actions-alternatives)

### 5. GitHub Actions

[GitHub Actions](https://docs.github.com/en/actions) is a powerful CI/CD automation tool built directly into GitHub, making it an excellent choice for teams already using GitHub for version control. It allows developers to create custom workflows that automate building, testing, and deployment processes with minimal setup.

![](https://assets.northflank.com/image_78_a8806c2331.png)

**Pros:**

- Seamless GitHub integration
- Flexible and customizable
- Rich marketplace of pre-built actions
- Scalable and secure

**Cons:**

- Limited outside GitHub
- Costly for larger workloads
- Complex workflows require experience

## How to choose the right CircleCI alternative

When picking a new CI/CD tool, think about how your team works today.

If you need a simple, **Heroku-like experience** with CI/CD and hosting in one platform, [**Northflank**](https://northflank.com/) is a strong choice — no complex setup or tool sprawl.

For teams embracing **Kubernetes and GitOps**, **Northflank** and **Jenkins** are great options. Northflank makes it easier to get started with less overhead.

Already using **GitHub**? **GitHub Actions** is a solid pick. But if you need more flexibility and control, **Northflank** integrates smoothly and adds a lot of extra power for scaling.

If you need a more **full-stack DevOps solution**, **GitLab CI/CD** is powerful but heavy. **Northflank** delivers a more streamlined experience while covering similar needs, especially for containerized apps.

And for teams deep in the **AWS** ecosystem, **CodePipeline** might work, but **Northflank** offers cross-cloud flexibility without locking you into a single provider.

## Wrapping up

CircleCI paved the way for modern CI/CD, but the landscape has changed. Today’s teams need more than just fast builds — they need platforms that keep up with containers, Kubernetes, and how developers *actually* ship software in 2026.

If you’re feeling the friction — whether it’s YAML fatigue, billing headaches, or toolchain sprawl — it might be time to try something built for the way you work now.

That’s where [Northflank](https://northflank.com/) comes in. It combines CI/CD, deployment, hosting, and database management into one clean platform, designed for containerized apps and modern DevOps workflows. You get the power of Kubernetes without the ops burden, and the flexibility to grow without hidden costs.

Start small, spin up a preview env, or move your whole stack — no pressure. [Northflank](https://northflank.com/) makes it easy to test the waters with generous free tier, solid docs, and a workflow that just makes sense.

[Sign up](https://app.northflank.com/signup), check out the [guides](https://northflank.com/guides), or take it for a spin and see how smooth CI/CD can actually feel.]]>
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</item><item>
  <title>7 best Codefresh alternatives in 2026</title>
  <link>https://northflank.com/blog/codefresh-alternatives</link>
  <pubDate>2025-05-06T15:03:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for a Codefresh alternative? These 7 tools match its CI/CD, Kubernetes-native delivery, and rollout features, no matter if you're focused on pipelines, Helm support, or multi-cluster deployments.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/codefresh_alternatives_06e0464dd7.png" alt="7 best Codefresh alternatives in 2026" />If you’re using [Codefresh](https://codefresh.io/), you already know it’s built on top of ArgoCD. It gives you a hosted GitOps runtime, visual dashboards, and rollout tracking for Kubernetes deployments. That setup works well for many teams, but it might not be what your team needs.

How does your team work? Do you want:

- More control over your CI/CD setup
- Fewer layers between code and deployment
- A delivery model that doesn’t rely on ArgoCD
- A platform you can self-host or run in your own cloud ([BYOC](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment))
- Or just something simpler than what Codefresh offers right now?

If any of that sounds like what you’re looking for, you’re in the right place.

This guide breaks down seven (7) Codefresh alternatives, with enough context to help you decide which one best fits your team.

Let’s get into it.

<div>
	<center>
		<a href="https://app.northflank.com/signup">
			<Button variant={["large", "gradient"]}>
				Find the right platform for your next project >>>
			</Button>
		</a>
	</center>
</div>

<InfoBox className='BodyStyle'>

### Quick look: top Codefresh alternatives in 2026

Short on time? Here’s a quick breakdown of the best Codefresh alternatives and what they’re good at:

1. [**Northflank**](https://northflank.com/) – Kubernetes-native CI/CD, preview environments, and Bring Your Own Cloud (BYOC) support  
2. [**Argo CD**](https://argo-cd.readthedocs.io/) – Open-source GitOps controller that powers Codefresh's delivery engine  
3. [**GitLab CI/CD**](https://about.gitlab.com/stage-devops-lifecycle/continuous-integration/) – Built-in pipelines with Git and Kubernetes integration  
4. [**Octopus Deploy**](https://octopus.com/) – Release orchestration and deployment policies across environments  
5. [**Jenkins X**](https://jenkins-x.io/) – Kubernetes-native automation with GitOps-style triggers  
6. [**Harness**](https://harness.io/) – Enterprise-grade CI/CD with policy engines and canary support  
7. [**Spinnaker**](https://spinnaker.io/) – Multi-cloud app delivery with visual pipeline management

</InfoBox>

## What to look for in a Codefresh alternative

I know you plan to choose the best option for your team and your project, but if you’re unsure what to look out for, this section should help. It'll give you a clearer picture of what you need so you don’t waste time testing the wrong platform or run into avoidable issues later.

So, let’s see a few questions you should ask before making a decision.

### Do you need both CI and CD, or just deployment automation?

Codefresh gives you both pipelines to build and test your containers (CI) and tools to deploy them to Kubernetes (CD). But not every alternative does that. Some tools focus only on deployments. Others will expect you to plug in a separate CI engine like GitHub Actions, Jenkins, or GitLab CI.

![codefresh main 1.png](https://assets.northflank.com/codefresh_main_1_c992154958.png)

If you want an all-in-one setup where builds, tests, and deployments are handled in one place, that narrows your options. If you already have CI sorted and just want a better deployment experience, you have more flexibility.

### Is UI visibility and rollout tracking important to your team?

Some teams are fine running everything through CLI commands or Git-based syncs. Others need a UI to track what’s been deployed, see real-time logs, and understand what’s going on in staging or production.

![codefresh main 2.png](https://assets.northflank.com/codefresh_main_2_c7be68b262.png)

Codefresh gives you dashboards, rollout history, and diffs. If that kind of visibility matters to your workflow, especially when working across environments or with non-technical stakeholders, make sure your next platform includes it. Not every tool gives you that out of the box.

### Do you need support for Helm, Docker, Kubernetes, or multi-cluster delivery?

Codefresh supports all of these, but again, not all alternatives do. If your workloads are packaged as Helm charts or Docker images, or you’re deploying across multiple clusters, your platform has to support those formats natively.

Some tools assume a specific deployment model (like raw manifests or Kustomize), and others might not handle multi-cluster coordination well. Be clear about what your current setup looks like and what it needs to grow.

### Are you planning to use SaaS, self-hosted, or Bring Your Own Cloud (BYOC)?

Some platforms are only available as SaaS, meaning your workloads and deployment logic live on someone else’s infrastructure. That might be fine, but if you have security requirements and compliance rules or just prefer to run things in your own cloud, that rules out some options.

![codefresh main 3.png](https://assets.northflank.com/codefresh_main_3_48f87d667b.png)

[BYOC](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) is a good middle ground; you use the platform’s UI and features, but it runs in your own AWS, GCP, or Azure account. If that’s important to your team, look for tools that explicitly support it.

### Will you need integrations with GitHub, GitLab, or policy management tools?

Whatever platform you switch to should work cleanly with your source control and existing workflows. That means Git-based triggers, support for pull/merge requests, secret management, and access control.

If you rely on branch protections, pipeline policies, or approval workflows, make sure your next CI/CD tool can plug into those without you rebuilding everything from scratch.

## Quick comparison of 7 Codefresh alternatives

If you're short on time and just need a high-level view, the table below gives you a quick comparison of platforms that can replace Codefresh. It covers whether they support CI, how they handle Kubernetes-native delivery, their deployment strategies, and where they run.

The goal here isn’t just to check boxes, it's to show what each tool focuses on, so you can match it with your own setup and workflow.

| **Platform** | **CI support** | **Supported deployment features** | **Hosting options** | **Pricing** |
| --- | --- | --- | --- | --- |
| Northflank | Built-in CI/CD | [Rolling deploys](https://northflank.com/docs/v1/application/release/run-and-manage-releases#roll-back-a-release), [preview envs](https://northflank.com/use-cases/preview-environments-backend-for-kubernetes) | SaaS, [BYOC](https://northflank.com/features/bring-your-own-cloud) | [Free tier + usage-based](https://northflank.com/pricing) |
| Argo CD | CD only | GitOps sync, Helm, rollback | Self-hosted | Free (open source) |
| GitLab CI/CD | GitLab pipelines | K8s agent, merge-based deployments | SaaS, Self-hosted | Free + paid plans |
| Octopus Deploy | No native CI | Helm, manual approval gates | SaaS, Self-hosted | Paid only |
| Jenkins X | Built-in CI | GitOps, preview environments | Self-hosted | Free (open source) |
| Harness | Full CI/CD | Canary, blue-green, policy checks | SaaS | Enterprise pricing |
| Spinnaker | CD only | Rolling deploys, multi-cloud delivery | Self-hosted | Free (OSS) + vendor support |

## 7 best Codefresh alternatives in 2026

Let’s break down each platform in more detail, so you can compare their strengths, understand the trade-offs, and find the one that fits your team best.

### 1. Northflank – For built-in CI/CD, Kubernetes-native delivery, and BYOC support

[Northflank](https://northflank.com/) gives you everything in one place: [CI/CD pipelines](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank), [container builds](https://northflank.com/deploy/run-persistent-and-ephemeral-docker-containers), [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), and Kubernetes-native deployment. You can [ship from Git](https://northflank.com/docs/v1/application/build/build-code-from-a-git-repository) without needing to maintain a GitOps controller, and you get visibility with [visual logs](https://northflank.com/docs/v1/application/observe/view-logs), [rollback history](https://northflank.com/docs/v1/application/databases-and-persistence/backup-restore-and-import-data), and [deployment status](https://northflank.com/docs/v1/application/production-workloads/production-operations) built in.

![northflank's home page-min.png](https://assets.northflank.com/northflank_s_home_page_min_83c67d58f3.png)

You can run Northflank in your own infrastructure using [BYOC](https://northflank.com/features/bring-your-own-cloud) or use it [fully hosted](https://northflank.com/guides/category/deploy-on-northflank).

You also get:

- Integrated CI/CD pipelines with container builds and Git triggers
- Preview environments for testing changes before production
- A clean UI with deployment status, logs, and rollback history
- Run in your own cloud (BYOC) with full GUI support (no CLI setup needed)

Compared to Codefresh, it gives you a similar visual experience but removes the complexity of managing agents or relying on ArgoCD underneath.

> Go with this if you want everything in one place: CI, CD, observability, and your choice of cloud.
> 

*See [Scaling 30,000 deployments with 100% uptime - how Clock uses Northflank](https://northflank.com/blog/scaling-30-000-deployments-with-100-uptime-how-clock-uses-northflank-to-simplify-infrastructure)*

### 2. Argo CD – For teams who want more control and visibility into GitOps workflows

[Argo CD](https://argoproj.github.io/cd/) is the open-source GitOps engine that powers Codefresh’s deployment layer. It is a good option if your team prefers declarative infrastructure and wants full control over how applications sync from Git to Kubernetes.

It doesn’t come with built-in CI or a hosted UI like Codefresh, but it gives you fine-grained control over how you define, sync, and track deployments.

![argocd home page.png](https://assets.northflank.com/argocd_home_page_59fa1af37c.png)

With Argo CD, you get:

- Git-based declarative sync and drift detection
- Native support for Helm, Kustomize, and manifest diffs
- Clear visibility into application state and sync status
- Works well with external CI tools (like GitHub Actions, GitLab CI, Jenkins)
- No vendor lock-in, it’s fully open-source and self-hosted

You’ll need to set up and maintain it yourself, including RBAC, secrets, and (in some cases) multi-cluster config. But if your team is comfortable running Kubernetes operators and wants full observability into application state, Argo CD gives you that without relying on a SaaS layer.

> Go with this if you want GitOps delivery without relying on a hosted tool, and your team is comfortable managing a controller.
> 

*See [Argo CD alternatives – Northflank vs GitOps controllers](https://northflank.com/blog/argo-cd-alternatives-northflank-developer-platform-git-ops-self-service)*

### 3. GitLab CI/CD – For teams already managing code, pipelines, and deploys in GitLab

If you’re already using GitLab for your repos, issues, and merge requests, [GitLab CI/CD](https://docs.gitlab.com/ci/) might be the most straightforward alternative to Codefresh.

It comes with a built-in pipeline engine, container registry, and Kubernetes integration, so you can define your builds, tests, and deploys in one `.gitlab-ci.yml` file without introducing another tool.

![GitLab CICD.png](https://assets.northflank.com/Git_Lab_CICD_fad7b00f4f.png)

With GitLab CI/CD, you get:

- Native CI/CD pipelines with Git-based triggers
- Support for Helm, Docker builds, and Kubernetes deploys
- Built-in GitLab agent for Kubernetes
- Secure variable and secret management
- Clean UI for pipeline status and job logs

It’s not a GitOps tool in the same sense as Argo CD or Codefresh, but you can achieve similar workflows using the Kubernetes agent or by extending pipelines with custom scripts.

GitLab CI/CD is ideal if you’re looking to consolidate everything (code, CI, CD, and visibility) in a single platform.

> Go with this if your team is already in GitLab and wants to avoid combining together extra tools for CI/CD, container builds, and cluster deploys.
> 

*See [Best GitLab alternatives - CI/CD, security, and DevOps tools compared](https://northflank.com/blog/best-gitlab-alternatives)*

### 4. Octopus Deploy – For structured release flows across environments

[Octopus](https://octopus.com/) and Codefresh are now part of the same company, but they’re built for different use cases. While Codefresh leans into GitOps and Kubernetes-native delivery, Octopus focuses on release orchestration, especially when you need gated, step-by-step promotion across dev, staging, and production.

![octopus deploy home page.png](https://assets.northflank.com/octopus_deploy_home_page_ca731185a9.png)

With Octopus Deploy, you get:

- A UI for release approvals, multi-phase deployments, and environment-specific workflows
- Built-in support for Helm, Kubernetes, Terraform, and Azure/AWS infrastructure
- Role-based access controls and audit logs
- Integration with existing CI tools like GitHub Actions, TeamCity, and Jenkins

It doesn’t include a native CI engine, so you’ll still need something else to handle builds and tests. But for teams that care more about managing complex deployment pipelines, Octopus offers coordination features.

> Go with this if your team needs opinionated release flows, manual gates, and infrastructure-as-code support for non-Kubernetes targets.
> 

*See [Octopus Deploy alternatives - release automation and K8s orchestration tools](https://northflank.com/blog/octopus-deploy-alternatives)*

### 5. Jenkins X – For GitOps pipelines with preview environments and Tekton

[Jenkins X](https://jenkins-x.io/) is a Kubernetes-native rework of Jenkins, built to support Git-based workflows, Tekton pipelines, and automated previews. It’s open-source and designed for teams that want to build custom pipelines around GitOps, without a heavy UI or hosted control plane.

![jenkins x home page.png](https://assets.northflank.com/jenkins_x_home_page_ea832a2d5d.png)

With Jenkins X, you get:

- Automated promotion between environments using Git commits
- Preview environments for pull requests
- Support for Tekton as a CI/CD backend
- CLI-first workflows, suited for platform teams building internal tooling

It doesn’t come with a slick UI like Codefresh or Northflank, and it can take effort to maintain. But it's a flexible alternative for engineering teams that want to own their GitOps setup end-to-end and don’t mind a bit of YAML.

> Go with this if you want full control over your GitOps pipelines, use Tekton, and prefer building custom workflows from scratch.
> 

*See [Jenkins alternatives - Jenkins X, GitOps, and modern CI/CD tools](https://northflank.com/blog/jenkins-alternatives-2025)*

### 6. Harness – For enterprise teams that need fine-grained controls and delivery governance

[Harness](https://www.harness.io/) is a CI/CD platform focused on security, reliability, and policy-based delivery, especially for larger engineering organizations. It offers built-in CI pipelines, deployment verification, and controls like manual approvals and audit trails.

![harness.png](https://assets.northflank.com/harness_6ed883f12e.png)

Unlike Codefresh, which is tightly tied to ArgoCD and GitOps, Harness uses its own engine to manage pipelines and rollouts. It supports canary deployments, metrics-based rollbacks, and compliance enforcement.

With Harness, you get:

- Integrated CI and CD with support for Docker, Helm, and Kubernetes
- Built-in approval workflows and policy management
- Rollback verification using observability integrations (like Datadog or Prometheus)
- Secrets management and fine-grained RBAC
- Support for both pipelines-as-code and visual configuration

Harness works best for teams that care about governance and enterprise controls. It’s not open-source and requires a commercial plan, but it gives you a lot of operational confidence at scale.

> Go with this if your org needs strict deployment policies, rollback verification, and full CI/CD in a managed platform.
> 

*See [Top Harness alternatives - CI/CD pipelines and enterprise delivery platforms](https://northflank.com/blog/top-harness-alternatives)*

### 7. Spinnaker – For multi-cloud delivery and advanced deployment pipelines

[Spinnaker](https://spinnaker.io/) was originally built by Netflix for high-scale, multi-cloud deployments, and it’s still one of the most feature-rich CD tools if your team needs to manage complex delivery workflows across Kubernetes, EC2, GCP, and Azure.

![spinnaker home page.png](https://assets.northflank.com/spinnaker_home_page_3a22825196.png)

Unlike Codefresh, which is GitOps-first and Kubernetes-focused, Spinnaker is more flexible when it comes to targets. It includes a UI, deployment strategies like red/black (blue-green), and native integrations with cloud provider APIs.

With Spinnaker, you get:

- Support for multi-cloud deployments across Kubernetes and VM-based infra
- Manual gates, rollout dashboards, and visibility into pipeline execution
- Customizable stages for canary deploys, traffic splitting, and approval steps
- Flexible integrations with CI tools like Jenkins, GitHub Actions, or GitLab
- Open-source core with commercial distributions from vendors like Armory

It’s infrastructure-heavy and not the easiest tool to operate, but it gives you flexibility and control across diverse environments for large-scale delivery teams.

> Go with this if you need advanced deployment strategies across multiple clouds and your team is comfortable maintaining a dedicated CD platform.
> 

*See [Spinnaker alternatives - cloud-native deployment at scale](https://northflank.com/blog/spinnaker-alternatives)*

## Tips for choosing the right Codefresh alternative

You’ve now seen what each platform brings to the table. The best fit depends on your team's prioritization, like speed, control, visibility, or flexibility.

Use these quick takeaways to narrow down your options:

- **You want fast deploys with built-in CI/CD?** → [Northflank](https://northflank.com/blog/scaling-30-000-deployments-with-100-uptime-how-clock-uses-northflank-to-simplify-infrastructure), [GitLab CI/CD](https://northflank.com/blog/best-gitlab-alternatives)
- **You want open-source control over Kubernetes delivery?** → [Argo CD](https://northflank.com/blog/argo-cd-alternatives-northflank-developer-platform-git-ops-self-service), [Jenkins X](https://northflank.com/blog/jenkins-alternatives-2025)
- **You need structured environments and release coordination?** → [Octopus Deploy](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment), [Northflank](https://northflank.com/docs/v1/application/release/configure-a-release-flow)
- **You're in a compliance-heavy org?** → [Harness](https://northflank.com/blog/top-harness-alternatives)
- **You’re deploying to multiple clouds?** → [Spinnaker](https://northflank.com/blog/spinnaker-alternatives)
- **You want BYOC flexibility?** → [Northflank](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment)

## Common questions about Codefresh and alternatives

If you’re still deciding, these are some of the most frequently asked questions about Codefresh, especially from teams comparing it to Jenkins, Argo CD, and other CI/CD platforms.

This section provides clear answers, so you won’t have to keep switching tabs to understand.

### What is Codefresh used for?

Codefresh builds, tests, and deploys containerized applications to Kubernetes. It combines a CI engine with Argo CD for GitOps-style delivery. Teams use it to run pipelines, manage Helm-based deployments, and track rollout progress across clusters, all from a web UI.

### What is the difference between Jenkins and Codefresh?

Jenkins is a general-purpose CI server that supports any automation through plugins. Codefresh, on the other hand, is purpose-built for Kubernetes delivery. It has built-in support for Docker builds, GitOps-style deployments, and progressive rollout features, without relying on dozens of plugins or managing your infra.

### What is the difference between Codefresh and Octopus?

Codefresh is GitOps-first and focuses on container builds, Kubernetes pipelines, and deployment automation. Octopus Deploy is built around structured release flows, approval gates, and environment-specific workflows. They overlap in functionality but are optimized for different delivery models.

### What are the main differences between ArgoCD and Codefresh?

Argo CD is the open-source GitOps controller that powers Codefresh’s deployment layer. Codefresh wraps Argo CD in a hosted UI and adds its CI engine, rollout tracking, and team management features. If you want complete control, you can use Argo CD directly. If you wish to have something hosted with more features out of the box, Codefresh gives you that.

## Run Kubernetes-native CI/CD pipelines without unnecessary platform complexity

Codefresh provides many features out of the box, including CI pipelines, GitOps-based delivery, and visual dashboards. However, depending on how your team works, it might not cover everything you need or introduce layers you’d rather avoid.

You’ve now seen alternatives that give you more control, less setup, or better visibility, from open-source tools like Argo CD and Jenkins X to platforms like **Northflank** that simplify container builds and Kubernetes delivery without relying on external controllers or agents.

Some let you self-host, some support BYOC, and others focus on release coordination or enterprise guardrails.

> The key is picking a tool that fits your workflow, not one that forces you to rebuild it.
> 

If you want to deploy with rollback support, clear visibility into what’s happening, and the option to run everything in your own cloud, Northflank gives you that without the usual overhead.

You can now deploy with visibility, rollback, and BYOC flexibility. Start by [creating an account](https://app.northflank.com/signup).]]>
  </content:encoded>
</item><item>
  <title>Platform April 2025 Release</title>
  <link>https://northflank.com/changelog/platform-april-2025-release</link>
  <pubDate>2025-05-06T09:16:00.000Z</pubDate>
  <description>
    <![CDATA[Explore the latest Northflank updates: enhanced BYOC support, improved template editor, audit log filters, sticky session load balancing, GitLab fixes, UI upgrades, and performance boosts.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_April_changelog_min_cde1546e32.png" alt="Platform April 2025 Release" />### BYOC (Bring Your Own Cloud)
- Added PUT and PATCH endpoints for the BYOC API.  
- Added a BYOC Registry template node.  
- Improved the error messaging on BYOC cluster creation.  
- Fixed organisation BYOC clusters sometimes not appearing correctly during project creation.  
- Fixed miscellaneous issues for BYOC in Organisations.  
- Changing BYOC provider types no longer resets shared fields in the editor.  
- Fixed an issue with BYOC creation where the build plan sometimes could not be selected.
- Added support for public nodes on Azure.  
- Added support for non-zonal Azure regions.  
- Increased the maximum number of node pools to 20, for applicable providers.  
- Node pools can no longer be stuck in an uneditable state when adding a node pool without selecting a disk type.  
- Status errors are now displayed more prominently on the node pool page.  
- Node pool diffs for autoscaling are now ignored if previous node pool updates have failed.  
- Improved the UX for selecting Availability Zone subnets.

### Build & Deployment
- Workload autoscaling can now be configured using custom user metrics. 
- The build list now displays the build engine used, which can be hovered over to display additional data about the build settings.  
- Made some reliability improvements to build settings.


### Audit Logs & Events
- Infrastructure events can now be filtered by container.  
- Filters on the audit log and infrastructure events pages now support selecting multiple options, where applicable.  
- Added an audit log tab for clusters.  
- Made a number of visual improvements to infrastructure events.  
- Improved the performance of the audit log and infrastructure event tables.  
- Accessing filters and pagination on the audit log and infrastructure events pages now updates the URL to make sharing easier.  

### Template Editor
- The template editor now correctly allows references to be used in nested preview and release flow triggers.  
- Fixed the encoding of secret files in the template editor when refs or args are used.  
- The template editor no longer displays a 'view unsaved changes' prompt after saving.  
- The URL in the template editor now stores the currently accessed node, making it easier to share to team members.  
- Improved performance on the template run list page.  
- Cancelling a template run no longer displays as an error on an individual node.  
- Improved the performance of the template editor.  
- Fixed an issue where updating the arguments for a template without editing content could not be submitted when template drafts are enabled.  
- Saving a template no longer displays a commit message prompt when no commitable changes have been made.  
- Moving a workflow in the template editor now correctly displays the update immediately.  
- Template action nodes no longer have issues switching between kinds.
- Fixed an issue where the order of ports was not always consistent during creation which could lead to array indexing issues when referenced later in a template.
- Running a template via the API with non-string arguments no longer causes the run to fail as they are now correctly cast to strings.

### Git & Version Control
- Added support for Azure DevOps as a VCS provider
- Made a number of reliability improvements to release flows to improve the handling of git triggers and version control references.  
- Made performance improvements to version control repository syncing, with considerable improvements for teams with very large numbers of repositories.  
- Made a number of reliability improvements to self-hosted GitLab support.  
- The preview template Message node now works correctly with self-hosted GitLab.

### Networking
- Added support for sticky session load balancing.  
- Added consistent routing to services and subdomains.  
- Made a number of visual improvements to the Networking editor.  
- Improved the error display handling for health checks when switching protocol.

### Addons
- Improved performance of RabbitMQ healthcheck probes.
- Fixed the addon fork backup selector not displaying old backups correctly.  
- Resetting a paused addon no longer causes the addon to get stuck.  

### Secrets & Security
- Added additional options to generating secrets in the UI.  
- The password security prompt should no longer appear multiple times in short succession.  
- Fixed an issue with CLI login for organisation scoped API tokens.

### UI/UX Improvements
- GPU metrics now correctly display for jobs. 
- Improved the help information for timeslicing.  
- User avatars can now be uploaded as an .svg vector file.  
- Improved the display of stack templates on mobile devices.  
- Searching the domains list now stores the state in the URL.  
- Fixed the multiplayer online status sometimes not updating correctly.  


]]>
  </content:encoded>
</item><item>
  <title>7 Best Rancher alternatives in 2026</title>
  <link>https://northflank.com/blog/rancher-alternatives</link>
  <pubDate>2025-05-05T16:15:00.000Z</pubDate>
  <description>
    <![CDATA[Explore 7 top Rancher alternatives in 2026 for simpler Kubernetes management—developer-first platforms with built-in CI/CD, GitOps, and more to help teams ship faster with less overhead.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/rancher_alternatives_e8ad0b0fc5.png" alt="7 Best Rancher alternatives in 2026" />For years, Rancher has been a go-to for managing Kubernetes at scale—especially in ops-heavy, enterprise environments. But not every team needs that level of complexity. As Kubernetes becomes more of a baseline than a badge of honor, developers are rethinking the tools they use to work with it.

If you’re spending more time wiring up infrastructure than building your product, it might be time to look at alternatives.

In this article, you’ll find seven Rancher alternatives worth considering in 2026, and learn what makes each one a better fit for different kinds of teams.

## TL;DR: 7 Rancher alternatives to watch in 2026

Just want the list? Here are 7 Rancher alternatives developers are turning to in 2026:

1. [**Northflank**](https://northflank.com/) – Dev-first Kubernetes platform with built-in CI/CD, databases, preview environments, and Git-based workflows.
2. [**Portainer**](https://www.portainer.io/) – Simple UI for managing containers and clusters, with minimal resource requirements.
3. [**OpenShift**](https://www.redhat.com/en/technologies/cloud-computing/openshift) – Full-stack enterprise Kubernetes with strong RBAC and security.
4. [**Platform9**](https://platform9.com/) – SaaS-managed Kubernetes with a focus on hybrid, edge, and on-prem environments.
5. [**KubeSphere**](https://kubesphere.io/) – Open-source Kubernetes platform with a modular architecture and DevOps tools built-in.
6. [**Mirantis Kubernetes Engine (MKE)**](https://www.mirantis.com/software/mirantis-kubernetes-engine/) – Docker Enterprise’s spiritual successor, with enterprise-grade orchestration and governance.
7. [**Giant Swarm**](https://www.giantswarm.io/) – Fully managed multi-cluster Kubernetes targeting mid-sized and enterprise teams.

## Why look beyond Rancher?

Rancher earned its reputation as an open-source powerhouse for **multi-cluster Kubernetes management**. For platform teams and enterprises running dozens of clusters across clouds or on-prem, it's a strong fit. But for many developer-centric teams—especially those not looking to build a full platform internally—Rancher can feel like more tooling than necessary.

Common developer pain points with Rancher include:

- Requires **significant Kubernetes expertise** to set up and operate effectively.
- Designed more for **ops teams** than dev teams.
- Lacks built-in features like **CI/CD, managed databases, or preview environments**, so teams end up integrating multiple tools manually.
- Can feel **bloated** or overkill for simpler use cases or smaller projects.

If you're spending more time managing the platform than building on it, it might be time to consider a more modern alternative.

## What to look for in a Rancher alternative

Not every team needs to run dozens of Kubernetes clusters. In fact, many just need a platform that **abstracts complexity**, handles **infrastructure automatically**, and lets developers ship confidently without YAML deep-dives.

Here’s what to look for:

- **Developer-first workflows** – Git-driven deployments, automatic builds, preview environments, and one-click rollbacks.
- **Built-in services** – Managed databases, cron jobs, workers, and queues—no extra plugins required.
- **Integrated CI/CD** – Don’t bolt it on. The platform should include CI pipelines, container builds, and deployments natively.
- **Sane defaults** – Secure by default, with HTTPS, secrets management, and sensible RBAC(Role-Based Access Control).
- **Observability baked in** – Logs, metrics, and alerts should be first-class—not an afterthought.
- **Fast feedback loops** – Reduce the time between writing code and seeing it live.

## The top 7 Rancher alternatives in 2026

Looking for a simpler way to run Kubernetes? These alternatives to Rancher are making waves in 2026, each offering a fresh take on how teams build, deploy, and scale with less overhead.

### 1. Northflank – **Kubernetes without the complexity**

[Northflank](https://northflank.com/) is what Rancher *wants* to be for developers. It gives you all the power of Kubernetes—container orchestration, service discovery, and autoscaling—but wraps it in a developer-first experience.

No need to manage YAML files by hand. With Git-integrated workflows, built-in CI, automatic SSL, and managed databases, Northflank helps you go from code to running service in minutes. It also handles the heavy lifting like horizontal scaling, persistent storage, and background workers with a clean, intuitive UI and APIs.

![image (93).png](https://assets.northflank.com/image_93_2b254840ee.png)

**Key features:**

- Kubernetes-powered, full-stack platform
- Deploy containers, databases, and scheduled jobs
- [Bring your own cloud (AWS, GCP, Azure, etc.)](https://northflank.com/features/bring-your-own-cloud)
- CI/CD integration, real-time logs, with a developer-friendly and consistent experience across UI, CLI, API, and GitOps
- GPU support for AI workloads
- Automatic preview environments and seamless promotion to dev, staging, and production

**Best for:**

- Dev teams building APIs, microservices, and containerized web apps
- SaaS products needing multi-service architectures
- Teams looking for a fast, clean alternative to older, more rigid platforms like Rancher

**Potential drawbacks:**

- Highly experienced DevOps teams might find it restrictive compared to directly managing raw Kubernetes clusters. It’s a fine balance between ease of use, flexibility, and customization; that line differs for every organization.
- Less established compared to legacy platforms like Rancher.

*See how [Weights company uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### **2. Portainer**

[Portainer](https://www.portainer.io/) is a **lightweight container management UI** for Docker and Kubernetes environments. It’s not a full-fledged platform like others here, but it offers a user-friendly way to manage container infrastructure visually.

![](https://assets.northflank.com/image_71_4bed621467.png)

**Key features:**

- Simple dashboard for managing containers and clusters.
- Works with both Docker and Kubernetes.
- Role-based access control and team management.
- Minimal resource requirements.

**Best for:**

Self-hosters and small teams that want a visual layer over their existing container infrastructure.

**Potential drawbacks:**

- Lacks deeper DevOps features like CI/CD or GitOps.
- Not intended for large-scale enterprise workloads.

[Read more on Portainer](https://northflank.com/blog/portainer-alternatives)

### **3. OpenShift**

[OpenShift](https://www.redhat.com/en/technologies/cloud-computing/openshift) is a **comprehensive Kubernetes platform** developed by Red Hat. It’s designed for hybrid and multi-cloud deployments, offering strong security, compliance features, and enterprise support.

![](https://assets.northflank.com/image_2025_05_01_T201538_690_d20ad45e54.png)

**Key features:**

- Full-stack Kubernetes with integrated developer tools.
- Native CI/CD with Tekton and support for pipelines.
- Robust RBAC, policy enforcement, and compliance capabilities.
- Deep integration with Red Hat Linux and other enterprise tools.

**Best for:**

Large enterprises already invested in Red Hat infrastructure or needing high-security, compliance-ready Kubernetes environments.

**Potential drawbacks:**

- Complex to set up and maintain without dedicated platform teams.
- Can be resource-intensive and expensive.

[Read more on OpenShift](https://northflank.com/blog/best-open-shift-alternatives-finding-the-right-kubernetes-platform)

### 4. Platform9

[Platform9](https://platform9.com/) is a **managed Kubernetes solution** designed for **on-premises, edge, and hybrid cloud environments**. Unlike fully cloud-hosted Kubernetes services, Platform9 allows organizations to run Kubernetes anywhere while benefiting from a **SaaS-based management model**.

![](https://assets.northflank.com/image_40_6281cf93cd.png)

**Key features:**

- **Fully managed Kubernetes** with a 99.9% uptime SLA.
- **Works across on-prem, hybrid, and edge environments**.
- **Zero-touch upgrades and automated operations**.
- **Open-source foundation** with no vendor lock-in.

**Potential drawbacks:**

- Smaller market share compared to OpenShift, which may affect long-term support.
- Reliance on a SaaS-based model may not be suitable for some enterprises.

### 5. KubeSphere

[KubeSphere](https://kubesphere.io/) is an open-source layer on top of Kubernetes that adds a dashboard and a suite of DevOps tools. It’s modular and flexible, especially for teams that already run their own clusters and want to gradually enhance them with UI and automation.

![image - 2025-05-05T171518.934.png](https://assets.northflank.com/image_2025_05_05_T171518_934_726d236200.png)

**Key features:**

- Visual interface for Kubernetes resource management
- Built-in support for CI/CD, observability, and multi-tenancy
- Pluggable architecture: enable only what you need
- Self-hosted and fully open-source

**Best for:**

- Teams already managing their own clusters
- Organizations comfortable maintaining infrastructure but wanting better UX

**Potential drawbacks:**

- Still requires ops knowledge to run and scale effectively
- UI is improving, but the overall experience can feel fragmented
- Lacks the end-to-end polish of a fully integrated platform.

### 6. Mirantis Kubernetes Engine (MKE)

[Mirantis Kubernetes Engine (formerly Docker Enterprise)](https://www.mirantis.com/software/mirantis-kubernetes-engine/) is a governance-heavy platform designed for ops teams managing secure, large-scale deployments. It supports Kubernetes and Docker Swarm, which makes it good for legacy environments—but it’s not designed for fast-moving dev teams.

![image - 2025-05-05T171515.275.png](https://assets.northflank.com/image_2025_05_05_T171515_275_b20ecc1d70.png)

**Key features:**

- Secure container orchestration with strong compliance tooling
- RBAC, image scanning, and LDAP integration
- Hybrid support for Kubernetes and Swarm
- Enterprise support and SLAs

**Best for:**

- Organizations with strict compliance needs and legacy systems
- Large ops teams that need tight infrastructure control

**Potential drawbacks:**

- Dev teams may find it slow and heavy to work with
- Not developer-first; built around ops workflows and governance
- More complexity than many modern teams need

### 7. Giant Swarm

[Giant Swarm](https://www.giantswarm.io/) offers **white-glove Kubernetes management** for companies running dozens or even hundreds of clusters. It’s tailored for enterprises that want Kubernetes benefits without handling the infrastructure themselves—but it comes with enterprise-level complexity (and pricing).

![image - 2025-05-05T171511.817.png](https://assets.northflank.com/image_2025_05_05_T171511_817_278e3c5832.png)

**Key features:**

- Multi-cluster and multi-cloud management
- GitOps-native deployments
- Hands-on engineering support
- Pre-integrated observability and compliance tools

**Best for:**

- Large orgs with complex Kubernetes footprints
- Teams looking for long-term platform engineering support

**Potential drawbacks:**

- Expensive and tailored for large-scale needs
- Not designed for fast-moving product teams or startups
- Less flexibility for smaller teams or projects

## **How to choose the right Rancher alternative**

Choosing a Rancher alternative isn’t just about features—it’s about fit.

Ask yourself:

**Are you building a platform or building a product?**

If your team wants to own every layer of the stack, tweak every RBAC policy, and manage clusters across environments—something like OpenShift or Mirantis might make sense.

But if you’re a startup, SaaS team, or modern dev org that just wants to ship faster without spending cycles gluing tools together, it’s time to look beyond traditional platforms.

Here’s how to narrow it down:

- **Want to skip YAML and ship from Git?** Look for platforms with native GitOps and CI/CD baked in.
- **Need managed databases, background workers, or preview environments out of the box?** Choose tools that treat those as first-class, not plugins.
- **Working with a lean team, or no dedicated DevOps?** Prioritize simplicity, automation, and sane defaults.
- **Care about developer experience?** You shouldn’t need a PhD in Kubernetes to deploy your API.

That’s where a tool like [**Northflank**](https://northflank.com/) stands out. It gives developers what they actually need: containers, builds, databases, environments—all wired up, scalable, and ready to go.

## Conclusion

Kubernetes is no longer the exclusive domain of platform engineers and enterprise ops teams. Developers today want tools that let them deploy fast, scale with confidence, and stay focused on building—not wiring up infrastructure.

Rancher paved the way for Kubernetes at scale, but it was built for a different era—and a different audience.

If you're tired of YAML overload, pipeline patchwork, or managing three tools just to ship a feature, it's time to rethink what your platform should do for you.

**Northflank gives you the power of Kubernetes without the complexity.** Built-in CI/CD, preview environments, managed databases, and a clean Git-based workflow—no DevOps team required.

🚀 **Ready to ship faster with fewer headaches?** [Try Northflank for free →](https://app.northflank.com/signup)]]>
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  <title>OpenShift vs Kubernetes: What should you use to ship products in 2026?</title>
  <link>https://northflank.com/blog/openshift-vs-kubernetes</link>
  <pubDate>2025-05-01T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Comparing Kubernetes to OpenShift is a bit like comparing apples to oranges, or maybe more accurately, apples to an entire fruit salad. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/openshift_vs_kubernetes_fab4ac9211.png" alt="OpenShift vs Kubernetes: What should you use to ship products in 2026?" />Comparing Kubernetes to OpenShift is a bit like comparing apples to oranges, or maybe more accurately, apples to an entire fruit salad. 

Kubernetes isn't a platform. It's a container orchestration framework: a powerful toolkit for managing containerized workloads at scale. OpenShift builds directly on Kubernetes but wraps it in an enterprise-ready package. It simplifies many operational tasks but inevitably sacrifices flexibility.

When deciding between Kubernetes and OpenShift, teams face a key trade-off: total control versus ease-of-use. Kubernetes offers deep configurability but comes with substantial complexity, while OpenShift provides structured simplicity but imposes rigid workflows. 

But what if you didn't need to compromise?

## Kubernetes: Powerful, yet complex

![kubernetes.png](https://assets.northflank.com/kubernetes_2b0a85eb3b.png)

Kubernetes was never designed to be simple. It was built to solve complex infrastructure problems at the scale of Google. What that means in practice is a system composed of dozens of moving parts: pods, nodes, controllers, CRDs, operators, and so on. Every piece can be customized, extended, and tuned. That power is what makes Kubernetes the de facto standard, but also what makes it hard to use.

Setting up a working Kubernetes environment isn't just about installing it. You need to provision a cluster, configure ingress, manage secrets, set up network policies, configure storage classes, integrate observability tools, enforce RBAC, and stitch together CI/CD workflows. That's before you even deploy your actual application.

Even managed services like GKE, EKS, or AKS offload only part of that burden. You still need to be an infrastructure engineer to operate it well. Kubernetes doesn't give you batteries included. It gives you an instruction manual for building your own power grid.

### Strengths of Kubernetes

Kubernetes stands out for:

- **Customization:** Kubernetes gives users extensive control to adapt it precisely to their needs.
- **Portability:** It's cloud-agnostic, enabling migration of workloads across clouds and on-premise infrastructures.
- **Community and ecosystem:** With broad community support, numerous resources, plugins, and extensions, Kubernetes enjoys unparalleled ecosystem backing.

### Weaknesses of Kubernetes

Kubernetes' complexity comes with several challenges:

- **Steep learning curve:** Initial setup and ongoing management require deep technical expertise.
- **Operational overhead:** Running Kubernetes clusters involves considerable operational burden, often requiring dedicated DevOps teams.
- **Integration complexity:** Essential functionalities, including CI/CD pipelines, security enforcement, and monitoring, require manual integration.

## OpenShift: Kubernetes made enterprise-ready

![redhat.png](https://assets.northflank.com/redhat_aa067b7a9c.png)

Developed by Red Hat, OpenShift extends Kubernetes by incorporating enterprise-grade tools and structured workflows. It's positioned as Kubernetes made simpler, targeted at organizations that need comprehensive solutions straight out of the box.

OpenShift integrates continuous integration and delivery (CI/CD), built-in security policies, compliance features, monitoring, and logging. Its structured workflows reduce setup and operational complexity significantly.

Teams usually adopt it because they're already embedded in the Red Hat ecosystem or have specific compliance requirements that make OpenShift the safe choice. 

![reddit1.png](https://assets.northflank.com/reddit1_7bc83f34d3.png)
![reddit2.png](https://assets.northflank.com/reddit2_85c2e60c69.png)

<div>
  <center>
<a href="https://app.northflank.com/signup">
  <Button variant={["large", "gradient"]}>
    Find the right platform to ship your products
  </Button>
</a>
  </center>
</div>

### Strengths of OpenShift

OpenShift offers:

- **Integrated solutions:** CI/CD pipelines, security frameworks, and observability reduce the need for additional integrations.
- **Ease-of-use:** Structured workflows simplify managing complex deployments and enforcing best practices.
- **Red Hat support:** Robust, enterprise-grade support provides peace of mind for organizations needing reliability and accountability.

### Weaknesses of OpenShift

While Red Hat’s messaging frames OpenShift as a full platform for modern app development, there are blind spots. OpenShift is essentially a Kubernetes distribution. 

It manages orchestration and infrastructure well, but falls short when it comes to the application layer. It doesn’t help teams figure out how to build, test, and ship applications—just how to run them. It’s surprisingly unopinionated where it matters.

And it’s not easy to operate. OpenShift's operational burden is significant: standing up a cluster requires deep knowledge of IPI/UPI installs; upgrades are painful and often break CRDs or Operators unless done in strict version sequences; observability tooling is your responsibility to maintain; and backup/recovery is DIY (typically handled through tools like Velero).

On top of that, it demands a heavy VM or bare-metal footprint, especially for control plane nodes. All of this adds up to an expensive and fragile setup that’s overkill for many teams.

The developer experience isn’t much better. The UX is too complex, and often assumes the user is an infra engineer, not a developer trying to ship a service.

In addition:

- **Flexibility constraints:** Its structured nature limits customization and can feel restrictive for developers seeking full control.
- **Higher costs:** Subscription fees for licenses and support increase operational costs considerably compared to open-source Kubernetes.
- **Vendor lock-in risks:** OpenShift ties users closely to the Red Hat ecosystem, potentially creating dependency.

<InfoBox className='BodyStyle'>

## See more OpenShift alternatives [here](https://northflank.com/blog/best-open-shift-alternatives-finding-the-right-kubernetes-platform).

</InfoBox>

## Side-by-side comparison

To provide clarity, let's examine Kubernetes and OpenShift side by side:

| **Feature** | **Kubernetes** | **OpenShift** |
| --- | --- | --- |
| **Complexity** | High; requires deep operational expertise | Moderate; designed for simpler operations |
| **Flexibility** | Very High; customizable in detail | Moderate; structured, prescriptive workflows |
| **Integrated CI/CD** | No; external tools needed | Yes; built-in pipelines |
| **Security & compliance** | Manual configuration and integrations needed | Built-in enterprise-level security and compliance |
| **Cost** | Lower (open-source); costs from tools/support | Higher; subscription-based licensing |
| **Support & reliability** | Community-based or via cloud vendors | Robust enterprise support via Red Hat |

## Introducing Northflank: Best of both worlds

![image.png](https://assets.northflank.com/image_90cb168803.png)

Kubernetes is overpowered for most developers. It's a low-level abstraction that was never intended to be user-friendly. OpenShift attempts to solve this by wrapping Kubernetes in a friendlier UI and opinionated tooling, but it swings too far in the other direction.

[Northflank](https://northflank.com/) sits in the middle. It doesn't try to reinvent Kubernetes, nor does it hide it. Instead, it makes Kubernetes accessible by abstracting complexity where it matters, while still allowing teams to access the underlying primitives when they need to. This balance is what makes Northflank stand out as a real alternative to OpenShift.

Northflank addresses the limitations and builds on the strengths of both Kubernetes and OpenShift. As a fully managed Workload Delivery Platform, Northflank combines Kubernetes' flexibility with OpenShift's ease of operation.

### Flexibility without complexity

Northflank is opinionated about one thing: you should only care about your workloads. 

The rest, provisioning infrastructure, stitching together observability, setting up CI/CD, is handled for you, with sane defaults. But if you want to go deep, you can. Every deployment can be customized, every service tweaked. You can bring your own cloud, plug into your own registry, and define how jobs run. Unlike OpenShift, Northflank doesn't force you to do things a specific way, it just makes the path of least resistance the right one.

Northflank retains Kubernetes' flexibility, empowering users to design custom workflows, integrate preferred tooling, and adjust their operational setups easily. It eliminates Kubernetes' daunting operational overhead by providing managed solutions for common pain points like CI/CD integration, logging, monitoring, and security.

### Enterprise readiness without lock-in

Enterprise features usually come at the cost of flexibility. OpenShift is a clear example of this—comprehensive in scope but rigid in execution. Northflank takes a different stance. It delivers the security, compliance, and multi-cloud capabilities enterprises expect, but without imposing a heavy-handed architecture or forcing teams into specific workflows. You get serious infrastructure without the vendor gravity well.

### What makes Northflank stand out

Most OpenShift alternatives fall into two categories: they're either raw Kubernetes management layers (like Rancher or Tanzu), or they're managed services tightly coupled to a cloud provider (like GKE or EKS). Northflank offers a third path: full workload lifecycle automation across any environment, with a platform-first experience that developers actually enjoy using.

It doesn’t just support Kubernetes, it elevates it with smart defaults, templated deployments, and a dev-friendly abstraction that doesn’t sacrifice power.

- **Developer efficiency:** Northflank's intuitive UI and CLI simplify operations, reducing the Kubernetes learning curve.
- **Advanced security and compliance:** Built-in security measures rival OpenShift's, without vendor lock-in concerns.
- **Cost-efficiency:** Competitive pricing provides excellent value compared to the substantial costs of OpenShift licenses.
- **Operational Simplicity:** Reduces the need for extensive DevOps teams by automating and simplifying routine tasks.

### How Northflank handles what OpenShift only packages

Red Hat OpenShift bundles dozens of open-source tools under one roof:

- CI/CD via Tekton or Jenkins
- Monitoring via Prometheus
- Logging via Fluentd or Loki
- Image builds via Source-to-Image, and so on.

While these integrations are tested and validated by Red Hat, they are still distinct, separately maintained tools that need to be upgraded, patched, and occasionally debugged in isolation.

It’s more of a toolkit than a platform. You still operate Tekton. You still manage Prometheus. You still have to understand how each component works and what to do when they don’t. Red Hat helps by standardizing integrations, but it doesn’t eliminate the complexity. In fact, it often adds another layer on top with its Operator Lifecycle Manager and various Red Hat-specific interfaces.

Northflank takes a fundamentally different approach. Instead of handing you a set of tools and expecting you to figure out how they work together, it provides a fully integrated, purpose-built platform. CI/CD is just there. Observability is built-in. Secrets, services, environments, jobs, deployment workflows, they're all part of the same cohesive system, designed to work together seamlessly.

Northflank devlivers outcomes: deployments that work, metrics that are useful, pipelines that don’t need babysitting. You don’t need to know what’s under the hood unless you want to.

This is the difference between an orchestrated toolchain and a product. OpenShift gives you a stack to manage. Northflank gives you a platform that just works out of the box.

### When / where using Northflank makes sense

Northflank is particularly beneficial for:

- Startups and SMEs that have outgrown basic container management tools but lack resources for complex Kubernetes setups.
- Enterprise teams looking for Kubernetes-level flexibility without sacrificing compliance or ease of use.
- Organizations wishing to avoid vendor lock-in risks posed by traditional enterprise solutions.

## Choosing the right path forward

If you're choosing between Kubernetes and OpenShift, you're really choosing between two extremes: raw power with zero guardrails, or guardrails so thick you forget you're even using Kubernetes. Neither feels particularly modern.

Northflank offers a way out of that false binary. It's for teams that want power *and* speed, flexibility *and* guidance. It’s Kubernetes, minus the hair-pulling. It’s what OpenShift could have been if it had been designed for developers first, not enterprise procurement teams.

If you're tired of wrestling YAML but don't want to give up control, Northflank gives you a platform that just works, until you want to tinker. And when you do, it gets out of your way. That’s the path forward.

[**Get started now**](https://app.northflank.com/signup).]]>
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</item><item>
  <title>7 Top VMware Tanzu alternatives for DevOps in 2026</title>
  <link>https://northflank.com/blog/vmware-tanzu-alternatives</link>
  <pubDate>2025-05-01T19:00:00.000Z</pubDate>
  <description>
    <![CDATA[Struggling with VMware Tanzu's complexity? You're not alone. Developers are moving toward simpler, scalable platforms. This guide covers 7 top Tanzu alternatives for 2026, including Northflank.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/vmware_tanzu_alternatives_71c625d6a4.png" alt="7 Top VMware Tanzu alternatives for DevOps in 2026" />If you've ever tried to wrangle [VMware Tanzu](https://www.vmware.com/products/app-platform/tanzu) into shape, you probably understand the pain of over-complication. It’s powerful, sure, but it’s also sprawling, complex, and often better suited for enterprise-scale teams with dedicated platform engineers. For many developers and DevOps teams, Tanzu feels like using a sledgehammer to hang a picture frame.

As we head deeper into 2026, there's a noticeable shift: teams are looking for lightweight, developer-friendly platforms that still deliver powerful Kubernetes orchestration, automation, and scalability but without the heavy overhead.

In this article, we’ll explore **what the developer community is saying about VMware Tanzu**, what to look for in an alternative, and introduce **seven platforms that are gaining momentum**, especially [**Northflank**](https://northflank.com/), which is becoming a favorite for teams that want speed without compromise.

## TL;DR: 7 VMware Tanzu alternatives to watch in 2026

Here are the top VMware Tanzu alternatives developers are turning to this year:

1. [**Northflank**](https://northflank.com/) – Dev-first Kubernetes platform with built-in CI, database hosting, and automatic deployments.
2. [**Platform.sh**](https://platform.sh/) – Application-centric PaaS focused on Git-based workflows.
3. [**Red Hat OpenShift**](https://www.redhat.com/en/technologies/cloud-computing/openshift) – Enterprise-grade Kubernetes platform with strong hybrid cloud and security features.
4. [**Platform9**](https://platform9.com/) – SaaS-managed Kubernetes with a focus on hybrid and edge deployments.
5. [**Portainer**](https://www.portainer.io/) – Lightweight container management tool with a focus on simplicity.
6. [**Rancher**](https://www.rancher.com/) – Enterprise-grade Kubernetes management, still lighter than Tanzu.
7. [**Dokku**](https://dokku.com/) – Minimalist, self-hosted PaaS for smaller-scale deployments.

## What are developers saying about VMware Tanzu?

If you follow developer discussions on forums like Reddit, Hacker News, or X, the consensus on VMware Tanzu is mixed. Here’s a snapshot of what people are saying:

*“Great for large enterprises that are already committed to VMware. But it’s way too complex and pricey for smaller teams.”*

*“Integration with vSphere is seamless, but Tanzu still feels like it's in beta—documentation could definitely be better.”*

*“Love the idea, but licensing is a nightmare. I’m constantly trying to figure out what’s included and what’s not.”*

There are definitely some recurring themes. Many developers find Tanzu’s integration with VMware products to be a major selling point for large-scale environments, but there’s also frustration over the complexity and steep learning curve. On Reddit, users shared concerns like:

*“Do I really need Tanzu if I just want to manage Kubernetes clusters?”*

*“Trying to get pricing info was like pulling teeth. It’s almost like they don’t want you to know.”*

*“Good concept, but for the price, I’d rather just use Rancher and save the headache.”*

Ultimately, VMware Tanzu is a mixed bag. While it's perfect for large companies already using VMware infrastructure, smaller teams are still left scratching their heads over pricing, complexity, and whether it’s worth the cost.

[*You can read the original thread on Reddit here.*](https://www.reddit.com/r/kubernetes/comments/17w9h4d/thoughts_on_vmware_tanzu/)

## What to look for in VMware Tanzu alternatives

Choosing a VMware Tanzu alternative isn’t just about cutting costs or reducing complexity—it’s about finding a platform that aligns with how your team actually builds and ships software. Tanzu is powerful, but for many teams, it introduces friction: too much overhead, too many moving parts, and too steep a learning curve.

A good alternative should offer Kubernetes where it makes sense and get out of the way where it doesn’t.

**Key features to look for:**

- **Native Kubernetes support** – Kubernetes is the standard, but not everyone wants to write YAML or manage clusters manually. Look for platforms like [Northflank](https://northflank.com/) that offer Kubernetes under the hood, with smart abstractions that let developers focus on code, not container orchestration.
- **Built-in CI/CD pipelines** – Integration with your Git provider and automated pipelines should be table stakes. You shouldn’t need to duct tape a CI system to your deployment process just to push an update.
- **First-class database and service orchestration** – A modern platform should handle not just code, but the services around it: managed databases, queues, workers, cron jobs. These should be built-in, not bolt-ons.
- **Git-based workflows** – The best platforms treat Git as the source of truth. Whether it’s deploying on push, spinning up preview environments, or rolling back via commits, Git should drive the workflow.
- **Effortless scalability** – Whether you're scaling up for a product launch or spinning down idle services, your platform should adapt without requiring hands-on tuning of autoscalers or node pools.
- **Integrated observability** – Logs, metrics, and alerts should be baked in. You shouldn’t have to juggle third-party tools to understand what’s happening in production.
- **Sane, secure defaults** – From day one, you should be able to deploy a service securely, with automatic HTTPS, secrets management, and role-based access control in place. The platform should help you follow best practices without making it feel like a chore.

## The top 7 VMware Tanzu alternatives in 2026

### 1. **Northflank** – Kubernetes without the complexity

[Northflank](https://northflank.com/) is what Tanzu *wants* to be for developers. It gives you all the power of Kubernetes—container orchestration, service discovery, and autoscaling—but wraps it in a developer-first experience.

No need to manage YAML files by hand. With Git-integrated workflows, built-in CI, automatic SSL, and managed databases, Northflank helps you go from code to running service in minutes. It also handles the heavy lifting like horizontal scaling, persistent storage, and background workers with a clean, intuitive UI and APIs.

![image (93).png](https://assets.northflank.com/image_93_2b254840ee.png)

**Key features:**

- Kubernetes-powered, full-stack platform
- Deploy containers, databases, and scheduled jobs
- [Bring your own cloud (AWS, GCP, Azure, etc.)](https://northflank.com/features/bring-your-own-cloud)
- CI/CD integration, real-time logs, with a developer-friendly and consistent experience across UI, CLI, API, and GitOps
- GPU support for AI workloads
- Automatic preview environments and seamless promotion to dev, staging, and production

**Best for:**

- Dev teams building APIs, microservices, and containerized web apps
- SaaS products needing multi-service architectures
- Teams looking for a fast, clean alternative to older, more rigid platforms like Tanzu

**Potential drawbacks:**

- Highly experienced DevOps teams might find it restrictive compared to directly managing raw Kubernetes clusters. It’s a fine balance between ease of use, flexibility, and customization; that line differs for every organization.
- Less established compared to legacy platforms like VMware tanzu or Rancher.

*See how [Weights company uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### 2. Platform.sh

[Platform.sh](https://platform.sh/) is a **PaaS focused on Git-based environments**. Every Git branch can spin up its own isolated infrastructure, making it a great choice for teams that prioritize preview environments, testing workflows, and automation.

![image (100).png](https://assets.northflank.com/image_100_ba0eb5d43e.png)

**Key features:**

- Git-driven infrastructure with instant preview environments.
- Integrated CI/CD and environment cloning.
- Supports a wide range of runtimes (PHP, Python, Node.js, etc.).
- Multi-cloud deployment options.

**Best for:**

Agencies and teams managing multiple application versions in parallel, especially with PHP-heavy stacks.

**Potential drawbacks:**

- Less Kubernetes-native than other options.
- Limited flexibility for custom orchestration.

[Read more on Platform.sh](https://northflank.com/blog/platformsh-alternatives)

### 3. **OpenShift**

[OpenShift](https://www.redhat.com/en/technologies/cloud-computing/openshift) is a **comprehensive Kubernetes platform** developed by Red Hat. It’s designed for hybrid and multi-cloud deployments, offering strong security, compliance features, and enterprise support.

![image - 2025-05-01T201538.690.png](https://assets.northflank.com/image_2025_05_01_T201538_690_d20ad45e54.png)

**Key features:**

- Full-stack Kubernetes with integrated developer tools.
- Native CI/CD with Tekton and support for pipelines.
- Robust RBAC, policy enforcement, and compliance capabilities.
- Deep integration with Red Hat Linux and other enterprise tools.

**Best for:**

Large enterprises already invested in Red Hat infrastructure or needing high-security, compliance-ready Kubernetes environments.

**Potential drawbacks:**

- Complex to set up and maintain without dedicated platform teams.
- Can be resource-intensive and expensive.

[Read more on OpenShift](https://northflank.com/blog/best-open-shift-alternatives-finding-the-right-kubernetes-platform)

### **4. Platform9**

[Platform9](https://platform9.com/) is a **managed Kubernetes solution** designed for **on-premises, edge, and hybrid cloud environments**. Unlike fully cloud-hosted Kubernetes services, Platform9 allows organizations to run Kubernetes anywhere while benefiting from a **SaaS-based management model**.

![](https://assets.northflank.com/image_40_6281cf93cd.png)

**Key features:**

- **Fully managed Kubernetes** with a 99.9% uptime SLA.
- **Works across on-prem, hybrid, and edge environments**.
- **Zero-touch upgrades and automated operations**.
- **Open-source foundation** with no vendor lock-in.

**Potential drawbacks:**

- Smaller market share compared to OpenShift, which may affect long-term support.
- Reliance on a SaaS-based model may not be suitable for some enterprises.

### 5. Portainer

[Portainer](https://www.portainer.io/) is a **lightweight container management UI** for Docker and Kubernetes environments. It’s not a full-fledged platform like others here, but it offers a user-friendly way to manage container infrastructure visually.

![image (71).png](https://assets.northflank.com/image_71_4bed621467.png)

**Key features:**

- Simple dashboard for managing containers and clusters.
- Works with both Docker and Kubernetes.
- Role-based access control and team management.
- Minimal resource requirements.

**Best for:**

Self-hosters and small teams that want a visual layer over their existing container infrastructure.

**Potential drawbacks:**

- Lacks deeper DevOps features like CI/CD or GitOps.
- Not intended for large-scale enterprise workloads.

[Read more on Portainer](https://northflank.com/blog/portainer-alternatives)

### 6. Rancher

[Rancher](https://rancher.com/) is an **open-source Kubernetes platform** that simplifies cluster provisioning, monitoring, and governance. It supports multi-cluster setups and works well for organizations looking for an open solution that avoids vendor lock-in.

![image - 2025-05-01T201700.580.png](https://assets.northflank.com/image_2025_05_01_T201700_580_5cbffeaddd.png)

**Key features:**

- Centralized management of multiple Kubernetes clusters.
- Role-based access control and user authentication.
- Compatible with any certified Kubernetes distribution.
- Fully open-source and extensible.

**Best for:**

Enterprises and platform teams that want to retain full control of Kubernetes infrastructure without VMware or cloud vendor lock-in.

**Potential drawbacks:**

- More focused on operations than developer workflows.
- Requires some Kubernetes expertise to operate effectively.

### 7. Dokku

[Dokku](https://dokku.com/) is a **minimal, open-source PaaS** that acts like a lightweight version of Heroku. It’s designed for developers who want to deploy apps easily on their own servers using Git.

![image - 2025-05-01T201703.619.png](https://assets.northflank.com/image_2025_05_01_T201703_619_108695f886.png)

**Key features:**

- Simple, Git-push deployments for web apps.
- Plugin ecosystem for databases, SSL, and more.
- Very low system requirements.
- Full control over infrastructure.

**Best for:**

Tinkerers, indie developers, and small apps with simple deployment needs.

**Potential drawbacks:**

- Not suitable for complex or large-scale applications.
- Limited scalability and enterprise-grade features.

## How to choose the right Tanzu alternative

Start with your team’s real needs, not what sounds good in a cloud keynote.

Ask:

- Do we want to manage Kubernetes directly or abstract it?
- How much of our deployment pipeline do we want handled for us?
- Do we need managed databases or will we bring our own?
- How important is pricing transparency and cost control?

If you’re a developer or DevOps team that wants to **deploy quickly, scale automatically, and focus on product, not platform plumbing,** then a tool like [**Northflank**](https://northflank.com/) hits the sweet spot. It’s built with modern workflows in mind and removes the friction that tools like Tanzu often introduce.

## Conclusion

VMware Tanzu will always have a place in large enterprise environments, but 2026 is clearly the year of **simplified platforms** that give developers more power with less overhead. Whether you're a startup, a growing SaaS team, or a solo dev, there's a Tanzu alternative that fits your stack and your workflow.

Among them, [**Northflank**](https://northflank.com/) is emerging as the most developer-aligned option: clean interface, smart defaults, full Kubernetes power under the hood, and no need to hire a platform team to make it work.

[Try it once, and you may never look at Tanzu again.](https://app.northflank.com/signup)]]>
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  <title>7 best Flux CD alternatives in 2026</title>
  <link>https://northflank.com/blog/flux-cd-alternatives</link>
  <pubDate>2025-05-01T18:34:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for an alternative to Flux CD? Check out 7 GitOps-capable tools in 2026, from Argo CD to modern platforms like Northflank, without the controller overhead.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/flux_cd_alternatives_9770826913.png" alt="7 best Flux CD alternatives in 2026" />*When was the last time your team had to trace a deployment manually because Flux CD didn’t expose what was applied, when, or why it failed?*

That kind of issue becomes more common as teams scale and need clearer visibility across environments.

[Flux CD](https://fluxcd.io/) often works well for smaller setups. But as teams grow, the limitations become harder to ignore:

- No built-in UI to track sync status or changes
- Limited visibility across environments and clusters
- The ongoing effort of maintaining GitOps controllers as usage grows

You might’ve run into one of those recently.

And with Weaveworks (the company behind Flux) shutting down in 2024, even long-time users are questioning the project’s long-term future.

As one developer said:

> *“Flux is great for small and simple GitOps, but it doesn't scale well and lacks proper observability.” ~ [reddit](https://www.reddit.com/r/devops/comments/17kjlgg/comment/k7dvscq/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button)*
> 

So it’s no surprise that Flux CD alternatives are gaining traction. We’ll discuss seven (7) options that support GitOps workflows without the same maintenance burden or lack of visibility.

Let’s get into it.

<div>
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		<a href="https://app.northflank.com/signup">
			<Button variant={["large", "gradient"]}>Start deploying with Git-based workflows without managing controllers</Button>
		</a>
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<InfoBox className='BodyStyle'>

### Quick look: top Flux CD alternatives in 2026

In a hurry? Here's a quick breakdown of some of the best Flux CD alternatives for 2026:

1. [**Northflank**](https://northflank.com/) – Git-based deploys, BYOC ([Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud)), built-in logs and rollback, no controller required.

2. [**Argo CD**](https://argo-cd.readthedocs.io/) – Full GitOps controller with UI, Helm/Kustomize support, and strong community adoption.

3. [**Harness**](https://harness.io/) – Enterprise-grade CI/CD with GitOps pipelines, policy management, and visual workflows.

4. [**Spinnaker**](https://spinnaker.io/) – Multi-cloud delivery platform with GitOps plugin support and fine-grained release strategies.

5. [**GitLab CI/CD**](https://docs.gitlab.com/ee/ci/) – Git-based Kubernetes deploys via GitLab Agent, built-in UI, free and paid plans.

6. [**Jenkins X**](https://jenkins-x.io/) – Kubernetes-native GitOps automation, CLI-first, designed for Git-based multi-env delivery.

7. [**Codefresh**](https://codefresh.io/) – SaaS GitOps platform built on Argo with rollout tracking and dashboard visibility.

</InfoBox>

## What to look for in Flux CD alternatives

Before we get into the alternatives, let’s quickly go over the factors worth considering. You might have done a bit of research and seen that there’s no shortage of GitOps tools out there, but not all of them are built the same.

Some require you to install and manage your own controllers. Others give you the same Git-based workflows without needing to touch that layer at all.

![what to look for in a fluxcd alternative.png](https://assets.northflank.com/what_to_look_for_in_a_fluxcd_alternative_a32ba4ec91.png)

So, let’s look at a few things to think about before going for a Flux CD alternative:

1. **Do you want to manage a GitOps controller, or would you rather skip that step entirely?**
    
    Some tools expect you to operate and maintain a controller like Flux or Argo. Others like Northflank or Codefresh handle that under the hood, so you don’t need to deal with CRDs (Custom Resource Definitions) or reconciliation logic yourself.
    
2. **Do you need a UI with logs, diffs, and rollback features?**
    
    CLI-first might work early on, but teams often need better visibility once more people are involved or things start to break. Tools like Northflank provide a UI that is out of the box, making it easier to troubleshoot and manage deployments without digging through terminal logs.
    
3. **Is self-hosting a requirement, or is a SaaS (or BYOC) acceptable?**
    
    Depending on your infra policies, you might need something you can run in your own cloud, or you might prefer not to host anything at all. Platforms like Northflank support [Bring Your Own Cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) (BYOC), giving you control over where workloads run without managing the control plane yourself.
    
4. **Do you care about open source vs enterprise support?**
    
    Open source tools give you full control and flexibility, but they often come with the tradeoff of managing everything yourself. On the other hand, enterprise platforms tend to include support, SLAs, and team-ready features, things that matter more as your usage grows or requirements tighten.
    
5. **Is CI/CD integration important, or are you only focused on CD?**
    
    Some tools are GitOps-only. Others like Northflank or Harness include built-in CI or integrate closely with your pipelines, so you don’t have to manually integrate multiple tools to ship changes.
    

## Quick comparison table of 7 Flux CD alternatives

Now that we’ve broken down the key things to consider, let’s do a quick side-by-side look at how each alternative measures against the key things we just covered.

It isn’t just a feature checklist; it’s a practical view of what each tool expects from you and what it gives your team out of the box.

We’re comparing based on:

- GitOps-native: If Git-based deploys are built into the core design.
- Deployment model: Self-hosted, SaaS, or BYOC.
- Controller-free: If you need to manage a GitOps controller or not.
- Developer interface: CLI, UI, or both.
- Pricing: Free, open source, or commercial.

So, use this table to get a quick sense of where each tool sits, then jump into the detailed breakdowns to see which one fits your stack best.

| **Tool** | **GitOps-native** | **Deployment model** | **Controller-free** | **Developer interface** | **Pricing** |
| --- | --- | --- | --- | --- | --- |
| **Northflank** | Yes | SaaS / BYOC | Yes | UI + CLI | Free tier, paid plans |
| **Argo CD** | Yes | Self-hosted | No | UI + CLI | Open source |
| **Harness** | Yes | SaaS | Yes | UI | Commercial, usage-based |
| **Spinnaker** | Partially | Self-hosted | No | UI | Open source |
| **GitLab CI/CD** | Partially | SaaS / Self-hosted | Yes (via agent) | UI + YAML pipelines | Free & paid tiers |
| **Jenkins X** | Yes | Self-hosted | No | CLI | Open source |
| **Codefresh** | Yes | SaaS | No (built on Argo) | UI | Commercial, with free tier |

## 7 best Flux CD alternatives in 2026

If the comparison table helped you narrow things down, this section will give you a deeper look at each tool. We’ll go into what each tool is built for, how it works in practice, and when it makes sense to use.

We’re covering both open-source GitOps tools and platform-based options, depending on how much control or abstraction your team is looking for. They all support GitOps workflows but differ in how much infrastructure setup, controller management, and operational complexity they expect you to take on.

Let’s break them down one by one.

### 1. Northflank – GitOps workflows with built-in CI, UI, BYOC, and controller-free

If your team spends too much time managing Flux controllers, troubleshooting sync issues, or working without a clear view of what's actually deployed, Northflank removes all of that operational complexity.

[Northflank](https://northflank.com/) gives you Git-based deployments, container builds, and preview environments, without needing to install or maintain a GitOps controller. Every deployment is tied to your Git history, with built-in logs, diff views, and rollback support that’s ready from day one.

![northflank's home page-min.png](https://assets.northflank.com/northflank_s_home_page_min_cefd0ee938.png)

You also get:

- A clean UI for managing services, jobs, secrets, and environments
- [Bring Your Own Cloud (BYOC)](https://northflank.com/features/bring-your-own-cloud) support for running workloads in your own infrastructure
- Support for [persistent storage](https://northflank.com/docs/v1/application/production-workloads/persistent-storage-in-production), [background jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs), and [custom Docker builds](https://northflank.com/docs/v1/application/build/build-with-a-dockerfile)
- Deployment workflows without CRDs, sync loops, or controller maintenance

Compared to Flux CD, there are no CRDs to define, reconciliation loops to monitor, or controllers to debug. Northflank handles orchestration under the hood, giving your team a cleaner, more reliable deployment workflow.

> Go with this if you want GitOps workflows without managing a controller, and you care about visibility, rollback support, secrets management, and control over where and how your services run.
> 

*See how [Weights company uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### 2. Argo CD – For GitOps teams who want more visibility and structure

One of the biggest challenges with Flux CD is the lack of built-in visibility. If your team needs a UI to understand what’s deployed, what’s out of sync, and why something failed, Argo CD solves that out of the box.

![argocd home page.png](https://assets.northflank.com/argocd_home_page_d9b424f9f4.png)

[Argo CD](https://argo-cd.readthedocs.io/) is a self-managed GitOps controller designed specifically for Kubernetes. It continuously syncs application state from Git and provides a dashboard where you can see every deployment, compare desired vs actual state, and troubleshoot sync issues without relying solely on CLI tools.

It supports:

- GitOps workflows with Helm, Kustomize, plain YAML, and Jsonnet
- Automated sync and manual approval strategies
- RBAC, audit logs, and SSO integrations
- Multi-cluster deployment management

Where Flux expects you to work mostly through the CLI, Argo provides a full visual layer for inspecting and managing deployments. You’ll still be responsible for hosting and maintaining the controller, but the experience around it is more structured and easier to work with.

> Choose this if your team wants an open-source GitOps setup with a built-in UI and clearer control across multiple clusters.
> 

See [Flux vs Argo CD: Which GitOps tool fits your Kubernetes workflows best?](https://northflank.com/blog/flux-vs-argo-cd) and [Argo CD alternatives](https://northflank.com/blog/argo-cd-alternatives-northflank-developer-platform-git-ops-self-service)

### 3. Harness – For teams that need GitOps and policy control in one platform

If you’ve tried combining GitOps with feature flags, RBAC, or policy enforcement in Flux CD, you’ve likely had to integrate multiple tools manually. Harness includes these capabilities natively within its CI/CD platform.

[Harness](https://www.harness.io/) includes a GitOps module alongside full CI/CD pipelines, audit trails, and governance features. You can trigger deployments from Git commits, define automated rollout strategies, and connect your GitOps workflows to security and compliance policies.

![harness.png](https://assets.northflank.com/harness_6ed883f12e.png)

What it supports:

- Declarative GitOps pipelines with drift detection
- Policy-as-code using Open Policy Agent (OPA)
- Deployment strategies like canary, blue/green, and rolling
- Built-in CI engine, feature flags, and service catalog
- Centralized management across microservices and teams

Compared to Flux CD, Harness doesn’t require you to manage the GitOps engine. But it’s also not open source and comes with commercial pricing, which might not suit every team.

> Use Harness if you want a managed GitOps workflow with policy enforcement, governance, and deeper integrations across your delivery pipeline.
> 

See [Top alternatives to Harness for CI/CD and DevOps](https://northflank.com/blog/top-harness-alternatives)

### 4. Spinnaker – For multi-cloud delivery with GitOps extensions

Flux CD is great for syncing manifests to a Kubernetes cluster, but it’s limited when your deployments span multiple clouds or require more advanced orchestration. That’s where [Spinnaker](https://spinnaker.io/) comes in.

Originally built by Netflix, Spinnaker is a multi-cloud continuous delivery platform that supports Kubernetes, EC2, GCE, ECS, and more. It’s not GitOps-first by design, but it can be extended with GitOps plugins to pull deployment configs from Git and automate rollouts.

![spinnaker home page.png](https://assets.northflank.com/spinnaker_home_page_3a22825196.png)

Where it fits well:

- Multi-target, multi-cloud deployment pipelines
- Custom deployment workflows across services
- Integration with GitHub, Bitbucket, Jenkins, and more
- Fine-grained release controls with automated promotion and rollback
- Open-source core, with managed options like Armory available

It’s a heavier install than Flux and often overkill for smaller teams. But if you’re delivering to multiple cloud providers and want GitOps to be one part of a larger deployment strategy, Spinnaker offers that scope.

> Choose Spinnaker if you're operating across cloud environments and need a broader delivery system that can be extended with GitOps.
> 

See [9 best Spinnaker alternatives in 2026](https://northflank.com/blog/spinnaker-alternatives)

### 5. GitLab CI/CD – For teams already using GitLab and deploying to K8s

If your code, issues, and pipelines already live in GitLab, adding GitOps with the Kubernetes Agent can be a natural extension, without introducing another controller.

[GitLab CI/CD](https://docs.gitlab.com/ci/) isn’t a traditional GitOps engine, but it supports Git-based deploys through pipelines and its [Kubernetes Agent](https://docs.gitlab.com/ee/user/clusters/agent/). You can store manifests in your repo and trigger cluster changes based on pipeline events, merge requests, or tags.

![GitLab CICD.png](https://assets.northflank.com/Git_Lab_CICD_fad7b00f4f.png)

Why it works well:

- Built-in Git-native CI/CD pipelines
- Kubernetes Agent for pull-based GitOps sync
- Merge request-based deployments and diff previews
- Role-based permissions and audit logs baked in
- SaaS and self-hosted deployment models available

Unlike Flux, you don’t get a continuously reconciling controller, but for many teams, GitLab’s event-driven deploys are more than enough.

> This is a good match if your team already uses GitLab and wants GitOps-style deployments without adding another tool to manage.
> 

See [9 Best GitLab alternatives for CI/CD in 2026](https://northflank.com/blog/best-gitlab-alternatives)

### 6. Jenkins X – For GitOps-first automation with Kubernetes-native pipelines

[Jenkins X](https://jenkins-x.io/) aims to bring GitOps to CI/CD workflows with a Kubernetes-native approach, but it’s not the same as using classic Jenkins.

Built from the ground up for cloud-native apps, Jenkins X creates preview environments, automates promotion through Git pull 
requests, and runs CI/CD pipelines using Tekton. It stores everything in Git, from build outputs to environment configs.

![jenkins x home page.png](https://assets.northflank.com/jenkins_x_home_page_ea832a2d5d.png)

What it includes:

- GitOps-based environment promotion and version tracking
- Kubernetes-native pipelines via Tekton
- Integration with Helm, Kustomize, and Docker builds
- CLI-driven experience with automation around pull requests
- Focus on microservice and team-based delivery flows

There’s no dashboard or visual UI, and it takes effort to set up and manage. But for teams that want GitOps integrated into every step of the delivery process and are comfortable working through Git and CLI, Jenkins X provides automation that’s tightly aligned with Kubernetes workflows.

> Use Jenkins X if you want GitOps-first pipelines and don’t mind working without a UI.
> 

### 7. Codefresh – For teams that want GitOps on Argo, with more visibility

If your team likes the Argo CD model but needs something more enterprise-ready out of the box, [Codefresh](https://codefresh.io/) builds on top of Argo to give you a managed GitOps platform with more insight and control.

Codefresh uses Argo under the hood but adds visual dashboards, drift detection, service-level views, and release tracking across environments. It supports multi-cluster delivery and integrates tightly with Helm, Kustomize, and Terraform.

![codefresh home page.png](https://assets.northflank.com/codefresh_home_page_ff5d9ea4d2.png)

Key features:

- GitOps workflows powered by Argo CD
- Unified dashboard to track deployments across services
- Built-in audit logging, RBAC, and policy enforcement
- Rollback history and deployment metrics
- SaaS model, with multi-cluster and multi-tenant support

You’ll still be using an Argo-style controller behind the scenes, but Codefresh saves you from managing it directly and adds layers of visibility and automation that Argo doesn’t provide by default.

> Codefresh is a reliable choice if you want Argo’s capabilities without the maintenance, and need a central UI to track everything from Git to production.
> 

## Tips for choosing the best Flux CD alternative

Now that you’ve seen what each tool can do, it comes down to which one fits best with your team’s workflow, infrastructure, and priorities.

Some tools give you full control but expect you to host and maintain the GitOps engine yourself. Others abstract that layer away, so you don’t have to manage controllers or define custom resources.

Let’s see a quick breakdown based on what your team might be optimizing for:

- **Prefer self-hosted and open source?**
    
    Argo CD and Jenkins X are both GitOps-native, Kubernetes-focused, and give you full control over your deployment logic and infrastructure.
    
- **Want Git-based workflows without maintaining controllers?**
    
    Northflank and Codefresh provide GitOps without the operational cost of running your own controller, with built-in dashboards and automation.
    
- **Need policy enforcement, compliance, or enterprise-grade features?**
    
    Harness and Spinnaker support advanced deployment strategies, RBAC, audit logging, and centralized governance features out of the box.
    
- **Already using GitLab across your stack?**
    
    GitLab CI/CD can integrate GitOps-style deployments into your existing pipeline setup without adding another tool to manage.
    

Each of these GitOps tools makes different trade-offs between control, convenience, and setup time, so the right choice depends on how your team wants to deploy, operate, and scale.

## Common questions about Flux CD and alternatives

If you're still deciding or presenting options to your team, see some quick answers to common questions about Flux CD and the tools that come up around it.

1. What is the alternative to Flux CD?
    
    There’s no one-size-fits-all replacement, but tools like Northflank, Argo CD, and Codefresh come up most often. Your choice depends on whether you want to self-host and manage a GitOps controller or use a platform that handles that layer for you.
    
2. Which is better, Flux or Argo CD?
    
    Both are open-source GitOps controllers, but Argo CD includes a built-in UI, clearer observability, and broader adoption, especially in enterprise settings. **Flux CD** is lighter and more CLI-driven. We’ve broken this down in detail [here](https://northflank.com/blog/flux-vs-argo-cd).
    
3. What are the pros and cons of Flux CD?
    
    **Pros**: lightweight, flexible, works well with plain YAML, Helm, and Kustomize.
    
    **Cons**: no built-in UI, limited visibility, controller management overhead, and less adoption compared to Argo CD.
    
4. Does Flux CD have a UI?
    
    No. Flux CD does not include a native UI. You’ll need to rely on CLI tools or integrate with third-party dashboards. That’s one of the reasons teams move to alternatives like Argo CD, Northflank, or Codefresh.
    
5. What is the difference between Argo CD and Flux?
    
    Both sync your cluster state from Git, but Argo CD is more feature-complete out of the box. It comes with a UI, multi-cluster support, RBAC, and automated sync strategies. Flux is more modular, but that also means more manual setup and additional tools if you want similar capabilities.
    

## Keep your Git-based deployments running without complexity

If you’ve read up to this point, it means you’ve seen what else is out there and now have a clearer view of which Flux CD alternative might fit your team better.

We’ve looked at tools that give you full control over how deployments happen, like Argo CD and Jenkins X, and platforms that remove the need to manage controllers entirely, like Northflank and Codefresh. Each one solves a slightly different problem depending on how much infrastructure your team wants to manage and how much visibility and control you need.

If you’re unsure where to start, you can [create a free account](https://app.northflank.com/signup) on a platform like [Northflank](https://northflank.com/) and see how it handles Git-based deployments without asking you to install or maintain a controller. You get a UI, CLI, or API to work with, depending on what fits your team’s workflow, and it gives you a clearer view of how deployments are triggered, tracked, and rolled back across environments.]]>
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  <title>7 Best Octopus Deploy alternatives for modern deployment workflows (2026)</title>
  <link>https://northflank.com/blog/octopus-deploy-alternatives</link>
  <pubDate>2025-04-25T16:40:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for alternatives to Octopus Deploy? This guide covers 7 tools that support CI/CD, GitOps, Kubernetes, BYOC, and more, depending on how your team ships software.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/octopus_deploy_alternatives_3530c995f5.png" alt="7 Best Octopus Deploy alternatives for modern deployment workflows (2026)" />> Are you looking for Octopus Deploy alternatives? This will help!
> 

I know that for a long time, engineering teams have used [Octopus Deploy](https://octopus.com/) to manage deployment pipelines and promote releases across environments. It’s well known for things like:

- It's visual UI
- Step-based deployment process
- Its ability to handle multi-environment delivery

The thing now is, our infrastructure practices continue to develop, and the tools we use need to keep up. You’ve likely noticed the shift toward GitOps, Kubernetes, and cloud-native workflows. 

That’s why more developers and DevOps engineers are turning to tools that integrate better into their Git workflows, automate more of the release process, and support containers and microservices without being too complex.

Don’t get me wrong, Octopus Deploy still works. It’s just not always aligned with these new expectations.

In this article, I’ll walk you through 7 Octopus Deploy alternatives and help you find a solution that fits your needs.

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<a href="https://app.northflank.com/signup">
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    Find the right platform for your next project >>>
  </Button>
</a>
  </center>
</div>

<InfoBox className='BodyStyle'>

### Quick look: top Octopus Deploy alternatives in 2026

In a hurry? Here's a quick breakdown of some of the best Octopus Deploy alternatives for 2026:

1. [**Northflank**](https://northflank.com/) – Built-in CI/CD, Git-based workflows, Kubernetes support, and BYOC.  
2. [**Harness**](https://harness.io/) – Enterprise CI/CD with governance, cost tracking, and automated rollbacks.  
3. [**Argo CD**](https://argo-cd.readthedocs.io/) – GitOps-native tool for declarative delivery on Kubernetes.  
4. [**GitHub Actions**](https://github.com/features/actions) – Integrated CI/CD for GitHub users, simple for basic deploys.  
5. [**Jenkins + Spinnaker**](https://spinnaker.io/) – Classic combo for complex pipelines and multi-cloud deployments.  
6. [**Flux CD**](https://fluxcd.io/) – Lightweight GitOps tool with Helm and Kustomize support.  
7. [**Azure DevOps Pipelines**](https://azure.microsoft.com/en-us/services/devops/pipelines/) – Best for teams already using Microsoft tools.

</InfoBox>


## Quick comparison: 7 Octopus Deploy alternatives side by side

Before we go into the nitty-gritty of things, if you’re in a hurry, the table below compares these 7 Octopus Deploy alternatives to help you find a solution quickly.

| **Tool** | **CI/CD & GitOps support** | **Kubernetes support** | **BYOC ([Bring Your Own Cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment))** | **Ideal for** |
| --- | --- | --- | --- | --- |
| **Northflank** | Built-in CI/CD with Git-based deployment flows | Native support with zero config required | Yes | Teams that want Git-native workflows, built-in observability, and easy K8s + container deploys |
| **Harness** | Enterprise pipelines, not GitOps-native | Extensive support via pipelines and integrations | No | Enterprises needing policy enforcement, cost control, and complex deployment strategies |
| **Argo CD** | No CI, GitOps-first, declarative model | Kubernetes-native, syncs state from Git | No | Teams doing GitOps for K8s using Helm, Kustomize, or raw YAML |
| **GitHub Actions** | Integrated CI/CD, partial GitOps via workflows | Works with K8s using external actions | No | Developers looking for simple CI/CD built into GitHub, ideal for small apps and microservices |
| **Jenkins + Spinnaker** | Jenkins for CI, Spinnaker for CD with Git triggers | Comprehensive K8s support with advanced rollout logic | No | Large orgs with legacy systems or multi-cloud deployment needs |
| **Flux CD** | No CI, GitOps-native, lightweight | Designed for Kubernetes and Helm support | No | Platform engineers or SREs managing declarative delivery from Git |
| **Azure DevOps Pipelines** | Full CI/CD with YAML pipelines, no native GitOps | Basic support with custom config needed | No | Teams already using Azure Boards, Repos, and other Microsoft DevOps tooling |

## What to look for in an Octopus Deploy alternative

When comparing alternatives, it helps to think about your delivery approach, infrastructure stack, and team preferences. Here are some things you should look out for:

- **Do you prefer Git-based workflows or a visual UI?**

Octopus uses a drag-and-drop UI. Tools like Argo CD, Flux, and Northflank offer GitOps-style delivery where Git is the single source of truth.

- **Are you deploying to Kubernetes, VMs, or a mix?**

If your infrastructure is containerized or uses K8s, native support (like in Northflank, Argo, or Flux) makes deployments smoother.

- **Do you need secret management, observability, and service integration?**

Some tools integrate this out of the box. Others require connecting third-party services.

- **Do you prefer hosted or self-managed solutions?**

Tools like Jenkins and Spinnaker require hosting and maintenance. Platforms like Northflank or GitHub Actions take that burden off your hands.

- **Do you need Bring Your Own Cloud (BYOC)?**

Most platforms don't support it. Northflank allows teams to deploy into their own cloud while keeping things managed.

## In-depth breakdown: 7 Best Octopus Deploy alternatives

Now let’s go into the details of these alternatives to help you decide which you should go for or best suits your needs.

### 1. Northflank

If your team builds containerized apps and you want to deploy them quickly without stitching together five different tools, [Northflank](https://northflank.com/) gives you a unified workflow. It’s especially helpful for fast-moving product teams that want to spin up environments straight from Git without worrying about infrastructure setup.

![northflank's home page-min.png](https://assets.northflank.com/northflank_s_home_page_min_90a76ecbbe.png)
   
Northflank is a fully managed deployment platform that combines CI/CD, service orchestration, and infrastructure management in one place. It’s designed to reduce operational overhead while giving developers full control over their services.
    
What you can do with Northflank:

- Automate builds and deployments directly from your Git repo
- Spin up preview environments for every pull request
- Run cron jobs, background workers, and standalone services
- Inject secrets and environment variables securely
- Use built-in observability with logs, metrics, and resource insights
- Deploy to Northflank-managed infrastructure or your own cloud (BYOC)

**Ideal if you're looking for:** An all-in-one platform with Git-based workflows, managed services, and [BYOC](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) (Bring Your Own Cloud) flexibility without the overhead.

### 2. Harness

If you work in a large organization with strict compliance needs and multi-team delivery workflows, [Harness](https://www.harness.io/) gives you tight control over deployment logic, approval steps, rollback policies, and governance.

![harness-min.png](https://assets.northflank.com/harness_min_9051047de0.png)

Harness is an enterprise-focused CI/CD platform with features like policy enforcement, rollback automation, and cost governance. While it’s not GitOps-native, it offers powerful pipeline customization, extensive integrations, and smart deployment verification based on health checks and logs.

What you can do with Harness:

- Build complex pipelines with approvals and manual gates
- Set automated rollback triggers and health checks
- Track cloud spend and optimize resource usage
- Integrate with Jira, ServiceNow, and security tools
- Define granular RBAC and audit trails for compliance

**Ideal if you're looking for:** Enterprise-ready governance, rollback safety, and deep pipeline customization.

See [Top alternatives to Harness for CI/CD and DevOps](https://northflank.com/blog/top-harness-alternatives)


### 3. Argo CD

If your team wants to shift fully to GitOps for Kubernetes, [Argo CD](https://argoproj.github.io/cd/) gives you a clean way to manage and sync application state declaratively from Git, without relying on traditional deployment UIs or scripts.

![argocd home page.png](https://assets.northflank.com/argocd_home_page_3ba32128bf.png)

Argo CD is a GitOps-native delivery tool built for Kubernetes. It continuously syncs your app’s declared state from Git into your clusters, ensuring your deployments always reflect your Git history.

What you can do with Argo CD:

- Sync deployments automatically from Git to your cluster
- Visualize diffs between live and desired states
- Use Helm, Kustomize, or raw YAML to define infrastructure
- Integrate with Argo Rollouts for progressive delivery
- Set up access control and audit logs via SSO providers

**Ideal if you're looking for:** A GitOps-native CD tool to manage Kubernetes clusters declaratively from Git.

See [Argo CD alternatives that don’t give you brain damage](https://northflank.com/blog/argo-cd-alternatives-northflank-developer-platform-git-ops-self-service) and [Flux vs Argo CD: Which GitOps tool fits your Kubernetes workflows best?](https://northflank.com/blog/flux-vs-argo-cd)

### 4. GitHub Actions

If your team already works out of GitHub and needs a fast, familiar way to build and deploy code without managing external CI/CD tools, [GitHub Actions](https://github.com/features/actions) is a natural choice.

![github-actions home page.png](https://assets.northflank.com/github_actions_home_page_fc62c22e59.png)

GitHub Actions provides event-based CI/CD that is tightly integrated with your GitHub repositories. You can trigger builds, tests, and deploys with reusable YAML workflows and community-contributed actions.

What you can do with GitHub Actions:

- Automate workflows triggered by pushes, PRs, tags, and schedules
- Reuse shared actions from the GitHub Marketplace
- Run builds and tests across Linux, macOS, and Windows runners
- Add secrets, environment variables, and matrix builds
- Extend deploys to Kubernetes or cloud providers with community actions

**Ideal if you're looking for:** A fast and integrated way to build and deploy directly from your GitHub repo.

See [The best GitHub Actions alternatives for modern CI/CD in 2026](https://northflank.com/blog/github-actions-alternatives) and [GitHub Actions vs Jenkins (2026): Which CI/CD tool is right for you?](https://northflank.com/blog/github-actions-vs-jenkins)

### 5. Jenkins + Spinnaker

If you're working with complex release flows, multi-region targets, or hybrid infrastructure, combining [Jenkins](https://www.jenkins.io/) with [Spinnaker](https://spinnaker.io/) can give you full control over both CI and progressive delivery.

Jenkins is a highly customizable CI server, and when combined with Spinnaker, you get a powerful CD system that supports advanced deployment strategies like blue/green and canary rollouts. This combo requires more setup but offers deep flexibility for enterprise needs.

![jenkins+spinnaker design.png](https://assets.northflank.com/jenkins_spinnaker_design_2bcc951b30.png)

What you can do with Jenkins + Spinnaker:

- Automate builds and testing in Jenkins with a rich plugin ecosystem
- Set up canary or blue/green deployments in Spinnaker
- Use pipelines to manage multi-region or multi-cloud deploys
- Integrate with monitoring and rollback tools
- Connect to Kubernetes, EC2, and GCE targets

**Ideal if you're looking for:** A flexible setup that supports advanced strategies and integrates with legacy or hybrid stacks.

See [Jenkins alternatives in 2026](https://northflank.com/blog/jenkins-alternatives-2025) and [9 best Spinnaker alternatives in 2026](https://northflank.com/blog/spinnaker-alternatives) 

### 6. Flux CD

If your infra team prefers writing everything as code and wants to keep delivery lightweight and Git-driven, [Flux](https://fluxcd.io/) is a good alternative to Octopus that doesn't require a UI or pipeline editor.

![fluxcd-home-page.png](https://assets.northflank.com/fluxcd_home_page_dafb03db26.png)

Flux is a CNCF-backed GitOps tool designed for Kubernetes. It syncs workloads directly from Git, supports Helm and Kustomize, and is great for SREs and infrastructure teams wanting a lightweight, code-driven tool.

What you can do with Flux:

- Automatically sync changes from Git to your cluster
- Use Helm or Kustomize for templating and customization
- Control deployment frequency with reconciliation intervals
- Apply Git tags and commit messages to influence rollout behavior
- Integrate with image automation and policy-based approvals

**Ideal if you're looking for:** A minimal, Git-driven way to handle Kubernetes deployments with fine-grained control.

See [Flux vs Argo CD: Which GitOps tool fits your Kubernetes workflows best?](https://northflank.com/blog/flux-vs-argo-cd)

### 7. Azure DevOps Pipelines

If your team is already using Azure Repos or Boards and building .NET or Windows apps, [Azure DevOps Pipelines](https://azure.microsoft.com/en-us/products/devops/pipelines) fits naturally into your workflow and reduces the need for third-party integrations.

![azure pipelines home page.png](https://assets.northflank.com/azure_pipelines_home_page_59fc3e689c.png)

Azure Pipelines is part of the Azure DevOps suite and integrates well with Azure Boards, Repos, and other Microsoft services. It supports YAML and visual pipelines, and while Kubernetes support isn’t first-class, it works well enough with the right extensions.

What you can do with Azure DevOps Pipelines:

- Build and test .NET, Node.js, Python, and Java apps
- Run pipelines in the cloud or on your own agents
- Integrate directly with Azure Repos and GitHub
- Manage approvals and release gates
- Deploy to Azure Kubernetes Service and other Azure resources

**Ideal if you're looking for:** A native solution within the Microsoft ecosystem to manage CI/CD alongside Azure Boards and Repos.

## What developers also ask

**Is there a free version of Octopus Deploy?**

Yes, but it’s limited to 10 deployment targets and lacks some enterprise features. Most alternatives offer more flexibility in their free tier.

**What’s the difference between Jenkins and Octopus Deploy?**

Jenkins is a CI server that builds and tests code. Octopus Deploy focuses on deployments. They’re often used together, but newer tools like Northflank or Harness can cover both.

**What’s the difference between Octopus Deploy and TeamCity?**

TeamCity handles CI (builds), and Octopus Deploy handles CD (deployments). Some modern platforms combine both.

**How is Terraform different from Octopus Deploy?**

Terraform manages infrastructure provisioning, not deployments. You can use Terraform alongside Octopus or its alternatives to handle infrastructure and app delivery separately.

## The best Octopus Deploy alternative depends on your stack

Octopus Deploy paved the way for visual deployment automation, but today’s teams need tools that integrate better with Git workflows, containers, and Kubernetes. So, it doesn't matter if you’re looking for GitOps-native tools like Argo CD or Flux, enterprise-ready platforms like Harness, or a developer-first option like Northflank; there’s no shortage of reliable alternatives.

Northflank gives you CI/CD, service orchestration, observability, and BYOC ([Bring Your Own Cloud](https://northflank.com/features/bring-your-own-cloud)) in a single platform. If you’re ready to modernize your delivery workflow, it’s a great place to start.

Try it free by [signing up for Northflank today](https://app.northflank.com/signup).]]>
  </content:encoded>
</item><item>
  <title>Top 10 Terraform alternatives to optimize your infrastructure in 2026</title>
  <link>https://northflank.com/blog/terraform-alternatives</link>
  <pubDate>2025-04-25T13:00:00.000Z</pubDate>
  <description>
    <![CDATA[Terraform alternatives like Northflank, Pulumi, and Crossplane offer simpler, faster, and more scalable infrastructure-as-code solutions, addressing challenges like complex state management and steep learning curves.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/terraform_alternatives_0ad3ea81c6.png" alt="Top 10 Terraform alternatives to optimize your infrastructure in 2026" />If you’ve worked in DevOps or platform engineering recently, you’ve probably used Terraform. It’s been one of the most popular tools for managing infrastructure as code.

But things are changing.

More teams are starting to ask: ***“Is Terraform still the best choice?”***

Why? Well, it’s not just about writing code anymore. Today’s cloud infrastructure is more complex. And Terraform, while powerful, comes with some real headaches like confusing state files, harder learning curves, and recent license changes that some people aren’t happy with.

At the same time, **new tools** have come out. They’re easier to use, faster to set up, and built for modern cloud-native environments.

If you’re feeling stuck with Terraform or just curious about other options, this guide is for you.

## TL;DR: 10 Terraform alternatives to watch in 2026

Just want the list? Here are 10 standout Terraform alternatives, each solving IaC in their own way:

1. [**Northflank**](https://www.northflank.com/) – A full-service platform built on Kubernetes that simplifies deployments, CI/CD, databases, and infrastructure, all without the hassle.
2. [**Pulumi**](https://www.pulumi.com/) – Write IaC using real programming languages like TypeScript or Python.
3. [**Crossplane**](https://crossplane.io/) – Kubernetes-native IaC that plays great with GitOps and platform engineering.
4. [**Spacelift**](https://spacelift.io/) – Adds control, policy, and workflows on top of Terraform.
5. [**env0**](https://www.env0.com/) – Makes Terraform more collaborative with cost controls and governance.
6. [**AWS CDK**](https://aws.amazon.com/cdk/) – Ideal if your whole world lives in AWS.
7. [**Bicep**](https://learn.microsoft.com/en-us/azure/azure-resource-manager/bicep/) – Azure’s clean, modern IaC syntax.
8. [**Google Config Connector**](https://cloud.google.com/config-connector/docs/how-to/getting-started) – Kubernetes-native GCP resource management.
9. [**Ansible**](https://www.ansible.com/) – A classic automation tool that still pulls its weight.
10. [**Terragrunt**](https://terragrunt.gruntwork.io/) – A smarter wrapper around Terraform to reduce repetition and improve reuse.

## Why teams are rethinking Terraform

Let’s be clear: Terraform has earned its place. It pioneered the modern IaC movement, brought consistency across cloud providers, and has helped thousands of teams tame infrastructure chaos.

But over time, cracks have started to show:

- **State management is fragile.** One wrong move, and your state file turns into a single point of failure.
- **CI/CD isn't native.** You’ll often find yourself duct-taping scripts and pipelines together.
- **HCL (HashiCorp Configuration Language) can be awkward.** While readable, Terraform’s syntax is not always intuitive, especially for teams coming from app development.
- **Licensing is a curveball.** HashiCorp’s move to the BSL license spooked a lot of open-source fans and raised long-term concerns.
- **Scaling gets tricky.** As your infrastructure grows, so does the complexity of managing dependencies, modules, and long plan times.

For many teams, what started as a time-saver now feels like technical debt.

## What to look for in Terraform alternatives

Not every team needs the same thing from their infrastructure tooling. Before diving into the options, take a moment to think about your priorities:

- **Declarative vs imperative:** Do you want to describe the end state (like Terraform)? Or do you need step-by-step control?
- **Cloud-native readiness:** Will it play nice with containers, Kubernetes, and serverless?
- **Collaboration features:** Can your team safely work in parallel? Are there roles, approvals, and audit trails?
- **State handling:** Is state transparent and reliable—or better yet, abstracted away?
- **CI/CD integration:** Does it plug into your workflow easily? Or will you be writing glue scripts?
- **Licensing and cost:** Post-BSL, sustainability and openness matter more than ever.
- **Learning curve:** Can new team members ramp up quickly?

Now, let’s talk about what’s out there.

## Top 10 Terraform alternatives

Here’s a curated list of Terraform alternatives gaining traction in 2026, each offering a unique spin on IaC:

### **1. Northflank - A modern Terraform alternative built on Kubernetes**

Imagine deploying a full-stack application, complete with a database, DNS, and automated CI/CD in minutes, without touching a single YAML file or worrying about Terraform state files. That’s the experience [Northflank](https://northflank.com/) is aiming to deliver.

Northflank isn’t just an alternative to Terraform; it’s a leap forward for teams that want to ship faster and spend less time wrangling infrastructure. Designed with a developer-first mindset, it wraps deployment, provisioning, and operations into one sleek platform. Whether you’re spinning up microservices, deploying containers, or managing databases, everything just works without the usual friction.

[Learn more about Infrastructure as code on Northflank here](https://northflank.com/docs/v1/application/infrastructure-as-code/infrastructure-as-code)

![image (93).png](https://assets.northflank.com/image_93_2b254840ee.png)

**Key features:**

- Built-in CI/CD pipelines for streamlined deployments
- First-class support for containers, databases, and DNS out of the box
- A powerful API and intuitive UI that developers actually *enjoy* using
- No YAML, no Terraform state files, no provisioning headaches
- Fully managed infrastructure, so your team can focus on code, not cloud complexity
- Built-in monitoring and logging
- Automatic horizontal scaling
- Integrated secrets management
- [Bring your own cloud (BYOC)](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment)

**Best for:**

Teams who want to deliver software quickly and reliably without becoming infrastructure experts. If your team is tired of juggling Terraform, CI/CD tools, and cloud resources separately, Northflank brings everything together in one platform, freeing you to build, ship, and scale with ease.

### **2. Pulumi**

[Pulumi](https://www.pulumi.com/) is an infrastructure as code platform that lets you define cloud infrastructure using familiar programming languages like TypeScript, Python, Go, and C#.

![image (94).png](https://assets.northflank.com/image_94_5e0bb6e3d7.png)

**Key features:**

- Multi-cloud support (AWS, Azure, GCP, Kubernetes, and more)
- Strong integration with CI/CD tools
- Use code you already know
- Supports both imperative and declarative styles

**Best for:** Developers who prefer full control and flexibility using general-purpose code instead of domain-specific languages like HCL.

### **3. Crossplane**

[Crossplane](https://crossplane.io/) is a Kubernetes-native IaC framework that lets you manage infrastructure using custom Kubernetes resources (CRDs).

![image (95).png](https://assets.northflank.com/image_95_292e1d1127.png)

**Key Features:**

- Kubernetes-native infrastructure management
- Supports GitOps workflows
- Works well for building internal developer platforms
- Strong community and open-source support

**Best For:** Teams that are all-in on Kubernetes and want a unified control plane for apps and infra.

### **4. AWS CloudFormation / CDK**

CloudFormation is AWS’s native IaC service, and the [CDK](https://aws.amazon.com/cdk/) is a developer-friendly abstraction that lets you write infrastructure in real code.

![image (96).png](https://assets.northflank.com/image_96_05b2d5733f.png)

**Key features:**

- CDK lets you write infrastructure in TypeScript, Python, Java, and C#
- Tight integration with AWS services
- Fully supported and maintained by AWS
- CDK generates CloudFormation templates behind the scenes

**Best for:** Organizations fully invested in AWS that want first-party tooling and familiar languages.

### **5. Bicep (for Azure)**

[Bicep](https://learn.microsoft.com/en-us/azure/azure-resource-manager/bicep/) is a domain-specific language (DSL) from Microsoft that simplifies writing and managing Azure infrastructure.

**Key features:**

- Clean, easy-to-read syntax
- Built-in support in Azure CLI and Azure Portal
- First-party support from Microsoft
- Validates and compiles to ARM templates

**Best for:** Teams deploying infrastructure on Azure that want a smoother experience than raw ARM templates.

### **6. Google Cloud Config Connector / Deployment Manager**

These are [Google Cloud’s native tools](https://cloud.google.com/config-connector/docs/how-to/getting-started) for managing infrastructure as code, with strong Kubernetes integration.

**Key Features:**

- Use Kubernetes CRDs to manage GCP infrastructure
- Integrates with GitOps workflows
- Native GCP support
- YAML-based configuration

**Best For:** GCP-native teams that want to manage infrastructure using Kubernetes.

### **7. Ansible**

[Ansible](https://www.ansible.com/) is a powerful automation tool that can provision infrastructure and configure software using simple YAML playbooks.

**Key features:**

- Uses YAML for automation tasks
- Agentless—runs over SSH
- Great for managing both infra and app configuration
- Big ecosystem with tons of modules

**Best for:** Teams looking for one tool to manage both infrastructure and software setup (especially for VMs and bare metal).

### **8. Spacelift**

[Spacelift](https://spacelift.io/) is a CI/CD and governance platform for infrastructure as code, built specifically to enhance and extend Terraform workflows.

![image (97).png](https://assets.northflank.com/image_97_93ceb217cd.png)

**Key features:**

- Policy-as-code using Open Policy Agent (OPA)
- Role-based access control (RBAC), audit trails
- CI/CD and GitOps support
- Workflow automation and drift detection

**Best for:** Larger teams using Terraform that need more security, visibility, and control over how infrastructure is deployed.

### **9. env0**

[env0](https://www.env0.com/) is a governance and automation layer for infrastructure-as-code tools like Terraform, Pulumi, and Terragrunt.

![image (98).png](https://assets.northflank.com/image_98_aa1248cba9.png)

**Key features:**

- Team collaboration features
- Budget and cost tracking
- Self-service infrastructure
- Supports Terraform, Terragrunt, Pulumi

**Best for:** Platform engineering teams looking to offer self-service deployments while maintaining guardrails.

### **10. Terragrunt**

[Terragrunt](https://terragrunt.gruntwork.io/) is a wrapper for Terraform that helps teams better organize code, manage state, and reuse modules efficiently.

![image (99).png](https://assets.northflank.com/image_99_d8925ebff5.png)

**Key features:**

- Keeps your Terraform code DRY (Don’t Repeat Yourself)
- Simplifies managing multiple environments/modules
- Handles complex dependencies more cleanly
- Still uses Terraform underneath

**Best for:** Teams already using Terraform that want better organization and reusability without switching tools completely.

## How to choose the right Terraform alternative

Still figuring out which tool makes the most sense for your team? Start by thinking about what you actually want to be responsible for... and what you'd rather hand off.

Here’s a quick way to think through it:

- If you like writing real code and treating infrastructure like software, Pulumi is a strong choice.
- If your team lives inside Kubernetes and wants everything managed in-cluster, go with Crossplane or [Northflank](https://northflank.com/).
- If you’re fully invested in AWS and want official tooling that feels native, AWS CDK is a solid bet.
- If you want the power of Kubernetes without needing to understand every detail, [Northflank](https://northflank.com/) takes care of the complexity for you.
- If compliance, security, and team governance are top priorities [Northflank](https://northflank.com/), Spacelift or env0 are worth a look.
- If you're already using Terraform but wish it were less of a headache, Terragrunt helps clean things up.

Zooming out for a second: in 2026, managing infrastructure manually is starting to feel like writing raw SQL when you could just use an ORM. It works, but it's clunky, fragile, and way more work than it needs to be.

That’s why platforms like [Northflank](https://northflank.com/) are gaining momentum. You get the full power of containers, databases, autoscaling, and cloud-native workflows — all without touching YAML or managing state files.

Build fast, deploy confidently, and spend your time on what actually matters: shipping great software.

## Conclusion

Terraform was a game-changer when it arrived, but the game has evolved.

Today, teams are moving faster and embracing cloud-native architectures, and tools need to keep pace. Whether you’re looking for something more developer-friendly, more scalable, or just more *modern*, there’s no shortage of great options.

Platforms like **Pulumi**, **Crossplane**, and especially [**Northflank**](https://northflank.com/) are pushing the boundaries of what infrastructure can be. With [Northflank](https://northflank.com/), you get all the power of Kubernetes — containers, deployments, databases, and autoscaling — without the complexity. No YAML, no state files, just seamless developer workflows and automation.

So before you swap out one IaC tool for another, take a step back and ask yourself:

**What do we actually want from our infrastructure?**

If the answer is simplicity, speed, and reducing the overhead of managing cloud resources, **Northflank** is the solution you’ve been looking for.

Ready to build faster and smarter? [Start with Northflank today](https://app.northflank.com/signup) and see how it can transform your development workflow.]]>
  </content:encoded>
</item><item>
  <title>GitHub Actions vs Jenkins (2026): Which CI/CD tool is right for you?</title>
  <link>https://northflank.com/blog/github-actions-vs-jenkins</link>
  <pubDate>2025-04-24T17:32:00.000Z</pubDate>
  <description>
    <![CDATA[Trying to decide between Jenkins and GitHub Actions? This in-depth comparison breaks down setup, extensibility, workflows, and security to help you choose the right CI/CD tool in 2026.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/github_actions_vs_jenkins_0211f73cd8.png" alt="GitHub Actions vs Jenkins (2026): Which CI/CD tool is right for you?" />Have you been seeing the whole comparison discourse around GitHub Actions and Jenkins?

Oh yes, it didn’t start today. The discussion has been ongoing for a long time, especially in DevOps chats on Reddit.

I’ve seen a couple myself. Some devs say Jenkins is fading or outdated, while others complain about its maintenance and plugin issues, including security concerns.

I saw other developers say GitHub Actions is easier to adopt. Do you think so, too? It’s fine if you don’t have an answer to that question now; by the time you’re done reading this, you’ll have more insights to decide.

Let’s get into it!

<InfoBox className='BodyStyle'>

If you're caught between GitHub Actions feeling limited and Jenkins being too complex, there's a middle ground.

[Northflank](https://northflank.com/) supports:
- [CI/CD pipelines](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank) connected to GitHub, GitLab, or Bitbucket  
- [Automatic builds](https://northflank.com/docs/v1/application/getting-started/build-and-deploy-your-code) with [logs](https://northflank.com/docs/v1/application/observe/view-logs), history, and status in one place  
- Deployment from [Dockerfiles](https://northflank.com/docs/v1/application/build/build-with-a-dockerfile), [containers](https://northflank.com/deploy/run-persistent-and-ephemeral-docker-containers), or [buildpacks](https://northflank.com/docs/v1/application/build/build-with-buildpacks) 
- Background jobs, [cron tasks](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs), and [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment)  
- No plugin setup, no agent maintenance, no YAML lock-in  

Try it out by [starting for free](https://app.northflank.com/signup) — it only takes a few minutes to deploy your first service.

</InfoBox>


## Quick comparison of GitHub Actions vs Jenkins

If you don’t have time for the details, let’s quickly compare GitHub Actions and Jenkins to show you their differences in key areas like setup, config style, and use cases.

| **Feature** | **GitHub Actions** | **Jenkins** |
| --- | --- | --- |
| **Setup** | Zero config inside GitHub repos (no separate installation) | Manual installation, agent setup, plugin dependencies |
| **Hosting** | GitHub-hosted runners (or BYO self-hosted) | Self-hosted by default (cloud/on-prem) |
| **Config style** | YAML workflows inside `.github/workflows` | `Jenkinsfile` written in Groovy (or via UI jobs) |
| **Extensibility** | Reusable Actions from Marketplace (version-pinned, community-maintained) | 1800+ plugins (powerful but fragile, often outdated or unsupported) |
| **Debugging** | Console logs and step-by-step output in GitHub UI | Structured logs, but setup and plugin debugging can be complex |
| **Secrets** | Managed in GitHub repo/environment settings | Managed via Credentials Plugin |
| **Great fit for** | GitHub-native teams, startups, OSS contributors | Teams with legacy infra, regulated environments, or heavy customization needs |

## Overview of Jenkins

Let’s start with the long-time favorite in the CI/CD space (as many would agree) - [Jenkins](https://www.jenkins.io/).

![jenkins-review-1.png](https://assets.northflank.com/jenkins_review_1_8fadf67ce3.png)

As you’ve seen in the table in the previous section, Jenkins gives you a lot of control, but it comes with extra setup and maintenance. It’s been around for years and powers some serious complex workflows, especially in larger or more regulated teams.

### What is Jenkins?

Let’s say your team is building a product that needs to run automated tests, trigger deployments, and manage approvals - all across multiple environments. And you want full control over how:

- The pipeline runs
- Where it runs
- What tools plug into it

Such a scenario is where Jenkins comes into play.

![jenkins website.png](https://assets.northflank.com/jenkins_website_f279c50098.png)

Jenkins is an open-source automation server that has existed since 2011. It was originally a fork of a project called Hudson. You may have heard of it.

It was built to help developers automate everything from builds to testing to deployments.

Over the years, it’s become one of the most powerful (and customizable) CI/CD tools out there, especially for teams that need to host things themselves.

It runs on your infrastructure, supports over 1,800 [plugins](https://plugins.jenkins.io/), and still powers a ton of enterprise workflows where control and compliance are non-negotiable.

### What does a basic Jenkins pipeline look like?

Have you seen a `Jenkinsfile` before? If you haven’t, let’s see what a basic one looks like in action - nothing too complex, just a simple pipeline that installs dependencies and runs tests in a Node.js app:

```groovy
pipeline {
    agent any
    stages {
        stage('Install dependencies') {
            steps {
                sh 'npm install'
            }
        }
        stage('Run tests') {
            steps {
                sh 'npm test'
            }
        }
    }
}
```

Each stage represents a step in your pipeline, and Jenkins runs this on an agent (basically a server (physical or virtual) that executes your jobs). You’re the one setting it up and managing it, could be a VM in the cloud or a bare-metal box in your office.

The pipeline itself is written in Groovy, which gives you a lot of control but also means you’re working a bit closer to the internals than you would with newer, GitHub-native CI tools like GitHub Actions or platforms like Northflank, which connect directly to your repo and skip most of the setup.

### Why is Jenkins still used in 2026?

Now, that `Jenkinsfile` might look a bit old-school, and sure, setting things up takes more effort compared to newer tools. But that’s exactly why some teams still stick with Jenkins.

It gives you full control over your pipelines. You can customize almost every part of it, thanks to its massive plugin ecosystem (over 1,800 plugins and counting). If your workflow is complex, or your team has very specific requirements, Jenkins most likely has a plugin for it.

It’s also one of the few CI/CD tools that work well in air-gapped or on-prem environments, where cloud-based tools like GitHub Actions aren’t an option. That’s why you’ll still find Jenkins in heavily regulated industries and large enterprises.

As one Developer on Reddit put it:

> “*Personally I would suggest GitHub Actions if you can choose, Jenkins, albeit great, has been gradually fading.*”
~ [u/mparigas](https://www.reddit.com/r/devops/comments/1hir6a5/comment/m30vm4m/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button)
> 

But “fading” doesn’t mean forgotten. For teams that need deep customization, Jenkins still gets the job done.

### What are the common limitations of Jenkins?

We talked about the control and flexibility that Jenkins gives you, but it comes with a cost. And for many teams, it’s a steep one.

So, what cost are we referring to here?

First, there’s the **manual setup and ongoing maintenance**. With Jenkins, you’re not just writing pipelines - you're also in charge of managing agents, plugins, system updates, and sometimes even the infrastructure it runs on. When something breaks (and it will), you’re the one fixing it.

Then, there’s the **plugin ecosystem**. It’s one of Jenkins’ biggest strengths, but also a source of pain. Some plugins are outdated or no longer maintained, and updates can cause conflicts.

As one G2 reviewer put it:

> “*It is more difficult to trace some bugs and it is difficult to manage because of outdated UI and plugin configuration management.*”
~ [G2 reviewer, Nov 2024](https://www.g2.com/products/jenkins/reviews/jenkins-review-10555732)
> 

![jenkins review 2.png](https://assets.northflank.com/jenkins_review_2_711b8362bd.png)

And if you’re new to Jenkins? The learning curve is pretty steep. The UI feels outdated to some, and configuring pipelines in Groovy can be tough if you’re used to more modern, YAML-based tools.

Another reviewer noted:

> “*The user experience of Jenkins UI is not that good. For a first-time user, it will be difficult to understand the features.*”
~ [G2 reviewer, Jan 2025](https://www.g2.com/products/jenkins/reviews/jenkins-review-10753911)
> 

![jenkins review 3.png](https://assets.northflank.com/jenkins_review_3_66e1946f1a.png)

So, while Jenkins absolutely works and still powers some serious infrastructure, it’s not always the easiest tool to work with, especially if you’re a first-time user.

### Who should still use Jenkins today?

Now after all that, you might be thinking: “*Why would anyone still choose Jenkins in 2026?*”

Fair question - but the answer comes down to what kind of team you’re running and what your environment looks like.

If you’re working with **existing Jenkins pipelines** or your company has a lot of legacy infrastructure tied to it, switching tools might be worth the disruption. Jenkins also makes sense if you need to **run everything on-prem**, especially in **air-gapped or compliance-heavy environments** where internet access is restricted.

And for teams that need **deep customization**. Let’s say you have a complex approval flow and tons of moving parts, or need plugins that GitHub Actions or other tools don’t support. Then, Jenkins is still a great fit.

In short, Jenkins isn’t gone, it’s just no longer the default. But for teams that need full control and can manage the overhead, it’s still very much in play.

## Overview of GitHub Actions

Now let’s talk about the other side of the table - [GitHub Actions](https://github.com/features/actions).

Jenkins might’ve been around longer, but GitHub Actions launched in 2018 and quickly became the default for devs already on GitHub.

![github actions techcrunch.png](https://assets.northflank.com/github_actions_techcrunch_6d8b5021d6.png)*Source: Techcrunch*

It’s built into the platform, so you don’t need to leave your repo to set up CI/CD. That simplicity has made it a go-to choice, especially for smaller teams and open-source projects.

### What is GitHub Actions?

Let’s say you’re pushing code to a GitHub repo, and you want a few things to happen right after, like:

1. Tests should run
2. A build kicks off
3. A deployment to staging 

All without touching anything outside GitHub. That’s where GitHub Actions comes in.

![github-actions home page.png](https://assets.northflank.com/github_actions_home_page_e019c80dab.png)

It’s a CI/CD tool built directly into GitHub. You write your workflows using YAML and store them in a `.github/workflows` folder inside your repo. Those workflows can run on GitHub’s cloud-hosted runners or your own infrastructure if needed.

Since it’s built directly into the same platform where your code lives, you get version-controlled automation right next to your pull requests and issues. This means no separate UI, extra setup, or plugins to manage.

### What does a basic GitHub Action look like?

Have you seen a GitHub Actions workflow before? If you haven’t, let’s take a quick look at a basic one - something simple that runs tests whenever you push code to the main branch:

```yaml
name: CI

on:
  push:
    branches: [main]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Install dependencies
        run: npm install
      - name: Run tests
        run: npm test
```

The file lives inside `.github/workflows` in your repo. It tells GitHub to spin up a workflow every time you push to the `main` branch. It installs dependencies and runs tests using the latest Ubuntu runner - all in one place, right next to your code.

You don’t need to set up external agents or configure a separate CI server. Just commit the file and push.

### Why are so many teams using GitHub Actions now?

So, we’ve seen what GitHub Actions looks like in practice. But why has it become the go-to CI/CD tool for so many teams?

The answer is pretty simple: it’s already in the place most teams live - GitHub. If your code is there, setting up automation takes no time. You commit a YAML file, and your workflow kicks in. No separate UI. No server setup. No plugin drama.

It also comes with [cloud-hosted runners](https://docs.github.com/actions/using-github-hosted-runners/about-github-hosted-runners), built-in secrets management, and a growing [Marketplace](https://github.com/marketplace) full of reusable actions, so you don’t have to start from scratch every time.

Developers on Reddit say the same:

> “*GitHub Actions is closer to the code and the feedback loop is tighter. Jenkins is yet another tool to manage.*”
~ [u/puresoldat](https://www.reddit.com/r/devops/comments/1hir6a5/comment/m34fcpq/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button)
> 

![reddit 1 - github actions.png](https://assets.northflank.com/reddit_1_github_actions_470f7f21dc.png)

Another dev put it more bluntly:

> “*GitHub Actions... No doubt. Migrated our complete self-hosted Jenkins to GitHub Actions this year with self-hosted runners. Never looked back. Way easier to maintain. Way easier to implement your pipelines.*”
~ [u/ZealousIdeal-One5210](https://www.reddit.com/r/devops/comments/1hir6a5/comment/m3a0i44/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button)
> 

![reddit 2 - github actions.png](https://assets.northflank.com/reddit_2_github_actions_73baade777.png)

There’s a growing sentiment that GitHub Actions is becoming the default. As one person said:

> “*I wish GitHub Actions (or any other solution) would become a defacto industry standard. I like the approach Gitea takes with building a GitHub Actions-compatible solution*.” ~ [u/nevotheless](https://www.reddit.com/r/devops/comments/1hir6a5/comment/m313wn5/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button)
> 

![reddit 3 - github actions.png](https://assets.northflank.com/reddit_3_github_actions_149bee74d7.png)

That doesn’t mean it’s perfect, but for many teams, especially those working with fast-moving codebases and fewer infra constraints, GitHub Actions is the obvious choice.

### When GitHub Actions can feel limiting

But of course, no tool is perfect, and GitHub Actions isn’t an exception.

As simple as it is to start with, there are some noticeable constraints that show up once your setup grows beyond a few workflows.

For instance, let’s say you’re managing builds across 10+ repos. You’ll notice that things like sharing artifacts, coordinating workflows, or tracking status across repos quickly become hard to manage.

One of the most talked-about limitations is **centralized visibility**. If you’re managing CI across multiple repos, GitHub Actions doesn’t give you a single dashboard to monitor or control everything. In the same way that developers rely on a [Mac optimization app](https://macpaw.com/cleanmymac) to improve performance and streamline workflows on their machines, engineering teams often look for tools that reduce complexity and provide better visibility across projects. One dev said it straight:

> “*Centralized management and monitoring is non-existent – GitHub Actions doesn’t let you create a dashboard where you can manage every executing action across all repositories.*”
~ [u/Zenin](https://www.reddit.com/r/devops/comments/1bmn7ie/comment/kwh295m/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button)
> 

Then there’s **multi-repo coordination**. Sharing artifacts, triggering chained builds, or managing dependencies across different repos can be a pain point.

Someone said:

> “*We use both. GitHub Actions is great for simple or fast builds, but when you have complex CI processes that may trigger others or pass artifacts between steps - Jenkins wins*.”
~ [u/CloudyWater_](https://www.reddit.com/r/devops/comments/1hir6a5/comment/m38k4gc/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button)
> 

And of course, there’s **platform lock-in**. GitHub Actions only works if your repo is hosted on GitHub. If your team uses GitLab or Bitbucket, you’re either building workarounds or switching tools entirely.

So, yeah, GitHub Actions makes a lot of things easier. But if you’re working on something more complex, heavily regulated, or outside the GitHub ecosystem, it might not give you everything you need.

### Is GitHub Actions right for your team?

 So now that you’ve seen both the strengths and the gaps, let’s bring it back to you.

GitHub Actions is a great fit if your code lives on GitHub, your pipelines aren’t too complex, and you want something that just works without a ton of setup. It’s excellent for:

- Small to mid-sized teams
- Open source projects
- Startups shipping fast
- Teams that don’t want to maintain infra

If that’s you, then great. You’ll most likely be up and running in less than a day.

But if you’re dealing with **more complex CI flows**, need **multi-repo coordination**, or your company doesn’t use GitHub at all, Actions might start to feel limiting pretty fast.

This is also a good moment to ask what your team wants in the long term: speed and simplicity now, or more control and customization later?

And if you’re already asking those questions, you’ll want to check out this [GitHub Actions alternatives article](https://northflank.com/blog/github-actions-alternatives). It breaks down some great options if you feel like you’re outgrowing Actions.

## Which one should I finally go for? GitHub Actions or Jenkins?

If you’ve made it this far, or even if you skipped ahead, this is the part where we map each tool to the kind of team or setup it’s best for.

If your team is already on GitHub and you want a faster, simpler way to run tests, build, or deploy without setting up extra tooling, GitHub Actions will most likely get the job done. It’s clean, built-in, and great for small to mid-sized teams that don’t want to manage infra.

But if your team has more complex CI flows, needs to run things in a self-hosted or compliance-heavy environment, or relies on very specific plugins or job types, Jenkins might still be the right call, especially if you're already running it.

Still not sure? Or feeling like Jenkins might be more work than it’s worth?

Then you’ll want to check out this [Jenkins alternatives article](https://northflank.com/blog/jenkins-alternatives-2025). It walks through modern platforms that give you the power of Jenkins without all the manual setup and maintenance.

## Need more than GitHub Actions but less setup than Jenkins? Meet Northflank

So maybe you’re reading all this and thinking:

1. GitHub Actions feels too limited.
2. Jenkins looks too heavy.

Where’s the in-between? That’s where [Northflank](https://northflank.com/) comes in.

![northflank's home page-min.png](https://assets.northflank.com/northflank_s_home_page_min_37e3eb9e75.png)

Northflank gives you the flexibility of a fully featured CI/CD platform without the setup burden that comes with Jenkins.

You don’t need to manually configure agents or deal with plugin management. And unlike GitHub Actions, it’s not tied to a single code host.

It works with [GitHub](https://northflank.com/docs/v1/application/getting-started/link-your-git-account), GitLab, and Bitbucket, and lets you deploy from your repo using Docker, buildpacks, or your own custom pipelines. You can see how Northflank supports GitLab and Bitbucket in [this guide](https://northflank.com/blog/integrating-with-gitlab-and-bitbucket). 

You can also run background jobs, manage secrets, and connect services through a unified developer platform.

You get:

- Built-in CI/CD with no need to manage your own runners
- Logs, builds, and deployments all visible in one place
- More control than GitHub Actions, with less maintenance than Jenkins

If your team is stuck between too simple and too complex, Northflank might be exactly what you need.

And if you're already using GitHub Actions and want to keep your workflows, [this guide shows you how to connect them to Northflank in a few steps](https://northflank.com/docs/v1/application/infrastructure-as-code/use-github-actions-with-northflank).

### Questions devs ask about Jenkins and GitHub Actions

Still weighing your options? These are some of the questions devs are asking the most, and brief answers to them:

- **Is GitHub Actions better than Jenkins?**
    
    Depends on your team. GitHub Actions is easier to start with, Jenkins gives more control.
    
- **Can GitHub Actions fully replace Jenkins?**
    
    For many teams, yes. But Jenkins still makes sense for advanced, on-prem, or compliance-heavy pipelines.
    
- **What are the disadvantages of GitHub Actions?**
    
    Limited visibility across repos, YAML can get messy, and you’re tied to GitHub.
    
- **Why do teams still use Jenkins?**
    
    It’s still the go-to in setups that need deep customization, plugin flexibility, or tight infra control.
    
- **How do CircleCI and GitLab CI compare to Jenkins?**
    
    Easier to use than Jenkins, more flexible than Actions in some ways, but they come with their own trade-offs.
    
- **What’s the most popular CI tool in 2026?**
    
    There’s no universal answer, but GitHub Actions has definitely become the default for GitHub-first teams.
    

## What’s next?

If you’ve made it this far, you now have a much clearer picture of what Jenkins and GitHub Actions bring to the table and where they might fall short.

And if you’re looking for something that gives you more flexibility than GitHub Actions but without the setup load of Jenkins, Northflank could be the right fit.

Want to try it out?

Start with this quick [getting started guide](https://northflank.com/docs/v1/application/getting-started/introduction-to-northflank). It walks you through the steps to deploy your first service.

Or if you're ready to check it out yourself:

<div><center><a href="https://app.northflank.com/signup"><Button variant={["large", "gradient"]}>Sign up to deploy with Northflank</Button></a></center></div>]]>
  </content:encoded>
</item><item>
  <title>9 Best GitLab alternatives for CI/CD in 2026</title>
  <link>https://northflank.com/blog/best-gitlab-alternatives</link>
  <pubDate>2025-04-23T11:13:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for the best GitLab alternatives in 2026? Discover 9 top CI/CD platforms like Northflank built for speed, cloud-native dev, better UX, and pricing. Ideal for modern DevOps teams.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/openshift_alts_1_200d5d5ff3.png" alt="9 Best GitLab alternatives for CI/CD in 2026" />Let’s be honest — GitLab is a fantastic platform. It’s packed with features, offers built-in CI/CD, and does a good job of handling everything from code hosting to deployments. But like with any tool, it won’t be the perfect fit for every project or team. Maybe the pricing doesn’t scale well for your needs, the UX feels clunky, or you’re just after something more modern, cloud-native, or developer-friendly.

I’ve been there myself — juggling multiple tools, chasing better pipelines, and dreaming of that one platform that gets out of the way and lets me ship code faster. So, whether you're scaling up, tightening budgets, or just curious, it’s worth exploring what's out there in 2026.

## GitLab vs CI/CD alternatives at a glance

If you don’t have time to dive deep, here’s a quick comparison of how GitLab stacks up against other CI/CD alternatives in the areas that matter most:

| Platform | CI/CD built-in | Source hosting | Cloud-native / Kubernetes | Developer experience | Pricing model | Best suited for |
| --- | --- | --- | --- | --- | --- | --- |
| **GitLab** | Yes | Yes | Partial | Moderate | Tiered / Per-user | Teams needing an all-in-one DevOps suite |
| [**Northflank**](https://northflank.com/) | Yes (native) | External (GitHub, GitLab, Bitbucket) | Full support (built-in) | Excellent | Usage-based | Modern DevOps, fast CI/CD & cloud-native apps |
| [**GitHub Actions**](https://github.com/features/actions) | Yes | GitHub only | Limited | Good | Free + usage-based | Teams already using GitHub |
| [**Bitbucket Pipelines**](https://www.atlassian.com/software/bitbucket/features/pipelines) | Yes | Bitbucket only | Limited | Basic | Tiered (pipeline minutes) | Atlassian stack users |
| [**CircleCI**](https://circleci.com/) | Yes | External (GitHub, Bitbucket) | Partial | Good | Usage-based | Teams needing flexible, performant CI |
| [**Jenkins X**](https://jenkins-x.io/) | Yes | External | Full (K8s-native) | Complex | Open-source | Kubernetes-native CI/CD workflows |
| [**Buddy**](https://buddy.works/) | Yes (visual) | External | Partial | User-friendly | Tiered | Visual CI/CD, smaller teams |
| [**Buildkite**](https://buildkite.com/) | Yes | External | Customizable | DevOps-heavy | Self-hosted pricing | Security-conscious, enterprise teams |
| [**Codefresh**](https://codefresh.io/) | Yes | External | Full (K8s optimized) | Good | Tiered (K8s-centric) | GitOps, Helm, and cloud-native teams |
| [**Harness**](https://www.harness.io/) | Yes | External | Full | Enterprise-grade | Enterprise pricing | Governance, compliance, and progressive delivery |

## Why look for GitLab alternatives?

GitLab is one of the most popular DevOps platforms out there — and for good reason. It combines source code management, issue tracking, CI/CD, and even container registry support under one roof. But despite its strengths, it’s not the perfect tool for every team, project, or workflow. There are a few valid, practical reasons developers and engineering teams start exploring alternatives:

### 1. Cost can escalate quickly

GitLab has a generous free tier for individuals and small projects, but as your team scales or you start relying on premium features like advanced CI/CD runners, security scanning, and priority support, the pricing can climb fast. For growing startups or projects with fluctuating team sizes, these costs can become hard to justify.

### 2. Complexity and overhead

While GitLab offers an impressive all-in-one platform, that breadth can come with a learning curve and operational overhead. For small to mid-sized teams that don’t need every enterprise feature, GitLab can start to feel bloated, with features getting in the way rather than making things easier.

### 3. Performance issues with larger projects

As repositories grow and CI/CD pipelines become more demanding, some teams report performance slowdowns in GitLab — both in the UI and during pipeline executions. If you’ve ever watched a build queue crawl during peak hours, you know how frustrating this can be.

### 4. DevOps has evolved

The landscape has shifted towards **cloud-native tools**, **GitOps workflows**, and **containerized infrastructure**. Platforms designed from the ground up with these concepts in mind tend to offer faster, lighter, and more integrated experiences compared to monolithic systems like GitLab.

### 5. Better specialized tools are out there

Sometimes you don’t need an all-in-one solution. You might already have GitHub for code hosting, Jira for project management, and Terraform for infrastructure as code. In those cases, a purpose-built CI/CD and deployment platform like [**Northflank**](https://northflank.com/) might serve your workflow better.

## What to look for in a GitLab alternative

If you’re considering making a move, it’s important to evaluate alternatives based on what matters to your team and projects — not just what’s popular. Here’s a breakdown of the key criteria to focus on when comparing options:

### 1. CI/CD pipeline power and flexibility

At the heart of most DevOps workflows is the pipeline. Look for platforms that offer:

- Fast, reliable builds and deployments
- Configurable workflows (through visual editors or YAML)
- Container-native support (Docker, Kubernetes, Helm)
- Pipeline parallelization and caching for speed optimization

### 2. Source code management (if needed)

If you're replacing GitLab entirely, make sure the platform supports:

- Git hosting and branching models
- Merge requests (PRs), code reviews, and approvals
- Integration with third-party tools for issue tracking and CI/CD

Some alternatives like GitHub and Bitbucket handle this well, while others like Northflank integrate seamlessly with external Git providers.

### 3. Developer experience (DX)

You’ll spend a lot of time inside this tool, so it needs to:

- Have an intuitive, clean, responsive UI
- Provide clear, actionable logs and error reporting
- Offer good documentation, API access, and CLI support
- Not get in your way — let you focus on writing and shipping code, not wrestling with configs

### 4. Cloud-native and Kubernetes integration

If your infrastructure is containerized or Kubernetes-based (and it probably should be in 2026), look for:

- First-class container support
- Built-in Kubernetes deployments
- Managed infrastructure options or seamless cloud provider integrations
- Support for GitOps and declarative infrastructure setups

This is where modern platforms like [**Northflank**](https://northflank.com/) really shine — providing native, managed Kubernetes environments without overwhelming complexity.

### 5. Scalability and pricing transparency

As your project or team grows:

- Can the platform scale with you?
- Are pricing models clear and predictable?
- Do you pay for what you use, or get locked into rigid pricing tiers?

[Northflank](https://northflank.com/), for example, offers usage-based pricing that scales naturally, while other platforms might impose expensive per-user or per-runner fees.

### 6. Integrations and ecosystem

Modern development workflows rely on a whole stack of tools. Make sure your CI/CD platform integrates smoothly with:

- Version control providers (GitHub, Bitbucket, etc.)
- Container registries (Docker Hub, GitHub Container Registry, etc.)
- Infrastructure tools (Terraform, Pulumi, Ansible)
- Notification tools (Slack, Discord, email)
- Monitoring and observability tools (Prometheus, Grafana, Datadog)

### 7. Security and compliance

For larger projects or production systems:

- Look for built-in vulnerability scanning, secrets management, and audit logging
- Ensure support for secure pipeline execution (isolated runners, private networking)
- Consider compliance standards like SOC 2, ISO 27001, or GDPR if applicable

### 8. Community and support

No tool is perfect — so when things break (and they will), you want:

- Responsive, knowledgeable support
- An active, helpful community
- Detailed documentation and troubleshooting guides

## Top 10 GitLab Alternatives in 2026

### 1. **Northflank** — The cloud-native CI/CD devs actually enjoy using

Okay, full disclosure — this is the one I personally recommend most often these days. [**Northflank**](https://northflank.com/) is a modern CI/CD and deployment platform built for developers who prefer speed, simplicity, and cloud-native workflows.

It lets you build, deploy, and scale services directly from your Git repositories (GitHub, GitLab, Bitbucket, etc.) with beautifully integrated pipelines, containerized deployments, and Kubernetes under the hood — without making you touch YAML files for days.

 ![](https://assets.northflank.com/today1_843ac3c2a6.webp) 

**Why it's a great GitLab alternative:**

- **Built-in CI/CD and deployment** — no extra plugins, no brittle configs.
- **Managed infrastructure** — deploy to managed containers on scalable infrastructure without leaving the dashboard.
- **Great DX** — clean interface, clear logs, easy rollback, and environment previews.
- **Cloud-native by default** — containerized services, databases, cron jobs, and APIs, all under one roof.

If GitLab feels heavy and you're craving something sleek, fast, and purpose-built for modern DevOps, Northflank deserves a look.

### 2. **GitHub Actions**

[GitHub Actions](https://github.com/features/actions) has quietly grown into one of the best CI/CD options out there — especially if you're already hosting code on GitHub. It integrates directly with your repositories, offers a massive library of pre-built actions, and handles everything from testing to deployment.

 ![](https://assets.northflank.com/image_89_591fc08c20.png) 

**Why it’s a good pick:**

- Native integration with GitHub.
- Flexible workflows and matrix builds.
- Massive community of actions and templates.

[Read more on GitHub Actions.](https://northflank.com/blog/github-actions-alternatives)

### 3. **Bitbucket Pipelines**

If you're using Bitbucket for source control, [Pipelines](https://www.atlassian.com/software/bitbucket/features/pipelines) is a natural next step. It's Bitbucket’s native CI/CD tool, offering YAML-configured pipelines that run on Atlassian’s infrastructure.

 ![](https://assets.northflank.com/image_90_0afb2d03b5.png) 

**Pros:**

- Tight integration with Bitbucket repos.
- Simple pricing and built-in pipeline minutes.
- Good for teams already in the Atlassian ecosystem.

### 4. **CircleCI**

[CircleCI](https://circleci.com/) is a popular, cloud-hosted CI/CD service known for its speed and simplicity. It’s highly customizable and works with GitHub and Bitbucket.

 ![](https://assets.northflank.com/today2_5574af1db5.webp) 

**Highlights:**

- Optimized performance with parallelism and caching.
- Custom Docker images and machine executors.
- Great analytics and insights on builds and deployments.

### 5. **Jenkins X**

Not to be confused with classic Jenkins, [Jenkins X](https://jenkins-x.io/) is designed for Kubernetes-native CI/CD. It automates CI/CD pipelines for cloud-native applications, leveraging GitOps workflows.

 ![](https://assets.northflank.com/image_87_006583e2ae.png) 

**Good for:**

- Kubernetes-first teams.
- Cloud-native apps using Docker, Helm, and K8s deployments.
- GitOps automation fans.

[Read more on Jenkins.](https://northflank.com/blog/jenkins-alternatives-2025)

### 6. **Buddy**

[Buddy](https://buddy.works/) is an intuitive, visual CI/CD platform aimed at developers who prefer drag-and-drop simplicity. It integrates with popular Git services and supports container builds, deployments, and testing.

 ![](https://assets.northflank.com/image_91_bedcd35ecc.png) 

**Perks:**

- Clean, visual pipeline builder.
- Fast builds with smart caching.
- Pre-built actions for common workflows.

### 7. **Buildkite**

[Buildkite](https://buildkite.com/) offers hybrid CI/CD — you manage the infrastructure, and they handle the orchestration. It’s highly scalable and flexible, often favored by larger teams with security and compliance requirements.

 ![](https://assets.northflank.com/today4_e4baf62a38.webp) 

**Why it stands out:**

- Runs on your own infrastructure (including behind firewalls).
- Scales to thousands of parallel agents.
- Great for complex or self-hosted environments.

### 8. **Codefresh**

[Codefresh](https://codefresh.io/) is a powerful CI/CD platform optimized for Kubernetes and Docker workflows. It’s feature-rich, offering GitOps integrations, Helm charts, and Kubernetes dashboards.

 ![](https://assets.northflank.com/image_92_445c27a83d.png) 

**Best for:**

- Cloud-native, containerized applications.
- Teams using GitOps and Helm.
- Kubernetes-focused projects.

### 9. **Harness**

[**Harness**](https://www.harness.io/) is a powerful DevOps platform designed for teams that need robust delivery pipelines, governance, and visibility. It brings AI and automation into CI/CD, aiming to simplify deployment processes while optimizing for efficiency and cost.

 ![](https://assets.northflank.com/today6_7801f2b02f.webp) 

**Best for:**

- Large teams or orgs that need governance, compliance, and advanced deployment strategies.
- Engineering teams that want AI-powered insights into pipeline performance and cloud usage.
- Companies practicing progressive delivery or canary deployments.

[Read more on Harness.](https://northflank.com/blog/top-harness-alternatives)

## Conclusion: Finding the Right Fit

GitLab is solid — no denying it. But it’s not a one-size-fits-all solution. Whether you’re after faster pipelines, simpler deployments, better pricing, or a more cloud-native experience, there’s no shortage of great alternatives in 2026.

If you’re especially looking for a platform that makes CI/CD and deployments genuinely painless without sacrificing power, [**Northflank**](https://northflank.com/) is seriously worth trying. I’ve been impressed with how clean, fast, and developer-friendly it is compared to some of the old-school options out there.

Ultimately, the “best” alternative depends on your stack, your team’s preferences, and your workflow. Hopefully, this list helps narrow it down.

**Curious to see what a smoother DevOps setup feels like?** Give [**Northflank**](https://app.northflank.com/signup) a try — it's built for speed, simplicity, and scaling modern workflows without the usual friction.]]>
  </content:encoded>
</item><item>
  <title>9 best Spinnaker alternatives in 2026: CI/CD tools for better pricing, flexibility &amp; DX</title>
  <link>https://northflank.com/blog/spinnaker-alternatives</link>
  <pubDate>2025-04-22T13:50:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for better Spinnaker alternatives? Learn about 9 top CI/CD platforms with better pricing, observability, GitOps support, and developer experience, including Northflank, Argo CD, GitLab, and more.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/spinnaker_alternatives_eeb60174ca.png" alt="9 best Spinnaker alternatives in 2026: CI/CD tools for better pricing, flexibility &amp; DX" />> Spinnaker alternatives have been a hot topic lately, and for good reason. For a while now, I’ve seen developers complain about [Spinnaker](https://spinnaker.io/)’s complexity and how much effort it takes to keep things running.
> 

Some developers expressed their frustration about these issues on Reddit. They talked about maintenance-heavy setups, limited GitOps support, and the lack of flexibility. A few mentioned switching to alternatives like ArgoCD, tools they consider easier to manage and more aligned with modern DevOps practices.

Do you have similar complaints, or just looking for a Spinnaker alternative? If yes, then this article is for you.

We’ll look at 9 Spinnaker alternatives that are easier to deploy, maintain, and work with, and in many cases, much more budget-friendly than keeping Spinnaker running.

<div> <center> <a href="https://app.northflank.com/signup"> <Button variant={["large", "gradient"]}>Find the right platform for your next project</Button> </a> </center> </div>


<InfoBox className='BodyStyle'>

### Quick look: top 9 Spinnaker alternatives in 2026

In a hurry? Here's a quick breakdown of some of the best Spinnaker alternatives for 2026:

1. **[Northflank](https://northflank.com/)** – Full GitOps support, built-in logs & metrics, BYOC, and background jobs.
2. **[Argo CD](https://argo-cd.readthedocs.io/en/stable/)** – Kubernetes-native GitOps tool with declarative delivery.
3. **[Jenkins](https://www.jenkins.io/)** – Long-standing CI tool that pairs with CD solutions.
4. **[Azure DevOps](https://azure.microsoft.com/en-us/services/devops/)** – Microsoft-native CI/CD pipelines with tight Azure integration.
5. **[Harness](https://harness.io/)** – Enterprise-grade CD with machine learning-based verification.
6. **[Qovery](https://www.qovery.com/)** – Git-based deployment platform with strong developer UX.
7. **[GitHub Actions](https://github.com/features/actions)** – Built-in CI/CD for GitHub repositories.
8. **[OpenShift Pipelines](https://www.redhat.com/en/technologies/cloud-computing/openshift/pipelines)** – Tekton-powered pipelines for K8s-heavy teams.
9. **[Fly.io](https://fly.io/)** – Fast deploys with regional app hosting and Git-based flow.

</InfoBox>

## Quick comparison: 9 Spinnaker alternatives at a glance

Before we get into the details, here’s a quick comparison of how each tool compares across the basics.

We’re looking at four things that tend to matter most when teams like yours are moving away from Spinnaker:

- GitOps support
- Built-in observability (logs, metrics, debugging)
- CI/CD coverage (do you need to plug in other tools?)
- Pricing model (because not everyone has the budget for enterprise licenses)

Look at the comparison:

| **Platform** | **GitOps** | **Observability** | **CI/CD Coverage** | **Pricing Model / Starting Price** |
| --- | --- | --- | --- | --- |
| [Northflank](https://northflank.com/) | Yes | Full logs & metrics | Full CD, background jobs | Free plan + usage-based ([BYOC](https://northflank.com/features/bring-your-own-cloud) supported) |
| [Argo CD](https://argo-cd.readthedocs.io/en/stable/) | Yes | K8s-native only | CD only | Free (open-source) |
| [Jenkins](https://www.jenkins.io/) | No | Manual | CI only | Free (open-source) |
| [Azure DevOps](https://azure.microsoft.com/en-us/services/devops/) | Partial | Basic built-in | Full CI/CD | Free for 5 users, then $6/user/month |
| [Harness](https://harness.io/) | Yes | ML-based metrics | Full CI/CD | Paid only (contact sales) |
| [Qovery](https://www.qovery.com/) | Yes | Git-based deploy logs | CD only | Free for hobby, from $49/month |
| [GitHub Actions](https://github.com/features/actions) | Yes | Lightweight | Full CI/CD | Free up to 2k minutes, then pay-as-you-go |
| [OpenShift Pipelines](https://www.redhat.com/en/technologies/cloud-computing/openshift/pipelines) | Yes | K8s-native observability | CD | Included with OpenShift |
| [Fly.io](https://fly.io/) | No | Runtime & app logs | CD for small apps | Free tier + usage-based |

## What to look for in a Spinnaker alternative

If you’re thinking of switching from Spinnaker, it helps to know what to prioritize before picking a replacement.

For most teams like yours, it’s not just about finding another CI/CD tool. It’s about making sure you’re not spending hours managing infrastructure or piecing together several different tools just to ship code.

Let’s see the few things that are worth paying attention to as you look for a better fit.

 ![](https://assets.northflank.com/spinnaker_article_graphic_design_dc65b7bf6a.png) 

### 1. Git-based or declarative delivery

Tools that use Git as the source of truth help reduce manual steps and make your deployments more predictable and auditable. It’s a bonus if the tool supports [Helm](https://helm.sh/), [Kustomize](https://kustomize.io/), or other Kubernetes-native config tools.

### 2. Built-in observability

Look for platforms that give you logs, metrics, deployment history, and rollback options without needing third-party integrations. That alone saves hours of debugging and setup time.

### 3. CI/CD coverage

Some platforms handle both CI and CD. Others focus on CD and expect you to bring your own CI. Either is fine, what matters is “does it fit your workflow or make you add complexity?”

### 4. Secret management

Native secrets support is ideal, but it’s also fine if it integrates cleanly with Vault of Kubernetes secrets. What you don’t want is a setup where secrets are treated like an afterthought.

### 5. Support for background jobs

Not every app is a web service. If you rely on background workers, corn jobs, or task queues, make sure the platform supports those too, or gives you a way to manage them alongside services.

### 6. Developer experience and setup time

If the tools need a full-time DevOps engineer just to get it running (looking at you, Spinnaker), it might not be the right long-term fit. Look for something that’s easier to adopt and doesn’t fight your team.

### 7. Pricing and flexibility

Some tools are open-source. Others, like Northflank, give you usage-based pricing or bring-your-own-cloud options. So, depending on your needs, one model might make a lot more sense than the other, especially if you’re trying to reduce costs or avoid vendor-lock-in. 

## 9 best Spinnaker alternatives in 2026

We’ve covered the basics, now let’s look at each platform in a bit more detail.

If you’re here, you’re most likely trying to move away from Spinnaker without making things harder for your team. It could be one of the following reasons:

- You want a GitOps-friendly setup
- You’re done dealing with the stress that comes with on-prem
- You want something that’s easier to manage and works out of the box

Whatever the case, I’ve broken down these 9 Spinnaker alternatives by how they work, where they fit best, and why teams are switching to them.

Let’s start with Northflank.

### 1. Northflank – For fast deploys, observability, and full control

If you’re looking for something that’s GitOps-friendly out of the box, has great built-in [observability](https://northflank.com/docs/v1/application/observe/observability-on-northflank), and doesn’t require combining multiple tools like Prometheus for metrics, Loki for logs, or custom dashboards for deployment history, [Northflank](https://northflank.com/) might be exactly what you need.

It supports:

- Full [CD pipelines](https://northflank.com/docs/v1/application/release/create-a-pipeline-and-release-flow)
- [Background jobs](https://northflank.com/create-trigger-and-schedule-jobs-and-cron-jobs)
- Build [services](https://www.notion.so/spinnaker-alternatives-1cf6d14c7851807fb035d0e460d61551?pvs=21)
- BYOC (Bring Your Own Cloud) [features](https://northflank.com/features/bring-your-own-cloud)
- … and more

It lets you [bring your own cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) if that’s your setup. This means you can deploy workloads into your AWS or GCP account while using Northflank’s interface, monitoring, and deployment controls.

 ![](https://assets.northflank.com/northflank_s_home_page_min_3683176a08.png) 

Also, with Northflank, you get [logs](https://northflank.com/docs/v1/application/observe/view-logs), [metrics](https://northflank.com/docs/v1/application/observe/view-metrics), and [deployment history](https://northflank.com/docs/v1/application/observe/monitor-containers) directly in the platform, so you don’t have to integrate separate services or write extra config just to see what’s going on.

Northflank also gives you flexibility around how you deploy. You can:

- Push from Git ([See how](https://northflank.com/docs/v1/application/build/build-code-from-a-git-repository))
- Build from Dockerfiles ([See how)](https://northflank.com/docs/v1/application/build/build-with-a-dockerfile)
- Spin up preview environments ([See how](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment))
- Manage services ([See how](https://northflank.com/docs/v1/application/cloud-providers/manage-your-cluster))
- Cron jobs ([See how](https://www.notion.so/The-Northflank-Sales-Bible-3675234901ac4f9aa36bef25dd73ef94?pvs=21))
- Storage ([See how](https://northflank.com/docs/v1/application/production-workloads/persistent-storage-in-production))

The good part is that you can do all these in one place!

And if you’re working with a team, it’s easy to [assign access](https://northflank.com/docs/v1/application/secure/use-role-based-access-control), [manage secrets](https://northflank.com/docs/v1/application/secure/manage-secret-groups), and define custom roles without needing to dig into complex RBAC setups.

If Spinnaker has started to feel too rigid or time-consuming for your use case, Northflank gives you a simpler and more maintainable path forward.

[See Northflank in action](https://app.northflank.com/signup) or [book a live demo](https://cal.com/team/northflank/northflank-demo?duration=30) to see how it compares.

### 2. Argo CD – Kubernetes-native GitOps with declarative delivery

If your team is already running workloads on Kubernetes and you want a GitOps tool purpose-built for it, [ArgoCD](https://argoproj.github.io/cd/) is a good option. It’s fully open source, backed by the CNCF, and widely adopted by teams that are serious about declaratively managing Kubernetes.

With ArgoCD, your Git repo becomes your application state's single source of truth. It watches for changes in your manifests and automatically syncs them to your Kubernetes cluster. That means you get automated, version-controlled deployments using YAML or Helm (no need to trigger deploys manually or update config across different tools).

 ![](https://assets.northflank.com/argocd_home_page_min_4005b43f16.png) 

Some of the key features include:

- Declarative GitOps deployments for Kubernetes
- Support for Helm, Kustomize, Jsonnet, and plain YAML
- [Multiple cluster support](https://argo-cd.readthedocs.io/en/stable/operator-manual/cluster-bootstrapping/)
- [Application health status monitoring](https://argo-cd.readthedocs.io/en/latest/operator-manual/health/#:~:text=An%20Argo%20CD%20App's%20health,of%20its%20immediate%20child%20sources.)
- [Sync and rollback controls](https://argo-cd.readthedocs.io/en/stable/user-guide/sync-options/)

It’s worth noting that Argo CD doesn’t include CI capabilities, so you’ll need to pair it with a CI tool like GitHub Actions, CircleCI, or Northflank if you want a full pipeline from code to deploy.

Also, Argo doesn’t come with built-in secrets management. You can integrate it with tools like [HashiCorp Vault](https://argo-cd.readthedocs.io/en/stable/operator-manual/security/#secrets-management) or [Sealed Secrets](https://github.com/bitnami-labs/sealed-secrets), but you’ll need to handle that setup yourself.

That said, if you’re comfortable with Kubernetes and want a lightweight GitOps CD engine that fits cleanly into your workflow, Argo CD is definitely worth looking into.

You can check out [Argo CD’s documentation](https://argo-cd.readthedocs.io/en/stable/) or [try it on GitHub](https://github.com/argoproj/argo-cd) to get started.

If you're also looking into tools that improve on what Argo does (or fill in the gaps), check out this [Argo CD alternatives guide](https://northflank.com/blog/argo-cd-alternatives-northflank-developer-platform-git-ops-self-service).

### 3. Jenkins – Reliable, but starting to show its age

[Jenkins](https://www.jenkins.io/) is one of the oldest and most widely used CI tools out there. If you've ever built a pipeline before, chances are you've used Jenkins at some point. It’s open source, heavily extensible, and still used in a lot of production systems today.

That said, Jenkins on its own is more of a CI tool than a full CD platform. It doesn’t have built-in deployment tracking, rollback support, or native GitOps functionality, so if you’re thinking about replacing Spinnaker with Jenkins alone, you’ll likely need to add a few more tools to your stack.

 ![](https://assets.northflank.com/jenkins_website_min_455a49acba.png) 

You can still make it work for CD, especially if you're comfortable managing plugins and custom scripts. Jenkins has a massive [plugin ecosystem](https://plugins.jenkins.io/) that lets you hook into most services and cloud providers, and some teams use it for end-to-end automation. But the tradeoff is the amount of maintenance involved. You’ll spend time upgrading plugins, dealing with UI limitations, and handling user management manually unless you build your own solutions around it.

Some key things to keep in mind:

- Reliable [CI capabilities](https://www.jenkins.io/doc/book/pipeline/)
- Flexible, with [hundreds of plugins](https://plugins.jenkins.io/)
- Can be extended for CD, but not out of the box
- You’ll need to configure observability, RBAC, secrets, and rollback manually

If your use case is mostly CI or you're inheriting an existing Jenkins setup, it can still get the job done. But for modern, Git-based CD workflows with better built-in controls, you might want to pair Jenkins with another tool, or go for something that handles CI/CD together.

You can read more in this [Jenkins alternatives guide](https://northflank.com/blog/jenkins-alternatives-2025) if you're looking to move on from it completely.

### 4. Azure DevOps – Familiar if you’re already in the Microsoft ecosystem

If your team is already deep into Azure or using tools like Visual Studio and Git repos hosted on Azure, then [Azure DevOps](https://azure.microsoft.com/en-us/products/devops) might already be part of your stack. It’s Microsoft’s full DevOps suite, covering everything from code repos and CI/CD pipelines to project boards and test plans.

The CI/CD part comes from [Azure Pipelines](https://azure.microsoft.com/en-us/products/devops/pipelines), which supports both YAML-based and classic GUI pipelines. You can use it to build, test, and deploy to Azure, AWS, on-prem servers, or Kubernetes clusters. It’s flexible, and it integrates well with Microsoft tools out of the box.

 ![](https://assets.northflank.com/azure_devops_home_page_min_b0a5f3378f.png) 

That said, it can feel a bit clunky if you’re not fully bought into the Azure ecosystem. There’s a decent amount of setup required to get pipelines running, and you’ll likely be writing more YAML than you’d expect. GitOps support is also limited; it's there, but mostly for pipelines, not releases, so if you're looking for something closer to Argo CD or Northflank, this might feel like a step back.

Here’s what stands out:

- [CI/CD pipelines](https://learn.microsoft.com/en-us/azure/devops/pipelines/?view=azure-devops) with support for YAML and classic editors
- Built-in Git repos, boards, and test plans
- [Good integration with Azure](https://learn.microsoft.com/en-us/azure/devops/pipelines/targets/azure-services) and other Microsoft tools
- Works with self-hosted agents or Microsoft-hosted runners
- Some GitOps workflows possible, but not full support
- Secrets management, RBAC, and approval flows are built in, but often need configuration

If you’re already using Azure DevOps, it might make sense to keep building on top of it. But if you’re starting from scratch or want something more developer-friendly with less overhead, there are definitely lighter and more flexible tools out there.

And if you’re weighing it up against platforms that support GitOps and modern workflows better, [this Azure alternatives guide](https://northflank.com/blog/azure-alternatives) might help.

### 5. Harness – CD focused, with enterprise-grade features

If you’re looking for a more polished, enterprise-ready take on Continuous Delivery, [Harness](https://www.harness.io/) is worth checking out. It was created by the same co-founder of AppDynamics, and it’s positioned as a commercial alternative to Spinnaker, with a bigger focus on usability and automation.

Harness supports things like canary deployments, automated rollbacks, approval workflows, and continuous verification. It also has its own CI engine, but where it really leans in is CD. You get granular control over pipelines, built-in RBAC, and integrations with tools like Datadog, New Relic, and Prometheus for monitoring deployments.

 ![](https://assets.northflank.com/harness_min_d5bf0e85b1.png) 

One feature Harness is known for is [automated verification](https://www.harness.io/products/continuous-delivery/ai-assisted-deployment-verification) (basically using ML to compare pre-deploy and post-deploy metrics to help catch issues early). It’s designed for teams that want to move fast but still keep quality and compliance in check.

Here’s what stands out:

- [CD pipelines](https://harness.io/products/continuous-delivery) with rollback, canary, and blue/green support
- [Machine learning-based deployment verification](https://developer.harness.io/docs/continuous-delivery/verify/cv-concepts/machine-learning/)
- Secrets management, audit trails, and fine-grained access controls
- Built-in CI and feature flagging modules if you need them
- Works with Kubernetes, VMs, AWS, GCP, and Azure
- SaaS and on-prem options available

The main downside? It’s not cheap. There’s no free tier, and pricing is usage-based with custom quotes. You can request a trial, but it’s clearly targeted at mid-to-large teams with serious deployment volume and strict security needs.

If you’re replacing Spinnaker in an enterprise setting and want support, governance, and automation out of the box, Harness is a good option to look into.

You can read more in this [Harness alternatives guide](https://northflank.com/blog/top-harness-alternatives) if you’re weighing it against other tools.

### 6. Qovery – Git-based deployments with good developer experience

If you want a platform that takes care of infrastructure without getting in your way, [Qovery](https://www.qovery.com/) might be a good fit. It’s a deployment platform built for developers, especially teams that want to ship from Git without managing Kubernetes directly.

Qovery sits somewhere between a PaaS and a control plane. You connect your Git repo, configure your environment, and Qovery handles provisioning, deployment, and environment management. Under the hood, it’s running on Kubernetes, but you don’t need to touch YAML unless you want to.

 ![](https://assets.northflank.com/qovery_home_page_min_309378f6d5.png) 

It supports multi-service apps, preview environments, secrets management, and even cron jobs. You can choose between running on Qovery’s cloud or in your own AWS account, which gives you flexibility depending on your team’s needs.

Here’s what stands out:

- Preview environments for every pull request
- Secrets and config management built in
- Support for background jobs and cron scheduling
- Can be hosted on Qovery’s cloud or self-managed on AWS
- Docker and Kubernetes support under the hood, but abstracted away by default

Qovery doesn’t aim to replace every enterprise use case, but for developers who want to focus on writing and shipping code, without stitching together a full CI/CD pipeline.

If you're comparing platforms in this category, this [Qovery alternatives guide](https://northflank.com/blog/best-qovery-alternatives) might help you see how it stacks up.

### 7. GitHub Actions – CI/CD built into your Git workflow

If your code already lives on GitHub, [GitHub Actions](https://github.com/features/actions) is one of the easiest ways to start building and deploying your projects. It’s fully integrated into the GitHub UI, supports event-driven workflows, and can be used for everything from linting and testing to full application deployments.

You define your pipelines as YAML files inside your repo, and Actions takes care of the rest, including triggering workflows on every push, PR, or tag. You can deploy to Kubernetes, AWS, GCP, Azure, or even self-hosted servers, depending on how you set it up.

 ![](https://assets.northflank.com/Github_actions_home_page_6093a76be8.png) 

It’s not a full platform like Spinnaker, but with the right actions and integrations, you can build powerful pipelines without leaving your repo.

What stands out:

- [Tight GitHub integration](https://docs.github.com/en/actions) with event-based triggers
- Huge marketplace of [pre-built actions](https://github.com/marketplace?type=actions) to deploy to any cloud or service
- Simple syntax and quick setup for small teams
- Works well for CI/CD, infrastructure automation, or custom workflows
- Pay-as-you-go pricing with a generous free tier (2,000 minutes/month for public repos)

GitHub Actions is especially useful for smaller teams, hobby projects, or startups that want to keep things simple. It scales pretty well, but for large-scale delivery pipelines with approval flows, GitOps, or security policies, you might need to combine it with another tool.

Still, if you’re already using GitHub, it’s one of the most convenient places to start.

You can also check out this [GitHub Actions alternatives guide](https://northflank.com/blog/github-actions-alternatives) if you're thinking about building more complex workflows or want more control.

### 8. OpenShift Pipelines – Tekton-powered pipelines for Kubernetes teams

If you're working in a Red Hat environment or already using OpenShift, [OpenShift Pipelines](https://www.redhat.com/en/technologies/cloud-computing/openshift/pipelines) might be a natural fit. It's Red Hat’s CI/CD solution built on top of Tekton, which is an open-source framework for running Kubernetes-native pipelines.

OpenShift Pipelines lets you define tasks and pipelines as Kubernetes custom resources. It integrates with your cluster’s RBAC, supports triggers, and gives you full control over how your builds and deployments run, all in a declarative, container-native way.

 ![](https://assets.northflank.com/openshift_min_2d87ef258a.png) 

Unlike platforms like Spinnaker, OpenShift Pipelines is built for teams that are already deep in Kubernetes and want pipelines that behave like any other workload in their cluster.

Here’s what stands out:

- Built on [Tekton](https://tekton.dev/docs/pipelines/), a CNCF project for Kubernetes-native pipelines
- [Integrated with OpenShift’s developer console](https://docs.openshift.com/container-platform/4.13/cicd/pipelines/working-with-pipelines-using-the-developer-perspective.html) and RBAC model
- Declarative workflows defined as Kubernetes CRDs
- [Pipeline triggers](https://docs.openshift.com/container-platform/4.13/cicd/pipelines/using-triggers.html) for event-based CI/CD
- Can deploy to any Kubernetes target within your cluster
- Ideal for GitOps and Git-based deployments when paired with Argo CD

That said, it’s not the most beginner-friendly option. You’ll need to be comfortable working inside Kubernetes and managing pipelines as code. But if that’s your setup already, OpenShift Pipelines gives you a powerful, consistent way to build and deploy within your platform.

You can get started in the [OpenShift Pipelines docs](https://docs.openshift.com/container-platform/4.13/cicd/pipelines/understanding-openshift-pipelines.html) or check other [Kubernetes platform alternatives](https://northflank.com/blog/best-open-shift-alternatives-finding-the-right-kubernetes-platform) if you're looking for something lighter.

### 9. Fly.io – Fast global deploys with a developer-first feel

[Fly.io](https://fly.io/) is built for developers who want to deploy apps close to their users, without getting into Kubernetes or managing cloud infrastructure. You write your app, run a simple CLI command, and Fly handles the rest: provisioning, networking, certificates, scaling, and regional deploys.

It’s especially popular for full-stack apps that need fast cold starts, edge presence, or region-aware deployment logic. You can run databases, background jobs, and even scale apps across multiple regions with relatively little setup.

 ![](https://assets.northflank.com/fly_io_min_bfc65ba670.png) 

Here’s what makes Fly.io stand out:

- Global app hosting with regional scaling
- Simple deploys from Docker or buildpacks
- Support for Postgres, Redis, and other managed services
- Secrets management and app metrics included
- Free tier available, with usage-based billing after that

Fly.io doesn’t give you the same level of control as something like Spinnaker or Argo CD, no pipeline builders, no complex approval flows. But if you want to ship fast, monitor your apps easily, and deploy globally from the CLI, it’s one of the simplest ways to do it.

You can also check out [Fly.io alternatives](https://northflank.com/blog/flyio-alternatives) if you’re looking for more visibility or GitOps support.

## Frequently asked questions about Spinnaker

If you're still figuring out where Spinnaker fits into the bigger picture or trying to understand how it compares to other tools, this will help. These are some of the most common questions people ask when deciding if they should stick with it or switch to something else.

### What is Spinnaker used for?

Spinnaker is an open-source Continuous Delivery platform. It helps teams automate application deployments, especially in multi-cloud environments. It was originally built by Netflix to handle their production rollouts, and it’s known for supporting strategies like blue/green, canary, and rolling deployments.

### Does Netflix still use Spinnaker?

Yes, Netflix still uses Spinnaker internally. But it's worth noting that many other companies have moved away from it, mostly due to the maintenance effort involved and how hard it can be to scale without a dedicated team.

### What are the drawbacks of Spinnaker?

Spinnaker isn’t exactly lightweight. It requires a complex setup, doesn’t come with built-in secrets management, and isn’t very flexible if you’re trying to follow GitOps workflows. It also doesn’t support SaaS hosting, which makes it harder for teams that just want to deploy without managing infrastructure.

### What is the difference between Spinnaker and Argo CD?

The biggest difference is in the architecture and workflow. Argo CD is GitOps-first and Kubernetes-native; it deploys by syncing your cluster to a Git repo. Spinnaker, on the other hand, takes a more traditional approach to CD with pipelines and custom deploy stages, and it’s not limited to Kubernetes.

### Is Spinnaker similar to Jenkins?

Not really. Jenkins is primarily a CI tool, and while it can be extended for CD, it doesn’t come with deployment support out of the box. Spinnaker focuses entirely on CD, you still need to pair it with a CI tool like Jenkins, GitHub Actions, or something else to get a full pipeline.

### Is Spinnaker still relevant in 2026?

Spinnaker is still around and used in some enterprise setups, but it’s definitely lost traction in recent years. Many teams are moving to tools that are lighter, easier to manage, and fit better with modern GitOps and Kubernetes workflows.

### What’s the difference between Spinnaker and Terraform?

Spinnaker handles application deployments; it defines *how* and *when* apps should roll out. Terraform is an infrastructure-as-code tool that provisions infrastructure, like VMs, databases, or networks. You can use them together (and some teams do), but they solve different problems.

### Is Spinnaker a CI/CD tool?

Spinnaker is strictly a CD tool. It doesn’t handle Continuous Integration (like building and testing your code). You’ll need to pair it with a CI tool to get full CI/CD coverage.

## Choosing the right Spinnaker alternative for your team

By now, you’ve likely seen that there’s no one-size-fits-all answer. Some teams want something that’s GitOps-native and Kubernetes-ready out of the box. Others want a more developer-friendly experience, less manual setup, or a platform that doesn’t require constant maintenance.

Spinnaker can still work for certain use cases, but if maintaining it has started to slow your team down, it might be time to move on. The good news is, you’ve got plenty of options, no matter if you’re looking for something self-hosted, SaaS, simple, or flexible enough to bring your own cloud.

If you're already leaning toward one of the platforms above, the best next step is to try it out in your own workflow and see how it fits. Get started by [signing up for free](https://app.northflank.com/signup).]]>
  </content:encoded>
</item><item>
  <title>The best Platform.sh alternatives for fast, flexible app hosting</title>
  <link>https://northflank.com/blog/platformsh-alternatives</link>
  <pubDate>2025-04-21T10:10:00.000Z</pubDate>
  <description>
    <![CDATA[Platform.sh is a reliable PaaS for complex, multi-service apps with Git-driven workflows, but it struggles with flexibility, pricing, and modern container support. Newer platforms like Northflank offer better alternatives.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/20_oss_projects_to_check_out_at_Kube_Con_blog_post_1_5122a63ecd.png" alt="The best Platform.sh alternatives for fast, flexible app hosting" />If you’ve worked with [**Platform.sh**](http://platform.sh/), you probably know what makes it appealing — a reliable platform that combines app hosting, infrastructure management, and Git-driven deployment workflows in one place. It’s well-regarded for its multi-service support, cloning environments from Git branches, and managing infrastructure complexity so developers don’t have to.

But like any platform, it isn’t perfect for everyone. Maybe you’re starting to feel the limits around flexibility. Or the pricing isn’t scaling well with your needs. Or you’re curious if newer, faster, more developer-friendly options exist — ones that better fit today’s workflows and tech stacks.

If that sounds like you, this guide is for you.

## Platform.sh vs other hosting platforms at a glance

If you don’t have time to dive deep, here’s a quick comparison of how [Platform.sh](http://platform.sh/) stacks up against other hosting platforms in the areas that matter most:

| Feature / Platform | [**Platform.sh**](http://platform.sh/) | [**Northflank**](https://northflank.com/) | [**Render**](https://render.com/) | [**Railway**](https://railway.com/) | [**Fly.io**](https://fly.io/) | [**DigitalOcean App Platform**](https://www.google.com/search?client=safari&rls=en&q=digital+ocean+app+platform&ie=UTF-8&oe=UTF-8) |
| --- | --- | --- | --- | --- | --- | --- |
| Architecture Style | Git-driven, proprietary environments | Container-native, microservices-friendly | App & static hosting, Docker-based | Simple apps & databases | Global edge-deployed containers | PaaS for apps & static sites |
| Supports Containers | Limited (through buildpacks) | ✅ Full Docker support | ✅ Docker-based | ⚠️ Basic support | ✅ Full Docker-native | ⚠️ Limited (via buildpacks or container images) |
| Multi-Service / Microservices | ✅ Supported | ✅ First-class microservices support | ⚠️ Limited | ⚠️ Limited | ✅ Excellent (edge-distributed) | ⚠️ Limited multi-service support |
| [Bring Your Own Cloud (BYOC)](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) | ❌ Not supported | ✅ Supports AWS, Azure, GCP, [etc](https://northflank.com/features/bring-your-own-cloud). | ❌ Not supported | ❌ Not supported | ❌ Not supported | ❌ Not supported |
| Built-in CI/CD | ✅ Git-based, proprietary | ✅ Integrated Git CI/CD & Preview Envs | ✅ Git-based | ✅ Git deploys | ⚠️ Manual / limited | ✅ Git-based CI/CD |
| Managed Databases | ✅ PostgreSQL, MariaDB, Redis, MongoDB | ✅ PostgreSQL, MongoDB, Redis, more | ✅ PostgreSQL, Redis | ✅ PostgreSQL, Redis | ✅ LiteFS, SQLite, external DBs | ✅ PostgreSQL, MySQL, Redis |
| Preview Environments | ✅ Git branch-based | ✅ Per-branch deploy previews | ✅ Available | ✅ Simple preview environments | ⚠️ More manual | ⚠️ Limited / only for Pro tier |
| Pricing Transparency | ⚠️ Can be complex, scales up quickly | ✅ Transparent, fair pricing tiers | ✅ Clear and affordable | ✅ Pay-as-you-go | ✅ Pay-as-you-go | ✅ Transparent tiered pricing |
| Ease of Use / Dev Experience | ⚠️ Steeper learning curve | ✅ Modern, Developer-friendly, intuitive UI & CLI | ✅ Developer-friendly | ✅ Beginner-friendly | ⚠️ Requires infra knowledge | ✅ Simple, polished UI |
| Edge Hosting / Global Deploy | ⚠️ Limited | ✅ Available | ⚠️ Limited | ⚠️ Limited | ✅ Built for edge | ⚠️ Regional deploys only |
| Best For | Complex enterprise apps, tightly coupled stacks | APIs, microservices, SaaS apps, Docker-first projects | Web apps, APIs, simple services | Side projects, MVPs, indie SaaS | Real-time, global-first apps | Simple apps, startups, DigitalOcean fans |

## Where [Platform.sh](http://platform.sh/) works well — and where it struggles

[Platform.sh](http://platform.sh/) shines in a few key areas, especially for teams working with complex, tightly coupled applications. It’s particularly well-suited for projects that:

- **Need built-in support for multiple services** like PostgreSQL, Redis, MongoDB, and more.
- **Rely on Git-driven workflows**, where every branch can instantly spin up its own environment — perfect for staging, testing, and QA.
- **Value having infrastructure and hosting managed together** under a single platform, with less day-to-day DevOps overhead.
- **Support enterprise apps** with strict infrastructure policies, compliance needs, or legacy requirements that benefit from [Platform.sh](http://platform.sh/)’s managed services and structured workflow.

It’s a solid choice for **larger teams working in established ecosystems** where consistency, predictability, and managed infrastructure take priority over bleeding-edge flexibility.

### Where [Platform.sh](http://platform.sh/) struggles

As good as it is for certain use cases, [Platform.sh](http://platform.sh/) starts to feel limiting when your needs shift towards modern, containerized, microservices-driven architectures — or when your budget and workflows demand something leaner and faster.

Here’s where it tends to fall short:

- **Flexibility is limited.** You’re often working within proprietary build and deployment processes that don’t integrate cleanly with external CI/CD systems, container registries, or cloud services.
- **Container and microservices support feels bolted on.** While you can run multiple services, it’s not a native container-first platform — Docker support is limited, and multi-service orchestration lacks the simplicity found in newer platforms.
- **Pricing can escalate quickly.** As you add more services, environments, or databases, costs can balloon in a way that’s not always transparent or predictable.
- **Developer experience feels a little dated.** Compared to newer platforms with intuitive UIs, modern CLIs, and seamless environment management, [Platform.sh](http://platform.sh/) can feel clunky and harder to adopt, especially for newer teams or fast-moving projects.
- **Vendor lock-in is real.** Moving your infrastructure, databases, and services outside of [Platform.sh](http://platform.sh/)’s ecosystem can be painful, making scaling or migrating to other platforms more difficult down the road.

If your team’s tech stack is moving towards **containers, microservices, APIs, and lightweight, cloud-native services**— or if you need to control infrastructure more closely while keeping the developer experience frictionless — you’ll likely start feeling these limitations.

## What makes a good alternative?

When you're weighing alternatives, you’re not just chasing hype — you’re after something that actually makes your workflow better. Here’s what that looks like:

- **Feels fast and intuitive to work with** — from the first `git push` to a live deploy, it should feel effortless. Clean docs, a sane CLI, good defaults, and a dashboard you don’t dread opening.
- **Supports the tech stacks and architectures you actually use** — whether you’re running containers, microservices, databases, background workers, or static sites. A good platform should fit into your existing stack, not force you to rebuild it.
- **Gives you flexibility without burying you in DevOps work** — you should be able to tweak infrastructure when you need to, but not get stuck managing YAML forests or debugging obscure pipeline errors at midnight.
- **Offers predictable pricing** that scales fairly with your usage — no surprises, no unpredictable overages, and pricing models that make sense whether you’re a solo developer or running production workloads for a growing team.
- **Supports modern CI/CD workflows, preview environments, and instant rollbacks** — because shipping fast (and safely) is non-negotiable in 2026. Your tools should help you iterate confidently, not hold you back.

## The best [Platform.sh](http://platform.sh/) alternatives in 2026

Let’s go through some of the strongest alternatives — and where each one shines.

### 1. **Northflank**

[Northflank](https://northflank.com/) is a platform that enables developers to build, deploy, and scale applications, services, databases, and jobs on any cloud through a self-service approach. For DevOps and platform teams, Northflank provides a powerful abstraction layer over Kubernetes, enabling templated, standardized production releases with intelligent defaults while maintaining necessary configurability.

![](https://assets.northflank.com/image_5_fd06403bd1.png) 

**Key features:**

- Kubernetes-powered, full-stack platform
- Deploy containers, databases, and scheduled jobs
- [Bring your own cloud (AWS, GCP, Azure, etc.)](https://northflank.com/features/bring-your-own-cloud)
- CI/CD integration, real-time logs, with a developer-friendly and consistent experience across UI, CLI, API, and GitOps
- GPU support for AI workloads
- Automatic preview environments and seamless promotion to dev, staging, and production

**Best for:**

- Dev teams building APIs, microservices, and containerized web apps
- SaaS products needing multi-service architectures
- Teams looking for a fast, clean alternative to older, more rigid PaaS platforms

**Limitations:**

- Highly experienced DevOps teams might find it restrictive compared to directly managing raw Kubernetes clusters. It’s a fine balance between ease of use, flexibility, and customization; that line differs for every organization.

*See how [Weights company uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### 2. Render

[Render](https://render.com/) is a modern cloud platform that streamlines the hosting of web applications, static sites, APIs, and databases, providing automatic SSL certification and CDN integration.

 ![](https://assets.northflank.com/image_7_04cbeab21d.png) 

**Key features**:

- Zero-downtime deployments
- Automatic HTTPS and DDoS protection
- Native SSD storage
- Pull request preview environments
- Custom domain support

**Best for:**

Teams that need a clean, reliable platform without dealing with infrastructure.

**Limitations:**

Less flexibility for multi-service, microservices, or containerized apps compared to Northflank.

*For a closer look at how Fly compares to other platforms, this [article](https://northflank.com/blog/render-alternatives) offers a well-rounded analysis.*

### 3. Railway

Railway excels at simple, fast app deployments with built-in databases and auto-generated environments. Its UI is clean, intuitive, and geared towards indie developers, hackers, and small SaaS projects.

 ![](https://assets.northflank.com/image_84_b757a67aa3.png) 

**Best for:**

Rapid MVPs, hackathons, and indie SaaS products.

**Limitations:**

Limited fine-tuning for advanced multi-service projects or heavy containerized workloads.

*For a closer look at how Fly compares to other platforms, this [article](https://northflank.com/blog/railway-alternatives) offers a well-rounded analysis.*

### 4. [Fly.io](http://fly.io/)

[Fly.io](http://fly.io/) is a globally distributed application platform that positions your code closer to users, delivering exceptional performance without the complexity of traditional infrastructure management.

 ![](https://assets.northflank.com/image_85_eb52255c08.png) 

**Key features**:

- Intelligent global load balancing
- Integrated Postgres and Redis support
- Native IPv6 compatibility
- Docker-based deployment pipeline
- Extensive edge network coverage

**Best for:**

APIs, multiplayer games, real-time apps, or anything with a global audience.

**Limitations:**

Requires more operational knowledge (Docker, networking, CLI tools) compared to PaaS platforms like Northflank.

*For a closer look at how Fly compares to other platforms, this [article](https://northflank.com/blog/flyio-alternatives) offers a well-rounded analysis.*

### 5. DigitalOcean App Platform

[DigitalOcean App Platform](https://www.digitalocean.com/products/app-platform) is a PaaS solution built on DigitalOcean's robust infrastructure, striking an optimal balance between simplicity and control for growing applications.

 ![](https://assets.northflank.com/image_6_022540644b.png) 

**Key features**:

- Integrated CI/CD pipelines
- Automatic vertical and horizontal scaling
- Built-in monitoring and alerting
- Seamless integration with DigitalOcean's managed databases
- Global CDN support

**Best for:**

Startups, SaaS apps, and dev teams that need a no-nonsense PaaS with modern features.

**Limitations:**

Less mature in CI/CD automation and multi-service orchestration compared to Northflank.

*For a closer look at how Fly compares to other platforms, this [article](https://northflank.com/blog/best-digitalocean-alternatives-2025) offers a well-rounded analysis.*

## How to choose the best alternative

Selecting the optimal Platform alternative involves a systematic approach:

1. **Identify your primary challenges**: Pinpoint the specific limitations you're experiencing with [Platform.sh](http://platform.sh/).
2. **Prioritize requirements**: Create a weighted list of features and capabilities most crucial to your workflows.
3. **Consider team expertise**: Evaluate your team's familiarity with the underlying technologies of each platform.
4. **Conduct targeted proof of concept**: Test your most critical workloads on shortlisted platforms.
5. **Evaluate total cost of ownership**: Look beyond base pricing to include potential savings in developer time and infrastructure optimization.
6. **Plan for growth**: Select a platform that can accommodate your projected scaling needs.

Based on these criteria, [Northflank](https://northflank.com/) consistently emerges as the superior choice, particularly for teams seeking an optimal balance of power, flexibility, and usability. Its comprehensive feature set addresses common [Plaform.sh](http://plaform.sh/) limitations while providing additional capabilities that enhance productivity and control.

## Conclusion

While [Platform.sh](http://platform.sh/) continues to serve many organizations effectively, the evolving demands of modern development teams often necessitate alternatives with enhanced capabilities. Among the leading contenders, [Northflank](https://northflank.com/) stands out as the premier option, offering superior deployment flexibility, advanced CI/CD features, and exceptional developer experience without compromising on performance or scalability.

For organizations looking to optimize their cloud deployment strategy, [Northflank](https://northflank.com/) represents not merely an alternative to [Platform.sh](http://platform.sh/), but a significant advancement that can transform application delivery and management. As the PaaS landscape continues to evolve, platforms that successfully combine powerful features with intuitive interfaces—as [Northflank](https://northflank.com/) does—will continue to lead the market by enabling teams to focus on building great products rather than managing infrastructure.

[**Ready to make the switch? Try Northflank today and take your deployments to the next level!**](https://northflank.com/)]]>
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</item><item>
  <title>Render vs Heroku: Which platform-as-a-service is right for you in 2026?</title>
  <link>https://northflank.com/blog/render-vs-heroku</link>
  <pubDate>2025-04-21T08:52:00.000Z</pubDate>
  <description>
    <![CDATA[Compare Heroku vs Render for modern app hosting. Learn their pros, cons, pricing, and scaling limits. Discover why Northflank is the best scalable Heroku alternative with BYOC and Kubernetes support.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Render_vs_Heroku_b11e3d770b.png" alt="Render vs Heroku: Which platform-as-a-service is right for you in 2026?" />Developers want to spend their time shipping applications, not wrestling with infrastructure. Platforms like Render and Heroku promise exactly that: simplified deployments, effortless scaling, and freedom from server management headaches. This promise of simplicity is precisely what every engineering team aspires to achieve.

However, choosing the right platform means balancing convenience against potential limitations as your app evolves. Let's dive deeper into Render and Heroku, explore their strengths and weaknesses, and introduce an alternative designed specifically to grow with your workloads.

## Understanding Render and Heroku

### Heroku: Pioneering simplicity

Heroku revolutionized web deployment when it launched in 2007. Its innovative `git push` deployment method simplified app deployment dramatically, allowing developers to focus solely on writing code. After being acquired by Salesforce in 2010, Heroku expanded its ecosystem significantly, offering a robust marketplace of add-ons for databases, caching, monitoring, logging, and more.

### Heroku key features:

- Git-based deployments
- Dynos (containerized runtime environments)
- Extensive marketplace for add-ons
- Simple web UI and CLI

### Render: Modernizing the developer experience

Render, founded in 2019 by a former Stripe engineer, positions itself as a modern successor to Heroku. It offers a more diversified set of integrated services, modern container-based infrastructure, and an attractive pricing model to appeal to newer, growth-oriented teams.

### Render key features:

- Container-based infrastructure
- Built-in support for cron jobs, background workers, and static websites
- Automated SSL and DNS management
- Integrated CI/CD pipeline

## Comparing Render vs Heroku: Pros and cons

| Feature | Heroku | Render |
| --- | --- | --- |
| **Ease of use** | Excellent usability; simple CLI and UI | Equally intuitive; slightly broader service offerings |
| **Pricing at scale** | Quickly becomes costly; limited control over expenses | Competitive initially; still expensive at higher scale |
| **Complex workloads** | Struggles with complex architectures and microservices | Handles basic complexity better, but still limited |
| **Infrastructure control** | Limited visibility and control | Limited visibility; slightly more modern infrastructure |
| **BYOC support** | No BYOC support | No BYOC support |
| **Graduation problem** | High likelihood of outgrowing platform | Still likely to outgrow, but slightly later |

### Heroku pros:

Heroku offers unmatched ease of deployment and a mature ecosystem that lets developers quickly add necessary services. Its simple CLI and intuitive UI significantly reduce the learning curve, making it ideal for rapid prototyping and hobby projects. The platform’s extensive marketplace provides reliable integrations with services such as databases, caching layers, logging, and performance monitoring tools, enabling rapid setup of complex application stacks without infrastructure overhead. Its longstanding presence in the market ensures strong community support, ample documentation, and resources for troubleshooting.

[*Click here for a deeper dive about Heroku Enterprise*](https://northflank.com/blog/heroku-enterprise-capabilities-limitations-and-alternatives)

### Heroku cons:

Heroku becomes prohibitively expensive as workloads scale, largely due to its rigid pricing model based around dynos and add-ons. This makes Heroku increasingly less viable for resource-intensive applications. Additionally, its inflexible infrastructure and opaque abstraction layer offer limited visibility into system performance or the underlying configuration, significantly hampering troubleshooting for performance bottlenecks or downtime incidents. Heroku’s lack of BYOC (Bring Your Own Cloud) capability restricts your ability to leverage negotiated cloud pricing, enterprise discounts, or maintain stringent compliance requirements. 


>💡
Bring Your Own Cloud (BYOC) allows companies to deploy software directly within their own cloud accounts—like AWS, Azure, or Google Cloud. BYOC provides greater flexibility, cost control, security, compliance, and visibility into infrastructure, overcoming key limitations associated with traditional SaaS deployments.
[*Read more: Bring your own cloud (BYOC): What is it Why is it the future?*](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment)
>

As applications grow in complexity, particularly with distributed architectures, Heroku struggles to efficiently manage intricate dependencies, forcing many teams into complex and costly migrations to alternative solutions.

[*Top Heroku alternatives in 2026*](https://northflank.com/blog/top-heroku-alternatives)

### Render pros:

Render provides broader integrated services compared to Heroku, such as built-in support for cron jobs, background workers, static website hosting, and continuous deployment pipelines. Render’s modern, containerized infrastructure aligns better with contemporary application deployment best practices, making it attractive to fast-growing startups and dynamic teams. Initially, Render offers competitive and predictable pricing, making it appealing to budget-conscious organizations. It simplifies DNS management and automates SSL certification, removing tedious manual configuration tasks and further streamlining deployments.

### Render cons:

Despite its modern approach, Render shares several significant shortcomings with Heroku. Pricing remains a significant concern as workloads scale, with costs escalating substantially when services require higher compute or storage capacity. Like Heroku, Render offers limited visibility into the underlying infrastructure, restricting users’ ability to monitor and debug system-level issues comprehensively. Render also does not support BYOC, forcing customers to remain locked into Render’s cloud services, limiting control over cloud expenditures, compliance, and long-term flexibility. Although slightly better suited for handling complex workloads than Heroku, Render eventually encounters similar limitations, particularly with sophisticated microservices architectures, intensive background processing, or applications demanding highly customized infrastructure setups.

[*7 Best Render alternatives for simple app hosting in 2026*](https://northflank.com/blog/render-alternatives)

## Solving the graduation problem with Northflank

Both Render and Heroku eventually force teams to migrate as workloads grow more complex. Let's explore how Northflank approaches this differently.

### Why Northflank is built to scale with you

Most developer platforms act like they're afraid of developers, hiding complexity behind abstractions and treating DevOps as something special and isolated. Northflank flips this idea on its head.

[Northflank](https://northflank.com/) believes infrastructure shouldn't be abstracted—it should be *synthesized*. The entire post-commit workflow—building, deploying, running, scaling, and observing—should feel unified, not like disconnected pieces held together by tape.

Where platforms like Heroku attempt to remove complexity and inevitably hit scaling ceilings, Northflank absorbs that complexity. It's not just a better Heroku—it's more accurately described as "Kubernetes without tears."

Northflank uniquely supports BYOC, running seamlessly in your own managed or on-premises cloud environments. This unlocks powerful economic advantages and ensures maximum control, security, and compliance. Most importantly, Northflank is intentionally designed to eliminate the graduation problem, scaling effortlessly alongside your workloads without ever forcing a migration.

## Making the right choice

Render offers immediate advantages over Heroku, but neither solution will scale comfortably with complex and evolving workloads. Northflank, however, is purpose-built to offer simplicity without sacrificing flexibility or control. If your team anticipates growth—and wants to avoid future platform migrations—consider choosing Northflank from the start.

[Explore Northflank today, and never worry about outgrowing your deployment platform again.](https://app.northflank.com/signup)]]>
  </content:encoded>
</item><item>
  <title>7 Netlify alternatives in 2026: Where to go when your app grows up</title>
  <link>https://northflank.com/blog/netlify-alternatives</link>
  <pubDate>2025-04-19T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[If you're reading this, you've probably started hitting Netlify's limits and you're exploring alternatives that can better handle your app deployments.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Porter_alternatives_1_88dcdb2a92.png" alt="7 Netlify alternatives in 2026: Where to go when your app grows up" />If you're reading this, you've probably started hitting Netlify's limits and you're exploring alternatives that can better handle your app deployments.

In 2020, deploying a site on Netlify felt like magic. Push to GitHub, and seconds later, your landing page, blog, or demo app was live, fast, and globally distributed. No servers to configure. No CI pipelines to wire up. Netlify made the JAMstack feel like the future.

Fast forward to 2026, and things are more complicated.

Netlify is still a solid choice for personal sites, portfolios, and simple frontend apps. But a growing number of teams are finding its limits. Maybe you need more control over deployments. Maybe you’ve bolted on one too many third-party services. Or maybe you’re just tired of pretending serverless functions can replace a real backend.

Whatever the reason, you’re not alone. A wave of newer platforms are giving developers better options. They’re more flexible, more full-stack, and in many cases, more honest about what you actually need to ship real software.

In this post, we’ll walk through the best Netlify alternatives in 2026: who they’re for, where they shine, and why you might be better off skipping Netlify entirely, especially if you’re planning to build a real product.

<InfoBox className='BodyStyle'>



### 💡 Quick look: Top Netlify alternatives in 2026

In a rush? Here’s a breakdown of the best Netlify alternatives worth checking out:

1. **Northflank** – Full-stack deploys with frontend + backend + databases, PR-based preview environments, and BYOC support. Netlify-like DX without the ceiling.

2. **Vercel** – Perfect for frontend teams using Next.js; great DX and edge functions, but backend is bolt-on.

3. **Render** – Heroku-style simplicity with real backend support and managed databases.

4. **Cloud Run** – Containerized, serverless hosting with Google-grade infra—powerful, but not exactly plug-and-play.

5. **Cloudflare Pages & Workers** – Ultra-fast edge deployment for static sites and lightweight APIs.

6. **Heroku** – Still relevant for quick app deployments, with a huge ecosystem—but showing its age.

7. **DigitalOcean App Platform** – Managed hosting with Git-based deploys and simple autoscaling; more flexible than Netlify, less opinionated than Vercel.

</InfoBox>

## Why you might outgrow Netlify

Netlify’s main strengths (static hosting, basic serverless functions, and easy CI/CD) often become weaknesses as you scale. Key issues include:

- **Limited backend capabilities**: Great for static content, weak for dynamic or full-stack applications.
- **Pricing surprises**: What starts affordable can get expensive as your build times and bandwidth grow.
    
 ![](https://assets.northflank.com/2_01f6aade24.png)     
- **No full-stack previews**: Frontend previews are helpful, but you can't preview databases or backends, limiting your testing capabilities.

## Netlify alternatives at a glance

| Platform | Best for | Key strengths | Free tier | Pricing starts |
| --- | --- | --- | --- | --- |
| **Northflank** | Full-stack apps, databases, Kubernetes | Full-stack previews, databases, BYOC, robust CI/CD | 2 services, 2 jobs | Pay-as-you-go |
| **Vercel** | Frontend apps (especially Next.js) | Edge functions, SSR, ISR | 1M requests/month | $20/month |
| **Render** | Web apps, databases, APIs | Zero downtime, managed DBs | Static apps | $19/month |
| **Google Cloud Run** | Containerized workloads | Container support, autoscaling, GCP integration | 2M requests/month | Usage-based |
| **Cloudflare Pages** | Global static site hosting | Edge hosting, ultra-fast performance | Unlimited static sites | $20/month |
| **Heroku** | Rapid app deployments | Extensive add-on ecosystem, dyno scalability | Limited dynos | $5/month |
| **DigitalOcean App Platform** | Simplicity & moderate complexity | Managed infra, DB integration, easy deployments | 3 static sites | $5/month |

## Netlify alternatives explored

### 1. Northflank — Best overall alternative

 ![](https://assets.northflank.com/pawelzmarlak_2025_04_18_T15_38_26_542_Z_min_3bdc58e89e.png) 

**Why Northflank?**

Northflank uniquely bridges the gap between frontend ease-of-use and full backend power, essentially delivering what Netlify promises, without the usual compromises. It’s a complete platform designed to handle applications from the simplest static sites to the most complex enterprise-grade architectures effortlessly. 

Think of Northflank as the platform Netlify might be if it had been built for all of your workloads rather just frontend from day one.

**Key advantages:**

- **Full-stack previews**: Preview frontend, backend, and databases simultaneously with automated PR-based deployments.
- **Databases included and stateful services**: No more stitching together databases, caching layers, and jobs from multiple vendors. Northflank supports PostgreSQL, Redis, MongoDB, MySQL, and more out-of-the-box. It makes running stateful workloads as easy as deploying a static website.
- **Best-in-class CI/CD pipelines:** Northflank’s CI/CD integrates effortlessly with GitHub, GitLab, and Bitbucket. Pipelines are intuitive enough for small teams yet powerful enough to handle complex deployment workflows across different environments and clouds.
- **Advanced Kubernetes under the hood:** Unlike Render or DigitalOcean, which abstract away infrastructure at the expense of flexibility, Northflank gives you Kubernetes-grade reliability and scaling without requiring Kubernetes expertise. It auto-scales and self-heals, so your team spends less time firefighting and more time building.
- **BYOC (Bring Your Own Cloud)**: You’re never locked in. Deploy applications seamlessly to AWS, GCP, Azure, or your own Kubernetes cluster in order to maintain full compliance, data sovereignty, and cost transparency.

Northflank suits teams who need a Netlify-like experience but without its backend limitations. It’s powerful enough for enterprise use yet accessible to solo devs.

**Pricing**: Transparent pay-as-you-go model, generous free tier.

### 2. Vercel — Best for Next.js and front-end apps

 ![](https://assets.northflank.com/Clean_Shot_2025_04_18_at_14_12_02_2x_min_372102b986.png) 

**Why Vercel?**

Vercel specializes in frontend deployments, especially Next.js, offering good edge performance and rendering capabilities.

| **Advantages** | **Disadvantages** |
| --- | --- |
| **Advanced Edge Functions**: Serve dynamic content instantly from Vercel's global CDN. | **Backend limitations:** Vercel quickly becomes restrictive when deploying complex backends or databases. |
| **Optimized for Next.js**: Seamless integration, auto-rendering, incremental static regeneration (ISR). | **Vendor lock-in:** Deep integration with Next.js means migrating away later is challenging and costly. |
| **Excellent DX**: Git-based deploys, automated previews, robust logging and analytics. | **Cost at scale:** Can get expensive rapidly as your traffic and serverless usage grow. |

**Pricing**: Free for personal projects, $20/month for Pro features.

### 3. **Render**

 ![](https://assets.northflank.com/Clean_Shot_2025_04_18_at_14_13_08_2x_min_185d3bf259.png) 

**Why Render?**

Render simplifies full-stack app deployments while offering more powerful backend tools than Netlify or Vercel.

| **Advantages** | **Disadvantages** |
| --- | --- |
| **Zero-downtime deployments**: Update your application seamlessly without interruption. | **Limited customization**: Less flexibility in infrastructure management, limiting complex scenarios. |
| **Managed databases**: Easy PostgreSQL and Redis hosting included. | **Scaling cost**s: Pricing structure can get steep quickly with resource-intensive applications. |
| **Pull request previews**: Preview environments for each PR, though limited compared to Northflank’s full-stack previews. | **No multi-cloud/BYOC support**: Render locks you into its own infrastructure without the flexibility of BYOC. |

Render suits developers looking for simplicity, similar to Heroku but updated for modern stacks.

**Pricing**: Free static hosting, $19/month for more robust applications.

### 4. Google Cloud Run

 ![](https://assets.northflank.com/Clean_Shot_2025_04_18_at_14_14_04_2x_min_4597f1cedc.png) 

**Why Cloud Run?**

Google Cloud Run lets you deploy containerized applications in a fully managed serverless environment. This is ideal if you already use Docker containers or require deep integration with other Google Cloud services.

| **Advantages** | **Disadvantages** |
| --- | --- |
| **Container support**: Run any Docker container without server management. | **Complexity overhead**: Requires Docker/container experience, increasing onboarding complexity. |
| **Autoscaling**: Scales rapidly to zero and back, ideal for cost optimization. | **Unpredictable costs:** Pricing can lead to unexpected spikes. |
| **Google ecosystem integration**: Easily integrates with Cloud SQL, Cloud Build, Firebase, and more. | **Limited DX**: Not as intuitive or streamlined as dedicated app-hosting platforms like Northflank or Render. |

Ideal for teams comfortable with containers or Google Cloud’s ecosystem.

**Pricing**: Usage-based, generous free tier (2M requests/month).

### 5. Cloudflare Pages & Workers

 ![](https://assets.northflank.com/Clean_Shot_2025_04_18_at_14_15_06_2x_min_450c2bc3dc.png) 

**Why Cloudflare?**

Cloudflare offers Pages (for static sites) and Workers (for dynamic functionality), providing the fastest possible global hosting via their extensive edge network.

| **Advantages** | **Disadvantages** |
| --- | --- |
| **Global Edge Network**: Lowest latency worldwide with 115% faster performance than typical CDN setups. | **Limited backend capability:** Workers have resource limitations, restricting complex backends. |
| **Instant rollbacks**: Quickly revert to stable versions without downtime. | **Complex debugging:** Limited observability tools make debugging and troubleshooting harder. |
| **Enhanced security**: Built-in SSL, CDN, and DDoS protection. | **Cost concerns:** Pricing structure for dynamic workloads can escalate quickly. |

Ideal for global, latency-sensitive static and dynamic applications.

**Pricing**: Generous free tier, Pro tier from $20/month.

### 6. Heroku

 ![](https://assets.northflank.com/Clean_Shot_2025_04_18_at_14_15_41_2x_min_138a8a18c0.png) 

**Why Heroku?**

Heroku remains a reliable option, particularly for developers seeking ease of use, scalability, and a rich add-on ecosystem.

| **Advantages** | **Disadvantages** |
| --- | --- |
| **Dyno System**: Simple deployment and scaling via dynos (virtualized containers). | **Limited modernization**: Lacks advanced features like full-stack previews or sophisticated CI/CD pipelines. Got acquired and stopped innovating. |
| **Add-ons**: Huge marketplace for databases, caching, analytics, and more. | **Cost at scale**: Quickly becomes expensive as your application grows. |
| **Developer Experience**: Proven, straightforward workflow. | **Diminishing updates**: The platform's innovation has slowed dramatically compared to competitors like Northflank or Render. |

Best for developers familiar with its ecosystem and not requiring extensive Kubernetes-level control.

**Pricing**: Limited free dynos, $5-7/month for standard dynos.

### 7. DigitalOcean App Platform

 ![](https://assets.northflank.com/Clean_Shot_2025_04_18_at_14_16_12_2x_min_d3b5767f2e.png) 

**Why DigitalOcean?**

DigitalOcean offers a middle ground between Netlify’s simplicity and the complexity of AWS, with intuitive management of infrastructure.

| **Advantages** | **Disadvantages** |
| --- | --- |
| **Managed infrastructure**: Abstracts complexity while providing flexible scaling options. | **Limited advanced features:** No full-stack previews or extensive CI/CD options. |
| **Easy integration**: Deploy straight from Git, straightforward setup. | **Limited Scalability:** Less ideal for heavy enterprise use cases or intensive workloads. |
| **Managed databases**: Built-in PostgreSQL and Redis hosting. | **Support constraints:** Developer support and documentation aren’t as robust as competitors. |

Ideal for developers wanting more control without significant complexity.

**Pricing**: Starts at $5/month with a basic free tier for static apps.

## **Why Northflank stands out**

Northflank knocks the competition out of the park by combining the best attributes of all these alternatives. It provides:

- The intuitive frontend simplicity you'd expect from Vercel.
- The powerful backend capabilities and infrastructure of Google Cloud Run, without the complexity.
- Rich database management and seamless scaling that far exceeds Render, Heroku, and DigitalOcean.
- A truly multi-cloud, BYOC-friendly approach unmatched by any other platform listed here.

Where others stop, Northflank keeps going. Its ability to handle entire complex applications (front-to-back) without sacrificing simplicity or power is why teams of all sizes are increasingly adopting it as their go-to platform.

- [**Weights uses Northflank to scale to millions of users without a DevOps team**](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)
- [**Scaling 30,000 deployments with 100% uptime. How Clock uses Northflank to simplify infrastructure.**](https://northflank.com/blog/scaling-30-000-deployments-with-100-uptime-how-clock-uses-northflank-to-simplify-infrastructure)

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    <a href="https://salvo.sh/blog/tag/case-study">
      <Button variant={["large", "gradient"]}>Read more here</Button>
    </a>
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## When Netlify might still work for you

Now that we've covered the best Netlify alternatives, we still want to make sure you understand, Netlify isn’t useless. For certain projects and teams, it still delivers a smooth, reliable experience with minimal setup.

You’re probably fine sticking with Netlify if:

- You're building static sites, blogs, or marketing pages with minimal backend logic
- You don't need preview environments for databases or APIs
- You’re not planning to scale the project significantly or commercialize it
- You're okay wiring up your backend and storage manually (or just don’t need one)
- You value built-in extras like form handling, identity, or split testing (and they actually fit your use case)

The DX is clean, deploys are fast, and for simple frontends, it works. But once your project grows beyond static pages—or you need full-stack previews, backend services, or predictable pricing—Netlify starts to get in the way. At that point, it’s probably time to move on.

## **More resources worth checking out**

If you're still weighing your options or exploring related tools, these might help:

- [Bring Your Own Cloud (BYOC): The Future of Enterprise SaaS Deployment](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment)
- [Top Heroku Alternatives](https://northflank.com/blog/top-heroku-alternatives)
- [Best Vercel Alternatives for Scalable Deployments](https://northflank.com/blog/best-vercel-alternatives-for-scalable-deployments)
- [Best DigitalOcean Alternatives in 2026](https://northflank.com/blog/best-digitalocean-alternatives-2025)]]>
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  <title>Vercel vs Netlify: which deployment platform should you use in 2026?</title>
  <link>https://northflank.com/blog/vercel-vs-netlify-choosing-the-deployment-platform-in-2026</link>
  <pubDate>2025-04-18T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[If you're shipping a static site or building your first Next.js app, Netlify and Vercel both do the job well. They give you fast deploys, simple Git integrations, and zero infrastructure overhead. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/vercel_vs_netlify_5b9ccc6a76.png" alt="Vercel vs Netlify: which deployment platform should you use in 2026?" />*Vercel and Netlify are both Git-connected deployment platforms built for frontend and JAMstack workloads. Both offer automatic deploys, preview environments, and edge delivery. The right choice depends on your framework, your app's backend requirements, and how far you expect to scale.*
 
For static sites, marketing pages, and simple frontends, both platforms work well and the differences are minor. The gap widens for teams building full-stack applications with backend services, background workers, databases, or compliance requirements that neither platform was designed to handle.

## TL;DR: Vercel vs Netlify in 2026
 
| Category | Vercel | Netlify |
|---|---|---|
| **Best for** | Next.js and dynamic React apps | Static sites and JAMstack |
| **SSR support** | First-class | Limited |
| **Edge functions** | Yes | Yes |
| **Background jobs** | No | Via scheduled functions (limited) |
| **Managed databases** | No (Marketplace only) | Yes (Postgres, powered by Neon) |
| **Docker support** | No | No |
| **Preview environments** | Frontend only | Frontend only |
| **BYOC** | No | No |
| **Free tier commercial use** | Prohibited | Allowed |
| **Pro pricing** | $20/user/month | $20/user/month |
 
- **Choose Vercel** if you are building with Next.js and need first-class SSR, ISR, and edge middleware.
- **Choose Netlify** if you are building a static site or JAMstack app and want built-in forms, identity, and split testing.
- **Choose [Northflank](https://northflank.com/)** if your app needs backend services, managed databases, background workers, GPU workloads, or the ability to run inside your own cloud account. Both Vercel and Netlify hit a ceiling for full-stack production apps.

<InfoBox className="BodyStyle">

[Northflank](https://northflank.com/) is a full-stack cloud platform that covers what Vercel and Netlify cannot: managed databases, background workers, CI/CD pipelines, GPU workloads, preview environments for every service type (not just frontends), and self-serve BYOC into AWS, GCP, Azure, and on-premises. [Sign up to get started](https://app.northflank.com/signup) or [book a demo](https://cal.com/team/northflank/northflank-demo?duration=30).
 
 </InfoBox>
 
 ## What is Vercel?
 
Vercel is a deployment platform optimized for Next.js and React frameworks. It provides Git-based deployments, automatic preview environments per pull request, edge caching, and serverless functions under the `/api` directory. ISR (Incremental Static Regeneration), SSR (Server-Side Rendering), and edge middleware are first-class primitives on Vercel, tightly integrated with how Next.js works.
 
The platform is opinionated. The developer experience for Next.js is the best in the category. The further your stack diverges from the Next.js model, the less of that experience carries over. Long-running backend services, stateful workloads, managed databases, and background jobs require external providers.
 
## What is Netlify?
 
Netlify is a deployment platform built around the JAMstack model: static site generators, Git-based deploys, global CDN delivery, and serverless functions for dynamic behavior. It ships with built-in features that Vercel requires third-party services for: forms, identity, A/B split testing, and server-side analytics.
 
Netlify supports more frameworks than Vercel out of the box and is less opinionated about stack. SSR support exists via functions and Netlify Edge but is less capable than Vercel's native rendering model. For static sites, content-heavy blogs, and marketing pages, Netlify covers the deployment need with less configuration.

## Comparing Vercel vs Netlify side by side

Once you understand how Vercel and Netlify work on their own, it’s less about features and more about how they fit into your development workflow. You're making trade-offs around:

- How much backend you need
- How dynamic your app is
- How much you care about scale, lock-in, and extensibility

Let’s break down how they compare across key areas.

### 1. Deployment model

**Vercel** deploys apps via Git integration and is optimized for frameworks like Next.js. You get automatic builds, edge caching, and serverless APIs under the `/api` directory. Everything is tightly coupled, opinionated, and tuned for performance, BUT only within its preferred boundaries.

**Netlify** also offers Git-based deploys but is better suited to static sites. Its function system lives in a separate `/functions` directory, and while it can support dynamic behavior, the system isn’t built for heavy logic or SSR. It’s more flexible about frameworks, but less powerful when it comes to dynamic app needs.

### 2. SSR and dynamic content

**Vercel** excels here. It’s made for SSR, ISR, and edge functions baked directly into the platform. With Next.js, you get predictable performance and clear primitives for rendering strategies. But use a different framework and you’ll lose a lot of that magic.

**Netlify** can handle dynamic behavior through functions and Netlify Edge, but SSR support is clunky at best. Expect more configuration, more cold starts, and slower performance if your app requires personalized or time-sensitive content.

### 3. Observability and Developer Experience (DX)

**Vercel** has a strong UI, preview environments, and detailed build/deploy logs. If you’re building in a monorepo with multiple frontend apps, it handles projects cleanly and without friction. The CLI is solid, and the docs are great—if you're building with Next.js.

**Netlify** also has an intuitive UI and automatic previews, with extras like build plugins and form dashboards. It’s less polished in how it handles multi-project setups, but its simplicity is part of the appeal. Devs coming from the static site world will feel right at home.

### 4. Ecosystem and built-ins

**Netlify** comes with batteries included: forms, identity, split testing, and server-side analytics. It leans hard into JAMstack tooling, which is great if you want a lot of features without gluing together services.

**Vercel** is more minimal. You get edge functions, but forms, auth, and analytics require third-party services or roll-your-own. If you’re in a Next.js world, this isn’t a big deal—but it means more work for apps that need extra features.

## Pricing and scale

The appeal of both Vercel and Netlify is obvious when you’re starting out: generous free tiers, simple deploys, zero infra. But pricing shifts quickly once traffic picks up, more people join your team, or your app starts doing more than rendering static content.

Let’s unpack how their pricing works, and more importantly, how it breaks.

### Vercel

- **Hobby (Free)**
    - 100 GB bandwidth/month
    - 1,000,000 serverless function invocations/month
    - 4 hours Active CPU/month
    - 1 hour of runtime logs
    - Community support
    - Non-commercial use only
- **Pro ($20 per user/month, includes $20 monthly usage credit)**
    - 1 TB bandwidth/month
    - 10,000,000 Edge Requests/month
    - 1,000 GB-hours of function duration
    - Unlimited free viewer seats
    - Email support
- **Enterprise (Custom pricing)**
    - Custom bandwidth and function limits
    - SAML SSO, SCIM, 99.99% SLA
    - Dedicated support and isolated infrastructure

Vercel moved to credit-based billing in September 2025. Pro plans include a $20 monthly usage credit that applies before overage charges kick in. Bandwidth overages bill at $0.15/GB after 1TB. Teams deploying anything beyond a marketing site should monitor function GB-hours closely, as SSR-heavy apps burn through them quickly. Build costs also increased in February 2026 when Turbo machines became the default at $0.126/minute.

### Netlify

- **Free**
    - 300 credits/month (hard limit, no overages)
    - Community support
    - Commercial use allowed
- **Personal ($9/month)**
    - 1,000 credits/month
    - 1 concurrent build
- **Pro ($20/month flat, unlimited team members)**
    - 3,000 credits/month shared across the team
    - 3 concurrent builds
    - RBAC, audit logs, password-protected sites
    - Email support
- **Enterprise (Custom pricing)**
    - Custom credit allocation, 99.99% SLA, SAML SSO, dedicated support

Netlify moved to credit-based billing in September 2025. All usage, including bandwidth, compute, deploys, and web requests, consumes credits from a monthly allotment. As of April 14, 2026, the Pro plan includes unlimited team member seats at a flat $20/month, removing per-seat charges entirely. The credit system makes cost prediction harder than the old bandwidth and build minutes model. When credits are exhausted, all sites on the account pause until the next billing cycle.

Bottom line: Vercel is more predictable for frontend-heavy usage but expensive for teams, at $20 per seat per month before a single request is served. Netlify removed per-seat pricing in April 2026, making the Pro plan a flat $20/month for unlimited team members, which changes the cost comparison significantly for larger teams. Both platforms use credit-based billing that makes overage costs harder to predict than the old bandwidth and build-minutes model.

If you are building a real product with backend logic, background jobs, and team workflows, neither model covers the full stack. You will end up paying for Vercel or Netlify plus separate providers for databases, workers, and anything stateful, which adds cost and operational complexity that a full-stack platform like Northflank eliminates.

## A warning on monetization

Vercel's free tier explicitly prohibits commercial use. It is meant for hobby projects and personal sites. Teams with paying customers are expected to upgrade to the Pro tier immediately. Vercel does not strictly enforce this, but relying on Hobby infrastructure for a revenue-generating product violates the terms of service.

Netlify allows commercial use on the free tier within usage limits. For solo developers testing an idea with low traffic and early traction, Netlify gives more breathing room before a plan upgrade is required.

 ![](https://assets.northflank.com/Clean_Shot_2025_04_17_at_14_15_39_2x_min_eee5306bc3.png) 

Neither platform is designed to run a business at scale. They are good starting points, but teams with real users and real traffic will outgrow both. If monetization is a goal, factor in the platform's compliance posture and pricing ceiling early.

## Ok, I want to build a real product. What do I use instead?

Vercel and Netlify are fantastic for quick wins: personal sites, demo apps, and static frontends. They’re polished and self-serve. But once your product starts getting traction or your architecture gets even slightly more complex, you hit the ceiling. Pricing jumps. You start gluing on services. You end up migrating pieces to AWS or another cloud, undoing the simplicity that drew you in to begin with.

Northflank exists specifically to avoid this trap.

You get the same self-serve developer experience. No DevOps knowledge required. You can deploy a full-stack app in minutes, not hours. And then, as your product grows, Northflank grows with you.

Want to run a background worker? Easy. Need a Postgres instance alongside your frontend and API? Built-in. Deploy a GPU workload? Yep. Use Redis, set up CRON jobs, expose gRPC services, or deploy any Dockerized microservice? All supported.

Northflank is a robust, production-grade platform that supports full application lifecycles. It includes:

- Automatic HTTPS, secrets management, build & deploy pipelines
- Logging, metrics, and monitoring built-in
- Deployment of frontend apps, backend services, jobs, databases, cron tasks, and workers
- GPU support and custom resource allocation
- Preview environments for **every type of service,** not just frontends

Northflank is powering:

- **Solo developers** launching SaaS ideas.
- **Startups like [Weights](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)** scaling to millions of users without hiring a DevOps team.
- **Companies like [Ultralight](https://northflank.com/blog/ultralight-ditched-aws-ecs-for-eks-with-northflank)** ditching AWS ECS and migrating fully to Northflank-managed Kubernetes.
- **Enterprises like Writer and Sentry**, running production workloads with the confidence that they can scale, customize, and control their environments without vendor drama.

 ![](https://assets.northflank.com/Clean_Shot_2025_04_17_at_14_22_40_2x_29bd5e6d3e.png) 

#### There’s no lock-in. There’s no need to re-architect. There’s no point where you “graduate” off the platform. You can start small and scale to a billion-dollar business without switching platforms.

### Bring Your Own Cloud (BYOC)

This is a huge gap in platforms like Vercel and Netlify. They don’t let you run in your own infrastructure. Want to keep data in a specific region? Want to use your own AWS credits or your company’s VPC setup? Too bad.

Northflank supports BYOC, so you get the simplicity of a PaaS, with the security, compliance, and control of owning the underlying infrastructure. 

<div>
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    <a href="https://app.northflank.com/signup">
      <Button variant={["large", "gradient"]}>Deploy on your terms (and Cloud) ☁️</Button>
    </a>
  </center>
</div>

## There is no reason to migrate later

Northflank gives you the speed of Vercel, the convenience of Netlify, and the power of Kubernetes, AWS, and enterprise-grade infrastructure without making you choose between them. Start small and scale to production without switching platforms.

## FAQ: Vercel vs Netlify
 
### Which is better, Vercel or Netlify?
 
Neither is better in absolute terms. Vercel is stronger for Next.js and SSR-heavy applications. Netlify is stronger for static sites and JAMstack apps that need built-in forms, identity, and split testing. For full-stack apps with backend services, databases, or background workers, both platforms require external tooling that a full-stack platform like Northflank handles natively.
 
### What is the difference between Vercel and Netlify?
 
Vercel is optimized for dynamic React apps, especially Next.js, with first-class SSR, ISR, and edge middleware. Netlify is more general-purpose for static sites with a wider set of built-in features including forms, identity, and A/B testing. Vercel has stronger dynamic rendering. Netlify has a broader feature set for static workflows.
 
### Can you use Vercel or Netlify for full-stack apps?
 
Both support serverless functions for API routes, but neither supports long-running backend services or background workers natively. Netlify now includes a managed Postgres database powered by Neon. Full-stack apps that need long-running services, background workers, or more than one database type require external providers, which adds operational complexity and cost that a full-stack platform eliminates.

### Do Vercel or Netlify support BYOC or private cloud deployment?
 
No. Both platforms run exclusively on their own managed infrastructure. Teams with data residency requirements, HIPAA obligations, or existing cloud commitments cannot deploy to their own AWS, GCP, or Azure accounts on either platform.
 
### Do preview environments on Vercel and Netlify include backend services?
 
No. Preview environments on both platforms deploy frontend code only. Database state and backend services are not isolated per preview. For teams that need full-stack preview environments with isolated backend services and databases per pull request, Northflank supports this across all service types.
 
### Can I use Vercel's free tier for a commercial product?
 
No. Vercel's Hobby tier explicitly prohibits commercial use. Teams with paying customers must be on the Pro plan at $20/user/month. Netlify allows commercial use on the free tier within usage limits.]]>
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  <title>Top 10 Microsoft Azure alternatives in 2026: Best cloud platforms for your business</title>
  <link>https://northflank.com/blog/azure-alternatives</link>
  <pubDate>2025-04-18T17:07:00.000Z</pubDate>
  <description>
    <![CDATA[Find out about the best Microsoft Azure alternatives in 2026, from Northflank to AWS, GCP, and other cloud platforms. Learn why businesses are switching and how these options could be a better fit for your needs.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/azure_alternatives_507f2944e4.png" alt="Top 10 Microsoft Azure alternatives in 2026: Best cloud platforms for your business" />> With so many developers and businesses searching for cloud provider alternatives every day, it’s clear that cloud solutions are no longer a one-size-fits-all, especially when it comes to Azure alternatives.
> 

Isn’t it surprising how, even with so many options out there, it’s still hard to find the one that suits your needs? I totally get it. That’s why we put this guide together to help you find an Azure alternative that matches what you're looking for.

At the same time, we can’t ignore the fact that Azure has its strengths. Even at that, the confusing pricing, overwhelming complexity, and the feeling of being stuck in one ecosystem have pushed many businesses like yours to search for something different. If any of this rings a bell, keep reading.

For context, this article focuses mainly on alternatives to Azure App Service, which is the part of Azure that lets you host, run, and scale web apps. If you’re using it and want something simpler, faster, or more flexible, this should help.


<InfoBox className='BodyStyle'>

### Need a better cloud platform for your needs?

See for yourself how a cloud platform can provide more control, flexibility, and support as you scale. With affordable pricing, free-tier options, and pay-as-you-go models, you can avoid vendor lock-in and choose a solution that fits your business needs.

[Find out how this platform works for businesses like yours](https://app.northflank.com/signup)

</InfoBox>

## Quick comparison table of 10 Azure alternatives

If you're short on time, here's a quick overview of 10 Azure alternatives based on key factors like BYOC support, global infrastructure, pricing, and target audience. This table gives you a snapshot to help you compare and decide quickly.

| **Cloud provider** | **BYOC (Bring Your Own Cloud) support** | **Global infrastructure** | **Pricing** | **Target audience** |
| --- | --- | --- | --- | --- |
| [**Northflank**](https://northflank.com/) | Yes | Global, region-aware | Transparent, scalable (Free tier, pay-as-you-go) | Developers, startups, SMBs, enterprises, DevOps teams |
| [**Railway**](https://railway.com/) | No | Global | Usage-based with $5/month minimum | Developers, Startups |
| [**Fly.io**](https://fly.io/) | No | Global (30+ regions) | Pay-as-you-go | Developers, SMBs |
| [**Render**](https://render.com/) | No | Global | Free tier, then fixed pricing | Developers, SMBs |
| [**Harness**](https://www.harness.io/) | No | Global | Modular, per-developer pricing | Enterprises, DevOps |
| [**Porter**](https://www.porter.run/) | Yes | Global | Metered billing (startup credits available) | Developers, SMBs |
| [**OpenShift**](https://www.redhat.com/en/technologies/cloud-computing/openshift) | No | Global | Subscription-based | Enterprises, Kubernetes |
| [**AWS App Runner**](https://aws.amazon.com/apprunner/) | No | Global | Pay-as-you-go | Developers, Enterprises |
| [**Google Cloud Run**](https://cloud.google.com/run) | No | Global | Pay-as-you-go | Developers, Enterprises |
| [**Vercel**](https://vercel.com/) | No | Global | Free tier, then fixed pricing | Front-end developers |

## What to look for when choosing an Azure alternative

The decision to choose the right (most suitable) option from the many Azure alternatives available online is about more than finding something cheaper; cost has been a very popular discussion, especially on Reddit:

 ![](https://assets.northflank.com/azure_cost_design_min_9dd2d3639f.png) 

So, what’s it really about, then, you might ask? It comes down to choosing a platform that can answer a few key questions:

- Does it fit your specific needs?
- Can it scale with your business?
- Does it integrate smoothly with what you’re already using?
- … what else can you think of? Add more to the list…

Now that you know, let’s quickly go over what you should keep in mind when making this choice. You’ll see a breakdown of the key factors to help guide your decision:

### 1. Cost transparency

*How many times have you received an unexpected cloud bill?* It can catch you off guard, right? Look for alternatives that make pricing clear and easy to understand. You want to know what you’ll be paying upfront, not get hit with hidden or unpredictable costs.

A platform with transparent pricing, or even a **cost calculator like the one Northflank provides on the pricing page**, will help you stay in control of your budget.

Compared to Azure’s calculator, which makes you piece together dozens of services and understand SKUs, Northflank’s is built to be fast and simple. You can input what you want to run, get a full cost breakdown in seconds, and avoid decoding complex billing layers.

[Try it here](https://northflank.com/pricing) to see how the calculator works:

 ![](https://assets.northflank.com/northflank_pricing_calculator_page_min_dde5daed6c.png) 

### 2. Multicloud flexibility (Bring your own cloud (BYOC) support)

*How much freedom do you want with your cloud strategy?*

With BYOC, which means Bring Your Own Cloud, or multicloud support, you can take control by choosing the best cloud environment for your needs. This means you can deploy directly into your own GCP, AWS, Azure, Civo, and OCI accounts.

 ![](https://assets.northflank.com/byoc_2_min_09d9c7300d.png) 

Platforms like Northflank provide [BYOC features](https://northflank.com/features/bring-your-own-cloud) that allow you to deploy your applications on your preferred cloud provider or even your bare-metal infrastructure for more control and customization.

If you need to understand BYOC better, read [this article](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment).

### 3. Flexibility for growth

As your business grows, your cloud needs will change, that’s a fact, right?. Now the question is, *can your platform grow with you?*

A good Azure alternative should be able to scale quickly and easily as your requirements expand. It doesn't matter if you're handling more traffic, adding services, or expanding globally; make sure your platform grows at the same pace as your business.

Solutions like Northflank make scalability simple. You can easily scale your applications with just a few clicks, even if you need to [scale instances](https://northflank.com/docs/v1/application/scale/scale-instances) vertically or horizontally, or [automatically](https://northflank.com/docs/v1/application/scale/autoscale-deployments) adjust your resources based on demand. The [autoscaling feature](https://northflank.com/features/scale) helps you stay in control and ensures your applications remain responsive even as traffic increases.

 ![](https://assets.northflank.com/northflank_autoscaling_min_97b768dc5e.png) 

So, if you're used to worrying about downtime or performance issues, solutions like Northflank that support the scaling of both stateless and stateful workloads should definitely be on your radar.

To try this out yourself, check the [guide](https://northflank.com/docs/v1/application/scale/autoscale-deployments) on auto-scaling your deployments.

### 4. Integration with existing tools

You don’t want to start from scratch when switching platforms, right? So, it’s important to find an alternative that works well with the tools you already use. It could be your CI/CD pipeline, database, or other software. The platform should make your work easier and not more complicated. Look for integrations that help keep your workflow organized and running smoothly.

An Azure alternative like Northflank makes this entire process simple. For instance, integrating your Azure account with Northflank is easy. You can follow [step-by-step guides](https://northflank.com/docs/v1/application/cloud-providers/azure-on-northflank) to register your Azure Active Directory application, link it to Northflank, and manage your cloud resources effortlessly.

 ![](https://assets.northflank.com/create_azure_cluster_min_94396349af.png) 

## Top 10 Azure alternatives in 2026

Now that we’ve covered what to look for in an Azure alternative, let’s go into some of the best options available.

### 1. Northflank

[Northflank](https://northflank.com/) is a cloud platform built for developers and teams who want to focus on building and scaling applications without worrying about infrastructure management.

It provides [BYOC](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) (Bring Your Own Cloud) support and multicloud flexibility, allowing you to deploy on your preferred cloud environment or even bare-metal infrastructure.

It also provides features such as [autoscaling](https://northflank.com/features/scale), transparent [pricing](https://northflank.com/pricing), and easy integrations with tools like [GitHub](https://northflank.com/docs/v1/application/getting-started/link-your-git-account) and [Docker](https://northflank.com/guides/tag/docker).

 ![](https://assets.northflank.com/northflank_s_home_page_min_946d051433.png) 

If your needs or problems are:

1. ***“I need my apps to scale automatically without worrying about cost”***
    
    Northflank takes care of both with [autoscaling features](https://northflank.com/features/scale) that adjust resources based on demand. The [pricing calculator](https://northflank.com/pricing) helps you stay within budget by showing what you’ll pay before you deploy.
    
2. ***“I need flexibility to deploy on my cloud provider or on bare-metal”***
    
    Northflank’s [BYOC](https://northflank.com/features/bring-your-own-cloud) and multicloud support let you choose your cloud environment, providing full control over your infrastructure.
    
3. ***“I need predictable, simple pricing”***
    
    Northflank provides transparent pricing, including a free tier to help you get started and pay-as-you-go options as you grow.
    
4. ***“I am tired of being locked into one cloud provider”***
    
    Northflank solves this by letting you bring your own cloud, giving you the flexibility to choose the best environment for your needs.
    
5. ***“I don’t want to deal with managing infrastructure”***
    
    Northflank’s [managed cloud](https://northflank.com/features/managed-cloud) feature allows you to deploy apps without handling the setup yourself. Once deployed, you can easily [manage your clusters](https://northflank.com/docs/v1/application/cloud-providers/manage-your-cluster) from one place.
    

### 2. Railway

[Railway](https://railway.com/) is a cloud platform built for developers who want a fast, straightforward way to ship apps. It gives you flexibility to manage configuration through code, using **railway.toml** or **railway.json** files, so you can control how apps are built and deployed.

It also provides built-in integrations and environment management to simplify app deployment directly from your GitHub repo.

 ![](https://assets.northflank.com/railway_min_10957de907.png) 

If your needs or problems are:

1. ***“I need a quick, easy deployment process”***
    
    Railway makes it simple to deploy from your repository with minimal setup. You can launch apps in just a few steps.
    
2. ***“I want to avoid managing infrastructure myself”***
    
    Railway handles servers and infrastructure for you behind the scenes, so you can focus on writing and shipping code.
    
3. ***“I’m comparing options beyond Railway and Azure”***
    
    If you're unsure if Railway is the right fit for your long-term needs, check out this article on [Railway alternatives](https://northflank.com/blog/railway-alternatives) to see what other platforms can meet your goals.
    

### 3. Fly.io

[Fly.io](http://fly.io/) is a cloud platform that allows developers to deploy applications close to users by running them on servers located in over [30 regions](https://fly.io/docs/reference/regions/) worldwide. It gives a developer-friendly experience with features like autoscaling, private networking, and a global edge network.

It also supports both autostop/autostart and metrics-based autoscaling, allowing applications to scale based on demand or custom metrics.

 ![](https://assets.northflank.com/fly_io_min_00141b3afd.png) 

If your needs or problems are**:**

1. ***“I need to deploy my app close to users globally”***
    
    Fly.io's global network allows you to run your applications in multiple regions, reducing latency and improving performance.
    
2. ***“I want my app to scale automatically based on demand”***
    
    Fly.io gives you both autostop/autostart and metrics-based autoscaling options, which allow your application to adjust resources dynamically in response to traffic or custom metrics.
    
3. ***“I’m checking for more alternatives to Fly.io”***
    
    If you're looking at other options, this [Fly.io alternatives article](https://northflank.com/blog/flyio-alternatives) is helpful for comparing different platforms.
    

### 4. Render

[Render](https://render.com/) is a unified cloud platform designed to simplify the deployment and management of web applications, APIs, static sites, and databases.

It provides features like one-click deployments, automatic scaling, and integrated CI/CD pipelines, enabling developers to focus on building applications without worrying about infrastructure management.

 ![](https://assets.northflank.com/render_s_home_page_min_23e582c5c1.png) 

If your needs or problems are:

1. ***“I need automatic scaling based on traffic”***
    
    Render provides autoscaling capabilities that adjust the number of service instances based on traffic patterns, ensuring optimal performance without manual intervention.
    
2. ***“I want a straightforward deployment process”***
    
    With Render's one-click deployments and integrated CI/CD pipelines, you can deploy your applications quickly and efficiently, reducing the time from development to production.
    
3. ***“I’m looking for more alternatives to Render”***
    
    If you're considering other options, this [Render alternatives article](https://northflank.com/blog/render-alternatives) is helpful for comparing different platforms.
    

### 5. Harness

[Harness](https://www.harness.io/) is a comprehensive software delivery platform built to support the entire DevOps lifecycle. It includes modular tools for continuous integration, continuous delivery, feature flag management, chaos engineering, and cloud cost optimization.

It also comes with smart automation, robust security features, and helpful data to speed up how teams ship software.

 ![](https://assets.northflank.com/harness_min_08b227e635.png) 

If your needs or problems are:

1. ***“I need a platform that supports intelligent autoscaling”***
    
    Harness enables autoscaling through its [Delegate system](https://developer.harness.io/docs/platform/delegates/delegate-concepts/delegate-overview/), allowing for resource scaling based on CPU and memory utilization. This helps keep deployments running smoothly without downtime or resource overload.
    
2. ***“I prefer a flexible pricing model that scales with my team”***
    
    Harness offers a Developer 360 subscription model with Free, Startup, and Enterprise plans. This per-developer [pricing](https://www.harness.io/pricing) structure allows teams to select and pay for only the modules they need, providing predictability and scalability as your organization grows.
    
3. ***“I’m looking for more alternatives to Harness”***
    
    If you're looking for other options, this article on [Harness alternatives](https://northflank.com/blog/top-harness-alternatives) might help you compare different platforms.
    

### 6. Porter

[Porter](https://www.porter.run/) is a PaaS (Platform-as-a-Service) designed to help teams deploy and manage applications on their own cloud infrastructure using Kubernetes without the complexity that usually comes with it.

It provides a Heroku-style developer experience while letting you run everything in your AWS or GCP account.

 ![](https://assets.northflank.com/porter_homepage_min_3b23d4eca9.png) 

If your needs or problems are:

1. ***“I want a Heroku-like platform but with more control”***
    
    Porter gives you that familiar Heroku-style UI and workflow, but behind the scenes, it deploys your apps directly into your own AWS or GCP infrastructure, giving you more control and flexibility.
    
2. ***“Kubernetes is too complicated to manage”***
    
    Porter abstracts Kubernetes behind an easy-to-use interface. You don’t need to write Helm charts or deal with raw manifests; it automates the Kubernetes setup for you.
    
3. ***“I’m still weighing my options”***
    
    If you’re comparing Porter with other platforms, this article on [Porter alternatives](https://northflank.com/blog/best-porter-alternatives-for-scalable-deployments) provides a complete view.
    

### 7. OpenShift

[OpenShift](https://www.redhat.com/en/technologies/cloud-computing/openshift) is Red Hat’s enterprise-grade Kubernetes platform designed to help teams build, deploy, and manage containerized applications at scale.

It provides self-managed and fully managed options, including [OpenShift Dedicated](https://www.redhat.com/en/technologies/cloud-computing/openshift/dedicated), [Azure Red Hat OpenShift](https://azure.microsoft.com/en-gb/products/openshift), and [Red Hat OpenShift Servic](https://www.redhat.com/en/technologies/cloud-computing/openshift/aws)e on AWS (ROSA), providing flexibility across hybrid and multi-cloud environments.

 ![](https://assets.northflank.com/openshift_min_f6f48f8b2f.png) 

If your needs or problems are:

1. ***“I need a platform that supports automatic scaling”***
    
    OpenShift provides both Horizontal Pod Autoscaling (HPA) and Cluster Autoscaling. HPA adjusts the number of pod replicas based on CPU or memory usage, while Cluster Autoscaler adds or removes nodes to meet workload demands. 
    
2. ***“I want to deploy across hybrid or multicloud environments”***
    
    OpenShift supports deployments on various infrastructures, including AWS, Azure, Google Cloud, IBM Cloud, and on-premises data centers, providing flexibility for diverse deployment strategies. 
    
3. ***“I’m looking for more alternatives to OpenShift”***
    
    If you're considering other options, this article on [OpenShift alternatives](https://northflank.com/blog/best-open-shift-alternatives-finding-the-right-kubernetes-platform) might help you compare different platforms.
    

### 8. AWS App Runner

[AWS App Runner](https://aws.amazon.com/apprunner/) is a fully managed service that simplifies the process of deploying containerized web applications and APIs directly from source code or container images.

It automatically handles the infrastructure, including provisioning, scaling, and load balancing, allowing developers to focus on building applications without managing servers.

 ![](https://assets.northflank.com/aws_app_runner_home_page_min_ce0e6d05eb.png) 

If your needs or problems are:

1. ***“I want my applications to scale automatically based on demand”***
    
    App Runner provides configurable auto-scaling settings, allowing you to define parameters like maximum concurrency, minimum, and maximum instance counts. This helps your application scale in a way that matches changing traffic without overprovisioning or lag.
    
2. ***“I need a straightforward deployment process from my code repository”***
    
    With App Runner, you can set up automatic deployments that trigger whenever you push new code to your repository. This continuous deployment capability makes the release process faster and easier to manage.
    
3. ***“I’m looking for more alternatives to AWS App Runner”***
    
    If you're looking for other options, you might find this [AWS App Runner alternatives article](https://northflank.com/blog/aws-app-runner-alternatives) helpful for comparing different platforms.
    

### 9. Google Cloud Run

[Google Cloud Run](https://cloud.google.com/run) is a fully managed serverless platform that allows you to deploy and run containerized applications without managing infrastructure.

It automatically scales your applications up or down based on traffic, even down to zero when not in use, helping to optimize costs.

 ![](https://assets.northflank.com/google_cloud_run_home_page_min_25317b598a.png) 

If your needs or problems are:

1. ***“I need my applications to scale automatically based on demand”***
    
    Cloud Run supports request-based autoscaling, automatically adjusting the number of container instances to handle incoming requests as they come in. This helps your application respond to changes in traffic without needing manual adjustments.
    
2. ***“I want a straightforward deployment process from my code repository”***
    
    With Cloud Run, you can deploy applications directly from your source code or container images. It integrates easily with popular CI/CD tools, enabling continuous deployment workflows.
    
3. ***“I prefer a pay-as-you-go pricing model”***
    
    Cloud Run operates on a pay-as-you-go [pricing](https://cloud.google.com/run/pricing) model, charging you only for the resources your application uses.
    
4. ***“I’m looking for more alternatives to Google Cloud Run”***
    
    If you're looking for other options, you might find this [Google Cloud Run alternatives article](https://northflank.com/blog/best-google-cloud-run-alternatives-in-2025) helpful for comparing different platforms.
    

### 10. Vercel

[Vercel](https://vercel.com/) is a frontend-focused platform tailored for developers who are building with modern frameworks like Next.js, React, and Svelte.

It simplifies the deployment process by including automatic CI/CD, serverless functions, and a global edge network that helps maintain fast load times and scale as needed.

 ![](https://assets.northflank.com/vercel_min_c04c41400d.png) 

If your needs or problems are:

1. ***“I want my applications to scale automatically based on demand”***
    
    Vercel provides automatic scaling by deploying applications across its global edge network, helping maintain optimal performance during traffic spikes without manual intervention.
    
2. ***“I need a straightforward deployment process from my code repository”***
    
    Vercel connects easily with Git repositories, so every push triggers an automatic deployment. This helps keep the development workflow clear and straightforward.
    
3. ***“I’m looking for more alternatives to Vercel”***
    
    If you're looking for other options, this [Vercel alternatives article](https://northflank.com/blog/best-vercel-alternatives-for-scalable-deployments) might help you compare different platforms.
    

## Answers to common questions about Azure alternatives

Still trying to figure things out? That’s totally normal. Below are some common questions people have when comparing Azure with other cloud platforms, along with quick answers to help you get clarity.

- **What is an alternative to Azure?**
    
    An alternative to Azure is any cloud platform that provides similar services, such as hosting, scaling, and deploying applications. Examples include Northflank, AWS, Google Cloud, Render, and others.
    
- **Who competes with Microsoft Azure?**
    
    Azure's biggest competitors are Amazon Web Services (AWS) and Google Cloud Platform ****(GCP). Other growing platforms like Northflank, Render, and Fly.io also compete in specific areas, especially with developer-first features.
    
- **What replaced Azure?**
    
    Azure hasn’t been replaced, but many teams are moving away from it in favor of platforms that provide more flexibility, clearer pricing, and simpler setup.
    
- **Why is AWS better than Azure?**
    
    AWS has been around longer and generally provides more services and documentation. Some developers also find AWS’s ecosystem easier to use and better supported by third-party tools.
    
- **Is Azure still relevant?**
    
    Yes, Azure is still widely used, especially by companies already using Microsoft tools. But more teams are now choosing alternatives that give them more control and less complexity.
    
- **Is Azure better than Google Cloud?**
    
    It depends on your needs. Azure works well with Microsoft’s stack. Google Cloud is often preferred for its tools around data and machine learning. Both are popular, but not always the best fit for every team.
    

## Tips for making the right choice

Now that you’ve seen what each platform can do, the next step is figuring out which one fits best with your goals. The right cloud platform isn’t always the one with the most features. It’s the one that gives you what *you* need, without adding extra complexity.

A few things to keep in mind as you decide:

- Know what you want control over (pricing, infrastructure, or both)
- Check if the platform works with your current tools and setup
- Look for transparent pricing (a free tier helps too)
- See how much freedom you have to scale or switch providers
- Make sure the docs and support are easy to access

If you're trying to see how a developer-first platform with transparent pricing, autoscaling, and multi-cloud support can fit into your workflow, [see how it works for teams like yours.](https://app.northflank.com/signup)]]>
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</item><item>
  <title>Talking IDPs, PaaS, and Developer Experience (DX) on the Tech Lounge Podcast</title>
  <link>https://northflank.com/blog/talking-idp-paas-and-developer-experience-dx-on-the-tech-lounge-podcast</link>
  <pubDate>2025-04-17T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Back in September 2024, I joined Chris Ward on the Tech Lounge podcast at Civo Navigate Berlin to talk about how Northflank is building the next generation of developer infrastructure.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_civo_berlin_2024_7d96b79b85.png" alt="Talking IDPs, PaaS, and Developer Experience (DX) on the Tech Lounge Podcast" /><iframe
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Back in September 2024, I joined [Chris Ward](https://www.linkedin.com/in/chrischinchilla/) on the [Tech Lounge podcast](https://chrischinchilla.com/podcast/techlounge/) at [**Civo Navigate Berlin**](https://www.civo.com/navigate/berlin/2024) to talk about how Northflank is building the next generation of developer infrastructure.

The episode covers everything from multi-cloud deployments to our use of Kata containers and how we started out by deploying game servers on bare metal at age 11. Here’s a recap of the main takeaways:

### Northflank is not just another PaaS

There’s a crowded field of platforms claiming to “focus on developer experience.” But most of them are either glorified dashboards or shallow abstractions that break the moment you try to do something complex. We’ve spent five years going deep AND broad, building a platform with serious depth that works for real production workloads.

 ![](https://assets.northflank.com/1_min_abd155e9ff.png) 

Northflank is a post-commit platform to self-serve apps, databases, and jobs to any cloud. You can use our managed infrastructure, or plug in your own AWS, GCP, Azure, or even a private OpenShift cluster. You get consistency across environments, and the ability to scale from hobby projects to teams running thousands of microservices.

### Workloads, not infrastructure

Most infrastructure tools still think in terms of primitives: EC2 groups, load balancers, Kubernetes clusters. We don’t. **Northflank speaks the language of workloads.** 

Developers don’t want to provision a VPC. They want a Postgres database, a preview environment for their app, a cron job that just works.

That mindset shift drives everything we do. You push code, we build, deploy, manage logs, metrics, autoscaling, failover, and more.

### Security is non-negotiable

Multi-tenancy is hard to do well, and most people get it wrong. From day one, we’ve treated untrusted code execution as a security problem and a cornerstone of Northflank’s platform, not a feature. We initially used gVisor, but migrated to microVMs with Kata Containers to balance security and performance with support for QEMU, Firecracker, and Cloud Hypervisor. For edge cases (like public cloud not supporting nested virtualization on some hardware, such as GPU node types and non-metal nodes on AWS), we still support gVisor.

You don’t want tenant A snooping on tenant B because someone cut corners with container isolation.

### A golden path, with escape hatches

We have strong opinions about defaults—Istio for service mesh, sane CI/CD workflows, one-click preview environments. But we’re not dogmatic. If you want to bring your own logs, secrets manager, DNS, GPU workloads, or even your own cloud, we support it.

 ![](https://assets.northflank.com/12_min_33d117aa90.png) 

It’s a “pick and mix” model. Start with the golden path. Escape when needed.

### Designed for everyone, but built for power users

The same platform that powers complex enterprise workloads also supports parents deploying glucose monitor dashboards for their kids. 

They click a template, configure a couple variables, and deploy. No infra experience required. At the other end of the spectrum, we’ve got platform teams building full internal platforms on top of our API surface. Same platform. 

Our APIs are fully exposed. Every action in the UI is mirrored in our CLI, REST API, and GitOps layer. It’s why platform teams trust us to run thousands of workloads. 

### Why we started Northflank

My co-founder and I met playing games online when we were 11. We were hosting game servers on Hetzner and OVH, duct-taped together with Bash scripts. It was slow, painful, and fragile.

Later, we learned to code and built our own game server hosting platform. We used Rancher. Then Mesos. We were early to containers. But we eventually realized that if you can containerize a game server, you can containerize everything: databases, jobs, production apps, CI/CD workflows.

Northflank is the platform we wish we had then. One place to define and deploy workloads with speed and security, across any environment.

### Try it out

We offer a generous free tier called the **Developer Sandbox**. You can deploy apps, databases, and jobs on our infrastructure, for free. If you want to use your own cloud, go for it. 

We built Northflank for engineers who’d rather ship than stitch together infra. If that’s you, try it out.

<div>
  <center>
    <a href="https://app.northflank.com/signup">
      <Button variant={["large", "gradient"]}>Start here</Button>
    </a>
  </center>
</div>

---

**You’ll find the full conversation (lightly edited for clarity) below.**

**Chris:** Welcome to the *Tech Lounge*. My guest today is Will Stewart, CEO and co-founder of Northflank. We recorded this at Civo Navigate Berlin back in September 2024. Since then, Northflank has raised $22 million. Congrats on that. Let’s start from the top: Northflank is in a pretty crowded space. What exactly do you offer that’s different?

**Will:** At a high level, Northflank is a self-service platform for developers to deploy apps, databases, and jobs to the cloud. But we don’t think in terms of infrastructure, we think in workloads. Developers don’t want to create EC2 groups or Kubernetes clusters. They want Postgres, Mongo, Redis, or a preview environment for their app. That’s what we provide: a post-commit platform to build, deploy, and operate workloads with minimal friction.

**Chris:** So under the hood, you’re using Kubernetes?

**Will:** Yes, Kubernetes is our operating system. But the developer never needs to touch it. They can deploy on our managed infrastructure, or connect their own AWS, GCP, Azure, whatever they need. We take care of the lifecycle, automation, and developer experience on top of that.

**Chris:** Do you also offer your own cloud?

**Will:** Our managed product runs on Google Cloud and Azure. We operate large clusters and layer our secure runtime on top using Kata Containers, Cilium, and Istio. It’s multi-tenant, secure, and production-ready. And yes, we just hit general availability for our Civo integration, you can provision a production-ready workload platform on Civo in under 10 minutes.

**Chris:** What makes Northflank stand out from other PaaS or platform tools?

**Will:** Most tools in this space say they “focus on DX,” but don’t back it up. Northflank exposes every feature across UI, API, CLI, and GitHub integrations. Some of our enterprise customers chose us because we were the only platform offering that level of abstraction and control. We’re not just a dashboard over Kubernetes—we’ve built deep functionality around real developer workflows.

**Chris:** Do you see your main competition as other platforms, or DIY setups?

**Will:** Honestly, 99% of the time, our competition is DIY. Teams write their own Terraform, run their own Helm charts, stitch it all together. They’re doing that because there hasn’t been a credible alternative that’s flexible and complete enough. That’s our opportunity. If a platform only solves 85% of your needs, it’s not enough. We aim to get much closer to 100% by working tightly with our customers.

**Chris:** Why Kata Containers?

**Will:** Security. In a multi-tenant platform, you’re running untrusted code. You can’t rely on basic container isolation. We started with gVisor but hit performance issues. Kata gives us hardware-level isolation with better performance. We’ve deployed millions of pods in production using it.

**Chris:** What about users who don’t want Kata or can’t use nested virtualization?

**Will:** We still support gVisor for environments where Kata isn’t viable—like non-metal AWS nodes or certain GCP AMD configurations. We maintain a matrix of what works best depending on cloud, CPU type, and price-performance tradeoffs.

**Chris:** You use Istio for service mesh by default. Can users bring their own mesh?

**Will:** By default, it’s Istio, but yes, we support alternatives like Linkerd for customers who need it. We offer a golden path that works out of the box, but customers can customize everything: logs, DNS, secrets, GPUs, service mesh, cloud provider. You can even run Northflank on your own data center or OpenShift cluster.

**Chris:** And what about monitoring and logging?

**Will:** If you run on our infrastructure, we handle it. If you need data residency or privacy guarantees, you can plug in your own logging and observability stack. Bring-your-own-everything is something we support—BYO logs, secrets, cloud, GPU, etc.

**Chris:** You mentioned earlier that some pretty different kinds of people are using Northflank. What’s the range?

**Will:** We’ve got platform teams managing thousands of microservices. But we also have mothers deploying glucose monitoring dashboards for their kids. There’s an open source project that uploads glucose data to a database and visualizes it. With Northflank’s free tier, they can deploy that with one click, no infra experience required.

**Chris:** Let’s go back to the beginning. Why did you start Northflank?

**Will:** It started when my co-founder and I were 11. We met playing online games and started deploying game servers on Hetzner and OVH using Bash scripts. It was painful, but we learned a lot. Later, we built a game server hosting platform on Rancher, then on Mesos. Eventually, we realized if you can containerize a game server, you can containerize anything—databases, microservices, CI/CD pipelines.

**Chris:** Do you still use Northflank to run game servers?

**Will:** We’ve run some Minecraft servers on it, yeah. Kubernetes didn’t play well with UDP until recently, but it’s getting better.

**Chris:** You mentioned the shift between public and private cloud. Are you seeing that among customers?

**Will:** It’s all over the place. Some are moving off public cloud to private. Others are going the other way. Some are trying to do hybrid. The consistent thing is that nobody wants to throw away their investment, especially in data centers. With Northflank, if you can get us a Kubernetes endpoint, we can install and run the platform.

**Chris:** Is multi-cloud something your customers care about?

**Will:** Depends what you mean by multi-cloud. Some want active-active across providers. Others want DR in a second region or cloud. Some have teams in different orgs using different clouds. We support all of that. The goal is: no matter where you're running, the workflow stays the same.

**Chris:** And your pricing?

**Will:** Usage-based. If you're on your cloud, pay us a fraction of what you pay your provider—roughly 10%. If you’re using our infra, it’s metered too. We’re about half the price of Heroku and ~30x cheaper than OpenShift.

**Chris:** Where can people try it?

**Will:** https://northflank.com/. You can start with the Developer Sandbox, fully featured and free on our cloud. Or book a demo [here](https://cal.com/team/northflank/northflank-demo?duration=30).

**Chris:** Awesome. Thanks, Will.

**Will:** Appreciate it. Thanks for having me.]]>
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</item><item>
  <title>Ultralight ditched AWS ECS for EKS with Northflank. Here’s why.</title>
  <link>https://northflank.com/blog/ultralight-ditched-aws-ecs-for-eks-with-northflank</link>
  <pubDate>2025-04-15T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Ultralight is an early-stage company building software that helps medical device companies navigate FDA approvals.They started on AWS ECS, but deployments were slow, debugging was painful, and compliance was a nightmare.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_ultralight_casestudy_27b3c29c16.png" alt="Ultralight ditched AWS ECS for EKS with Northflank. Here’s why." />## TL;DR

[Ultralight](https://www.ultralightlabs.com/) is an early-stage, venture-backed company building software that helps medical device companies navigate FDA approvals. 

They started on AWS ECS, but deployments were slow, debugging was painful, and compliance was a nightmare.  

Co-founder and CTO [Shiv Ghai](https://www.linkedin.com/in/shivghai/) switched to Northflank, gaining full control over deployments, automating infrastructure, and eliminating DevOps overhead. 

After an initial short learning curve, they set up both staging and production environments in under an hour.

## Who is Ultralight?

 ![](https://assets.northflank.com/ultralight_9acca55280.png) 

Ultralight was co-founded by Shiv Ghai, an engineer with experience at Bloomberg, Two Sigma, and Meta. 

Having worked in finance, big tech, and now healthcare, he saw firsthand how different industries handle compliance-heavy software.

Hedge funds and FAANG companies had streamlined, powerful infrastructure. Medical device companies? Not so much.

> “I was lucky to work at places that had good tools. Bloomberg, Two Sigma, Meta, they all had solid infra for compliance-heavy software. Medical device companies don’t.”

Ultralight was created to fill that gap. A platform that helps medical device companies develop software without struggling with compliance, documentation, and FDA approval workflows.

## The problem 


AWS ECS was holding them back.

From day one, Ultralight needed infrastructure that was secure, compliant, and scalable, but also developer-friendly. 

They started on AWS ECS, hoping it would provide low-maintenance, containerized deployments.

It worked… for a while. Then the cracks started showing:

- Deployments were slow – ECS required more manual effort than expected.

- Not enough control – Shiv’s team couldn’t customize workflows the way they needed.

- ECS felt like a black box – Debugging and troubleshooting issues took too long.

> “We were doing ECS deployments and wanted a little bit more control. We wanted more power.”

Ultralight wasn’t trying to reinvent the wheel. They aren’t an infrastructure company; they are a medical software company. 

ECS was forcing them to spend too much time on infrastructure, pulling focus away from building their core product.

### Looking at alternatives

Shiv and his team explored multiple platforms to replace ECS, looking for something that gave them more control but without the complexity of running everything from scratch.

They tested several alternatives, but none felt quite right:

- Some were too restrictive, abstracting away too much control.
- Others left behind resources after deletion, creating unnecessary infrastructure clutter.
- Most required too much upfront investment in learning and setup.

> “With other platforms, leftover resources caused problems and trust issues. When I started from scratch on Northflank, everything cleaned up perfectly. It inspired more trust.”

## The solution

Northflank struck the perfect balance: powerful and flexible, yet easy to use. 

It gave Ultralight the benefits of Kubernetes without forcing them to deal with Kubernetes.

### Fast, developer-friendly onboarding

> “The self-serve process within Northflank was the best out of all the other providers I tried.”

Shiv didn’t have to book a sales call. He didn’t need a multi-week onboarding process. He and his team simply signed up and started deploying.

### Kubernetes with 0 headaches
Ultralight didn’t have deep Kubernetes experience. 

But with Northflank, that didn’t matter:

> “We had no idea how to manage Kubernetes initially. Northflank allowed us to quickly and easily leverage its full potential.”

Instead of wrestling with raw Kubernetes configurations, Ultralight could deploy applications quickly, while still having the flexibility to customize infrastructure as needed.

### Infrastructure that cleans up after itself

Unlike other platforms that left stray infrastructure behind, Northflank ensured clean state resets, reducing clutter and keeping their AWS environment tidy.

No lingering infrastructure, no rogue AWS charges, no mystery services you forgot about.

### Built-in compliance support

Ultralight needed complete isolation between staging and production, which Northflank made simple:

- Dedicated Kubernetes clusters for staging and production.
- Clear network separation and access controls.
- Compliance-focused deployment workflows.

### Crazy fast support 😌

During a critical migration from ECS, Northflank shined:

> “We needed to complete the migration quickly, and Northflank’s self-service, documentation and first-class Slack support got us through it quickly.”

## The results

Once they got comfortable with Northflank, Ultralight saw immediate benefits.

> “Once set up, I had one engineer handle staging, another handle production, and both were fully operational within an hour on a single call.”

Other immediate wins:

- Faster builds. They switched to Northflank’s build infrastructure, making builds more stable and cost-efficient:

> “We previously managed builds within our own cluster. Now, Northflank’s build infrastructure provides much greater stability and lower costs.”

- More reliable infrastructure. No more unexpected downtime or manual workarounds.
- Simplified deployments. One-click deployments for staging, manual releases for production, all configured exactly how they wanted.

## Where to now?

Ultralight is doubling down on automation. 

They’re now working on advanced caching optimizations and leveraging Northflank’s caching features to further reduce build times.
Shiv summed it up:

> “Northflank transformed our infrastructure from a frustration into a genuine strength.”

With their infrastructure running smoothly, Ultralight is fully focused on helping medical device companies bring new products to market faster, without getting slowed down by DevOps headaches.

Unlike most teams that rely on Vercel or Netlify for static sites and separate backend infrastructure, Ultralight kept everything simple and low-cost by deploying both through Northflank. 

The platform’s flexibility allowed them to stand up a full-stack setup (static frontend and backend) without stitching together multiple services or overcomplicating orchestration.]]>
  </content:encoded>
</item><item>
  <title>Top 6 Fly.io alternatives in 2026</title>
  <link>https://northflank.com/blog/flyio-alternatives</link>
  <pubDate>2025-04-15T21:16:00.000Z</pubDate>
  <description>
    <![CDATA[Fly.io is a great platform for global infrastructure and Docker-based workflows but has limitations in pricing, scalability, add-ons, and user-friendliness. Alternatives like Northflank, Render, and Vercel offer simpler, more predictable solutions.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Porter_alternatives_4_ff78d4d9de.png" alt="Top 6 Fly.io alternatives in 2026" />If you’ve been deploying apps on **Fly.io**, you’ve probably enjoyed its global infrastructure, Docker-based workflows, and performance-first design. It’s a solid platform — no doubt about it.

But maybe you’ve run into a few limits. Maybe the pricing gets unpredictable as you scale. Maybe you need a richer add-on ecosystem, simpler deployments, or tools that better fit the way *your team* works.

The good news? In 2026, there’s a whole ecosystem of modern alternatives out there — each with its own strengths, trade-offs, and ideal use cases. Whether you're after Heroku-style simplicity, enterprise-ready infrastructure, or cutting-edge edge deployments, there’s a platform out there that might fit you better.

## Fly.io alternatives at a glance

If you’re already familiar with the space and just want a quick side-by-side, here’s a breakdown of the key differences between top platforms:

| Platform | Best For | Key Strengths | Free Tier | Pricing Starts |
| --- | --- | --- | --- | --- |
| [**Northflank**](https://northflank.com/) | Full-stack apps, databases, CI/CD, multi-cloud | Built-in CI/CD, integrated databases, flexible scaling, great DX, multi-cloud, VPC support | 2 services, 2 jobs | Pay-as-you-go |
| [**DigitalOcean App Platform**](https://www.digitalocean.com/products/app-platform) | Simplicity with control | Integrated database, autoscaling, global CDN | 3 static sites | $5/mo |
| [**Render**](https://render.com/) | Web apps, APIs, databases | Zero-downtime deploys, PR previews | Low-traffic apps | $19/mo |
| [**Vercel**](https://vercel.com/) | Frontend apps & static sites | Edge deploys, performance tools, serverless focus | 1M monthly requests | $20/mo |
| [**Cloudflare Workers**](https://workers.cloudflare.com/) | Serverless at the edge | Ultra-low latency, global scale | 100K daily requests | $5/10M requests |
| [**Heroku**](https://www.heroku.com/) | Simplicity, hobby & production | Zero-config deploys, massive add-on ecosystem | Limited free dynos | $5-7/dyno/mo |

## Why consider Fly.io alternatives?

Let’s be clear: **Fly.io is a great platform**. It nails things like global latency optimization, built-in Postgres, edge deployments, and a slick, developer-friendly CLI. Many developers love it for hobby projects, microservices, and fast global APIs.

But like any tool, it’s not a perfect fit for every situation. Here’s where folks start looking elsewhere:

- **Pricing surprises at scale:** Fly’s pricing is fine for small apps, but as soon as you start scaling instances, adding regions, or bumping up resources, things get murky. Several devs on Reddit mention how it’s easy to rack up unexpected charges or how the pricing model feels more complicated than it needs to be.
- **Limited add-on ecosystem:** Compared to platforms like Heroku or Render, Fly has fewer plug-and-play services — things like background workers, queues, object storage, or third-party integrations. You’ll often end up wiring together external services manually.
- **A Docker-focused learning curve:** If your team isn’t already fluent in Docker or container-based workflows, onboarding can feel steep. Unlike Render or Vercel, which abstract away a lot of deployment details, Fly requires you to be comfortable with images, processes, volumes, and networking quirks.
- **Niche tooling and platform feel:** Fly has its own workflow philosophy — CLI-first, config-driven, and region-centric. Some developers dig this; others find themselves missing the simplicity and conventional “click-to-deploy” feel of more traditional PaaS platforms.
- **Scalability and infrastructure trade-offs:** While Fly shines for globally distributed apps and edge workloads, some teams need different scaling patterns, VPC peering, private networking, or more traditional regional infrastructure setups — areas where platforms like AWS, DigitalOcean, or Railway might be a better match.
- **Recent shift away from GPU support:** In 2024, [Fly.io](http://Fly.io) [publicly retracted its GPU hosting ambitions](https://fly.io/blog/wrong-about-gpu/) after realizing that its platform's strengths didn't align well with the infrastructure demands of GPU-heavy workloads like AI inference and video processing. If your stack includes GPU-dependent apps, you’ll need to look elsewhere; platforms like [Northflank](https://northflank.com/) are better suited for those cases.

## What are developers saying about Fly.io?

If you spend time in dev communities like Reddit, Hacker News, or X, you’ll hear a mix of praise and pain points:

> “Love how easy it is to deploy globally. But wish it had more third-party add-ons like Heroku.”
> 

> “Great for small apps and personal projects. Gets tricky when scaling up.”
> 

> “CLI-first workflow is solid, but onboarding junior devs takes longer compared to Render or Vercel.”
> 

Beyond these common takes, some developers have voiced frustrations about reliability and pricing clarity. On Reddit, people have shared concerns like:

> “[I just want pricing. I don't want to have to study.](https://www.reddit.com/r/laravel/comments/1bajds9/comment/ku5x67v/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button)”
> 

> *“[Our app was completely down for hours, and the status page said everything was fine.](https://www.reddit.com/r/fly_io/comments/1ga932z/flyio_lost_my_trust_what_do_you_think_about_it/)”*
> 

> *“[Even with enough capacity, we still hit weird latency issues and it’s not reflected anywhere.](https://www.reddit.com/r/fly_io/comments/1ga932z/comment/ltk3ydb/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button)”*
> 

## Top criteria for evaluating Fly.io alternatives

Here’s what people consistently look for when comparing alternatives and why these things actually matter in real-world projects:

### Simple, no-hassle deployments

Most developers want a platform that lets them focus on building, not fiddling with infrastructure. The ability to deploy via a `git push` or a straightforward CLI command is huge, especially when you’re iterating fast.

Tools like **Northflank** and **Vercel** have nailed this with zero-config deploys that just work out of the box. If you’re used to that, anything more involved like writing Dockerfiles, configuring processes, or debugging networking configs (which Fly sometimes requires) — feels like friction.

### CI/CD integration that just works

CI/CD pipelines should feel invisible, reliable, fast, and easy to hook up. If your platform doesn’t have solid built-in CI/CD (or at least effortless GitHub/GitLab integration), you’re losing precious time. Developers value automatic deploys on main or PR merges, staging/previews for pull requests, and easy rollback if a deploy goes sideways.

### Scaling without headaches

Early-stage apps might run fine on a $5 instance, but growth happens fast and you don’t want scaling to turn into a research project. Developers look for **Horizontal scaling (**adding instances to handle traffic), **Vertical scaling** (upgrading CPU/memory as needed), and **Autoscaling** (without manual babysitting). Fly.io does edge scaling well for some workloads, but if you need predictable autoscaling rules, VPC peering, or per-region controls, other platforms might offer more out-of-the-box.

### Transparent, predictable pricing

This comes up *a lot*. Developers hate ambiguous billing and unexpected spikes. What seems cheap at first can get messy with hidden fees for extra regions, charges for network egress or storage and confusing per-second instance pricing. Platforms like **Northflank** offer flat-rate plans or extremely clear usage dashboards, which is comforting for both solo developers and teams on a budget.

### A healthy add-on ecosystem

When you’re deploying an app, you rarely just need a server. You also need:

- Managed databases (Postgres, MySQL, Redis)
- Queues (Sidekiq, RabbitMQ, etc.)
- Object storage (S3-style)
- Background workers
- Monitoring, logging, analytics

**Northflank** and **Heroku**, for example, have a long list of ready-to-go add-ons you can provision in seconds.

### Multi-region or global infrastructure

One of Fly’s biggest selling points is edge-first hosting. If your app has a global audience, you’ll want to serve content as close to users as possible.

But developers also weigh:

- How easy is it to control regions per service?
- Are all regions equally reliable and fast?
- Can I easily add/remove regions without downtime?
- How’s the pricing for multi-region setups?

Some alternatives (like **Vercel, Northflank,** and **Cloudflare Workers**) simplify global deployment more than Fly, especially if you don’t need persistent storage everywhere.

### **A developer experience that doesn’t fight you**

Good platforms are invisible. Great platforms actually feel good to use. Devs care a ton about:

- Clean, well-structured docs
- Helpful, responsive support
- A CLI that behaves consistently
- A dashboard that isn’t slow or confusing
- Straightforward logs and debugging tools

When something goes wrong at 2am, you don’t want to be stuck reading vague errors or chasing logs across regions.

## Top Fly.io alternatives

Are you looking for a new home for your applications? Here are seven powerful alternatives that combine Heroku's simplicity with compelling modern features.

### 1. Northflank

[Northflank](https://northflank.com/) is a platform that enables developers to build, deploy, and scale applications, services, databases, and jobs on any cloud through a self-service approach. For DevOps and platform teams, Northflank provides a powerful abstraction layer over Kubernetes, enabling templated, standardized production releases with intelligent defaults while maintaining necessary configurability.

Northflank advances the legacy of pioneers like Heroku and Pivotal Cloud Foundry. While Heroku perfected the self-service developer experience, it didn't support complex workloads in enterprise cloud accounts. Cloud Foundry offered the right application abstraction to simplify complexity, but its underlying infrastructure proved costly and difficult to implement. Northflank delivers the best of both worlds: support for complex workloads, exceptional developer experience, and appropriate abstractions in your cloud environment—all within minutes and at a reasonable cost.

 ![](https://assets.northflank.com/image_5_fd06403bd1.png) 

**Key features**:

- End-to-end CI/CD pipeline automation
- Built-in monitoring and logging
- Automatic horizontal scaling
- Private networking and VPC support
- Integrated secrets management
- Database as a Service
- Multi-cloud support (AWS, Azure, GCP, and others)

**Pricing**:
Northflank offers a generous [free tier](https://northflank.com/pricing) that includes deployment of 2 services, 2 jobs, and 1 addon. Users can connect their existing cloud account, with limited resources and plans available. A Pay-as-you-go Pro plan provides additional capabilities.

*See how [Weights company uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### 2. DigitalOcean App Platform

[DigitalOcean App Platform](https://www.digitalocean.com/products/app-platform) is a PaaS solution built on DigitalOcean's robust infrastructure, striking an optimal balance between simplicity and control for growing applications.

 ![](https://assets.northflank.com/image_6_022540644b.png) 

**Key features**:

- Integrated CI/CD pipelines
- Automatic vertical and horizontal scaling
- Built-in monitoring and alerting
- Seamless integration with DigitalOcean's managed databases
- Global CDN support

**Pricing**:
[DigitalOcean App Platform](https://www.digitalocean.com/pricing/app-platform) includes a free tier supporting up to 3 static sites with 1GiB data transfer allowance per app. Paid plans begin at $5 per month with enhanced features.

### 3. Render

[Render](https://render.com/) is a modern cloud platform that streamlines the hosting of web applications, static sites, APIs, and databases, providing automatic SSL certification and CDN integration.

 ![](https://assets.northflank.com/image_7_04cbeab21d.png) 

**Key features**:

- Zero-downtime deployments
- Automatic HTTPS and DDoS protection
- Native SSD storage
- Pull request preview environments
- Custom domain support

**Pricing**:
Render provides a [free tier](https://render.com/pricing) for low-traffic applications, with paid plans starting at $19 per user monthly.

### 4. Vercel

[Vercel](https://vercel.com/) is a cloud platform optimized for frontend frameworks and static sites, delivering exceptional performance and developer experience.

 ![](https://assets.northflank.com/image_9_36a9aaf661.png) 

**Key features**:

- Advanced performance optimization
- Comprehensive serverless function support
- Integrated CI/CD pipeline
- Global edge network deployment
- Sophisticated analytics and monitoring

**Pricing**:
Vercel's [free tier](https://vercel.com/pricing) accommodates frontend applications with up to 1,000,000 monthly requests. Premium plans start at $20 monthly with advanced features.

### 5. Cloudflare Workers

[Cloudflare Workers](https://workers.cloudflare.com/) is a unique serverless platform that allows you to run your app at the edge, anywhere in the world. It’s particularly powerful for serverless functions, APIs, and applications that need extreme global distribution without maintaining traditional infrastructure.

 ![](https://assets.northflank.com/image_82_916d61ffdc.png) 

**Key features:**

- Deploy at the edge for low-latency performance
- Built-in integrations with Cloudflare's global CDN
- Scalable without managing servers

**Pricing:**

The first 100,000 requests each day are free, and paid plans start at just $5/10 million requests.

### 6. Heroku

[Heroku](https://www.heroku.com/) has long been the go-to platform for developers looking for simple, scalable, and easy-to-use cloud hosting. With a large catalog of add-ons and seamless Git-based deployments, Heroku offers a truly developer-friendly experience.

 ![](https://assets.northflank.com/image_81_ed869cd124.png) 

**Key features**:

- Zero-config deployments via Git
- Huge selection of add-ons (databases, caching, storage, etc.)
- Automated scaling
- Integrated monitoring and alerting
- Easy CI/CD integration

**Pricing:**

This [article](https://northflank.com/heroku-pricing-comparison-and-reduction) provides a deeper look into Heroku's pricing, breaking it down in an easy-to-understand way.

*For a closer look at how Heroku compares to other tools, this [article](https://northflank.com/blog/heroku-enterprise-capabilities-limitations-and-alternatives) offers a well-rounded analysis.*

### Wrapping Up

Fly.io is a great platform, but like any tool, it has quirks that might not align with your evolving needs as your app grows. Whether it’s unpredictable pricing, scaling limitations, or a need for better integrations, exploring alternatives can help you find a platform that better fits your workflow and budget.

If you’re looking for something that balances powerful features with an exceptional developer experience, [Northflank](https://northflank.com/) could be just what you need. With automated CI/CD pipelines, flexible scaling, and transparent pricing, it’s a platform designed to help teams ship fast without the headaches. [Why not give it a try and see if it’s the right fit for your next project?](https://app.northflank.com/signup)]]>
  </content:encoded>
</item><item>
  <title>Flux vs Argo CD: Which GitOps tool fits your Kubernetes workflows best?</title>
  <link>https://northflank.com/blog/flux-vs-argo-cd</link>
  <pubDate>2025-04-15T16:56:00.000Z</pubDate>
  <description>
    <![CDATA[Looking to choose between Flux and Argo CD? This detailed comparison breaks down their architectures, use cases, pros and cons, and introduces Northflank as a third GitOps alternative for Kubernetes.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Flux_vs_Argo_CD_Blog_post_2ac322a5a3.png" alt="Flux vs Argo CD: Which GitOps tool fits your Kubernetes workflows best?" />Have you noticed that recently, there have been many arguments around GitOps tools, especially Flux vs. Argo CD?

You’ll see most of these conversations all over Reddit, with people comparing Flux vs. Argo CD, not just because they’re both open source and CNCF-backed, but also because they take different approaches to solving similar problems.

If you're running workloads on Kubernetes and looking for a GitOps solution, you've likely come across both tools.

In this guide, we’ll explain how each one works, how it’s structured, and what that means for your deployments. You’ll also see how they compare in areas like observability, access control, and extensibility, with technical details to help you choose the best tool for your setup.

If you're building internal platforms, managing clusters at scale, or standardizing how applications get deployed, this walkthrough is for you.

<div>
  <center>
    <a href="https://app.northflank.com/signup">
      <Button variant={["large", "gradient"]}>Try a simpler GitOps experience for your Kubernetes apps &gt;&gt;&gt;</Button>
    </a>
  </center>
</div>

## Flux vs Argo CD at a glance

If you’re already familiar with both tools and just want a quick side-by-side comparison, look at a breakdown of their key differences:

| **Feature** | **Flux CD** | **Argo CD** |
| --- | --- | --- |
| **Deployment model** | Pull-based GitOps using Kubernetes controllers | Pull-based GitOps with optional manual sync and web UI |
| **UI** | No native UI (uses CLI + Grafana dashboards) | Full-featured web UI with visual status and controls |
| **Observability** | Exposes metrics via Prometheus, relies on external dashboards | Built-in status views, diffs, pod logs, and health status |
| **Multi-tenancy** | Based on Kubernetes namespaces and standard RBAC | Built-in AppProject abstraction for multitenancy |
| **Access control** | Uses native Kubernetes RBAC | Custom RBAC system configured via UI or config files |
| **Helm support** | Native Helm controller, supports all Helm features | Uses Helm via plugins or built-in Helm rendering |
| **Custom resources** | Everything is declarative with CRDs (Kustomize, Helm, etc.) | Applications managed via Argo CD `Application` CRD |
| **Extensibility** | Extend via additional controllers | Extend via config management plugins |
| **Secrets support** | Native support for SOPS | External tools via plugins (e.g., helm-secrets) |
| **Multi-cluster support** | Install one Flux per cluster | Single UI to manage multiple clusters centrally |
| **Community & governance** | CNCF graduated project, led by Weaveworks | CNCF incubating project, led by Intuit |
| **Git provider support** | GitHub, GitLab, Bitbucket, Azure Repos | GitHub, GitLab, Bitbucket, Azure Repos |

## What is Flux CD?

[Flux CD](https://fluxcd.io/) is a GitOps controller built natively for Kubernetes. It keeps your cluster in sync with what's defined in your source of truth, usually a Git repository. Compared to CI/CD tools that trigger one-time deploys, Flux continuously reconciles state. If someone makes a manual change in the cluster, Flux detects the mismatch and reverts it. It doesn’t just deploy; it enforces consistency.

 ![](https://assets.northflank.com/fluxcd_home_page_04e9912134.png) 

Behind the scenes, Flux runs a set of Kubernetes controllers that watch your source repositories, apply updates with tools like [Kustomize](https://fluxcd.io/flux/components/kustomize/) and [Helm](https://fluxcd.io/flux/components/helm/), and handle things like image automation and notifications. You define everything as [CRDs](https://helm.sh/docs/chart_best_practices/custom_resource_definitions/) (Custom Resource Definitions), Git repositories, Helm charts, alerting rules, and Flux continuously ensures that your cluster reflects those resources.

You can see this play out in a [Reddit post from r/devops](https://www.reddit.com/r/devops/comments/j8rs0g/continuous_delivery_to_k8s_with_fluxcd/) where a user explains their real-world pipeline. They describe how Flux CD watches an ECR (Amazon Elastic Container Registry) repo for new images, detects changes, and triggers rolling updates (no manual kubectl needed).

> *“CD - Fluxcd operator running in k8s detects a new image in my ECR repo and updates the k8s manifest in my repo. This initiates a rolling update to my deployment.”*
> 

If you have similar needs, see this [guide](https://fluxcd.io/flux/guides/cron-job-image-auth/) from Flux documentation on configuring image automation authentication.

### Why teams choose Flux CD

You’ll often see Flux recommended by teams who are already deep into Kubernetes and prefer infrastructure that’s built from primitives. If your team already thinks in CRDs (Custom Resource Definitions) and reconciliation loops, Flux will feel like home.

It also fits well in scenarios where you want GitOps as a building block, not an opinionated full-stack experience. If you’re creating your own internal platform or need full control over how GitOps is integrated into your pipelines, Flux gives you that flexibility.

Here's a Reddit user explaining why Flux works well for them:

> *“The most useful features I found were the dependsOn and wait parameters that help me better manage dependencies...”*
~ [r/kubernetes](https://www.reddit.com/r/kubernetes/comments/1il9d9q/fluxcd_useful_features/)
> 

 ![](https://assets.northflank.com/argocd2_03c8383fa2.png) 

That’s the kind of low-level control Flux gives you; you can build very specific deployment flows without needing external plugins or UI workarounds.

### What to watch out for

Flux doesn’t ship with a built-in dashboard. That’s intentional; it follows the Unix philosophy of doing one thing well and leaving visualizations to tools like Grafana, Prometheus, or external dashboards. If your team relies heavily on UI for troubleshooting or monitoring, this may require some adjustment.

You’ll also find that onboarding can take a bit longer if your team isn’t already used to Kubernetes-native workflows. Everything is declarative, but it assumes you know your way around CRDs and controllers.

This [Reddit post](https://www.reddit.com/r/devops/comments/1bv9697/what_are_argocd_fluxcd_used_for/) captures that initial friction clearly:

> *“What is the issue with just using a CI/CD tool such as GitHub Actions? From what I understand, they are mainly for k8s... can I not just use GitHub Actions for this?”*
> 

 ![](https://assets.northflank.com/argocd3_232335c399.png) 

That question comes up often, and it shows why understanding how GitOps fits into modern cluster management is important before choosing a tool like Flux.

## What is Argo CD?

[Argo CD](https://argoproj.github.io/cd/) is a declarative GitOps tool that syncs Kubernetes clusters with what’s defined in Git. Like Flux, it constantly watches for differences between your live cluster and your Git repository. When there’s a difference, Argo CD applies the changes automatically. The idea is that Git holds the source of truth, and Argo CD handles keeping everything aligned.

 ![](https://assets.northflank.com/argocd_home_page_f7367a427a.png) 

What sets Argo CD apart is its web UI. You get a real-time view of your deployments, things like:

- Which apps are out of sync
- What’s currently being deployed
- Where something is stuck

It also lets you dig into [pod logs](https://argo-cd.readthedocs.io/en/stable/user-guide/commands/argocd_app_logs/), run [diffs](https://argo-cd.readthedocs.io/en/stable/user-guide/diffing/) on YAML, and even [exec](https://argo-cd.readthedocs.io/en/latest/operator-manual/web_based_terminal/) into containers if needed. It’s a practical tool for getting visual feedback on cluster state without leaving the browser.

This is one reason you’ll see Argo CD brought up more often in [Reddit threads like this](https://www.reddit.com/r/kubernetes/comments/1937tty/argo_cd_or_flux_cd_what_is_the_most_used_cicd_tool/), asking which GitOps tool teams reach for the most.

> *“Although I personally prefer Flux over Argo CD, I got the impression that more people are using Argo CD...”*
> 

 ![](https://assets.northflank.com/argocd4_0ad9f8eec9.png) 

Argo CD’s user-friendly experience and multi-cluster management capabilities make it a go-to for teams that need visibility and control across multiple environments.

### Why teams choose Argo CD

Argo CD makes GitOps feel more accessible. You don’t need to think in controllers or CRDs from day one. Most teams start with the web UI, and from there, they go deeper into how Argo CD fits into the rest of their tooling. It’s especially helpful when you want to give developers or app owners visibility into deployments without giving them direct Kubernetes access.

For platform teams managing several apps across staging and production clusters, Argo CD gives you one place to see it all. It’s also easier to standardize on policies when everyone is working through the same interface.

This [Reddit post](https://www.reddit.com/r/devops/comments/16fr3id/those_who_have_chosen_between_argo_and_flux_which/) sums up a common reason:

> *“Besides what seems like the mainly-CLI focus of Flux vs. Argo, I can’t really find much to differentiate them in terms of capabilities...”*
> 

 ![](https://assets.northflank.com/argocd5_cee0ef1ea1.png) 

If you go through the comments on that post, you'll see that for many teams, it's less about core features and more about how they prefer to work. Some want a clean web UI with visual status and controls. Others prefer CLI-driven workflows and building with primitives.

Even though Argo CD and Flux both solve the same core problem, Argo CD’s focus on usability often tips the balance for teams that don’t want to build around lower-level abstractions.

### What to watch out for

The UI makes it easy to work with Argo CD, but that can come with trade-offs. For example, the [access control model](https://argo-cd.readthedocs.io/en/stable/operator-manual/rbac/) is separate from [Kubernetes RBAC](https://kubernetes.io/docs/reference/access-authn-authz/rbac/), and managing it at scale can get tedious. It’s also common to run into edge cases with [Helm hooks](https://argo-cd.readthedocs.io/en/stable/user-guide/helm/#helm-hooks) or [drift](https://argo-cd.readthedocs.io/en/stable/user-guide/diffing/) when charts are rendered differently than expected.

Here’s a [thread on r/kubernetes](https://www.reddit.com/r/kubernetes/comments/1bogcs9/argo_cd_vs_flux_cd/) where someone compares both tools in terms of configuration and flexibility:

> *“Those who have had to use them in real world PoCs or in production, what did you love or hate?”*
> 

If you read through the comments, you'll see a pattern: many teams say Argo CD works well out of the box, especially for getting started quickly. But when it comes to customizing workflows or plugging into more complex pipelines, the visual-first approach can feel limiting.

Also, if you’re self-hosting Argo CD, you’ll also need to manage the Kubernetes cluster it runs on. That includes the nodes, networking, and security updates. Compared to platforms where GitOps behavior is baked in, this setup can slow down time to value, especially for teams that just want to get deployments going without managing the GitOps engine itself.

*See [Argo CD alternatives that don’t give you brain damage and simplify DX for GitOps, clusters & deployments](https://northflank.com/blog/argo-cd-alternatives-northflank-developer-platform-git-ops-self-service).*

## Comparing Flux CD and Argo CD side by side

Once you understand how each tool works on its own, the main decision comes down to how they fit into your workflow. You’re not just choosing based on features, you’re choosing:

- How much control you want
- How you manage clusters
- How much you want to build around GitOps.

Let’s see how Flux CD and Argo CD compare across key areas.

### Deployment model

Flux CD uses native Kubernetes controllers to apply changes. Everything is defined as a CRD and handled through reconciliation loops. You push to Git, and the controllers pull and apply changes based on your setup. That means no need for templating engines or external plugins, just CRDs working the way Kubernetes intended.

Argo CD handles things a bit differently. It renders your manifests first, usually through [Helm](https://argo-cd.readthedocs.io/en/stable/user-guide/helm/), [Kustomize](https://argo-cd.readthedocs.io/en/stable/user-guide/kustomize/), or plain YAML, and then applies them with its [GitOps Engine](https://argo-cd.readthedocs.io/en/stable/developer-guide/dependencies/#gitops-engine-githubcomargoprojgitops-engine). That gives you visibility into [diffs](https://argo-cd.readthedocs.io/en/stable/user-guide/diffing/#diff-strategies) before things are applied, but also introduces room for drift if Helm charts don’t render the same way every time.

> If you're using a lot of Helm charts with complex hooks or dynamic behavior, Flux might feel more stable. Argo CD’s model gives you more insight into what’s happening, but it might require extra configuration to avoid unexpected behavior.
> 

### UI and observability

This is one of the biggest differences. Argo CD ships with a full-featured UI. You can browse all your apps, see their live status, compare against Git, and take actions like syncing or rolling back, all from the browser.

Flux CD doesn’t come with a UI. You interact through the [CLI](https://fluxcd.io/flux/cmd/) or integrate it into other dashboards (e.g., with Grafana or Weaveworks' Web UI). If you prefer a command-line-first experience or want to plug it into your own observability stack, Flux gives you the space to do that.

### RBAC and multi-tenancy

Flux CD uses standard [Kubernetes RBAC](https://kubernetes.io/docs/reference/access-authn-authz/rbac/), which makes it easy to apply the same access model across all workloads, for [Flux controllers](https://fluxcd.io/flux/releases/controllers/) or other workloads.

Argo CD uses its own [internal RBAC system](https://argo-cd.readthedocs.io/en/stable/operator-manual/rbac/). You configure access through roles and permissions defined in the Argo CD config, separate from Kubernetes. This works well when all team members go through the UI, but it adds complexity if you need to manage access outside that context.

### Community and vendor support

Both tools are CNCF projects, but they have slightly different paths. Argo CD has wider adoption, more GitHub stars, and a bigger ecosystem of community plugins and integrations. You’ll find more tutorials, YouTube videos, and templates.

 ![](https://assets.northflank.com/argocd_github_stars_b937c3265c.png) 

Flux CD has fewer GitHub stars, but while its community is smaller, it has a very active group of maintainers and a growing list of adopters in production.

 ![](https://assets.northflank.com/fluxcd_github_stars_91fb9159b1.png) 

The focus is more on GitOps as a concept, with technical depth that appeals to teams already used to writing controllers or working with [Kustomize](https://kustomize.io/).

### Integrations and extensibility

Flux gives you building blocks. You can use sources like Git, [Helm repos](https://fluxcd.io/flux/components/source/helmrepositories/), [OCI images](https://fluxcd.io/flux/components/source/ocirepositories/), and [S3 buckets](https://fluxcd.io/flux/components/source/buckets/) as inputs, then define how to apply them with [Kustomize controllers](https://fluxcd.io/flux/components/kustomize/) or [Helm controllers](https://fluxcd.io/flux/components/helm/).

 ![](https://assets.northflank.com/kustomize_controller_b5180e262d.png) *Kustomize controllers (Source: Flux documentation)*

It’s very composable, and there’s built-in support for secrets management with SOPS.

 ![](https://assets.northflank.com/helm_controller_f59510518c.png) *Helm controllers (Source: Flux documentation)*

Argo CD supports plugins, and you can write your own [config management plugins](https://argo-cd.readthedocs.io/en/stable/operator-manual/config-management-plugins/) to work with tools like [Helm Secrets](https://github.com/jkroepke/helm-secrets). You can also extend the Argo CD UI and workflows, but it’s not as composable out of the box. It tends to work better when you follow its opinionated model rather than trying to bend it to your own.

## When to use Flux CD or Argo CD

Once you understand how each tool is built and how it behaves in production, the next question is: which one should you use for your team?

There’s no one-size-fits-all answer. The choice often comes down to how much abstraction you want and how your team prefers to work with Kubernetes.

### For Flux CD

Use Flux CD if:

- You want to keep everything in Git and use native Kubernetes patterns.
- Your team is already familiar with CRDs, reconciliation loops, and declarative infrastructure.
- You’re building an internal platform or pipeline that uses GitOps as a foundation, not an end-to-end UI.
- You want fine-grained control over the deployment flow, especially around ordering and dependencies.

Teams that go with Flux often value minimalism and transparency. You’re working with plain Kubernetes objects, so you can inspect, modify, and extend the system without relying on additional tools or custom dashboards.

This Reddit thread captures that mindset well:

> *“The most useful features I found were the dependsOn and wait parameters that help me better manage dependencies...”*
~ [r/kubernetes](https://www.reddit.com/r/kubernetes/comments/1il9d9q/fluxcd_useful_features/)
> 

If you’re working on infrastructure that requires high customization and you’re comfortable stitching things together, Flux gives you the primitives to build exactly what you need.

### For Argo CD

Use Argo CD if:

- You want a visual interface to manage deployments, view app status, and control syncs or rollbacks.
- Your developers need visibility without full access to the Kubernetes cluster.
- You’re managing multiple clusters from a central location and want to standardize how GitOps is applied across them.
- You prefer opinionated workflows that reduce setup time and abstract away some of the underlying complexity.

Argo CD is especially useful when you want teams to manage apps without touching kubectl. The UI helps onboard new users, and the built-in RBAC system gives you a way to control who can see and modify what.

You can also set up Argo CD to sync automatically or require manual approvals, making it a good fit for staged environments like dev → staging → prod.

This comment from [r/devops](https://www.reddit.com/r/devops/comments/17kjlgg/fluxcd_vs_argocd/) reflects that:

> *“Argo CD just makes it easier to show devs what’s happening in the cluster. They can roll back or trigger syncs without needing to understand Helm or kubectl.”*
> 

## Another approach to GitOps: How Northflank compares

Choosing between Flux and Argo CD often comes down to how much control you want versus how much setup you're willing to manage. Some teams prefer stitching together tools and building workflows from primitives.

Others want visual feedback and a clear UI. But there's a third group, teams that want GitOps behavior without maintaining the GitOps engine themselves.

That’s where [Northflank](https://northflank.com/) fits in. It’s not a GitOps operator like Flux or Argo CD, but it brings Git-based deployment flows into a full platform.

 ![](https://assets.northflank.com/northflank_s_home_page_cf7948babf.png) 

You connect your repo, define your services in Git, and Northflank handles your builds, deployments, and syncing. It applies the principles of GitOps without asking you to manage CRDs, build custom dashboards, or run extra controllers.

That kind of experience also avoids some of the overhead teams deal with when managing GitOps themselves. Flux already supports more than just Helm, it works with Kustomize, plain YAML, and more. But if you’re using Helm, you’re still writing and maintaining templated YAML. For many teams, that’s where GitOps starts to feel too hands-on. Northflank removes that layer entirely. You define your services and configs in simple Git structure, and the platform handles the deploys. No Helm, no YAML templating, no mismatches between what’s in Git and what’s running.

> Note: If you’re wondering how this kind of platform approach compares to managing your own GitOps tooling, [this breakdown of build vs buy for platform teams](https://northflank.com/blog/build-vs-buy-the-platform-engineers-conundrum) goes deeper into the trade-offs.
> 

## Common questions about Flux CD and Argo CD

If you’ve been comparing Flux CD and Argo CD, chances are you’ve had some of these questions. Here’s a quick rundown of what people usually ask, and what you should know before picking a tool.

### Which is better, Flux or Argo CD?

It depends on what you need. Flux is better for low-level control and Kubernetes-native workflows. Argo CD works well if you want a UI and app-focused experience.

### What is Flux and Argo CD?

They're GitOps tools that sync your Kubernetes cluster with what's defined in Git. Flux is controller-based; Argo CD runs as a separate service with a UI.

### What is the difference between Flux and Argo CD?

Flux integrates deeply into Kubernetes and is modular. Argo CD is centralized and comes with built-in app management features.

### What are the disadvantages of Argo CD?

It can be heavy at scale, and the custom RBAC config isn’t always intuitive. You’ll also rely on its central UI service being up and stable.

### What are the pros and cons of Flux CD?

**Pros**: Native to Kubernetes, highly flexible, ideal for infra.

**Cons**: No UI out of the box, steeper learning curve for beginners.

### What is better than Argo CD?

Tools like Northflank offer GitOps with built-in logs, CI, and infra control. It depends on how much abstraction you want.

### What is the difference between Flux and Argo Rollouts?

Argo Rollouts handles canaries and blue-green deploys. Flux doesn't include rollout strategies by default; you’ll need to configure that yourself.

### Can I use Flux and Argo CD together?

Yes. Some teams use Flux for infra and Argo CD for app teams. It’s common in large orgs that split platform and app responsibilities.

## Choosing the right GitOps solution

If you’re optimizing for developer experience and want a centralized UI to manage app deployments across clusters, Argo CD is a reliable choice. It gives you visibility and controls that dev teams appreciate out of the box.

If you’re building internal platforms or managing infrastructure as code at scale, Flux gives you more control. It fits right into Kubernetes with native CRDs and lets you compose complex setups without extra tooling.

You don’t need to pick one forever. Some teams even use both: Flux for infra, Argo CD for apps. And if you’re looking for a third path, something that wraps GitOps into a developer-friendly PaaS, you’ll find options like Northflank helpful.

Want to try a platform that simplifies GitOps without limiting your control? [Sign up for Northflank](https://app.northflank.com/signup) and see how it works in practice.]]>
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</item><item>
  <title>Best Qovery alternatives in 2026</title>
  <link>https://northflank.com/blog/best-qovery-alternatives</link>
  <pubDate>2025-04-11T11:02:00.000Z</pubDate>
  <description>
    <![CDATA[Qovery made app deployment easier, but many teams are now hitting its limits. This guide explores top alternatives like Northflank that offer better CI/CD, flexibility, and scaling options.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Porter_alternatives_2_c32df8cb63.png" alt="Best Qovery alternatives in 2026" />Remember when deploying apps was the most frustrating part of your week? For many dev teams, it still is. That’s why Qovery made such a strong impression early on. It brought a much-needed layer of simplicity and automation to modern app deployments, helping countless teams ship faster without deep DevOps expertise.

But as projects scale and infrastructure needs grow more complex, the cracks can start to show. Teams often encounter limitations around customization, cost control, and scalability that can slow progress and increase operational overhead.

The good news is that the deployment landscape has evolved. Platforms like [Northflank](https://northflank.com/) and others are pushing the boundaries with greater flexibility, deeper CI/CD features, and more scalable developer experiences.

Let’s cut through the marketing hype and explore the best Qovery alternatives that are helping real teams build, deploy, and scale smarter in 2026.

## Why consider Qovery alternatives?

Qovery gained traction by making modern app deployments more accessible, but for many growing teams, it’s started to show its limits. As architectures evolve and developer expectations rise, certain recurring challenges are driving teams to look elsewhere. Here’s a more detailed breakdown of the most common friction points:

### 1. Deployment flexibility limitations

Qovery simplifies the deployment process with opinionated defaults, but that same simplicity can become a bottleneck. As applications grow more complex (microservices, background workers, jobs with custom schedules, etc.), teams often hit limitations in how workloads are defined, scheduled, or deployed. Custom routing, granular autoscaling rules, or non-standard workloads may require awkward workarounds or fall outside Qovery’s support altogether.

### 2. Feature gaps around CI/CD and workflows

While Qovery handles basic deployment pipelines, it lacks the kind of deep CI/CD integration that more advanced teams rely on, like dynamic environment provisioning per branch, matrix builds, promotion workflows, or native GitOps support. This often forces teams to bolt on additional CI/CD tooling, creating fragmented workflows and longer debug loops.

### 3. Toolchain Integration challenges

Qovery’s black-box architecture can make it hard to plug into established DevOps stacks. Whether it’s secrets management, custom observability tooling, infrastructure as code (IaC), or policy-as-code systems, the lack of extensibility can slow teams down, or worse, force them to compromise on standards and security.

### 4. Scaling economics don’t always add up

Qovery’s pricing is attractive for early-stage projects. However, as usage increases with more environments, services, and cloud consumption, costs can rise quickly. Maintaining cost transparency becomes increasingly difficult, especially when compared to platforms that offer finer-grained control over resource usage and built-in cost optimization tools.

### 5. Support and visibility limitations

Fast-moving teams need fast, clear answers—especially when things break. Qovery’s support model can lag behind the needs of larger or more regulated teams. Combined with limited visibility into underlying infrastructure, it can leave developers feeling stuck when trying to debug or trace performance issues.

## Top criteria for evaluating Qovery alternatives

If you're considering a switch, it's not just about “what’s missing”—it’s about what your team *actually needs* to build faster and scale smarter. Here are the most important factors to weigh when evaluating alternative platforms:

### 1. Multi-Cloud support

Does the platform allow you to deploy across AWS, GCP, Azure, or even self-hosted environments? Even if you might not need it today, multi-cloud readiness ensures future flexibility and avoids lock-in.

### 2. Developer experience (DX)

From local dev parity to intuitive UIs and painless environment management, the best platforms prioritize a great DX. Think: clear logs, fast deploy feedback loops, CLI/GUI/API consistency, and minimal context switching.

### 3. Scalability and performance management

As your team and workloads grow, you’ll want a platform that scales horizontally and vertically. Bonus points for built-in autoscaling, job scheduling, and support for both stateless and stateful workloads—without you having to configure Kubernetes by hand.

### 4. First-Class integrations and extensibility

Look for platforms that support Git-based workflows, secrets managers, monitoring and alerting tools (e.g. Datadog, Prometheus), container registries, and infra tools like Terraform or Pulumi. Native integrations reduce glue code and help your team move faster.

### 5. Transparent and scalable pricing

Cost shouldn’t be a mystery. Top platforms offer usage-based pricing with clear tiers, visibility into spend per environment or service, and optimization tools that help you stay within budget as you grow.

### 6. Security, compliance, and enterprise readiness

Does the platform support encryption at rest and in transit? Role-based access control (RBAC)? Audit logging? SOC 2, GDPR, HIPAA, or ISO 27001 compliance? These features aren’t just for enterprises—they matter to every team managing sensitive data or regulated workloads.

### 7. Reliability and operational confidence

You need confidence that your platform can deliver. Check for documented SLAs, robust rollback and redeploy options, self-healing infrastructure, and mature incident response processes. Bonus if the platform surfaces key metrics directly in the dashboard.

## Top 5 alternatives to Qovery

### **1. Northflank – The best Qovery alternative for fully managed deployments**

[Northflank](https://northflank.com/) is a platform that enables developers to build, deploy, and scale applications, services, databases, and jobs on any cloud through a self-service approach. For DevOps and platform teams, Northflank provides a powerful abstraction layer over Kubernetes, enabling templated, standardized production releases with intelligent defaults while maintaining necessary configurability.

 ![](https://assets.northflank.com/image_67_32edd90929.png) 

**Key features:**

- Kubernetes-powered, full-stack platform
- Deploy containers, databases, and scheduled jobs
- [Bring your own cloud (AWS, GCP, Azure, etc.)](https://northflank.com/features/bring-your-own-cloud)
- CI/CD integration, real-time logs, with a developer-friendly and consistent experience across UI, CLI, API, and GitOps
- GPU support for AI workloads
- Automatic preview environments and seamless promotion to dev, staging, and production

**Why choose Northflank over Qovery?**

- More advanced **automation and CI/CD features**.
- Greater **flexibility in cloud provider selection**.
- **Enterprise-grade security and monitoring tools**.
- **Lower costs with transparent pricing models**.

**Potential drawbacks:**

- Highly experienced DevOps teams might find it restrictive compared to directly managing raw Kubernetes clusters. It’s a fine balance between ease of use, flexibility, and customization; that line differs for every organization.

*See how [Weights company uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### 2. Flightcontrol

[Flightcontrol](https://www.flightcontrol.dev/) enables **Heroku-like deployments on AWS**, allowing developers to leverage cloud infrastructure without deep DevOps knowledge.

 ![](https://assets.northflank.com/image_69_dd7ef3c5b4.png) 

**Key features:**

- Deploy directly to AWS without managing Kubernetes
- Automated scaling and infrastructure provisioning
- Built-in CI/CD integration

**Potential drawbacks:**

- Limited to AWS, no support for GCP or Azure
- Lacks Kubernetes-level customization for advanced use cases

### 3. Porter

[Porter](https://www.porter.run/) provides a Kubernetes-based platform that aims to simplify container management and deployment.

 ![](https://assets.northflank.com/image_80_95bebd606d.png) 

**Key features**:

- Kubernetes-native approach with simplified UI
- Good template system for common deployments

**Potential considerations**:

- Steeper learning curve for teams without Kubernetes experience
- Less robust CI/CD capabilities compared to Northflank
- Limited enterprise support options
-

*For a closer look at how Porter compares to other tools, [this article offers a well-rounded analysis.](https://northflank.com/blog/best-porter-alternatives-for-scalable-deployments)*

### 4. Cloud66

[Cloud66](https://www.cloud66.com/) is a **DevOps automation platform** that provides production-ready Kubernetes deployments with **multi-cloud and on-premise support**.

 ![](https://assets.northflank.com/image_70_4fb7db4ef4.png) 

**Key features:**

- Infrastructure as code for Kubernetes clusters
- Multi-cloud compatibility (AWS, GCP, Azure, on-prem)
- Advanced security and compliance features

**Potential drawbacks:**

- Requires more hands-on infrastructure management
- More complex setup compared to fully managed platforms

### 5. Portainer

[Portainer](https://www.portainer.io/) simplifies **container and Kubernetes management**, offering a GUI-based approach for managing deployments across **on-prem, hybrid, and cloud environments**.

 ![](https://assets.northflank.com/image_71_4bed621467.png) 

**Key features:**

- GUI-based Kubernetes management
- Multi-cloud and on-prem deployment support
- Role-based access control for secure operations

**Potential drawbacks:**

- More focused on Kubernetes management rather than full deployment automation
- Lacks built-in CI/CD and GitOps features

*For a closer look at how Portainer compares to other tools, [this article offers a well-rounded analysis.](https://northflank.com/blog/portainer-alternatives)*

## How to choose the best alternative

Selecting the optimal Qovery alternative involves a systematic approach:

1. **Identify your primary challenges**: Pinpoint the specific limitations you're experiencing with Qovery.
2. **Prioritize requirements**: Create a weighted list of features and capabilities most crucial to your workflows.
3. **Consider team expertise**: Evaluate your team's familiarity with the underlying technologies of each platform.
4. **Conduct targeted proof of concept**: Test your most critical workloads on shortlisted platforms.
5. **Evaluate total cost of ownership**: Look beyond base pricing to include potential savings in developer time and infrastructure optimization.
6. **Plan for growth**: Select a platform that can accommodate your projected scaling needs.

Based on these criteria, [Northflank](https://northflank.com/) consistently emerges as the superior choice in 2026, particularly for teams seeking an optimal balance of power, flexibility, and usability. Its comprehensive feature set addresses common Qovery limitations while providing additional capabilities that enhance productivity and control.

## Conclusion

While Qovery continues to serve many organizations effectively, the evolving demands of modern development teams often necessitate alternatives with enhanced capabilities. Among the leading contenders, [Northflank](https://northflank.com/) stands out as the premier option, offering superior deployment flexibility, advanced CI/CD features, and exceptional developer experience without compromising on performance or scalability.

For organizations looking to optimize their cloud deployment strategy, [Northflank](https://northflank.com/) represents not merely an alternative to Qovery, but a significant advancement that can transform application delivery and management. As the PaaS landscape continues to evolve, platforms that successfully combine powerful features with intuitive interfaces—as [Northflank](https://northflank.com/) does—will continue to lead the market by enabling teams to focus on building great products rather than managing infrastructure.

[**Ready to make the switch? Try Northflank today and take your deployments to the next level!**](https://northflank.com/)]]>
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</item><item>
  <title>7 Best Render alternatives for simple app hosting in 2026</title>
  <link>https://northflank.com/blog/render-alternatives</link>
  <pubDate>2025-04-10T07:11:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for Render alternatives in 2026? Check out 7 platforms that support static IPs, BYOC, and more predictable pricing, plus options for complex workloads that don’t fit Render’s limits.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/render_alternatives_03060cefd9.png" alt="7 Best Render alternatives for simple app hosting in 2026" />> *“I just want something simple like Render where I connect my GitHub repo and get a link to the backend.” ~ Someone on [Reddit](https://www.reddit.com/r/node/comments/1bbwyvi/alternative_to_render/) asking for Render alternatives*
> 

After Heroku shut down its free tier in 2022, a lot of people moved their smaller apps and side projects somewhere else. During the shift away from Heroku, Render became more popular because of its free plan, clean UI, and easy setup, especially for smaller apps. It ticked the right boxes.

But it wasn’t long before people started searching for Render alternatives, especially once they ran into some of the platform’s limitations.

You need a card to access basic features, apps go idle unless you pay, and static IPs aren’t always available unless you’re on a higher plan.

If any of that’s gotten in your way, this list should help.


<div>
  <center>
    <a href="https://app.northflank.com/signup">
      <Button variant={["large", "gradient"]}>Start building without the usual friction &gt;&gt;&gt;</Button>
    </a>
  </center>
</div>


<InfoBox className='BodyStyle'>

### Quick look: top Render alternatives in 2026

If you're short on time, here’s a quick breakdown of some of the best Render alternatives right now:

1. [**Northflank**](https://northflank.com/) – Container-based hosting with static IPs, GitHub builds, and Bring Your Own Cloud (BYOC).

2. [**Railway**](https://railway.app/) – Super simple Git-based deployments, great for quick backend apps.

3. [**Fly.io**](https://fly.io/) – Run full-stack apps close to your users with global deployment and IPv4 support.

4. [**Coolify**](https://coolify.io/) – Self-hosted Render alternative with a clean UI and Docker support.

5. [**DigitalOcean App Platform**](https://www.digitalocean.com/products/app-platform) - Managed infra with a familiar feel 

6. [**Vercel**](https://vercel.com/) – Built for frontend teams working with Next.js and React.

7. [**Heroku**](https://www.heroku.com/) – Still going strong (but now paid), with a developer-friendly CLI and mature ecosystem.

</InfoBox>


Let’s break each one down so you can find what fits your project best.

## What to look for in Render alternatives

By now, you probably know what you *don’t want.* But what should you be looking for instead?

If you're looking to move away from Render, it's usually for one of a few reasons:

→ You need a static IP for external services, but it's locked behind higher plans

→ You want to deploy on your own cloud (AWS, GCP, Azure)

→ You're working with workloads that Render can’t handle cleanly

→ Pricing, limitations, or support have gotten in the way

The good news is that some platforms get this and actually build it for how developers work today.

So, let’s look at a few things you’ll probably want in your next platform:

### Static IP support

If your backend connects to external services, think payment providers, CRMs, or anything with IP allowlists, you’ll need static IPs. Render supports this, but it’s not easy to find in other free-tier platforms. A reliable alternative should give you static IPs or let you bring your own.

 ![Showing how a static IP from Northflank lets your app connect to APIs that require allowlisting](https://assets.northflank.com/static_ip_support_65e79894b3.png) *Showing how a static IP from Northflank lets your app connect to APIs that require allowlisting*

### No Docker knowledge required

Docker is great until you’re forced to use it just to get a basic app online. Most developers, especially solo devs or small teams, just want to deploy code and not worry about containers. Look for a platform that either handles Docker behind the scenes or doesn’t require it at all unless you want it.

### A free tier that doesn’t shut down your app

A lot of Render competitors have a "free tier," but what they don't tell you up front is that your app might go to sleep after 15 minutes of inactivity, forcing you to upgrade just to keep basic services online.

With Northflank, your free-tier apps stay running 24/7, so you can focus on building and testing. When you're ready to scale with more resources, advanced features, and higher limits, our paid plans are designed to grow with you.

### Easy custom domain setup

You should be able to point your domain in minutes, not hours. Some platforms make this way harder than it needs to be. A good Render alternative should support quick domain linking, automatic HTTPS, and ideally, some DNS help built into the dashboard. 

### A genuinely good developer experience

Clean UI. Helpful logs. Clear documentation. It sounds basic, but not every platform nails this. You’ll want something that feels good to use every day, because if you’re building something serious, you’ll be spending a lot of time inside that dashboard.

 ![How much context is visible by default — Northflank shows logs, settings, and build info upfront](https://assets.northflank.com/dashboard_uis_8e36f247da.png) 

## Quick comparison of 7 Render alternatives

To help you make a quick decision, here’s a comparison table that breaks down 7 Render alternatives based on features developers care about most: GitHub deploys, static IPs, always-on free tiers, and more.

| **Platform** | **Static IPs** | **Free Tier (No Sleep)** | **Best For** |
| --- | --- | --- | --- |
| **Northflank** | Yes | Yes | Developers who want control without DevOps overhead (includes BYOC) |
| **Fly.io** | Yes (IPv4 + IPv6) | Yes | Global apps needing static IPs and edge presence |
| **Railway** | No | No | Fast MVPs and prototypes |
| **DO Platform** | Limited (via LB) | Yes (static sites) | Managed infra for startups and small teams |
| **Coolify** | Yes (via VPS) | Yes (if VPS runs 24/7) | Full control with a self-hosted setup |
| **Vercel** | No | Yes | Frontend apps built with Next.js or React |
| **Heroku** | No | No | Legacy apps and teams familiar with Heroku workflows |

## 7 Best Render alternatives in 2026

Let’s break down the top options worth your time, not just based on feature lists but also on how they actually perform in practice.

### 1. Northflank – Full control, no DevOps stress

 ![](https://assets.northflank.com/northflank_s_home_page_a4c423be98.png) 

Northflank gives you container-based hosting without making you “do containers.” You get Git-based deploys, built-in CI/CD, secrets management, and full logs, all visible in one UI. It also offers static IPs, no forced sleep on the free tier, custom domains, and preview environments by default. If you want to deploy fast but still have serious flexibility later, it’s the right choice.

**How it compares to Render**

- Render gives you Git-based deploys, but doesn’t show as much upfront in the UI.
- You need a higher plan on Render to get static IPs; Northflank includes them.
- Render apps go idle on the free tier; Northflank doesn’t do that.
- Northflank also supports Bring Your Own Cloud (BYOC), which Render doesn’t.

**Best for**

Dev teams and solo builders who want production-grade features without learning Docker or managing cloud infrastructure. It's also great if you want to scale without re-platforming later.

**Pros**

- Static IPs, preview environments, and custom domains out of the box
- GitHub builds with detailed logs
- BYOC support for enterprise use cases
- Built-in databases, secrets, metrics

**Cons**

- UI has a lot of options, and can feel heavy if you're just testing an idea
- Slightly more setup than ultra-minimal tools like Railway

### 2. Fly.io – Deploy close to your users, get static IPs too
 ![](https://assets.northflank.com/fly_io_9ea9a9cf93.png)

[Fly](https://fly.io/) lets you run your apps on servers near your users, globally. It uses microVMs behind the scenes, but you don’t need to understand all of that to get started. You can deploy with the CLI, assign IPv4 or IPv6 addresses, and scale across regions. It’s one of the few platforms where static IPs are baked in, even on the free tier.

**How it compares to Render**

- Both support Git-based deploys, but Fly leans CLI-first while Render is more UI-driven
- Fly gives you static IPs on all plans, no upgrade required
- Render is easier to start with from the dashboard
- Fly has a steeper entry point but more flexibility long term

**Best for**

Apps that need low latency across regions, or anything that relies on static IPs to talk to external services. Also great if you want to grow into multi-region infra without switching platforms.

**Pros**

- Global deployment with static IPs
- Edge presence by default
- Great community and open roadmap
- Free tier has meaningful power

**Cons**

- Learning curve if you’re new to CLI workflows
- Limited dashboard UI compared to Render or Railway
- Logs and build visibility could be better

### 3. Railway – Probably the fastest way to deploy from GitHub

 ![](https://assets.northflank.com/railway_76e4c28512.png) 

[Railway](https://railway.com/) keeps things simple. You connect your GitHub repo, hit deploy, and your backend is live in minutes. The UI is clean, logs are easy to find, and environment variables are handled well. It’s built for speed, not for deep customization, which makes it great for early-stage projects and prototypes.

**How it compares to Render**

- Both focus on Git-based deploys and dev-friendly dashboards
- Railway’s UI is arguably cleaner and faster to use
- But on the free tier, Railway puts your app to sleep after inactivity
- No static IP support, even on paid plans

**Best for**

Developers building MVPs, prototypes, or small tools that don’t need static IPs or 24/7 uptime. It’s also great for hackathons and personal projects.

**Pros**

- Super simple GitHub integration
- Fast deploys and a smooth dashboard
- Great for solo devs and early-stage products
- Easy-to-use environment variable management

**Cons**

- Free tier apps go idle after inactivity
- No static IP support
- Less flexibility for more complex infrastructure needs

### 4. DigitalOcean App Platform – Managed infra with a familiar feel

 ![](https://assets.northflank.com/Digitalocean_app_platform_s_home_page_d3551fe31d.png) 

[App Platform](https://www.digitalocean.com/) gives you GitHub-based deployments on top of DigitalOcean’s infrastructure. You can deploy from a repo, scale easily, and avoid managing your own servers. It’s more “platform” than “playground,” so the experience is tailored for teams building production apps, not just personal projects.

**How it compares to Render**

- Both offer Git-based deploys and clean dashboards
- DigitalOcean doesn’t include static IPs per app, only through a load balancer
- The free tier only applies to static sites, not backend services
- The ecosystem around databases, monitoring, and backups is more mature

**Best for**

Startups or dev teams who want to stay hands-off with infrastructure, but still need something production-ready. Good if you’re already in the DigitalOcean ecosystem.

**Pros**

- Managed infrastructure with autoscaling
- Good CI/CD experience via GitHub
- Native database and storage integrations
- Supports Docker and custom containers

**Cons**

- No static IPs unless you add a load balancer
- Free tier is limited to static sites
- Slightly more setup than platforms like Railway or Render

### 5. Coolify + VPS – Self-hosted control if you're willing to set it up

 ![](https://assets.northflank.com/coolify_0ae360c8c9.png) 

[Coolify](https://www.coolify.io/) is an open-source alternative to Render that you can host yourself. It gives you a dashboard similar to Vercel or Render, but it runs on your own server, so you get full control. You’ll need to set up a VPS (like Linode or Hetzner), but once it’s up, you get GitHub deploys, Docker support, and static IPs through your VPS.

**How it compares to Render**

- Both have clean UIs and Git-based deploys
- Coolify doesn’t come with a hosted platform, you bring the server
- You get static IPs through your VPS provider, not Coolify itself
- Slightly steeper learning curve, but much more flexibility

**Best for**

Developers who want full control over where and how their apps run. Great if you’re comfortable managing a VPS and want predictable infrastructure costs.

**Pros**

- You control everything, including IPs and resource limits
- Self-hosted and open-source
- Looks and feels similar to Render or Vercel
- Works with Docker and GitHub

**Cons**

- Requires VPS setup and basic server knowledge
- No hosted version, you run the whole stack
- No out-of-the-box support team if something breaks

### 6. Vercel – Great for frontend teams, not built for backend logic

 ![](https://assets.northflank.com/vercel_41ecdbb2ae.png) 

[Vercel](https://vercel.com/) is a strong option if you're building with Next.js, React, or any frontend-first framework. You get instant previews, GitHub integration, and fast global deployments. The platform is optimized for frontend workflows, so if you're running a backend API or need static IPs, you’ll hit limitations pretty quickly.

**How it compares to Render**

- Vercel’s frontend DX is one of the best, especially for Next.js
- Render offers more backend flexibility, including background workers and databases
- No static IPs on Vercel, and backend use cases are limited to serverless functions
- Vercel’s free tier is generous and reliable for frontend projects

**Best for**

Frontend developers working with Next.js, React, or Svelte who want fast previews and minimal config. Not ideal if your app has a complex backend or relies on static IPs.

**Pros**

- Excellent integration with GitHub and frontend frameworks
- Fast deploys with preview URLs
- Clean dashboard and CLI tools
- Global edge network built-in

**Cons**

- No support for static IPs
- Limited backend capabilities beyond serverless functions
- Not built for full-stack apps that need long-running services

  

### 7. Heroku – Still familiar, but no longer free

 ![](https://assets.northflank.com/heroku_78bb3d605e.png) 

[Heroku](https://www.heroku.com/) used to be the default way to deploy apps, especially for early-stage projects. It offered Git-based deploys, one-click addons, and a simple CLI experience that just worked. But since removing its free tier in 2022, it’s been less attractive for personal projects or quick experiments. That said, it’s still stable and predictable for teams already invested in the platform.

**How it compares to Render**

- Heroku and Render both support GitHub-based deploys and simple workflows
- Render still has a free tier (with limits), while Heroku is fully paid
- Heroku doesn’t offer static IPs unless you set up more advanced networking
- The add-on ecosystem around Heroku is still more mature than Render’s

**Best for**

Teams maintaining older apps built on Heroku or developers who prefer its CLI-first workflow. Also a good fit if you need something stable and you’re okay with paying for it.

**Pros**

- Stable platform with a long track record
- Mature plugin/add-on ecosystem
- Simple CLI and Git-based deployment
- Great docs and community support

**Cons**

- No free tier, everything is paid now
- No static IP support
- Slower evolution compared to newer platforms

## When Render might still work for you

Now that we've gone through the top alternatives, it’s worth saying: Render isn’t unusable. If the limitations we’ve talked about don’t bother you, it can still be a decent option for the right kind of project.

You’re probably fine sticking with Render if:

- You don’t need a static IP for external integrations
- You’re okay with apps going to sleep after inactivity
- Your credit card works on the platform
- You’re building quick test environments, internal tools, or personal apps

It’s easy to set up, has a clean UI, and still gives you GitHub deploys and some helpful extras. But if you’ve run into blockers, like idle timeouts, missing IPs, or account setup issues, there are better tools out there now.

## Common questions about Render and its alternatives

### What can I use instead of Render?

If you're hitting limitations with Render, like sleep mode, static IP issues, or credit card setup problems, tools like Northflank, Fly.io, Railway, and Zeet are solid options depending on your needs. You’ll find better free tiers, more control, or support for things like BYOC and static IPs.

### How do I replace Render?

Start by identifying what Render isn’t giving you, maybe you need static IPs, support for complex workloads, or the ability to run on your own cloud infrastructure. Tools like Northflank and Fly.io offer more flexibility, better resource control, and features like BYOC or always-on apps.

### Does Render have a free tier?

Yes, but it comes with limits. Your app can go idle after inactivity unless you upgrade, and some features, like static IPs, require higher plans. You might also need to add a credit card before unlocking certain things.

### Is Heroku or Render better?

Depends on what you're doing. Render feels more modern and UI-driven, and it still has a limited free tier. Heroku is stable and familiar, but it removed its free tier in 2022. If you're already used to Heroku’s CLI or working on a legacy app, it might still make sense. Otherwise, newer platforms might fit better.

### Is Render cheaper than AWS?

Yes, for most small projects. Render is a platform-as-a-service (PaaS), while AWS is infrastructure-as-a-service (IaaS). That means Render handles more of the setup, but also comes with fewer knobs to turn. AWS can be cheaper at scale, but you’ll need to manage more yourself.

### What is the difference between Render and Vercel?

Render supports backend services, background workers, static sites, and databases. Vercel is focused on frontend frameworks like Next.js and React, with serverless functions for backend logic. If you're building a full-stack app with a custom backend, Render is more flexible. If it's all frontend, Vercel is likely faster to ship.

### Is Vercel better than Heroku?

Yes, especially for frontend projects, if you’re using Next.js or React. Vercel is built for that flow. Heroku is better suited for full backend apps or projects that need a more traditional server setup.

## Wrapping up

Render still works for a lot of projects. But there are better options now if you’ve run into limits, sleep mode, static IP gaps, or missing features. If you want full control, global deployments, or something that gets out of your way, you have choices.

Northflank is one of them. You get GitHub deploys, static IPs, preview environments, and no forced sleep, even on the free tier. And if you're building something serious, you can scale with built-in CI/CD, managed databases, and Bring Your Own Cloud support.


<div>
<center>
<a href="https://app.northflank.com/signup">
<Button variant={["large", "gradient"]}>Deploy your next app without limits →</Button>
</a>
</center>
</div>


## More resources worth checking out

If you're still weighing your options or exploring related tools, these might help:

- [Bring Your Own Cloud (BYOC): The Future of Enterprise SaaS Deployment](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment)
- [Top Heroku Alternatives](https://northflank.com/blog/top-heroku-alternatives)
- [Best Vercel Alternatives for Scalable Deployments](https://northflank.com/blog/best-vercel-alternatives-for-scalable-deployments)
- [Best DigitalOcean Alternatives in 2026](https://northflank.com/blog/best-digitalocean-alternatives-2025)]]>
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  <title>Build vs. buy: The platform engineer’s conundrum</title>
  <link>https://northflank.com/blog/build-vs-buy-the-platform-engineers-conundrum</link>
  <pubDate>2025-04-08T01:12:00.000Z</pubDate>
  <description>
    <![CDATA[Congratulations—you’ve either built a platform or you’re deep into the process of creating one. But here’s the uncomfortable reality: many engineering teams conflate “building the platform” with the ultimate goal. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/unnamed_705cf6938d.png" alt="Build vs. buy: The platform engineer’s conundrum" />Congratulations—you’ve either built a platform or you’re deep into the process of creating one. But here’s the uncomfortable reality: many engineering teams conflate “building the platform” with the ultimate goal. 

We get so wrapped up in the technical details—scripting everything in YAML, wrestling with container orchestration, navigating Kubernetes, setting up custom monitoring dashboards—that we lose sight of what really matters. 

The true objective is simple: make it easier to deliver workloads, enhance the developer experience, and ship business logic that delights customers and drives revenue.

I talk to engineering leaders daily who, after months or even years of effort, are left with Internal Developer Platforms that don’t get any adoption. It’s not that these leaders don’t know how to code or how to set up Kubernetes clusters—it’s that the building blocks they’re working with are too raw. 

Imagine juggling tasks like spinning up servers with custom scripts, maintaining ephemeral containers for development, cobbling together manual solutions for log aggregation, and writing endless Terraform modules for multi-cloud support. 

Each of these steps is crucial in isolation, but none inherently serves your company’s end customers. In fact, the sheer magnitude of these low-level primitives can grind even the most proactive teams to a halt.

## The problem with homegrown platforms

This painstaking process is akin to building a bespoke factory just to ship a single product. 

Let’s say you have a bakery. Instead of focusing on perfecting your pastries, you spend months constructing each oven by hand, forging the mixers, and tinkering with the ventilation system. 

By the time your ovens are operational, customers have already found other bakeries—or lost interest in buying pastries altogether. 

The product itself—fresh, warm bread—gets overshadowed by the never-ending quest to optimize how the bread is made.

In the world of software, it’s all too common to see developers drowning in the “factory” details:

-  **K8s objects** (Pods, Deployments, Services, Ingresses, etc.) for running workloads.
- **Security and policies** (RBAC, Pod security, Network Policies, Secrets) to lock down the environment.
- **Networking** (CNI, Services, Ingress, possible service mesh) to route traffic, with an eye on performance overhead.
- **Storage** (PVs, PVCs, StatefulSets) for persistent data requirements.
- **Observability** (logging, metrics, tracing) for performance visibility.
- **Automation** (Autoscalers, Operators, GitOps) to reduce manual toil.
- **Developer experience** (Helm, Kustomize, ephemeral environments, CI/CD pipelines, and a self-service interface).

Platform teams must take these foundational Kubernetes primitives and build a cohesive post-commit platform capable of running diverse workloads—microservices, stateful applications, and scheduled jobs—while delivering a consistent experience across multiple cloud providers. The platform must be secure, self-service, and multi-tenant, offering zonal and regional redundancy, robust networking, autoscaling, and multi-cloud consistency. It’s a tall order that can feel daunting just thinking about it.

You can't overstate the importance of developer experience. Platform teams must meet engineers where they are or risk losing adoption. That means offering a consistent experience across multiple interaction points—UI, API, CLI, and GitOps. 

This is where a platform that sees tens of thousands of workflows and developers' preferences daily comes into its own.

## From software factories to software delivery

The irony, of course, is that we promised ourselves something different. With containers and cloud-native tech, we envisioned frictionless deployment pipelines and near-instant scalability. Yet somewhere along the line, many of us went from building products to building factories to produce those products. It doesn’t have to be this way, and there are precedents from other industries to prove it.

Consider game development. In the early days, studios often had to craft their own game engines from scratch. They built the rendering systems, physics engines, and level editors—all so they could eventually make a single game. Today, most studios don’t do that. Instead, they rely on battle-tested engines like Unreal Engine (UE), which abstract away the underlying complexities and let developers concentrate on creating immersive experiences. Building your own game engine is still possible—but it’s usually only reserved for the largest companies with extremely specialized needs. And even then, those engines are often maintained by entire teams dedicated solely to that purpose.

## Mirroring the game engine analogy

Much like game developers who have turned to Unreal or Unity, software developers deserve a robust platform that eliminates the guesswork of building ephemeral environments, ensuring regional redundancy, and implementing zero-downtime deployments. The reason is obvious: your energy should go into making the next killer feature for your user base, not wrangling with hand-crafted automation just to keep the lights on.

Yet, many Internal Developer Platforms (IDPs) end up struggling to deliver tangible benefits. Sure, you might get them running in a year or two, but does that timeline really make sense in a fast-paced market? Does it make sense to sink significant resources into a homegrown solution that could be outdated by the time it goes live? As we’ve learned from the game engine world, leveraging an existing platform frees your engineers to innovate at the product level—and do so much more quickly.

## Why buying a platform makes sense

“Buying” got a bad rep because the first generation of tools nailed the vision but fumbled the execution. Heroku excelled at providing a seamless, self-service developer experience, but it fell short when enterprises needed to run complex workloads in their own cloud environments. Meanwhile, Cloud Foundry offered an excellent application abstraction for reducing complexity, yet the underlying infrastructure proved both expensive and difficult to manage.

Both Cloud Foundry and Heroku, backed by significant resources and engineering talent, ultimately fell short. This raises the question: how can an internal platform team with fewer resources succeed in delivering a homegrown platform that meets all your requirements and truly delights your engineers?

This is why a new generation of off-the-shelf solutions is gaining traction. I’ve seen dozens of companies and spoken with countless engineers who have found that purchasing a ready-made platform is the difference between shipping on time and missing critical business milestones. And this doesn’t mean you lose control or flexibility. Modern platforms are built with extensibility in mind, giving you the power to customize workflows, integrate with existing toolchains, and deploy to your cloud environment of choice.

Take Northflank as an example. We’ve had hundreds of companies and over 30,000 developers adopt our platform to deploy microservices, jobs, and databases across pre-production and production environments. Instead of creating yet another “factory,” these teams tap into a platform that’s already equipped to handle the complexities of deployments, testing, auto-scaling, and regional redundancy. In other words, it’s a jump-start on the real work: delivering features that make a difference to end users.

## Putting the focus back on customers

Ultimately, the question is not whether you can build your own platform—plenty of teams can. The question is whether it’s the best use of your time and resources. If you’re stuck in a loop of never-ending code pipelines, manual environment setups, or do-it-yourself monitoring solutions, ask yourself: Is this really bringing value to our customers? If not, it may be time to break free from the build mindset and refocus on shipping features.

After all, the goal isn’t to become an expert at building factories. The goal is to bake, package, and deliver the finest pastries in town. Similarly, your software team’s mission is to create new features, serve your users, and drive revenue. By leveraging platforms that already handle the grunt work of infrastructure and orchestration, you can reclaim your bandwidth and do what matters most: deliver compelling experiences to your customers.
]]>
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</item><item>
  <title>Top alternatives to Harness for CI/CD and DevOps</title>
  <link>https://northflank.com/blog/top-harness-alternatives</link>
  <pubDate>2025-04-07T12:24:00.000Z</pubDate>
  <description>
    <![CDATA[Harness is a powerful CI/CD platform, but it may be costly or complex for some teams. This article explores top alternatives like Northflank, Jenkins, GitLab CI, and GitHub Actions for flexible DevOps.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Porter_alternatives_1_a3c10a9671.png" alt="Top alternatives to Harness for CI/CD and DevOps" />Continuous Integration and Continuous Deployment (CI/CD) are at the heart of modern software development, enabling faster and more reliable releases. Harness has become a big name in this space, offering automation, security, and cost-saving features. But does it work for everyone?

For some teams, Harness can feel like overkill—whether due to its price, complexity, or infrastructure limitations. Maybe you need something simpler, more customizable, or that fits better with your existing tools. The good news is, there are plenty of great alternatives out there.

In this article, we’ll dive into why teams are moving away from Harness, what to look for when choosing a CI/CD platform, and how the top alternatives compare.

## What is Harness?

Harness is an intelligent software delivery platform designed to simplify CI/CD pipelines, automate deployments, and integrate security, compliance, and cost optimization. It leverages AI and machine learning to streamline DevOps workflows, helping organizations reduce manual intervention and minimize deployment risks.

While Harness provides robust capabilities, it isn’t the perfect fit for every team. Some businesses find the platform expensive, complex, or limiting in terms of customization. As a result, many are evaluating other CI/CD solutions that offer similar functionality with different strengths.

## Why consider alternatives to Harness?

There are several reasons why teams may look beyond Harness for their CI/CD needs:

### Rising costs

Harness's pricing structure can be a challenge for startups and smaller teams. While it offers a powerful feature set, its cost may be prohibitive compared to open-source or lower-cost alternatives.

### Complexity and learning curve

Harness offers a sophisticated automation engine, but some users find it difficult to set up and configure, especially if they lack DevOps expertise. Simpler, more intuitive alternatives can offer faster adoption and quicker deployment cycles.

### Customization limitations

Organizations with unique deployment strategies or specific infrastructure requirements may find Harness’s abstraction layer restrictive. Some teams need more granular control over their CI/CD workflows, which certain alternatives provide.

### Vendor lock-in concerns

Harness is a proprietary platform, which may raise concerns about long-term dependency on a single vendor. Open-source or multi-cloud solutions offer more flexibility and control over infrastructure.

### Infrastructure-specific needs

Teams using Kubernetes, AWS-native tools, or GitOps methodologies may prefer alternatives like [Northflank](https://northflank.com/) that integrate more naturally with their infrastructure.

## What to consider when choosing a Harness alternative

Before switching to another CI/CD tool, consider the following key factors:

### Ease of deployment

Your chosen alternative should be easy to set up and manage. Look for modern solutions that provide user-friendly interfaces, intuitive configurations, and minimal overhead in managing pipelines.

### Integration capabilities

CI/CD tools should integrate seamlessly with your existing stack, including GitHub, GitLab, AWS, Kubernetes, and other cloud services. A tool with extensive plugin support can help streamline your DevOps workflows.

### Scalability

Ensure that your alternative can handle your team’s growth. To optimize performance, a platform should support distributed builds, parallel execution, and auto-scaling capabilities.

### Customization & flexibility

If your team has specific deployment needs, look for flexible scripting tools, robust API support, and deep configuration options.

### Security & compliance

Security is critical in CI/CD. Consider platforms that provide role-based access control (RBAC), automated security scanning, secrets management, and compliance with industry standards.

### Cost & licensing model

Open-source tools can reduce costs, while cloud-based platforms may offer flexible pricing models. Evaluate whether the tool’s pricing aligns with your team’s budget and expected usage.

## Top alternatives to Harness: **Quick comparison** table

If you're looking for a quick comparison, here's a table to help you easily compare the top alternatives to Harness.

| **CI/CD Tool** | **VCS Support** | **Ease of Use** | **Scalability** | **Hosting Options** | **Pricing** |
| --- | --- | --- | --- | --- | --- |
| [**Northflank**](https://northflank.com/) | GitHub, GitLab, Bitbucket | 5/5 | Auto-scaling | Cloud | Transparent, usage-based |
| [**Jenkins**](https://www.jenkins.io/) | GitHub, GitLab, Bitbucket | 3/5 | Manual scaling | Self-hosted, Cloud | Free (Open-source) |
| [**GitLab CI/CD**](https://docs.gitlab.com/ci/) | GitLab | 4/5 | Scalable runners | Self-hosted, Cloud | Free & Paid Plans |
| [**CircleCI**](https://circleci.com/) | GitHub, GitLab, Bitbucket | 4/5 | Highly scalable | Cloud, Self-hosted | Usage-based pricing |
| [**AWS CodePipeline**](https://aws.amazon.com/codepipeline/) | GitHub, Bitbucket | 3/5 | Auto-scaling | AWS Cloud | Pay-as-you-go |
| [**GitHub Actions**](https://docs.github.com/en/actions) | GitHub | 4/5 | Auto-scaling | Cloud, Self-hosted | Free & Paid Plans |

## Top Alternatives to Harness: Detailed comparison

Let’s take a closer look at some of the best CI/CD platforms that serve as strong alternatives to Harness.

### **1. Northflank** – A modern alternative with Kubernetes focus

[Northflank](https://northflank.com/) is a CI/CD platform that’s optimized for Kubernetes-native and containerized applications. It’s designed to simplify the deployment pipeline, offering powerful automation while maintaining a focus on scalability, flexibility, and ease of use.

Northflank stands out with its deep integration into cloud-native technologies, particularly Kubernetes, making it ideal for modern, containerized workloads. Unlike some traditional CI/CD platforms, it doesn’t require complex configuration or manual scaling, allowing teams to quickly set up, scale, and automate their deployment processes without hassle.


 ![](https://assets.northflank.com/image_73_4960b1b179.png) 

**Pros:**

- Seamless Kubernetes integration
- Automatic horizontal scaling
- Built-in CI/CD and logging

**Cons:**

- Smaller community compared to Harness

**Why Choose Northflank Over Harness?**

- More advanced automation and CI/CD features.
- Greater flexibility in cloud provider selection.
- Kubernetes-native with built-in auto-scaling.
- Lower costs with transparent pricing models.
- Enterprise-grade security and compliance tools.

### **2. Jenkins**

[Jenkins](https://www.jenkins.io/) remains one of the most popular CI/CD tools due to its flexibility and extensive plugin ecosystem. While it requires manual setup and maintenance, it’s a great option for teams that want full control over their pipelines.


 ![](https://assets.northflank.com/image_74_46326a2940.png) 

**Pros:**

- Highly customizable with thousands of plugins
- Free and open-source
- Supports self-hosted and cloud deployments

**Cons:**

- Requires manual scaling
- Steeper learning curve for beginners

[Read more on Jenkins.](https://northflank.com/blog/jenkins-alternatives-2025)

### **3. GitLab CI/CD**

[GitLab CI/CD](https://docs.gitlab.com/ci/) is a natural choice for teams already using GitLab. It offers built-in CI/CD functionality, making it easy to integrate with repositories.


 ![](https://assets.northflank.com/image_75_1f6b5d553a.png) 

**Pros:**

- Seamlessly integrated with GitLab repositories
- Built-in security scanning and compliance tools
- Supports both cloud and self-hosted deployment

**Cons:**

- Can be resource-intensive for large projects
- Limited customization compared to Jenkins

### **4. CircleCI**

[CircleCI](https://circleci.com/) is designed for teams looking for a cloud-based CI/CD solution that’s easy to set up and scale.


 ![](https://assets.northflank.com/image_75_1f6b5d553a.png)

**Pros:**

- Fast and efficient cloud-based builds
- Simple configuration using YAML files
- Strong integration with cloud services

**Cons:**

- Pricing can be high for large teams
- Limited self-hosted capabilities

### **5. AWS CodePipeline**

[AWS CodePipeline](https://aws.amazon.com/codepipeline/) is a fully managed CI/CD service that integrates seamlessly with other AWS services, making it ideal for teams deeply embedded in the AWS ecosystem.

 ![](https://assets.northflank.com/image_79_08ab9bfc6d.png) 

**Pros:**

- Deep integration with AWS services
- Fully managed with automatic scaling
- Pay-as-you-go pricing

**Cons:**

- Limited flexibility outside AWS
- Can be complex to configure for non-AWS users

### **6. GitHub Actions**

[GitHub Actions](https://docs.github.com/en/actions) is a powerful CI/CD automation tool built directly into GitHub, making it an excellent choice for teams already using GitHub for version control. It allows developers to create custom workflows that automate building, testing, and deployment processes with minimal setup.

 ![](https://assets.northflank.com/image_78_a8806c2331.png) 

**Pros:**

- Seamless GitHub integration
- Flexible and customizable
- Rich marketplace of pre-built actions
- Scalable and secure

**Cons:**

- Limited outside GitHub
- Costly for larger workloads
- Complex workflows require experience

## Wrapping up

If you're looking for an easy-to-use, flexible, and cost-effective alternative to Harness, [Northflank](https://northflank.com/) might be the solution you’ve been searching for. It simplifies CI/CD, especially for Kubernetes and containerized apps, without the complexity or high costs that some other platforms impose.

With [Northflank](https://northflank.com/), you can get up and running quickly, with automatic scaling and seamless integration into your existing stack. Its transparent, usage-based pricing makes it easy to scale without worrying about unexpected costs.

Why not give it a try? You can start for free and experience how CI/CD can be streamlined without the headaches. Check out the [guides](https://northflank.com/guides), browse the [documentation](https://northflank.com/docs), or just [sign up](https://app.northflank.com/signup) and start building today.]]>
  </content:encoded>
</item><item>
  <title>Best Porter alternatives for scalable deployments</title>
  <link>https://northflank.com/blog/best-porter-alternatives-for-scalable-deployments</link>
  <pubDate>2025-03-27T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Porter simplifies Kubernetes deployments, but rising costs, stability issues, and workflow limitations push teams to seek alternatives. Northflank, Qovery, and others offer better pricing, automation, and flexibility.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Porter_alternatives_b3fec4d213.png" alt="Best Porter alternatives for scalable deployments" />Deploying applications on Kubernetes can be complex, but [**Porter**](https://www.porter.run/) has made it easier for many teams. Still, as projects grow and infrastructure needs evolve, some developers start looking for alternatives—whether to cut costs, streamline workflows, or unlock more advanced features.

If you're considering a switch, you're not alone. **Many teams have expressed frustrations with Porter, particularly after the transition from Porter 1 to V2, which led to stability issues and lost trust in the platform.** Platforms like [**Northflank**](https://northflank.com/) offer a modern take on deployment and DevOps automation, giving teams greater flexibility and efficiency.

In this guide, we'll break down why teams explore alternatives to Porter, what to look for in a new platform, and the best options available today.

## **Why Consider Porter Alternatives?**

While Porter offers a solid Kubernetes-based deployment experience, there are several reasons why developers and teams might look for alternatives:

### **1. Pricing Concerns**

Porter’s pricing can become expensive as applications scale. For startups and growing teams, the cost of infrastructure management, Kubernetes clusters, and additional services can add up quickly. Many teams **have reported paying thousands per month for Porter but feel that the value doesn’t match the cost.** Alternative platforms often provide more transparent and cost-effective pricing structures.

### **2. Stability and Reliability Issues**

Teams have cited **frustration with the quality of Porter's software**, especially after moving from Porter 1 to V2. Some companies **lost trust in the platform** due to performance inconsistencies and migration difficulties. One enterprise customer noted that **Porter 1 to V2 migration caused them to completely lose faith in the platform**, prompting a switch to [Northflank](https://northflank.com/) for a more stable and reliable experience. Others have reported **major production issues only discovered after scaling workloads**, making Porter unreliable for mission-critical applications.

### **3. Limited Customization and Flexibility**

Porter abstracts much of the Kubernetes complexity, which is great for ease of use but can be limiting for advanced users. Developers who need fine-tuned control over their Kubernetes setup, networking configurations, or infrastructure choices may find Porter too restrictive. Some customers have also **expressed concerns about being forced into certain workflows**, limiting their ability to optimize infrastructure for their needs.

### **4. CI/CD and Workflow Restrictions**

While Porter simplifies Kubernetes deployments, some teams need more advanced CI/CD integration, automated scaling, and direct cloud provider integration. **Customers have noted that Porter’s workflow lacks flexibility and that tools like Northflank provide a much smoother automation experience.** One team mentioned that **Porter’s GitHub Actions integration wasn't sufficient for their complex pipelines**, leading them to seek a more robust solution.

### **5. Infrastructure and Cloud Compatibility**

While Porter allows **Bring Your Own Cloud (BYOC)** deployments, some teams have reported **challenges in fully leveraging cloud provider credits** or configuring their infrastructure in a way that meets their needs. One customer noted that **despite having AWS startup credits, Porter's setup didn't allow them to optimize their usage efficiently**. Additionally, some teams prefer **platforms with stronger multi-cloud support**, enabling seamless deployments across AWS, GCP, and Azure without additional complexity.

### **6. Enterprise-Grade Features**

Larger organizations require **security, compliance, and monitoring** features that go beyond Porter’s core offerings. **Teams have also pointed out that Porter’s lack of robust permission management has been a security concern.** One customer highlighted that **Porter required full admin access for every engineer**, raising security risks and compliance challenges. Alternatives like Northflank provide enhanced **observability, logging, security policies, and private networking** options for enterprise-scale applications.

## **Top Criteria for Evaluating Porter Alternatives**

When assessing Porter alternatives, consider the following factors to determine the best fit for your development and infrastructure needs:

### **1. Ease of Deployment**

A good Porter alternative should simplify deployments with automated processes, intuitive interfaces, and minimal DevOps overhead. Look for platforms that offer **one-click deployments, Git-based workflows, and container-based hosting**.

### **2. CI/CD Integration**

Seamless CI/CD pipelines help automate builds, tests, and deployments. The ideal alternative should support **GitHub, GitLab, Bitbucket**, and integrate with **Docker, Kubernetes, and other DevOps tools**.

### **3. Scalability and Performance**

As your application grows, your platform should support **automatic horizontal scaling, efficient resource allocation, and high availability** to handle traffic spikes without manual intervention.

### **4. Multi-Cloud and BYOC Support**

If you prefer to deploy across AWS, GCP, Azure, or even on-premise, choose a platform that allows multi-cloud deployment or Bring Your Own Cloud (BYOC) for better flexibility.

### **5. Pricing Transparency**

Avoid surprise costs by selecting a platform with clear, predictable pricing models. Some alternatives offer free tiers for small projects, while others provide **pay-as-you-go models** based on actual resource usage.

### **6. Developer Experience**

Good developer experience includes **great documentation, an intuitive CLI, API access, and strong community support**. A strong ecosystem ensures that troubleshooting and development remain smooth.

### **7. Security and Compliance**

For businesses handling sensitive data, compliance with standards like **SOC 2, ISO 27001, and GDPR** is essential. Look for alternatives with built-in security features like **private networking, role-based access control (RBAC), automated backups, and encryption**.

## **Top 5 Porter Alternatives**

### **1. Northflank – The Best Porter Alternative for Fully Managed Deployments**

[Northflank](https://northflank.com/) is a platform that enables developers to build, deploy, and scale applications, services, databases, and jobs on any cloud through a self-service approach. For DevOps and platform teams, Northflank provides a powerful abstraction layer over Kubernetes, enabling templated, standardized production releases with intelligent defaults while maintaining necessary configurability.

 ![](https://assets.northflank.com/image_67_32edd90929.png) 

**Key Features:**

- Kubernetes-powered, full-stack platform
- Deploy containers, databases, and scheduled jobs
- [Bring your own cloud (AWS, GCP, Azure, etc.)](https://northflank.com/features/bring-your-own-cloud)
- CI/CD integration, real-time logs, with a developer-friendly and consistent experience across UI, CLI, API, and GitOps
- GPU support for AI workloads
- Automatic preview environments and seamless promotion to dev, staging, and production

**Why Choose Northflank Over Porter?**

- More advanced **automation and CI/CD features**.
- Greater **flexibility in cloud provider selection**.
- **Enterprise-grade security and monitoring tools**.
- **Lower costs with transparent pricing models**.

**Potential Drawbacks:**

- Highly experienced DevOps teams might find it restrictive compared to directly managing raw Kubernetes clusters. It’s a fine balance between ease of use, flexibility, and customization; that line differs for every organization.

*See how [Weights company uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### **2. Qovery**

[Qovery](https://www.qovery.com/) is a DevOps automation platform and an internal developer platform (IDP) that simplifies cloud infrastructure management. It allows developers to deploy applications quickly and efficiently without needing deep knowledge of underlying infrastructure like Kubernetes.

 ![](https://assets.northflank.com/image_68_3b0184a6a2.png) 

**Key Features:**

- Fully managed Kubernetes with deep cloud provider integration
- Built-in CI/CD and GitOps workflows
- Automatic scaling and cost optimization

**Potential Drawbacks:**

- Pricing at scale can be expensive, especially as additional deployment minutes and features are required
- While it supports multiple cloud providers, configuring multi-cloud deployments can be complex

### **3. Flightcontrol**

[Flightcontrol](https://www.flightcontrol.dev/) enables **Heroku-like deployments on AWS**, allowing developers to leverage cloud infrastructure without deep DevOps knowledge.

 ![](https://assets.northflank.com/image_69_dd7ef3c5b4.png) 

**Key Features:**

- Deploy directly to AWS without managing Kubernetes
- Automated scaling and infrastructure provisioning
- Built-in CI/CD integration

**Potential Drawbacks:**

- Limited to AWS, no support for GCP or Azure
- Lacks Kubernetes-level customization for advanced use cases

### **4. Cloud66**

[Cloud66](https://www.cloud66.com/) is a **DevOps automation platform** that provides production-ready Kubernetes deployments with **multi-cloud and on-premise support**.

 ![](https://assets.northflank.com/image_70_4fb7db4ef4.png) 

**Key Features:**

- Infrastructure as code for Kubernetes clusters
- Multi-cloud compatibility (AWS, GCP, Azure, on-prem)
- Advanced security and compliance features

**Potential Drawbacks:**

- Requires more hands-on infrastructure management
- More complex setup compared to fully managed platforms

### **5. Portainer**

[Portainer](https://www.portainer.io/) simplifies **container and Kubernetes management**, offering a GUI-based approach for managing deployments across **on-prem, hybrid, and cloud environments**.

 ![](https://assets.northflank.com/image_71_4bed621467.png) 

**Key Features:**

- GUI-based Kubernetes management
- Multi-cloud and on-prem deployment support
- Role-based access control for secure operations

**Potential Drawbacks:**

- More focused on Kubernetes management rather than full deployment automation
- Lacks built-in CI/CD and GitOps features

## **Comparison Table: Porter Alternatives**

| Feature | Northflank | Qovery | Flightcontrol | Cloud66 | Portainer |
| --- | --- | --- | --- | --- | --- |
| **Bring Your Own Cloud** | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes |
| **Managed Kubernetes** | ✅ Yes | ✅ Yes | ❌ No | ✅ Yes | ✅ Yes |
| **Built-in CI/CD** | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes | ❌ No |
| **Auto-scaling** | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes |
| **Multi-cloud support** | ✅ Yes | ✅ Yes | ❌ No | ✅ Yes | ✅ Yes |
| **Enterprise security** | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes |

## Wrapping up

Choosing the right deployment platform is essential for ensuring scalability, reliability, and cost efficiency. While Porter has served many teams well, its limitations—ranging from pricing concerns to workflow restrictions—have pushed many to seek better alternatives.

Among the available options, [Northflank](https://northflank.com/) stands out as the best Porter alternative** due to its **fully managed Kubernetes, enterprise-grade security, and support for Bring Your Own Cloud (BYOC)**. Unlike other platforms that limit cloud flexibility, **Northflank empowers teams to optimize their infrastructure while maintaining control over costs and deployments**.

With its **powerful automation, real-time observability, and seamless CI/CD integrations**, Northflank provides a **developer-friendly yet highly scalable solution**. Whether you're a startup looking to streamline cloud operations or an enterprise requiring advanced security and compliance, Northflank offers the best balance of flexibility, automation, and cost efficiency.

[**Ready to make the switch? Try Northflank today and take your deployments to the next level!**](https://northflank.com/)]]>
  </content:encoded>
</item><item>
  <title>KubeCon Europe London 2025 – 27 open-source projects you should check out</title>
  <link>https://northflank.com/blog/kube-con-europe-london-2025-27-open-source-projects-you-should-check-out</link>
  <pubDate>2025-03-24T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[KubeCon 2025 highlights 27 open-source projects tackling key challenges in Kubernetes, from security to cluster management. Tools like Shipwright, Koordinator, and LoxiLB simplify deployment, networking, and scaling.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/20_oss_projects_to_check_out_at_Kube_Con_blog_post_ca666839a3.png" alt="KubeCon Europe London 2025 – 27 open-source projects you should check out" />If you’re going to KubeCon 2025, you’re probably looking for more than just keynotes and free swag. You want to see what’s new, what’s solving real problems in platform engineering, and which tools are making Kubernetes more effective. If that’s you, here’s a look at some top open-source tools worth checking out—each built to address real challenges in cloud-native environments.

## Why these tools matter
Kubernetes is powerful, but managing production workloads takes more than just the core platform. From building and deploying apps to security, networking, [supply chain inventory](https://timly.com/en/inventory-management-control-in-supply-chain/), and scaling, the right tools can either add complexity or make everything run smoothly. The projects below aren’t just hype—they’re solving real problems for platform teams, whether it’s automating deployments, securing the software supply chain, or managing data across clusters.

## Why we curated this list

At [Northflank](https://northflank.com/), we’re all about simplifying and making Kubernetes more efficient for engineering teams. We’ve helped companies like [Sentry](https://sentry.io/welcome/), [Writer](https://writer.com/), and [Weights](https://www.weights.com/)—along with thousands of developers and platform teams worldwide—streamline their workflows, automate deployments, and get the most out of their cloud infrastructure.

We put this list together because we know firsthand how the right tools can transform how teams build and ship software. Whether you're scaling a startup or managing complex enterprise workloads, these open-source projects can help you solve real problems and move faster with Kubernetes.

Northflank wouldn’t be possible without the incredible open-source ecosystem. We rely on projects like Kata Containers, Cloud Hypervisor, Istio, Cilium, and Ceph to power our platform. Where we can, we contribute upstream to help improve these tools and support the broader community.


<FancyQuote
  body={
    <>
Northflank is way easier than gluing a bunch of tools together to spin up apps and databases. It’s the ideal platform to deploy containers in our cloud account, avoiding the brain damage of big cloud and Kubernetes. It’s more powerful and flexible than traditional PaaS – all within our VPC. Northflank has become a go-to way to deploy workloads at Sentry
       </>
  }
   attribution={
    <TestimonialHeader
      name="David Cramer"
      position="Co-Founder and CPO @ Sentry"
      avatar="https://northflank.com/images/landing/quotes/david-c.jpeg"
      linkedin="https://www.linkedin.com/in/dmcramer/"
      mb={0}
    />
  }
/>

Looking to simplify Kubernetes for your team? [Try Northflank](https://northflank.com/) and see how we can help you build, deploy, and scale easily. 

## 1. Build and deploy tools

**Shipwright** ([shipwright.io](https://shipwright.io/))

Shipwright makes building container images simple and Kubernetes-native. It lets you define, run, and scale image builds directly in your cluster, making it a great fit for teams integrating builds into their CI/CD pipelines.

 ![](https://assets.northflank.com/image_52_a48297f47b.png) 

**Stacker** ([stackerbuild.io/v1.0.0/](https://stackerbuild.io/v1.0.0/))

Stacker provides a secure, unprivileged way to build OCI-compliant container images. If security is a concern in your image builds, Stacker helps you build safely without added complexity.(Take out)

 ![](https://assets.northflank.com/image_41_c7fc8b67e4.png) 

**Atlantis** ([runatlantis.io](https://www.runatlantis.io/))

Managing Terraform workflows can be tricky, but Atlantis simplifies collaboration by automating Terraform pull requests. It helps platform teams streamline infrastructure changes while reducing manual errors.(Take out)

 ![](https://assets.northflank.com/image_53_e6e1f07d6a.png) 

**KitOps** ([kitops.org](https://kitops.org/))

For AI/ML teams, KitOps packages models, code, and data into reproducible artifacts. It ensures AI projects are as easy to deploy and manage as containerized apps.

 ![](https://assets.northflank.com/image_42_73556caf9e.png) 

## 2. Cluster management & orchestration tools

**Koordinator** ([koordinator.sh](https://koordinator.sh/))

Koordinator is all about optimizing Kubernetes resource scheduling and management. It helps you squeeze the best performance out of your clusters by intelligently managing workloads—a must-have when every CPU cycle counts.

 ![](https://assets.northflank.com/image_43_96d5d24795.png) 

**KubeSlice** ([kubeslice.io](https://kubeslice.io/documentation/open-source/1.4.0))

Multi-cluster management can get messy fast. KubeSlice simplifies connectivity and security across clusters, making it easier to orchestrate services in a distributed environment without the headache of traditional networking challenges.

 ![](https://assets.northflank.com/image_44_20a1682f9b.png) 

**Kubean** ([kubean-io.github.io/kubean/en/](https://kubean-io.github.io/kubean/en/))

Kubean leverages the power of Ansible to automate Kubernetes cluster management. It’s ideal for teams that love the flexibility of Ansible and want to extend that into their Kubernetes operations—keeping your clusters consistent and reliable.

 ![](https://assets.northflank.com/image_45_36dbc17163.png) 

**KusionStack** ([github.com/KusionStack/kusion](https://github.com/KusionStack/kusion))

Think of KusionStack as a programmable infrastructure. It allows you to define and manage your infrastructure as code with a high-level language, reducing the friction of manual configurations and paving the way for faster, error-free deployments.

 ![](https://assets.northflank.com/image_54_8d24c653e9.png) 

**hami** ([project-hami.github.io/HAMi/](https://project-hami.github.io/HAMi/))

High availability is non-negotiable in production. hami focuses on ensuring that your Kubernetes clusters are resilient, automating failover and recovery processes so you can keep your services running smoothly—even when the unexpected happens.

 ![](https://assets.northflank.com/image_55_4936e11aa9.png) 

**Kairos** ([kairos.io](https://kairos.io/))

Edge computing is more than a buzzword—it’s the future. Kairos brings Kubernetes to the edge, offering immutable, self-healing nodes that are ideal for remote or resource-constrained environments. For platform engineers pushing workloads to the edge, Kairos is a must-see.

 ![](https://assets.northflank.com/image_46_09f4bdd9fb.png) 

## 3. Networking, load balancing & service mesh tools

**LoxiLB** ([loxilb.io](https://www.loxilb.io/))

When it comes to high-performance load balancing, LoxiLB is your go-to solution. Designed for Kubernetes, it delivers low latency and high throughput, ensuring that traffic is routed efficiently even in the busiest environments.

 ![](https://assets.northflank.com/image_56_f3ad9b74e5.png) 

**OVN-Kubernetes** ([ovn-kubernetes.io](https://ovn-kubernetes.io/))

Networking in Kubernetes can be a labyrinth, but OVN-Kubernetes brings clarity with its advanced virtual networking features. It simplifies the creation and management of secure, isolated networks within your cluster.

 ![](https://assets.northflank.com/image_47_4a7298bb4d.png) 

**Connect RPC** ([connectrpc.com](https://connectrpc.com/))

Building microservices often means dealing with complex communication protocols. Connect RPC offers a language-agnostic framework for remote procedure calls, making inter-service communication straightforward and reliable.

**Kuadrant** ([kuadrant.io](https://kuadrant.io/))

APIs are the backbone of modern applications. Kuadrant provides robust API management for Kubernetes, helping you secure, observe, and control API traffic in a scalable way—ideal for teams integrating multiple services.

 ![](https://assets.northflank.com/image_57_35eccab34b.png) 

**Sermant** ([sermant.io/zh/](https://sermant.io/zh/))

Service meshes are becoming essential for managing microservices at scale. Sermant enhances observability, security, and traffic management within your Kubernetes environment, making deploying, monitoring, and securing your services easier.

 ![](https://assets.northflank.com/image_58_37cb15e89e.png) 

**kGateway** ([kgateway.dev](https://kgateway.dev/))

API gateways are critical for routing and securing traffic. kGateway is a lightweight, Kubernetes-native API gateway that simplifies the exposure and management of APIs, ensuring that your services remain accessible and secure.

 ![](https://assets.northflank.com/image_48_47be27cef6.png) 

## 4. Security, observability & compliance tools

**Bank-Vaults** ([bank-vaults.dev](https://bank-vaults.dev/))

Managing secrets in a dynamic environment is challenging. Bank-Vaults automates the initialization, unsealing, and configuration of HashiCorp Vault, giving you a secure way to manage secrets and protect sensitive data.

 ![](https://assets.northflank.com/image_59_6c77bfcb9b.png) 

**Ratify** ([ratify.dev](https://ratify.dev/))

In today’s supply chain security landscape, ensuring the integrity of your container images is vital. Ratify validates image signatures and attestations, providing an extra layer of trust in your deployment pipelines.

 ![](https://assets.northflank.com/image_60_dcd27c4875.png) 

**OSCAL-COMPASS** ([github.com/oscal-compass/community](https://github.com/oscal-compass/community))

Compliance doesn’t have to be a chore. OSCAL-COMPASS leverages the Open Security Controls Assessment Language (OSCAL) to streamline security assessments and compliance reporting, helping you stay audit-ready without the usual headaches.

**Cartography** ([cartography-cncf.github.io/cartography/](https://cartography-cncf.github.io/cartography/))

Visualizing your infrastructure can reveal hidden vulnerabilities and misconfigurations. Cartography maps out your assets and their relationships, providing an insightful overview that aids in security audits and operational management.

 ![](https://assets.northflank.com/image_61_680cb8bbd3.png) 

**bpfman** ([bpfman.io/v0.5.6/](https://bpfman.io/v0.5.6/))

For those looking to harness the power of eBPF, bpfman is a handy tool. It simplifies the management of eBPF programs, enabling dynamic loading and unloading that can enhance observability and performance monitoring in your clusters.

 ![](https://assets.northflank.com/image_62_c982fc69a6.png) 

## 5. Data & storage tools

**openGemini** ([opengemini.org](https://opengemini.org/))

When monitoring performance and scaling analytics, a robust time-series database is indispensable. openGemini is built for high-performance, large-scale monitoring, making it ideal for tracking metrics in complex environments.

 ![](https://assets.northflank.com/image_63_2e003db8d0.png) 

**OpenEBS** ([openebs.io](https://openebs.io/))

Stateful applications demand reliable storage. OpenEBS provides containerized block storage solutions that integrate seamlessly with Kubernetes, ensuring your stateful workloads are supported with high-performance, persistent storage.

 ![](https://assets.northflank.com/image_64_db47791f6a.png) 

**Kmesh** ([kmesh.net/en/](https://kmesh.net/en/))

Data orchestration can be the difference between a sluggish application and one that scales effortlessly. Kmesh is designed to facilitate seamless data mobility across hybrid and multi-cloud environments, ensuring that your data is consistent, accessible, and secure.

 ![](https://assets.northflank.com/image_50_66577826e1.png) 

## 6. Developer & micro services tools

**youki** ([youki-dev.github.io/youki/](https://youki-dev.github.io/youki/))

Performance and security start at the runtime. youki is a container runtime written in Rust that emphasizes efficiency and compliance with OCI standards, ensuring your containerized applications run smoothly and securely.

 ![](https://assets.northflank.com/image_65_d71705e417.png) 

## 7. Next-generation tools for 2025

**Hyperlight** ([github.com/hyperlight-dev/hyperlight](https://github.com/hyperlight-dev/hyperlight))

Imagine a virtual machine manager that is lightweight and can be embedded directly within your applications. Hyperlight pushes the boundaries of performance, offering near-zero overhead for running untrusted code safely—a visionary tool for the future of cloud-native execution.

 ![](https://assets.northflank.com/image_66_8dcac9df82.png) 

**Interlink** ([intertwin-eu.github.io/interLink/](https://intertwin-eu.github.io/interLink/))

Integration is key in today’s heterogeneous environments. Interlink focuses on bridging systems and facilitating seamless communication between disparate services. It’s the glue that holds modern, polyglot architectures together.

 ![](https://assets.northflank.com/image_51_8a93606e9b.png) 

## Wrapping Up

KubeCon 2025 is packed with innovations that can change how we build, deploy, and manage cloud-native infrastructure. Whether you're improving build pipelines, managing clusters, strengthening security, or optimizing data orchestration, these tools offer real solutions to real challenges.

As you explore the expo or join virtual sessions, keep an eye on these projects—they’re not just concepts but actively solving everyday problems for platform teams. Let us know which ones catch your interest and how they could impact your work.

Enjoy KubeCon 2025!]]>
  </content:encoded>
</item><item>
  <title>KubeCon Europe London 2025 – 20 companies you should check out</title>
  <link>https://northflank.com/blog/kubecon-europe-london-2025-20-companies-you-should-check-out</link>
  <pubDate>2025-03-24T19:24:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for startups addressing meaningful platform engineering challenges at KubeCon? Here are 20 thoughtful picks that stood out to us, teams rethinking how we build, ship, and manage cloud native infrastructure.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/20_companies_to_check_out_at_Kube_Con_blog_post_aca00f97de.png" alt="KubeCon Europe London 2025 – 20 companies you should check out" />If you’re heading to KubeCon, you’re probably looking for more than just swag and sticker packs. You want to see what’s new, what’s solving pressing problems, and honestly, what’s worth your time, right? If yes, then this list is for you.

Kubernetes is great, but running and scaling it? That’s where things get interesting. From managing infrastructure and optimizing performance to securing workloads and making deployments less painful, platform engineers like you are the ones handling the tough challenges.

That’s why I put together this list of 20 companies you should check out at KubeCon. Not just the big names you already know but the ones bringing fresh ideas to platform engineering. Some are redefining observability, some are fixing the pain points in CI/CD at scale, and others are making security something you don’t have to babysit.

Just a heads up, this isn’t a generic list of vendors. These are companies that:

- Deal with the kinds of real-world problems platform teams face every day.
- Bring fresh thinking instead of just repackaging old ideas.
- Actually make cloud-native infrastructure less chaotic to work with.

So, if you’re walking the expo floor and wondering, “Where should I stop first?” this list is for you. Let’s get into it!

*And yes — we’re starting with Northflank (that’s us, had to plug ourselves).*


## Who to check out at Kubecon (and why it’s worth your time)

You’ll find teams solving very specific pain points, rethinking parts of the stack we’ve all struggled with, and, in some cases, quietly building the next wave of platform tooling. Let’s take a closer look.

### 1. Northflank (Cloud-native build, deployment, and automation platform)

 ![](https://assets.northflank.com/northflank_s_home_page_efd1b749fb.png) 

[Northflank](https://northflank.com/) is a platform built for engineers who don’t want to fight their tooling. For example, if you’ve had to manually integrate CI/CD, networking, and databases, just to get a basic service up and running, you’ll know exactly what I mean.

Northflank helps bring together CI/CD, container orchestration, databases, networking, and multi-cloud support in one place. The good news is that you no longer have to orchestrate across five different services just to ship a microservice.

If you’re the one responsible for making deployments less painful or spinning up environments quickly for your team, Northflank’s setup will feel like a breath of fresh air. It supports [Bring Your Own Cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) (BYOC), handles multi-region deployments, and automates the infrastructure work you usually have to script or babysit. It’s the kind of tool that helps you move faster without giving up control.

### 2. NetBird (Secure peer-to-peer networking built on WireGuard)

 ![](https://assets.northflank.com/netbird_abe7011324.png)

[NetBird](https://netbird.io/) gives you a simpler way to connect distributed environments without the overhead of managing VPNs. It’s built on top of WireGuard and handles peer discovery, NAT traversal, and key rotation for you. No manual configs or IP whitelisting needed.

If you're managing services across clouds, self-hosted systems, or even developer laptops, NetBird creates a private mesh network that lets them talk to each other securely. Everything is encrypted, and access is managed centrally, so you’re not chasing down credentials or debugging why traffic isn’t flowing.

And if you're deploying microservices with Northflank or running workloads across multiple environments, NetBird makes it easy to keep them connected behind the scenes without opening up unnecessary ports or relying on public endpoints.

It’s open source, easy to self-host, and works with the tooling you already use.

### 3. Root (Infrastructure-as-code security automation)

 ![](https://assets.northflank.com/root_home_page_d0dff48146.png) 

[Root](https://www.root.io/) helps secure your infrastructure before it’s deployed by scanning Terraform and Kubernetes manifests for misconfigurations and policy violations. It plugs into your pipeline and flags risk early.

If you're thinking about how to shift [security](https://www.root.io/blog/hands-on-guide-to-container-vuln-management/) earlier in the process, Root helps you do exactly that. It scans your Terraform and Kubernetes configs before anything goes live, plugs into your pipeline, and flags misconfigurations or policy violations right inside version control.

So, while others react to issues after they hit production, you’re already catching risks upstream and keeping your infrastructure secure by default.

### 4. Tetrate (Service mesh and application networking)

 ![](https://assets.northflank.com/tetrate_home_page_a74eca4779.png) 

[Tetrate](https://tetrate.io/) builds on top of Istio to bring service mesh to enterprise production environments without the usual stress. If you're dealing with service-to-service communication, security policies, or multi-cluster setups, you know how much overhead it takes to maintain all of that consistently.

Tetrate simplifies application networking and security by making service mesh more production-ready. You get [workload identity](https://docs.tetrate.io/service-bridge/refs/onboarding/config/types/identity/v1alpha1/identity), fine-grained access control, observability out of the box, and support for hybrid and multi-cloud environments.

If you’re managing a growing network of microservices, Tetrate gives you a cleaner way to handle service communication, apply consistent policies, and route traffic across clusters without duct-taping together multiple tools.

### 5. Stackit (GDPR-compliant cloud infrastructure)

 ![](https://assets.northflank.com/stackit_home_page_8d3417cc8f.png)

[Stackit](https://www.stackit.de/en/) is a European cloud provider built on data sovereignty and compliance. If you work with teams that operate under strict data protection requirements like GDPR, Stackit provides a path that doesn’t require making trade-offs between compliance and flexibility.

It gives you a full IaaS and PaaS experience while keeping infrastructure hosted in sovereign data centers. As a platform engineer, you know it’s about having more control over where and how your workloads run, without giving up modern tooling or scalability.

If you're building for organizations that care deeply about data residency, Stackit gives you the flexibility to meet [compliance needs](https://www.stackit.de/en/compliance/) without locking yourself into outdated infrastructure choices.

### 6. Synadia (Cloud-native messaging built on NATS)

 ![](https://assets.northflank.com/synadia_home_page_4f73caaa6d.png) 

[Synadia](https://www.synadia.com/) is the company behind [NATS](https://nats.io/), a lightweight, high-performance messaging system designed for modern distributed systems. If you're building event-driven architectures or need low-latency pub-sub communication between services, NATS gives you a clean, scalable way to do that.

[Synadia Cloud](https://www.synadia.com/cloud) builds on top of the NATS protocol and offers global connectivity, security features, and operational tooling for teams that want messaging infrastructure without running it themselves. For Kubernetes users, NATS integrates well through Helm charts and operators.

If your stack relies on real-time communication or service decoupling, Synadia makes that easier without locking you into a heavy messaging broker.

### 7. StormForge (AI-powered Kubernetes resource optimization)

 ![](https://assets.northflank.com/stormforge_home_page_3e9fad26a1.png)

[StormForge](https://stormforge.io/) helps platform teams manage one of the more annoying parts of Kubernetes: right-sizing. Getting resource requests and limits right is critical, but it’s often based on guesswork or time-consuming tuning.

StormForge uses [machine learning to optimize](https://www.notion.so/20-companies-to-check-out-at-KubeCon-1bc6d14c785180d3a711ed27cbc0b025?pvs=21) how workloads request CPU and memory based on actual usage patterns. This means fewer overprovisioned clusters and fewer scaling surprises.

If you're managing multiple environments or trying to keep cloud costs under control, you already know how painful it is to constantly tune resource requests by hand. StormForge brings structure and automation to that process, so you're not stuck tweaking CPU and memory limits every time traffic shifts.

### 8. Chainguard (Software supply chain security)

 ![](https://assets.northflank.com/chainguard_home_page_9af31810bc.png) 

[Chainguard](https://www.chainguard.dev/) focuses on securing the software supply chain by making sure the container images you run are signed, verified, and built using minimal, trusted components. This matters more than ever if you're responsible for what gets deployed in your environment.

It fits naturally into CI/CD pipelines and works well with platforms like Northflank. You can ensure that what you build is what you run and reduce the surface area for security risks without disrupting your workflows.

If you're taking [supply chain security](https://www.chainguard.dev/solutions/software-supply-chain-security) seriously, Chainguard gives you verifiable guarantees for your container infrastructure without forcing you to change how you work.

### 9. Infisical (Secrets management for cloud-native applications)

 ![](https://assets.northflank.com/infiscal_home_page_7e8dae9f36.png) 

[Infisical](https://infisical.com/) helps platform teams manage secrets in a way that actually fits into modern pipelines. It’s open source, self-hostable, and integrates with Kubernetes, GitHub Actions, and other tools you already use.

Managing secrets across multiple environments and cloud providers is usually painful. Infisical makes it easier by giving you a central place to [store and rotate secrets](https://infisical.com/docs/documentation/platform/secret-rotation/overview) with built-in [access controls](https://infisical.com/docs/documentation/platform/access-controls/overview) and [audit logs](https://infisical.com/docs/documentation/platform/audit-log-streams/audit-log-streams).

If you're deploying with Northflank or any other CI/CD platform, Infisical gives you the security layer for your environment variables and API keys without needing to duct-tape a solution together.

### 10. Depot (Remote Docker build acceleration)

 ![](https://assets.northflank.com/depot_home_page_398cb3c0c1.png)

[Depot](https://depot.dev/) speeds up Docker builds by offloading them to remote, cached environments. If you’ve watched build minutes disappear while waiting on images to compile or cache layers to settle, Depot gives you those minutes back.

It works with existing Dockerfiles and plugs into CI systems, so you don’t need to refactor your build pipelines. If you're supporting dev environments or maintaining internal tooling, you know how important fast feedback loops are. Depot helps you [speed things up](https://depot.dev/blog/depot-magic-explained) without burning extra compute.

And if you pair Depot with a platform like Northflank, you get faster pipelines without giving up reproducibility or control over your build environment.

### 11. SigNoz (Open-source observability and monitoring)

 ![](https://assets.northflank.com/signoz_home_page_12741e07e1.png)

[SigNoz](https://signoz.io/) gives you full observability in a single open-source platform: metrics, logs, and traces, all tied together and stored in [ClickHouse](https://signoz.io/docs/operate/clickhouse/). If you're trying to consolidate monitoring tools or avoid SaaS lock-in, SigNoz is worth looking into.

It supports [OpenTelemetry](https://signoz.io/guides/is-opentelemetry-ready-for-production/) out of the box and integrates easily with Kubernetes. The UI is clean, the performance is reliable, and you control where your data lives.

If you’re running workloads on Northflank or any other managed K8s setup, SigNoz gives you [full observability](https://signoz.io/guides/full-stack-observability-essentials/) without handing your telemetry to a third party.

### 12. Rootly (Incident management automation)
 ![](https://assets.northflank.com/rootly_home_page_454e90b078.png) 

[Rootly](https://rootly.com/) automates [incident response](https://docs.rootly.com/incidents/incidents) workflows so teams can focus on resolution instead of manual coordination. It plugs into your existing stack and helps formalize incident creation, status updates, and on-call routing with minimal effort.

If you manage Slack threads, Google Docs, and status updates during high-pressure incidents, Rootly takes that weight off your shoulders. It standardizes the entire flow and can even trigger automated actions through Runbooks so your team can focus on fixing the issue, not coordinating the response.

If you want incident response to feel less reactive and more like a process your team can trust, Rootly gives you the framework to do that.

### 13. PerfectScale (Kubernetes cost optimization and scaling)

 ![](https://assets.northflank.com/perfectscale_7f7c047e1e.png)

[PerfectScale](https://www.perfectscale.io/) gives you a handle on your Kubernetes costs without forcing you to dig through dashboards or export spreadsheets. It [analyzes usage patterns](https://www.perfectscale.io/blog/perfectscales-saas-platform-is-now-available) and provides recommendations for how to right-size clusters across environments.

The platform connects directly to your infrastructure, watches how workloads behave over time, and flags overprovisioned resources or underutilized capacity. If you're tired of guessing or relying on static limits, PerfectScale gives you clear, actionable recommendations that help reduce waste and improve performance.

And if you're responsible for keeping Kubernetes costs in check without spending your day buried in metrics, it gives you meaningful [visibility](https://www.perfectscale.io/article/kubernetes-cost-visibility) and clarity to make smarter scaling decisions.

### 14. Scarf (Developer analytics for open-source adoption)

 ![](https://assets.northflank.com/scarf_home_page_54193590d9.png)

[Scarf](https://about.scarf.sh/) helps you understand how your open-source projects are being used, something that’s often hard to track. If you maintain internal tooling or contribute to OSS, knowing who’s downloading, using, or sharing your packages can shape how you support them.

Scarf works at the edge of [distribution](https://about.scarf.sh/post/direct-downloads-via-scarf-gateway): registry traffic, downloads, referrals, and more. It’s like Google Analytics for your container images or CLIs.

If you're maintaining shared tooling or building out your developer platform, Scarf gives you that missing layer of insight to understand adoption and make better decisions about where to focus next.

### 15. Traefik Labs (Cloud-native application proxy and ingress controller)

 ![](https://assets.northflank.com/traefiklabs_home_page_86a63fb98d.png) 

[Traefik](https://traefik.io/) simplifies traffic management across services and clusters by handling [ingress](https://doc.traefik.io/traefik/routing/providers/kubernetes-ingress/), routing, TLS, and more through a declarative, Kubernetes-native approach. It supports multiple protocols, dynamic service discovery, and zero-downtime reloads.

If you're deploying microservices or APIs, Traefik saves you from manually [managing NGINX](https://community.traefik.io/t/running-traefik-and-nginx-proxy-manager-on-the-same-server/15573) or HAProxy configs. You get a proper control plane for your traffic that works smoothly with CRDs and integrates with service meshes when you need it.

You can pair Traefik with a platform like Northflank if you're managing services across multiple environments or need a centralized way to handle ingress outside of what Northflank already provides.

### 16. KubeDB (Kubernetes-native database management)

 ![](https://assets.northflank.com/kubedb_home_page_9acece406a.png)

[KubeDB](https://kubedb.com/) handles the lifecycle of databases within Kubernetes: provisioning, backups, scaling, failover, and more. It supports [PostgreSQL](https://kubedb.com/kubernetes/databases/run-and-manage-postgres-on-kubernetes/), [MySQL](https://kubedb.com/datasheet/mysql/), [MongoDB](https://kubedb.com/kubernetes/databases/run-and-manage-mongodb-on-kubernetes/), [Redis](https://kubedb.com/kubernetes/databases/run-and-manage-redis-on-kubernetes/), and others, all with custom resources that align with [Kubernetes-native](https://kubedb.com/features/deploy-databases-in-kubernetes-native-way/) workflows.

If you're running your databases off-cluster or maintaining them with manual scripts, KubeDB gives you a cleaner way to manage everything inside Kubernetes. You define what you need in YAML, and it handles provisioning, scaling, backups, and failover without all the usual operational overhead.

And if you prefer to keep stateful services inside your Kubernetes control plane, KubeDB lets you do that without breaking the patterns you're already using.

### 17. Kubiya (AI-driven DevOps assistant)
 ![](https://assets.northflank.com/kubiya_home_page_b006781fff.png) 

[Kubiya](https://www.kubiya.ai/) acts like a chatbot for your infrastructure. It connects to your systems and lets developers or platform teams trigger actions in natural language, like provisioning a dev environment or restarting a pod, all through Slack or a CLI

It’s [powered by AI](https://www.kubiya.ai/resource-post/ai-for-devops-a-practical-view) and tightly scoped permissions, so users can safely self-serve tasks. That means fewer interruptions for platform engineers like you and faster internal workflows overall.

If your team is scaling fast and you’re looking for ways to offload common platform requests, Kubiya gives you automation with a human-friendly interface.

### 18. SigLens (Cloud observability and logging platform)

 ![](https://assets.northflank.com/siglens_home_page_421fea9acb.png) 

[SigLens](https://www.siglens.com/index.html) provides ultra-fast log analytics and [observability](https://signoz.io/blog/kubernetes-observability/) designed for massive data volumes. It’s optimized for speed, low storage cost, and real-time search performance.

You can ingest logs, metrics, and traces from your Kubernetes clusters and search them with low latency. It’s designed to be cost-aware at scale, making it a great option for teams that want to run observability in-house without ballooning costs.

If you're working in a high-volume environment or managing noisy workloads, SigLens gives you a log pipeline that stays responsive even when everything else feels like it’s pushing the limits.

### 19. VictoriaMetrics (High-performance time-series database for monitoring)

 ![](https://assets.northflank.com/victoriametrics_home_page_37c51c703d.png)

[VictoriaMetrics](https://victoriametrics.com/) is a fast, [scalable time-series database](https://victoriametrics.com/blog/the-cost-of-scale/) that’s [compatible with Prometheus](https://docs.victoriametrics.com/sd_configs/) and supports long-term retention at low resource cost.

It’s ideal for setups where metrics volume is high but infrastructure budget is tight. It works as a drop-in backend for Prometheus and [integrates with Grafana](https://grafana.com/grafana/plugins/victoriametrics-metrics-datasource/) and other monitoring tools.

If you’re maintaining custom observability stacks or scaling beyond what vanilla Prometheus handles well, VictoriaMetrics is a smart addition.

### 20. Testkube (Testing framework for Kubernetes-native applications)

 ![](https://assets.northflank.com/testkube_home_page_0e9678d1d0.png) 

[Testkube](https://testkube.io/) makes it easier to [run tests](https://testkube.io/learn/end-to-end-testing-in-kubernetes) directly in Kubernetes as part of your deployment lifecycle. If you’re writing integration, performance, or E2E tests, you can trigger and manage them like native Kubernetes jobs.

It supports multiple testing tools, integrates with CI/CD platforms, and keeps test results visible across your environments.

If you're building paved paths and reusable delivery workflows, Testkube lets you bring testing into the platform itself without relying on external test runners.

## Found something worth checking out?

That’s the list. If you’re walking the floor at KubeCon or scanning through projects from home, a few of these might spark ideas or even solve a problem you’ve been quietly ignoring for weeks. If it’s better builds, clearer observability, or infrastructure you can rely on, there’s something here worth checking out.

Let us know which ones you’re looking to test in your stack!]]>
  </content:encoded>
</item><item>
  <title>Docker Swarm vs Kubernetes</title>
  <link>https://northflank.com/blog/docker-swarm-vs-kubernetes</link>
  <pubDate>2025-03-20T01:22:00.000Z</pubDate>
  <description>
    <![CDATA[Kubernetes is powerful but complex; Docker Swarm is simpler but less advanced. Kubernetes dominates due to scalability and cloud support. Tools like Northflank simplify Kubernetes for easier management.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Docker_Swarm_vs_K8s_c90825d00c.png" alt="Docker Swarm vs Kubernetes" />Let's be honest—containerization is amazing, but managing hundreds of containers? That’s where things start getting complicated.

I've seen teams spend weeks debating between Kubernetes and Docker Swarm, getting caught up in all the technical details instead of focusing on what actually matters for their project.

So, let’s break it down. Both tools help you manage containers, but they cater to different needs. Kubernetes is powerful, flexible, and scalable but comes with a steep learning curve. Docker Swarm is simpler and easier to use but lacks some advanced features.

In this comparison, we’ll skip the marketing fluff and focus on what really matters: Which tool is the right fit for your team?

## What are Containers?

Think of a container as a tiny, self-sufficient environment that holds everything an application needs to run—code, libraries, dependencies, and configuration files. The magic of containers is that they work the same way everywhere, whether on your laptop or in a massive cloud data center. That means no more “*but it worked on my machine!*” issues.

Containers are lightweight and fast because they share the same operating system kernel instead of running separate virtual machines. This efficiency allows teams to deploy applications quickly, scale easily, and maintain consistency across development and production environments.

## What is Kubernetes?

[Kubernetes](https://kubernetes.io/) (often abbreviated as K8s) is an open-source container orchestration tool originally developed by Google. It's now maintained by the [Cloud Native Computing Foundation (CNCF)](https://www.cncf.io/).

In simple terms, Kubernetes helps you deploy, manage, and scale containerized applications automatically. It ensures your app runs smoothly even if something breaks by restarting failed containers, distributing traffic, and balancing workloads across multiple servers.

But here’s something important to understand: Kubernetes was never meant to offer a seamless, out-of-the-box developer experience. Instead, it serves as a foundation for building platforms, leaving teams to navigate its complexity. Many struggle with Kubernetes because they try to use it directly rather than leveraging tools that simplify its management.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img 
    src="https://assets.northflank.com/kubernetes_cluster_architecture_60e0cf5042.svg" 
    alt="Kubernetes" 
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{ 
    marginTop: "8px", 
    fontSize: "14px", 
    color: "#555", 
    textDecoration: "none", 
    display: "block" 
  }}>
    Source - kubernetes.io
  </figcaption>
</figure>

### Key features of Kubernetes

- **Automated scaling:** If your app gets a surge of traffic, Kubernetes adds more containers to handle the load.
- **Self-healing:** If a container crashes, Kubernetes replaces it automatically.
- **Load balancing:** Ensures traffic is evenly distributed so no single container gets overwhelmed.
- **Extensibility:** Works with third-party tools and allows custom configurations.
- **Multi-Cloud support:** Runs across on-premises, cloud, and hybrid environments.
- **Declarative configuration:** Instead of manually managing deployments, you define how your application should behave, and Kubernetes makes it happen.

## What Is Docker Swarm?

[Docker Swarm](https://docs.docker.com/engine/swarm/) is Docker’s built-in orchestration tool. If you’re already using Docker, it’s a natural next step. Swarm is lightweight and easy to set up, making it a great choice for simpler projects.

Unlike Kubernetes, which is designed for large-scale operations, Swarm focuses on simplicity. It’s a good option if you just need basic orchestration without the extra complexity.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img 
    src="https://assets.northflank.com/image_4_77bad8d839.png" 
    alt="Docker Swarm" 
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{ 
    marginTop: "8px", 
    fontSize: "14px", 
    color: "#555", 
    textDecoration: "none", 
    display: "block" 
  }}>
    Source - GeeksforGeeks.org
  </figcaption>
</figure>

### Key features of Docker Swarm

- **Simple deployment:** Setting up Swarm takes just one command: `docker swarm init`.
- **Seamless Integration:** Uses the same Docker CLI and API that developers are already familiar with.
- **Built-in load balancing:** Automatically distributes traffic between running services.
- **Automatic failover:** If a service fails, Swarm restarts it to keep your app running.
- **Lightweight Architecture:** Less resource-intensive than Kubernetes, making it ideal for smaller projects.
- **Rolling updates:** Allows you to update services without downtime, though less robust than Kubernetes.

## **Docker Swarm vs. K**ubernetes: a detailed comparison

### 1. Installation & setup

- **Kubernetes**: Requires multiple components such as the API server, controller manager, and etcd, making setup complex and time-consuming. Most users rely on managed Kubernetes services like AWS EKS, Google GKE, or Azure AKS to simplify deployment.
- **Docker Swarm**: Much easier to set up. Since it’s built into Docker, initializing a Swarm cluster requires just a single command: `docker swarm init`.

### 2. Scalability

- **Kubernetes**: Designed for enterprise scalability, capable of managing thousands of nodes across multiple data centers.
- **Docker Swarm**: Can handle scaling but is more suited for small to medium-sized applications.

### 3. Load balancing & networking

- **Kubernetes**: Offers advanced load balancing, network policies, and service discovery mechanisms. Supports multiple networking solutions such as Calico and Flannel.
- **Docker Swarm**: Uses built-in load balancing with ingress routing but lacks the advanced networking features Kubernetes offers.

### 4. Community & ecosystem

- **Kubernetes**: Huge open-source community with thousands of contributors, extensive documentation, and third-party integrations.
- **Docker Swarm**: Smaller community and fewer integrations, leading to less innovation and slower adoption.

### 5. High availability & fault tolerance

- **Kubernetes**: Automatically replaces failed containers, supports multi-master setups, and provides self-healing capabilities.
- **Docker Swarm**: Supports high availability but lacks Kubernetes’ self-healing mechanisms.

### 6. Security & access control

- **Kubernetes**: Implements Role-Based Access Control (RBAC), network policies, and built-in secrets management.
- **Docker Swarm**: Offers basic security controls but lacks advanced RBAC and network policy enforcement.

### 7. Monitoring & logging

- **Kubernetes**: Provides built-in monitoring via Prometheus and integrates with logging solutions like Fluentd and ELK.
- **Docker Swarm**: Requires third-party tools for comprehensive monitoring and logging.

### 8. Resource management & efficiency

- **Kubernetes**: Uses advanced scheduling and autoscaling to optimize resource usage.
- **Docker Swarm**: Simpler but less efficient resource management compared to Kubernetes.

### 9. Flexibility & portability

- **Kubernetes**: Supports multi-cloud, hybrid-cloud, and on-premises deployments.
- **Docker Swarm**: Less flexible, primarily optimized for Docker-native environments.

### 10. Developer experience & ease of use

- **Kubernetes**: Has a steep learning curve but offers powerful features.
- **Docker Swarm**: Easier to learn and deploy, making it a great choice for beginners.

## Quick comparison table: Kubernetes vs. Docker Swarm

| Feature | Kubernetes | Docker Swarm |
| --- | --- | --- |
| **Installation** | Complex | Simple |
| **Scalability** | High | Moderate |
| **Load Balancing** | Advanced | Basic |
| **Community Support** | Large | Smaller |
| **High Availability** | Strong | Limited |
| **Security** | Strong RBAC | Basic |
| **Monitoring** | Built-in integrations | Requires third-party |
| **Flexibility** | Multi-cloud support | Limited |

## How to Choose the Right Container Orchestration Tool

- Choose **Docker Swarm** if:
    - You need a simple, lightweight solution.
    - Your project is small to medium-scale.
    - You want a minimal setup and learning curve.
- Choose **Kubernetes** if:
    - You need enterprise-grade scalability and high availability.
    - You require advanced networking, security, and monitoring.
    - You are working in a multi-cloud or hybrid-cloud environment.

## How Kubernetes Won Over Docker Swarm

Before Kubernetes became the industry standard, Docker Swarm was a viable option for container orchestration, especially for small to medium-scale deployments. Swarm’s simplicity and tight integration with Docker made it an attractive choice. However, Kubernetes' rapid adoption was fueled by its scalability, robust community support, and extensive ecosystem of tools and services.

Cloud providers like AWS, Google Cloud, and Azure introduced managed Kubernetes solutions (EKS, GKE, AKS), further solidifying its dominance. Additionally, Swarm lacked advanced networking, security, and auto-scaling features, making it less suitable for enterprise-grade applications. Over time, as Kubernetes evolved and became more accessible through managed services, Docker Swarm's adoption dwindled, leaving Kubernetes as the clear leader in container orchestration.

## How Northflank simplifies Kubernetes for you

Kubernetes is here to stay, but it doesn’t have to be painful. While some teams may benefit from Docker Swarm, others can use platforms like [Northflank](https://northflank.com/) to make Kubernetes more accessible without the operational burden.

Neither Docker Swarm nor Kubernetes offers **zonal redundancy** or **health checks out of the box** to fully support high availability. You’d need a solution like **Northflank** for that.

Northflank gives you the power of Kubernetes without the complexity. It provides a seamless developer experience, built-in CI/CD, and automation that takes the headache out of managing workloads. Whether you need multi-cloud support, real-time logs, or simple scaling, Northflank lets your team focus on building, not maintaining infrastructure.

[**Try Northflank today and make Kubernetes work for your team!**](https://northflank.com/)

 ![](https://assets.northflank.com/northflank_byoc_cloud_foundry_d2435ad6c2.png) ]]>
  </content:encoded>
</item><item>
  <title>Best OpenShift alternatives: finding the right Kubernetes platform</title>
  <link>https://northflank.com/blog/best-open-shift-alternatives-finding-the-right-kubernetes-platform</link>
  <pubDate>2025-03-16T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[OpenShift enhances Kubernetes with security and automation but is costly and complex. Alternatives like Rancher, EKS, and Northflank offer flexible, scalable, and cost-effective solutions based on your needs.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/openshift_alts_6591f3afcd.png" alt="Best OpenShift alternatives: finding the right Kubernetes platform" />As containerized applications become the standard for modern software development, Kubernetes has emerged as the go-to platform for orchestrating and managing these workloads. However, Kubernetes can be complex to configure and operate at scale, prompting many enterprises to seek solutions that simplify deployment, security, and scalability.

One of the most well-known enterprise Kubernetes platforms is **Red Hat OpenShift**, which builds upon Kubernetes by adding security features, developer-friendly workflows, and enterprise-grade support. OpenShift makes it easier to manage containers, which is why many large companies rely on it for a secure and stable setup.

But OpenShift isn’t for everyone. It can be expensive and complex and sometimes lock you into Red Hat’s ecosystem. Additionally, since OpenShift is a **fork of Kubernetes**, it often lags behind upstream Kubernetes releases, which may impact access to the latest features and improvements. That’s why many teams explore other options. In this guide, we’ll break down what OpenShift offers, why some teams switch, and the best alternatives available today.

## What is OpenShift?

Red Hat OpenShift is an enterprise-grade **container platform** based on Kubernetes. It provides a comprehensive environment for developers and operations teams to deploy, scale, and manage containerized applications efficiently. By enhancing Kubernetes with **built-in security, automation tools, and a developer-friendly experience**, OpenShift helps organizations streamline application delivery while maintaining strong governance and compliance.

## Key features of OpenShift

- **Enterprise-grade Kubernetes** – Built on Kubernetes but with additional security, networking, and monitoring capabilities tailored for enterprise use.
- **Automated deployment & scaling** – Integrated DevOps tools enable CI/CD pipelines and auto-scaling for smoother application lifecycle management.
- **Security & compliance** – Features like role-based access control (RBAC), built-in image scanning, and policy enforcement help maintain a secure infrastructure.
- **Multi-cloud & hybrid cloud support** – OpenShift can run on-premises, in public clouds (AWS, GCP, Azure), or in hybrid cloud environments.
- **Developer experience enhancements** – Tools like **Source-to-Image (S2I)** and a streamlined web console improve development workflows and accelerate app delivery.

## Why consider OpenShift alternatives?

OpenShift has a lot to offer, but it's not a perfect fit for everyone. Here are some common reasons teams explore alternatives:

- **Cost** – OpenShift can be costly, particularly for smaller teams or startups, due to licensing fees and required infrastructure investments. Its default architecture has higher resource demands, including **dedicated control plane nodes** and additional **monitoring and logging components**, further increasing infrastructure costs.
- **Complexity** – While OpenShift simplifies Kubernetes in some ways, it still has a learning curve and requires expertise to manage effectively.
- **Vendor lock-in** – OpenShift is deeply integrated with Red Hat technologies, which may limit flexibility for teams that rely on other ecosystems.
- **Customization needs** – Some organizations prefer a **lighter-weight or more customizable** Kubernetes distribution tailored to their specific workloads.
- **Performance & scalability** – Depending on the use case, other Kubernetes distributions may offer better performance, faster scaling, or improved cloud-native capabilities.

## Top criteria for evaluating OpenShift alternatives

Not sure if OpenShift is right for you? Here’s what to look for in an alternative:

- **Ease of Use** – A user-friendly interface and automation tools can simplify Kubernetes management and reduce operational overhead.
- **Cost Efficiency** – Consider total ownership costs, including licensing, infrastructure, and support fees.
- **Multi-cloud & Hybrid Support** – Ensure compatibility with cloud providers like **AWS, Google Cloud, and Azure**, especially if your workloads run across multiple environments.
- **Security & Compliance** – Look for platforms with built-in security features, role-based access controls, and compliance certifications for your industry.
- **CI/CD Integration** – Seamless integration with continuous integration/continuous deployment (CI/CD) tools can improve development speed and reliability.
- **Scalability & Performance** – Evaluate how well the platform handles large workloads, auto-scaling, and high-traffic applications.
- **Community & Support** – A strong open-source community or reliable enterprise support can be crucial for long-term platform success.

## Best OpenShift alternatives

If OpenShift doesn’t align with your organization’s needs, there are several alternatives that offer different levels of flexibility, management, and cost efficiency. Here are some of the top options:

### 1. **Northflank**

[Northflank](https://northflank.com/) is a platform that enables developers to build, deploy, and scale applications, services, databases, and jobs on any cloud through a self-service approach. For DevOps and platform teams, Northflank provides a powerful abstraction layer over Kubernetes, enabling templated, standardized production releases with intelligent defaults while maintaining necessary configurability.

 ![](https://assets.northflank.com/image_34_9c9a7f8675.png) 

**Key features:**

- Fully managed Kubernetes-based platform with a developer-friendly UI.
- Integrated [CI/CD](https://northflank.com/docs/v1/application/release/continuous-integration-and-delivery-on-northflank), real-time logs, and autoscaling.
- Supports [Bring Your Own Cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) (AWS, GCP, Azure).
- It focuses on developer experience and removes Kubernetes complexity.

**Potential drawbacks:**

- Highly experienced DevOps teams might find it restrictive compared to directly managing raw Kubernetes clusters. It’s a fine balance between ease of use, flexibility, and customization; that line differs for every organization.
- Less established compared to legacy platforms like OpenShift or Rancher.

*See how [Weights company uses Northflank to scale to millions of users without a DevOps team](https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s)*

### 2. **Rancher**

[Rancher](https://www.rancher.com/) is an open-source **Kubernetes management platform** that simplifies deployment and administration, especially in **multi-cluster and multi-cloud environments**. It provides **centralized cluster management**, making it ideal for enterprises running Kubernetes across multiple providers.

 ![](https://assets.northflank.com/image_39_6cdc97389f.png) 

**Key features:**

- **Easy cluster provisioning** and lifecycle management.
- **Built-in security, monitoring, and policy management**.
- Supports **on-premises, hybrid, and multi-cloud environments**.

**Potential drawbacks:**

- Requires some Kubernetes expertise to configure and manage.
- May not have as extensive enterprise support as OpenShift.

### 3. **VMware Tanzu**

[VMware Tanzu](https://www.vmware.com/products/app-platform/tanzu) is an enterprise-grade **Kubernetes and application modernization** platform. It offers deep integration with **VMware’s existing infrastructure**, making it a strong choice for companies already using VMware products.

 ![](https://assets.northflank.com/image_36_0e06e1e049.png) 

**Key features:**

- **Enterprise-level security and compliance** controls.
- Seamless **integration with VMware vSphere and other VMware tools**.
- **Multi-cloud Kubernetes support**, including on-premises and cloud deployments.

**Potential drawbacks:**

- Best suited for VMware environments, making it less ideal for teams using other infrastructure solutions.
- Licensing costs may be high for some organizations.

### 4. **Amazon EKS, Google GKE & Azure AKS**

For teams that prefer **fully managed Kubernetes services**, **[Amazon Elastic Kubernetes Service (EKS)](https://aws.amazon.com/eks/), [Google Kubernetes Engine](https://cloud.google.com/kubernetes-engine),** and **[Azure Kubernetes Service (AKS)](https://azure.microsoft.com/en-us/products/kubernetes-service)** provide **scalable, managed Kubernetes clusters** with seamless integration into their respective cloud ecosystems.

 ![](https://assets.northflank.com/image_28_3d89f4ced2.png) 

**Key features:**

- **Managed Kubernetes clusters** with automated updates and security patches.
- **Integrated cloud-native services** for storage, networking, and monitoring.
- Reduced **operational overhead** compared to self-managed Kubernetes.

**Potential drawbacks:**

- Deeply tied to their respective cloud ecosystems, making multi-cloud strategies more complex.
- Limited customization compared to self-managed Kubernetes.

### 5. **Platform9**

[Platform9](https://platform9.com/) is a **managed Kubernetes solution** designed for **on-premises, edge, and hybrid cloud environments**. Unlike fully cloud-hosted Kubernetes services, Platform9 allows organizations to run Kubernetes anywhere while benefiting from a **SaaS-based management model**.

 ![](https://assets.northflank.com/image_40_6281cf93cd.png) 

**Key features:**

- **Fully managed Kubernetes** with a 99.9% uptime SLA.
- **Works across on-prem, hybrid, and edge environments**.
- **Zero-touch upgrades and automated operations**.
- **Open-source foundation** with no vendor lock-in.

**Potential drawbacks:**

- Smaller market share compared to OpenShift, which may affect long-term support.
- Reliance on a SaaS-based model may not be suitable for some enterprises.

## When to choose an alternative

Choosing an OpenShift alternative depends on your organization's needs. Here are some scenarios where an alternative may be a better fit:

- **If cost is a concern**: OpenShift licensing fees can be expensive, making free or lower-cost alternatives like [Northflank](https://northflank.com/) more attractive.
- **If simplicity is needed**: Teams without dedicated DevOps resources may prefer an easier-to-use Kubernetes platform like [Northflank](https://northflank.com/).
- **If avoiding vendor lock-in**: Organizations that want flexibility in their cloud and infrastructure choices may prefer a more open solution.
- **If scalability is a priority**: Some platforms offer better performance for large-scale or multi-cloud deployments.
- **If you need a developer-friendly Kubernetes experience**: Some platforms, like [Northflank](https://northflank.com/), offer simplified workflows and automation tools that enhance productivity.

## How Northflank can help

Managing Kubernetes shouldn’t be complicated. While OpenShift offers an enterprise-ready solution, its cost and complexity don’t work for everyone.

[Northflank](https://northflank.com/) simplifies Kubernetes with an intuitive platform that includes **built-in CI/CD, automated scaling, and multi-cloud support**—all without vendor lock-in. Whether you're migrating from OpenShift or adopting Kubernetes for the first time, Northflank makes it **faster, easier, and more cost-effective** to deploy and manage applications.

If OpenShift feels like a burden, **Northflank is a lightweight, scalable alternative** that lets you focus on building, not infrastructure. Try **Northflank** today and experience seamless deployments, built-in automation, and flexible multi-cloud support—without the complexity of OpenShift. [**Get started now**](https://app.northflank.com/signup).]]>
  </content:encoded>
</item><item>
  <title>Kubernetes alternatives: finding the right fit for your team</title>
  <link>https://northflank.com/blog/kubernetes-alternatives-finding-the-right-fit-for-your-team</link>
  <pubDate>2025-03-12T18:58:00.000Z</pubDate>
  <description>
    <![CDATA[Kubernetes is powerful but complex. Teams can choose simpler alternatives like Nomad or Docker Swarm or use enhancements like Northflank, OpenShift, or Rancher to streamline Kubernetes management.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_reflex_2_ad5db11832.png" alt="Kubernetes alternatives: finding the right fit for your team" />Kubernetes has become the go-to platform for container orchestration, but that doesn’t mean it’s the best fit for every team. It comes with complexity, operational overhead, and a learning curve that not everyone wants or needs to tackle. Some teams might be better off with alternatives, while others may need tools that make managing Kubernetes easier.

In this guide, we’ll explore both paths:

- **Alternatives:** Platforms that offer simpler ways to orchestrate containers without Kubernetes.
- **Enhancements:** Tools that streamline and simplify Kubernetes management.

## What is Kubernetes?

Kubernetes is a powerful container orchestration platform, but it was never meant to offer a seamless, out-of-the-box developer experience. Instead, it serves as a foundation for building platforms, leaving teams to navigate its complexity. Many struggle with Kubernetes because they try to use it directly rather than leveraging tools that simplify its management.

Rather than asking, *“How do we avoid Kubernetes?”* a better question is, *“How do we make Kubernetes work for our developers?”* That’s where Kubernetes enhancements come in.

## How Kubernetes Won Over Mesos

Before Kubernetes became the dominant container orchestration platform, Apache Mesos was a strong contender. Mesos, designed for large-scale cluster management, was popular among enterprises like Twitter and Airbnb. However, Kubernetes' rapid adoption was driven by its strong open-source community, ease of deploying and managing microservices, and a more developer-friendly and standardized approach. Google’s support for Kubernetes secured its long-term future, while managed Kubernetes offerings like GKE, EKS, and AKS provided greater stability compared to Mesos’ DC/OS. Additionally, the Mesos developer experience was slow and tedious when making changes. Over time, Kubernetes' ecosystem grew, while Mesos declined due to its complexity and a lack of widespread adoption outside niche use cases.

## Why consider Kubernetes alternatives?

While Kubernetes is a strong choice for managing large-scale containerized applications, it isn’t always necessary or the best option for every team. Some of the biggest reasons teams look for alternatives include:

- **Complexity and learning curve:** Kubernetes requires deep expertise in container orchestration, networking, and cluster management, making it challenging for teams without dedicated DevOps engineers.
- **Operational overhead:** Managing Kubernetes clusters involves constant monitoring, scaling, security management, and troubleshooting, which can increase workload and slow down development.
- **Resource intensity:** Kubernetes consumes significant computing and memory resources, making it impractical for smaller teams or lightweight applications.
- **Cost concerns:** While Kubernetes is open-source, the infrastructure and skilled personnel required to manage it can add up.
- **Faster deployment needs:** Some teams need a simpler approach with quick setup and minimal management effort.

## Top criteria for evaluating Kubernetes alternatives

When assessing Kubernetes alternatives, consider these key factors:

- **Ease of use and developer experience:** Some platforms prioritize simplicity with intuitive UIs, automation, and minimal configuration, making them ideal for teams without DevOps expertise.
- **Workload support:** While Kubernetes is container-focused, some alternatives support non-containerized workloads, offering greater flexibility for legacy applications.
- **Multi-cloud and hybrid support:** The ability to run workloads across different cloud providers and on-premises environments is crucial for scalability and flexibility.
- **Security and networking capabilities:** Built-in security, service discovery, and networking options can simplify management while maintaining robust protections.
- **CI/CD integration:** Some alternatives offer built-in CI/CD tools, reducing complexity and improving deployment workflows.
- **Scalability and performance:** Alternatives should efficiently scale to meet growing business needs while maintaining performance.
- **Cost-effectiveness:** Some platforms offer lower infrastructure and operational costs compared to running Kubernetes clusters.

## Best Kubernetes alternatives

If Kubernetes is too complex or resource-intensive for your needs, these alternatives provide lighter-weight solutions:

### 1. HashiCorp Nomad

Nomad is a simple, flexible orchestrator that supports multiple workloads beyond containers. It’s ideal for teams looking for a lightweight alternative that doesn’t require the complexity of Kubernetes.

 ![](https://assets.northflank.com/image_38_73ebc7dfb5.png) 

**Key features:**

- Supports containerized and non-containerized workloads.
- Lightweight and easy to deploy.
- Multi-cloud and multi-region support.

**Potential drawbacks:**

- Lacks built-in service discovery and networking.
- Smaller community compared to Kubernetes.

### 2. Docker Swarm

For teams already using Docker, Docker Swarm provides a built-in orchestration tool that is much simpler than Kubernetes.

**Key features:**

- Native integration with Docker.
- Easy setup and maintenance.
- Simpler networking model.

**Potential drawbacks:**

- Less powerful than Kubernetes for complex workloads.
- Smaller ecosystem and less active development.

## Best Kubernetes enhancements

If you need the power of Kubernetes but want a smoother developer experience, these platforms build on Kubernetes to provide abstraction, automation, and enhanced usability.

### 1. Northflank - Kubernetes with a seamless developer experience

[Northflank](https://northflank.com/) is a platform that enables developers to build, deploy, and scale applications, services, databases, and jobs on any cloud through a self-service approach. For DevOps and platform teams, Northflank provides a powerful abstraction layer over Kubernetes, enabling templated, standardized production releases with intelligent defaults while maintaining necessary configurability.

 ![](https://assets.northflank.com/image_34_9c9a7f8675.png) 

**Key features:**

- Fully managed Kubernetes-based platform with a developer-friendly UI.
- Integrated CI/CD, real-time logs, and autoscaling.
- Supports [Bring Your Own Cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) (AWS, GCP, Azure).
- It focuses on developer experience and removes Kubernetes complexity.

**Potential drawbacks:**

- Highly experienced DevOps teams might find it restrictive compared to directly managing raw Kubernetes clusters. It’s a fine balance between ease of use, flexibility, and customization; that line differs for every organization.

### 2. Red Hat OpenShift

OpenShift enhances Kubernetes with enterprise-grade security, CI/CD, and built-in automation, making it easier to manage at scale.

 ![](https://assets.northflank.com/image_35_c850e1a0f5.png) 

**Key features:**

- Enterprise Kubernetes platform with built-in security and automation.
- A strong ecosystem with Red Hat support.
- Integrated CI/CD capabilities.

**Potential drawbacks:**

- More expensive than self-managed Kubernetes.
- Requires Kubernetes knowledge.

### 3. VMware Tanzu

Tanzu streamlines Kubernetes deployment and management, particularly for enterprises using VMware infrastructure.

 ![](https://assets.northflank.com/image_36_0e06e1e049.png) 

**Key features:**

- Simplifies Kubernetes management across multi-cloud environments.
- Strong security and governance features.
- Deep integration with VMware products.

**Potential drawbacks:**

- Higher cost compared to open-source alternatives.
- Requires VMware infrastructure.

### 4. Rancher

Rancher provides an easy-to-use Kubernetes management platform with a focus on multi-cluster management.

 ![](https://assets.northflank.com/image_37_9b0c3f9bd5.png) 

**Key features:**

- Simplifies Kubernetes cluster deployment and management.
- Multi-cloud and multi-cluster support.
- Built-in security and monitoring features.

**Potential drawbacks:**

- Requires knowledge of Kubernetes.
- May not be necessary for small-scale Kubernetes deployments.

## Comparison Table: Alternatives vs. Enhancements

| Feature | Nomad | Docker Swarm | Northflank | OpenShift | Tanzu | Rancher |
| --- | --- | --- | --- | --- | --- | --- |
| **Ease of Use** | Simple | Very Simple | Developer-friendly | Moderate | Moderate | Moderate |
| **Container Support** | Yes | Yes | Yes | Yes | Yes | Yes |
| **Non-Container Workloads** | Yes | No | No | No | No | No |
| **Multi-Cloud Support** | Yes | Limited | Yes | Yes | Yes | Yes |
| **CI/CD Integration** | No | No | Yes | Yes | Yes | Limited |
| **Security Features** | Basic | Basic | Strong | Enterprise-grade | Enterprise-grade | Strong |
| **Built-in Monitoring** | No | No | Yes | Yes | Yes | Yes |
| **Scalability** | High | Limited | High | High | High | High |
| **Cost** | Lower | Lower | Flexible | Higher | Higher | Flexible |
| **Best For** | Simple orchestration | Small Docker-based apps | Dev-friendly K8s | Enterprise K8s | VMware users | Multi-cluster K8s |

### When to choose an alternative

- If you want a simpler orchestration solution without Kubernetes complexity.
- If your team is small and doesn’t have dedicated DevOps expertise, it's good to note that you don’t need DevOps expertise to use platforms like Northflank.
- If you primarily run non-containerized workloads (Nomad is a strong choice).

### When to choose an enhancement

- If you need the power of Kubernetes but want a better developer experience.
- If you’re looking for automation, security, and easier management.
- If you want to keep Kubernetes but reduce operational overhead.

## How Northflank can help

Kubernetes is here to stay, but it doesn’t have to be painful. While some teams may benefit from alternatives like Nomad or Docker Swarm, others can use platforms like [Northflank](https://northflank.com/) to make Kubernetes more accessible without the operational burden.

Northflank gives you the power of Kubernetes without the complexity. It provides a seamless developer experience, built-in CI/CD, and automation that takes the headache out of managing workloads. Whether you need multi-cloud support, real-time logs, or simple scaling, Northflank lets your team focus on building, not maintaining infrastructure.

[**Try Northflank today and make Kubernetes work for your team!**](https://northflank.com/)]]>
  </content:encoded>
</item><item>
  <title>Best Google Cloud Run alternatives in 2026</title>
  <link>https://northflank.com/blog/best-google-cloud-run-alternatives-in-2026</link>
  <pubDate>2025-03-11T02:00:00.000Z</pubDate>
  <description>
    <![CDATA[Google Cloud Run simplifies deployment but has CI/CD, execution, and cost limits. Alternatives like Northflank, AWS Fargate, and Kubernetes offer more flexibility, scalability, and multi-cloud support for diverse needs.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_google_cloud_run_alternatives_c4b866fc69.png" alt="Best Google Cloud Run alternatives in 2026" />Google Cloud Run has long been a popular choice for deploying containerized applications without the headache of managing servers, automatically scaling services, integrating tightly with other Google Cloud offerings, and offering a straightforward developer experience.

However, as teams grow and their requirements evolve, several limitations become apparent. For instance, Cloud Run lacks built-in CI/CD, requiring developers to integrate external tools such as Google Cloud Build, GitHub Actions, or GitLab CI/CD to automate deployments. Additionally, the platform enforces a 24-hour execution limit on jobs, posing challenges for longer-running tasks. Cloud Run’s services also operate exclusively within your GCP account, restricting flexibility for teams needing multi-cloud capabilities or greater control. Furthermore, networking, logging, and monitoring services incur separate charges, complicating accurate cost estimation.

If these constraints are impacting your workflow, it may be time to consider modern alternatives that provide greater flexibility and deeper integration.

## What Is Google Cloud Run?

Cloud Run was designed to simplify the deployment of stateless containerized applications. By abstracting the underlying infrastructure, it lets developers focus on writing code rather than managing servers. Despite its ease of use, the absence of native CI/CD, strict execution limits, and account restrictions often prompt teams to explore other platforms that can better accommodate evolving DevOps needs.

## The problems with Google Cloud Run

Many teams use Cloud Run, but it isn’t always the best fit. Here’s a detailed look at some of the challenges developers face:

### Limited CI/CD integration

Cloud Run lacks built-in CI/CD functionality, but you can integrate it with Google Cloud Build to automate deployments. However, this requires additional setup and may complicate workflows for those who prefer an all-in-one solution.

### Execution and scalability constraints

- **Timeouts:** HTTP requests are capped at 60 minutes and background jobs at 24 hours, which may restrict long-running or batch processes.
- **Resource & concurrency limits:** While each instance can handle up to 80 concurrent requests (configurable up to 1,000) and scale to 32 vCPUs and 128GB RAM, the stateless design means persistent connections aren’t supported.
- **Lack of stateful workload support:** Cloud Run is designed for stateless applications, making it difficult to manage stateful workloads that require persistent storage or long-term connections.

### Networking and storage limitations

- **Ephemeral storage:** Each instance has access to only 50GB of temporary storage, with data lost upon instance termination.
- **Inbound traffic & IPs:** Only HTTPS is supported (unless using Cloud Run for Anthos), and dynamic IP addresses can complicate scenarios needing static egress IPs.
- **Outbound costs:** While egress to Google APIs is free, traffic to other endpoints can add extra charges.

### Cost and pricing complexity

- **Fragmented billing:** Separate charges for networking, logging, and monitoring make it challenging to predict total costs.
- **Per-request pricing:** Without optimization, costs can escalate with increased traffic.
- **Idle instance charges:** Keeping a minimum number of instances running to avoid cold starts may incur extra costs even when idle.

### Restricted execution environment

- **No SSH access:** Debugging is limited because you cannot SSH into Cloud Run instances.
- **No background processes:** Containers are designed to exit after handling requests, so running daemon processes isn’t supported.
- **System call restrictions:** Certain low-level operations are prohibited, limiting functionality within containers.

### GCP-exclusive deployment

Cloud Run is confined to your GCP account, making it less suitable for organizations that require full control over multi-cloud or hybrid deployments.

### Cold starts and latency Issues

Scaling from zero can introduce delays (cold starts), and dynamic IP addresses may affect latency—issues that are critical for latency-sensitive applications.

## Best alternatives to Google Cloud Run

If you’re encountering these limitations, here are 8 alternatives to consider:

### 1. Northflank

[Northflank](https://northflank.com/) is a production workload platform that lets you deploy any service, database, or job on a managed Kubernetes infrastructure. It provides a unified experience to run containers, backend APIs, cron tasks, and frontends together.

Northflank supports “[Bring Your Own Cloud](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment),” meaning you can deploy workloads to your own AWS, GCP, Azure, etc., giving you control over infrastructure and avoiding lock-in.

 ![](https://assets.northflank.com/image_33_95a52ae8ff.png) 

**Key features:**

- Kubernetes-powered, full-stack platform
- Deploy containers, databases, and scheduled jobs
- Bring your own cloud (AWS, GCP, Azure, etc.)
- CI/CD integration, real-time logs, with a developer-friendly and consistent experience across UI, CLI, API, and GitOps
- GPU support for AI workloads
- Automatic preview environments and seamless promotion to dev, staging, and production

**Potential drawback:** While Northflank offers a seamless developer experience and is easy to use, it lacks native support for hosted GCP services like Cloud SQL for PostgreSQL. Although it can support these services through Bring Your Own Addon, this is not considered "first-class" support as it would be in Cloud Run.

### 2. AWS Fargate

[AWS Fargate](https://aws.amazon.com/fargate/) is Amazon’s serverless container service that lets you run containers without managing servers.

 ![](https://assets.northflank.com/image_32_9b6a965cc3.png) 

**Key features:**

- Fully managed by AWS with deep integration into the AWS ecosystem
- Automatic scaling based on demand
- Supports both ECS and Kubernetes workloads

**Potential drawback:** AWS Fargate simplifies container management but is tightly coupled with the AWS ecosystem, limiting portability. Additionally, pricing is based on vCPU and memory usage, which can become costly at scale.

### 3. Azure Container Apps

This Microsoft-managed service provides a serverless environment for containerized applications on Azure.

 ![](https://assets.northflank.com/image_31_dfa539e691.png) 

**Key features:**

- Optimized for Azure workloads
- Supports Kubernetes-based scaling (via KEDA)
- Simplifies networking and authentication

**Potential drawback:** While Azure Container Apps integrates well with Azure services and supports Kubernetes-based scaling, it may not be ideal for organizations relying on other cloud providers. Some advanced networking configurations are also limited compared to full Kubernetes solutions.

### 4. AWS App Runner

AWS App Runner offers a fully managed service for deploying containerized web applications and APIs.

 ![](https://assets.northflank.com/image_30_733bb287fe.png) 

**Key features:**

- Simplifies deployments with minimal configuration
- Integrates with AWS’s ecosystem
- Provides automatic scaling without managing infrastructure

**Potential drawback:** AWS App Runner abstracts infrastructure management but has limited customization options compared to Kubernetes-based solutions. Its pricing model, based on active instances and requests, can make cost estimation challenging for high-traffic applications.

### 5. Amazon Elastic Container Service (ECS)

ECS is Amazon’s highly scalable container orchestration service, often used in conjunction with Fargate.

 ![](https://assets.northflank.com/image_29_1a62a2d419.png) 

**Key features:**

- Supports both serverless and self-managed deployment options
- Deep integration with AWS services
- Highly customizable for diverse workloads

**Potential drawback:** ECS provides deep integration with AWS but requires either AWS Fargate (for serverless execution) or EC2 instances, adding complexity. Multi-cloud deployments are not natively supported, and additional setup is needed for networking and service discovery.

[*Learn more about Amazon Elastic Container Service (ECS)*](https://northflank.com/blog/aws-ecs-elastic-container-service-deep-dive-and-alternatives)

### 6. Kubernetes-based alternatives

Managed Kubernetes services give you full control and flexibility for container orchestration.

 ![](https://assets.northflank.com/image_28_3d89f4ced2.png) 

**Popular options:**

- **GKE (Google Kubernetes Engine):** Managed by Google Cloud
- **AKS (Azure Kubernetes Service):** Managed by Microsoft
- **EKS (Amazon Elastic Kubernetes Service):** Managed by AWS

**Key features:**

- High customizability and control over cluster configuration
- Ability to support both stateful and stateless applications

Potential drawback: Managed Kubernetes services like GKE, AKS, and EKS offer high flexibility but require expertise in Kubernetes administration. Predicting costs can be difficult, especially when factoring in networking, storage, and computing resources. Additionally, these services provide low-level infrastructure abstractions, meaning you must build a developer platform on top, as they are not designed with developer experience in mind.

### 7. Heroku

Heroku is a platform-as-a-service (PaaS) known for its simplicity and developer-friendly interface.

 ![](https://assets.northflank.com/image_27_d2368590bd.png) 

**Key features:**

- Quick deployments with minimal configuration
- Extensive add-on ecosystem
- Ideal for rapid prototyping and smaller-scale applications

**Potential drawback:** Heroku is extremely developer-friendly but can be costly for production workloads. Additionally, it lacks advanced networking configurations and is less suited for complex enterprise applications requiring fine-grained control.

*See "[Top Heroku alternatives in 2026](https://northflank.com/blog/top-heroku-alternatives)" and "[Heroku Enterprise: deep dive](https://northflank.com/blog/heroku-enterprise-capabilities-limitations-and-alternatives)"*

### 8. Digital Ocean App Platform

Digital Ocean’s App Platform simplifies the deployment of containerized applications with predictable pricing.

 ![](https://assets.northflank.com/image_26_413c6685ed.png) 

**Key features:**

- Easy setup and scaling
- Transparent, straightforward pricing
- Designed for developers seeking a balance of simplicity and power

**Potential drawback:** While DigitalOcean App Platform is straightforward and cost-effective, it has fewer enterprise-grade features compared to Kubernetes-based solutions. Scalability options are more limited, making it less ideal for highly dynamic workloads.

## Cloud Run vs. alternatives: a quick comparison

| Feature | Google Cloud Run | Northflank | AWS Fargate | Azure Container Apps | AWS App Runner | Amazon ECS | Kubernetes-Based Alternatives | Heroku | Digital Ocean App Platform |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| **CI/CD integration** | External tools | Built-in | External tools | External tools | External tools | External tools | DIY / External tools | Built-in | Built-in |
| **Execution limits** | 24-hour job limit | No limits | No limits | No limits | No limits | No limits | No limits | No strict limits | No strict limits |
| **Cloud account control** | GCP-only | [Your own cloud (multi-cloud support)](https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment) | AWS-only | Azure-only | AWS-only | AWS-only | Depends on provider (multi-cloud) | Hosted (limited control) | Digital Ocean only |
| **Cost & integration** | Separate billing for networking, logging | Transparent, straightforward pricing | Integrated with AWS pricing | Integrated with Azure pricing | Integrated pricing | Varies (can be complex) | Varies (self-managed complexity) | Transparent pricing with add-ons | Transparent, straightforward pricing |

## How to choose the right alternative

When evaluating which platform best suits your needs, consider the following:

- **Built-in CI/CD:** Do you need a fully integrated deployment pipeline? Platforms like [Northflank](https://northflank.com/) provide native CI/CD.
- **Job duration:** Are long-running or batch processes critical for your workflow? Look for alternatives without strict execution time limits.
- **Cloud account control:** Is running services in your own cloud account or even across multiple clouds important? Solutions such as [Northflank](https://northflank.com/) and managed Kubernetes services offer this flexibility.
- **Ecosystem integration:** Consider how well the platform integrates with your existing tools and cloud services.

## **How Northank can help**

[Northflank](https://northflank.com/) tackles the core limitations of Google Cloud Run by offering built-in CI/CD, eliminating execution time limits, and providing flexible deployment options that let you run tasks directly within your own cloud environment. With [Northflank](https://northflank.com/), you gain the advantages of Kubernetes-powered scalability and integrated cost management, ensuring that your deployments remain efficient, predictable, and well-controlled—no more juggling multiple external tools or dealing with strict runtime constraints.

If you’re ready to overcome the restrictions of Cloud Run and streamline your DevOps workflow, it’s time to explore [Northflank](https://northflank.com/). Experience a more flexible, transparent, and scalable cloud solution that adapts to your growing needs. [Get started with Northflank today and take control of your cloud strategy.](https://app.northflank.com/signup)]]>
  </content:encoded>
</item><item>
  <title>Platform February 2025 Release</title>
  <link>https://northflank.com/changelog/platform-february-2025-release</link>
  <pubDate>2025-02-28T10:16:00.000Z</pubDate>
  <description>
    <![CDATA[Northflank’s latest changelog update brings new features, enhanced metrics, and critical fixes to boost stability and performance in your deployments.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_feb_changelog_07c3574cde.png" alt="Platform February 2025 Release" />This update introduces several new features, improvements, and fixes aimed at enhancing stability, performance, and usability. The following changes reflect our ongoing commitment to providing a robust platform for your deployments and management tasks.

## New features

- BYOC clusters: Addon ports are no longer exposed by default – ports are now dynamically exposed and removed as required by exposed addons.
- Tailscale support: Added support for Tailscale on addons, including restore jobs.
- Tailscale keys: Keys will now be automatically re-generated.
- Secret file uploads: When uploading a secret file, you now have the option to specify whether it is UTF or binary.
- Additional UI metrics: Introduced GPU-specific metrics for workloads deployed on GPU nodes (such as GPU consumption and ephemeral storage usage).
- Enhanced volume UI: New management and monitoring capabilities for volumes at the project level.
- Stack templates: Public one‑click templates for the simple deployment of popular software such as DeepSeek.
- Custom BYOC node pool IDs: You can now define custom IDs for your BYOC node pools.
- Organisation-level BYOC clusters: Clusters can now be defined on an organisation level, allowing you to share access to clusters across multiple teams.
- GitOps commit messages: When making changes to a GitOps‑enabled template, you can now specify a commit message.
- Easier template creation: Added a button to the View specification modal to create a new template from spec.
- Audit log: Added an Audit Log subpages to secrets, pipelines, and templates.
- Volume metrics: Added support for volume IOPS and throughput metrics.
- BYOC metric graphs: Added node  IO Wait to metric graphs.

## Improvements

- AKS update: Updated AKS version to 1.31.
- Buildpack update: Added the HEROKU_24 builder as the new default buildpack stack; HEROKU_22_CLASSIC is now deprecated.
- BYOA import: Improved error message parsing for a more seamless Helm chart import.
- Addon triage page: Refined layout for easier navigation.
- Template cluster node: Switching the provider type now omits irrelevant data.
- Deployment timestamps: Improved handling of deployment timestamp ranges when filtering logs or metrics.
- Build logs: Buildpack now displays a warning when invalid build arguments are passed.
- Template node editing: Users with access to custom plans can now correctly edit the name on templates if the resource already exists.
- Plan updates: Enhanced feedback when changing plans to indicate a successful update.
- Shell controls: Additional controls are now visible without needing to open the terminal.
- Autoscaling info: Service autoscaling information now correctly appears in the header after enabling an autoscaling configuration.
- AWS autoscaling: Reliability improvements have been made.
- Preview environment: Deployment services now allow selection of a start build node as the source.
- Shell terminal: Made major performance improvements to the UI components.
- Volume usage graph: Redesigned for better readability on services with multiple volumes.
- Template project node: When creating a new template, the empty project node is expanded by default.
- Tailscale auth keys: Automatic workload redeployment when keys are regenerated ensures maintained connectivity.
- Pod count: Increased the maximum pod count for GCP and Azure to 256 and 250 respectively.
- BYOC AWS VPC validation: Extended logic to handle additional edge cases.
- Template Execute action: Added the option to skip awaiting an action.
- Addon updates: Updated PgBouncer to 1.24.0 and RMQ to 4.0.5.
- Triage view: Now displays runtime errors from pods terminated incorrectly.
- Run time display: Added a “latest run time” display and a 50‑item option to pages.
- Cluster creation navigation: Improved when creating a cluster during project creation.
- Container logs: Viewing logs within a release flow now updates in real time.
- Preview environment expiry: Reliability improvements to prevent unexpected expiries.
- GitLab reliability: Several improvements made for self‑hosted GitLab.
- Release flows: Various UX improvements to the release flow UI.
- Build lists: Now show a Build Health button.

## Fixes

- Template Dockerfile handling: Template service nodes using a dynamic branch will no longer cause the Dockerfile editor to get stuck.
- Branch selection: Resolved a bug causing the branch selection to reset incorrectly when a reference was input.
- Cluster deletion: Added an additional confirmation prompt when deleting a cluster.
- Log tailing: Fixed an issue with the log tailing and metrics APIs where some containers were not found when filtering.
- Addon versions: Updated RabbitMQ and PostgreSQL addon versions.
- Team creation prompt: Corrected the organisation create team prompt to display an insufficient permissions error when required.
- Container memory errors: Fixed an issue where containers that ran out of memory were marked as failing if the issue occurred more than five minutes earlier.
- Organisation roles: Fixed roles not syncing correctly on user signup.
- Log view search: Resolved issues with the RegEx search functionality.
- UI overflow: Fixed overflowing issues in the Subdomain path Service selector.
- AWS custom subnet validation: Fixed an incorrect check on AWS clusters.
- Azure node pool error: Implemented error detection for a specific provisioning error.
- Template errors: Templates no longer throw an error for a missing argument if passed as an empty string.
- Build nodes: Release build nodes now have their values prefilled.
- Template scrolling: Fixed scrolling issues on the template issue page.]]>
  </content:encoded>
</item><item>
  <title>Jenkins alternatives in 2026: CI/CD tools that won’t frustrate DevOps engineers</title>
  <link>https://northflank.com/blog/jenkins-alternatives-2026</link>
  <pubDate>2025-02-25T06:52:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for Jenkins alternatives in 2026? Find out cloud-based continuous integration tools and DevOps automation tools that simplify CI/CD workflows.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/jenkins_alternatives_d803cc874e.png" alt="Jenkins alternatives in 2026: CI/CD tools that won’t frustrate DevOps engineers" />> What are people saying about Jenkins? Has it become more of a headache than a help?

While Jenkins was once the go-to CI/CD tool, many DevOps engineers now feel frustrated with its maintenance, scalability, and plugin management. If you’ve spent hours debugging pipelines or dealing with an outdated UI, it’s something many DevOps engineers run into.

Don’t get me wrong; Jenkins still has its place, but 2026 brings better options with better cloud integration, simpler configurations, and faster builds. Cloud-based continuous integration is becoming the norm, so looking at Jenkins alternatives that fit modern workflows makes sense.

If you’re ready to move on, here are some of the best CI/CD tools to consider:


1. [Northflank](https://northflank.com/) → A modern alternative with cloud-first CI/CD and Kubernetes support.
2. [GitHub Actions](https://docs.github.com/actions) → Built into GitHub, great for repository-based workflows.
3. [GitLab CI/CD](https://docs.gitlab.com/ci/) → Deep integration with GitLab and flexible automation.
4. [CircleCI](https://circleci.com/) → Known for speed and cloud-based builds.
5. [Harness](https://www.harness.io/) → AI-powered CI/CD with cost and security optimization.
6. [Travis CI](https://www.travis-ci.com/) → Simple setup, popular for open-source projects.
7. [Bitbucket Pipelines](https://www.atlassian.com/software/bitbucket/features/pipelines) → Direct integration with BitBucket repositories.
8. [TeamCity](https://www.jetbrains.com/teamcity/) → Self-hosted option with customizable build processes.
9. [AWS CodePipeline](https://aws.amazon.com/codepipeline/) → Works well with AWS services.
10. [Azure DevOps](https://azure.microsoft.com/en-us/products/devops) → Microsoft’s CI/CD solution with broad enterprise support.

Let’s break down what makes these Jenkins alternatives worth your time.


## What is Jenkins?

[Jenkins](https://www.jenkins.io/) has been around for a long time. It started in 2004 as “Hudson” before changing its name in 2011, and for years, it was the go-to CI/CD tool for DevOps teams. Back then, it made sense. Jenkins gave developers a way to automate builds and deployments when most setups were still running on on-premise servers.

Even now, Jenkins is everywhere. As of 2023, it still holds around [44% of the CI/CD market](https://cd.foundation/announcement/2023/08/29/jenkins-project-growth/), with over a million active users and over 200,000 installations worldwide. That is a lot, but it does not mean people are not looking for something better.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img 
    src="https://assets.northflank.com/jenkins_dashboard_8a83a7c232.png" 
    alt="A Jenkins pipeline showing automated build and deployment stages" 
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{ 
    marginTop: "8px", 
    fontSize: "14px", 
    color: "#555", 
    textDecoration: "none", 
    display: "block" 
  }}>
    A Jenkins pipeline showing automated build and deployment stages
  </figcaption>
</figure>


The problem? Jenkins was not built for how teams work today. It comes with a lot of setup, plugin management, and maintenance. Cloud-based CI/CD tools take that extra work out of the picture. You know exactly what that means if you have spent time tweaking configurations, fixing broken pipelines, or dealing with self-hosted servers.

Jenkins played a huge role in CI/CD, but things have changed. Cloud-based CI/CD tools scale better, run faster, and do not require constant upkeep. Now teams are looking for automation without the extra maintenance, and many are moving on to better alternatives.


## The problems with Jenkins

Many teams still use Jenkins, but that does not mean it is keeping up. If you have used it, you already know how much work goes into maintaining it. What started as a solution for automation has become a system that demands constant upkeep. The more teams try to make Jenkins work for modern workflows, the more problems they encounter.

Let’s look at why so many teams are moving on.


### Jenkins is outdated software
Jenkins was built at a different time. It was designed for on-premise setups where teams managed their servers and manually handled everything. That is not how things work anymore, except for teams in industries like finance and government that still rely on self-hosted infrastructure.

DevOps tools in 2026 are built for cloud-based automation, and Jenkins was never designed for that. It lacks native cloud support, making integrating with modern CI/CD workflows harder. The result? More work just to keep things running.

> "... while Jenkins offers a wide range of plugins and integrations, managing these plugins and ensuring that they are up-to-date can be time-consuming and challenging. This can lead to compatibility issues and other problems that can slow down the software development process.” ~  [Customer review](https://www.g2.com/products/jenkins/reviews/jenkins-review-7715065)


### Security concerns
Security is one of the major reasons teams look for alternatives. Jenkins has a long history of vulnerabilities, and because it relies so heavily on plugins, the risk only increases. Each plugin is a potential entry point, and not all of them are well-maintained. Attackers know this. There have been multiple reports of Jenkins being exploited, including [this one about hackers using the script console to gain access](https://thehackernews.com/2024/07/hackers-exploiting-jenkins-script.html).

> “Jenkins may not be suitable, for beginners as it has a bit of a learning curve. Its interface appears outdated. It can be resource intensive. Security concerns have been raised by some users. Working with plugins can pose challenges at times.” ~ [Customer review](https://www.g2.com/products/jenkins/reviews/jenkins-review-9320856)


### Plugin overload & complexity
Jenkins relies on plugins for almost everything, but that comes with a cost. Some plugins are outdated, poorly maintained, or even abandoned. Teams end up in situations where one update breaks an entire pipeline because of a plugin compatibility issue.

Jenkins tried to fix its plugin issues with [Jenkins X](https://jenkins-x.io/) and [Tekton](https://tekton.dev/). Jenkins X was introduced as a cloud-native solution simplifying CI/CD with automated environments and Kubernetes integration. Meanwhile, Tekton started as an open-source framework for building CI/CD systems on Kubernetes. Both tools had complex setups and steep learning curves, making adoption difficult for teams.

Rather than making CI/CD easier, Jenkins adds another layer of complexity that teams have to manage.

> Jenkins can be a bit challenging to set up and maintain, especially with complex pipelines. The UI feels outdated, and managing plugins can sometimes lead to compatibility issues. ~ [Customer review](https://www.g2.com/products/jenkins/reviews/jenkins-review-10571023)


### Lack of built-in cloud integration
Most teams are no longer running CI/CD on self-hosted servers. They need automation that fits into cloud-based workflows, and Jenkins does not do that natively. It requires extra configurations, workarounds, and constant adjustments to fit into modern DevOps pipelines. Other tools are built for the cloud from day one, and that is why so many teams are moving on.

> “Jenkins UI visually outdated and complex configurations may require a learning curve. Now github and gitlab also provide CI/CD with code hosting platform. For containerization technologies like kubernetes and docker it may lack of built-in support for this.” ~ [Customer review](https://www.g2.com/products/jenkins/reviews/jenkins-review-9117460)

<br/>

Now that we’ve broken down the challenges with Jenkins think about what you really want from a CI/CD solution. You want a setup that works the way you work through a UI, CLI, API, or GitOps. You want to run tests, catch issues early, and ship without stressing over security or infrastructure. The goal is a reliable process for every workload so you can stay focused on building.


## Jenkins vs. modern CI/CD tools (best Jenkins alternatives in 2026)

Let’s go through some of the top 10 CI/CD alternatives that teams are using in 2026.

### 1. Northflank
 [Northflank](https://northflank.com/) is built for cloud-native workflows, making deployments easier without the complexity that comes with Jenkins. If you’re tired of managing self-hosted setups and relying on so many plugins, Northflank handles your infrastructure. [Kubernetes support](https://northflank.com/blog/northflank-raises-22m-to-make-kubernetes-work-for-your-developers-ship-workloads-not-infrastructure) is built-in, so your [containerized applications deploy](https://northflank.com/deploy/run-persistent-and-ephemeral-docker-containers) and [scale](https://northflank.com/blog/scaling-30-000-deployments-with-100-uptime-how-clock-uses-northflank-to-simplify-infrastructure) automatically as needed.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img 
    src="https://assets.northflank.com/northflank_site_01497cf09c.png" 
    alt="Northflank" 
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{ 
    marginTop: "8px", 
    fontSize: "14px", 
    color: "#555", 
    textDecoration: "none", 
    display: "block" 
  }}>
    Northflank
  </figcaption>
</figure>


It connects directly to [GitLab](https://northflank.com/blog/integrating-with-gitlab-and-bitbucket), [Bitbucket](https://northflank.com/blog/integrating-with-gitlab-and-bitbucket), and [GitHub](https://northflank.com/blog/integrating-with-github-github-apps-and-oauth), skipping extra configuration steps. Pipelines are easy to set up, and automation is ready out of the box, so you won’t have to write custom scripts or get caught up managing third-party integrations or in YAML files. For teams working with microservices, this means spending less time on infrastructure and more time getting features out the door.

See [how to build a scalable software architecture (monolith vs. microservices)](https://northflank.com/blog/how-to-build-a-scalable-software-architecture-part-1-monolith-vs-microservices).


### 2. Bitbucket pipelines
If your team already uses Bitbucket, [Bitbucket Pipelines](https://www.atlassian.com/software/bitbucket/features/pipelines) makes it easy to automate builds and deployments without adding more tools to your stack. It is built right into the Bitbucket environment, so everything stays in one place without managing separate CI/CD services or handling complex setups.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img 
    src="https://assets.northflank.com/Bitbucket_pipelines_a8b6d63a24.png" 
    alt="Bitbucket pipelines" 
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{ 
    marginTop: "8px", 
    fontSize: "14px", 
    color: "#555", 
    textDecoration: "none", 
    display: "block" 
  }}>
    Bitbucket pipelines
  </figcaption>
</figure>

Since it’s part of Atlassian’s ecosystem, it connects naturally with tools like Jira, making tracking issues easier and linking deployments to tasks. Compared to Jenkins, getting started is quicker because there are fewer manual configurations. You can define your pipeline with a simple YAML file, commit it, and start automating without getting stuck on plugin compatibility or infrastructure problems.

See [how to integrate your application with GitLab and Bitbucket](https://northflank.com/blog/integrating-with-gitlab-and-bitbucket).


### 3. GitHub Actions
If your team already works with GitHub, [GitHub Actions](https://docs.github.com/en/actions) makes sense as a CI/CD option. It connects directly to your repositories, so you can define and run pipelines right alongside your code without managing separate servers or hunting for third-party plugins.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img 
    src="https://assets.northflank.com/Git_Hub_Actions_9df410abd8.png" 
    alt="GitHub Actions" 
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{ 
    marginTop: "8px", 
    fontSize: "14px", 
    color: "#555", 
    textDecoration: "none", 
    display: "block" 
  }}>
    GitHub Actions
  </figcaption>
</figure>

Since GitHub handles the infrastructure, you do not have to think about updates, security patches, or maintenance. The [marketplace](https://github.com/marketplace?type=actions) has plenty of pre-built actions, so automating tasks feels easier to manage than Jenkins’ plugin-heavy setup. You can spend more time building and shipping your projects without stressing over the underlying systems.

See [how to use GitHub Action to deploy to Northflank](https://northflank.com/guides/use-a-git-hub-action-to-deploy-to-northflank).


### 4. Harness
[Harness](https://www.harness.io/) takes automation a step further with AI that analyzes your deployment patterns to help optimize performance and cut cloud costs. Manually tracking usage and tweaking configurations takes a lot of time. Harness handles it for you, so you can avoid the back-and-forth and keep moving forward.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img 
    src="https://assets.northflank.com/harness_a7e980f95a.png" 
    alt="Harness" 
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{ 
    marginTop: "8px", 
    fontSize: "14px", 
    color: "#555", 
    textDecoration: "none", 
    display: "block" 
  }}>
    Harness
  </figcaption>
</figure>

Security is built right in, with [automatic rollbacks](https://developer.harness.io/docs/database-devops/use-database-devops/rollback-for-database-schemas/) and [governance policies](https://developer.harness.io/docs/platform/governance/policy-as-code/harness-governance-overview/) that catch issues before they spiral into bigger problems. You won’t have to patch things up after failed deployments or rely on third-party tools to stay compliant.



### 5. GitLab CI/CD
If your team already used GitLab, setting up [GitLab CI/CD](https://about.gitlab.com/) becomes much simpler. It’s built right in, so you can automate your workflows without extra tools or complicated integrations. Security scanning, container registry support, and cloud-native features are ready to use from the start.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img 
    src="https://assets.northflank.com/Git_Lab_CI_CD_85551f7adf.png" 
    alt="GitLab CI/CD" 
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{ 
    marginTop: "8px", 
    fontSize: "14px", 
    color: "#555", 
    textDecoration: "none", 
    display: "block" 
  }}>
    GitLab CI/CD
  </figcaption>
</figure>

Pipelines are defined in a simple YAML file, and [GitLab Runners](https://docs.gitlab.com/runner/) take care of execution across multiple machines. This saves you from handling Jenkins agents or constantly fixing configuration issues. Rather than spending hours debugging scripts, you can keep shipping features without unnecessary delays.

See [how to integrate GitLab with your applications](https://northflank.com/blog/integrating-with-gitlab-and-bitbucket).


### 6. Travis CI
For years, [Travis CI](https://www.travis-ci.com/) has been the go-to choice for open-source projects. If you’re hosting your code on GitHub and Bitbucket, it connects easily, letting you automate builds and deployments without spending hours on configuration.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img 
    src="https://assets.northflank.com/travis_ci_1225c7977f.png" 
    alt="Travis CI" 
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{ 
    marginTop: "8px", 
    fontSize: "14px", 
    color: "#555", 
    textDecoration: "none", 
    display: "block" 
  }}>
   Travis CI
  </figcaption>
</figure>

It might not have every advanced feature newer platforms provide, but it can be a reliable option for smaller teams and open-source maintainers. You can run tests, catch issues early, and ship updates without getting lost in complex settings or maintaining your own infrastructure.


### 7. CircleCI
[CircleCI](https://circleci.com/) helps you build and ship faster. It speeds up execution with [parallelism](https://circleci.com/docs/parallelism-faster-jobs/) and [caching](https://circleci.com/docs/caching-strategy/), so you do not lose time waiting for builds to finish. You do not need to handle your own build infrastructure because everything runs in the cloud.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img 
    src="https://assets.northflank.com/circleci_dashboard_0ffef4656f.png" 
    alt="CircleCI" 
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{ 
    marginTop: "8px", 
    fontSize: "14px", 
    color: "#555", 
    textDecoration: "none", 
    display: "block" 
  }}>
   CircleCI
  </figcaption>
</figure>

The platform works with [multiple programming languages](https://support.circleci.com/hc/en-us/categories/115001913988-Languages-and-databases#:~:text=CircleCI%20supports%20a%20wide%20range,as%20well%20as%20supported%20databases.) and containerized environments, making it easy to switch between projects without extra effort. Scaling up for larger deployments happens automatically, so you can handle big releases without constantly adjusting configurations.


### 8. TeamCity
[TeamCity](https://www.jetbrains.com/teamcity/) gives you more control over your CI/CD pipelines without adding extra complexity. You can set up multiple build configurations in a single project, which is helpful when handling larger or more complex workflows.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img 
    src="https://assets.northflank.com/Team_City_8dd2dcf76b.png" 
    alt="TeamCity" 
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{ 
    marginTop: "8px", 
    fontSize: "14px", 
    color: "#555", 
    textDecoration: "none", 
    display: "block" 
  }}>
   TeamCity
  </figcaption>
</figure>

You will not have to spend time managing many plugins like Jenkins. TeamCity has built-in features for test reporting, dependency management, and artifact storage. That means you can keep your builds running without spending hours adjusting configurations.


### 9. AWS CodePipeline
[AWS CodePipeline](https://aws.amazon.com/codepipeline/) is a good fit if your team already relies on AWS. It connects with other AWS services, automating application deployments so you can skip third-party tools and unnecessary complexity.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img 
    src="https://assets.northflank.com/AWS_Codepipeline_50156bd820.png" 
    alt="AWS CodePipeline" 
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{ 
    marginTop: "8px", 
    fontSize: "14px", 
    color: "#555", 
    textDecoration: "none", 
    display: "block" 
  }}>
   AWS CodePipeline
  </figcaption>
</figure>

The pay-as-you-go pricing model helps you scale based on what you actually use, reducing the costs of maintaining Jenkins infrastructure.



### 10. Azure DevOps
If your team works with Microsoft technologies, [Azure DevOps](https://azure.microsoft.com/en-us/products/devops) gives you a full set of CI/CD tools for Azure environments. You get version control, build automation, and release management all in one place.

<figure style={{ textAlign: "center", maxWidth: "800px", margin: "auto" }}>
  <img 
    src="https://assets.northflank.com/Azure_devops_35c10bdf6c.png" 
    alt="Azure DevOps" 
    style={{ width: "100%", height: "auto", borderRadius: "8px" }}
  />
  <figcaption style={{ 
    marginTop: "8px", 
    fontSize: "14px", 
    color: "#555", 
    textDecoration: "none", 
    display: "block" 
  }}>
   Azure DevOps
  </figcaption>
</figure>

Its integration with [Azure cloud services](https://www.google.com/aclk?sa=l&ai=DChcSEwje_fX-2NuLAxVQmFAGHWHUOxgYABAAGgJkZw&ae=2&aspm=1&co=1&ase=5&gclid=Cj0KCQiAq-u9BhCjARIsANLj-s06sUo6efBET1WFhW8FHu6pnvCu9hb50AH8kk18WufVX8EK3_AUxNkaAm-2EALw_wcB&sig=AOD64_36cfdZGqUZzRbNKI48JYN3LAb4xA&q&adurl&ved=2ahUKEwiuq_D-2NuLAxVxVkEAHcNFHO4Q0Qx6BAgVEAE) makes application deployment easier without layers of complex configurations. This provides enterprises with a CI/CD solution within the Microsoft ecosystem.

See [how to deploy, release, and scale workloads on Azure with automation and DevOps collaboration through Northflank](https://northflank.com/cloud/azure).


## Jenkins vs. Modern CI/CD Tools: A Quick Comparison

Before wrapping up, let’s break down the key differences between Jenkins and modern CI/CD tools like Northflank. The table below highlights how they stack up in key areas like cloud support, security, and maintenance.


| Feature               | Jenkins                                                         | Modern CI/CD Tools                              |
|-----------------------|----------------------------------------------------------------|------------------------------------------------|
| **Cloud-Native Support** | Limited, requires additional setup                             | Built-in, designed for cloud workflows          |
| **Security**            | Frequent vulnerabilities, requires constant monitoring         | Built-in protections with fewer risks           |
| **Scalability**         | Difficult, requires manual optimization                        | Scales easily with cloud infrastructure         |
| **Plugin Reliance**     | High, many dependencies                                        | Low, most features are native                   |
| **Maintenance**         | Requires ongoing updates and troubleshooting                   | Minimal upkeep, managed environments            |


The comparison table shows that modern CI/CD tools simplify the build process, but not all handle deployment. Note that Jenkins focuses on continuous integration, leaving deployment to plugins or manual processes.

In contrast, tools like Northflank automate the full pipeline, including build, release, and preview environments for testing microservices in production-like containers.

This includes Docker builds, making it easier to create and optimize container images. If you want to optimize your Docker build process, check out [this guide on Docker Build and Buildx best practices](https://northflank.com/blog/docker-build-and-buildx-best-practices-for-optimized-builds). 

## Find a CI/CD solution that works for your team

Teams are moving away from Jenkins because nobody wants to spend their day fixing broken plugins or babysitting servers. The best CI/CD tools handle the heavy lifting, so you can push updates and ship features without constant roadblocks.

If you want to try something new, Northflank makes it easy to get started for free. You can run builds and test deployments and see what it’s like when your CI/CD setup works without constant maintenance. Not sure where to begin? Check out these [guides](https://northflank.com/guides) or browse the [documentation](https://northflank.com/docs). Or just [create a free account](https://app.northflank.com/signup) and start building right away.]]>
  </content:encoded>
</item><item>
  <title>Why we ditched Next.js and never looked back</title>
  <link>https://northflank.com/blog/why-we-ditched-next-js-and-never-looked-back</link>
  <pubDate>2025-02-20T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[When we started running into issues with Next.js, we did what any sane team would do: we tried to fix them. Then we tried again. Then we realized the problem was Next.js itself. So we pulled the plug.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Northflank_Next_js_Design_System_2_5eb72430b0.png" alt="Why we ditched Next.js and never looked back" />At Northflank, we obsess over performance, reliability, and developer experience. 

We help companies [deploy and scale](https://northflank.com/product/deployments) applications that handle tens of billions of requests. We’ve built and optimized everything from marketing sites to complex, real-time web applications, so we know what it takes to scale both. Our core platform runs on hundreds of routes, real-time RPC methods, and WebSocket subscriptions, all managing intricate real-time state. 

We’ve successfully scaled SSR-based marketing and documentation sites as well as high-performance, real-time applications.

When we first chose Next.js, we understood the trade-offs—SSR frameworks work well for marketing and docs, while SPAs are better for real-time, data-heavy apps. We made that decision with years of hands-on experience in both. 

But even for our marketing site—where Next.js should have been the ideal choice—we hit significant challenges that ultimately led us to look for alternatives. 

When we started running into issues with Next.js, we did what any sane team would do: we tried to fix them. Then we tried again. Then we realized the problem was Next.js itself. So we pulled the plug.

Server-side rendering (SSR) isn’t hard if you know what you’re doing. Next.js and Vercel have boxed developers into an unnecessarily complex ecosystem.  

If you’re struggling with Next.js, this might be useful.

## The promise vs. the reality of Next.js

Next.js sells itself as an all-in-one solution: great performance, built-in static site generation (SSG), server-side rendering (SSR), seamless SEO, and a smooth developer experience.  

In theory, it’s perfect. In practice, not so much.

## A framework without conviction

Next.js’s performance issues are one thing, but the constant shifts in its philosophy are even worse.  

The same company that built its entire identity around Jamstack and static site generation pivoted to serverless, then became the cheerleaders of SSR—the complete opposite of what they originally pushed.  

Now, they’re moving away from serverless again in favor of microVMs—who’d have thought? (We did).  

At this point, it’s become a pattern. Roll out a new paradigm, sell it hard, ignore the warning signs, then drop it and move on when the problems become too obvious to hide. Every time, developers are left scrambling to keep up, and companies are stuck cleaning up the mess.  

At Northflank, we’ve felt the pull of trends, just like everyone else. But from the start, we’ve stayed clear on what we’re building and why. Our product has changed a lot (it’s grown, improved, and adapted), but the mission has never wavered.  

We’ve never chased whatever was trending just for the sake of it. You don’t have to build that way.  

Now back to what made us switch.



### 1. Poor performance

Next.js loves to talk about how fast it is. Yet it’s painfully slow at scale. Here’s what we saw:

- Basic page renders were taking **200-400ms**.  
- Large pages, especially ones with dynamic content, could spike well beyond **700ms**.  
- If a Google crawler or our Ahrefs SEO monitoring tool hit multiple pages at once, the site would start crashing **multiple times per week**.  
- Next.js’s built-in caching was **unpredictable and inefficient** across multiple replicas.  

Performance isn’t just a "nice to have." Or at least, not for us. It’s everything.  

In a world where milliseconds determine conversion rates, Next.js forces businesses into a suboptimal reality where every interaction carries unnecessary latency.  

> **Google doesn’t wait. Users don’t wait. Next.js makes them wait.**

At Northflank, we’ve always had a culture of building things ourselves when existing solutions don’t cut it. Ironically, many of our customers come to us after realizing that DIY infrastructure is harder than expected—but in this case, the choice was obvious.  

The alternatives weren’t just bad; they actively got in our way. And because building a high-performance SSR setup isn’t actually difficult when done right, we knew we could fix this faster than continuing to fight Next.js.  

To illustrate the problem, we ran tests comparing Next.js against our custom-built solution under the same conditions (**same browser, same system resources, no CDN**).  

After switching from Next.js to our custom-built React SSR solution, we saw **massive performance improvements**:

#### Landing page performance:
- **First Contentful Paint:** Improved from **2.1s → 0.5s** (**4x faster**)  
- **Largest Contentful Paint:** Improved from **5.1s → 0.8s** (**6x faster**)  
- **Speed Index:** Improved from **8.4s → 1.7s** (**5x faster**)  



 ![](https://assets.northflank.com/landing_next_7e99b9afbe.png) 

 ![](https://assets.northflank.com/landing_react_20fcebbc5e.png) 


Even if you have a CDN in front, it doesn’t solve Next.js's deeper inefficiencies. Performance bottlenecks persisted regardless of CDN usage.

Additionally, if you are seeking to enhance your [landing page](https://www.sender.net/blog/landing-page-optimization/), you may find these tips beneficial.

#### Cloud Provider Page (Data-Heavy Page):
- **First Contentful Paint:** Improved from **2.0s → 0.5s** (**4x faster**)  
- **Largest Contentful Paint:** Improved from **3.6s → 0.8s** (**4.5x faster**)  
- **Total Blocking Time:** Improved from **2,810ms → 870ms** (**3x improvement**)  
- **Speed Index:** Improved from **11.0s → 1.9s** (**6x faster**)  



 ![](https://assets.northflank.com/cloud_provider_next_7ce94bf350.png) 

 ![](https://assets.northflank.com/cloud_provider_react_4cebbbe1fc.png) 


### 2. SEO took a hit

Next.js is supposed to be good for SEO because of SSR and SSG.  

Except when your page takes forever to render, search engines don’t care how "static" it is.  

We saw this firsthand:
- Our **SEO performance halved in December**.  
- We **lost valuable rankings** because of **slow server-side rendering and JSON bloat**.  
- **Google was actively penalizing us** for slow speeds.  
- Customers noticed and **reported issues** with our slow-loading documentation pages.  


 ![](https://assets.northflank.com/image_1_0fc029e113.png) 

The irony is that companies buy into Next.js because they think it’s good for SEO, only to find that under real-world scale, it actually works against them.  

It’s a textbook example of **marketing hype masking systemic inefficiencies**.

### 3. It was a black box

- When Next.js crashed, we didn’t get **useful errors**.  
- When things slowed down, we couldn’t **pinpoint why**.  
- Instead of control, we got **a black box** with unpredictable performance issues.  

There were **no good ways to debug slow renders**.  

At some point, we stopped asking, *"How do we fix this?"* and started asking, *"Why are we using this?"*


## When enough was enough

We first felt the pain with our **documentation platform**. Our docs were incredibly slow—customers noticed, we noticed, and our SEO rankings noticed.

Then our **marketing site** started experiencing the same problems.  

At this point, we had two choices:
1. Fight Next.js for another few months.  
2. Rip it out and build something better.  

We ripped it out. 

<figure>

![](https://assets.northflank.com/ssr_ab64e7390a.png)

<figcaption style={{ textAlign: 'center' }}>

It really is *that* simple.

</figcaption>

</figure>


We didn’t move to Astro or another framework. We built our own **server-side rendering system** using **plain React and Express**.  

Why?
- We **already knew how to build SSR**—it’s not hard.  
- It took us **3 days to replace Next.js for docs**.  
- It took us **a couple of days to replace it for our marketing site**.  
- The result? **Pages that render in 20ms instead of 700ms**.  




 ![](https://assets.northflank.com/hydrate_f4e14db92a.png) 

## The real cost of Next.js and Vercel

One of the biggest issues with Next.js is how **tightly it’s coupled with Vercel**.  

- Next.js features often **work best (or only) on Vercel**.  
- **Vercel locks you in** with expensive hosting.  
- **Scaling Next.js is a headache** because of bad cache invalidation across replicas.  

Next.js makes it **easy to start**, but **harder to grow**.

## The results of our DIY

Since the switch, here’s what we’ve seen:
- **SEO rankings bounced back** (faster site = happier Google).  
- **Pages load 50x faster** (**20ms vs. 700ms**).  
- **No more crashes when a crawler visits**.  
- **Deployments don’t cause massive slowdowns**.  
- **We don’t have to babysit our website anymore**.  

## Should you do it?

If you have a **tiny site**, Next.js is fine.  

But if you care about **performance, SEO, scalability, or having control over your tech stack**, you might want to reconsider.  

Next.js is **bloated, slow, and unnecessarily complex**.  

With Northflank handling our deployments and infrastructure, we built something that **scales seamlessly, deploys effortlessly, and actually performs under real-world conditions**.  

If you're tired of sluggish builds, bloated frameworks, and arbitrary limitations, there are **better ways to ship software**.
]]>
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</item><item>
  <title>Self-host Deepseek R1 on AWS, GCP, Azure &amp; K8s in Three Easy Steps</title>
  <link>https://northflank.com/blog/self-host-deepseek-r1-on-aws-gcp-azure-and-k8s-in-three-easy-steps</link>
  <pubDate>2025-02-17T08:00:00.000Z</pubDate>
  <description>
    <![CDATA[Deploy DeepSeek R1 on spot-priced GPUs in your own cloud for cost-effective, private, production-ready AI. Enjoy fast setup with Northflank BYOC and flexible cloud options.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_deepseek_1f4c848cd8.png" alt="Self-host Deepseek R1 on AWS, GCP, Azure &amp; K8s in Three Easy Steps" />If you've been searching for a way to run your own large language model (LLM) without sacrificing performance, privacy, or cost-effectiveness, look no further than DeepSeek R1. This cutting-edge LLM solution enables you to deploy on spot-priced A100s and H100s in your own cloud infrastructure - Amazon Web Services, Google Cloud Platform, or Microsoft Azure - using Northflank's "Bring Your Own Cloud" (BYOC) feature. In under an hour, you can harness the power of DeepSeek while keeping your data entirely within your own cloud account.

DeepSeek R1 captures the zeitgeist of modern AI: it's fast, flexible, and ready to build on for production. Below is a quick walkthrough on how to self-host DeepSeek in your own cloud account using Northflank. Remove the complexity of Kubernetes while retaining control over your infrastructure and data.

* * * * *

### Why Self-Host DeepSeek R1?

1.  **Complete Data Privacy**: Maintain full control of your chat history, logs, and any uploaded data.
2.  **Lower Costs with Spot Instances**: DeepSeek R1 can leverage spot-priced GPUs - like A100s and H100s - to deliver massive compute power without draining your budget.
3.  **Fast Setup**: Get up and running in less than an hour - no advanced DevOps expertise needed.
4.  **Flexibility Across Clouds**: Use AWS, GCP, or Azure - whichever platform works best for you.

* * * * *

### Step 1: Prepare Your Cloud Provider and Northflank Account

1.  **Create/Log In to Your Cloud Provider Account**

    -   Set up a new project or resource group in [AWS](https://aws.amazon.com/), [GCP](https://cloud.google.com/), or [Azure](https://azure.microsoft.com/en-gb/).
    -   Make sure your account has permissions to spin up GPU-based VMs or container instances (e.g., A100, H100, or [whatever GPU you prefer](https://northflank.com/cloud/gpus)).
2.  **Sign Up for Northflank**

    -   [Create a Northflank account](https://app.northflank.com/signup), then enable the BYOC functionality by [linking your cloud provider credentials](https://app.northflank.com/s/account/cloud/clusters/integrations/new).
3.  **Check Your Cloud Quotas**

    -   Before deploying, ensure you have sufficient quota for the GPU resources you intend to use. Spot instances are cheaper but can be reclaimed by the cloud provider, so you'll want to plan for that.

* * * * *

### Step 2: Deploy DeepSeek R1 via Northflank BYOC

1.  **Deploy DeepSeek from a Northflank stack template**

    -   Northflank offers templates which allow you to deploy DeepSeek into your chosen cloud provider with a couple of clicks. Click Deploy Deepseek Now for [GCP](https://northflank.com/stacks/deploy-deepseek-r1-70b-gcp), [AWS](https://northflank.com/stacks/deploy-deepseek-r1-70b-aws), or [Azure](https://northflank.com/stacks/deploy-deepseek-r1-70b-aks).

2.  **Configure the DeepSeek template**

    -   Select the integration for your chosen cloud provider. This ensures that all compute and storage come from your own cloud account.
    - Review the cluster configuration and resources that the template will create. You can proceed with the defaults, or select a different region or node types for your new cluster.
    - Deploy the stack to save the template in your team.

3. **Run the DeepSeek template**

    - Run the template and Northflank will provision a new cluster in your cloud account. When that's done, it'll create a new project and deploy the DeepSeek resources.

* * * * *

### Step 3: Configure & Test Your LLM

1.  **Access DeepSeek via Open-WebUI**

    -   Once deployment finishes, navigate to the Open-WebUI endpoint and create a new account.

2.  **Run Sample Queries**

    -   Configure how you want DeepSeek R1 to respond to queries---tailor it to your business logic, data, or unique domain knowledge.
    -   Test the LLM with a few prompts to ensure it's functioning as intended.
    -   Fine-tune any parameters for latency, output style, or memory constraints.

4.  **Expand, Secure, and Iterate**

    -   Add more GPU nodes if you need additional throughput, or deploy nodes with more GPUs.
    -    Get ready for production and switch to on-demand instances.
    -   Keep iterating: one of the best parts about self-hosting is you can adapt as quickly as your business demands.

* * * * *

### Watch the Demo

Watch how Northflank simplifies DevOps. A new cluster is created in the linked cloud provider account, and the required GPU workload - DeepSeek on OIlama in this case - is also provisioned along with Open-WebUI and persistent volumes. The command to download and run the DeepSeek model is executed when the application is running, meaning it's ready to use immediately. What would normally be a hassle at best and a nightmare at worst - provisioning a Kubernetes cluster in your own cloud account _*with all the tools required for a developer to immediately begin deploying workloads*_ - can be done in a click!

<video controls autoPlay playsInline loop muted width="100%">
    <source src="https://assets.northflank.com/deploy_deepseek_95b7ee84fb.mp4"/>
</video>

This demo shows the [DeepSeek 70B stack template for Azure](https://northflank.com/stacks/deploy-deepseek-r1-70b-aks).

* * * * *

### Conclusion

DeepSeek R1 brings the promise of cutting-edge AI directly into your own environment, allowing you to tap into spot-priced GPUs with complete confidence in your data's security. With Northflank BYOC, orchestration becomes a breeze - spend less time worrying about infrastructure and more time developing your product.

Spin up DeepSeek R1 in [GCP](https://northflank.com/stacks/deploy-deepseek-r1-70b-gcp), [AWS](https://northflank.com/stacks/deploy-deepseek-r1-70b-aws), or [Azure](https://northflank.com/stacks/deploy-deepseek-r1-70b-aks) today using Northflank, and experience the perfect blend of performance, security, and cost-efficiency.

<InfoBox className="BodyStyle">

## Ready to get started?

Northflank allows you to deploy clusters, code, and databases within minutes. Sign up for a Northflank account and create a free project to get started. 

- Create and manage clusters in your AWS, GCP, and Azure accounts
- Deploy Docker containers
- Create your own stateful workloads
- Backup, restore and fork databases
- Observe & monitor with real-time metrics & logs
- Low latency and high performance

<div>
    <a href="https://app.northflank.com/signup">
        <Button variant={["large", "gradient"]}>Get started now</Button>
    </a>
</div>

</InfoBox>]]>
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</item><item>
  <title>Weights uses Northflank to scale to millions of users without a DevOps team</title>
  <link>https://northflank.com/blog/weights-uses-northflank-to-scale-to-millions-of-users-without-a-devops-team-ai-k8s</link>
  <pubDate>2025-02-06T17:54:00.000Z</pubDate>
  <description>
    <![CDATA[With 9 clusters across AWS, GCP, and Azure, 40+ microservices, 250+ concurrent GPUs, and 10,000+ AI training jobs per day, Weights operates at scale—and does it so seamlessly that most Series B+ startups wish they could be them.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/weights_casestudy_6fbc265ee5.png" alt="Weights uses Northflank to scale to millions of users without a DevOps team" />TL;DR

[JonLuca DeCaro](https://www.linkedin.com/in/jonluca/), ex-Citadel and Pinterest engineer, could have built his own infrastructure from scratch. Instead, he used Northflank to scale [Weights](https://www.weights.com) into a multi-cloud, GPU-optimized AI platform serving millions.

With 9 clusters across AWS, GCP, and Azure, 40+ microservices, 250+ concurrent GPUs, 10,000+ AI training jobs and half a million inference runs per day, Weights operates at scale—and does it so seamlessly that most Series B+ startups wish they could be them. 

Northflank automates everything—from container orchestration to workload scheduling—so a two-person team can run what would typically take an entire infra org.

The results: seamless cloud migration in hours instead of weeks, aggressive spot instance optimization, and a 7-minute model load time slashed to 55 seconds, cutting GPU costs dramatically.

For Weights, Northflank eliminated the need for Kubernetes management, CI/CD headaches, multi-cloud balancing, and endless DevOps overhead. 

If JonLuca uses it, so should you. 🙂

 ![](https://assets.northflank.com/northflank_weights_byoc_c510edb3d0.png) 


Sometimes, the best engineering teams are the ones you don't need to hire. 

That's what JonLuca DeCaro, founder of Weights, discovered when he turned to Northflank for infrastructure.

JonLuca isn't just any startup founder—he's a former Citadel and Pinterest engineer, where he built and scaled complex systems handling millions of users. If anyone could have built their own infra from scratch, it was him. Instead, he chose Northflank.

With only two engineers, they've built a consumer AI platform that serves millions of users—all without a dedicated DevOps team.

## The problem

## Scaling AI with constrained resources

In late 2023, Weights began as a local AI application for voice cloning. Their technical edge came from rewriting open-source AI models to run efficiently on consumer hardware, optimizing for edge inference rather than cloud deployment. 

Users loved the performance, but they wanted more: a web version that could run on any device, including phones.

The transition from edge to cloud wasn't as much of a technical challenge as it was an existential one. 

"We were a bootstrapped consumer startup. We faced this chicken-and-egg problem where we needed to monetize, but we couldn't until we launched and had no startup capital." 

They had cloud credits but lacked the infrastructure expertise to leverage them effectively.

## Switching from manual deployments to automated infrastructure

### Phase 1: The Manual Era

Weights started out in a very hacky way.

"We were spinning up a single instance with a spot A100, SSH-ing in, doing a git pull, and starting services manually." 

This approach worked for about a week before user demand exposed its limitations.

### Phase 2: We need scalability!

As demand grew, they evaluated several options:

-   Self-managed Kubernetes clusters

-   Cloud-native deployment solutions

-   Managed container platforms

-   DevOps automation tools

-   Fractional DevOps consultants

### Phase 3: ✨ Northflank ✨

 ![](https://assets.northflank.com/weightsggnorthflankk8sbyoc_casestudy_1_19d0b4815b.png) 

"We wanted something that felt like Vercel for the backend. Where I can hook up my GitHub repo, write a single Dockerfile, and with one click, everything else just deploys. Autoscaling, builds, container registry, networking—everything just works."

## The solution

## Building a multi-cloud AI platform

"The average Series B startup doesn't have nine clusters across three separate clouds, Most startups wouldn't be able to reach this point without a full team of DevOps and deployment engineers. We're able to do it without one at all."

The infrastructure Weights built with Northflank is sophisticated yet manageable by a small team. Here's how it breaks down:

### Architecture 

-   9 clusters across AWS, GCP, and Azure

-   40+ microservices handling different AI workloads

-   250+ instances running simultaneously

-   Custom node pools for specific workload types

-   Integrated logging and monitoring systems


* * * * *

 ![](https://assets.northflank.com/weightsggnorthflankk8sbyoc_casestudy_3_60d1b5125c.png) 

### Workloads

-   10,000 daily AI training jobs

-   500,000 content creations per day

-   150TB monthly data transfer

-   Half a petabyte of user-generated content

### Optimizing GPU 

 ![](https://assets.northflank.com/weightsggnorthflankk8sbyoc_casestudy_4_1da2e469a4.png) 

Weights implemented a sophisticated approach to GPU resource management:

1.  Workloads designed for interruptibility and self-healing

2.  Spot instance orchestration across clouds

3.  VRAM-based GPU type selection

4.  Time-slicing for optimal resource utilization

5.  Multi-read-write cache layers for model loading

When you're paying by the minute for GPUs, every optimization counts.

"We cut our model loading time from 7 minutes to 55 seconds with Northflank's multi-read-write cache layer—that's direct savings on our GPU costs."

## Infrastructure as Code (IaC)

 ![](https://assets.northflank.com/weightsggnorthflankk8sbyoc_casestudy_2_38e74101ec.png) 


### First-class developer experience

The deployment workflow at Weights exemplifies modern DevOps practices without the overhead:

### CI/CD pipeline

1.  Code  push  →  GitHub  repository

2.  Northflank  build  trigger  analysis

3.  Automated  environment  variable  configuration

4.  Docker  build  with  optimized  cache  layers

5.  Artifact  registry  push

6.  Health  check  validation

7.  Zero-downtime  deployment

"The entire setup for launching a new service is probably five minutes. You point it to the Dockerfile, set the build rules and environment variables, click save, and then just don't think about it again."

### There's more

![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXcSXdMTUZBJIn7XEZemst1g1q8siQ7m8fjfLxxbxqzYY3_-uOHu3HLUp0-kVcOyuxhOWbRX3YC-Gg6dVpd6Ut1LJQznBMEqchGuL-UfT7DRk632rDkYEKfyVmvyxnNn0aU4mHMGSQ?key=QEiixSoIkw7baGndFRrDfXIT)

As their platform evolved, Weights leveraged Northflank's ecosystem for additional capabilities:

### Development workflow integration

-   TypeScript client for API automation

-   Template-based resource provisioning

-   Automated health checks and rollbacks

-   Cross-cluster resource orchestration

-   Integrated metrics and alerting

### Operational tooling

-   Native job scheduling for 30-35 cron jobs

-   Datadog log aggregation and analysis

-   Redis deployment for acquired products

-   Custom node pool management

-   Cross-cloud resource balancing

## The results (💸)

The cost benefits came in multiple forms.

### 1\. Cloud flexibility

"Moving from Azure to GCP would typically be a massive migration—something you'd debate if it's worth 4-6 weeks of engineer time. With Northflank, it was an afternoon. We could one-click redeploy all our jobs and instantly leverage new cloud credits."

### 2\. Spot instance optimization

Weights wrote their workloads to be interruptible and self-healing, then let Northflank handle the orchestration. This gave them a significant cost advantage through spot pricing.

### 3\. Performance optimizations

A multi-read-write cache layer implemented through Northflank cut their model loading time from 7 minutes to 55 seconds—crucial savings when you're paying by the minute for GPUs.

### 4\. Team size

"If we didn't have Northflank managing everything, just keeping track of the Kubernetes clusters, setting up registries, actually running all of it—I think it's three to five people at this point," JonLuca estimates.

## Looking forward 🫡

"When you're a small seed-stage startup, the founder's time is invaluable. Any time spent fiddling with builds and DevOps pipelines is not spent building your product or finding product-market fit."

As they continue to scale, Weights is exploring advanced features like:

-   Automated spot market arbitrage across clouds

-   Enhanced cost optimization through usage analytics

-   Advanced performance monitoring and optimization

-   Cross-cluster resource sharing and balancing

-   Automated workload distribution based on regional demand

## Focus on workloads, not infrastructure

"Speed is everything," JonLuca advises other startups. 

"Now that something like Northflank exists, there's no reason not to use it. It'll let you move faster, figure out what your company is doing, save you money, and save you time."

For Weights, this meant transforming from a local AI app to serving millions of users across nine clusters—all while maintaining a lean, product-focused team. 

That's the result of having infrastructure that WORKS.]]>
  </content:encoded>
</item><item>
  <title>Bring Your Own Cloud (BYOC): What is it and why it's the future of deployment</title>
  <link>https://northflank.com/blog/bring-your-own-cloud-byoc-future-of-enterprise-saas-deployment</link>
  <pubDate>2025-01-23T15:00:00.000Z</pubDate>
  <description>
    <![CDATA[Discover how Bring Your Own Cloud (BYOC) empowers enterprises to maintain security, reduce costs, and gain operational consistency by deploying software within their own cloud environments. Learn more]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_byoc_aa3e81b96a.png" alt="Bring Your Own Cloud (BYOC): What is it and why it's the future of deployment" />For years, enterprises have faced a costly trade-off: maintain control with in-house hosting or embrace innovation through vendor-hosted solutions. This false choice has resulted in costly duplicate infrastructure, fragmented security, and operational inefficiencies. Organizations are spending millions without achieving the balance they need. The real solution lies in rethinking how we deploy modern software.

As organizations invest in cloud optimization, security, and compliance, forcing critical applications outside these environments is increasingly untenasdasable. Traditional SaaS deployment models no longer align with modern enterprise needs.

Bring your own cloud (BYOC) breaks this deadlock by rethinking how we deploy software. To see why it matters, we need to explore how traditional models are falling short.

## **What is BYOC?**

Bring your own cloud (BYOC) lets enterprises deploy software directly within their own cloud infrastructure instead of vendor-hosted environments. This approach preserves control over data, security, and operations while benefiting from cloud-native innovation.

Bring your own cloud (BYOC) is becoming the default because it aligns with modern enterprise needs, eliminating the trade-offs of traditional SaaS models by integrating seamlessly with existing cloud environments.

![Bring your own cloud architecture diagram - BYOC](https://assets.northflank.com/byoc_bring_your_own_cloud_diagram_9f24a175ec.png) 

## **The different types of BYOC**

Bring your own cloud (BYOC) implementations reflect the diverse needs of enterprise environments, from organizations requiring partial control to those demanding complete isolation. Understanding these approaches helps organizations choose the right model for their specific requirements.

### 1. SaaS control plane with BYOC runtime

In this model, the vendor maintains the control plane while workloads run in the customer's cloud infrastructure. This hybrid approach has evolved into two distinct implementations:

- **IAM Credentials Sharing** - represents the traditional approach, where customers provide cloud provider credentials to vendors. While straightforward to implement, this method introduces significant operational overhead. Organizations must manage credential rotation, handle security risks from potential credential compromise, and maintain ongoing access controls across multiple vendors.
- **Cross-Account Links** - offer a more sophisticated solution by establishing permanent, secure connections between vendor and customer environments. This approach, also adopted by [Northflank](https://northflank.com/features/bring-your-own-cloud), eliminates credential management challenges while providing enhanced security through revocable access and simplified integration processes. Organizations gain the benefits of vendor expertise without compromising their security posture.

### 2. BYOC control plane and runtime

Some organizations require complete control over both their control plane and runtime environments. This self-managed or air-gapped deployment model serves industries with strict security and compliance requirements, such as banking, defense, and healthcare where the [core software](https://amlyze.com/core-banking-software/) functions as the backbone of the operations. By maintaining all components within their environment, organizations achieve maximum security isolation and compliance control.

## **How Kubernetes has made BYOC feasible**

Kubernetes has revolutionized software deployment by standardizing container orchestration and enabling applications to run consistently across different cloud environments. Its ability to automate scaling, manage workloads, and handle failover has made it the foundation of modern cloud-native infrastructure.

However, while Kubernetes makes bring your own cloud (BYOC) possible, it's not a complete solution. The rise of managed Kubernetes services like GKE, EKS, and AKS has reduced the operational burden for teams, allowing organizations to access Kubernetes' capabilities without managing the underlying infrastructure. If organizations can ensure their software runs on Kubernetes, they can leverage these managed services to reduce operational complexity and focus on their applications.

Still, managed Kubernetes alone doesn't solve all the challenges of bring your own cloud (BYOC). Platforms like [**Northflank**](https://northflank.com/features/bring-your-own-cloud) take this a step further by integrating with managed Kubernetes services to provide a seamless, out-of-the-box experience across major cloud providers. By bridging the gap between Kubernetes' raw potential and practical implementation, Northflank enables organizations to deploy, monitor, and scale applications effortlessly while maintaining control over their cloud environments.

To truly harness the benefits of bring your own cloud (BYOC), organizations need more than Kubernetes—they need platforms like Northflank that abstract its complexity and make bring your own cloud (BYOC) a reality for enterprises of any size.

## **Why enterprise software requires a BYOC model**

Enterprise software deployment has reached an inflection point where traditional SaaS models no longer align with modern organizational needs. The rapid maturation of cloud infrastructure within enterprises has created an environment where bring your own cloud (BYOC) isn't just beneficial—it's becoming essential. Here's why this shift is happening:

- **Operational consistency and tooling -** Organizations have spent years building sophisticated monitoring systems, implementing logging solutions, and developing automated deployment pipelines. When software runs outside this ecosystem in vendor-hosted environments, it creates operational blind spots. Teams must context switch between different monitoring systems, manage separate alert channels, and maintain duplicate tooling. This fragmentation increases operational risks of missing critical issues. Bring your own cloud (BYOC) eliminates these problems by allowing enterprises to leverage their existing operational tools and practices across all their applications.
- **Cost management & optimization -** Organizations have already negotiated complex cloud pricing agreements and implemented sophisticated resource management strategies. Running applications in vendor-hosted environments means paying premium prices for resources that could be more efficiently managed within existing cloud infrastructure. Moreover, data transfer between vendor environments and internal systems often incurs significant costs. BYOC enables organizations to consolidate their cloud spending and optimize resource utilization across all applications. A key advantage comes through enterprise-grade multi-tenancy capabilities, where organizations can run multiple workloads on the same pool of compute resources. This approach dramatically reduces costs by eliminating hardware duplication across different vendor environments and minimizing unused capacity through intelligent workload distribution. The ability to consolidate workloads onto a single, efficiently managed infrastructure creates significant cost advantages that simply aren't possible with separate vendor-hosted solutions.
- **Security & control -** Organizations have established comprehensive security frameworks within their cloud environments, including intrusion detection systems, encryption standards, and access controls. These security measures represent significant investments and are carefully tailored to meet specific compliance requirements. When applications run in vendor environments, organizations must rely on the vendor's security measures, which may not align with internal standards or compliance needs. This misalignment becomes particularly critical as enterprises face increasingly stringent deployment requirements that can block the adoption of new tools entirely. BYOC solves these challenges by allowing enterprises to maintain consistent security controls across all applications, ensure compliance with data sovereignty requirements, and maintain complete visibility into their security posture.
- **Performance optimization** - BYOC enables organizations to achieve exceptional performance by leveraging their existing cloud infrastructure and specialized hardware configurations. By deploying applications within their own Virtual Private Cloud (VPC), enterprises can minimize network latency and maximize throughput for critical workloads. This becomes particularly important for applications that require real-time processing or handle sensitive data that needs to stay within specific network boundaries. Furthermore, organizations can take advantage of their cloud provider's specific instance types and hardware configurations that best match their workload requirements. For example, they can utilize specialized hardware such as ARM-based processors (including AWS EC2 Graviton, GCP ARM, and Azure ARM instances) to achieve superior performance characteristics while maintaining cost efficiency. These performance optimizations extend beyond what's typically possible in vendor-hosted environments, where hardware choices are often limited to standard configurations.

## Reasons to use BYOC

Remember the last time you had to jump between different dashboards just to figure out why your app was running slow? Or that moment when your security team discovered some critical data was living in a vendor's cloud somewhere outside your carefully crafted security perimeter? We've all been there. That's why more companies are bringing their software back home—running it in their own cloud instead of scattered across vendor-hosted services. 

Let's dive into why deploying software in your own cloud infrastructure isn't just a tech choice—it's a game-changer for how your entire organization operates.

### **1. Seamless networking with shared VPC**

Running all applications within the same Virtual Private Cloud (VPC) creates a seamless networking environment that's both efficient and secure. When applications share a VPC, they can communicate directly without leaving your network perimeter, reducing latency and eliminating the need for complex network configurations. This integration becomes particularly valuable when applications need to interact frequently or share sensitive data. For instance, your customer relationship management system can directly communicate with your billing system without data ever traversing the public internet.

### 2. Workload portability

Workload portability through bring your own cloud (BYOC) provides organizations with unprecedented flexibility in managing their cloud resources. For startups, this means capitalizing on cloud provider credits offered through various startup programs, potentially saving thousands of dollars in infrastructure costs during critical growth phases. As organizations scale, they can strategically shift workloads to lower-cost providers or regions, optimizing their cloud spending without being locked into a single vendor's pricing structure.

### 3. Data residency and compliance

In today’s regulatory environment, controlling where and how data is stored isn’t just helpful—it’s often required. Bring your own cloud (BYOC) gives organizations full control over their data to comply with [regional rules like GDPR](https://sprinto.com/blog/gdpr-compliance/), which strictly govern how data is handled and where it’s stored. With bring your own cloud (BYOC), organizations can keep sensitive data within specific regions, ensuring it stays compliant with legal requirements while having full visibility into how and where it’s processed.

### 4. Secure operations in air-gapped environments

For enterprises operating in highly regulated industries or handling sensitive information, air-gapped environments provide an essential security measure. Bring your own cloud (BYOC) enables organizations to run critical applications within these isolated environments while maintaining their security posture. This capability is particularly valuable for government agencies, financial institutions, and healthcare organizations that must maintain strict isolation between their systems and external networks.

### 5. Building custom hosting platforms

Some organizations aspire to create their own hosting platforms, either for internal use or as a service offering. Bring your own cloud (BYOC) provides the foundation for building these custom platforms, allowing organizations to tailor their infrastructure to specific requirements. This approach enables precise control over resource allocation, security configurations, and operational procedures while maintaining the flexibility to evolve the platform as needs change.

Solutions like [Northflank](https://northflank.com) enable open-source projects and software companies to offer one-click deployment without building their own runtime infrastructure. This eliminates the need for dedicated platform teams while maintaining all the benefits of BYOC, making sophisticated deployment capabilities accessible to a broader range of organizations.

## How Northflank can help

The path from understanding bring your own cloud (BYOC) to implementing it effectively requires bridging several technical gaps. A unified solution must address the core challenges that organizations face when adopting bring your own cloud (BYOC) architectures.

True multi-cloud capability with a unified control plane forms the foundation of an effective bring your own cloud (BYOC) implementation. Think of this as having a single dashboard that lets you manage all your cloud resources, regardless of where they live. Without this unified approach, organizations often find themselves switching between different tools and interfaces, creating the same kind of fragmentation they sought to eliminate.

Kubernetes serves as a powerful foundation, but understanding its role helps set realistic expectations. Just as Linux provides an operating system for computers, Kubernetes provides an operating system for containers. And like any operating system, it needs additional layers built on top to create a complete, usable platform.

[Northflank](https://northflank.com/features/bring-your-own-cloud) builds upon these fundamentals by providing a common interface for software deployment across any cloud environment. Whether your team deploys to internal infrastructure or customer cloud accounts, they interact with a consistent set of tools and workflows. This consistency proves crucial for maintaining efficiency as organizations scale their bring your own cloud (BYOC) implementations.

 ![Bring Your Own Cloud (BYOC): made easy with Northflank](https://assets.northflank.com/bring_your_own_cloud_byoc_northflank_decd10e261.png) 

The result is multi-cloud deployment without the traditional operational complexity. Development teams can focus on building and deploying software while the platform handles the intricacies of managing different cloud environments. This unified experience across clouds means teams spend less time managing infrastructure and more time delivering value. [Schedule a live demo here](https://cal.com/team/northflank/northflank-demo?date=2025-01-13&month=2025-01)*, or* [get started with Northflank bring your own cloud (BYOC) here](https://app.northflank.com/signup)*.*]]>
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  <title>Scaling 30,000 deployments with 100% uptime. How Clock uses Northflank to simplify infrastructure.</title>
  <link>https://northflank.com/blog/scaling-30-000-deployments-with-100-uptime-how-clock-uses-northflank-to-simplify-infrastructure</link>
  <pubDate>2025-01-17T20:04:00.000Z</pubDate>
  <description>
    <![CDATA[Discover how Northflank's PaaS transformed Clock’s infrastructure with rapid container deployment, seamless scaling, transparent costs, and empowered developers. Learn more.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_clock_in_stats_casestudy_c540435701.png" alt="Scaling 30,000 deployments with 100% uptime. How Clock uses Northflank to simplify infrastructure." />[Clock](https://clock.co.uk), a digital agency working with household names like Riot Games, Epic Games, Times Plus and Critical Role, has a knack for delivering. Their engineering team built a reliable infrastructure that had served them well for years. But as new client requests surged and project complexity grew, the cracks began to show. Environments took longer to provision, scaling demanded hands-on coordination, and costs weren’t always transparent.

As they grew, their tech stack wasn’t exactly aging like fine wine. With over 70 environments they needed infrastructure as reliable and innovative as their own work. Enter **Northflank**.

(Now, we know this is a case study, and yes, we’re about to toot our own horn. But when someone like Clock tells us we’ve changed the way they work, we can’t help but feel a little proud.)

<FancyQuote
  body={
    <>
      We didn’t just find a tool; we found a teammate. Northflank took the chaos out of our operations. {' '}
      <Text as="span" color="success" fontWeight={500}>
        Now, we can focus on delivering for our clients instead of putting out fires.
      </Text>
     </>
  }
   attribution={
    <TestimonialHeader
      name="Ash Summers"
      position="Head of Engineering @ Clock"
      avatar="https://media.licdn.com/dms/image/v2/C4E03AQEEHGVcYiSrIg/profile-displayphoto-shrink_800_800/profile-displayphoto-shrink_800_800/0/1634935558371?e=1742428800&v=beta&t=ypT4ICGXB8F8ARtcuH_mOQhPdSzEjxjS8mvctwYxPa8"
      linkedin="https://www.linkedin.com/in/ashley-summers-651b53223"
      mb={0}
    />
  }
/>

### The problem

Clock’s in-house infrastructure was good—built on robust principles and a dedicated ops team. As they took on more enterprise clients and high-traffic launches, however, the system required more manual oversight.

- **Prolonged staging provisioning**: Even with a seasoned engineering team, rolling out staging environments could balloon into a weeks-long process. In a fast-moving agency culture, this delay added overhead and slowed product teams that thrived on quick feedback loops.

- **Scaling demands**: Many of Clock's customers would see massive concurrent audiences, pushing Clock to scale hundreds of containers in minutes on bare-metal. While they had a functional process for this, it required heavy involvement from the ops team each time.

- **Opaque costs**: Hosting bills would arrive as a lump sum, making it tough to pinpoint which projects or environments drove usage. This made forecasting and client cost breakdowns more difficult.

- **Limited self-service**: Though the system was reliable, in-depth logs, backups, and debugging tools often needed ops involvement. This created bottlenecks when product engineers needed immediate insights to troubleshoot issues.


### **The solution**

When Clock started exploring alternatives, Northflank stood out immediately. Our platform-as-a-service (PaaS) approach makes complex infrastructure tasks intuitive, accessible, and fast.

“We looked at other solutions like AWS Fargate and App Runner, but they all felt like trying to assemble IKEA furniture without instructions. Northflank was different—it just made sense,” Ash says.

![container deployment dashboard for Clock with Northflank](https://assets.northflank.com/northflank_service_dashboard_clock2_bf4abd4233.png) 

Northflank, on the other hand, felt intuitive from day one. Our platform-as-a-service approach offered exactly what Clock was looking for:

1. **Ease of use**: Developers could spin up services, pipelines, and environments without needing a PhD in cloud computing.  
2. **Flexibility**: Whether deploying to Northflank's Managed Cloud, AWS, Google Cloud, or Azure, Northflank made it easy to adapt to clients’ unique requirements.  
3. **Empowerment**: With tools like self-service backups, real-time logs, and autoscaling, every developer—from junior to senior—gained hands-on infrastructure experience.

Here's what made us stand out 😇

**Staging in hours, not weeks**  
Creating new environments became as simple as clicking a few buttons. “Before Northflank, staging was an ordeal. Now, I can spin up a fully functional environment in minutes. It’s a game-changer.”

**Effortless scaling**  
For creator economy streams, we automatically scaled services to over 200 containers, handling up to 20,000 requests per second. “We don’t even think about it anymore. Northflank just works.”

**Integrated code and infrastructure**  
With GitOps, developers could bundle infrastructure changes with application code, ensuring deployments were always aligned. “It’s beautiful,” Beth says. “We can now easily see what's running in each environment and where there might be differences and promoting releases from stage to prod is seamless”

**Transparent costs**  
We broke down hosting costs by project and client, giving Clock full visibility for the first time. “We finally have a granular cost breakdown per client, project and service. That alone has been worth it.”

 ![IaC - Infrastructure as Code for Clock with Northflank](https://assets.northflank.com/northflank_clock_template_iac_fa79f99e12.png) 

### **The results**

Two years into their Northflank journey, Clock’s workflow has transformed. Deployments are faster, scaling is seamless, and the entire team has gained confidence in their infrastructure.

* **Speed**: New environments are live in hours, not weeks.  
* **Reliability**: Some workloads have achieved 12+ months of 100% uptime.  
* **Scalability**: Autoscaling effortlessly handles major client launches without manual intervention.  
* **Developer Happiness**: Logs, backups, and metrics are now self-service, empowering every engineer.

“We’ve gone from infrastructure being a constant headache to it being invisible. That’s the highest compliment I can give,” Ash says.

### **Why Clock recommends Northflank**

<FancyQuote
  body={
    <>
      <Text as="span" color="success" fontWeight={500}>The support has been incredible</Text>. Whether it’s helping us scale for a launch or answering questions about new features, the Northflank team is always there for us—even on weekends.
</>
  }
   attribution={
    <TestimonialHeader
      name="Beth Gavin"
      position="Senior Client Solutions Manager"
 avatar="https://media.licdn.com/dms/image/v2/C4D03AQE02ISTc7MjWg/profile-displayphoto-shrink_800_800/profile-displayphoto-shrink_800_800/0/1612536881599?e=1742428800&v=beta&t=W6vt-WRIz5ZZ6oee_AUndh0Ur0wgt2-tWVGCzXm6pFk"
      linkedin="https://www.linkedin.com/in/beth-gavin-1b5949bb"
      mb={0}
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Ash agrees. “It’s rare to find a platform that genuinely empowers your team while making your life easier. With Northflank, we’ve found that. I can’t recommend it enough.”

When a customer says something like this, it reminds us why we do what we do here at Northflank.

If you want to solve your infrastructure woes and give your team the tools to move faster, book a [live demo](https://cal.com/team/northflank/northflank-demo) with Northflank today. We can make your work easier, too. 🙂

<GetStartedCta />

<Box>
<a target="_blank" href="https://www.clock.co.uk/work">
    <Button variant={["large", "gradient"]} width="100%">Work with Clock</Button>
  </a>
</Box>

 ![northflank and clock case study metrics](https://assets.northflank.com/northflank_clock_in_stats_casestudy_c540435701.png) 

#### **Key Stats**

* **30,000+ deployments** since adopting Northflank 
* **35 projects** managed across Clock’s team.  
* **350+ services** running on the platform.  
* **Scaling to 250+ containers** during a creator economy launch, handling **20,000+ RPS**.  
* **12 months of 100% uptime** on most workloads.  
* **Weeks reduced to hours** for provisioning new environments


#### **Core Features Used**

 ![Case study Clock Northflank features used](https://assets.northflank.com/Case_Study_Clock_Northflank_2x_d31652d493.png) 

* **Services & addons**: Simplifies deployments across hundreds of services and databases.  
* **Pipelines & release flows**: Enabled smooth CI/CD workflows and infrastructure automation.  
* **Templates**: Made creating and duplicating environments fast and reliable.  
* **Autoscaling**: Scaled services to handle 100s of containers at peak load at over 20,000 RPS.  
* **GitOps**: Integrated infrastructure changes with application code for seamless deployments.  
* **Backups & observability**: Allowed self-service backups and visibility into logs, CPU, and network metrics.  
* **Slack notifications**: Centralized alerts for scaling, failures, backups, failed builds and traffic spikes.

#### **Enterprise features**

* **Multi-Read/Write volumes**: Managed complex data replication needs.  
* **BYOC (Bring Your Own Cloud)**: Supported deployments across AWS, Google Cloud, and Azure with ease.  
* **Docker SHM Size**: Provided enhanced Docker container configurations for high-performance needs.]]>
  </content:encoded>
</item><item>
  <title>The what and why of ephemeral preview environments on Kubernetes</title>
  <link>https://northflank.com/blog/the-what-and-why-of-ephemeral-preview-environments-on-kubernetes-sandbox-testing</link>
  <pubDate>2025-01-14T05:00:00.000Z</pubDate>
  <description>
    <![CDATA[This post is an overview of preview environments. It covers why they’re crucial for modern development teams, what makes them challenging to implement (particularly for backend and full-stack apps), and how Northflank simplifies the entire process.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/preview_ephemeral_and_sandbox_environments_63a4d24009.png" alt="The what and why of ephemeral preview environments on Kubernetes" />This post is an overview of preview environments. It covers why they’re crucial for modern development teams, what makes them challenging to implement (particularly for backend and full-stack apps), and how Northflank simplifies the entire process. This guide focuses on preview environments for apps running in Kubernetes clusters and tools within the cloud native ecosystem. That said, most of this should still be broadly applicable to any other type of deployment target.

Note that preview environments are also sometimes colloquially known as ephemeral or sandbox environments. All of these terms are interchangeable.

## What is a preview environment?

Preview environments are temporary, full-stack environments spun up to test and validate new features before they merge into the main codebase.

They give developers, QA teams, product managers, and other stakeholders a fully functional environment. This is often an entire microservices-based stack plus supporting databases. Stakeholders can then inspect how an upcoming feature behaves in real-world scenarios.

These environments are automatically created, usually triggered by a Git pull request or merge request. Once the code is validated or a feature is merged, the environment is typically torn down, freeing up resources and efficiently managing costs.

### How are preview envs different from other envs?

Preview envs are like staging since both environments run on non-local environments. They differ from staging and other pre-prod environments, because the code you would run on a preview environment is usually unmerged and on its own branch.

Preview environments (PEs) might also seem similar to how you share code running on your local machine. Preview envs differ from your local development environment since sharing your local just points to what you’re working on and only works when your computer is on. In contrast, multiple preview environments can co-exist for multiple branches, and they’re accessible even when devs are offline.

One of the major challenges with setting up any pre-production environment is matching production. Preview environments may require more effort than prod. This is because deployments to prod are usually sent to existing, already made environments. In contrast, deploying to a PE means you have to create the cluster and associated state from scratch for each new environment. 

 ![](https://assets.northflank.com/Preview_env_diagrams_7_64caafc8a7.png) 
> A diagram comparing ephemeral preview environments and staging environments. Both environments are important for testing, but slight differences in their implementation shifts where they find their best use.

## Benefits of preview environments

Challenges aside, the benefits of ephemeral preview envs are massive. Preview environments are the perfect way to share work-in-progress with business stakeholders. 

Being private and isolated to a specific change is critical. The trend in DevOps is for work to shift left. In other words, business stakeholders are involved earlier in the process. Preview environments have become a standard practice, because they are an ideal way to get stakeholders involved earlier in the development process.

- **Faster feedback loops**  
When developers can spin up a complete environment on every feature branch or pull request, it’s easier to identify bugs, performance issues, and integration gaps early in the development process.  When a PR is opened as sometimes you push to a branch and it’s in no state to deploy - when a PR is made it’s more in a ready state.
- **Consistent testing**  
“It works on my machine” is no longer a problem. With preview environments replicating production settings (frameworks, databases, services, and more), teams can reliably test new features without environment discrepancies.
- **Improved collaboration**  
Stakeholders from QA, product, marketing, and sales can visit a live URL to see a feature in action. QA in particular can be idle while waiting for features to be ready for testing. Getting access to PEs earlier empowers other roles to offer feedback early, leading to more refined products.
- **Reduced bottlenecks**  
Large teams no longer have to queue for a shared dev or staging environment. Parallel preview environments let multiple feature branches proceed without waiting for a “free” test environment.

Ultimately, the biggest advantage is fast, continuous feedback. Agile methodologies and Extreme Programming emphasize rapid feedback loops for good reason—it improves both code quality and developer productivity.

The faster feedback loops that preview environments fit into is normally a deadzone in most developer’s feedback cycles. When code is local, it can be shared with Ngrok or Tilt in conjunction with tools like Docker Compose or Minikube. Once code is committed, there’s no real way to see what it’s like before you merge and promote to staging. 

Of course, you could ask developers to keep a local version running. But, what happens when a stakeholder is asked to review a change and other work in local development introduces a breaking change? What if the developer has already clocked out for the day? Only a solution like preview environments keeps feedback flowing while code lives in limbo after commit but before [software integration testing]( https://www.opkey.com/blog/integration-testing-a-comprehensive-guide-with-best-practices) on staging.

## Key features of a preview environment

- **Made per branch or PR**  
Every branch or pull request (PR) can have its own environment. This allows developers and stakeholders to preview changes without impacting other deployments.
- **Isolated**  
Changes in a preview environment are self-contained. In addition to keeping shared envs like staging and QA clean, isolation also means PEs are often on a private branch that’s isolated from other developer’s work-in-progress.
- **Production-like testing**  
Preview environments are typically configured to mimic the production environment as closely as possible. This means there are similar dependencies, configuration, and underlying infrastructure. That matching is crucial for accurate testing.
- **Ephemeral**  
These environments are temporary and automatically created and destroyed as needed (for example, upon PR creation and merge). The ephemeral approach means unused environments will scale down so that you can avoid unnecessary costs.
- **Automated**  
Like almost everything in the cloud, preview environments have to be automated. If you’re manually managing PEs, you're likely losing track of them or wasting time waiting on manual provisioning.

 ![](https://assets.northflank.com/create_preview_template_form_7c0af75c7d.png) 
> _An example of creating a new preview environment template, using Northflank's visual editor. Everything seen on this screen may also be done by using the CLI or API calls._

## Typical workflow and challenges

When code is local, developers can share work-in-progress with dedicated tools. However, once changes are pushed to a repository, there’s a “dead zone” before code merges into staging. PEs fill this gap. This list is a simplified workflow.
1. Developer commits code and opens a PR.
1. CI/CD detects the commit, checks for keywords in the commit message, and automatically spins up a preview environment.
1. The new code (along with test data) is deployed.
1. The feature is reviewed and tested by stakeholders.
1. When the branch is merged or closed, the preview environment is scaled down or destroyed.

This workflow keeps feedback flowing during a critical phase—after code is committed but before it merges with the main branch.

Preview environments usually exist in large numbers because there might be one per PR or even one per feature branch. Unsurprisingly, that makes scalability and automation absolutely crucial. Implementing preview environments manually results in human error, delays, and higher cloud bills.

 ![](https://assets.northflank.com/Preview_env_diagrams_6_d3d6c9d725.png) 
> A comparison of ephemeral preview environments and staging. This diagram shows how ephemeral previews shift the usual development cycle left. QA, PMs, and security are all able to get involved much earlier in the process.

There are a number of other challenges worth highlighting, as well.

### Complexity of full-stack workloads

Preview environments are less complex for simple static websites (like using Next.js on Vercel or Jamstack on Netlify). However, full-stack or backend workloads involve containerized microservices, databases, job schedulers, and more -- meaning you need to implement Kubernetes, increasing the DevOps complexity and overhead. At Northflank, we’ve seen some organisations run up to 30 microservices in a single PE. Setting up ephemeral Kubernetes environments for each pull request balloons your environment complexity without a proper platform.

### Infrastructure and cost

Spinning up dozens (or hundreds) of environments in a large team could skyrocket infrastructure bills. If not managed well, ephemeral environments can eat up compute, storage, and other resources. [Intelligent cost controls](https://productive.io/blog/workforce-planning-software/) are essential and may include strategies like leveraging spot/preemptible instances and time-based auto-shutdown.

### Data handling and migrations

Preview environments, like most test envs, need realistic datasets. Manually cloning databases, managing migrations, and ensuring data security can be cumbersome. A single mistake can break a feature branch or corrupt shared data, delaying progress.

### Tooling fragmentation

Many teams have multiple repositories, microservices written in different languages, and a variety of pipelines like Argo and Tekton. Stitching these together into a cohesive, automated workflow often turns into “YAML whack-a-mole,” where developers spend more time on DevOps plumbing than coding.

### Tracking cloud costs

Tracking infrastructure sprawl is trivial with staging. The staging cluster is tagged, and there is just a single version of each service to run. It’s no big deal to track that manually. Tracking preview environments, on the other hand, is a lot more involved. Each version for every commit gets its own deployment and potentially its own cluster.  

A manual process is chaos. Human error makes tagging and tracking resources inconsistent. Since there’s no automated way to scale down, dangling resources are a threat. 

### Access control

At some level, security is just who has access to what. With ephemeral preview environments, you have customer data and topology info about your production environment. As a result, ephemeral preview environments need security that is at least on-par with staging. Ideally, you’re providing access through SSO. 

### Secrets and security

Keep it secret, keep it safe. The secrets you use in your environment need to be securely stored and you need a system for organising and injecting different secret groups based on the environment. Connection details for staging are different from production. Likewise, connection details for a database you spin up in a preview environment are unique to that environment. You need a way to create and securely share that connection info with the right environment.

### Scaling down

It’s rare to scale down a staging environment. It’s exceptionally common to scale down preview environments. For effective ephemeral environments, you must have a solution for identifying which images are in active use, and which can be snapshotted and spun down.

### Commit message trigger words

You might not always want to create an ephemeral preview for a commit. That’s why it’s essential to hook your PE tools in with Git. That way, commit messages can be parsed for keywords and trigger CI/CD actions.

### Promoting workloads

It’s great having access to all of these different environments, but how do you promote workloads between envs? There are a glut of options for CI/CD and many have their own approach. Preview environments are dynamically created, and thus quite different from something like staging. Ideally, this process should be highly automated so that developers are able to self-serve as idealized by the DevOps philosophy.

 ![](https://assets.northflank.com/release_flow_migration_e9d1afd805_7ffaa45947.png) 
> *An example of a release flow on Northflank. This is Northflank's visual editor for making re-usable release flow templates.*

## DIY versus buy

There are a number of tools for creating ephemeral preview environments on the cloud native landscape[^2]. A DIY approach might start with an automated provisioning tool like Karpenter or Istio. Argo CD and other projects can be tied together to create a pipeline. 

Building it yourself with open source is free as in beer, but it’s also free as in puppies. In other words, you have to continue to take care of it. You might find you also need to fill in the gaps if you’re unable to find solutions for tracking costs, detecting idle environments, and scaling up and down.

This shortened checklist of requirements summarizes and hopefully offers a bit of guidance as you consider what features should be on your preview environments (even if they’re DIY).

- **Provisioning and release flows**: Select containerization, orchestration (Kubernetes/PaaS), and CI/CD options.
- **Configuration**: Define Infrastructure-as-Code, environment variables, and secrets management.
- **Secure access**: Enforce SSO, securely generate preview URLs, and determine how you want to surface those links to stakeholders.
- **Data cloning and migrations**: Programmatically create and seed your environment with test data.
- **Build caching for warm starts**: Minimize waiting time and maximize cloud savings by snapshotting, and restoring PEs that have already spun down due to inactivity.
- **Tear down**: Systematize how you want to monitor, track, and destroy unused resources.

I'm biased (this is the Northflank blog after all), but Northflank's features are a good example of what to look for when considering how to approach ephemeral preview environments.

Northflank resolves the complexity mentioned above with an out-of-the-box platform for preview environments. No matter the workload complexity, if you can dream (build) it, we can support it. Northflank offers granular billing to control costs, automates spinning up and down, and simplifies promoting workloads. The result? Well, you should already know the benefits if you're still reading.

- **User-friendly interfaces and IaC** means devs are equipped to manage their own [pipelines and release flows](https://northflank.com/docs/v1/application/release/create-a-pipeline-and-release-flow#create-a-release-flow) for preview environments. No need for scattered YAML files and partial solutions. Drag and drop components, sync changes to git, and create workflows for promoting workloads between environments. Configure and reference your version control metadata in one place.
- **Automatically create PEs on [pull requests or merge requests](https://northflank.com/docs/v1/application/release/create-a-pipeline-and-release-flow#automatically-run-a-release-flow)**. Native GitHub, GitLab, and Bitbucket integrations trigger ephemeral environments automatically upon pull requests or feature branches. 
- **Security and secrets management** encrypts data at rest and inflight,  keeping sensitive PII[^3] safe. Northflank’s [secret groups](https://northflank.com/docs/v1/application/secure/manage-secret-groups) inject environment variables at runtime without exposing them in your repositories.
- **[Track costs and locate dangling resources](https://northflank.com/docs/v1/application/billing/monitor-spending#view-project-billing)** with the per account, per project, and per resource billing data. [Scale down environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment#set-preview-environment-duration-and-creation-times) by setting how long you want to wait before tearing down, and set working hours so PEs automatically shut down on nights and weekends. Deploy on spot or preemptible servers to cut infrastructure costs by up to 90%. When PRs are closed or merged the related infra is deleted. Previews can be paused manually or automatically by time based logic. For example, set your preview envs to scale down during the night when developers are out of the office. 
- **[Spin up databases](https://northflank.com/docs/v1/application/databases-and-persistence/fork-an-addon)**, so that they are ready for preview using cloned data from staging in seconds. Reset environments if a migration goes wrong and eliminate complex rollback procedures. Set up a job to regularly obfuscate[^1] your prod data, and clone that output as seed data for any PE.
- **Build caching and container start times** are improved by Northflank’s caching mechanisms, ensuring previews are ready in minutes rather than hours. The average PE creation time is under a minute.
- **Observability and logging** on every PE. Gain full visibility with real-time logs, metrics, and monitoring dashboards. Quickly troubleshoot issues and provide feedback to developers.

If you’re interested in reading a full guide on how to set up preview environments with Northflank, check the [preview environment page](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment) on our docs. 

 ![](https://assets.northflank.com/pipeline_overview_09f11fb95d_83c2cd1785.png) 
> _An example of using Northflank's pipelines and templates to re-use code for preview environments alongside dev, staging, and prod setups._

## In conclusion

The typical challenges revolve around cost control, configuration complexity, and external integrations. With the right approach, the benefit of preview environments is they vastly improve the speed and quality of your software development lifecycle. 
Many of those benefits can be broken down by role.

- **Quality assurance (QA)**: 
Test new features earlier in the process and more thoroughly, ensuring changes don’t break existing workflows before reaching staging or production.
- **Product management (PM)**:
Get a live preview of how new features integrate with the rest of the product in real-time, enabling faster iteration and more informed roadmapping.
- **Engineering**:
Parallelize development with multiple ephemeral environments. Reduce dependency on busy dev/staging environments and accelerate your developer feedback loop.
- **Marketing, sales, and leadership**:
Demonstrate new features to clients or internal stakeholders before they go live. Start refining marketing materials and sales pitches earlier in the process.

If you’re working on projects without ephemeral previews, it’s time to give them a shot. They can be set up in minutes to hours with the right provider (like, Northflank) and they’re sure to have a big impact.

_Foot notes are listed below this point._

[^1]: Obfuscation is sometimes also called pseudonymization, masking, or [munging](https://en.wikipedia.org/wiki/Mung_(computer_term)). These all mean the process of filtering and removing sensitive data from a data set. A few examples of data that needs to be munged are fields for names, addresses, and financials.

[^2]: I am unaware of any single project on the [Cloud Native Landscape](https://landscape.cncf.io/) that completely handles preview environments. A composition of multiple tools is usually what’s needed to build your own PE.

[^3]: PII is personally identifying information. It’s the type of data that regulations like [GDPR](https://en.wikipedia.org/wiki/General_Data_Protection_Regulation) and [CCPA](https://www.oag.ca.gov/privacy/ccpa) are meant to protect.
]]>
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  <title>Dev, QA, preview, test, staging, and production environments. What's the deal?</title>
  <link>https://northflank.com/blog/what-are-dev-qa-preview-test-staging-and-production-environments</link>
  <pubDate>2025-01-04T09:30:00.000Z</pubDate>
  <description>
    <![CDATA[Understand dev, QA, preview, test, staging, and production environments in the software development lifecycle. Learn their roles for successful deployments.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_environments_0447528004.png" alt="Dev, QA, preview, test, staging, and production environments. What's the deal?" />Imagine you’re a developer. You’ve just nailed down a new feature, and you’re pumped to see it in action. But wait—where does it go first? Do you test it on your local setup? Push it straight to production (yikes)? Or maybe you’ve heard whispers of QA, staging, and other mystical environments.

![preview, development, staging and production environment overview](https://assets.northflank.com/pipeline_overview_09f11fb95d.png)

If you’ve ever wondered what all these development environments are for and how they fit together, we’ve got you. 🙂

Development environments—like QA, dev, test, staging, preview, and production—are essential stages in the software development lifecycle (SDLC). They ensure your software works perfectly at every phase, from initial builds to final deployment.

In this guide, we’ll break it all down and show how Northflank can take the stress out of managing these environments. Further reading can be found in the Northflank documentation: [Release for production](https://northflank.com/docs/v1/application/production-workloads/release-for-production), [getting production ready](https://northflank.com/docs/v1/application/production-workloads/get-production-ready-on-northflank), [create a pipeline and release flow](https://northflank.com/docs/v1/application/release/create-a-pipeline-and-release-flow), [and creating a preview environment](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment).

## What is a development environment?

The dev environment is where the magic begins. It’s your sandbox in the software development lifecycle, where developers write, debug, and refine their code. This software environment allows teams, including [nearshore IT](https://www.tecla.io/blog/nearshore-it) teams, to experiment safely without impacting live systems.

### Key features of a dev environment:

- Fast feedback loop: Allows for quick code changes and testing.  
- Minimal dependencies: Typically uses mocked data or stubbed APIs for simplicity.  
- Experimental: A safe space to try new things without impacting anyone else.

### Use cases for a dev environment:

- Writing new features.  
- Debugging code.  
- Experimenting with creative solutions.

### Common dev environment issues:

- Limited visibility: It’s hard to know how changes will work with the full system.  
- Consistency issues: If local setups differ among team members, bugs might slip through.

### How Northflank helps:

Northflank provides lightweight containerized dev environments, so every developer has an identical setup, eliminating the classic “it works on my machine” issue.

## What is a preview environment? (the short-lived testing ground)

A **preview environment** is a temporary, fully functional replica of your application—often created automatically for every pull request or feature branch. It’s designed to streamline collaboration and code review, enabling teams to see changes in action before they are merged into main or staging.

### Key features of a preview environment:

- Ephemeral: Automatically spun up and torn down based on pull requests or short-term testing needs.  
- Close to production: Mimics the production setup as closely as possible—down to configuration and environment variables.  
- Shareable: Generates a unique URL so team members (and even stakeholders) can easily review and test the new feature in real time.

### Use cases of a preview environment:

- UI/UX validation: Quickly share new UI or layout changes without impacting the dev or staging environment.  
- Team & stakeholder feedback: Gather direct feedback from QA, product managers, or designers on in-progress changes by sharing a [QR code](https://www.qrcode-tiger.com/free-form-generator) that links to a live preview or feedback form.
- Quick regression checks: Confirm that new code doesn’t accidentally break existing features in an environment that behaves like production.

### Common preview environment issues:

- Ephemeral environment sprawl: Managing multiple short-lived environments can become complex if not automated properly.  
- Resource constraints: Large or resource-intensive apps might strain infrastructure if many preview environments run concurrently.  
- Environment drift: Ensuring each preview environment accurately reflects the latest configuration and dependencies can be challenging.

### How Northflank helps:

Northflank’s integrated preview environments automatically create isolated, full-stack deployments for each new branch or pull request. This eliminates manual environment configuration and speeds up reviews:

- Automated provisioning: Hooks into your Git provider to spin up environments based on PR activity.  
- One-click tear-down: Safely remove preview environments once the feature is merged or closed.  
- Consistent configurations: Uses the same container images and environment variables to prevent “configuration drift.”

## What is a QA environment? (a.k.a. the quality assurance hub)

![tonight we test in production meme](https://assets.northflank.com/tonight_we_test_in_production_99e23aab94.jpg)

The **QA environment** is your testing battleground, where code is validated to ensure it meets functional, security, and usability standards. This plays a critical role in the testing workflow of your development lifecycle. Unlike the dev environment, QA focuses on structured testing in a controlled environment.

### Key features of a QA environment:
- Structured testing: Automated and manual test suites ensure features behave as expected. Teams often rely on [QA automation tools](https://testrigor.com/blog/test-automation-tools/) to streamline repetitive test cases, enhance accuracy, and accelerate regression testing across builds.  
- Stable data: Often uses snapshots or controlled test data to replicate real-world conditions.  
- Isolated: Dedicated to testing, preventing interference from other environments.

### Use cases of a QA environment:

- Verifying bug fixes.  
- Running automated regression tests.  
- Ensuring new features don’t break existing functionality.

### Common QA environment issues:

- Synchronization: Keeping QA aligned with staging and production environments can be tricky.  
- Bottlenecks: Automated tests can slow down when dealing with complex or large systems.

#### Adding manual testing/approval:

**Manual testing** is the process where testers manually execute test cases without using automation tools to validate the application’s functionality. It’s especially crucial for ensuring usability, exploratory testing, and verifying features that require human judgment.

The **approval stage** follows manual testing and involves key stakeholders—including PMs, QA engineers, and developers—collaborating to review the results and approve the release for the next phase. This stage ensures every detail is **scrutinized** before the code progresses further.

When planning project budgets, understanding ranges of [software developer salary](https://alcor-bpo.com/developer-salary-colombia/) can help allocate resources effectively during the approval stage.

### How Northflank helps:

Northflank’s environment management simplifies this process by creating consistent, isolated setups that streamline collaboration. Teams can efficiently identify issues, ensure all checks are completed, and confidently sign off on releases.

## What is a test environment?

The **test environment** focuses on specific validation tasks, often serving as a complement to QA. It’s especially critical for integration testing, where different components are tested together to ensure they work seamlessly.

### Key features of a test environment:

- Integration focused: Tests APIs, databases, and external services working together.  
- Data accuracy: Uses controlled data to mimic production scenarios.  
- Repeatable: Designed for consistent testing conditions.

### Use cases of a test environment:

- Validating API requests and responses.  
- Testing database migrations.  
- Ensuring compatibility with third-party integrations.

### Common test environment issues:

- Setup time: Creating realistic test environments can be time-consuming.  
- Data integrity: Ensuring data doesn’t accidentally leak into production.

### How Northflank helps:

Northflank’s pre-configured test environments with real-world data replicas allow faster integration tests without the hassle of manual setup. Learn how to [set up release workflows](https://northflank.com/docs/v1/application/release/configure-a-release-flow) with Northflank, or how to [setup migrations or job runs](https://northflank.com/docs/v1/application/release/run-migrations).

![release flow with migration, backup and a promotion](https://assets.northflank.com/release_flow_migration_e9d1afd805.png)

## What is a staging environment? (the final dress rehearsal)

The **staging environment** is a near-perfect replica of production, providing the last line of defense before deployment. Here, everything from performance to user experience is tested.

### Key features of a staging environment:

- Production parity: Mirrors production in terms of data, configuration, and load.  
- End-to-end (E2E) testing: Validates real-world workflows and scenarios.  
- User Acceptance Testing (UAT): Stakeholders sign off before deployment.

### Use cases of a staging environment:

- Load testing to ensure performance under peak conditions.  
- Testing deployment pipelines.  
- Running final checks on user experience and design.

### Common staging environment issues:

- Resource heavy: Staging environments can be costly to maintain.  
- Drift: Even small differences from production can introduce issues.

### How Northflank helps:

With Northflank, creating staging environments that mirror production is seamless. You can quickly replicate configurations, run end-to-end tests, and tear down the environment when done.

#### ✨ Our recommendation

<InfoBox className="BodyStyle">

We recommend leveraging a combination of robust, large-scale staging environments that closely replicate production and lightweight, ephemeral dev environments. Large staging environments serve as high-fidelity replicas for end-to-end testing, performance checks, and stakeholder sign-offs. Meanwhile, ephemeral dev environments, created dynamically for specific branches or pull requests, enable engineers to quickly deploy and validate feature benches in isolation.

This eliminates the need to wait for shared staging environments, minimizes contention for resources, and ensures faster feedback loops. By doing this, teams can accelerate the merge, test, and deployment process while avoiding infrastructure bottlenecks, ultimately reducing time-to-release.

</InfoBox>


## What is a production environment? (the real deal)

The **production environment** is where your application lives for real users. Stability, performance, and reliability are non-negotiable. Any downtime or bugs here directly impact users.

### Key features of a prod environment:

- Live data: Interacts with actual user data.  
- Monitored: Tracks performance, errors, and user behavior in real-time.  
- Highly secure: Implements strict access controls and backup systems.

### Use cases of a prod environment:

- Running the live application.  
- Monitoring user activity and feedback.  
- Responding to issues in real time.

### Common prod environment issues:

- Debugging risks: Diagnosing issues without disrupting users is tough.  
- Change management: Every update carries potential risks.

### How Northflank helps:

Northflank simplifies production deployments with features like rolling updates, automatic rollbacks, and real-time monitoring. This ensures smooth operation and a great user experience. Get your production environment ready [with this checklist](https://northflank.com/docs/v1/application/production-workloads/get-production-ready-on-northflank).

#### So there you have it 🙂

A whirlwind tour through dev environments, preview environments, QA environments, test environments, staging environments, and production environments. ALL OF THE ENVIRONMENTS.

**Over 35,000 developers** already trust Northflank to handle their environments.

### Why use Northflank?

Wrestling with mismatched configurations, buggy setups, or endless DIY solutions is the coding equivalent of trying to cut down a tree with a butter knife. Sure, you *could* do it, but why would you when there's a chainsaw sitting right there?

Book a demo [here](https://cal.com/team/northflank/northflank-demo) or try it for yourself [here](https://app.northflank.com/signup).

### How Northflank thinks about environments

![How Northflank thinks about environments](https://assets.northflank.com/Northflank_Environments_Diagram_2x_7f3489d314.svg)

### TL;DR - Table summarizing environment types

| Environment | Purpose | Key features |  Use cases   | Challenges | How Northflank helps  |
| ----- | ----- | ----- | ----- | ----- | ----- |
| **Dev environment**   | A sandbox for developers to write, debug, and refine their code.   | \- Fast feedback loop \- Minimal dependencies \- Experimental | \- Writing new features \- Debugging code \- Experimenting with solutions | \- Limited visibility \- Consistency issues across setups | Provides lightweight, containerized setups to ensure consistency and eliminate "it works on my machine" issues. |
| **QA environment**   | Validates code functionality, security, and usability through structured testing.   | \- Structured testing (manual & automated) \- Stable data \- Isolated | \- Verifying bug fixes \- Regression tests \- Ensuring feature compatibility | \- Synchronization with staging/production \- Automated test bottlenecks | Enables realistic, production-replicated QA environments with automated provisioning and tear-down. |
| **Test environment**   | Focused on integration and validation of components and services working together.   | \- Integration testing \- Controlled data \- Repeatable conditions | \- API validation \- Testing database migrations \- Third-party integrations | \- Time-consuming setups \- Data leakage risks | Offers pre-configured environments with real-world data replicas for faster integration testing. |
| **Preview environment**   | Temporary, isolated replicas for each PR or feature branch.  | \- Ephemeral \-Close to production \- Shareable unique URLs \- Controlled data \- Repeatable conditions | \- UI/UX validation \- Team & stakeholder feedback \- Quick regressions | \- Environment sprawl \- Resource constraints \- Environment drift  | Automates provisioning for each pull request, offering consistent configs and easy tear-down.
| **Staging environment**   | A near-perfect replica of production for end-to-end testing before deployment.   | \- Production parity \- End-to-end testing \- User Acceptance Testing (UAT)  | \- Load testing \- Testing pipelines \- User experience validation | \- High resource costs \- Potential drift from production | Supports seamless creation of production-mirroring staging environments with ephemeral alternatives.   |
| **Production environment**   | The live environment where the application runs for end-users.   | \- Live data \- Real-time monitoring \- High security | \- Running live applications \- Monitoring performance \- Debugging issues | \- Difficult debugging without disruption \- Managing risks of updates | Simplifies deployments with rolling updates, rollbacks, and real-time monitoring. |

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  <title>Case Study: How Catalog Built a Scalable Streaming Music Platform with Northflank</title>
  <link>https://northflank.com/blog/case-study-how-catalog-built-a-scalable-streaming-music-platform-with-northflank-cloud-platform-idp</link>
  <pubDate>2025-01-03T05:00:00.000Z</pubDate>
  <description>
    <![CDATA[The challenge? Building a highly flexible, developer-friendly backend that could support complex workloads. Their services relied on CPU-intensive transcoding, high-bandwidth streaming (HLS), and multi-node read-write (RWX) volumes.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_catalog_works_casestudy_brett_53e8251007.png" alt="Case Study: How Catalog Built a Scalable Streaming Music Platform with Northflank" /><a href="https://catalog.works" target="_blank">Catalog</a> set out to revolutionize how artists connect with fans. They’re creating a self-publishing and audience engagement platform with a next-generation music streaming service and internet radio channels. 

The challenge? Building a highly flexible, developer-friendly backend that could support complex workloads. Their services relied on CPU-intensive transcoding, high-bandwidth streaming (HLS), and multi-node read-write (RWX) volumes. They needed all of this while leveraging existing cloud credits and maximizing developer efficiency.

With Northflank, Catalog found an ideal alternative to traditional PaaS offerings like Heroku, Render, and Railway. They also found Northflank a more robust and approachable solution than managed services like Google Cloud Run or raw Google Kubernetes Engine (GKE). 

We spoke with Brett Henderson, Senior Staff Engineer at Catalog, about his experience with Northflank. 
> “I’ve never had this level of interaction with any other vendor really. I’ve never gotten to work with them this closely. It’s not even comparable with anyone else. I don’t actually think we would have been able to release the product that we did without Northflank in any sort of comparable timeline.”

 ![catalog.works case study](https://assets.northflank.com/Catalog_case_study_a83dc62213.png) 
> An image of Catalog’s site showing how they help artists self-publish and engage directly with listeners.

## Challenges: Why Legacy PaaS Couldn’t Keep Up
By harnessing Northflank’s managed Kubernetes platform, Catalog unlocked a polyglot, self-service infrastructure. Northflank provided significantly simplified complex CI/CD pipelines, ensured high availability for HLS media streaming, leveraged BYOC (Bring Your Own Cloud) to utilize existing cloud credits, and integrated tightly with advanced CDN and logging solutions like Fastly.

Catalog’s specialized streaming workloads quickly revealed the limitations of conventional PaaS platforms.

1. **Complex CI/CD Requirements and Self-Service DevOps**: Catalog’s engineers wanted a platform that automated their DevOps, integrated seamlessly with GitOps workflows, and eliminated the complexity of manually managing Kubernetes. They also sought a solution that offered a simple, self-service UI without sacrificing power.
1. **ReadWriteMany (RWX) Volumes for Shared Storage**:
Few PaaS solutions support RWX volumes, essential for running CPU-intensive transcoding workloads in parallel while sharing common storage. Without RWX, scaling a streaming service becomes inefficient and expensive. RWX allows for horizontal scaling of a stateful workload that traditionally couldn’t be scaled horizontally.
1. **HLS (HTTP Live Streaming) Support**:
HLS is adaptive bitrate streaming for high-quality, near-lossless audio. Most legacy PaaS providers lack first-class support for media streaming protocols like HLS, making it difficult to serve large audiences at low latency.
1. **Advanced Fastly and CDN Integration for Listener Metrics**:
Catalog leverages Fastly as their CDN for distributing audio streams. To accurately track listener counts, session data, and geographical distribution, they needed a solution that could easily pipe CDN logs into their observability stack.
1. **Polyglot and Specialized Stacks**:
Catalog’s backend stack is diverse. It features stacks like TypeScript + Bun and domain-specific languages like LiquidSoap (OCaml-based) for streaming logic. The team needed a platform that didn’t lock them into a single language or framework.
1. **Needed to run on their own cloud account**:
Catalog wanted to run workloads in their own cloud. It was too hard to do with the tools provided by Google. They needed BYOC to make use of cloud credits and to configure infrastructure to the unique requirements of a media streaming app. BYOC was required for performance, so their data stayed close to their compute for transcoding and serving music streams efficiently. A single cloud interface like BYOC was also necessary so developers could move fast and unlock self-service while keeping the guard rails on for safety. Finally, BYOC and Northflank as the abstraction, Catalog could avoid the tax of training all of their developers to implement and run Kubernetes at scale. 

## Discovering Northflank
After evaluating various Kubernetes-based developer platforms and PaaS alternatives, Catalog selected Northflank. They found a managed Kubernetes platform that combines the flexibility of raw Kubernetes with an intuitive developer experience. For Brett, he found this was exactly what his team needed.

> “Anyone who’s messed with Kubernetes before knows it’s not easy to work with. With Northflank, I didn’t have to dedicate time to managing the cluster while building our product. The managed Kubernetes experience made everything real easy.”

### Key Northflank Features for Catalog’s Use Case
1. **Managed Kubernetes + BYOC**:
Catalog needed a number of things from BYOC. Things like better DX / UX for their devs to self-serve, more locality and control over their data, and improved automation of day 2 operations. They also needed to leverage their existing ~$100k Google Cloud Startup Program credits. Northflank’s Bring Your Own Cloud (BYOC) approach allowed them to use GKE while layering an easy-to-use interface, CI/CD, cluster management, and workflow automation on top.
1. **Support for Specialized Workloads (HLS & RWX)**:
Unlike traditional PaaS providers, Northflank supports complex workloads like HLS streaming and ReadWriteMany volumes. By combining the simplicity of a next-gen PaaS with the power of Kubernetes, Northflank eliminated the “graduation problem” that often arises when startups outgrow their initial platforms.
1. **Developer Experience and Self-Service Infrastructure**:
Catalog needed integrated build pipelines and GitOps workflows. That’s exactly what they got with Northflank. Without needing to touch raw YAML Catalog’s team could connect their GitHub repo, define workloads, and start deploying quickly. This eliminated the need for multiple standalone tools and reduced the complexity of their release process.
1. **Polyglot Workloads and Unique Stacks**:
Catalog’s stack spanned everything from TypeScript + Bun for their web framework to LiquidSoap (an OCaml-based DSL) for streaming logic. Northflank’s flexible build system and language-agnostic approach made it straightforward to spin up workloads regardless of the runtime or framework.
1. **Advanced CDN & Fastly Log Integration**:
Streaming made Catalog’s CDN setup a challenge. They needed a log sink to seamlessly route CDN logs from Fastly. Northflank’s log sink integration granted them access to real-time analytics on listener counts, geographic data, and engagement metrics. This not only simplified understanding their listener metrics, but also allowed them to build real-time dashboards showing how many users were tuning in and where they were located geographically.

## Results: Developer Productivity and Operational Ease
By adopting Northflank as their Kubernetes-based developer platform, Catalog saved significant time and reduced operational complexity. According to Brett Henderson, Senior Staff Engineer at Catalog, Northflank saved an estimated six months of engineering time, or a little over 1000+ hours. Instead of wrestling with fragmented tooling or spinning up custom Kubernetes operators, the team could focus on innovation and rapid feature delivery.
> “The developer experience is great. Working with Northflank is easy, and their proactive support is unmatched. It’s so developer-friendly and technical, without being overly complex like a typical cloud dashboard.”

With Northflank Catalog has the freedom to iterate on new streaming features, handle scaling demands, and maintain top-tier performance for their HLS streams. The platform unlocked integrations with Fastly, support for advanced caching and logging strategies, and the ability to run on any cloud.

Northflank means Catalog can continue growing their streaming service without hitting operational roadblocks.

## Looking Ahead
With their streaming platform in production and performing at scale, Catalog plans to continue iterating and improving their platform on Northflank. They are confident that as they continue to iterate Northflank will remain a stable, flexible platform that helps them innovate quickly.

## Have a Challenging Workload?
If you’re looking to streamline your complex Kubernetes deployments, enhance CI/CD, or leverage your own cloud credits then Northflank is here to help.

[Contact Us](https://northflank.com/contact) to learn how Northflank can help you conquer complexity, streamline deployments, and deliver exceptional developer experiences.]]>
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</item><item>
  <title>Northflank’s 2024 PR: merged, approved, and deployed</title>
  <link>https://northflank.com/blog/northflank-2024-pr-merged-approved-and-deployed</link>
  <pubDate>2024-12-22T19:30:00.000Z</pubDate>
  <description>
    <![CDATA[Northflank had an action-packed year, enhancing Kubernetes for 35k+ developers, shipping 2,000+ updates, expanding globally with BYOC, tripling revenue, and supporting 1M deployments monthly.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_2024_review_ec0bf9dbd2.png" alt="Northflank’s 2024 PR: merged, approved, and deployed" />2024 was a [pivotal](https://en.wikipedia.org/wiki/Pivotal_Software) year for Northflank. We announced [$22 million in funding](https://northflank.com/blog/northflank-raises-22m-to-make-kubernetes-work-for-your-developers-ship-workloads-not-infrastructure) and took huge steps toward our vision: **making the developer experience seamless, no matter how complex their workloads get**. By simplifying Kubernetes and cloud infrastructure, we’ve earned the trust of more than 35,000 developers—and some of the world’s largest development organizations.

We’re a fully remote team spread across six time zones, and people love telling us you can’t achieve this level of success or tackle something this complex without being in a traditional office. But here we are, growing faster than ever (and proving them wrong). It’s not like the Linux kernel or Kubernetes were cranked out in some cubicle farm either.


This past year, we shipped over [2,000 product enhancements](https://northflank.com/changelog) (yes, we still sleep occasionally, unless our enterprise customers’ PagerDuties keep us awake). A big priority for us has been ensuring the Northflank platform runs exactly where you need it—whether that’s on hyperscalers, niche providers, or hardware you’ve got in-house.

 ![preview trigger and naming](https://assets.northflank.com/create_preview_template_form_cad05bbfa9.png) 


This emphasis on “Bring Your Own Cloud” [(BYOC)](https://northflank.com/use-cases/bring-your-own-cloud-app-platform-kubernetes) and enterprise-grade capabilities is core to our platform. Over the past year, we’ve laid the groundwork for Bring Your Own Kubernetes (BYOK) and our Self-Deployable Control Plane (SDCP), while expanding our global presence with additional PaaS regions.

For Fred and me, this journey has been over six years in the making. It’s been challenging at times and humbling always. Seeing the platform evolve and knowing how much more there is to create reminds us why we started this company in the first place. It still feels like we’re just getting started, and luckily, we’ve still got the same excitement as we did on day one.

This year, we hit a major milestone with our $22M founding announcement—a huge validation of our focus on product innovation, customer growth, and revenue growth.

Today, we’re proud to support over 1 million container deployments each month (and tripling our revenue, again 😇), thanks to new customers like Sentry, Weights, Writer, and hundreds more we’re excited to keep building on this momentum.

If that wasn’t enough humble bragging for one paragraph, we’re [hiring](https://northflank.com/careers) across both Engineering and GTM roles. Below is a snapshot of some of the product announcements we’re particularly proud of (in case you care as much as we do about building great products).

## 1. Elevated developer experience: Enhanced preview environments, templates & release flows

 ![preview templates](https://assets.northflank.com/build_on_trigger_node_063886af52.png) 


We’ve been hard at work unifying [preview environments](https://northflank.com/docs/v1/application/release/set-up-a-preview-environment), templates, and release flows to simplify the path from commit to preview to production release—even for the most complex projects (actually, especially for those). By streamlining pipelines and release alongside our core resource primitives, we’ve made every step more efficient without sacrificing flexibility. Nested templates now provide rich visual feedback, so you can see exactly how changes progress throughout your stack.

Enterprise customers benefit from audit logs and [draft templates](https://northflank.com/docs/v1/application/infrastructure-as-code/manage-template-versions) that mimic Pull Requests with approval workflows. We’ve also rolled out advanced networking capabilities, including cross-project [private networking](https://northflank.com/docs/v1/application/network/networking-on-northflank#private-networking), VPN Tailscale support, [path-based routing](https://northflank.com/docs/v1/application/network/create-path-based-security-policies), and [global CDN with Fastly](https://northflank.com/docs/v1/application/domains/use-a-cdn) per-port enablement.


![audit logs](https://assets.northflank.com/Screenshot_2024_12_22_at_19_37_03_7e4e22c3af.png) 


That’s a lot of information for developers, developers, developers.  
We’re doing this with a very precise purpose: Workload delivery, workload delivery, workload delivery.

 ![developers developers developer](https://assets.northflank.com/developers_a23c035542.gif) 

## 2. Broad regional coverage & enhanced Cloud flexibility

We expanded Northflank’s global reach, adding [PaaS regions](https://northflank.com/docs/v1/application/run/deploy-to-a-region) in the U.S. (East, West), Europe (Amsterdam), and Asia (Singapore). Customers can now run workloads closer to their users, improving performance, reliability, and compliance.

We also leveled up our Bring Your Own Cloud [(BYOC) capabilities](https://northflank.com/docs/v1/application/cloud-providers/use-other-cloud-providers-with-northflank). welcoming Oracle Cloud Infrastructure (OCI) and Civo to the mix alongside AWS, GCP, and Azure. With even more providers to choose from, teams now have the freedom to optimize for what matters most to them—whether that’s cost, performance, or regional requirements.

## 3. Radical enhancements to managed Kubernetes (AKS, EKS, GKE)

Northflank now offers advanced private cluster and node networking options,  and streamlined onboarding. We’ve also nailed secure [integration with existing cloud IAM setups](https://northflank.com/docs/v1/application/cloud-providers/aws-on-northflank#add-your-account-with-a-cross-account-role) (even if you’re juggling multiple accounts). Plus, teams can deploy self-hosted registries and tap into managed Kubernetes services—minus the usual headaches.

## 4. GPU support for AI & ML workloads

One of our biggest wins was [adding GPU support,](https://northflank.com/docs/v1/application/cloud-providers/run-gpu-workloads-in-your-cluster) unlocking new opportunities for AI and ML workloads on Northflank.

From inference to model training, users can now run resource-intensive applications more efficiently.  
Integrated tools like templates, deployments, logs, metrics, and CI/CD keep things simple, so you can focus on building great products.

We’re already seeing customers scale to hundreds of GPUs, which just goes to show the platform can handle a lot (and then some).

## 5. Bring Your Own Addon (BYOA)

BYOA lets teams [deploy custom addons](https://northflank.com/docs/v1/application/databases-and-persistence/create-a-custom-addon-type) with Helm charts, enabling support for specialized databases and applications. Some of my favorites I’ve seen deployed are Flyte, Airbyte, and ClickHouse. It’s a simple way to tailor the platform to your needs while keeping operations consistent.

## Northflank in 2025

 ![gordon no pressure 2025](https://assets.northflank.com/gordon_pressure_c92f32022f.webp) 

As we wrap up the year, I feel grateful to everyone who’s been part of 2024—our team, our customers, our investors, and everyone who’s believed in what we’re building.

I can’t wait to show you what’s next. Here’s a sneak peek at what’s on the horizon for 2025:

- New regions and providers to keep you closer to your users
- ARM support and faster CPUs for better performance across the board
- Super-fast build caches and configurable database storage to give you even more control
- Niche workload support, BYOK, and self-hosted control plane
- Deeper integrations with your existing tools and cloud provider resources

So much to build. Let’s get to work.]]>
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</item><item>
  <title>Platform December 2024 Release</title>
  <link>https://northflank.com/changelog/platform-december-2024-release</link>
  <pubDate>2024-12-20T15:00:00.000Z</pubDate>
  <description>
    <![CDATA[Improves addon resets, template reruns, build triggers, and SSO sign-in errors. Enhances UI, filtering, searching, and template ops. Better overall usability for all users.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_december_changelog_cdcd441b63.png" alt="Platform December 2024 Release" />This release improves addon resetting behavior, template reruns, and build triggers, as well as refining SSO sign-in error messages. It also includes UI and usability enhancements throughout the platform, including better error handling, more consistent filtering and searching of projects, and refined template operations.

## Enterprise
- First-time SSO sign-in attempts with inactive accounts now show an informative error instead of creating and deleting the account.
- Signing in via SSO no longer fails due to issues synchronizing irrelevant organizational data.


## Addons
- When resetting an addon that is used as the source for another forked addon, the original source data is now removed from the forked addon. Previously, it was set to a backup of the fork source, potentially causing addon failures if the backup was deleted.
- Deprecated several old versions of MySQL, MongoDB, and RabbitMQ.

## Templates
- Selecting a template run now displays the arguments used by that run.
- Added the option to retrigger a template run with the same arguments.
- For Release flow and Preview environment runs triggered by a Git commit, Git data is re-sent to enable reruns without new commits.
- The template editor no longer prompts about unsaved changes when switching teams.
- The preview environment template Message node no longer appears as an option in non-Preview templates.
- Updating a service via a template no longer resets Continuous Deployment.
- Improved the interface for selecting success conditions in build and job run Condition nodes.
- Fixed an issue where the job run condition could be incorrectly marked as failed.
- Pipeline nodes no longer fail when secret files are passed in.
- Fixed issues with autosave when closing or submitting a node in code view.

## Builds
- Fixed an issue where builds sometimes failed to trigger if multiple pushes happened in quick succession.
- Merge commits now properly respect file path build rules and will not trigger builds that do not match the set criteria.

## BYOC
- Improved error messaging during cluster creation, providing more actionable information.

## Miscellaneous
- Syncing the repository list for self-hosted GitLab integrations now works reliably even with very large numbers of accessible repositories.
- Accessing a job run through the job dashboard no longer risks crashing if other data has not fully loaded.
- Fixed erratic behavior in the date picker occurring in certain months.
- Short project lists now correctly support navigation between pages.
- Improved filtering performance and reliability for project lists, fixing issues with searches getting stuck.
- Added a namespace selector to the public egress bandwidth metric.
- Fixed an issue causing the multiplayer menu not to display correctly when another team member is on the same page.
]]>
  </content:encoded>
</item><item>
  <title>Argo CD alternatives that don’t give you brain damage and simplify DX for GitOps, clusters &amp; deployments</title>
  <link>https://northflank.com/blog/argo-cd-alternatives-northflank-developer-platform-git-ops-self-service</link>
  <pubDate>2024-12-19T20:42:00.000Z</pubDate>
  <description>
    <![CDATA[Frustrated by Argo CD’s complexity? Try simpler Kubernetes/GitOps alternatives. Northflank offers integrated abstractions, less YAML, smoother workflows—focus on features!]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_argo_alts_0b406a2b7b.png" alt="Argo CD alternatives that don’t give you brain damage and simplify DX for GitOps, clusters &amp; deployments" />Feeling swamped by Kubernetes deployments? I'm with you. As Kubernetes continues to blow up and workloads get increasingly complex daily, teams are catching on that manual, on-the-fly deployments are holding them back. Infrastructure sprawl keeps growing ever larger and more intricate. Keeping configurations consistent across multiple environments is a headache developers don't need (or want). Developers demanded a faster, more reliable path from code to production. As Infrastructure as Code (IaC) practices emerged to tackle these challenges, it became clear that manual interaction, ClickOps, or simply having everything defined as code wasn’t enough—there had to be a structured, automated way to release changes and keep systems in sync.

This is where Argo CD soared. By championing a GitOps-driven approach, Argo CD provided a declarative model anchored in Git. Instead of wrestling endlessly with manual updates or scattered scripts, teams could version control their Kubernetes manifests, rely on Git as a single source of truth, and let Argo CD handle the continuous synchronization of desired states. Developers gained a more predictable, scalable workflow for managing releases, and the friction of dealing with raw Kubernetes complexity started to subside.

Despite these initial wins, many organizations began to realize that Argo CD wasn’t delivering the deep simplifications they had hoped for. While it improved workflows on paper, developers continued to grapple with low-level Kubernetes details and intricate YAML files. Day-to-day work often felt like navigating a maze of complexity rather than enjoying meaningful abstractions. The core issue is that GitOps in its current form frequently stops short of true simplification. Instead of enabling a frictionless developer experience, it often introduces another layer of operational overhead—a different flavor of complexity rather than a solution to it.

Beyond just easing the pain of YAML management, emerging platforms like Northflank are redefining the developer experience by turning Kubernetes and GitOps into a cohesive, self-service developer platform. Rather than forcing engineers to think in terms of low-level infrastructure, Northflank encourages them to define their microservices, databases, and scheduled jobs at a workload level—abstracting away the operational grunt work. This streamlined approach results in a consistent, unified platform that operates across a real-time UI, accessible APIs, a command-line interface, GitOps workflows, and ready-made templates supporting any language and any cloud environment.


 ![Northflank template example ui](https://assets.northflank.com/argo_alternative_northflank_templates_gitops_ui_442264ff7c.png) 

By adopting this new paradigm, developers can seamlessly manage preview, staging, and production environments, and gain instant access to integrated observability, autoscaling, disaster recovery, RBAC, and cluster lifecycle management. All of these capabilities remain codified in Git while still offering a bi-directional, human-friendly interface—making it infinitely easier to release software without the complexity that has long burdened Kubernetes adoption.

## Northflank vs. Argo: a new standard in Kubernetes abstraction

In the early days of Kubernetes adoption, **Argo CD** rose to prominence by offering a GitOps-driven approach to managing configurations. It promised to tame the chaos of sprawling YAML files and infrastructure sprawl, giving teams a declarative model anchored in version control. However, as companies matured and scaled their operations, many found that while Argo CD alleviated some pain points, it fell short of providing a complete platform experience. Developers still wrestled with low-level Kubernetes details, and operational overhead persisted—just in different forms.

**Northflank**, by contrast, represents a paradigm shift. It goes beyond optimizing workflows or YAML management and moves towards full-stack abstraction. By transforming Kubernetes and GitOps into a unified, self-service developer platform, Northflank eliminates the friction and complexity that have long plagued the ecosystem. Instead of tweaking YAML or juggling multiple tools, developers define their microservices, databases, and jobs at a workload level, manage environments across staging and production, and enjoy integrated observability, autoscaling, and cluster lifecycle management. Everything is codified in Git, accessible via a bi-directional UI, and consistent across UI, CLI, API, and templated GitOps flows—turning what was once a tangled mess into a smooth, cohesive developer experience.

## Beyond YAML management

- **Argo CD:** Focuses on GitOps workflows and YAML management but still leaves raw Kubernetes details front and center.  
- **Northflank:** Elevates developers above the complexity of Kubernetes, offering streamlined abstractions that keep the machinery hidden. Work with workloads, not YAML.

## Reducing operational overhead

- **Argo CD:** Reduces some pain but requires building and maintaining additional tooling for logs, metrics, backups, and scaling—similar to Terraform-level complexity.  
- **Northflank:** Delivers an all-in-one platform with integrated build systems, CI/CD, logging, monitoring, autoscaling, disaster recovery, and more, letting developers focus on features, not infrastructure.

<br/>

 ![Northflank template example snippets](https://assets.northflank.com/argo_alternative_northflank_templates_gitops_iac_aea433b42b.png) 

## Developer experience first

- **Argo CD:** Improves workflows but remains tied to YAML, Helm, and intricate Kubernetes knowledge.  
- **Northflank:** Prioritizes a frictionless developer experience. Applications become first-class citizens, manageable through a real-time UI, APIs, CLI, and GitOps templates. It’s a platform where developers think in terms of services and code, not clusters and configs.

## Streamlined release management

- **Argo CD:** Encapsulates releases as YAML operations on individual components, making holistic release management cumbersome.  
- **Northflank:** Offers an application-centric approach, enabling you to define services, databases, and jobs that seamlessly progress from preview to staging to production. Releases are simplified, managed at the workload level, and codified in Git for easy traceability.

## Unified platform for scalable, multi-cloud deployment

As organizations embrace multi-cloud architectures, complexity escalates. **Northflank** meets this challenge head-on:

- **Cloud-agnostic scaling:** Run and scale workloads across any major cloud seamlessly.  
- **Integrated tooling:** Enjoy a consolidated platform with everything from CI/CD to resource management baked in.  
- **Enterprise-grade primitives:** Enforce policies, manage resources, and comply with enterprise requirements—all without adding layers of overhead.

## Unlock developer productivity with Northflank

By transcending the limitations of YAML and configuration-centric tools, Northflank empowers teams to:

1. **Ship faster:** Move from code to live services without the operational drag.  
2. **Reduce complexity:** Eliminate the burden of integrating and maintaining multiple tools.  
3. **Enhance security & reliability:** Standardize workflows, enforce RBAC, and leverage built-in observability for safer releases.  
4. **Improve developer happiness:** Free developers to innovate at the application layer instead of wrestling with infrastructure details.

While Argo CD offered a step forward in GitOps adoption, it never fully dismantled Kubernetes complexity. **Northflank** takes the next leap by providing a complete, integrated developer platform that renders infrastructure complexities virtually invisible. The result is a more productive, agile, and enjoyable development environment—one that empowers teams to focus on what truly matters: building great software.

## Other Argo CD alternatives for modern dev teams

While Argo CD set the stage by automating Kubernetes deployments through GitOps, developers today often need more than just YAML management. Below are some other platforms and tools that challenge the status quo, offering diverse approaches to CI/CD and infrastructure management. Think of these as starting points for teams who crave a frictionless, self-service experience—one that empowers you to ship features faster without getting lost in the weeds.

### Flux CD
**What It Is:** A GitOps toolkit that continuously applies your Git-stored configurations into Kubernetes clusters, ensuring everything stays in sync.  
**Where It Shines:**  
- **Developer-friendly GitOps:** Git is the single source of truth for your environment, so you’re working with familiar tools.  
- **Lightweight & extensible:** Flux focuses on doing one thing well—automating deployments—letting you layer in other services as needed.  
- **CNCF-backed:** An active community and broad ecosystem support mean you’ll find lots of tutorials, plugins, and extensions.

### Spinnaker
**What It Is:** A multi-cloud continuous delivery platform originally developed at Netflix for releasing code at scale.  
**Where It Shines:**  
- **Multi-cloud support:** Deploy confidently across AWS, GCP, and more, without rebuilding your pipelines from scratch.  
- **Complex delivery strategies:** Blue/green, canary, or rolling updates—Spinnaker makes them accessible to any team.  
- **Rich integrations:** Plug in monitoring tools, feature flags, and compliance checks for a more holistic approach.

### Harness
**What It Is:** A SaaS-driven CD platform that cuts out repetitive tasks and introduces intelligent automation into your pipelines.  
**Where It Shines:**  
- **Frictionless CI/CD:** Pipelines you can set and forget, with automatic rollbacks, analytics, and approvals built-in.  
- **AIOps-powered:** Predictive analysis and recommendations simplify troubleshooting and boost release quality.  
- **Security & compliance:** Enforce policies and governance without manual overhead.

### Jenkins X
**What It Is:** CI/CD for Kubernetes, reimagined from traditional Jenkins to deliver a more cloud-native experience.  
**Where It Shines:**  
- **Built for Kubernetes:** Embrace GitOps, preview environments, and faster feedback loops without leaving the Kubernetes ecosystem.  
- **Opinionated & dev-centric:** Pre-built best practices and workflows mean less time deciding on tooling and more time shipping.  
- **Flexible but familiar:** Jenkins X uses Jenkins under the hood, so it’s friendlier if you’re migrating from legacy setups.

### Tekton Pipelines
**What It Is:** A Kubernetes-native CI/CD engine that defines everything as declarative resources, letting you model pipelines as code.  
**Where It Shines:**  
- **Kubernetes-native:** Work directly with pipelines that feel at home in your cluster.  
- **Highly extensible:** Add your own tasks and steps to create bespoke, repeatable pipelines that reflect your team’s workflow.  
- **Seamless GitOps:** Tekton plays nice with Git-based workflows and can integrate directly with Flux, Argo, or other GitOps tools.

---

**Finding your perfect match:**  
Just as Northflank redefines how developers interact with Kubernetes and GitOps, these Argo CD alternatives each bring their own philosophy and approach. Think about your team’s pain points: Are you drowning in YAML? Wrestling with multiple cloud providers? Hungry for a simpler workflow that empowers application-level thinking?

- **If you crave a fully integrated, end-to-end platform experience that abstracts away Kubernetes complexity:** Northflank is your jam.  
- **If you want a more vanilla GitOps approach or incremental improvements:** Tools like Flux or Tekton might be the next natural step.  
- **If you need robust multi-cloud support or advanced deployment strategies:** Explore Spinnaker, Harness, or Jenkins X.

The key is to pick a platform or tool that matches your team’s culture, growth trajectory, and complexity tolerance. Your developers will thank you—and your code will ship that much faster.

Interested in trying Argo CD alternatives with Northflank? Get started for free with Northflank's generous developer tier with GitOps and bi-directional UI!

<Box>
  <a target="_blank" href="https://app.northflank.com/signup">
    <Button variant={["large", "gradient"]} width="100%">Unleash your developers today</Button>
  </a>
<br/>
<a target="_blank" href="https://cal.com/team/northflank/platform-overview">
    <Button variant={["large", "gradient"]} width="100%">Book a demo</Button>
  </a>
</Box>]]>
  </content:encoded>
</item><item>
  <title>Platform November 2024 Release</title>
  <link>https://northflank.com/changelog/platform-november-2024-release</link>
  <pubDate>2024-12-02T09:00:00.000Z</pubDate>
  <description>
    <![CDATA[November updates: Enhanced performance, improved observability, nested visual template editor, new addons, BYOC enhancements, and enterprise SSO support.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_november_changelog_min_1a9c287d30.png" alt="Platform November 2024 Release" />This November, we've been hard at work enhancing the performance and reliability of our platform to provide you with a smoother experience. We've made significant improvements to Observability, including a more reliable Observe tab. Template creation is now more intuitive with a fully visual editor for nested templates. We've added support for new addon versions and BYOC options, including GKE 1.31 and new node types. Enterprise users can now benefit from IdP initiated SSO login. Plus, we've implemented performance optimizations across the platform for faster load times and better responsiveness.


## Enterprise

* Added support for IdP initiated SSO login
* Improved the handling of duplicate email addresses when logging in via SSO
* Improved the reliability of adding and removing users from an active directory

## Templates

* Creating nested templates via the UI has been greatly improved. Adding a pipeline to the template will allow you to access a full visual editor for the release flows and previews in that pipeline, instead of having to use the code view only
* Saving a template with a timeout duration no longer appears as a diff when it wasn’t changed
* Fixed an issue with copying project specification when there is a pipeline in the project
* Subdomains created as part of a preview are now correctly associated with that preview
* Job run settings overrides will no longer disappear when editing their environment variables
* Condition nodes now display team level clusters
* The override changes modal no longer renders behind the main editor window
* Job run overrides will no longer automatically be populated in the template if they haven’t been edited
* Added timestamps to more template node responses
* Job run credential overrides are now correctly formatted
* Added the option to automatically upgrade addons on template run if they are using an out of date version
* Added resource links on condition nodes
* When running a release flow manually, you can now directly select a branch and commit to run it based on  
* Templates with GitOps enabled will no longer automatically overwrite themselves when the template is initially loaded, stopping an issue where templates could get stuck on an old version if you changed the GitOps settings in Git  
* Improved the handling of the drag to pan feature
* Holding space bar to pan no longer causes issues with the space key in node forms
* When setting active hours for a preview template, new days will automatically be filled with the hours you have previously set
* Fetching from Git no longer clears any template arguments
* The pipeline list now displays more useful metadata
* Fixed GitOps templates sometimes not running correctly when updated
* You can no longer create a git trigger with a duplicate name or ref
* Improved the handling for the Message node

## Addons

* Added support for new addon versions:  
  * MySQL: `8.4.3`, `8.0.40`  
  * RabbitMQ: `3.13.7`, `4.0.3`  
  * MongoDB: `7.0.15`, `8.0.3`  
* Implemented support for Global backups having a custom retention period
* Increase `max_standby_streaming_delay` and `max_standby_archive_delay` configuration setting to 10 minutes for PostgreSQL addons to avoid rare conflicts during queries on standby replicas
* Improve addon state validation for disk backups  
* Fix issue for PostgreSQL addons with version 16 resulting from default privilege changes
* Fix issue with replica creation when doing a restore of a zonally redundant addon

## BYOC

* Added support for BYOC versions  
  * GKE: 1.31  
* Added support for N4 and C4 nodes on GCP
* Fixed an issue with soft-disable for AWS BYOC
* Creating a project from the cluster information screen now automatically scopes you to that cluster
* Added support for restricting allowed IP ranges for EKS

## Observability

* Improved the reliability of the Observe tab and improved the accuracy of some stats
* Fixed issues with the UI that could occur when switching quickly between views or date ranges
* Added focus for terminated containers to easily review logs during its runtime
* Reduce look back window of logs to 30 days to speed up log load if the workload has been active for a long time
* Pods no longer show as being out of memory after they have become healthy
* Deleted nodes no longer show in the node triage screen
* Eviction notices are now displayed on the build health tab and popover
* Added the option to fetch extra lines for the current log line context
* Fixed the copy indicator on log ranges
* Istio mesh logs are now sent to the correct log sink destination
* Fixed a bug causing build logs to not load if the build was clicked on while in the cloning stage  
* Pods that are in staging now show in triage
* Scrolling on graphs no longer causes the metrics to go back in time


## API

* Added GPU details to the Get service endpoint
* Added additional fields to the schema of the Get service endpoint response
* Added API endpoints for backup destinations
* Added native pagination support in js-client and CLI with `getNextPage` and `all` functions

## Miscellaneous

* We’ve made many performance improvements to all parts of the platform, resulting in faster load times and better responsiveness
* Added compression to networking gateways for egress traffic with `Brotli` and `gzip` protocols
* Custom domains now appear correctly in the UI when a service has zero deployments
* VCL snippets editor no longer displays incorrect syntax errors
* Adds the option to delete cloud provider integrations via the integration menu
* Links between pipeline stages no longer disappear when hovering over a service
* Name fields now don’t falsely state a resource with the name already exists for users with insufficient permissions
* Onboarding now prompts you to select a team to onboard with
* Job runs now operate correctly when no manual overrides are provided
* Fixed an issue with the Edit deployment modal not loading repository branches
* Viewing the list of builds for a commit on a specific branch doesn't incorrectly show builds associated with another branch
* Stopped a crash that could occur when adding a Cloudfront CDN
* Fixed an issue with toggling VPC egress for AWS BYOC
* Audit logs no longer crash when making certain date selections
* Cron job schedules correctly update when no other fields are updated
* Merging a pull request for a service with both branch and PR rules no longer causes the same commit to be built twice
* Added support for custom deployment annotation and labels
* Usability improvements to the Cluster IP Allow List settings
* Usability improvements to the addon backup source selection
* Added manual TLS certificate controls
* Users can now clear list filters
* Jobs now automatically fetch Dockerfiles in creation forms and templates
* Fixed searching and selecting pull requests in some commit selection components
* Job run names will no longer be generated longer than the maximum allowed length
* The service environment page no longer displays as empty when not authenticated
* Improved validation for backup schedules
* Timeslice is now unset for workloads in a GPU node pool when that node pool is deleted
* Repositories with many branches will no longer use too much browser memory
* Searching for domains now works as expected
* Added internal handling for CNB shim which was causing some Buildpack builds to fail due to it being deprecated
* Update handling for CNB shim to support deprecated Heroku Buildpacks
* The service list now displays when the last build for a service has failed
* Docker CMD overrides display correctly on the service dashboard
* Made the team and organisation list filtering more consistent]]>
  </content:encoded>
</item><item>
  <title>Platform Eng Day Keynote: Make Workloads, not Infrastructure</title>
  <link>https://northflank.com/blog/make-workloads-deployments-platform-eng-day-NA-2024-not-infrastructure</link>
  <pubDate>2024-12-02T05:00:00.000Z</pubDate>
  <description>
    <![CDATA[You’ve built a platform, are currently building one, or recently re-platformed.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_kubecon_talk_min_1_90a5ee00ad.png" alt="Platform Eng Day Keynote: Make Workloads, not Infrastructure" />You’ve built a platform, are currently building one, or recently re-platformed. Everyone in the infrastructure and cloud native space is working on an internal developer platform (IDP). 

That’s how Will Stewart opened his keynote at Platform Engineering Day, an event co-located with KubeCon NA 2024\. This six minute talk explores how developers often find themselves bogged down by configuring infrastructure and deploying clusters rather than focusing on their primary goal—delivering valuable workloads.

## Overview
1. Cloud infrastructure is getting more complex.  
2. Why devs need better abstractions than Terraform and YAML.  
3. Examples of good abstractions in popular tools.  
4. Why is self-service developer experience (DX) difficult to achieve?  
5. Microservices and data storage are the essentials for a workload.  
6. Unifying abstractions for logs, Disaster Recovery (DR), autoscaling, preview environments, and releases management.

<iframe width="100%" style={{ aspectRatio: '16 / 9' }} src="https://www.youtube.com/embed/jqNa0nkzKxI?si=fmrmiaCE8No-Bush" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

## About the Talk
Will Stewart is the Co-Founder & CEO of Northflank. He’s been building infrastructure since he was a teenager hosting gameservers on Mesos. This talk is born from his experience building Northflank, an app platform for Kubernetes. 

 ![](https://assets.northflank.com/2024_11_Platform_Eng_Day_NA_Will_Stewart_Abstractions_slide_1d2f76ceb3.jpg) 
> Will showcases some abstractions that organise and reduce complexity well.

Can cloud complexity keep increasing forever? When building with the best cloud-native solutions today, the answer seems to be “yes.” This talk is Will’s perspective on what needs to be done to organise and reduce that complexity for app developers. 

App developers work at the level of Go, .NET, or Next.js. They are experts in those languages and frameworks. Platform teams often get app developers to self-manage infrastructure through solutions like Terraform or YAML. This is almost always the wrong approach, because it is so far from the level developers are accustomed to working at.

Will points out that most of us are starting at infrastructure and building forward towards the app. From that starting point tools like Terraform and YAML are the natural choice. There is another way to work, however. What if we started at the app and worked back towards infrastructure? 

He uses the metaphor that we’re spending all of our time building a factory without knowing what we want to actually produce. The unruly and seemingly ever-growing complexity is too much to expose developers to when all they want to do is deploy their workload. That workload is what creates your business value. Everything else should be oriented around supporting that. We don’t ask line workers to understand the ins and outs of factory logistics, and we don’t need to ask app developers to do that on their platforms, either.

Will closes the talk by noting that organising and reducing complexity means abstracting away the minutia needed to run an actual workload. A unified abstraction needs to encapsulate details like logging, metrics, disaster recovery (DR), autoscaling, preview environments, and release management. 

Encapsulating the complexity like this means developers can work at the level where they’re most effective. They don’t have to be distracted and slowed down by the sprawling demands of cloud knowledge.

## What is Northflank

Northflank is a unified abstraction layer for managing the multitudes of moving parts in your cloud. It is both a framework for building IDPs and an example of a polished developer experience on Kubernetes. You can see for yourself by [deploying with Northflank](https://app.northflank.com/s/account/cloud/clusters) today.]]>
  </content:encoded>
</item><item>
  <title>A Summary of KubeCon for Busy People</title>
  <link>https://northflank.com/blog/a-kube-con-na-2024-slc-summary-for-busy-people</link>
  <pubDate>2024-11-25T17:00:00.000Z</pubDate>
  <description>
    <![CDATA[Looking for a summary of a few of the topics covered during KubeCon NA 2024 in SLC? This post is for you if you need a quick and easy way to catch up on the news. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Kube_Con_NA_24_SLC_Opening_Remarks_eac76ae3b3.jpg" alt="A Summary of KubeCon for Busy People" />Looking for a summary of a few of the topics covered during KubeCon NA 2024 in SLC? Consider this post a quick and easy way to catch up on the news at and surrounding KubeCon. 

KubeCon was split across three days, with a major focus area for each day. Day one went to artificial intelligence and platform engineering. Day two to security. And finally, day three was about the community looking back and looking forward.

AI and the unique attributes of those types of workloads were a major overarching theme. This theme has persisted from past KubeCons and is likely to continue at future events. 

If you’re looking for a single quick takeaway, it’s that **AI/ML workloads are unique, and we need to modify our solutions and approaches to fit those distinct characteristics**. The ideology applied to cloud native and most solutions still works well. The techniques and technologies just need to be adapted to new requirements. For instance, consider the longer-lived requests for an answer from a LLM. Best practices are still being discovered and shared. Other important and buzzy topics from KubeCons past also carried over. Things like security, networking, multi-tenancy, eBPF, developer experience, and wasm.

Anyways, here are the highlights announcements from KubeCon NA 2024 SLC\!

## Day one, AI and platform engineering

KubeCon day one kicked off with a non-AI and non-platform engineering topic, but one that’s still very important. That is, fighting patent trolls. The CNCF announced an initiative to rally the community to help find prior art and other info that’s critical for fighting off patent trolls.

The initiative is called [the cloud native heroes challenge](https://www.cncf.io/blog/2024/11/13/announcing-the-cloud-native-heroes-challenge/). Read more about how to get involved in the link.

Other highlights from day one include:

* In 2020 CERN processed 100+ petabytes of data, and they expect to 10x that by 2030\.  
  * [Kueue](https://kueue.sigs.k8s.io) is the project CERN expects to help them meet that challenge.  
* A 5G satellite network is running on cloud native tech like k3s.  
* Close to 80% of interruptions tracked across 50 days of training Meta’s Llama model were due to hardware interruptions.  
  * CoreWeave highlighted how they perform additional checks and collect additional metrics as their way of cutting failure rates for jobs in half. For improved reliability, metrics like temperature, and hardware health need to happen in addition to the usual suspects. 
* Lunar shared how platform engineering principles translate nicely when working with GenAI. They are a bank, and now see 60% of their customer interactions resolved with AI.

The days leading up to KubeCon also saw a number of announcements and donations to the CNCF.

* Intel started [Open Platform for Enterprise AI, or OPEA](https://github.com/opea-project). It’s now being donated to the Linux Foundation (the organisation that governs the CNCF).
  * Think of OPEA like a reference architecture. It’s a set of patterns for building microservices and other infra that supports GenAI.  
* [WasmCloud was accepted](https://www.cncf.io/blog/2024/11/12/cncf-welcomes-wasmcloud-to-the-cncf-incubator/) as an incubating project.
  * Ever wanted to run WASM at scale instead of containers? [wasmCloud](https://wasmcloud.com) is worth checking out.  
* [Karpenter’s beta for 1.0.0](https://www.cncf.io/blog/2024/11/06/karpenter-v1-0-0-beta/) is here.  
  * Karpenter is a project from AWS for managing compute capacity, and works by watching for pods that are unschedulable.

If I had to recommend just one video to watch related to the themes of AI and platform engineering, I would check out Idit Levine’s presentation. It’s on the many ways that GenAI traffic and web traffic differ, and why it’s smart to look to [Envoy as an LLM Gateway](https://www.youtube.com/watch?v=b1C7nt8Mv4I&list=PLj6h78yzYM2Pw4mRw4S-1p_xLARMqPkA7&index=60).

 ![](https://assets.northflank.com/2024_11_idit_levine_talk_kubecon_LLM_gateway_a96b75be4c.jpg) 

What is an LLM Gateway? The goal is similar to a service mesh, where there’s a lot of logic needed for managing traffic, so you want to pull the logic for handling that outside of your actual apps. Idit points out that traffic for GenAI is quite different from the usual web traffic we see. Web traffic might send a response within milliseconds, while an LLM might need seconds to minutes to send a response. As a result, it’s clear that LLM usage is different enough to warrant a new product category like LLM Gateways.

You might find you want to switch which LLM is providing answers for you based on current costs and time to respond. Traditionally, changing that might mean you need to re-deploy all of your apps to change your LLM target. Contrast that with a solution like Envoy, where you can push configuration changes to the proxies all at once.

## Day two, Security

Similar to day one, the security day had a few additional themes. Contributors were celebrated with an award ceremony, with Tim Hockin landing a lifetime achievement award.

Other highlights from day two include:

* A closer look at the history of [In-Toto](https://in-toto.io), a supply chain security tool.  
  * We’re all invited to get involved with the CNCF security community by checking out the [OpenSSF](https://openssf.org) or the [security TAG group](https://tag-security.cncf.io).  
* The [Guac project](https://github.com/guacsec/guac) was highlighted as a great way to visualise points of failure in your software supply chain and [supply chain inventory]( https://timly.com/en/inventory-management-control-in-supply-chain/
). 
* Predictions were made about AI BOMs becoming a standard in the future.  
* [Reference architectures](https://architecture.cncf.io/) were announced by the End-user TAB.  
  * Currently, you can view architectures from Adobe and Allianz-Direct.  
* Bringing back the [CNCF technology landscape radar](https://www.cncf.io/reports/cncf-technology-landscape-radar/).  
  * Think of it like a report on the maturity and usage of cloud native tech.

Similarly, some of the announcements at or leading up to KubeCon got some stage time or were announced during speaking sessions.

* RedHat promised to donate PodMan and bootc to the CNCF.  
  * [PodMan](https://podman.io) is a container toolkit and Docker alternative.  
  * [bootc](https://github.com/containers/bootc) is a way of managing bootable host systems and updates through the containerized approach of applying layers.  
* A collaboration between engineers at Bloomberg and Tetrate produced [Envoy AI Gateway](https://github.com/envoyproxy/ai-gateway).  
* Solo.io’s Gloo project has been donated to the CNCF under a new name, [K8sGateway](https://k8sgateway.io).  
  * Solo.io suggested it's best thought of as a way to create consistency and unify approaches for ingress, egress, and east-west traffic.  
* Prometheus released v3.0. The announcement and highlights are on [their blog](https://prometheus.io/blog/2024/11/14/prometheus-3-0/) and [in a recorded talk](https://youtu.be/9ROvfBqpdu4?si=cHYeUOle7s9ZRir8).  
  * Prometheus is one of the most popular ways to collect and query metrics about cloud native workloads.

If I had to recommend just one video to watch related to security, it would be [Mish-mesh: Abusing the service mesh to compromise Kubernetes environments by H. Ben-Sasson and N. Ohfeld](https://www.youtube.com/watch?v=wJGKVQmeDns). This presentation is about legitimate features that attackers might use to escalate privileges. In other words, there’s nothing like exploiting out-of-date software. The talk showcased one compromise of Linkerd, and one for Istio.

For the Linkerd case, the Wiz researchers made some assumptions about how Azure’s ML infrastructure works. They assumed it was likely Kubernetes, and their first goal was to learn more about what they were allowed to interact with, and what they were not. They focused on a part of the Azure ML system that allows you to input a URL for the system to analyze. They found the URL was not sanitized, which they smartly saw as a Server Side Request Forgery (SSRF) opportunity. 

Armed with this new knowledge, Ben-Sasson and Ohfeld decided to use the form to scan the entire port range of localhost (127.0.0.1). After filtering the results, they found an open port for 4191\. That’s the Linkerd sidecar container port. Researching Linkerd, they found endpoints for /shutdown, /env.json, and /metrics. Is the metrics endpoint useful? Sure is\! It gives IP addresses for internal hosts, ports, and service account names.

 ![](https://assets.northflank.com/2024_11_Kube_Con_NA_SLC_Wiz_talk_Linkerd_Istio_security_c8bcab75ad.jpg) 

Fast forwarding, this eventually led to them gaining access to Prometheus, GoldPinger, Nginx Ingress Controller, and Secret Store Metrics. For details on how they got there, I highly recommend checking out the talk.

Ben-Sasson and Ohfeld closed by noting that both Linkerd and Istio are robust solutions. Any solution will have some kind of attack surface area. The best way to deal with that threat is defence in depth. They had more specific recommendations, as well.

* Observability features are valuable, but they should only be accessible from trusted environments.  
  * Assess new Kubernetes components with an offensive outlook. 
  * Properly segment your Kubernetes networks.  
    * Separate between the data plane and the control plane.  
    * Enforce critical rules at the Kubernetes level in addition to the service mesh.  
  * Use multiple security barriers. 
    * Assume the first line of defence will always be bypassed. What’s beyond that?

 To hear more about how the Wiz team found issues with Linkerd and Istio, [check out the full talk](https://www.youtube.com/watch?v=wJGKVQmeDns).

## Day three, looking forward and back

Day three kicked off with a [presentation about the 12 Factor app](https://www.youtube.com/watch?v=JG1nGgirkB4) and the historical context of where the manifesto came from. Undoubtedly the most fun of the day three keynotes was the [Family Fued style gameshow](https://www.youtube.com/watch?v=4d4X8S4Zeks), pitting Kubernetes aficionados against each other.

Other highlights from day three include:

* A review of the traits that make a successful project in the CNCF by the technical oversight committee (TOC).  
  * There is a clear governance, regular release cadence, clearly defined roadmap, and a well defined scope.  
  * The most successful projects are simple to adopt and abstract away complexity from the practitioner.  
* Congratulations to the projects that graduated this year. Those are [cert-manager](https://cert-manager.io), [dapr](https://dapr.io), [KubeEdge](https://kubeedge.io), and [Falco](https://falco.org).  
* The observation that when Kubernetes was released, it was unfinished. Kubernetes is still not “done” yet. The tech we use is always going to be evolving, and that’s part of what makes the cloud native community fun to be a part of.

Similar to previous days, some of what was announced leading up to KubeCon got some stage time, like:

* [Heroku open sourcing the 12 Factor App](https://blog.heroku.com/heroku-open-sources-twelve-factor-app-definition).  
  * The 12 Factor app was a set of principles proposed as best practices for developing SaaS apps in the early days.  
* [Microsoft donating Hyperlight to the CNCF](https://opensource.microsoft.com/blog/2024/11/07/introducing-hyperlight-virtual-machine-based-security-for-functions-at-scale/) as a sandbox project.  
  * Hyperlight creates microVMs in as littles as one to two milliseconds. The use case is to run individual functions in these tiny VMs, just for the lifecycle of the function.

If I had to recommend just one video to watch related to looking forward and looking back, I’d check out Andrew L'Ecuyer from Crunchy Data’s presentation. That is [Engineering a Kubernetes Operator: Lessons Learned from Versions 1 to 5](https://www.youtube.com/watch?v=p2v7bPJkrVU). Operators are essential for running any kind of stateful workload on Kubernetes. This was a look at CrunchyData’s Postgres operator, which goes by the short name PGO.

Andrew’s talk covered three major areas. They are high availability (HA), upgrades, and disaster recovery.

High availability for an Operator is complex, because you’re managing both the availability of the Operator itself in addition to whatever it happens to be managing. Additionally, at every step of making the Operator they were faced with a design choice between doing things the Kubernetes way or by using standard Postgres tools.

Andrew noted that versions 1 through 3 of the Operator worked well, but there was definitely some room for improvement. Specifically, that there was only a single instance of the Operator deployed in these early versions. So, the Operator crashing could result in your Postgres instances being unmanaged. Additionally, he noted that all operators use a queuing mechanism to capture and respond to events in a K8s cluster. This is problematic if multiple DBs crash at the same time. You don’t want to be stuck waiting in line for the Operator to take action.

 ![](https://assets.northflank.com/2024_11_Crunchy_Data_Kube_Con_NA_SLC_talk_postgres_operator_9793e09851.jpg) 

For upgrades, Andrew found that different strategies worked best for minor and major version updates. Minor updates were relatively simple, since there’s usually API compatibility guarantees. A rolling update strategy works well in that case.

For major updates, CrunchyData needed to use the PGUpgrade API. This is a process that can potentially result in brief downtime, so a design decision was made to ask engineers to annotate the instances they wanted to upgrade. 

For disaster recovery, they realised these features might serve additional purposes. For example, PGO might need to cover scenarios that involved crossing k8s cluster and potentially even cloud boundaries. That meant that the DR features also had the potential to assist with data mobility.

Andrew wrapped up the talk with some lessons learned. The two that stood out to me were:

* Prevention is better than preparedness (although, you certainly need to do both).  
  * Operators will ultimately want to combine both Postgres native solutions and Kubernetes native solutions. For example, PGBackRest and k8s volume snapshots together in concert can help to prevent corruption.

## List of all KubeCon keynotes & talks

If you’re curious to hear and see more about KubeCon NA 2024 in SLC, you can find a list of all of the keynotes and sessions in [this CNCF playlist](https://www.youtube.com/playlist?list=PLj6h78yzYM2Pw4mRw4S-1p_xLARMqPkA7). As of this writing, there are 373 videos in the playlist. So, if you are browsing, I recommend checking [the Sched app](https://kccncna2024.sched.com) for better descriptions of sessions, and tools for filtering by topic.

<iframe width="100%" style={{ aspectRatio: '16 / 9' }} src="https://www.youtube.com/embed/jqNa0nkzKxI?si=k1-NwveW2ELADNdv" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

I would also be remiss not to mention that Northflank’s CEO, Will Stewart, gave a lightning talk at Platform Engineering Day. You can catch his \~6 minute talk on [creating better abstractions for platform engineering](https://www.youtube.com/watch?v=jqNa0nkzKxI).]]>
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</item><item>
  <title>KubeCon NA 2024 SLC, Themes from the Hallway track</title>
  <link>https://northflank.com/blog/kube-con-na-2024-slc-YAML-observability-themes-from-the-hallway-track</link>
  <pubDate>2024-11-21T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[KubeCon is a great time for sharing war stories and learning from others. You can get some of that from talks, but a whole lot more by striking up conversations.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_platform_engineering_day_kubecon_2_min_66c1028d2e.png" alt="KubeCon NA 2024 SLC, Themes from the Hallway track" />Informal conversations are NOT something you will see on the [CNCF’s YouTube channel](https://youtube.com/playlist?list=PLj6h78yzYM2Pw4mRw4S-1p_xLARMqPkA7&si=aHFaNUWADs4gLnTk). If you’re curious about what we heard among attendees at KubeCon, this post is for you.

 ![](https://assets.northflank.com/northflank_platform_engineering_day_kubecon_1_min_c679bdcea6.png) 
> *It was nice to see that platform engineering day was  popular.*

KubeCon is a great time for sharing war stories and learning from others. You can get some of that from talks, but a whole lot more by striking up conversations. That dynamic and unplanned sharing of perspectives is often affectionately called the Hallway track. Consider this Northflank's roundup from the Hallway track.

## YAML and the Buffet problem

Have you ever sat down at a Buffet for a meal, and been overwhelmed with 200+ menu options? I hear that’s a pretty good approximation of what figuring out the bazillion fields in k8s manifests and Helm charts feels like. 

YAML is still a pain for just about everyone, it seems. Files are extremely long, highly structured, and sensitive to silly issues like whitespace. Are you ready for Helm 4?

Finding new ways to wrangle YAML seems almost like a tradition for the k8s community. From the early days of KSonnet, to approaches like Kustomize and now YAMLscript. Are templates or overlays the way to go? When I talk to attendees who have customised Helm charts, I hear stories about the mind-numbing task of combing through hundreds of fields. Every so often you stumble across a field that sounds familiar, but you can’t quite remember what it means. That means you get to go on a journey to that package’s docs to figure it out. 

There are many solutions around YAML currently, but none completely remove the pain and complexity. In other words, there’s no silver bullet. The current solutions do a noble job of fighting cloud complexity. The fact that we are all still discussing pain points just indicates there’s still room to do more.

## Deployment is still a challenge

Talk to any infrastructure team, and you’ll find a new way to deploy code. Kubernetes deployments are usually bespoke, and almost always unique to the teams and organisations that create them. 

The uniqueness makes sense when you think about Conway’s Law. Communication structures are reflected in the code you create. Each team has its slight variance in how they communicate and accordingly, app deployment methods inherit that variance.

This is good when it comes to adapting the cloud-native world to your internal business processes. It’s bad when you look at long-term maintainability. Nearly everyone I spoke to at the Northflank booth shared stories about obscure parts of their infrastructure that might only really be understood by the one engineer who built it. 

Everyone seems to have a team for specialised parts of their distributed system. I’ve heard of teams dedicated to service mesh, observability, and databases. With that kind of logistical overhead, good luck getting anything done\! At least, that's the way it feels when teams revert from collaborators back to gatekeepers. I appreciate that in large organisations this is currently a necessary evil. It’s also an area where people feel pain, and I believe we are likely to see continued refinement.

The result of this wide variety of deployment strategies is lower team velocity, and an enormous duplication of effort. That’s basically the opposite of what you hope for in an open source community.

Pausing this honest review, just to shill Northflank a little bit… The ability to standardise deployments is the feature that people are most excited about after trying Northflank.

The new [Reference Architecture Initiative](https://architecture.cncf.io/) from the CNCF is one possible sign of relief. More companies sharing how they deploy and run workloads is one way the community can start to identify common patterns. Reference architectures are great, but they’re more likely to be just one part of the solution. The goal we’re all working towards is fewer bespoke handcrafted solutions, in favor of more standard deployment practices.

Because everyone’s deployments are unique, the reference architecture is likely to work less like a sure-fire recipe for success, and more like a place to look for inspiration. These architectures also took the best part of a decade to refine and implement, and you should expect that copying parts of them to take time, as well.

From my conversations it sounds like the lack of good deployment standards, best practices, and a pervasive fear of being too opinionated are all likely contributing factors. 

Distributed systems like Kubernetes need better abstractions in order to get better standards. I predict that just like programming languages push developers towards certain idioms and best practices, the CNCF community should also expect to see more idiomatic Kubernetes abstractions in the future.

## Observability advancements
I heard about more than one cool observability trends at the Northflank booth. One of which is continuous profiling. The other is improvements in how the standards for cross-referencing and correlating data. Finally, the release of Prometheus 3.0 is big news in the observability space.

Continuous profiling is a word many coders are probably at least partially familiar with. Profiling tools have been around since the dawn of programming. You set up your program in profiling mode, and data about the flow and performance of every function pours out.

Profiling usually just happens on a developer’s local machine, since in addition to the glut of profiling data, the impact on performance is high. Continuous profiling gives developers the same ability to track function-level performance, with as little as a 1% impact to system resources. It does so with stochastic sampling (in other words random) of eBPF data as your programs run in production. With enough random samples nice looking approximations of performance emerge. Continuous profiling is another observability solution to add to the usual mix of metrics, logs, and traces.

 ![](https://assets.northflank.com/Scooby_doo_It_was_ebpf_the_whole_time_40beb88827.png)
> *Jinkies\! Continuous Profiling was just eBPF wearing a mask all along.* 

[Pyroscope](https://pyroscope.io/) from Grafana Labs, and the eponymous [Polar Signals](https://www.polarsignals.com/) are two names I hear are worth checking out for continuous profiling. 

When it comes to improvements in how to cross reference and correlate data, I’ve heard this [presentation from Apple on enabling exemplars](https://www.youtube.com/watch?v=zYF1JIZmpI4) is a great talk to catch. [Exemplars are a standard in the Open Telemetry spec](https://github.com/open-telemetry/oteps/blob/main/text/metrics/0113-exemplars.md) for showing disparate types of data in a unified UI. Imagine if while looking at metrics, you could click a button and also see traces and logs for the same set of requests? That’s what exemplars bring to the table.

[Prometheus 3.0 was released along with a nice presentation](https://youtu.be/9ROvfBqpdu4?si=ueo8VsqmvVFYuVz_) and deep dive of the new UI and features. It’s already a standard of many Kubernetes stacks, so there’s something for everyone here. The Prometheus team also made a [post about the 3.0 release](https://prometheus.io/blog/2024/11/14/prometheus-3-0/). One big item worth checking out is the performance gains since Prometheus 2.0. Some of us might see more than 200% improvement in resource usage compared to 2.0.

## Developer Experience is still broken

In conversation, I'm hearing that the challenge of deployments also involves a bit of [Not Invented Here](https://en.wikipedia.org/wiki/Not_invented_here) (NIH). Every shop operates in its own unique way. That’s often seen as one of the benefits of Kubernetes. What’s not unique, and not a benefit, is the fact that developer experience (DX) is still broken. In other words, the majority of solutions around Kubernetes work great for Ops teams, but are still considered broken for Dev teams. No wonder so many teams spent years building a platform, only to find that their developers loathe it. 

If the point of DevOps is to unite Dev and Ops teams, then why are so many of us still stuck here? Why engineering orgs happy with poor DX as the status quo? Conversations with attendees have made it clear most are still struggling when it comes to DX.

KubeCon attendees frequently noted that in addition to understanding the structure of YAML manifests, developers are also saddled with the responsibility of learning what the various K8s resources do and how Kubernetes works. It’s a lot, and when I heard from attendees who were happy with their DX, it was usually because they reduced or wholly removed this burden from developers. In other words, better abstractions were put to use.

## Conclusion

KubeCon was a lot, and I'm sure I missed some interesting topics in this post. One attendee, Nathan Bohman, shared that his smartwatch tracked his walk around the just vendor area as 6.5+ miles in total. There was a lot of ground to cover at KubeCon.

What about the event itself? I'm empathetic to the challenge of making a lot of meals at scale. Jokes aside, the lunch bags were pretty good, although the screen printed cookies were an eyebrow raiser. Funnily, it was easier to get coffee than it was to grab water. Maybe the CNCF were expecting a massive number of pagers to go off in unison?

Overall there was too much for any one post to cover. If you think I missed something major in this walk through, let me know. Or, take it as an opportunity to share your own personal highlights from KubeCon.

 ![](https://assets.northflank.com/Northflank_team_at_the_booth_for_Kube_Con_NA_24_SLC_5526699a9a.jpeg) 

At Northflank we’re on a mission to reduce the effort and investment required to build in the cloud. The conversations we had were a nice validation of that mission. I appreciate everyone who chatted with us at the booth, gave a talk, or otherwise shared the technologies and techniques that drive their approach to the cloud.

Until next time! I'll see you in London for the next KubeCon EU.]]>
  </content:encoded>
</item><item>
  <title>WARNING: Kubernetes Undefined as Platform</title>
  <link>https://northflank.com/blog/warning-kubernetes-undefined-as-platform</link>
  <pubDate>2024-11-19T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[While Kubernetes has become the cloud's OS, it remains complex for developers. The industry seeks a platform that bridges ops and dev needs - automating deployment while maintaining flexibility. Is a &ldquo;Rails moment&ldquo; for K8s possible?]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/kubernetesplatform_min_ddfb7cff01.png" alt="WARNING: Kubernetes Undefined as Platform" />[Kubernetes is a platform for building platforms](https://x.com/kelseyhightower/status/935252923721793536). Kubernetes is for operators, not developers. Grabbing a big Cloud-hosted flavor of Kubernetes is sure to delight your ops team, but it's just as likely to leave your dev team grumbling. The reason? Kubernetes is not the platform developers need; It's a complex set of primitives misaligned with their primary focus—building applications.

Platforms are defined by your ability to build on them. If you’re a platform engineer, Kubernetes really is a platform. You can build what you need on top of it. If you’re an app developer, Kubernetes is overwhelming. And if you’re a platform engineer, it’s overwhelming for you, too\! That might be why so many IDP projects built on Kubernetes go sideways and get re-platformed. Despite all of the good that Kubernetes does, we still lack a post-commit platform that developers love.

Getting to a place where both operations and development are happy is a problem that other platforms struggled with in the past. KubeCon even hosted keynotes that compared Kubernetes to one platform that really broke through. As we head into KubeCon Salt Lake City 2024, It’s worth revisiting that, and some of the other platforms that led to Kubernetes.

## **In Search of a Rails Moment**

In 2019 Bryan Liles keynoted KubeCon with his talk, [In Search of the Kubernetes "Rails" Moment](https://www.youtube.com/watch?v=ZqQTEdHVaCw). He made a bold point by saying that YAML kind of sucks. In the world of Kubernetes, YAML manifests mean screens full of undefined fields and a dizzying array of tasks. That's a far cry from an experience like `rails new blog`. In other words, YAML is the wrong abstraction for app developers. 

Ruby on Rails was a platform built in an era where LAMP (Linux, Apache, MySQL, and PHP) was a dominant stack. Like Kubernetes, the problem with LAMP was figuring out how to make it usable for software engineers.

Today, Kubernetes feels akin to the L in LAMP. Both Linux and Kubernetes are platforms that other components build on. Linux is definitively an OS, and Kubernetes is the OS for the Cloud. It’s wild to think of an app developer wrangling kernel-level Linux APIs. But, with Kubernetes, wrangling is the status quo.

As platform engineers, we need a platform that not only abstracts away the complexities but also frees developers to focus on writing the code they get paid for.

## **Cloud Foundry was Almost THE Platform** 

Pivotal’s Cloud Foundry was an early attempt at providing a sophisticated platform as a service. They nailed the vision of simplifying application deployment and enabling the “you build it, you run it” ethos. PCF had an easy onboarding like Rails; instead of `rails blog new` there was `cf push`. The experience felt similar, but the big leap made by Cloud Foundry was supporting nearly every language and framework (not just Ruby). Developers just needed to commit their code. PCF is what drove everything post-commit. 

Yet, the platform still required large teams to maintain and operationalize alongside a hefty hardware investment that took months to provision. Because the effort required to adopt PCF, it didn’t quite live up to its full potential, nor did it adapt fast enough to a cloud-native era. Remember how the missing piece of Kubernetes was good developer experience? The missing piece of Cloud Foundry was an adaptable and pleasant operations experience. 

The cloud-native ecosystem is much more robust, as is the size of the problem, considering how many more software engineers are shipping workloads – with considerable effort and sometimes unsuccessfully – compared to a decade ago. 

I should mention that Cloud Foundry rose to prominence in the early 2010s, around the same time as Apache Mesos. Mesos was on the other end of the spectrum from PCF. It focused heavily on operational experience, but never quite found its footing. Heroku was from a similar era, but focused on developer experience while hiding the operational aspects. 

## **Kubernetes became the OS for the Cloud**

When Kubernetes rose to prominence, its success was partly fueled by its flexibility. There are many reasons why Kubernetes succeeded all of these platforms. K8s gave the Cloud a standard API, it was declarative, and its focus on containers abstracted nicely over VMs. Another reason Kubernetes succeeded is because ingredients can be swapped in and out. As an example, K3s swaps out etcd for a more traditional relational database. 

The emergence of EKS, GKS, and AKS cemented Kubernetes as the definitive OS for the Cloud – each with their own flavors and challenges. It’s worth remembering that the application abstraction is still a task left to platform builders. It’s easy to see why. How do you want to get your code from Dev to Prod? Every team and organization is going to do that a little bit differently. That’s an important detail to keep in mind when recalling the “Kubernetes is a platform for building platforms” mantra. Finding the right DX is quite a challenging task.

## **Defining the Future State: A Platform That Developers and Operators Love**

So, what should a platform actually look like? I can share some of the philosophies that have guided me as I’ve built the [Northflank platform](https://northflank.com/). Most platform engineers share an overarching vision, already. That vision is that everything post-commit is abstracted by the platform. That kind of abstraction frees developers to ship their workloads in a self-service way. They should be able to build, deploy, and scale their workloads without being infrastructure experts. As long as the controls and levers are still available for tuning APIs below the surface of the platform, we’ve got a winning solution.

That’s the broad vision that I think most of us share. That broad vision translates into design philosophies, and ultimately requirements. I’d love to hear about the philosophies and requirements that guide other platform builders. In that spirit, here are a few of mine.

* **IaC is a starting point.** Infra-as-code is essential, but it's too static, yet the release process is inherently dynamic. It leaves open questions like “How do I get code from Dev to Staging to Prod?” and “How do I restore production in another region or cloud?” The platform should provide a golden path that answers that question.  
* **Automate CI/CD pipelines**. CI/CD is where the post-commit journey begins. Minimize manual intervention, and live the GitOps dream.  
* **You build it, you run it**. Developers must be able to deploy and scale their applications with a few clicks or commands.  
* **Polyglot is standard**. Most businesses making software are too big to not build with multiple languages and frameworks. The platform must be polyglot — not just for ephemeral, but also stateful and scheduled.  
* **All workloads**. A platform should be agnostic about how complex the workload type and be able to support all containerized frameworks.  
* **Make Troubleshooting Easy.** One of the largest headaches when running software is troubleshooting. All of the APIs hidden from app developers need to still be accessible to SREs.  
* **Bi-directional, real-time intefaces**. If I update the workloads in Git, the UI should reflect those changes and vice versa. And don’t make your teams guess where info about their workloads lives. We shouldn’t accept stale information in cloud UIs.

In essence, the future platform should empower teams to "make workloads, not infrastructure."

By embracing a platform that prioritizes developer experience without compromising operational flexibility, organizations can accelerate their delivery cycles, reduce overhead, and stay competitive. A good platform frees developers to do what they do best—write code—while operations ensures that the supporting infrastructure continues to run smoothly.

## **Conclusion** 

DevOps is about uniting developers and operations. Platforms aren’t really platforms if they cater to one over the other. That’s something I’ll keep in mind as we head into KubeCon 2024 in Salt Lake City. There are over [a dozen talks about platforms](https://kccncna2024.sched.com/overview/type/Platform+Engineering) at the main event, and a whole [co-located platform engineering day](https://colocatedeventsna2024.sched.com/overview/type/Platform+Engineering+Day), as well. 

 ![](https://assets.northflank.com/northflank_platform_kubernetes_e2eef62d12.png) 

What I’ve shared here flows from my experience building platforms on Kubernetes at [Northflank](https://northflank.com/). If you spot me wandering around KubeCon, I would love to hear what you think. Is it possible to make a successful platform that de-prioritizes either half of DevOps? What philosophies guide you as you build your IDP? What do you see as the major challenges while platform engineering?
]]>
  </content:encoded>
</item><item>
  <title>Northflank raises $22M to make Kubernetes work for your developers – ship workloads, not infrastructure</title>
  <link>https://northflank.com/blog/northflank-raises-22m-to-make-kubernetes-work-for-your-developers-ship-workloads-not-infrastructure</link>
  <pubDate>2024-11-11T14:00:00.000Z</pubDate>
  <description>
    <![CDATA[Northflank raises $22M to make Kubernetes work for your developers – ship workloads, not infrastructure]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_series_a_graphic_min_db57d929d6.png" alt="Northflank raises $22M to make Kubernetes work for your developers – ship workloads, not infrastructure" />In an era where deploying software has become increasingly convoluted—with endless YAML files, Terraform scripts, and Kubernetes clusters—engineering teams are spending more time managing infrastructure than creating the products that drive business outcomes. **As an industry, we've somehow shifted our focus from building products to building the factories that produce them.** This makes no sense, and it's time to change that.

That's why we're excited to announce **$22 million in funding**, accelerating our mission to help developers create workloads, not infrastructure. But what is a workload? It's any microservice, any database, any job—any software that can run on a server. Workloads are the building blocks that power your business.

### **The problem: Infrastructure overload**

The optimism of the early days of containers—when technologies like Docker and Kubernetes promised to revolutionize deployment—has been overshadowed by the complexities of managing them. Today, developers are entangled in building and maintaining complex CI/CD pipelines and grappling with infrastructure sprawl. Companies pour resources into infrastructure setup and maintenance, inflating costs and stretching teams thin.

Why is it acceptable to spend months or even years setting up automated environments, ensuring zonal and regional redundancy, and crafting intricate deployment processes? Why are we tolerating cumbersome release cycles and error-prone manual interventions? It's time to break free from this cycle.

Consider the world of game development. Building a game engine like Unreal Engine (UE) from scratch is an unrealistic endeavour for most developers due to the immense resources required to keep up with the latest technology. Instead of investing time and effort into engine development, game creators now mostly rely on UE to start building their games immediately. This shift allows them to focus on what truly matters: delivering engaging gaming experiences.

This analogy mirrors the pain software developers feel today when working atop incomplete or feature-scarce platforms. Too many Internal Developer Platforms (IDP) fail to produce the kind of output that justifies the months to years invested in building and managing them. We should be building on abstractions that act as a force multiplier and get IDP initiatives in a usable state in days to weeks, not the current standard of months to years.

Recognizing this parallel, we founded Northflank to eliminate these hurdles. Just as UE empowers game developers to focus on their creations without worrying about the underlying engine, Northflank enables software teams to concentrate on shipping software quickly and efficiently—without the overhead of building and maintaining their own IDPs.

### **Our solution: Workloads over infrastructure**

Northflank is picking up where pioneers like Heroku and Pivotal Cloud Foundry left off. Heroku got the self-service developer experience right but didn’t allow for complex workloads where enterprises want them – in their cloud accounts. Cloud Foundry was the right application abstraction to simplify the complex but the underlying infrastructure was too costly and painful to implement. Northflank gives you the best of both worlds: complex workloads, great developer experience, and the right abstractions in your cloud in minutes without breaking the bank.

We're advocating for a new paradigm: **make workloads, not infrastructure**. Northflank provides an application-level abstraction for Kubernetes, enabling developers and DevOps teams to focus on delivering valuable applications without the overhead of managing complex infrastructure. 

> Customers don’t pay you to write YAML.

 ![](https://assets.northflank.com/northflank_platform_kubernetes_e2eef62d12.png) 

Our platform empowers developers to build, deploy, and scale apps, services, databases, and jobs on any cloud in a self-service way. For DevOps and platform teams, we offer a powerful abstraction over Kubernetes clusters, allowing for templated, standardized production releases with smart defaults and the configurability you need.

### **Abstraction over Kubernetes: Unlocking simplicity and power**

Kubernetes has emerged as the de facto standard for container orchestration, offering powerful primitives for building scalable and resilient applications. Yet, its inherent complexity can be a barrier for many development teams.

Think of Kubernetes as an operating system upon which platforms can be built. Managed Kubernetes services have significantly reduced complexity and dramatically enhanced teams' ability to run single-tenant container orchestrators. This is where Kubernetes (K8s) has excelled, surpassing competitors like Mesos, Nomad, and Rancher. Managed Kubernetes could be considered the eighth wonder of the world. Every major cloud provider has standardized on providing an open, consistent API and control plane, enabling platforms like Northflank to run inside your cloud account within 30 minutes—reliably and securely.

Major cloud providers—including Google Kubernetes Engine (GKE), Amazon Elastic Kubernetes Service (EKS), and Azure Kubernetes Service (AKS)—offer managed K8s services that make it easier to get a cluster up and running, accelerating adoption. However, they haven't solved the challenge of deploying workloads onto these clusters. That's where Northflank comes in.

By simplifying Kubernetes into an accessible platform, Northflank enables teams to deploy and manage applications without getting bogged down in the intricacies of infrastructure management. We leverage Kubernetes' robust capabilities such as controllers, custom resource definitions (CRDs), daemonsets, runtime classes, and storage and network drivers. Northflank does this while providing an interface that is composable and approachable.

The Kubernetes ecosystem is thriving, with a vibrant community and powerful open-source tools like Istio and Cilium adding immense value on top of Kubernetes. By building on this foundation, Northflank allows you to tap into this ecosystem without the steep learning curve. We are proud to be a member of the Linux Foundation and support many tools in the CNCF Landscape.

### **Why Bring Your Own Cloud (BYOC) matters**

Enterprises require flexibility and control over their cloud environments. That's why Northflank supports **Bring Your Own Cloud (BYOC)**, enabling you to deploy workloads on your own cloud infrastructure—whether it's AWS, GCP, Azure, or even on-premises. This approach offers several key benefits:

* **Ownership and control**: Running within your own Virtual Private Cloud (VPC) gives you greater control over your data and infrastructure, addressing concerns around security, compliance, and regulation.  
* **Solving the graduation problem**: Traditional platforms often falter with a "graduation problem," where scaling beyond a certain point becomes challenging. By pairing a powerful Kubernetes abstraction with the ability to run inside your own cloud account, Northflank solves this issue.  
* **Reshoring workloads**: As more microservices and third-party tools are dispersed across various locations, BYOC allows you to bring these workloads back into your VPC, consolidating your infrastructure.  
* **Cost efficiency**: Companies can leverage their existing cloud commitments and credits to save money and maximise efficiency.  
* **Access to latest hardware and regions**: BYOC allows you to take advantage of the latest hardware offerings and regional availability from major cloud providers, ensuring optimal performance and proximity to your customers.  
* **Hybrid cloud flexibility**: BYOC enables a hybrid cloud approach, allowing you to run workloads across multiple cloud providers or on-premises environments. This flexibility not only counters vendor lock-in but also provides an "insurance policy" for your most critical workloads. For most engineering teams, a consistent release process with workload portability is a pipe dream. And you want disaster recovery? Good luck. Northflank unlocks the multi-cloud end state (don’t worry, I cringe at that phrase too).

### **The importance of self-service**

Developers are constantly bogged down by bureaucratic hurdles that prevent them from spinning up workloads and databases instantly. It makes no sense that deploying something as essential as PostgreSQL requires submitting a Jira ticket and then waiting around for weeks. You should be able to deploy it in seconds, right alongside your new backend workload in a preview environment. Waiting on other teams that are already swamped not only grinds development to a halt but also breeds unnecessary frustration. This isn't just a minor inconvenience—it's a glaring inefficiency that's crippling your engineering team’s productivity.

Not every team member is a platform or DevOps engineer, nor should they need to be. Developers want to focus on building applications, not getting tangled up in complex YAML configurations or wrestling with Helm charts. Northflank cuts through the nonsense by providing a self-service Platform as a Service (PaaS) that supercharges the developer experience across an expansive range of software and deployment configurations. This empowers organizations to adopt a robust platform from the get-go, scale seamlessly with it, and effortlessly transition to Northflank's Bring Your Own Cloud (BYOC) or on-premises solutions when the time is right. Choose your own adventure: Northflank’s PaaS or BYOC; start small and graduate later.

### **But, why now?**

Several industry trends amplify the need for solutions like Northflank:

* **Emphasis on developer experience**: Developers and organisations increasingly prioritise tools that enhance productivity and satisfaction through self-service.  
* **Rising costs**: Building software and maintaining cloud infrastructure have become more expensive, driving the need for more efficient solutions.  
* **Tooling sprawl**: The proliferation of tools has added complexity to software development, making unified platforms more appealing.  
* **Desire for optionality**: Companies want to avoid vendor lock-in and seek flexibility to operate across multiple cloud providers.  
* **Hybrid cloud adoption**: Enterprises are looking to leverage existing on-premises investments while adopting cloud-like experiences, often as a strategy for cost management and risk mitigation.

The tailwinds are not going away, and they’ll only get worse. So, let’s make workloads, not infrastructure.

### **Our journey and rapid growth**

Today marks a significant milestone for Northflank—**we've secured $22 million in Series A and Seed funding**, led by Bain Capital Ventures and Vertex Ventures US, with participation from Kindred Ventures, Pebblebed, and Uncorrelated Ventures. This investment fuels our mission to revolutionize software deployment, enabling developers to focus on delivering valuable applications rather than wrestling with complex infrastructure.

Since our founding in early 2019, Northflank has been on a mission to streamline developer workflows. In 2020, I shared our journey in the announcement titled "[Northflank Enters the Fray](https://northflank.com/blog/northflank-joins-the-fray)," marking our bold entry into the developer tools landscape. Since then, Northflank has grown to empower tens of thousands of developers to deploy production workloads across more than six cloud providers, deploying over a million containers monthly. We expect to surpass 10 billion public egress requests processed monthly by the end of the year.

We are grateful to collaborate with amazing customers like Sentry, Writer, Northfield, Chai Discovery, Clock,  and thousands more, who have been instrumental in shaping the platform into what it is today.

> "Northflank is way easier than glueing a bunch of tools together to spin up apps and databases. It's the ideal platform to deploy containers in our cloud account, avoiding the brain damage of big cloud and Kubernetes. It's more powerful and flexible than traditional PaaS – all within our VPC. Northflank has become a go-to way to deploy workloads at Sentry."

*— David Cramer, Co-Founder and CPO @ Sentry*

This sustained increase in usage and revenue growth, along with a platform that solves the graduation problem for enterprise and self-service users, has enabled us to raise a successful Series A. We're now rapidly scaling our Product, Site Reliability Engineering (SRE), and Go-To-Market (GTM) teams to meet the growing demand and ensure that our platform remains robust, secure, and user-friendly (if this excites you, check out our [careers here](https://northflank.com/careers)).

### **What's next: Our product roadmap**

We are committed to continuous improvement and innovation. In the coming months, we plan to:

* **Enhance our managed offerings**:  
  * Add more global regions.  
  * Support newer hardware, including ARM.  
  * Provide standard DDoS protection.  
  * Achieve faster build times with local caching.  
* **Expand our platform and enterprise capabilities**:  
  * Extend BYOC support to additional cloud providers.  
  * Introduce **Bring Your Own Kubernetes (BYOK)**.  
  * Offer a **self-deployable control plane** for greater autonomy.  
  * Enhance project and cluster-level observability and awareness.

This brief snapshot of our team's upcoming efforts showcases how we ensure Northflank grows alongside our expanding customer base, positioning it to be your production workload platform well beyond your IPO.

### **Join us in shaping the future**

Infrastructure should exist to serve us—the developers, the innovators—not the other way around. Too much time is spent managing the complexities of infrastructure when the real focus should be on the workloads that deliver value to users and drive revenue.

If you've ever felt the frustration of:

* Waiting too long for environment setups  
* Dealing with error-prone release processes  
* Lacking access to critical logs and metrics  
* Navigating infrastructure sprawl  
* Being confined by rigid deployment stacks  
* Overloading your DevOps team  
* Re-platforming without resolving core issues  
* Struggling to harness Kubernetes effectively  
* Using outdated cloud console UIs

...then Northflank is built for you.

### **Let's build the future together**

 ![](https://assets.northflank.com/IMG_0009_b5ef0775e4.jpg) 

We're thrilled about the road ahead and invite you to join us in making workloads—not infrastructure—the priority again. Let's empower teams to focus on innovation and delivering exceptional products to users.

If this vision resonates with you, **we'd love to chat**. Together, we can redefine what's possible and shape the future of software development.

*— Will Stewart, CEO and Co-Founder and Frederik Brix CTO and Co-Founder Northflank*]]>
  </content:encoded>
</item><item>
  <title>Platform September 2024 Release</title>
  <link>https://northflank.com/changelog/platform-september-2024-release</link>
  <pubDate>2024-10-11T08:00:00.000Z</pubDate>
  <description>
    <![CDATA[Adds preview environment controls (pause/resume, scheduling), two US regions, improved BYOC on Azure and GCP and added support for OCI with GPU workloads, enhanced UI/performance, better resource management and new addon features.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_september_changelog_fd341dc6c1.png" alt="Platform September 2024 Release" />Adds preview environment controls (pause/resume, scheduling), two US regions, OCI Cloud support (incl GPUs), GCP & Azure BYOC enhancements, enhanced UI/performance, better resource management, new addon features, and various bug fixes for GitHub deployments and builds.

## Preview Environments
* Added support for pausing and resuming preview environments
  * Clicking pause scales down all resources belonging to that environment
* Preview templates in BYOC projects can have active hours assigned
  * Previews automatically scale up/down at set times and days
  * Configurable for entire pipeline or individual previews
* Added automatic deletion configuration for previews
  * Timer can be reset when template is run
  * Configurable for pipeline or individual previews
* Improved concurrency policy enforcement on environment level, runs are now queued per environment
* Added option to toggle ignoring draft pull requests in preview template/release flow git triggers

## Performance Improvements
* Improved template visual editor performance for large templates
* Enhanced platform performance when loading project contexts
* Improved template validation speed
* Fixed slow loading of release flow run overrides UI when many overrides were present

## Cloud Infrastructure
* Added two new PaaS regions: us-west and us-east
* Added support for Azure local NVMe disks enhancing performance and reduces costs
* Added support for OCI cloud clusters and OCI GPUs
* Added cross-account service accounts for GCP BYOC integrations making it more secure and easier to adopt
* Improved handling of Azure provisioning errors
* Added validation for adequate Azure system node pool resources
* Added support for draining and cordoning nodes and node pools

## Resource Management
* Fixed deletion of dangling platform resources on deleted BYOC clusters
* Made volume storage requirements clearer for templates and API
* Fixed race condition where builds could trigger twice for same commit
* Enhanced validation of custom BYOA annotations, ensuring only string values can be submitted
* Added live certificate renewal for addons allowing certificate refresh without downtime

## User Experience
* Improved error display in templates showing brief error summary without clicking individual nodes
* Added clipboard options when viewing template runs:
  * Copy node's `spec`
  * Copy node's `response`
  * Copy entire node
* Added link to create new cluster when viewing BYOC integration
* Improved error messaging for expired Azure credentials
* Added user alerts for BYOC cluster and node pool provisioning errors via notifications

## Addons and Integration
* Added support for RabbitMQ Web-MQTT and Web-AMQP plugins
* Added "latest" as fork source option for addon provisioning via templates enabling preview environments to always use latest data
* Added support for searching backup selection dropdown

## Bug Fixes
* Fixed issue preventing resource selection in template 'action' node
* Resolved build initiation failures for old commits
* Improved GitHub deployment cleanup preventing orphaned deployments when Northflank resources are deleted]]>
  </content:encoded>
</item><item>
  <title>Introducing new regions: US East and US West</title>
  <link>https://northflank.com/changelog/introducing-new-regions-us-east-and-us-west</link>
  <pubDate>2024-10-07T12:00:00.000Z</pubDate>
  <description>
    <![CDATA[Northflank is thrilled to announce the addition of US East and US West to our globally available compute regions. Northflank continues to provide robust, scalable, and efficient cloud services, now even closer to your users.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_new_cloud_regions_smaller_min_34ee25d5da.png" alt="Introducing new regions: US East and US West" />
We're thrilled to announce the addition of US East and US West. This adds to our globally available compute regions to support US Central in NA. Northflank continues to provide robust, scalable, and efficient cloud services, now even closer to your users.

### Live regions supported by Northflank:

- US - Central
- US - East
- US - West
- Europe West - London
- Europe West - Amsterdam
- Asia - Singapore

Your team should focus on building great products and automate the deployment of your applications, databases, and jobs effortlessly with Northflank.

Leverage a unified developer experience (DX) across our UI, CLI, APIs, and GitOps. Whether you're a startup or a large enterprise, our platform is designed to cater to your unique needs. Northflank is used in production by tens of thousands of engineers for all kinds of workloads, from database deployment to game servers to SaaS applications.

### Your Preferred Region and Cloud

Don't see a region that fits your workload? No problem! With Northflank's 'Bring Your Own Cloud' capability, deploy in over 100 regions and 300 Availability Zones (AZs) with your AWS EKS, GCP GKE, Azure AKS, and Civo clusters. Enjoy the freedom to choose your cloud environment while leveraging our powerful platform.

### We Want to Hear from You!

Tell us where you'd like to see our next cloud region. We're constantly evolving and expanding to meet your demands. Please reach out through our <a href="https://northflank.com/contact" target="_blank">contact page</a> or other direct channels. We’d love to hear from you!

To stay up to date on everything new at Northflank be sure to follow us on <a href="https://twitter.com/northflank" target="_blank">Twitter</a> and <a href="https://www.linkedin.com/company/northflank/" target="_blank">LinkedIn</a>.

We look forward to seeing what you deploy. Start  deploying now <a href="https://app.northflank.com/s/account/projects/new" target="_blank">here</a>.

]]>
  </content:encoded>
</item><item>
  <title>Platform August 2024 Release</title>
  <link>https://northflank.com/changelog/platform-august-2024-release</link>
  <pubDate>2024-08-31T08:00:00.000Z</pubDate>
  <description>
    <![CDATA[BYOC GPU and Ceph support, enhanced templates, improved networking, better observability, and UI upgrades. Enjoy streamlined workflows, increased flexibility, and better performance with these latest updates.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_august_changelog_min_e26a0854b0.png" alt="Platform August 2024 Release" />This Northflank update introduces a range of new features, enhancements, and bug fixes to improve your experience. Highlights include BYOC upgrades with GPU, Ceph and custom VPC support, expanded templates and pipelines functionality, improved networking and security options, enhanced observability, and logging capabilities, and a host of user interface improvements and bug fixes.

## New Features

### BYOC (Bring Your Own Cloud)
- Added support for nested virtualization on BYOC GKE nodepools
- Added GKE GPU support on BYOC
- Added support for running Ceph as an alternative storage backend on BYOC
- Added support for cross-account role on AWS
- Enabled access entries on AWS BYOC clusters
- Added the ability to duplicate BYOC node pools in the UI
- Added AWS BYOC beta support for Amazon Linux 2023
- Added new API endpoint to return nodes in the BYOC cluster
- Improved BYOC billing UI to break down spend by clusters, CPU and memory
- Improved AWS BYOC permission check based on enabled features
- Improved handling for AWS BYOC creation when using custom VPCs
- Added support for Bring your own Addons (beta) allowing to install any helm chart on BYOC clusters
- Added additional permission requirements and checks for AWS BYOC
- Improved handling of AWS BYOC with regards to custom VPCs
- Ensure GPU node labels have consistent formatting in BYOC

### Templates and Pipelines
- Implemented template drafts
- Added 'skip node' functionality to template editor UI
- Added the ability to override preview environment template trigger variables when running manually
- Improved template creation UI:
  - Added progress tracker for improved status visibility
  - More obvious template name field
  - Better visibility around project inheritance and template errors

### Networking and Security
- Added support for hard-coded DNS entries via `/etc/hosts` in services/jobs/builds
- Added ability to configure port security matching certain paths (SSO, Header-based Authentication, IP Policies)
- Added enhanced support for CIDR blocks in the network security proxy
- Added support for enabling skipping security proxy if endpoint requested within the same cluster or project

### Observability and Logging
- Added audit log support to the platform
- Implemented audit logging
- Added the ability to configure batching options for HTTP log sinks (maximum number of logs per request, maximum size per request)
- Added new observe page for resources for greatly enhanced container, deployment overview and performance monitoring

### Authentication and Access Control
- Added support for newer self-hosted GitLab application tokens
- API roles can now be created on an organisation level, which are inherited by selected teams and Directory Sync roles
- Added an option to organisations to allow SSO with emails from external domains

### Storage and Databases
- Released support for new addon versions: 
  - PostgreSQL 16 (major)
  - MySQL: 8.4 (LTS), 9.0 (major)
  - MongoDB: 7.0.12
  - RabbitMQ: 3.13.3 (major), 3.12.14
- PostgreSQL addons now support installing the `pg_uuidv7` extension

### Integrations
- Added native support for AWS ECR with automatic docker pull secret refresh

### User Interface Improvements
- Expanded template editor node code editor to full viewport height
- Improved visibility of preview environment template run status & status history
- Improved visual presentation of Git triggers in release flows
- Implemented custom plan builder enabling teams to self-service a custom plan with Northflank vs Kubernetes modes
- Added links to billing items for easier navigation to resources
- Improved tag selection UI
- Added UI for creation of API roles for organisations

## Enhancements

### Performance and Stability
- Improved performance of GitOps calls for faster template saving
- Improved MongoDB addon disk restore performance for volumes which support disk cloning
- Upgraded Istio to 1.22
- Improved secret injector handling to reduce unexpected zombie processes in addons

### User Experience
- Improved display of run status on templates list
- Pipeline creation - resource selectors will remain open as you make selections
- Pre-fill GitOps file path field with default value when enabling GitOps
- Improved handling of AWS permission checks for accounts with organizational service control policies
- Improved text selection contrast for better accessibility
- Improved rendering of long commit messages in service deployment information
- Improved rendering of long names in the template and pipeline views
- Ensure relative time values (e.g. '3 minutes ago') are updated in real time across UI
- Improved the display of organisation team members, making it clearer who has access to a team due to an organisation-level role

### Functionality
- Importing a version 1.0 or 1.1 template no longer resets the existing GitOps settings
- Projects can no longer be defined in preview environment template editor
- Organisation SSO users now correctly sync on user creation
- Added support for selecting a specific build stage in your dockerfile via node override
- Increase API time limit to 31 days for logs / metrics
- Added support for parsing the build name to the build ARG context
- Improved UID and GID handling with the Northflank secret injector

## Bug Fixes

- Fixed the branch list on service creation sometimes failing to load
- Fixed an issue where updating a template's settings via Git would not correctly update the Northflank UI
- Fixed an issue where saving a deployment service while an external image was still verifying would not save changes
- Fixed various issues in template editor resulting from values being `${args}`
- Fixed an issue where fetching a template from GitOps could overwrite unsaved changes
- Fixed metrics crash when going to all builds view
- Fixed issue with saving an addon await condition node
- Fixed issue where template project information would be unset on fetching from GitOps
- Fixed handling of default storage classes on Civo BYOC
- Fixed issue in addon form where custom database name could not be unset
- Fixed issue where 'await completion' checkbox would set incorrect value
- Fixed issue where addon connection details could sometimes not be fetched in template editor UI
- Fixed issue where subdomain paths could not be edited more than once without reloading the page
- Fixed rendering of domains in template editor UI that contain `${args}` or `${refs}`
- Removed forcing of lower-case on regions and clusterIds in templates
- Fixed a redirect issue with the Manage domains button on a service
- The visual editor for selecting a subdomain path now correctly references the prior nodes in the template

## Other Changes

- Display 'skip node' status on each node in template editor
- Do not allow resources that belong to a preview environment to be added to pipelines in the UI
- Remove redundant description text from team & organisation lists
- Make timeout value optional in await condition template node
- Add RBAC permission checks to view roles in CLI log in flow UI
- Added certificate and CDN statuses on the subdomain GET endpoint]]>
  </content:encoded>
</item><item>
  <title>Platform May 2024 Release</title>
  <link>https://northflank.com/changelog/platform-may-2024-release</link>
  <pubDate>2024-05-31T08:00:00.000Z</pubDate>
  <description>
    <![CDATA[Path-based routing for custom subdomains, archive deployments, enhanced secret management, plus improvements to addons, BYOC, and templates. Streamline your workflow now!]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_may_changelog_min_3e4759528b.png" alt="Platform May 2024 Release" />This month, we have added some exciting new features. Path based routing will make it easier to set up your custom subdomains, services can now be built and deployed from an archive, and we're giving you more granular control of your secret groups with new config permissions. We've also got a host of improvements to addons, BYOC, and templates, as well as an assortment of bugfixes to make your experience smoother.

## New Features

- Added support for path based routing. On the domains page, you can now add one or more paths to a given subdomain, allowing you to send users to different Northflank services based on the URL path. Each subdomain path can be configured to match an exact string, a prefix, or a regular expression. You can configure the priority of each path in the case where multiple paths match. You can also add additional options to each path such as a timeout, rewrite, headers, CORS policy, and retries.
- Services can now be built and deployed from an archive, as an alternative to git.
- Secret groups now have two levels - secrets and config. These have separate RBAC and API permissions, allowing you to give team members access to non-sensitive variables while not giving them access to sensitive secrets.

## Addons

- MySQL admin users now have permission to create functions.
- MySQL addons no longer display a deprecated native password warning.
- Improved reliability of Redis health checks.
- Addon forking is now supported also when the two addons have different minor versions (but the same major version).
- PostgreSQL addons no longer fail when given a long name.

## BYOC

- Upgraded all BYOC clusters to Kubernetes version 1.28
- Made the error messages clearer when cluster creation pre-flight checks fail.
- Azure BYOC clusters now have automatic security upgrades disabled by default to avoid weekly node replacements.

## Templates

- Made a number of UX improvements to the layout and design of the template editor, making it easier to jump right in without clicking through unnecessary forms first.
- Templates list no longer flickers when loading the page or applying filters.
- Creating a service in a template no longer displays an error when referencing a build service that has yet to have a build triggered.
- Pipeline spec now correctly returns release flow data.
- Backup nodes in preview environment template runs now correctly redirect to the backup logs page.
- GitOps settings no longer fail to display the current branch.
- Internal DNS is now correctly displayed when creating a service name that includes a reference or argument.
- Adding release flows to templates no longer causes a validation error.
- Saving a template no longer prompts the user to recover old data when the template has already been saved.
- Preview environment templates no longer fail to run if an unrelated build fails to start.
- Release flow template nodes no longer try to submit the form when clicking the expand button.
- Release flows no longer fail to trigger due to their triggers being overwritten.
- Release flows and preview environment templates now have a copy spec button.

## Organisations

- Improved sort and filter handling on the organisation member list, allowing you to filter on display name and email address.

## API, CLI, and JS Client

- The Get Addon Backup endpoint no longer returns data about deleted backups.
- POST and PUT project endpoints now return more data.
- Secret group creation no longer fails when a token only has access to modifying configs.

## Miscellaneous

- The 'Not sure where to start?' dashboard section now links to the resource creation pages.
- Release nodes now have additional fields timeoutDuration and initialCheckTime allowing you to configure the node to fail after a set time if the release has not yet been successful.
- Added support for increasing the maximum length of resource names.
- Service creation no longer displays an error when toggling autoscaling on and then off again.
- Authenticating with the Northflank Docker Registry no longer fails when inputting the authentication scheme with an inconsistent case.
- Port creation no longer displays a false-negative error.
- Dockerfile can correctly be fetched from public repos that you don't have write permissions for.
- The domain list copy to clipboard button works correctly now on Firefox.
- Trying to delete a secret group from the list page no longer displays an 'Insufficient permissions' error.
- Tags can now be searched for in the tags list page and the tag selection popover.
- The tags popover can now correctly be scrolled and no longer appears offscreen when you have a large number of tags.
- The Edit GitHub Installation button now correctly redirects to the settings page for GitHub organisations.
- Passing in an empty secret file no longer errors on submission.
- Displaying billing information by project or by resource type correctly shows additional line items.
- Unlinking a build service now clears the deployment information of relevant services.]]>
  </content:encoded>
</item><item>
  <title>Northflank: App Delivery &amp; Internal Developer Platforms</title>
  <link>https://northflank.com/blog/northflank-app-delivery-and-internal-developer-platforms</link>
  <pubDate>2024-05-27T19:00:00.000Z</pubDate>
  <description>
    <![CDATA[Northflank's co-founder and CEO discusses application delivery and IDP (internal Developer Platforms) in Kubernetes and hybrid cloud environments. Developers are clamouring for self-service, and leadership is looking for a golden path to production.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/will_vertex_northflank_interview_241030ad91.png" alt="Northflank: App Delivery &amp; Internal Developer Platforms" />Northflank's co-founder and CEO discusses application delivery and IDP (internal Developer Platforms) in Kubernetes and hybrid cloud environments. Developers are clamouring for self-service, and leadership is looking for a golden path to production.

 <iframe id="ytplayer" type="text/html" width="100%"
  src="https://www.youtube.com/embed/JZ_EluQ-YVM?autoplay=1&origin=http://example.com"
  frameborder="0" style={{ aspectRatio: '16 / 9' }}></iframe>]]>
  </content:encoded>
</item><item>
  <title>Platform March 2024 Release</title>
  <link>https://northflank.com/changelog/platform-march-2024-release</link>
  <pubDate>2024-04-23T08:00:00.000Z</pubDate>
  <description>
    <![CDATA[Enhanced release flows with usability improvements, better pipeline stage status display and real-time logs via release flows.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_march_changelog_edit_opt_ceea13ba83.png" alt="Platform March 2024 Release" />This month included enhanced release flows with usability improvements, better pipeline stage status display and real-time logs via release flows. We've added additional functionality with commit and build rules on Release nodes and support for build overrides on Job Run nodes. Also, fixed various bugs, improved error handling, and updated user interface elements for smoother navigation and reliability.


- Added several usability improvements to release flows including:
    - Improved the stage header to make it easier to view runs and see the status of previous runs.
    - Clicking a node in a release flow run now opens up a side panel with metadata and logs.
    - Each release flow stage shows the status of resources that belong to it.
    - Improved error handling, making it easier to see when your release flow has failed and navigate directly to a failed node.
    - Improved the UX for navigating directly from a node to that resource.
- Added support for commit rules and file path rules to release flow build nodes, allowing you to automatically select a previous build if the triggering commit does not pass the provided checks.
- Added support for rich overrides in release flows. These can be configured in the code view and allow you to add dynamic build and commit selectors to the run modal which automatically populate the release flow arguments.
- Added support for build overrides for template job run nodes.
- Updated Buildpack API version support, set the new default buildpack to heroku:22 and deprecated heroku:18.
- Added additional safeguards to prevent a BYOC cluster from being deleted accidentally.
- Improved the reliability of autoscaling due to some events not being sent reliably.
- Added BYOC support for Kubernetes 1.27.
- Improved failure handling for failed BYOC node pool node provisioning.
- Made the dashboard icons more consistent in styling.
- Added a deployment history list to services showing a list of active pods per deployment.
- Secret group variable editor now autocompletes linked addon keys and aliases.
- Node pool labels and taints are now set correctly at creation for Civo clusters.
- Navigating to organisation pages correctly includes the url prefix.
- Secret group restriction setting no longer crashes due to undefined tags or resources.
- Improved autoscaling handling by increasing rate limits.
- Addons will no longer fail to have their TLS certificates updated if the expiry is checked while the addon is being backed up.
- Organisation Single Sign-on no longer fails for automatically provisioned users.
- Secret group template nodes now correctly remove the array of nfObjects when the restricted setting is toggled off.
- Addon forking in templates now correctly uses the human-readable backup ID.
- The site no longer crashes when exiting the avatar upload file selection without selecting a file.
- Documentation links now correctly open in a new tab.
- Service pages no longer crash when a domain entry is not defined correctly.
- Changed the BYOC redeploy strategy to restart to avoid update conflicts.
- Project creation no longer crashes when an invalid colour code is inputted.
- Aborting a backup upload no longer crashes the page.
- Users can request via support an egress gateway IP on EKS BYOC clusters provided by AWS Internet Gateway.
- Improved performance of environment injection.
- Improved reliability of template code editor auto-completion.
- Registering an organisation for Single Sign-On no longer causes an error to be returned.
- Trying to select a tag that cannot be applied to a specific resource no longer causes the form to get stuck.
- Templates with the autorun feature turned on can now correctly access their arguments.
- Trying to add a UDP and TCP port on the same port number no longer returns an error.
- The Select suggested keys option on secret group addon linking now behaves correctly for Postgres.
- Autoscaling information is now correctly returned for services in templates and the API.
- Azure registry credentials now correctly authenticate when accessing images.
- When editing the command override for a job, release flows containing that job will correctly use the updated override, if the override hasn’t been set manually on the release flow node.
- Commit and Build information on the container view is now shown by default, if there is enough space.
- During addon creation, the resource plan is no longer greyed out.
- Inviting members to a team now correctly displays the number of team members invited.
- Fixed some minor formatting issues with pipeline addons.
- Corrected the example command for fetching tail logs via the Northflank CLI.
- Secret file upload no longer crashes when not selecting a file.
- Build service node overview no longer displays the plan as ‘not defined’.
- Signing in no longer redirects organisation members to an invalid URL.]]>
  </content:encoded>
</item><item>
  <title>Platform February 2024 Release</title>
  <link>https://northflank.com/changelog/platform-february-2024-release</link>
  <pubDate>2024-03-07T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[New addon versions, backup fixes, template enhancements, BYOC updates.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_feb_changelog_2x_cb07847222.png" alt="Platform February 2024 Release" />This update rolls out new versions for Redis, MinIO, RabbitMQ, MySQL, and MongoDB addons, while deprecating older versions. It addresses critical fixes, notably preventing addons from becoming unresponsive during backups and streamlining service creation from templates. Enhancements include improvements to template functionality, removal of the cap for BYOC project resources, and clearer service creation processes. Additionally, the update offers separate billing plans for build time and runtime containers in jobs and services, accurate multi-factor authentication display for organization members, and fixes for the API volume update endpoint.



### Addons

- New addon versions released:
    - Redis: 7.2.4
    - MinIO: 2024.1.31
    - RabbitMQ: 3.11.28, 3.12.12
    - MySQL: 8.0.36, 8.3.0
    - MongoDB: 5.0.24, 7.0.5, 6.0.13
- Deprecated old addon versions.

- Fixed addons from being paused at the same time as triggering a backup, which would cause the addon to be stuck in an unresponsive state.

### Templates

- Fixed template and release flow runs so that they no longer can get stuck and not be able to be aborted.
- Improved stability of the template visual editor, removing a number of instances where it could crash the page.
- Refreshing the page with the template runs interface open returns you to the same place.
- Templates that are run immediately on creation now can correctly access their arguments.
- Fixed the formatting of the project template view.
- Jobs and addon specifications now show fewer settings when they have not been modified from their default value.
- Creating a service from a template no longer errors on submission when only one version control account is linked.


### BYOC

- Removed the instances cap for resources running in BYOC projects.
- Prevented the node pool creation form from resetting itself randomly.
- Azure BYOC clusters are now priced consistently with other providers.

### Services

- Made it clearer when a service cannot be created due to an attached volume sharing the name with an existing volume.
- Split the billing plans for jobs and services into separate build plans and deployment plans, allowing you to modify them separately. 

### Miscellaneous

- The organisation member list now correctly displays whether a user has multi-factor authentication enabled.
- Fixed API volume update endpoint returning a 500 Internal Server Error when an empty object was passed in.





]]>
  </content:encoded>
</item><item>
  <title>Platform January 2024 Release</title>
  <link>https://northflank.com/changelog/platform-january-2024-release</link>
  <pubDate>2024-03-06T23:59:00.000Z</pubDate>
  <description>
    <![CDATA[Expanded BYOC support, template improvements, organizational features, UI/UX enhancements, API/CLI fixes, and multiple bug fixes.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_jan_changelog_2x_50cf6a8ea4.png" alt="Platform January 2024 Release" />This update brings significant enhancements across various features, including BYOC integrations, template functionalities, organizational support, and user interface improvements. Key highlights include the removal of unnecessary permissions for AWS BYOC, the introduction of Civo BYOC support, refined template editing and specification viewing, and streamlined organization and team management. Additionally, the update addresses issues in logs, metrics, API, CLI, and the JavaScript client, alongside various bug fixes and UI enhancements to improve overall platform stability and user experience.


### BYOC
- Removed unnecessary permission requirements from AWS BYOC integrations.
- Added support for Civo BYOC integration.

### Templates
- Preview environment build source nodes link to the correct build logs page.
- Fixed template secret group nodes linking multiple addons without user input.
- Job run template nodes can be saved when no deployment exists.
- Improved UI for viewing template specification.
- Template addon backup nodes validation error fixed.
- Service specification shows correct image path for external registries.
- Template code editor updates correctly for previous versions.
- Fixed display issues in template node specifications.
- Enhanced UX in template visual editor for dragging nodes.
- Template difference editor no longer appears after deleting a template.
- Added override options in release flows for specific builds.
- Save button functions correctly in template editor node code view.
- Encrypted content displays correctly in template difference editor.
- External connection details configurable in template secret groups.
- Fixed visual issue with editing health checks in templates.

### Teams & Organisations
- Introduced support for Organisations with multiple teams and enterprise features.
- Users now belong to a default team, facilitating easier team expansion.
- Fixed crash when accepting a team invite on a team's page.

### Logs and Metrics
- Fixed zoom issue in metrics charts.
- Resolved display issue with old log lines.
- Fixed crash when viewing a single log line item.
- Added pagination to List Addon Backups API.

### API, CLI, and JS Client
- Fixed CLI login team selection issue.
- Resolved JavaScript client authentication issue for new API tokens.

### Miscellaneous
- Ephemeral storage configuration now available for builds.
- Secret groups with multiple linked addons fixed to prevent key mix-ups.
- Fixed crash on PostgreSQL resources page with enabled connection pooling.
- Notification webhooks no longer include resource secret values by default.
- Visual improvements to subdomain routing diagram.
- Wildcard redirects show registered region in UI.
- Fixed service list filtering redirection issues.
- Corrected random name generation button functionality.
- Fixed ordering in dashboard addon list.
- Environment variable editor accounts for Northflank managed secrets correctly.
- Secret group list shows restricted secrets by tags.
- Fixed issue with resource name field deleting the last character.
- Resolved flickering in services list search.
- Pipeline display name updates properly.
- Fixed navigation issues on alerts page.




]]>
  </content:encoded>
</item><item>
  <title>Platform December 2023 Release</title>
  <link>https://northflank.com/changelog/platform-december-2023-release</link>
  <pubDate>2024-03-05T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[Enhanced templates with autosave, improved addons, CDN integration, better UI/UX, expanded variable management, and onboarding improvements.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_dec_changelog_2x_cf3d107710.png" alt="Platform December 2023 Release" />This update introduces significant enhancements and fixes across templates, addons, and miscellaneous features. Template improvements include autosave functionality, usability enhancements, and performance optimizations. Addons see improved probe performance for PostgreSQL, better UI information for Redis, and fixes for backup and restart functionalities. Miscellaneous updates include CDN integration, improved navigation and metrics display, expanded variable management, and onboarding improvements. Additionally, there are fixes for CLI login, deployment configurations, and user interface issues, contributing to a more stable and user-friendly experience.

### Templates
- The template editor now autosaves recent changes for restoration if the page is exited without saving.
- Usability improvements in the template editor.
- Fixed deployment issues for jobs created from templates via version control.
- Enhanced UX for Build source nodes in previews, especially for services from different repositories.
- Corrected resource link issues in Release flow runs.
- Template nodes visually indicate the 'Wait for completion' setting.
- Switching between node form and code view retains current changes.
- Simplified deployment from build source nodes in preview templates.
- Addressed performance issues in the template run view, preventing page crashes.
- Fixed page crashes on the template Start build node.
- Resolved Deploy build node failures in release flow templates when using references.
- Templates and API now support CIDR notation for IP addresses.
- Added reuse of existing builds in release flow build nodes.
- Performance improvements in template run views.
- Expanded argument support in template fields.

### Addons
- Fixed addon creation issues when switching types.
- Enhanced probe performance for PostgreSQL High Availability.
- Detailed UI information for Redis addons with Sentinel enabled.
- Fixed addon Import backup page crashes caused by pasting connection strings.
- Enabled creation of hourly and daily addon backups of different types.
- Resolved double pod restarts when restarting an addon.
- Corrected scheduled addon backup compression type settings.
- Improved UI for PostgreSQL High Availability with component connection overviews.
- PostgreSQL fixes to support Supabase integration.

### Miscellaneous
- Introduced CDN integrations.
- Resolved issues with the multiplayer navigation pop-up.
- Improved metrics graphs display in the All instances view.
- Enhanced display of region information for easier selection.
- Corrected display of team owner information.
- Deleted BYOC clusters no longer show components as ready.
- Expanded list of Northflank managed variables in the environment variable editor.
- Fixed CLI login issues.
- Increased visibility of job status on small screens.
- Addressed registry credentials editor validation issues at page load.
- Deployment plan information now available on instance hover.
- Restricted navigation menu items are now hidden for insufficient permissions.
- Deployment storage SHM-size is configurable.
- SSO login now correctly redirects to the user page.
- Fixed loading issues in the Deploy build modal for deployment services.
- Added Tailscale sidecar support and options to disable service mesh and MTLS.
- Improved user onboarding for a smoother transition from account creation to first service.
- Corrected display issues in the secret file editor and base64 encoded content.
- Fixed validation issues in the port importer for unique port names.
- Resolved file path build rule issues causing unnecessary build triggers.


]]>
  </content:encoded>
</item><item>
  <title>Platform Engineering with Northflank and Civo</title>
  <link>https://northflank.com/blog/platform-engineering-with-northflank-and-civo</link>
  <pubDate>2024-01-31T18:00:00.000Z</pubDate>
  <description>
    <![CDATA[Watch and join Will Stewart (Co-Founder &amp; CEO at Northflank) and Dinesh Majrekar (CTO at Civo) as they explore platform engineering and how to build robust, scalable platforms using Northflank on Civo's cloud infrastructure. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/1705508622696_5bb2379a09.png" alt="Platform Engineering with Northflank and Civo" />Dive deep into the forefront of platform engineering with Northflank and Civo as they join forces to explore platform engineering and techniques to enhance developer experience, improve collaboration, and simplify the scaling of cloud infrastructure.

1. What is a Platform?
2. What makes a Platform? 
3. Why do Platforms Fail?
4. Platform live demo of Preview and Production Environments with UI and GitOps
5. Future of Platform Engineering
6. Benefits of Northflank and Civo

 <iframe id="ytplayer" type="text/html" width="750px" height="422px"
  src="https://www.youtube.com/embed/JiPT04U2I6M?autoplay=1&origin=http://example.com"
  frameborder="0"></iframe>

This session featured insights from Will Stewart, Co-Founder & CEO of Northflank, and Dinesh Majrekar, CTO at Civo, highlighting the integration between Northflank's advanced platform and Civo's cloud infrastructure. Our experts will unpack the strategies for effective application delivery and the essential building blocks of a successful platform.


### Typical Platform Building Blocks
 ![platform engineering diagram](https://assets.northflank.com/platform_engineering_diagram_af2ce75f84.png) 


### Deploy now

We look forward to seeing what you deploy. Start deploying with Civo and Northflank <a href="https://app.northflank.com/s/account/cloud/clusters" target="_blank">here</a> or sign up to <a href="http://civo.com" target="_blank">Civo</a>. 

### Meet us in person
You'll be able to meet Northflank and Civo in person in Austin Texas at Civo Navigate 2024 (February 20 & 21st). https://www.civo.com/navigate/north-america

To stay up to date on everything new at Northflank be sure to follow us on <a href="https://twitter.com/northflank" target="_blank">Twitter</a> and <a href="https://www.linkedin.com/company/northflank/" target="_blank">LinkedIn</a>.]]>
  </content:encoded>
</item><item>
  <title>Introducing new Regions: Amsterdam and Singapore</title>
  <link>https://northflank.com/changelog/introducing-new-regions-amsterdam-and-singapore</link>
  <pubDate>2024-01-19T13:00:00.000Z</pubDate>
  <description>
    <![CDATA[Northflank is thrilled to announce the addition of Amsterdam and Singapore to our globally available compute regions. Northflank continues to provide robust, scalable, and efficient cloud services, now even closer to your users.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/newregions_15aba55872.png" alt="Introducing new Regions: Amsterdam and Singapore" />🚀 Northflank Expands Global Reach: Welcome Amsterdam and Singapore!

We're thrilled to announce the addition of Amsterdam and Singapore to our globally available compute regions. Northflank continues to provide robust, scalable, and efficient cloud services, now even closer to your users.

### Live regions used by tens of thousands of engineers:

- US - Central
- Europe West - London
- Europe West - Amsterdam
- Asia - Singapore

Focus on building great products and automate the deployment of your applications, databases, and jobs effortlessly with Northflank.

Leverage a unified developer experience (DX) across our UI, CLI, APIs, and GitOps. Whether you're a startup or a large enterprise, our platform is designed to cater to your unique needs.

### Your Preferred Region and Cloud

Don't see a region that fits your workload? No problem! With Northflank's 'Bring Your Own Cloud' capability, deploy in over 100 regions and 300 Availability Zones (AZs) with your AWS EKS, GCP GKE, Azure AKS, and Civo clusters. Enjoy the freedom to choose your cloud environment while leveraging our powerful platform.

### We Want to Hear from You!

Tell us where you'd like to see our next cloud region. We're constantly evolving and expanding to meet your demands. Please reach out through our <a href="https://northflank.com/contact" target="_blank">contact page</a> or other direct channels. We’d love to hear from you!

To stay up to date on everything new at Northflank be sure to follow us on <a href="https://twitter.com/northflank" target="_blank">Twitter</a> and <a href="https://www.linkedin.com/company/northflank/" target="_blank">LinkedIn</a>.

Happy New Year! We look forward to seeing what you deploy. Start  deploying now <a href="https://app.northflank.com/s/account/projects/new" target="_blank">here</a>.

]]>
  </content:encoded>
</item><item>
  <title>Introducing Navigation V2</title>
  <link>https://northflank.com/changelog/introducing-navigation-v2</link>
  <pubDate>2023-12-05T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[We’re excited to launch a completely overhauled navigation experience in the Northflank platform!]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/changelog_nav_2_0_bb8450c9f7.png" alt="Introducing Navigation V2" />We’re excited to launch a completely overhauled navigation experience in the Northflank platform! These changes make it quicker and easier to understand where you are and get to where you need to be in the UI.

The context and project selectors have been condensed into a sleeker layout, and the resource navigation has been relocated from the sidebar to the top of the page with the rest of the navigation. This keeps everything together in one place, so it’s simpler to understand and easier to use. 

Additionally, the resource navigation bar is now hidden when you are not inside a project. Previously, navigating to resources could be confusing when you had not selected a project yet.

The primary navigation also now includes an account menu, regardless of which context you have selected. This means that you can always see which account you are logged into and can jump straight to your primary user settings even if you are viewing a team context.

Finally, there are a number of other small tweaks that should make the navigation experience more pleasant. Check it out and let us know what you think! 

<video autoPlay muted playsInline loop>
    <source src="https://assets.northflank.com/navigation_57db44378d.mp4" />
</video>

## Additional New Features

- You can now [set a subdomain to wildcard mode](https://northflank.com/docs/v1/application/domains/wildcard-domains-and-certificates#redirect-all-subdomains) to redirect all traffic for a domain to a single port. This is helpful in cases where the domain prefix can be dynamic. 

- You can now import a new certificate to an existing domain if that domain was created using another imported certificate.

- Added new versions for all addon types:

     - MongoDB®: 5.0.21, 6.0.10, 7.0
     - Redis®*: 6.2.13, 7.2.1
     - MinIO™: 2023.9.7 
     - MySQL™: 8.0.34, 8.1.0
     - RabbitMQ®: 3.10.25, 3.11.23, 3.12.4
     - Postgres®: 11.21.0, 12.16.0, 13.12.0, 14.9.0

## Bug Fixes
- Fixed an issue with Restricted mode for GitLab integrations where repositories in GitLab subgroups were not accessible when the main group was selected.
- Fixed an issue for mobile users where branches could not be selected in the Edit deployment modal.
- Importing a MongoDB database from a connection string no longer displays an error when multiple hostnames are provided.
- Fixed an issue where trying to create an addon with a backup schedule via the CLI would fail.
- Optimised log lookback period to speed up query for long running / terminated workloads.
- When creating a new build, linked deployment services no longer appear to have no current build.

]]>
  </content:encoded>
</item><item>
  <title>What You Missed at KubeCon North America 2023</title>
  <link>https://northflank.com/blog/kubecon-north-america-2023-recap</link>
  <pubDate>2023-11-21T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[KubeCon North America 2023 was abuzz with compelling sessions on AI, security, and the environmental impact of software development.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/kubecon_recap_3bda808e40.png" alt="What You Missed at KubeCon North America 2023" />KubeCon North America 2023 was abuzz with compelling sessions on AI, security, and the environmental impact of software development. In case you missed it, here are some of the key takeaways:

## AI & GPU Utilisation are on Everyone’s Mind 
KubeCon Chicago featured 47 sessions on ML/AI, data processing, and storage (not to mention KubeCon’s inaugural co-located AI Hub). There were many discussions around the growing opportunities for AI innovation in the cloud space. Not least of these was a compelling spotlight on AI’s potential for driving improvements across security ecosystems - especially when it comes to malicious pattern recognition.

However, conference sessions did not shy away from the ethics, security, and environmental sustainability concerns that loom over AI innovation. Shane Lawrence of Shopify led an excellent session on some of the potential security issues that accompany the adoption of AI, including information leakage and exploit obfuscation - especially in cases of phishing attacks. 

Another big concern with AI, especially LLMs, is the requirement for vast, power-hungry computing resources. Currently, Kubernetes provides limited support for resource sharing and allocation. This means it’s often difficult to right-size workloads so that GPU resources are used efficiently. Kevin Klues shared some excellent insights from the work Nvidia is doing to tackle issues in this space via dynamic resource allocation (DRA), which allows platform engineers to describe GPU resources more efficiently. This allows multiple GPU types per node, and easier allocation of GPU resources via time slices and multi-instance groups. Optimising resource consumption for AI workloads will become increasingly important as large language models grow - especially while GPU hardware remains scarce.

## Supply Chain Security is Crucial
Security, Auditing, and Compliance were prominent themes at KubeCon Chicago. For vendors of software, platforms, and open-source tooling, ensuring security within cloud-native ecosystems is vital for building trust and positive relationships both within the industry and with end users.

A big focus of this year's event was how we can ensure supply chain security. Cloud-native technology is an ever-changing landscape that supports rapid development, and simplifying adoption. To achieve this, the community shares its knowledge and resources. Supply-chain security is crucial for securing open-source and community-based channels of information.

A solution that stood out across the security sessions was In-Toto. In-Toto is an attestation framework that focuses on improving supply chain security by creating secure documents detailing the contents and operations applied to each step of building secure software. These documents are signed through cryptographic primitives, ensuring their contents are secure, and allowing an end user to verify the chain of software development. In-Toto’s ability to be applied on top of existing build infrastructure to produce a secure and verifiable build chain makes it an exciting option for enhancing supply-chain security.

## eBPF Has Received Some Exciting Upgrades
For those who aren’t familiar, eBPF is a kernel technology that allows efficient interaction with kernel state through a programmable interface. The power and application of eBPF are becoming increasingly apparent, and there are a number exciting new developments in eBPF across security, networking, and observability.

Isovalent and Cilium, some of the main propagators of eBPF, have further diversified their eBPF-based operations into security via Tetragon. The first major release of this tool was published at the start of November, bringing exciting new features for monitoring runtime security in Kubernetes. Daniel Borkmann of Isovalent detailed their work in improving the performance of networking within Kubernetes. This included some impressive features such as a new veth replacement called netkit, that aims to improve the performance of eBPF when applied in the network namespace context. Support and development for BIG TCP were also covered, allowing cluster operators to achieve the maximal performance of their network stack, whilst also reducing latency and system load.

There were also a number of presentations covering eBPF in the observability space. Mauricio, Principal Software Engineer at Microsoft, talked about leveraging eBPF for gathering low-level system metrics in Linux through projects like ebpf_exporter, Inspektor Gadget, and Tetragon. Due to eBPF being low-overhead and fast, application of it for system monitoring is an ingenious idea.

## Environmental Sustainability is Increasingly Important

Environmental sustainability is a hot-topic across industries, and KubeCon was no different. The first keynote of the conference saw panellists from various environmentally-focused advisory boards discussing the current challenges of measuring the environmental impact of software. Together, they promoted a directive for software development to achieve net zero carbon emissions. 

The environmental sustainability keynote introduced a compelling technique called Software Carbon Intensity specification, which can be used to determine the environmental impact of software and hardware development. The team of panellists leading this discussion also highlighted the meaningful sustainability impacts of High-Performance Computing, AI and ML, and generic workloads. It’s clear that widespread adoption of sustainability efforts will be necessary to improve the environmental impact of the software industry.

And that’s a wrap for KubeCon North America 2023. See you in Paris for KubeCon Europe 2024! 
]]>
  </content:encoded>
</item><item>
  <title>Performance Testing for CoreDNS</title>
  <link>https://northflank.com/blog/performance-testing-for-core-dns</link>
  <pubDate>2023-10-31T00:00:00.000Z</pubDate>
  <description>
    <![CDATA[Performance Testing for CoreDNS]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/coredns_perf_b83f1ee5c3.png" alt="Performance Testing for CoreDNS" />## Introduction

In cloud native environments the infrastructure is dynamic. This is especially true for Kubernetes. Pods can come and go; services can be scaled up or down, and the underlying IP addresses can change frequently. For components to communicate reliably, you need a system that can quickly and accurately provide the correct IP addresses for given service names. KubeDNS plays the role of default DNS server in Kubernetes with CoreDNS often seen as an optional but important upgrade to increase flexibility of your DNS infrastructure. Both of these tools resolve service names to their current IP addresses and facilitate communication between different services.

At Northflank, we rely heavily on a custom version of CoreDNS to support all our internal and external routing requirements and security demands. As a piece of mission-critical infrastructure, it’s essential that our CoreDNS servers are always available, and able to serve requests within an acceptable time frame during both normal and peak hours. 

These requirements have led our team to continuously monitor our DNS performance and conduct regular performance tests. Along the way, we’ve learned an array of best practices for evaluating DNS performance for Kubernetes CoreDNS deployments. Today, we’ll take a closer look at performance testing CoreDNS. Then, we’ll walk through how you can test your own CoreDNS setup and how to interpret the results. 

## What is CoreDNS?
CoreDNS is considered an authoritative DNS server for queries within a Kubernetes cluster. Queries to external domains (like a REST API or database connection string) are sent to external resolvers, and their responses are cached by CoreDNS. The authoritative DNS server behaviour is achieved through the `dnsController` structure, which watches for changes in the Services and Endpoints Kubernetes resources. The data returned from those resources is stored in an in-memory structure, which is then utilised when an in-cluster address is queried.

CoreDNS has seen mass adoption, due in part to its performance and pluggability. CoreDNS is written in Go, and like most Go programs, it’s lightweight and efficient. CoreDNS is multi-threaded whereas KubeDNS is single-threaded which is essential when your DNS server is under load. Additionally, unlike many DNS servers, CoreDNS has a plugin architecture. This enables users of CoreDNS to extend its functionality to support their specific use cases.

At Northflank, our CoreDNS service is highly customised with both community and internal plugins, which helps us deliver an exceptional Kubernetes experience to our users. For example, we rely on the [firewall](https://github.com/coredns/policy) plugin to ensure isolation of DNS records across namespaces. We’ve also customised plugins to enable seamless resolution of workload domains, both internally and externally, while avoiding conflicts across clusters in different regions.

But a DNS server is only as good as its ability to handle requests. That’s why we rigorously performance test our CoreDNS setup, to ensure stability and reliability. 

## Performance Testing for CoreDNS

At Northflank, we measure our CoreDNS service performance via Prometheus metrics monitoring: response time, throughput, latency, and bandwidth. Our performance testing helps us determine if there are enough resources allocated to handle both normal and peak loads. It also helps us determine the point at which we need to scale a service vertically and horizontally. 
 
We also run performance tests to identify any potential regressions or performance drawbacks due to configuration changes, version upgrades, or external and internal plugins. Performance testing comes in handy any time we release a new version, as performance is a high priority for our team and our users.

Today, we’ll be assessing our DNS server performance through QPS (queries per second), response time, success and error rates, and CPU resource usage. 

Some common tools for benchmarking DNS server performance include: 

- [**DNSPerf**](https://github.com/DNS-OARC/dnsperf) – An open-source benchmarking tool for testing authoritative DNS servers created by Nominum. 

- [**Resperf**](https://github.com/DNS-OARC/dnsperf) –  An open-source benchmarking tool specifically built for testing recursive DNS servers and caching capabilities in a lab environment.

- [**Dnsdiag**](https://github.com/farrokhi/dnsdiag) – Contains three DNS testing tools: dnseval, dnsping, and dnstraceroute. It is also useful for testing DNS servers. 

- [**Queryperf**](https://github.com/jinmei/queryperfpp) – Mainly built for benchmarking performance for authoritative DNS servers. 

We’ll focus on how to employ DNSPerf to assess the performance of our DNS servers. This is the solution that we use, and it has proven useful and simple-to-use for our performance testing objectives.
 
To reproduce a production scenario, we will use a combination of both internal domains (`svc.cluster.local`) and external domains, including non-existent domains. The aim is to simulate production traffic to ensure the results are relevant.

The final results will include queries per second, average latency, max and min latency, and success/failure rates.

## Testing Methodology 

We’ll be testing within a controlled environment, using a mix of external, internal, and non-existent domains via the ‘A’ record DNS request type. The goal is to run different configurations of compute resources and evaluate the results reported by the DNSPerf tool. Then, we’ll look at how you can use these results to help improve the reliability of your CoreDNS servers during peak times and determine scalability for future growth.

Today’s tests will be executed on a Google Cloud GKE Kubernetes cluster.  As such, it’s recommended that the `DNSPerf` pod runs in a separate node to the CoreDNS pod, and both with the same resource specification.  
 
We created a series of service manifests to simulate real world workloads running in the cluster. The domains for those services, along with some external and non-existent domains, will be used as part of the DNSPerf test file. We’re also going to rely on <a href="https://prometheus.io/docs/introduction/first_steps/" target="_blank">Prometheus</a> to query the behaviour of CoreDNS CPU resource usage throughout the tests.

## Experiment Set-up

 ![Diagram of Prometheus, Grafana, CoreDNS and DNSPerf components](https://assets.northflank.com/coredns_dnsperf_diagram_a0a07ec1cc.png) 

**1. Provisioning and configuring a GKE cluster**

A GKE cluster will be used to conduct the performance test for the CoreDNS deployment. The default node pool in the cluster  will use one node of the `n2-standard-4` machine type (4vCPU 16GiB). Two extra node pools will be created: `coredns-pool` and `dnsperf-pool` each one with the appropriate `coredns-test: coredns` and `coredns-test: dnsperf` node labels to ensure pods are scheduled to the relevant worker nodes. Each new node pool consists of only one node - in this case using the `n2-standard-8` machine type (8vcpu 32GiB ram). All of the nodes are deployed in the same zone, `us-central1-a`, using a boot disk size of `100GB` and `SSD persistent disk` disk type. The result from the GCP console is the following:

 ![Screenshot from Google Cloud Platform, showing 3 node pools named coredns-pool, default-pool, and dnsperf-pool](https://assets.northflank.com/image4_d6f22f0534.png) 

The next step is to import the Kubernetes cluster credentials on the local machine and start creating the relevant workloads.

**2. Deploying CoreDNS**

CoreDNS will be deployed to the cluster using helm, provided by <a href="https://github.com/coredns/helm" target="_blank">this chart</a>.

Assuming helm is installed locally, run the following command to add the CoreDNS helm repository.

```
helm repo add coredns https://coredns.github.io/helm
```

Then, install the CoreDNS release on the `coredns-test` namespace.

```
helm --namespace=coredns-test install coredns coredns/coredns --create-namespace --f dns-performance-tests/coredns-values.yaml
```

The CoreDNS release will be provisioned in the `coredns-test` namespace. The install will create a service to access the DNS server, which will need to be configured later in the DNSPerf tool.

The `values.yaml` used is relevant to ensure compute resources match the performance test constraints, and that the CoreDNS pod is allocated on the correct node. The following configuration sets compute resources, image tag, and node selector:

```yaml
nodeSelector:
  coredns-test: coredns
resources:
  limits:
    cpu: 1
    memory: 512Mi
  requests:
    cpu: 1
    memory: 512Mi
image:
  tag: '1.11.1'
```

For this performance test run, we used version `1.11.1`.

**3. Generating DNSPerf input domains**

To properly validate CoreDNS performance, a mix of external, internal, and non-existent domains are used to replicate the variety of responses that would be experienced when running a DNS service in production. The domains to be queried are of `A` type. Expected response types include `NOERROR` and `NXDOMAIN`. 

Internal domains were created using a script that relies on the local default KubeConfig file to create the specified number of services. These follow the structure of `<svcName>.coredns-services.svc.cluster.local`. For this experiment, we created 500 services in the `coredns-services` namespace. Another 500 random service domains were generated but not created on the cluster itself; these will be the ones that should return `NXDOMAIN` from CoreDNS. 

After that, the script itself takes care of generating the input file using 100 hardcoded external domains. The resulting file contains 100 external domains, 500 internal, and 500 internal non-existent domains to use with the DNSPerf tool.

It’s worth noting that this deployment is scheduled to a dedicated node through the `nodeSelector` attribute with the node label defined in previous steps.

The script used to generate the domains is available <a href="https://github.com/northflank-guides/dns-performance-tests/tree/main/kubernetes-client-script" target="_blank">here</a>. 
 
**4. Deploying DNSPerf**
 
The DNSPerf image used is <a href="https://hub.docker.com/r/guessi/dnsperf/" target="_blank">guessi/dnsperf</a>, and the applicable manifests are available <a href="https://github.com/northflank-guides/dns-performance-tests/blob/main/dnsperf.yaml" target="_blank">here</a>. The manifest will automatically create a deployment and config map for running DNSPerf, however the config map contents will need to be updated with the output generated by the script used to set up the domains. The IP address environment variable should be assigned the value from the `coredns-coredns` service which can be obtained through:

```
kubectl get svc coredns-coredns -n coredns-test -o jsonpath='{.spec.clusterIP}{"\n"}'
```

Other configuration settings like the MAX_QPS should be set to a large value. In this case, `1000000` has been set to maximise the load placed on the DNS server. It’s expected that some queries will fail due to the CPU limits applied to the CoreDNS deployment.

The MAX_TEST_SECONDS is set to 600. The goal is to run the performance test multiple times with certain compute resources assigned and let it run for 10 minutes to gather relevant data.

Once the above attributes have been updated, the manifest can be applied to the cluster with kubectl:

```
kubectl apply -f dns-performance-tests/dnsperf.yaml
```

This will automatically create the pod, which once running will begin the DNS performance test.

To verify the performance test is running, `kubectl top pods` can be used to quickly check the CPU usage of the CoreDNS pods:

```
kubectl top pods -n coredns-test | grep coredns
```

It’s expected for CPU usage to be near the CPU limit that was set during creation.

## Testing Results

The aim of the experiment was to determine how CoreDNS performs under different resource configurations. We ran both the DNS performance tool and CoreDNS with 1, 2 and 4 vCPUs, repeating the test 10 times on each vCPU configuration. The below table details the throughput, average latency, and success rate of each test configuration.

#### Table 1: Results from DNSPerf

| CPU/DNSPerf metrics | Queries Sent | Queries completed | Queries lost | QPS       | Avg latency (s) | Success rate (%) | Failure rate (%) |
|---------------------|--------------|-------------------|--------------|-----------|-----------------|------------------|------------------|
| 1vCPU CoreDNS       | 9,195,050    | 9,195,028         | 21.3         | 15,324.57 | 0.0064973       | 99.9997          | 0.0002           |
| 2vCPU CoreDNS       | 19,428,544   | 19,428,520        | 23.4         | 32,380.13 | 0.0030644       | 99.9998          | 0.0001           |
| 4vCPU CoreDNS       | 39,182,222   | 39,182,220        | 2.7          | 65,303.55 | 0.0015155       | 99.9999          | 0.0000           |

As expected, the higher the CPU limit, the more queries per second (QPS) CoreDNS is able to handle. More processing power allows for faster processing of requests, and as a consequence, it’ll contribute to higher throughput (QPS).

Over all of the test runs, `NOERROR` responses made up 54.60% of requests, while `NXDOMAIN` came in at 45.40%. This is aligned with the proportion of existing and non-existent domains used in the input file.

The average latency is within an acceptable range, being up to 6.5ms for 1vCPU, 3ms for 2vCPU and 1.5ms for 4vCPU. Take into account that these measures are for a system under very high load.

The failure rate of requests, which in this case refers to timeout responses detected by DNSPerf, is negatively correlated with the amount of CPU allocated. The percentage was very low compared to the total number of requests. It’s worth noting here that the default timeout used by DNSPerf is 5 seconds, after that it will report the query as failed.

The following query was used to fetch the average CPU usage during the execution of the experiment:

```
sum(rate(container_cpu_usage_seconds_total{namespace="coredns-test",pod=~"coredns-coredns-.+",container="coredns"}[1m]))
```

The same query was used on different occasions to fetch the CPU based on the experiment configuration: 1, 2 and 4 vCPUs.

#### Figure: 1vCPU Usage trend

 ![Graph showing CPU usage using a 1 vCPU configuration](https://assets.northflank.com/image1_e22a91b17c.png) 

The usage for the 1vCPU configuration can be seen near the 1 core limit. There are some fluctuations that happen very quickly throughout the test execution, at some point going close to 0.5.

#### Figure: 2vCPU Usage trend

 ![Graph showing CPU usage using a 2 vCPU configuration](https://assets.northflank.com/image2_9bb4df5b1b.png) 

Similar to the 1vCPU setup, for 2vCPU we have the same situation where most of the time usage is near the limit of 2 CPU cores, with some downward spikes.

#### Figure: 4vCPU Usage trend

 ![Graph showing CPU usage using a 4 vCPU configuration](https://assets.northflank.com/image3_94b7e83f0f.png) 

Finally, for 4vCPU, we can see a similar trend as the previous samples with 3.95 CPU usage, close to the limit. Downward spikes can also be observed on some occasions. All of these spikes can be attributed to DNSPerf finishing the test run after 10 minutes, DNS timeouts, or CPU throttling due to usage approaching the limit.

For this experiment, we assigned an upper limit for the MAX_QPS parameter of 1,000,000 QPS. It’s notable that DNSPerf was adapting itself to what CoreDNS was able to handle. Occasionally, some of the queries were cancelled as a result of the high load.

However, depending on your SLOs for DNS resolution, these errors may not be acceptable. That’s why continuously monitoring QPS, latency and CPU usage metrics is essential to avoid these types of issues.

## Optimising DNS Server Performance Based on Test Results

Now that we have a solid understanding of how CoreDNS should perform according to expected load from a production perspective, we can take things one step further and consider the implementation of different strategies to improve the stability and reliability of our DNS servers.

There are a number of ways we can improve performance, reliability and stability, including:

- **Provisioning compute resources:** According to the performance data we gathered, we can now determine suitable compute resources to match the predicted load in both normal and peak cases.

- **High availability:** This is best achieved by spreading the DNS infrastructure across a pool of nodes. Kubernetes provides specific primitives like affinity settings, that allow pods to be scheduled based on placement of existing pods and node metadata. With this configuration, planned or unplanned maintenance should have a limited impact on the DNS infrastructure.

- **Vertical scalability considerations:** This refers to increasing the compute resources of CoreDNS deployment. We’ve done tests with different configurations and know the impact of more CPU and its advantages. However, as explained on <a href="https://github.com/coredns/coredns/issues/5595" target="_blank">this issue</a> there’s a point at which adding more CPU cores won’t yield higher throughput for the same CoreDNS server block. In this case, we’ve seen 4 vCPU cmes with higher throughput. Tests with higher CPU resources are beyond the scope of this guide. 

- **Horizontal scalability considerations:** Kubernetes allows simple horizontal scalability with the deployment and replica set model. When paired with vertical scaling, the replica count should consider the available resources of the underlying infrastructure and the expected max load per DNS server.  This can also be extended with horizontal pod autoscaling (HPA), either via the default Kubernetes HPA or a custom setup that monitors the various metrics that CoreDNS exposes. In the end, a balance between proper compute resources and the number of pods that handle production traffic with acceptable latency should be the goal in order to efficiently use the resources provisioned.

- **DNS Metrics:** This considers both metrics exported by CoreDNS and compute metrics like CPU and RAM. These experiments can be extended to also consider metrics coming from CoreDNS and evaluate their behaviour under load, especially request duration, request and response types. That will allow infrastructure teams to have a complete picture and understand better the conditions that influence DNS server behaviour depending on load.

## Conclusions 
In summary, there are a variety of reasons you might want to test your CoreDNS server including:

- **Scalability:** CoreDNS will likely be handling a significant amount of DNS queries, especially in large Kubernetes clusters with many services and pods. Performance testing helps ensure that as the demand increases, CoreDNS can handle the extra load without noticeable degradation in performance.

- **Configuration Optimisation:** By performance testing CoreDNS, you can identify potential bottlenecks or suboptimal configurations in your setup. For instance, determining the appropriate caching settings can significantly influence performance.

- **Resilience Under Stress:** It's not just about ensuring CoreDNS responds quickly. It's also about making sure it remains stable and doesn't crash under high loads or during spike traffic conditions.

- **Benchmarking:** If you're considering switching to CoreDNS from another DNS solution, or if you're evaluating different plugins or configurations, performance testing gives you empirical data to compare performance and make informed decisions.

- **Capacity Planning:** If you anticipate growth in your infrastructure or application usage, performance testing can help you understand when you might need to scale or modify your CoreDNS setup.

- **Validating SLAs:** If you have service level agreements (SLAs) around response times or uptime, performance testing can help validate that you are meeting these SLAs, even under high load scenarios.

- **Plugin Impact:** CoreDNS's extensible nature means that you can add various plugins to modify its behaviour. Each plugin might have its own performance implications. By testing, you can measure the impact of specific plugins and decide whether their benefits are worth the potential performance trade-offs.

- **Resource Utilisation:** Understanding how CoreDNS utilises underlying resources (CPU, memory, etc.) under different load conditions can guide decisions around infrastructure provisioning and optimisation.

- **Reliability:** Performance tests, especially when combined with chaos engineering principles, can help identify points of failure in a system. By understanding these points, you can make the necessary changes to improve a system's reliability.

DNS monitoring and testing has been critical to the resilience of our platform, ensuring all our customers’ workloads maintain high availability and operate at peak performance 24/7. We hope this deep dive into CoreDNS performance testing has equipped you with the information you need to do your own testing, and to optimise your own service discovery setup.

If you’d like to discuss CoreDNS, performance of other cloud native technologies, or the Northflank platform, we’d love to connect. Send us an email at contact@northflank.com, or drop us a comment on <a href="https://twitter.com/northflank" target="_blank">Twitter</a> or <a href="https://www.linkedin.com/company/northflank" target="_blank">LinkedIn</a>.

Thanks for reading, and we’ll see you next time!]]>
  </content:encoded>
</item><item>
  <title>Northflank Joins the Cloud Native Computing Foundation (CNCF)</title>
  <link>https://northflank.com/blog/northflank-joins-the-cloud-native-computing-foundation-cncf</link>
  <pubDate>2023-10-24T15:00:00.000Z</pubDate>
  <description>
    <![CDATA[We’re excited to announce that Northflank has joined the Cloud Native Computing and Linux Foundation as a silver member!]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_joins_cncf_small_78e23f1259.jpg" alt="Northflank Joins the Cloud Native Computing Foundation (CNCF)" />We’re excited to announce that Northflank, provider of a self-service developer platform to automate deployments, has joined the Cloud Native Computing Foundation (CNCF) as a silver member. We’re proud to be part of a community that has had such an incredible impact on the way modern software is built, deployed, orchestrated, and productionized.


<img src="https://assets.northflank.com/cncf_continuous_integration_delivery_bf3115214b.png" alt="cncf continuous integration delivery with northflank" width="750"/>

Northflank is built on [Kubernetes](https://kubernetes.io/), [Kata Containers](https://katacontainers.io/), [gVisor](https://gvisor.dev/), [Istio](https://istio.io/), and [Cilium](https://cilium.io/). Open source cloud native software is at our core, and allows us to deliver an exceptional cloud native experience for developers, devops teams, and platform engineers alike. 

The Northflank engineering team has contributed to cloud-native projects including [Kaniko](https://github.com/GoogleContainerTools/kaniko), Buildkit, Kata Containers, [Cloud Hypervisor](https://github.com/cloud-hypervisor/cloud-hypervisor), and more. When we’re not committing upstream, we’re deploying cloud-native projects into production, collecting performance data across thousands of diverse workloads, and presenting data upstream to enhance the cloud native technology stack. We’re committed to actively contributing to a community we believe has pulled the entire industry forward, and ensuring that open source cloud native technologies are secure, well-maintained, and accessible to everyone.

If you want to learn more about the CNCF, you can check out their website [here](https://www.cncf.io). If you need a fully self-service unified developer platform to automate deployment of any workload, on any cloud, at any scale, take a few seconds to experience Northflank. You can launch your first app for free [here →](https://app.northflank.com/signup)

###### *CNCF and the CNCF logo design are registered trademarks of the Cloud Native Computing Foundation.*]]>
  </content:encoded>
</item><item>
  <title> Introducing Wildcard Certificates, Template Functions &amp; Northflank Registry</title>
  <link>https://northflank.com/changelog/introducing-wildcard-certificates-template-functions-and-northflank-registry</link>
  <pubDate>2023-10-01T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Introducing updates to certificate generation, templates, addons, and more! ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/15_09_2023_cd22c6bf12.png" alt=" Introducing Wildcard Certificates, Template Functions &amp; Northflank Registry" />We’re happy to introduce a series of updates to certificate generation, templates, addons, and more! 

## Certificate Generation

Northflank now supports [wildcard certificate generation](https://northflank.com/docs/v1/application/domains/wildcard-domains-and-certificates#certificate-generation) via DCV (Domain Control Validation) or user imported certificates, which allows you to avoid the [rate limits](https://letsencrypt.org/docs/rate-limits/) set by Let’s Encrypt. This is especially useful for users with a large number of subdomains. You can set up wildcard certificates by either setting a DNS entry to delegate ACME challenges to Northflank, or by importing your own custom certificate.

We also support the addition of [domains with wildcard](https://northflank.com/docs/v1/application/domains/wildcard-domains-and-certificates) redirects to Northflank. Subdomains with wildcard redirects will be automatically verified when added. This should help you save time when adding multiple subdomains under the same domain. If your domain is also set up with a wildcard certificate, you can dynamically provision subdomains by including them inside templates.

## Templates

Templates now support more advanced handling for string interpolation, including a wide variety of functions. We’ve added [functions](https://northflank.com/docs/v1/application/infrastructure-as-code/write-a-template#functions) for string manipulation, arithmetic, and boolean logic to give you more flexibility. These functions can take arguments, references, and literals as parameters, and can also be nested. This lets you handle all sorts of use cases: convert references to base64 for use in secret files, perform conditional logic based on your template arguments, or extract data from a URL.

For more information, please [visit our documentation](https://northflank.com/docs/v1/application/infrastructure-as-code/write-a-template#functions).

Additional Template Updates:
- Secret files can now contain [references](https://northflank.com/docs/v1/application/secure/inject-secrets#dynamic-templating) in line with other fields.
- Git triggers no longer run templates on opening a pull request if the commit does not pass the file path rules.
- Empty ref fields no longer disappear when switching between editor modes.
- Trying to access a reference or argument that doesn’t exist will cause the template to throw an error rather than being replaced with an empty string. References and arguments used as function parameters will continue to be replaced with the empty string.
- You can now use multiple interpolated strings within the same field.

Bug Fixes:
- Fixed an issue with addon creation in templates where `customCredentials.dbName` could not be patched.
- Fixed an issue where JobRun nodes could not be saved in the case where a job without a valid build was added and the deployment settings had not been modified.
- Fixed an issue where cron jobs would not be suspended correctly when configuring the setting via templates.

## Addons

Addons received several updates to performance, UX, and imports including:

- Upgraded performance of MongoDB addon restores for larger databases and resource plans.
- The addon dashboard now prioritises important addon variables. More variables are accessible on the Connection Details tab.
- When importing an addon via file upload, the suggested backup name will now default to the filename instead of a random identifier.
- Importing a MongoDB addon now requires you to specify which database to import. You will receive a warning when trying to import the admin database or another system database.

## Registry

We’ve made it easier for you to [pull container images from Northflank’s container registry](https://northflank.com/docs/v1/application/build/pull-images-from-Northflank). Images are now accessible from the following URL:

```
registry.northflank.com/[projectId]/[serviceId]:[buildId]
```

Additionally, `buildId` can now be a commit SHA or `latest`, which will return the latest relevant build. Now that the image url is deterministic, it should be much easier to integrate Northflank’s registry into your workflow. 

## API & CLI

We’ve added new API endpoints for managing notification integrations, so you can dynamically add new alerts and webhooks. We’ve also added endpoints for accessing backup and restore logs. For details please visit [our API docs](https://northflank.com/docs/v1/api/integrations/create-notification-integration). 

## Usability

Updates:
- The metrics UI now allows you to quickly select a specific time period by clicking and dragging on one of the graphs.
- Added an option to permanently hide the banner prompting you to link a version control account.
- Resources can now be filtered by tag.
- Hovering over a commit SHA on the build list now displays the commit message for that commit.

Bug Fixes:
- Log filters now apply correctly to live tail logs and when navigating to the page from an external link.
- Fixed an issue that occasionally caused the logging UI to crash.
- Fixed an issue that sometimes caused the page to crash when trying to restrict a container registry integration to specific projects.
- Fixed an issue where Slack notification integrations failed to be created if lots of events were selected.
- Fixed an issue where Dockerfiles would not be fetched correctly for services using public repositories.
- Fixed formatting issues with the instances list when autoscaling scaled down the number of replicas.
- Fixed an issue where multiplayer status would be shown on top of the observability modal.
- Fixed an issue where the list of domains failed to add new entries while a search term was entered.
- Fixed an issue where pressing enter on the domain search field would open the Add Subdomain modal.
- When updating buildpack configuration for services or jobs, you will no longer receive unnecessary error messages.
]]>
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</item><item>
  <title>How to Build a Scalable Software Architecture Part 1: Monolith vs. Microservices</title>
  <link>https://northflank.com/blog/how-to-build-a-scalable-software-architecture-part-1-monolith-vs-microservices</link>
  <pubDate>2023-08-31T07:00:00.000Z</pubDate>
  <description>
    <![CDATA[In this blog, we’ll discuss the nuances of the monolith vs. microservices debate, and dig into how we at Northflank applied these principles to the software architecture for our developer platform.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/monolith_vs_microservices_85e8286c5e.png" alt="How to Build a Scalable Software Architecture Part 1: Monolith vs. Microservices" />In March, Amazon published a <a href="https://www.primevideotech.com/video-streaming/scaling-up-the-prime-video-audio-video-monitoring-service-and-reducing-costs-by-90" target="_blank">blog post</a> stating they’d reduced costs for a Prime Video monitoring service by 90% after restructuring their microservices architecture into a monolithic one. For many, this article was confirmation of a growing bias in the industry towards microservices - even when they don’t make sense.

Knowing when to use a monolith vs. when to use microservices can play a pivotal role in shaping business outcomes and customer experiences. These architectural decisions are critical for maximising the performance, reliability, cost-efficiency, and scalability of your applications. 

In this blog, we will discuss:

- [Why the monolith vs. microservices debate matters](#why-it-matters)
- [How to determine when to use a monolith and when to use microservices](#when-to-use)
- [What logical modularity is and how you can use it to develop a scalable architecture](#logical-modularity)
- [How we implemented both monoliths and microservices into the software architecture for our developer platform at Northflank](#at-northflank).

<span id="why-it-matters"></span>

## Why the Monolith vs. Microservices Debate Matters: Amazon Prime Video Case Study

To understand the importance of sound architectural decisions, we can look at the Prime Video monitoring service <a href="https://www.primevideotech.com/video-streaming/scaling-up-the-prime-video-audio-video-monitoring-service-and-reducing-costs-by-90" target="_blank">blog post</a>. In their article, the Amazon team discussed how they reduced their cloud spend by 90%, and improved their system’s scalability by consolidating several microservices into a monolith. Now it’s worth noting that multiple factors contributed to that cost reduction, including migration from expensive AWS Lambda and Step Functions to Elastic Container Service (ECS) on EC2 instances. However, the main architectural improvement was the elimination of excessive data shuffling. Initially, Amazon’s system decoded videos into frames and analysed them in several distinct services using S3 as a buffer for frame data. By consolidating their services into a single process, intermediate data could be kept in memory, eliminating the need for S3.
 
The team at Amazon Prime Video fell into a common pitfall with their initial microservice-based implementation. Their services needed to exchange large volumes of data, which wouldn’t have been the case if they implemented their system with a centralised monolithic architecture. The 90% difference in their resulting cloud spend speaks to the consequences of implementing the wrong architectural paradigm. 

What can we learn from this? When choosing between a monolith or microservices architecture, there are many angles to consider, including the cost and implementation of auxiliary tools and resources. It’s also important to prevent industry bias from clouding decision making for your specific use case.
 
<span id="when-to-use"></span>

## How to Determine When to Use a Monolith and When to Use Microservices

As with most aspects of software engineering, there is no one-size-fits-all solution. It’s important to approach your decisions thoughtfully, and with a comprehensive understanding of the tradeoffs involved. 

First, gather a thorough list of your system requirements for:
- Resource fluctuations
- Fault tolerance
- Data consistency 
- Scalability

You also need to consider the resources and expertise of your engineering organisation, and your capacity for automated build and release processes.
From there, use the below as guiding principles to assist in the decision-making process.

#### Monolithic architecture

 ![Monolith diagram](https://assets.northflank.com/monolith_wide_2e3db148ff.png)

Monolithic architectures are generally cheaper to build and maintain. If your project’s scope is relatively limited or needs to be built quickly, first consider a monolithic implementation. Monoliths are also best for use cases where a high degree of data consistency is required.

#### Microservices architecture

 ![Microservices diagram](https://assets.northflank.com/microservices_wide_0db06a3b4e.png) 

Only start considering microservices when one of the following is true:
- Your system has components with highly-variant resource requirements
- Your system will be worked on by multiple teams with different domains of expertise
- Your system has high fault-tolerance requirements
- Your application can tolerate weaker data consistency guarantees

Under the right circumstances, a microservices architecture can facilitate higher agility and reliability. 

If you choose to adopt a microservices architecture, it’s important to adhere to loose coupling between services. Also, be prepared for unexpected performance bottlenecks and weaker consistency guarantees.

The other downside to microservices is the high cost to set up, maintain, and manage them. As a minimum, teams considering a microservices architecture should plan to implement CI/CD. Luckily, there are a number of tools and platforms that make microservices architecture accessible to teams that might otherwise lack the resources required to manage a complex deployment from scratch. 

<p></p>
<a href="https://northflank.com" target="_blank">Northflank</a> is a good example, as it provides the simplicity, pricing, and performance of container services like AWS ECS, but it can accommodate microservices just as easily as monoliths. It also provides built-in <a href="https://northflank.com/docs/v1/application/release/manage-ci-cd" target="_blank">CI/CD</a>, so managing complex deployments remains simple.

When evaluating cloud platforms and other auxiliary tooling, make sure they can scale with your team and the complexity of your application both at a functional level, and a financial one. Many solutions lock you in with specific features, don’t allow for high levels of configurability, or become exorbitantly expensive as you scale. 

Regardless of your chosen architecture and tooling, it is crucial to bear in mind that good coding practices win the game. One fundamental objective is to strive for loose coupling between application modules, and strong cohesion within them. If a monolithic implementation aligns with this principle, it provides the flexibility to separate some of its modules into microservices more seamlessly as you scale. It’s possible to start out with a monolithic service and gradually transition to a microservice architecture when the need for it arises. It’s also possible to employ different architectural patterns for different parts of your application. In other words, monoliths and microservices are not mutually exclusive, and can be hybridised. 

 
<span id="logical-modularity"></span>

## How to Leverage Logical Modularity to Build a Scalable Software Architecture

There is a spectrum between purely monolithic and fully distributed architectures. In building Northflank, we’ve found that the best architecture implementations don’t demand 100% commitment to microservices or monoliths. However, it is essential to strive for logical modularity.

Logical modularity refers to the degree to which an application’s modules are independent of one another. High logical modularity implies that it’s easier to replace the implementation of a module without affecting other parts of the application. It also means that functionality can be changed more easily while modifying a minimum of modules.

Neglecting to design with logical modularity in mind can result in a system that becomes complicated and eventually untenable. This phenomenon is often dubbed as a “big ball of mud”. Perhaps an even worse result is the distributed monolith, which combines the drawbacks of both microservices and monoliths.

When striving for logical modularity, follow proven software design patterns and adhere to the following guidelines:
- Application modules should be as independent of each other as possible. In particular, there should be no dependencies to a module’s specific implementation details (low coupling)
- The logic within a module should belong together and be responsible for exactly one particular function of the application (high cohesion)

By focusing on the above best practices, we have been able to build a scalable system that gets the best out of both monolithic and microservice architectures.


<span id="at-northflank"></span>

## How We Built a Scalable Software Architecture at Northflank

At <a href="https://northflank.com/" target="_blank">Northflank</a>, we built a platform that empowers developers to build, deploy, and manage cloud-native applications from a unified interface. To do this, we needed a system that could coordinate thousands of user deployments at a time. Thus, fine-grained scalability and fault-isolation were top concerns.

To meet these requirements, we chose to build several monolithic services that are supplemented by smaller, more specialised “satellite” services. Our core components like our <a href="https://northflank.com/docs/v1/api/use-the-api" target="_blank">API</a> and platform services are monolithic. We also went with a monolithic approach for more complex subsystems, like the controllers for <a href="https://northflank.com/features/bring-your-own-cloud" target="_blank">"Bring Your Own Cloud"</a> clusters or our <a href="https://northflank.com/features/databases" target="_blank">addon</a> system. By focusing on monoliths for our core services, we were able to develop and deploy them faster and more easily. In separating the monolithic components, we’ve enabled them to scale independently. For example, our add-on controller component can be scaled separately from our user-facing platform.

In addition to our monoliths are our satellite services. They cover clear-cut concerns like workload monitoring, log forwarding, event dispatching, and so on. These specialised services enjoy all the benefits of a microservices architecture - they can be shared across our monoliths and easily added or removed.

Our platform consists of about 40 services of varying complexity and size. We believe that the modularity of our architecture enables our team to work more efficiently on independent parts of our platform in parallel. It also allows performance-critical components of the platform to be (re-)implemented with low-level languages more readily.

So far, this approach has proven itself quite effective. 
 

## Takeaways
- The choice between monolithic and microservices architectures isn't binary, and hybridising both paradigms can be a viable option. 
- Whether you choose to go with a monolith, microservices, or a combination of the two, make sure to apply good coding practices. Always aim for logical modularity, which will empower your team to manoeuvre effectively, no matter how business requirements change.
- Be sure to consider the impact of auxiliary tooling when making architectural decisions, so you can effectively manage your expenses and human resources.


For more content like this follow us on <a href="https://www.linkedin.com/company/northflank" target="_blank">LinkedIn</a> and <a href="https://twitter.com/northflank" target="_blank">Twitter</a>.
]]>
  </content:encoded>
</item><item>
  <title>New Infrastructure Alerts &amp; Improved Addon Performance</title>
  <link>https://northflank.com/changelog/new-infrastructure-alerts-and-improved-addon-performance</link>
  <pubDate>2023-08-28T23:00:00.000Z</pubDate>
  <description>
    <![CDATA[Exciting updates to infrastructure alerts, add-ons, and DX on Northflank.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2023_eb80856aa0.png" alt="New Infrastructure Alerts &amp; Improved Addon Performance" />Today’s changelog features exciting updates to infrastructure alerts, addons, and DX on Northflank.

### Infrastructure Alerts

Infrastructure alerts are now available for resource usage events. You can add new alert types from the notification settings <a href="https://app.northflank.com/s/account/integrations/notifications" target="_blank">here</a> to be alerted via webhook, Discord, Teams, or Slack. You can also configure infrastructure alert settings <a href="https://app.northflank.com/s/account/integrations/alerts" target="_blank">here</a>. This makes it easier to monitor your workloads and manage resource requirements, allowing you to have peace of mind that your production workloads are running as expected.

New alerts include:

* Events for CPU and Memory usage
* 90% Usage Spike events when a resource hits the 90% usage threshold for a short amount of time.
* 90% Usage Sustained events when a resource has been over the threshold for five minutes or more.

### Addons

Addons received several improvements to performance and configurability. These improvements mean your Addons will be more responsive to user initiated actions and faster to create and scale, leading to a much improved user experience.

These include:

* Faster TLS certificate generation
* Improved reaction time to actions like backup triggers
* Faster MongoDB replica creation through a new disk cloning mechanism
* Support for the following stateful workload versions:
  * Redis: 7.0.11, 6.2.12
  * MySQL: 8.0.33
  * MinIO: 2023.6.9
  * MongoDB: 6.0.7, 5.0.18
  * RabbitMQ: 3.10.24, 3.11.18, 3.12.0
  * PostgreSQL: 11.20.0, 12.15.0, 13.11.0

Bug fixes include:

* Fix for issue where Postgres native backup restores were not correctly owned by the provided admin user.
* Fix for issue where Postgres addon could not be properly created when using a custom database name with uppercase letters.

Additionally, you can now set an <a href="https://northflank.com/docs/v1/application/databases-and-persistence/deploy-databases-on-northflank/deploy-redis-on-northflank#redis-specifications" target="_blank">eviction policy</a> on your Redis deployment. This enables fine-grained control over your Redis deployment's behaviour as it reaches its memory limit. Under the hood, you are configuring the Redis `maxmemory-policy` directive. Learn more about managing key eviction in Redis <a href="https://redis.io/docs/reference/eviction/#eviction-policies" target="_blank">here</a>.

Lastly, you can now reset your addon, completely wiping the addon of all data and returning it to its initial state. This process does not change your existing addon settings.

### Services

You can now configure the horizontal autoscaling of your services by Requests Per Second, for an extra degree of control.

Bug fixes include:

* Fix for service deletion volume check when deleting a service via the dashboard header.
* Fix for build duration not displaying correctly. 

### Jobs

The cron schedule is now viewable on the cron job settings page on initial page load.

### Usability

You no longer have to be an administrator for a GitHub organisation to connect with Northflank. After the organisation admin has authorised the GitHub App installation, users can return to the Northflank Git settings to finalise the link.

Bug fixes include:

* Fix to address load failure for code editor view in certain countries (due to blocked CDN).
* Fix to address issue with GitLab linking, where users who have linked their GitLab account to multiple teams could have their link invalidated.
* Fix to address certain translation browser extensions causing the site to crash.
* Minor UI fixes to the project selection modal and health check status icon.

### API & CLI

API signature updates:

* The list and get endpoints for Addons, Services, Jobs, and Secrets now return the field `tags`, an array of the resource tags associated with the resource.
* The list repositories endpoint now returns the repository id field as a string for type consistency across all version control providers. Previously, GitHub and GitLab repos returned an integer while Bitbucket repos returned a string.
* Get logs endpoints now have timestamp-based pagination, and the maximum number of log lines returned has been lifted to 1000. Additionally, when used with the Northflank CLI, get logs endpoints alert the user when no tall log lines can be returned due to hitting the maximum number.
* Get metrics endpoints now have improved error messages for invalid api calls.]]>
  </content:encoded>
</item><item>
  <title>Introducing Autoscaling</title>
  <link>https://northflank.com/changelog/introducing-autoscaling</link>
  <pubDate>2023-06-08T14:00:00.000Z</pubDate>
  <description>
    <![CDATA[Automatically handle fluctuations in traffic by adjusting the number of instances based on demand, eliminating the need for manual intervention.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Changelog_Autoscaling_c38af4b053.jpg" alt="Introducing Autoscaling" />We are thrilled to release autoscaling, a powerful feature designed to automate resource management and ensure optimal performance of your deployments. Automatically handle fluctuations in traffic by adjusting the number of instances based on demand, eliminating the need for manual intervention.

<video autoPlay playsInline loop muted width="100%">
    <source src="https://assets.northflank.com/Autoscaling_V3_70a8b6b33f.mp4"/>
</video>

With autoscaling on Northflank, you can expect:

### Seamless Horizontal Scaling
<Text mt={6}>Automatically scale up the number of instances for your deployment. Northflank’s load-balancers intelligently distribute incoming traffic across multiple instances, and ensure a smooth experience for your users during traffic fluctuations.</Text>

### Right-Sized Workloads
<Text mt={6}>Set your desired minimum and maximum number of instances to optimise resource allocation and control costs. No more over provisioning.</Text>

### Intelligent Triggering Mechanisms
Trigger autoscaling based on CPU and memory thresholds, or both, to precisely respond to changes in usage patterns.

### Responsive Provisioning
<Text mt={6}>Our autoscaling mechanism evaluates your deployment every 15 seconds to determine the appropriate scaling action. By considering the average usage across all instances, we ensure the correct number of instances are provisioned for your workload at any given time.</Text>

### Stability and Reliability
<Text mt={6}>Autoscaling takes into account a moving 5-minute window of checks, preventing rapid capacity reduction due to short-term drops in activity. This ensures stability and avoids unnecessary churn and fluctuations in your deployment's capacity.</Text>

### Getting Started
Autoscaling enables teams to strike a balance between traffic spikes, performance, and cost on all paid projects. To get started with autoscaling, simply navigate to the advanced resource options in your service's resource page or during the deployment creation process. You can check out the full documentation  <a href="https://northflank.com/docs/v1/application/scale/autoscaling" target="_blank">here</a>.

Have feedback, questions, or suggestions? Please reach out through our <a href="https://northflank.com/contact" target="_blank">contact page</a> or other direct channels. We’d love to hear from you!

To stay up to date on everything new at Northflank be sure to follow us on <a href="https://twitter.com/northflank" target="_blank">Twitter</a> and <a href="https://www.linkedin.com/company/northflank/" target="_blank">LinkedIn</a>.

Happy Scaling!


]]>
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</item><item>
  <title>CNAME Chaining and Performance Enhancements</title>
  <link>https://northflank.com/changelog/cname-chaining-and-performance-enhancements</link>
  <pubDate>2023-05-19T16:00:00.000Z</pubDate>
  <description>
    <![CDATA[Introducing CNAME chaining and a suite of platform enhancements for increased speed and reliability.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/performance_bug_fixes_changelog4_e9dfe0a540.jpg" alt="CNAME Chaining and Performance Enhancements" />### Platform enhancements and bug fixes:

- Added support for CNAME chaining with custom domains to make bulk import of custom domains easier
- Improved performance to domains page to better handle teams with 100s of domains
- Added edit mode for subdomains
- Increased speed of database queries and pub/sub for job runs when loading pages with >1000 job runs
- Reduced load times for projects with >100 resources (services, jobs, addons and secret groups)
- Fixed issue where Dockerfiles failed to load on the Build options page for some combined services using GitLab and Bitbucket.
- Enhanced ability to rapidly recreate recently deleted addons with the same name. This should be helpful when working with preview environments.
- Fixed an error with Team API permission scoping that previously allowed team members to make requests against another project without permissions in certain circumstances
- Updated Luxembourg and Portugal tax rates
- Fixed issue that prevented loading of logs and metrics for deployments with long names 
- Fixed issue that caused addon backup schedules to be unset by template spec if undefined
- Updated the Fig Northflank spec https://fig.io/manual/northflank https://github.com/withfig/autocomplete/pull/1953

### Update on last week’s *code.run Google security outage:

On May 11th around 10:44 UTC, notice of 2 malicious subdomains caused Google’s Safe Browsing to mistakenly mark all subdomains of *.code.run as dangerous. Although the malicious domains were immediately removed, this action affected thousands of *.code.run domains. Affected customers have already been notified, and the scope of this issue primarily impacted development environments. We were back online within 2 hours and have partnered closely with the folks at Google to ensure that code.run is now flagged so that only malicious subdomains are impacted if similar circumstances arise in the future. We’d like to thank all the affected customers for their patience and assistance in sending unblock requests en masse.

Updates to Documentation:

- Corrected skip CI message documentation, strings do not have to be in square brackets: https://northflank.com/docs/v1/application/build/build-code-from-a-git-repository#skip-ci-with-commit-messages 
- Documented secret file injection behaviour for builds: https://northflank.com/docs/v1/application/build/inject-build-arguments#add-a-secret-file-to-a-build 
- Added infrastructure alerts: https://northflank.com/docs/v1/application/observe/set-infrastructure-alerts 
- Added logz.io log sink instructions: https://northflank.com/docs/v1/application/observe/configure-log-sinks#create-a-logzio-log-sink 
- Added guide to configure addons for high-availability: https://northflank.com/docs/v1/application/databases-and-persistence/scale-a-database#configure-addons-for-high-availability

Have feedback, questions, or suggestions? Please reach out through our <a href="https://northflank.com/contact" target="_blank">contact page</a> or other direct channels. We’d love to hear from you!

To stay up to date on everything new at Northflank be sure to follow us on <a href="https://twitter.com/northflank" target="_blank">Twitter</a> and <a href="https://www.linkedin.com/company/northflank/" target="_blank">LinkedIn</a>.

]]>
  </content:encoded>
</item><item>
  <title>Introducing Infrastructure Alerts and Logs &amp; Metrics via CLI/API</title>
  <link>https://northflank.com/changelog/introducing-infrastructure-alerts-and-logs-and-metrics-via-cli-api</link>
  <pubDate>2023-05-12T17:50:00.000Z</pubDate>
  <description>
    <![CDATA[Improve monitoring, diagnosis, and optimization of production workloads with new alerts for container restarts and volume usage, as well as access to logs and metrics via the API and CLI.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/logmetrics_cli_infra_alerts_e20d3a6806.jpg" alt="Introducing Infrastructure Alerts and Logs &amp; Metrics via CLI/API" />We are pleased to release a handful of highly requested features for monitoring, diagnosing, and optimising production workloads on Northflank. Highlights include infrastructure alerts for container restarts and volume usage, as well as access to logs and metrics via the API and CLI.


### Infrastructure Alerts:

Users will now receive alerts when a container crashes or is evicted. Additionally, users can enable volume usage alerts to receive notifications when addon or service persistent disks hit 75% or 90% volume usage. These alerts can be integrated into users’ existing notifications so they can be kept up to date via Slack, Webhooks, and Discord. This will help users pre-emptively respond to database storage limits and OOM crashes.

Learn how to setup <a href="https://northflank.com/docs/v1/application/observe/set-infrastructure-alerts" target="_blank"> alerts</a>.

 ![Northflank Infrastructure alerts: CPU, memory, volume usage](https://assets.northflank.com/infra_alerts_time_windows_818d9fce62.jpg) 



### Logs via CLI, API, and JS Client

At Northflank, we are committed to letting our users choose between a GUI and a CLI for ultimate flexibility. In our continuing efforts to deliver parity across these experiences, we’re excited to introduce log search and tailing via CLI, API, and JS Client.

Log retrieval endpoints have been added to the API to make it possible to fetch logs programmatically through HTTP requests or WebSockets. Users can now:

- Fetch a range of logs within a time window or stream logs live as they are generated (log tailing).
- Apply filters including time constraints, text, and regex.
- Fetch logs from multiple service, job, or addon containers and filter at container level.
- Quickly copy a CLI query from the logs dashboard to get started.

Learn how to programmatically search and stream logs using the <a href="https://northflank.com/docs/v1/api/log-tailing" target="_blank">API and Websockets</a>.

<video autoPlay playsInline loop muted width="100%">
    <source src="https://assets.northflank.com/cli_tailing_gif_1_3fe6d86def.mp4"/>
</video>

### Metrics via CLI, API, and JS Client

We have also added metrics querying and graphing via CLI, API, and JS client. This enables users to:

- Fetch metrics at a single point in time or within a specified time window.
- Fetch different types of metrics or multiple metrics at once.
- Fetch metrics from multiple service or job containers or filter on container level.


 ![Northflank cli showing metrics usage of a container](https://assets.northflank.com/cli_metrics_command_2_d1fb73ea1c.png) 

### CLI and JS Client Releases

Update versions with: `npm i -g @northflank/cli` or `npm i -g @northflank/js-client`

- CLI (v0.9.9) and js-client (v0.7.9) release:
    - CLI dynamic container selection for logs and metrics endpoint if –containerId is specified
    - CLI pagination support for dynamic domain and registry selection
    - js-client exec methods return proper command result object when exit is not zero
    - js-client fix of improper handling of falsy optional arguments for log and metrics endpoints

Have feedback, questions, or suggestions? Please reach out through our <a href="https://northflank.com/contact" target="_blank">contact page</a> or other direct channels. We’d love to hear from you!

To stay up to date on everything new at Northflank be sure to follow us on <a href="https://twitter.com/northflank" target="_blank">Twitter</a> and <a href="https://www.linkedin.com/company/northflank/" target="_blank">LinkedIn</a>.

]]>
  </content:encoded>
</item><item>
  <title>Enhanced Container Observability: Metrics, Logs, and Health-Checks</title>
  <link>https://northflank.com/changelog/enhanced-container-observability-metrics-logs-and-health-checks</link>
  <pubDate>2023-05-05T13:00:00.000Z</pubDate>
  <description>
    <![CDATA[Significant enhancements to container observability: Log direction toggling, custom query ranges for metrics, health-check visibility, and overview of all containers in a resource. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Changelog_observability_48ec21b77f.jpg" alt="Enhanced Container Observability: Metrics, Logs, and Health-Checks" />We’re happy to announce significant enhancements to container observability on Northflank. These enhancements should make it easier to debug deployed workloads, and understand the performance of your application. Key features include: log direction toggling, custom query ranges for metrics, ability to export logs via UI, health-check visibility, and overview of all containers in a resource.

-   Added new modal interface to observe a specific container or all containers in a resource
-   Added instance metadata with resource states, metadata, and access to logs, metrics, health-checks, and terminal shell
-   Added logs and metrics tabs for the all containers view
-   Added logs, metrics, health-checks, and shell for specific container views
-   Added Docker pull command to run your image builds locally

![Screenshot of health check interface](https://assets.northflank.com/app_northflank_3_3897f174b7.png) 

-   Added health-checks tab
    -   Added dedicated container health page
    -   Now shows container failure reason if applicable, e.g. OOM
    -   Now shows total number of times a container has restarted (due to health check or crashes)
    -   Now shows active health checks with greater detail about:
        -   type: startup, liveness, and readiness probes
        -   status
        -   last update times
        -   responses (status code + message)
        -   response latencies

![Screenshot of logs interface](https://assets.northflank.com/app_northflank_1a4dd47795.png) 

-   Enhanced logs tab
    -   Added log direction preference: either new log lines come in at the top of the window, or new log lines come in at the bottom of the window more like a terminal
    -   Added log line panel when expanding a log line with additional metadata
    -   Added new controls to select and copy a specific range of logs
    -   Added ability to select a log range and export/download the logs

![Screenshot of metrics interface](https://assets.northflank.com/image_342_1425a356c7.png) 

-   Enhanced metrics tab
    -   Added support to query metrics for all containers on the metrics page
    -   Added support to query metrics for suggested or custom time intervals
    -   Volume usage graph for services with volumes now have the same visibility as those used in addons
    -   Added events timeline with support for start, terminate & restart events
    -   Metrics view is now customisable: metrics graphs can be resized and moved around within the grid layout

![Screenshot of shell interface](https://assets.northflank.com/app_northflank_1_ab60e54144.png) 

-   Updated and moved container shell access UI into observability view for easy access when reviewing the logs, metrics and health of an individual container]]>
  </content:encoded>
</item><item>
  <title>Addon Features, Performance Enhancements, and Volume Cleanup</title>
  <link>https://northflank.com/changelog/addon-features-performance-enhancements-and-volume-cleanup-2</link>
  <pubDate>2023-04-28T10:30:00.000Z</pubDate>
  <description>
    <![CDATA[Introducing UI performance enhancements, deleting volumes for a service, faster project clean-up and more improvements.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/changelog26042023_d959e9a3bd.jpg" alt="Addon Features, Performance Enhancements, and Volume Cleanup" />- Added additional supported features per addon type via API:
     ```json
     "features": {
       "backupsDump": false,
       "customDBName": false,
       "forkAddon": true,
       "importDump": true,
       "importLive": true,
       "scaleReplicas": false,
       "tls": true,
       "externalAccess": true
     }
    ```

- Added a prompt to trigger a build after configuration changes on a build service
- Added feedback shortcut and dedicated URL: https://app.northflank.com/s/account/feedback for increased accessibility
- Added fallback values on undefined metric series values to ensure graphs are rendered correctly
- Added option for team members to skip project creation during onboarding to get started more quickly
- Added ability to delete mounted services volumes via an opt-in checkbox during service deletion
- Added ability to delete all sub-resources at once via an opt-in checkbox during project deletion
- The UI will now correctly inherit the last project and team context from local storage
- Fixed a bug that was causing logs and metrics to get stuck in an indefinite loading state under certain circumstances
- Improved log search and tailing to use a more accurate timestamp to ensure all logs are returned for the lifetime of container(s) in all circumstances.
- Fixed a bug that caused the Github link to fail when a user was missing an OAuth link for a linked VCS team
- Fixed incorrect sorting of instance list by status
- Improved readability of text on health-check tooltips
- Fixed a bug that caused occasional crashing when renaming a resource's title
- Several performance optimisations have been applied to the application:
    -   Dropdowns are now virtualised when over 20 items are present
    -   Project modal and list sector are now virtualised
    -   Filter and sorting of project subscriptions is now handled server-side
    -   Added additional subscriptions for paged project lists and a full project view
    -   Enhanced container subscriptions for pipelines when many resources are managed per stage
    -   Addons and service container subscriptions are now only listening to resources currently visible

- Released new versions of the JS Client and CLI. This release is laying important infrastructure for further improvements to logging and metrics usage via CLI. Please update using the packages below:
    - https://www.npmjs.com/package/@northflank/cli
    - `yarn global add @northflank/cli@0.9.8`
    - https://www.npmjs.com/package/@northflank/js-client
    - `npm i @northflank/js-client@0.7.8`
- New guide walking through log sinks and Logz.io <a href="https://northflank.com/guides/send-logs-to-logz-io-from-northflank" target="_blank">here</a>
    

  
To keep up with everything new on Northflank, follow us on <a href="https://www.linkedin.com/company/northflank/" target="_blank">LinkedIn</a> or <a href="https://twitter.com/northflank" target="_blank">Twitter</a>.]]>
  </content:encoded>
</item><item>
  <title>Introducing Log Sinks</title>
  <link>https://northflank.com/changelog/introducing-log-sinks</link>
  <pubDate>2023-04-17T20:30:00.000Z</pubDate>
  <description>
    <![CDATA[Log sinks enable users to seamlessly integrate their preferred log aggregators and observability platforms with their Northflank builds and deployments.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/log_sinks_northflank_45a1df77c8.jpg" alt="Introducing Log Sinks" />   We are excited to release log sinks to general availability! Log sinks allow users to seamlessly integrate their preferred log aggregators and observability platforms with their Northflank builds and deployments.

Users can now send all their Northflank container logs to one or multiple providers for any project. This makes it possible to integrate with existing observability platform(s), build dashboards across multiple platforms and providers, and access the features of a preferred log service. It also improves the ability to analyse and visualise log metrics, perform searches, set up real-time alerts, and meet log auditing requirements for resources hosted on Northflank.

This new feature enables users to improve debugging with a comprehensive view of application performance across different services in their organisation's stack. In turn, this will make it easier to identify any issues that arise during builds or deployments. It will also provide a simpler way for teams to securely audit historical logs using their existing automation tools.
 
Ready to implement log sinks? You can view the complete documentation <a href="https://northflank.com/docs/v1/application/observe/configure-log-sinks" target="_blank">here</a>.

### Supported Integrations

- Datadog
- Loki
- Papertrail
- HTTP
- AWS S3
- Mezmo
- Logtail
- Honeycomb
- Logz.io
  

### Features

- Log encoding - By default, logs will be sent as plain text. There’s also support for sending logs in a JSON format instead.
- Project restrictions - You can restrict logs from certain projects, giving you granular control over which logs reach your log sink.
- Custom label extraction - When you need to extract custom labels from a JSON message body, you can enable Custom Labels.
    - Supported for Logtail, Honeycomb, Logz.io and Datadog.
    - Your JSON messages will be parsed, and then key value pairs will be extracted and added as labels and message, or msg will be picked from the object and set as the message body that is then forwarded on to the observability provider.
    - For example: `{ message: 'hello', custom: 'field', status: 'my-status' }`

To use the API, check out the documentation <a href="https://northflank.com/docs/v1/api/integrations/create-log-sink" target="_blank">here</a>. The following is an example of how to create a Datadog log sink via the API:

 ```javascript
await apiClient.create.logSink({
  data: {
    'name': 'example-log-sink',
    'description': 'This is an example log sink.',
    'restricted': true,
    'useCustomLabels': true,
    'projects': ['default-project'],
    'sinkType': 'datadog_logs',
    'sinkData': {
      'default_api_key': 'abcdef12345678900000000000000000',
      'region': 'eu',
    },
  },
});
```

Additional new releases and updates are on the horizon for observability and alerting. To keep up with everything new on Northflank, follow us on <a href="https://www.linkedin.com/company/northflank/" target="_blank">LinkedIn</a> or <a href="https://twitter.com/northflank" target="_blank">Twitter</a>.]]>
  </content:encoded>
</item><item>
  <title>What's New at Northflank: Q1 2023</title>
  <link>https://northflank.com/changelog/whats-new-at-northflank-q1-2023</link>
  <pubDate>2023-04-04T17:30:00.000Z</pubDate>
  <description>
    <![CDATA[Over the past quarter, the Northflank team has delivered an array of new features and enhancements to streamline the process of importing, building, and deploying production infrastructure on the Northflank platform.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Changelog_Q1_2023_f8b7450606.jpg" alt="What's New at Northflank: Q1 2023" />Over the past quarter, the Northflank team has delivered an exciting array of new features and enhancements to streamline the process of importing, building, and deploying production infrastructure on the Northflank platform.

To better support migration to Northflank from other platforms, we have significantly improved the import process with changes including: increased import speeds, new live import by connection string, and automated data import into users’ Northflank generated databases.

We also recognize the importance of integrating Northflank's build platform seamlessly into your existing workflows. To this end, we have introduced multi-stage build targets, expedited cloning speeds, added options to ignore commit messages, and enabled full Git history support for a wider range of workloads and use cases.

Additionally, we have improved the overall DX with a wide range of changes. Highlights include: making managed environment variables accessible during build and runtime, extending resource name limits from 20 to 39 characters, providing visible resource IDs for easier dashboard and API usage, and refining Docker command/entrypoint and Buildpack workflows. For a complete list of improvements, please read the full release notes below.

Thank you to everyone who has provided feedback and input over the last quarter. Over the coming months, Northflank will be rolling out a number of powerful new features. To keep up to date with all that's new at Northflank, please follow us on <a href="https://twitter.com/Northflank" target="_blank">Twitter</a>. If you have any questions, comments, or suggestions, please reach out through <a href="https://northflank.com/contact" target="_blank">our contact page</a> or other direct channels. We’d love to hear from you!


## Release Notes

### Services

![Services.jpg](https://assets.northflank.com/Services_10f3b6f963.jpg)

-   Improved Docker command/entrypoint and Buildpack workflows with the following updates:
	-   Simplified custom override creation with an updated UI.
	-   Docker users can now choose between: default config, custom entrypoint, custom command, or both.
	- Buildpack users can now choose between: default config, custom process (from your Procfile), custom command (either using the default process or the Buildpack launcher as entrypoint) or custom entrypoint & command.
	-  To learn more check out: <a href="https://northflank.com/docs/v1/application/run/override-command-entrypoint" target="_blank">Documentation for Command and Entrypoint Override</a>.

-   Added ability to import Heroku apps and pipelines.
	-   You can now link your Heroku account in your user or team settings, and select which apps and pipelines you wish to import from Heroku.
	-   Added options to configure the resources and settings for each app, and choose how to integrate with your existing version control setup.
	-   Your Heroku resources are now automatically migrated to Northflank resources and saved as a new template that can be rerun or reconfigured as needed.
	-  To learn more check out: <a href="https://northflank.com/docs/v1/application/migrate-from-heroku" target="_blank">Documentation on How to Migrate from Heroku</a>.

-   Enhanced flexibility and performance of build platform.
	-   Added advanced build settings to expand VCS functionality.
		-   Added the option to include the .git folder and full git history in builds.
		-   Added commit message ignore flags. When enabled, a commit won't be built as part of CI if the commit message contains an ignore flag. After enabling the setting, we include a list of preconfigured messages such as [skip ci]. These can be added or removed to fit your workflow.
		-   Added an Allow toggle to the path rules setting. When enabled, a commit will only be built if it modifies one or more files that match any of the path rules. This is useful for monorepos, where you may only want to build commits that affect a specific folder.
	-   Added an optional target build stage for users who provide multi-stage Dockerfiles.
	-   The status of Northflank deployment services are now displayed on GitHub, and deployment statuses will be cleaned up in cases where the build source for a service is changed.
	-   Increased the speed of Git repository cloning when creating a service from a template.
	-   The dashboard of a new service is now accessible immediately, providing you with information about the current status of cloning.
	-   Improved the handling for unlinking and relinking version control accounts.
		-   When linking to a new version control account, your existing Northflank resources will automatically switch over to the new version control account. Note: the new version control account must have access to your original repository.

### Addons

![Addons.jpg](https://assets.northflank.com/Addons_d266148a34.jpg)

-   Added support for the following stateful workload versions: 
	-   MySQL: `8.0.32`
	-   RabbitMQ `3.9.29`, `3.10.19`, `3.11.10`
	-   Redis `6.2.11`,`7.0.9`
	-   MongoDB `4.2.21`, `4.4.15`, `5.0.15`, `6.0.5`
	-   Postgres `11.19`, `12.14`, `13.10`, `14.7`
	-   Minio `2023.3.13`
-   Restores of imported data are now imported to the default database. 
-   Imports from connection strings are now supported for MySQL, Postgres and MongoDB addons.
-   Added ability to override default database name during creation for: MySQL, Postgres, MongoDB, and RabbitMQ (vhost).
-   Added support for Zstandard (zstd) to compress backups and imports.
-   Improved compression speed with multi-threaded compression.
-   Added support for the following Postgres extensions: earthdistance, pgRouting, PostGIS,  and pg_stat_statements.
-   Added rollout strategy improvements when using a multi-replica configuration in your MySQL, Postgres, or Redis addons.
	-   Database replicas will now be restarted sequentially when scaled or re-deployed, ensuring minimal to zero downtime.
-   Improved connection details by including tooltips for environment variable descriptions and introducing one-click filtering on external connection details.

### Jobs

![Jobs.jpg](https://assets.northflank.com/Jobs_42976d22d3.jpg)

-   You can now specify ephemeral storage of more than 1 GB to better complete tasks that require more disk usage (eg. file processing).
-   Added ability to preview or override job configuration when manually triggering a job run. Override deployment configuration, environment variables, Docker command/entrypoint and Buildpack workflows and resources.
-   Added shareable job trigger with pre-filled configuration URL for repeatability, external workflows and collaboration with teammates.

### Usability

![Usability.jpg](https://assets.northflank.com/Usability_d0fcf13a5f.jpg)

-   Resource ID is now shown in the dashboard resource header for use with the API.
-   Resources can be renamed from the header by hovering over the existing name.
-   Resource descriptions can be edited from the header by hovering over the description field.
-   Project colours can now be updated after creation.
-   You can now initiate a CLI session with a browser login.
-   Added <a href="https://fig.io/manual/northflank" target="_blank">Fig autocomplete for Northflank/Fig CLI</a>.
-   Fixed bug that caused certain browsers to block VCS linking flow with pop-up windows.
-   Added page titles to improve navigation experience.
-   Updated first and second level sidebar navigation with hover states and icons for better readability.
-   Added ability to `forward` proxy in the CLI without requiring root user privileges using `--skipHostnames`.
-   Improved terminal output when forwarding ports for Northflank resources.
-   Added ability to delete projects with active resources.
-   Improved team invite notifications and management.
-   Updated the UI for account limits for better readability.

### API

-   Added new API endpoints for:
	-   Pausing & resuming jobs
	-   Backup schedule & retention 
	-   Billing details and invoice(s)
-   Added ability to pick the latest version, latest major version or latest patch version for addon deployments via API using `latest` or `{version}-latest`, ie `14-latest`.

### Additional Updates and Improvements

-   Added one time password support (Multi-factor Authentication).
	-   Teams can view whether or not teammates have MFA enabled.
-   Improved invoices with ability to specify additional emails for delivery, add EU/UK VAT IDs with associated tax addresses, and override invoice name from default user/team name.
-   Extended resource name limits from 20 to 39 characters.
-   Added ability to configure resource list length app-wide.
-   Removed password requirement for SSO accounts.
-   Added new managed variables to access deployed image versions, resources, hosts and domains via injected environment variables:
	-   Build time:  such as `NF_GIT_SHA`, `NF_GIT_BRANCH` and `NF_PREVIOUS_BUILD_GIT_SHA`
	-   Runtime: `NF_PLAN_ID`, `NF_CPU_RESOURCES`, `NF_RAM_RESOURCES`, `NF_EPHEMERAL_STORAGE`, `NF_OBJECT_ID`, `NF_PROJECT_ID`, `NF_DEPLOYMENT_SHA`, `NF_DEPLOYMENT_REPO`, `NF_DEPLOYMENT_BRANCH`, `NF_HOSTS`
-   Env/secret file templating can now use managed variables.
-   Added guides for <a href="https://northflank.com/guides/deploy-kong-gateway-on-northflank-to-proxy-microservices" target="_blank">Kong</a> and <a href="https://northflank.com/guides/deploy-traefik-on-northflank-to-proxy-microservices" target="_blank">Traefik</a>.]]>
  </content:encoded>
</item><item>
  <title>June Changelog - Read replicas, networking and volumes</title>
  <link>https://northflank.com/changelog/june-changelog-read-replicas-networking-and-volumes</link>
  <pubDate>2022-07-04T08:00:00.000Z</pubDate>
  <description>
    <![CDATA[Developer experience enhancements, new documentation and API endpoints with networking and volume improvements.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Group_412_318f89738e.jpg" alt="June Changelog - Read replicas, networking and volumes" />Welcome to Northflank's June changelog. We've been working on a wide variety of product and developer experience enhancements across the platform. For production workloads improvements to read replicas and networking are most notable.

### June's improvements and fixes:
- Added `HOST_READ` connection detail to multi-replica MySQL and Postgres addons
- Added platform handling for volume resize cooldowns on AWS clusters
- Added support for overriding image entrypoints providing full flexibility for starting arbitrary processes within the container. This also adds full support for multiple process types in buildpack images that use Procfiles
- Added team invite components onto entity dashboards and on-boarding flow allowing developers to jump right into their teams
- Added support for MySQL global transaction ID (GTID) mode. GTID mode is now activated by default and improves read replication stability
- Added ability to disable routing on default Northflank domains when custom domains are added to a port
- Added query parameter syncing across team multiplayer, ensuring team filtering was correctly propagated
- Added icons to notification integration event selection
- Disabled TLS 1.0 and TLS 1.1 to improve SSL rating and security
- Improved addon connection details with external connection details now highlighted and increased search capabilities
- Improved domain lists with reactivity to external changes from other uses
- Improved handling of users, group and volume permissions: Workloads are started with the correct user group if the image defines a non-root user which fixes several issues with volume permissions for buildpack-based images. Also, the metadata of files on persistent volumes will be automatically changed such that their group owner matches the group of the image’s main process ensuring that workloads can always access attached persistent volumes.
- Improved service mesh TCP idle timeouts for internal and external endpoints allowing connections to be maintained for longer than 60 minutes
- Improved handling of unexpected error codes from self-hosted providers, and are now displayed to the user rather than being suppressed to make it clearer when there is an issue with the provider
- Improved performance when handling git cloning for template repositories with use of a queue
- Improved developer experience of the service creation and type selection
- Improved stability of metrics-server inside Kubernetes to improve platform stability
- Improved MySQL dump imports. Importing a dump will now import to the default database if no database is specified in the dump
- Improved account limits UI to show current usage and percentage of total limit
- Updated default branch rule for build services to be `main` in cases where the default branch has not been retrieved from a repository
- Improved global docker registry caching to better work with Kaniko and Buildkit cache
- Improved latency and responsiveness of environment editors
- Fixed creating a volume during service creation for teams
- Fixed environment editor crash on null secrets array
- Fixed issue with volume being marked as deleted before being provisioned
- Fixed volume configuration allowing selection of volume size smaller than current volume configuration
- Fixed issue with utf-8 encoding for secret files and config maps
- Fixed an issue with GitLab account links where builds could in rare cases fail to be triggered if multiple users with the same account linked triggered builds simultaneously
- Fixed Ruby on Rails template
- Fixed cron schedule text to show a 24 hour clock instead of 12 hour

### Guides

- [Updated how to deploy Payload CMS](https://northflank.com/guides/deploying-payload-cms)
- [Deploy 1Password Connect](https://northflank.com/guides/deploy-a-onepassword-connect-server-on-northflank)
- [Deploy YT-Spammer-Purge](https://northflank.com/guides/deploy-yt-spammer-purge-to-automatically-remove-spam-comments-on-your-youtube-videos)
 
### API, JS-client and CLI:
- Added support for setting Docker entrypoint overrides during service and job creation
- Added `Set service Docker entrypoint override` and `Set job Docker entrypoint override` endpoints to modify Docker entrypoint override after resource creation
- `Update job deployment endpoint` can now be called without passing in a build service id, which will cause it to keep using the existing build service.
- Improved performance of `Update service runtime environment` endpoint to make response time faster
- Fixed an issue with `Scale service` endpoint where scaling to zero instances would not update correctly.
- Improved error handling when linking an addon to a secret group to make it clearer which keys are valid for each addon
- Fixed issue with `Update job settings` endpoint where `runOnSourceChange` setting would sometimes not update correctly

### Documentation
- Added Migrate from Heroku section: [https://northflank.com/docs/v1/application/migrate-from-heroku](https://northflank.com/docs/v1/application/migrate-from-heroku)
- Improved forwarding section: [https://northflank.com/docs/v1/api/forwarding](https://northflank.com/docs/v1/api/forwarding)
- Added documentation for Slack and Discord notification integrations, updated Webhook integration documentation: [https://northflank.com/docs/v1/application/notifications/notification-integrations](https://northflank.com/docs/v1/application/notifications/notification-integrations)
- Added Docker command override documentation: [https://northflank.com/docs/v1/application/run/cmd-override](https://northflank.com/docs/v1/application/run/cmd-override)
- Added videos to command execute documentation: [https://northflank.com/docs/v1/api/execute-command](https://northflank.com/docs/v1/api/execute-command)
- Made guide to creating and editing deployment services clearer: [https://northflank.com/docs/v1/application/run/run-an-image-continuously](https://northflank.com/docs/v1/application/run/run-an-image-continuously)
- Added information on ephemeral storage: [https://northflank.com/docs/v1/application/scale/increase-storage#scale-ephemeral-storage](https://northflank.com/docs/v1/application/scale/increase-storage#scale-ephemeral-storage)
- Added scheduled addon backups documentation: [https://northflank.com/docs/v1/application/databases-and-persistence/backup-restore-and-import-data#schedule-backups](https://northflank.com/docs/v1/application/databases-and-persistence/backup-restore-and-import-data#schedule-backups)
- Updated billing section to include BYOC and enterprise options: [https://northflank.com/docs/v1/application/billing/pricing-on-northflank](https://northflank.com/docs/v1/application/billing/pricing-on-northflank)
- Updated domain and networking instructions: [https://northflank.com/docs/v1/application/network/networking-on-northflank](https://northflank.com/docs/v1/application/network/networking-on-northflank)
- Improved in app documentation and updated links


]]>
  </content:encoded>
</item><item>
  <title>May Changelog - Guides, Enhancements and more</title>
  <link>https://northflank.com/changelog/may-changelog-guides-enhancements-and-more</link>
  <pubDate>2022-06-01T14:00:00.000Z</pubDate>
  <description>
    <![CDATA[Introducing Northflank’s May Changelog with DX improvements, fixes and container isolation case study.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_may_changelog_3d33487195.jpg" alt="May Changelog - Guides, Enhancements and more" />Welcome to the May changelog, this has been a busy month of releases, iterations and many new faces migrating production workloads from Heroku to Northflank!

Will and Cameron sat down with the Kata Containers team to talk about how Northflank leverages Kata, KVM and sandbox isolation to securely operate our multi-tenant infrastructure. https://medium.com/kata-containers/kata-containers-northflank-case-study-6ff0ce17bfd1

### Guides
This month we were excited to welcome Jesbin and Amit to the Northflank guides blog, thank you for your contributions and excited to work together on more deployment guides!

- [How to deploy Hasura](https://northflank.com/guides/deploy-hasura-on-northflank) (Thanks Amit)
- [How to deploy Bitwarden](https://northflank.com/guides/deploy-bitwarden-on-northflank) (Thanks Jesbin)
- [How to deploy Dragonfly](https://northflank.com/guides/deploy-dragonfly-on-northflank-with-docker) 
- [How to deploy Meilisearch](https://northflank.com/guides/deploy-meilisearch-on-northflank)
- [How to deploy Uptime Kuma](https://northflank.com/guides/deploy-uptime-kuma-on-northflank)
- [How to deploy Joomla](https://northflank.com/guides/deploy-joomla-on-northflank)
- [How to deploy Wiki JS](https://northflank.com/guides/deploy-wiki-js-on-northflank)
- [How to deploy Shynet](https://northflank.com/guides/deploy-shynet-on-northflank)

### May’s improvements and fixes:
- Added force certificate re-generation button in the UI
- Added volume configuration during service creation
- Added additional DNS purge when a custom domain is linked to a service
- Added quotes correctly to original Docker CMD when copying
- Added additional shortlinks to add new Git integrations in areas listing VCS data
- Added support for reaping zombie processes which are not always properly cleaned up by applications spawning child processes. This also fixes RabbitMQ restarting due to accumulation of zombie processes created when running health checks
- Fixed distributed MinIO addons failing during startup phase in some circumstances
- Fixed Buildpack builds relating to CVE-2022-24765
- Fixed cursor hiding text when configuring alias in addon secret group
- Fixed termination signalling for containers. Containers are now being sent a SIGTERM signal before being terminated. This allows the workload to intercept the exit signal and shut down properly before final termination 
- Fixed clock drift within sandboxed containers that led to deviating system time in different containers
- Fixed an issue with service creation from template where a build would not be initiated automatically
- Fixed inconsistent addon upgrade icons and added shortcut to upgrades page
- Fixed issue where jobs deploying from a build service that were set to run on image change would sometimes fail to run after a build was completed
- Improved performance for fetching user version control data (repositories, branches, pull requests)
- Improved handling of xterm when a browser window is resized for terminal and exec
- Improved handling for copying and paste for terminal and exec
- Improved Postgres addon startup times using optimised probe settings 
- Correctly display usage in years for invoice item breakdowns
- Improved handling of sudo mode when loading secret files and other secrets
- Improved validation triggers for branch option selection, port duplication, and health-checks
- Removed health checks from project quick starts
 
### API, JS-client and CLI:
- Run Job endpoint now optionally takes a billing object with a deployment plan to override the current deployment plan for a single job run
- Get Job runs and Get Job details endpoints now return `concludedAt`, the timestamp when the run has concluded
- Fixed an issue where creating a service could sometimes return an error code despite the creation being successful
]]>
  </content:encoded>
</item><item>
  <title>Heroku, alternatives, and how to migrate</title>
  <link>https://northflank.com/blog/heroku-alternatives-and-how-to-migrate</link>
  <pubDate>2022-05-12T11:00:00.000Z</pubDate>
  <description>
    <![CDATA[Heroku was a great developer platform. Times have moved on, what is next? Migrating away from Heroku is now a priority for many. We dive into the reasons why and what are your options.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Heroku_Migrate_2b6d829eff.png" alt="Heroku, alternatives, and how to migrate" />Heroku is a cloud service platform that makes it simple to deploy your code using buildpacks and hosting databases using addons like Postgres and Redis. For years it has been popular because it reimagined how fullstack applications could be deployed into production without hiring infrastructure engineers. But recent developments have caused users to start leaving the platform and looking for alternatives.

Heroku raised $13m in funding and was acquired for $212m in cash by Salesforce in 2010. As time has moved on, they have not been able to keep up, the <a href="https://devcenter.heroku.com/changelog" target="_blank">changelog</a> paints a picture of a company unable to ship any product, with the last two years of updates being little more than version bumps and feature deprecations. Recent security incidents and outages have left Heroku users asking what next? There is frequent mention of project Periwinkle where Heroku could be discontinued in favour of a Salesforce cloud offering. Today Heroku handles over 60bn requests a day, 600k requests a second with $500m ARR, broken down by $200m in self-service and $300m in enterprise revenue. Heroku now has ~123 employees according to LinkedIn, down 30% in 2 years, and much below the peak in 2018/2019 at over 400 people working on the platform.

<InfoBox className="BodyStyle">

### Other relevant Heroku content on Northflank

<a href="https://northflank.com/docs/v1/application/migrate-from-heroku" target="_blank">Documentation on how to migrate from Heroku.</a>
<br/>
<a href="https://northflank.com/heroku-pricing-comparison-and-reduction" target="_blank">Heroku pricing comparison and reduction.</a>

</InfoBox>

### What does Heroku do well?

<br/>

<b>Dyno</b> - Heroku's version of containers allow for a serverless like experience in deploying your software. Scale up and down, horizontally or vertically as required.

<br/>
<br/>

<b>Buildpacks</b> - Heroku uses buildpacks to compile application code and create a “slug”, a pre-packaged copy of the application that is optimised for distribution into a dyno. Heroku supports the most common programming languages (Java, Node.js, Scala, Clojure, Python, PHP, Ruby, and Go) but for deployments in any other programming language, custom buildpacks can be used. Buildpacks allow you to get started quickly without wrangling with dependencies or configuration. 

<br/>
<br/>

<b>Environments variables</b> - either through their CLI or UI, users can change the configuration variables. 

<br/>
<br/>

<b>Rollbacks</b> - offer an overview of release history, allowing users to roll back to a prior state with ease. 

<br/>
<br/>

<b>Databases and addons</b> -  Heroku offers Postgres, Redis and Kafka as 1st party databases. Any other databases or addons required are available through their large library of addons developed by third-parties for data stores, database UIs, detailed logs and monitoring, notifications, caching, CMS, testing queuing, networking services (DNS; proxies management), security…

<br/>
<br/>

<b>Database restores and backups</b> - using <a href="https://devcenter.heroku.com/articles/heroku-postgres-logical-backups" target="_blank">Logical Backups</a> users can create a single snapshot file from Heroku Postgres databases. Via the CLI users can manually capture or schedule logical backups, which will run a series of SQL COPY statements in a single transaction to produce a consistent snapshot across the database. 

### What does Heroku do badly?

Heroku publicly <a href="https://status.heroku.com/incidents?page=1" target="_blank">shares their outages</a>, which occur on average every week. The latest and most concerning is the catastrophic hack suffered on and before the 15th April 2022, in which multiple Heroku GitHub OAuth applications were targeted, potentially exposing code source repositories, log sinks and service environment variables. Users have been unable to deploy new code to their Heroku services using GitHub for weeks. It is unclear if Heroku understands exactly what attackers were able to gain access to. These two articles by <a href="https://www.theregister.com/2022/04/21/github-stolen-oauth-tokens-used-in-breaches/" target="_blank">The Register</a> and the <a href="https://github.blog/2022-04-15-security-alert-stolen-oauth-user-tokens/" target="_blank">GitHub Blog</a> dive deeper into the details of the attack. Following are some Tweets sharing the sentiment of some Heroku users: 

<blockquote class="twitter-tweet" data-theme="dark"><p lang="en" dir="ltr">&quot;We're noticing issues with dyno restarts, dyno scaling, release phase, builds, app setups and Heroku CI&quot;<br/><br/>so, all the things, then. <a href="https://t.co/NPqOszQM1u">https://t.co/NPqOszQM1u</a></p>&mdash; Jon Yongfook (@yongfook) <a href="https://twitter.com/yongfook/status/1518834706673856512?ref_src=twsrc%5Etfw">April 26, 2022</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

<blockquote class="twitter-tweet" data-theme="dark"><p lang="en" dir="ltr">I&#39;m pretty salty Heroku hasn&#39;t communicated when they&#39;re planning on fixing or making their GitHub integration work again.<br/><br/>I totally understand the desire to not give a firm date, but come on, it&#39;s been like 11 days and we&#39;re still unable to use review apps among other features.</p>&mdash; Andrea Fomera (@afomera) <a href="https://twitter.com/afomera/status/1518962693461348352?ref_src=twsrc%5Etfw">April 26, 2022</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

Another recent disappointment from a large percentage of Heroku customers is their refusal to accept payments from non-enterprise Indian customers with Indian credit cards. On 15th December 2021, Heroku announced ​​that they were unable to verify and process India-issued credit cards for Heroku Online customers. This came after the new Reserve Bank of India (RBI) regulations stated that they can no longer process automatic recurring payments.

<blockquote class="twitter-tweet" data-theme="dark"><p lang="en" dir="ltr">If you&#39;re an Indian company, don&#39;t bother trying to pay for your Heroku account right now. Because Heroku is just flat out refusing to accept any India-issued cards. We even asked them for one-time payment links, but no luck. Thank you RBI for protecting us 🙏🏾 <a href="https://t.co/ffQxscVcWA">pic.twitter.com/ffQxscVcWA</a></p>&mdash; Rohin Dharmakumar (@r0h1n) <a href="https://twitter.com/r0h1n/status/1514475620599996418?ref_src=twsrc%5Etfw">April 14, 2022</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

<blockquote class="twitter-tweet" data-theme="dark"><p lang="en" dir="ltr">Owning a credit card in India is almost pointless for subscriptions/online services!<a href="https://twitter.com/heroku?ref_src=twsrc%5Etfw">@heroku</a> is there anyway I can pay for your service?<a href="https://t.co/ljvf1sC7jP">https://t.co/ljvf1sC7jP</a></p>&mdash; Mustafa (@mufasaYC) <a href="https://twitter.com/mufasaYC/status/1502989351575203842?ref_src=twsrc%5Etfw">March 13, 2022</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

<blockquote class="twitter-tweet" data-theme="dark"><p lang="en" dir="ltr">This is not an acceptable. I’m an Indian and I stay in India, from where can I get a non Indian card?⁰When I started using <a href="https://twitter.com/heroku?ref_src=twsrc%5Etfw">@heroku</a> you used to accept Indian cards, now you can’t accept, that’s your problem, why’re you suspending my account? Why should I suffer?<a href="https://twitter.com/SalesforceOrg?ref_src=twsrc%5Etfw">@SalesforceOrg</a> <a href="https://t.co/UwCzxr2nGo">pic.twitter.com/UwCzxr2nGo</a></p>&mdash; Rivu Chakraborty (@rivuchakraborty) <a href="https://twitter.com/rivuchakraborty/status/1496910767551295490?ref_src=twsrc%5Etfw">February 24, 2022</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

Since the acquisition by Salesforce in 2010, Heroku has renounced their leadership in product delivery, engineering, security and reliability. Once a pioneer they are now stuck in maintenance mode bordering on sunsetting the entire project. Heroku’s revenue of $500m would be a very large independent cloud technology company but only makes up 2% of Salesforce’s global revenue in 2021.

<blockquote class="twitter-tweet" data-theme="dark"><p lang="en" dir="ltr">I&#39;m sad to say but I think we&#39;re witnessing the demise of Heroku:<br/><br/>· Stagnated product offering<br/>· Customer-hostile practices (e.g. no easy way to access Performance dynos)<br/>· Recent security incident with GitHub tokens (including poor/lack of communication)<br/>· Reliability issues</p>&mdash; Greg Navis (@gregnavis) <a href="https://twitter.com/gregnavis/status/1519359638390595585?ref_src=twsrc%5Etfw">April 27, 2022</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

<blockquote class="twitter-tweet" data-conversation="none" data-theme="dark"><p lang="en" dir="ltr">Depending on who you ask, Heroku has been in steady decline - or at the very least, has stagnated at the technological level - since its acquisition by Salesforce in 2010.</p>&mdash; Hacker News (@Hacker__News) <a href="https://twitter.com/Hacker__News/status/1519453527508234243?ref_src=twsrc%5Etfw">April 27, 2022</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

<blockquote class="twitter-tweet" data-conversation="none" data-theme="dark"><p lang="en" dir="ltr">I feel like they exploded on the scene, got acquired by Salesforce, and then stagnated as the Enterprise sales team moved in. Heroku ops are still first-class (until last week at least 😅), but there’s been no product or pricing innovation in years. Only two regions, in 2022??</p>&mdash; Dave (@davemetrics) <a href="https://twitter.com/davemetrics/status/1519362480404762624?ref_src=twsrc%5Etfw">April 27, 2022</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

### What are the alternatives to Heroku?

Several production ready, credible alternatives to Heroku include <a href="https://northflank.com/" target="_blank">Northflank</a>, Cloud.run, Render, Platform.sh,  and Fly.io.

Developers want a platform to deploy their code which is flexible, reliable and secure. With rapidly evolving technologies and industries, a PaaS has to be constantly evolving and adding support for new databases and other developer needs. Ensuring reliability and security will make a PaaS trustworthy for production environments.

Northflank is a developer platform to build and scale microservices, jobs and managed databases with the ease of use of Heroku and  pricing and comprehensiveness of AWS in a delightful UI, API and CLI.

In minutes, developers can integrate with GitLab, GitHub & Bitbucket, provision highly available databases, schedule cron jobs, configure networking, deploy services and collaborate with their team of other developers. Northflank will build and deploy any workload: ephemeral, stateful, horizontal or vertically scalable. Northflank can be accessed through a flexible and comprehensive API while offering a severless like experience, without any of the drawbacks of existing serverless providers. Builds, containers, jobs and database ‘as a service’ in a unified experience.

### How do you migrate away from Heroku?

Northflank faithfully enables developers to deploy their 12factor applications, written by Heroku's co-founder Adam Wiggins <a href="https://12factor.net/" target="_blank">here</a>.

The first step to migrate from Heroku, is to <a href="https://app.northflank.com/signup" target="_blank">create a Northflank account</a> and connect your version control, either GitHub, Bitbucket or Gitlab. <a href="https://northflank.com/docs/v1/application/migrate-from-heroku" target="_blank">Our docs on how to migrate from Heroku to Northflank</a> will provide more detail.

All your code will have been built using buildpacks on Heroku, which are also supported by Northflank. Whether you use Heroku buildpacks or custom ones for unsupported languages or frameworks, you will be able to use the same buildpacks to build your services on Northflank.

Once that is set up, you can import your existing databases from external archives or existing live databases. Our <a href="https://northflank.com/docs/v1/api/addons/import-addon-backup" target="_blank">detailed docs on how to import an existing database</a> will be useful in this process.

#### Heroku concepts

  | Heroku | Northflank |
  | --- | --- |
  | App | Service|
  | Dynos | Instance |
  | Standard Heroku git push & build & deploy | Combined service |
  | Worker dynos | Service with no public networking |
  | Scheduler | Cron Jobs |
  | Postgres | Addons Postgres |
  | Papertrail addon | Logs (search, tail, 90+ day retention) - log sink coming soon |

A streamlined feature to import your Heroku project to Northflank in seconds is arriving soon. You will be able to login with your Heroku account, select the projects and services you wish to import. Setting up databases, Git repositories and your environment variables is all handled seamlessly and will be running in production in your Northflank account.

<div>
    <a href="https://app.northflank.com/signup">
        <Button variant={["large", "gradient"]}>Get started now</Button>
    </a>
</div>]]>
  </content:encoded>
</item><item>
  <title>Scheduled backups, guides and enhancements</title>
  <link>https://northflank.com/changelog/scheduled-backups-guides-and-enhancements</link>
  <pubDate>2022-05-03T12:00:00.000Z</pubDate>
  <description>
    <![CDATA[Create backup schedules to keep your production data safe and ready for restore]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_backup_schedules_d3ed22686e.jpg" alt="Scheduled backups, guides and enhancements" />Today we are excited to release backup schedules. You can now create regular backups for your databases and addons, enhancing your production workloads on Northflank. Having regular backups is essential as it allows you to restore your addon in case of data loss, bad migrations or queries. Scheduled backups can be configured flexibly to ensure your individual backup requirements can be met.

Backup schedules on your addon allow up to three schedules. Configure the following parameters:
- **Backup type**: snapshot (recommended) or dump [(Backup Documentation)](https://northflank.com/docs/v1/application/databases-and-persistence/backup-restore-and-import-data#create-a-backup)
- **Backup frequency**: choose between hourly, daily or weekly backups and select the appropriate time of the day
- **Retention period**: how long the backup will be stored until it's deleted


Notable features:
- Configure multiple schedules, one per type (hourly, daily or weekly)
- The total number of backups created for a schedule before any backups expire is limited to 500 backups
- Backups created by a schedule can be manually changed to not expire. To retain the backup indefinitely, click on the backup and hit `Retain backup forever` using the `save` button
- For backups that were created by a schedule, you can look up the schedule and the retention time on a per backup basis
- If two or more schedules overlap, only one backup is created. The UI and API will show all associated schedules and the highest retention period is applied.

![northflank-backup-schedules.png](https://assets.northflank.com/northflank_backup_schedules_b25e8f5881.png)

New Guides:
- Dgraph [https://northflank.com/guides/deploy-dgraph-on-northflank](https://northflank.com/guides/deploy-dgraph-on-northflank)
- Sourcegraph [https://northflank.com/guides/deploy-sourcegraph-on-northflank](https://northflank.com/guides/deploy-sourcegraph-on-northflank)
- NocoDB [https://northflank.com/guides/deploy-nocodb-on-northflank](https://northflank.com/guides/deploy-nocodb-on-northflank)
- Nextcloud [https://northflank.com/guides/deploy-nextcloud-on-northflank](https://northflank.com/guides/deploy-nextcloud-on-northflank)
- Keystone [https://northflank.com/guides/deploy-keystonejs-on-northflank](https://northflank.com/guides/deploy-keystonejs-on-northflank)

Other features & fixes:
- Added new GitLab application handling for upcoming token rotation requirements
- Added team invitation expiry and ability to regenerate an invitation
- Added editing of Dockerfile during service creation
- Improved performance of backup and VCS subscriptions
- Improved UI handling of log search between two time ranges
- Updated Flask template repository
- Fixed project icon disappearing from documentation
- Fixed build service handling in deployment component when no build is present
- Fixed secret group creation when modifying alias keys in a particular order

]]>
  </content:encoded>
</item><item>
  <title>Supporting Expiring OAuth Access Tokens for GitLab</title>
  <link>https://northflank.com/blog/supporting-expiring-oauth-access-tokens-for-gitlab</link>
  <pubDate>2022-04-20T11:00:00.000Z</pubDate>
  <description>
    <![CDATA[Deep dive into handling token rotation for your GitLab integration including how to handle expiring tokens, how to store access tokens and how to implement a lock to prevent multiple refreshes at once.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Oauth_Tokens_22f69a7e08.png" alt="Supporting Expiring OAuth Access Tokens for GitLab" />GitLab added an option to OAuth integrations <a href="https://gitlab.com/gitlab-org/gitlab/-/issues/340848" target="_blank">to have your access tokens expire after two hours</a>, and are deprecating support for non-expiring tokens in their <a href="https://gitlab.com/groups/gitlab-org/-/milestones/65#tab-issues" target="_blank">May 2022 release</a>. As I have talked about GitLab OAuth integration before, I’m writing this as an update to tell you how you might want to handle token rotation for your GitLab integration, whether you are creating a new application or updating your existing one.

### What is an expiring OAuth access token?

As I touched on previously, after creating an OAuth Application on GitLab, you can send your users to GitLab’s OAuth endpoint, where they are prompted to sign into their account and authorise your application. This redirects them to your website with a code in the query parameters, and you can exchange that code with GitLab to receive an access token. When you make the request, you will receive a response like this:

```jsx
{
 "access_token": "de6780bc506a0446309bd9362820ba8aed28aa506c71eedbe1c5c4f9dd350e54",
 "token_type": "bearer",
 "expires_in": 7200,
 "refresh_token": "8257e65c97202ed1726cf9571600918f3bffb2544b26e00a61df9897668c33a1",
 "created_at": 1607635748
}
```

This access_token can then be passed as a bearer token in the Authentication header when making HTTP requests to the <a href="https://docs.gitlab.com/ee/api/oauth2.html" target="_blank">GitLab API or to authenticate via git</a>. Previously, this access token would not expire unless you made a request to revoke it. This posed a possible security issue - if a malicious actor obtained the token, or the token was leaked, the malicious actor would be able to authenticate as the user, and the stolen token would remain valid until it is manually revoked by either you or the user - meaning they may have access for a very long time without you knowing!

To solve this issue, GitLab’s access tokens now expire after two hours. This means if a token is stolen, the malicious actor would have a very limited window to use the token. Whilst a lot of damage can still be done in the space of two hours, it helps to minimise the damage from some ways a token might get leaked, for example an access token being pushed to a public repository or a database dump being leaked.

### How do you handle expiring tokens?

Once a token has expired, your API requests will fail and you will be prompted by GitLab to generate a new token. To do this, you must make another request to GitLab’s OAuth endpoint. Much like the initial link, you must provide your application’s Client ID and Client Secret, but instead of passing the linking code, you will pass in the user’s refresh token. This will invalidate both the existing access token (if it is still valid) and the refresh token you just used, and return a new access token and refresh token. The access token will be valid for another two hours. You will need to store the new refresh token, as this token will be used the next time you request a new token.

```jsx
import fetch from 'node-fetch';

const CLIENT_ID = process.env.GITLAB_CLIENT_ID;
const CLIENT_SECRET = process.env.GITLAB_CLIENT_SECRET;

export const refreshGitlabOAuthToken = async ({ refreshToken }) => {
  const formBody = {
    client_id: CLIENT_ID,
    client_secret: CLIENT_SECRET,
    refresh_token: refreshToken,
    grant_type: 'refresh_token',
  };

  const body = new URLSearchParams(formBody).toString();

  const options = {
    method: 'POST',
    headers: { 'content-type': 'application/x-www-form-urlencoded' },
    body,
  };

  const url = 'https://gitlab.example.com/oauth/token';

  const response = await fetch(url, options);

  if (!response.ok) {
    const message = await response.text();
    throw new Error(`Failed to refresh token. Status ${response.status}. Message: ${message}`)
  }

  const { access_token: accessToken, refresh_token: newRefreshToken, expires_in: expiresIn } = await response.json();

  return {
    accessToken,
    refreshToken: newRefreshToken,
    expiresIn,
  };
}
```

By requiring the application’s Client ID and Client Secret, tokens are more secure. Even if a user’s refresh token is stolen by a malicious actor, that refresh token cannot be exchanged for a new token unless they also have access to your application’s secrets. This means the malicious actor would have to compromise your entire application to authenticate as a user, rather than compromising a single user’s token.

### Storing access tokens in Redis

When you are making API requests to GitLab, you want to try and minimise the amount of overhead to make your requests as fast as possible. One way of doing this is by caching the access token using Redis, which has a fast read speed making it ideal for this situation. After making a request to generate a GitLab access token, we can store it in Redis with a time to live slightly shorter than the `expires_in` time returned by the refresh response. Then, whenever we want to make a request, we can check the Redis cache for the access token. If it exists in the cache, we know it is (probably) valid, and if it doesn’t exist, we know we need to generate a new token. 

```jsx
import { refreshGitlabOAuthToken } from './refresh-token';
import { encryptToken, decryptToken } from './encryption-utils';

// This needs to be deterministic and use the user's GitLab ID in case multiple users have linked the same GitLab account
const getCacheTokenName = (gitlabId) => `gl-cache-${gitlabId}`;

export const getGitlabTokenWithCache = async ({ userId, RedisClient, db, ignoreCache }) => {
  const userObject = await db.getCollection('users').findOne({ _id: userId });
  const { gitlabId, refreshToken: oldEncryptedRefreshToken } = userObject;

  const cacheTokenName = getCacheTokenName(gitlabId);

  // Check the cache if we haven't explicitly chosen to ignore it.
  const cachedToken = ignoreCache ? null : await RedisClient.get(cacheTokenName);

  if (cachedToken) {
    return decryptToken(cachedToken);
  }

  const oldDecryptedRefreshToken = decryptToken(oldEncryptedRefreshToken);

  const { accessToken, refreshToken, expiresIn } = await refreshGitlabOAuthToken({ refreshToken: oldDecryptedRefreshToken });

  // If the refresh request returned an expiry time, we should expire a little before that.
  // If it doesn't, set an expiry time anyway for security.
  const expiryTime = expiresIn ? expiresIn - 30 : 6000;

  // Encrypt the tokens for storage
  const encryptedAccessToken = encryptToken(accessToken);
  const encryptedRefreshToken = encryptToken(refreshToken);

  // Set the cache
  await RedisClient.set(cacheTokenName, encryptedAccessToken, 'EX', expiryTime);

  // Update the refresh token in the database
  await db.getCollection('users').updateMany({ gitlabId }, { $set: { refreshToken: encryptedRefreshToken }});

  return accessToken;
}
```

In the above example, we first fetch the user’s details from a database. Here we are using MongoDB to store user data, but this will work with any database. When performing the initial OAuth link, you should make sure to store the user’s refresh token as well as their GitLab user ID which you can fetch via the GitLab API. Then, we check whether there is an access token in the Redis cache for that GitLab user, and return the cached token if it’s available. If it’s not available, we call the `refreshGitlabOAuthToken` function from before and cache the result in Redis. Make sure not to store tokens as plaintext, especially the refresh token as that does not expire until used. When generating a key for the cache, you should use a function that is deterministic so it always returns the same result, and it should use the user’s GitLab user ID. Access tokens and refresh tokens are linked to a specific GitLab user, so if you want to allow multiple users of your application to link the same GitLab account, you need to make sure to update all the tokens correctly.

### Implementing a lock to prevent multiple refreshes at once

One issue you might experience with the above is the situation where multiple refresh requests come in quick succession. For example, if you make three requests at the same time, and there is no token in the cache, all three requests will try to generate a new access token with the same refresh token, and only the first token will be successful. To prevent this, we can implement a lock - when a request wants to try and refresh the token, it tries to take the lock first. If the lock is available, it takes the lock and performs the refresh as normal, releasing the lock afterwards. If the lock is not available, it waits until the lock is released and takes the new cached value.

There are many ways to implement locks and many existing packages that will do it for you. Redis recommends using the <a href="https://redis.io/docs/reference/patterns/distributed-locks/" target="_blank">Redlock algorithm</a>. Here, we’re just implementing a simple lock primitive using the Redis SETEX command - there’s lots of improvements you can make here.

```jsx
// If lock has been held for longer than this, force open the lock
const expireLockTime = 120000;

const getLockName = (gitlabId) => `gl-lock-${gitlabId}`;

const timeout = async (ms) =>
  await new Promise((resolve) => {
    setTimeout(resolve, ms);
  });

// Performs exponential backoff
const backoff = async (f, options) => {
  let { initialWait, maxRetries } = options || {};
  initialWait = initialWait || 1000;
  maxRetries = maxRetries || 4;

  let currentWait = initialWait;
  let currentRetries = 0;

  while (true) {
    try {
      return await f();
    } catch (e) {
      if (currentRetries > maxRetries) {
        throw e;
      }
      await timeout(currentWait + Math.floor(Math.random() * 300));
      currentWait *= 2;
      currentRetries += 1;
    }
  }
};


export const getRefreshLock = async ({ gitlabId, RedisClient }) => {
  const lockKeyName = getLockName(gitlabId);
  const lockTime = await RedisClient.get(lockKeyName);
  const currentTime = Date.now();

  let receivedLockImmediately = true;
  
  if (lockTime && (currentTime - lockTime) > expireLockTime) {
    const lockTime2 = await RedisClient.set(lockKeyName, 'GET');

    if (!lockTime2) {
      return { receivedLockImmediately };
    }

    if ((currentTime - lockTime2) > expireLockTime) {
      return { receivedLockImmediately };
    }
  }

  await backoff(async () => {
    const receivedLock = await RedisClient.setnx(lockKeyName, Date.now());

    if (!receivedLock) {
      receivedLockImmediately = false;

      throw new Error('Failed to refresh token - lock was not released in time.');
    }
  });

  return { receivedLockImmediately };
}

export const releaseRefreshLock = async ({ gitlabId, RedisClient }) => {
  const lockKeyName = getLockName(gitlabId);

  await RedisClient.destroy(lockKeyName);
}
```

In the above, we create a simple lock utility, which tries to receive the lock if it is available and waits with an exponential backoff if the lock isn’t available. It also has some simple handling to obtain the lock if it has been locked for too long, in case something goes wrong with the handling somewhere else. We return whether the lock was immediately received, and we use this handling in the refresh function. If the lock was received immediately, we can refresh the token, and if the thread had to wait for the lock, we take the new cached value.

```jsx
import { refreshGitlabOAuthToken } from './refresh-token';
import { encryptToken, decryptToken } from './encryption-utils';
import { getRefreshLock, releaseRefreshLock } from './lock-utils';

// This needs to be deterministic and use the user's GitLab ID in case multiple users have linked the same GitLab account
const getCacheTokenName = (gitlabId) => `gl-cache-${gitlabId}`;

export const getGitlabTokenWithCache = async ({ userId, RedisClient, db, ignoreCache }) => {
  const userObject = await db.getCollection('users').findOne({ _id: userId });
  const { gitlabId, refreshToken: oldEncryptedRefreshToken } = userObject;

  const cacheTokenName = getCacheTokenName(gitlabId);

  // Check the cache if we haven't explicitly chosen to ignore it.
  const cachedToken = ignoreCache ? null : await RedisClient.get(cacheTokenName);

  if (cachedToken) {
    return decryptToken(cachedToken);
  }

  const { receivedLockImmediately } = await getRefreshLock({ gitlabId, RedisClient });

  try {
    if (!receivedLockImmediately) {
      const cachedToken2 = await RedisClient.get(cacheTokenName);

      if (cachedToken2) {
        await releaseRefreshLock({ gitlabId, RedisClient });
        return decryptToken(cachedToken2);
      }
    }

    const oldDecryptedRefreshToken = decryptToken(oldEncryptedRefreshToken);

    const { accessToken, refreshToken, expiresIn } = await refreshGitlabOAuthToken({ refreshToken: oldDecryptedRefreshToken });

    // If the refresh request returned an expiry time, we should expire a little before that.
    // If it doesn't, set an expiry time anyway for security.
    const expiryTime = expiresIn ? expiresIn - 30 : 6000;

    // Encrypt the tokens for storage
    const encryptedAccessToken = encryptToken(accessToken);
    const encryptedRefreshToken = encryptToken(refreshToken);

    // Set the cache
    await RedisClient.set(cacheTokenName, encryptedAccessToken, 'EX', expiryTime);

    // Update the refresh token in the database
    await db.getCollection('users').updateMany({ gitlabId }, { $set: { refreshToken: encryptedRefreshToken }});

    return accessToken;
  } finally {
    await releaseRefreshLock({ gitlabId, RedisClient });
  }
}
```


We can add in the lock utility to the `getGitlabTokenWithCache` function as seen above. Now, if multiple requests all come in at the same time, the first request can refresh the token and subsequent requests can receive the access token cached by the first request.

### Error handling for invalid access tokens

Whilst the system we have implemented should handle things correctly the vast majority of the time, it’s useful for us to add in some handling just in case something goes wrong and we end up with a revoked access token in the cache. When you make an API request to GitLab using a token that has been revoked, it will return the following error message:

```jsx
{"error":"invalid_token","error_description":"Token was revoked. You have to re-authorize from the user."}
```

Knowing this, we can wrap our API calls with a try catch block, and in the catch we can check whether the GitLab error code is `invalid_token`. If it is, we can try the request again using the `ignoreCache` flag to generate a fresh token.

```jsx
import { getGitlabTokenWithCache } from './get-token-with-cache';
import { RedisClient, db } from './addons';

export const gitlabAPIRequestWrapper = async ({ userId }, func) => {
  const { accessToken } = await getGitlabTokenWithCache({ userId, RedisClient, db });

  try {
    return (args) => func({ ...args, accessToken });
  } catch (e) {
    if (e?.message && e.message.includes("invalid_token")) {
      const { accessToken: accessToken2 } = await getGitlabTokenWithCache({ userId, RedisClient, db, ignoreCache: true });

      return (args) => func({ ...args, accessToken: accessToken2 });
    }
  }
}
```

Implementing refresh token support for your GitLab OAuth application will help keep things safe and secure. The same handling can be used for other OAuth providers that support token rotation, however each provider can have their own differences so you should look into their documentation before implementing this. For example, Bitbucket uses refresh tokens that don’t expire after use, meaning you don’t have to store the new refresh token in the database every time.

Thank you for reading and I hope you found this useful. If you want more information about version control providers and OAuth integration, you can check out my previous posts on <a href="https://northflank.com/blog/integrating-with-github-github-apps-and-oauth" target="_blank">Github</a> and <a href="https://northflank.com/blog/integrating-with-gitlab-and-bitbucket" target="_blank">Gitlab and Bitbucket</a>. If you have any questions you can contact me at [first name] @northflank.com.





]]>
  </content:encoded>
</item><item>
  <title>Introducing Exec &amp; Terminal via UI, CLI and API</title>
  <link>https://northflank.com/changelog/introducing-exec-and-terminal-via-ui-cli-and-api</link>
  <pubDate>2022-04-13T18:00:00.000Z</pubDate>
  <description>
    <![CDATA[Access and debug containers in real-time with new shell, terminal and exec capabilities.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/northflank_exec_terminal_shell_container_access_4efc772f7e.png" alt="Introducing Exec &amp; Terminal via UI, CLI and API" />Today, we are happy to announce a long requested feature: Container shell access for services and jobs! You were previously able to run custom commands at startup via CMD overrides or health checks, but now it's time to introduce a dedicated capability. Enhance your debug operations and stateful workload auditing via API or CLI.

Seamlessly connect to individual service or job run containers via the UI or the CLI. As always, this has been fully integrated with our access control system for both the UI and CLI/API, allowing you to tightly control which team members can access this functionality.

Shell access on Northflank works by spawning a new shell process inside your container with access to everything inside your container (environment, filesystem, processes, ability to run commands like `top`, `npm`, `sed`, `vi`, `df`). It also provides command completion and command history where possible.

#### Using shell access from the Northflank UI:

<video autoPlay playsInline loop muted width="100%">
    <source src="https://assets.northflank.com/final_625697ef9cd872006530ba5c_540478_41ed551bab.mp4"/>
</video>

- **UI**: simply click the new `Shell access` button on the container list to access the shell
- **UI Permissions**: `Services / Jobs > Deployment > Command Exec`

#### Using shell access via terminal & the Northflank CLI:

<video autoPlay playsInline loop muted width="100%">
    <source src="https://assets.northflank.com/final_625699ab1c0ef600c2c71177_368313_8cbd6b5a00.mp4"/>
</video>

- **CLI**: the new `northflank exec` command allows you to choose a container in a specific project, service or job, a shell will be started in your terminal
- **API Permissions**: `Services / Jobs > Deployment > Command Exec`


Other features & fixes:
- Fixed an issue where PostgreSQL native backups could fail silently
- Fixed a rare crash on the addon dashboard for addons created early in 2021
- Command override copy to clipboard now copies the command as displayed

API & CLI:
- Added new command `northflank exec` to the CLI
- Added new permission to the API for services & jobs called `Command Exec`

]]>
  </content:encoded>
</item><item>
  <title>Enhanced Deployment Overview, Notifications &amp; Container Endpoints</title>
  <link>https://northflank.com/changelog/enhanced-deployment-overview-notifications-and-container-endpoints</link>
  <pubDate>2022-04-03T17:00:00.000Z</pubDate>
  <description>
    <![CDATA[More detailed deployment information for services and jobs and improved build and runtime log accessiblity]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/deployment_overview_shortcuts_logs_sourcenorthflank_dbb39ef3b9.png" alt="Enhanced Deployment Overview, Notifications &amp; Container Endpoints" />We have improved the deployment overview component to give you clearer and more detailed information about the code or container image deployed on your services or running jobs.

Key three areas to highlight:
- **Currently deployed**: details about the code or container image such as the name, when it was built and when it was deployed
- **Deployment source**: displays the linked Northflank builder, its Git repository, whether a build is in progress or the external container registry source
- **Deployment workflow** whether the deployment is set to deploy every new successful build from a specific branch or whether it is pinned to deploy a specific build or registry image

Accessing relevant information about your builds and deployment quickly is important. We have added helpful shortcuts to view deployment or build logs, change the command override and a shortcut to edit the deployment source.

Other features & fixes:
- Fixed an issue where user webhook notifications could be delayed
- Fixed an issue where Notification integrations could not be updated when missing a secret

API & CLI:
- Improved the container endpoint to reflect the updated platform schema
- Fixed an issue where the API rate limit cache would not be cleared as expected

]]>
  </content:encoded>
</item><item>
  <title>Introducing ConfigMap and Secret Files</title>
  <link>https://northflank.com/changelog/introducing-config-map-and-secret-files</link>
  <pubDate>2022-03-28T17:30:00.000Z</pubDate>
  <description>
    <![CDATA[Configure secret files with variable templating and securely inject them at runtime.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/secret_files_configmap_overview_d32c6b29bd.png" alt="Introducing ConfigMap and Secret Files" />Today we are pleased to introduce secret files on Northflank. For Kubernetes users this capability is known as ConfigMaps, Docker users as volume `-v ./config.yaml:/var/application/config.yaml` and PaaS users as secret files. Ensuring compatibility with these three different use-cases was essential. Users will be able to use secret files in their workflows alongside secret groups, environment variables in deployments and builds seamlessly.

### Secret Files
- Allow writing of files to specific paths in your runtime container or build process
- Encrypted at rest and injected at runtime with environment variables
- Has access to variable templating enabling inheritance of other environment values or connection strings

### Use Cases:
- You need to configure a third party docker image via config files located at specific paths
- You need to create text based configuration files like `.json`, `.html`, `.css`, `.yaml` to power software like nginx, kong, supabase and more
- You need to add a certificate file or complex secret that cannot be handled via an environment variable
- Create manifest files with build or runtime variable configuration


![configmaps-secret-files-containers-northflank.jpg](https://assets.northflank.com/configmaps_secret_files_containers_northflank_9f300c4fb4.jpg)

### API & CLI:
- PR numbers in responses are now returned as integers in line with the response schema

### Features & fixes:
- Fixed an issue where two builds could be triggered at once on initial creation from template
- Links in the instance log feed are now marked up



]]>
  </content:encoded>
</item><item>
  <title>Integrating with GitLab and Bitbucket</title>
  <link>https://northflank.com/blog/integrating-with-gitlab-and-bitbucket</link>
  <pubDate>2022-03-24T10:00:00.000Z</pubDate>
  <description>
    <![CDATA[Introducing GitLab and Bitbucket: how to integrate your applications, how they are structured and what to look out for when implementing support for them.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Group_394_1875ce8976.png" alt="Integrating with GitLab and Bitbucket" />When developing an application to improve your DevOps experience you will probably want to have a way of accessing the code you have stored on your version control provider. This can be done with an OAuth application, which will allow users to link their version control account with your application, then you can access their version control data via the version control provider’s API. In <a href="https://northflank.com/blog/integrating-with-github-github-apps-and-oauth" target="_blank">my previous article</a>, I talked about some of the things you should know when integrating with GitHub. Today, I’ll be talking about two more version control providers, GitLab and Bitbucket, including how to create an application as well as some of the ways those providers are different to each other and GitHub.

## GitLab

To integrate your application with GitLab, you must first create an application in the <a href="https://gitlab.com/-/profile/applications" target="_blank">GitLab profile settings</a>. This will allow your users to link their GitLab accounts through OAuth. On this page, you can set a name for your application and provide one or more redirect URLs for the linking process. The confidential box determines what kind of OAuth linking flow you will need to use, and is based on whether you can keep the OAuth client secret hidden from the users of your application. If you are creating a web application that will store the user’s data on a backend server, you will likely want to tick the Confidential box as you can keep the client secret on the backend server and users won’t be able to access it. On the other hand, if you are creating something like a native application where there is no backend server, you will need to store the client secret on the user’s device, meaning you should not tick the Confidential option.

GitLab has a number of options for scopes though they are lacking somewhat in granularity. If you are only using GitLab as a single sign-on provider you can get away with just the email scope, however if you want to integrate with GitLab’s features you will probably want to choose the api scope which is a very broad scope giving you access to nearly all of GitLab’s features. If you are only working with a small amount of the platform you may be able to get away with the registry permissions or the repository permissions, but it’s important to note that the write_repository permission only provides write access to repos via HTTP, and does not grant you access to GitLab’s file API endpoints. Applications that don’t intend to modify the repository in any way can use read_api, with the caveat that if you need repository webhooks you will need full api access so that you can create a webhook on a repository.

![GitlabApplications.png](https://assets.northflank.com/image4_972795c3b7.png)

## How is GitLab structured?

GitLab is structured a little bit differently to its peers GitHub and Bitbucket. Whilst there shouldn’t be anything too surprising, there are a few little quirks that it’s useful to know about.

GitLab’s building blocks are ‘projects’. Each project contains a repository and repository related features such as merge requests (pull requests) and issues. A project also contains other features such as CI/CD handling, registry package management and a wiki.

This distinction between repositories and projects is sometimes important - when accessing the API many of your calls will be to the <a href="https://docs.gitlab.com/ee/api/projects.html" target="_blank">Projects API</a>. You won’t find a way to list all of a user’s repositories - you’ll need to list all of a user’s projects. 

Additionally, it’s a useful distinction when it comes to project permissions. As a repository is different from a project, it’s possible for a user to have access to a project without having any access to that project’s repository. By default, a user who has the ‘guest’ role on a private project is able to see some of the project’s details but not the repo. Therefore, if you are trying to list all the repositories a user can access, you may need to filter the project list to only show projects that the user has repository access on.

### Groups and subgroups

GitLab’s main collaboration tool is groups - much like GitHub’s organizations, GitLab’s groups allow you to invite other GitLab users and create projects that can be accessed by everyone in the group. GitLab’s group permissions are very granular - a number of permissions can be customised such as which roles can create projects. This means, when using the API to work out which actions a user can perform, you cannot always assume the permissions a user has from their role. For example, you might have a feature where you create a new repository for a user and want to list all the groups where the user can create a project. By default the Developer and Maintainer role can both create projects, but if you just search for all groups where the user has the Developer role or higher, you may get groups where the user isn’t able to create a new project because the group has modified the default permissions.

Additionally, each project also has its own permission roles. This means a user outside of a group may be invited to work on a specific project, or a member of the group might have elevated permissions on an individual project. For example, if you are checking whether a user has administrator access to a project, you need to make sure you check both the group access level and the project access level (generally these are returned together by the API).

GitLab’s groups also have the unique feature of subgroups. A subgroup is a group belonging to a group. Subgroups inherit the permissions of the groups they belong to, but additionally can have their own permissions overriding that. Users may be members of a subgroup without being a member of the group it belongs to. Subgroups generally have the same features as groups, including the ability to have their own subgroups. In this way, subgroups are kind of like folders.

One pitfall to look out for if you are parsing the URL of a repository is that GitLab’s subgroups mean that there can be multiple forward slashes in the URL as each subgroup name is preceded by a slash. 

### Webhooks

Webhooks allow you to receive notifications when specific events happen to a project such as commits being pushed to a repo, merge requests being opened and issues being created. Unlike GitHub Apps, GitLab’s webhooks are not account wide but are instead set up on an individual repository.

You can manually create a webhook in a project’s settings. Here you can add a url where you want GitLab to send the webhook events to. You can also select which events you want to receive. You can also set two security features here. If you set a secret token, GitLab will send that token with all its webhooks in the `X-Gitlab-Token` header. When receiving webhooks, you can verify that the token is correct to make sure that webhook requests you receive are legitimate requests from GitLab. The Enable SSL verification option makes it so that GitLab will verify the webhook endpoint has a valid SSL certificate before sending any webhooks.

![GitlabWebhooks.png](https://assets.northflank.com/Gitlab_Webhooks_75db6809ba.png)

You might use this manual webhook creation if you are making an application for personal use. However, if you are making an application designed to integrate with other users’ GitLab accounts, you will likely create webhooks using the GitLab webhooks API. You will have to call the <a href="https://docs.gitlab.com/ee/api/projects.html#add-project-hook" target="_blank">Add project hook</a> endpoint for each project you want to receive webhooks for. It is important to know that webhooks can be edited by any member of that project with administration access, so it is possible for users to delete or edit your webhooks. You can use the <a href="https://docs.gitlab.com/ee/api/projects.html#list-project-hooks" target="_blank">List project hooks</a> and <a href="https://docs.gitlab.com/ee/api/projects.html#get-project-hook" target="_blank">Get project hook</a> endpoints to check whether your webhook still exists and has the necessary events enabled, however there is no way to validate that the webhook secret has not been modified so you may find you do not receive valid webhooks if a user has modified the secret.

When creating secret tokens for webhooks, you should generate a different token for each repository and ensure that it is random and cannot be guessed. This ensures that users cannot spoof webhooks for repositories that they do not own.

```jsx
import crypto from 'crypto';

// Function for verifying GitLab webhook tokens in a koa server
export const verifyGitLabToken = async ({ receivedToken, repoUrl, db }, ctx) => {
  if (!receivedToken) {
    ctx.throw(401, 'Webhook token not provided.');
  }

  const tokenObject = await db.getCollection('webhook-tokens').find({ repoUrl });

  const ourToken = tokenObject?.token;

  if (!ourToken) {
    ctx.throw(401, 'Failed to verify webhook token.');
  }

  // crypto.timingSafeEqual requires the two inputs to be equal length.
  if (receivedToken.length !== ourToken.length) {
    ctx.throw(401, 'Failed to verify webhook token.')
  }

  // Using timingSafeEqual prevents malicious actors from guessing your webhook tokens using a timing attack.
  const validToken = crypto.timingSafeEqual(Buffer.from(receivedToken), Buffer.from(ourToken));
  if (!validToken) {
    ctx.throw(401, 'Failed to verify webhook token.')
  }
}
```

## Bitbucket

Integrating with Bitbucket is very similar to integrating with GitLab. You can create a new Bitbucket application when in a workspace under Settings > Apps and Features > OAuth Consumers. BitBucket requires a callback url for your OAuth handling but also optionally takes a URL for users to learn more about your application, a privacy policy URL and a end user license agreement URL which are all shown to the user when linking your application. Similar to GitLab’s confidential setting, you should tick the ‘This is a private consumer’ checkbox if you are able to keep the OAuth client secret secure on a backend server.

The permissions here are more granular than GitLab and you should be able to pick whatever scopes your application requires without giving it more permissions than necessary.

![BitbucketApplication.png](https://assets.northflank.com/Bitbucket_Application_9c0027a48d.png)

## How is Bitbucket structured?

Bitbucket’s structure is fairly simple to understand, though like the other version control providers it also has its own quirks that it is important to be aware of.

### Workspaces

All Bitbucket repositories belong to a workspace. Unlike GitHub and GitLab, Bitbucket does not have a hard distinction between users and teams. When you create an account, Bitbucket will create a workspace for you, however other Bitbucket users can be invited to your workspace. From a developer standpoint, there is no canonical workspace for a given user. You cannot list repos belonging to a user, or create a repo in the authenticated user’s namespace, because there is no such thing - every workspace a user is a part of is as valid as any other. Therefore, most API requests you make will be on a specific workspace - you can list repos belonging to a workspace, or create a repo in that workspace.

Similar to GitLab, repositories can have separate permissions to the workspaces they belong to.

### Projects

Bitbucket workspaces can have projects. Similar to GitLab’s subgroups, these act similar to folders, though unlike GitLab, Bitbucket’s projects exist mostly for organizational use rather than for access control. Projects have a name and a key, and can have a separate visibility level to the workspace as a whole, but otherwise offer no additional features. All repositories belong to a project - Bitbucket creates a default project if none exists and will put new repos in that project if a project isn’t specified. Some endpoints such as repository creation take a project key to allow you to decide which project the repo should be a part of but otherwise projects are unintrusive. They are not part of the repo url and generally shouldn’t cause you much issue. 

### Webhooks

Webhooks in Bitbucket function mostly the same as the webhooks in GitLab. You set a webhook url, determine which events you wish to receive, and whether to enable SSL verification. Unlike GitLab however, Bitbucket is missing any kind of verification token. This makes it easy for attackers to potentially spoof webhook events. Atlassian suggests that you solve this by setting up a whitelist to only accept webhooks from <a href="https://support.atlassian.com/bitbucket-cloud/docs/what-are-the-bitbucket-cloud-ip-addresses-i-should-use-to-configure-my-corporate-firewall/" target="_blank">their IP addresses</a> (Bitbucket server supports webhook tokens so this is not an issue there). However, there is one workaround you can use if you wish to implement token verification in a similar way to GitLab - you can put the webhook token in the webhook URL. This could be done either as a path parameter or a query parameter. Then, on your webhook server, you can take the token from the path and verify it as normal. Obviously, this isn’t perfect and I hope Bitbucket implements webhook tokens in the headers like GitLab but it is certainly better than having no verification whatsoever!

Supporting multiple version control providers allows you to reach more users and give them more flexibility over how to use your application. Whilst each provider has their own quirks and differences, hopefully you now have a good understanding of some of things you need to look out for when implementing support for GitLab and Bitbucket. If you missed it, you can check out my <a href="https://northflank.com/blog/integrating-with-github-github-apps-and-oauth" target="_blank">previous post on GitHub</a>.

Thank you for reading! I hope you found this brief guide to GitLab and Bitbucket useful when developing your own applications. If you have any questions you can contact me at [first name] @northflank.com.












]]>
  </content:encoded>
</item><item>
  <title>Introducing HTTP/2 and gRPC support</title>
  <link>https://northflank.com/changelog/introducing-http-2-and-grpc-support</link>
  <pubDate>2022-03-21T14:00:00.000Z</pubDate>
  <description>
    <![CDATA[Flexible network configuration with new enhanced HTTP/2 and gRPC protocol support for deployed workloads.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/http2_grpc_support_northflank_a86809fdab.png" alt="Introducing HTTP/2 and gRPC support" />Today we are very excited to announce end-to-end HTTP/2 and gRPC support. This further extends the advanced networking capabilities that can be deployed and configured on Northflank. We have historically upgraded HTTP/1 traffic to HTTP/2 at the edge load-balancer, downgrading it to HTTP/1 as it reached your workload, with the primary focus of improving performance and request efficiency. Now, you can make full use of HTTP/2 benefits in your web and microservices as traffic will now continue to flow upstream to your web server for processing as HTTP/2.

### HTTP/2 and gPRC on Northflank
- Allows using advanced features such a gRPC on your services
- Additional protocol can now be selected during port configuration for both private and public ports
- The platform will continue to upgrade all incoming traffic to HTTP/2 and downgrade if the workload doesn’t support it
- Existing HTTP ports will remain on HTTP/1 but can be switched by the user
- You can still configure multiple HTTP/1, HTTP/2, TCP and UDP ports per service

### Benefits of HTTP/2 and use-cases
- Reduce latency by enabling full request and response multiplexing
- Minimise protocol overhead via efficient compression of HTTP header fields
- Add support for request prioritisation and server push

### Benefits of gRPC and use-cases
- designed for low latency and high throughput communication. gRPC is great for lightweight microservices where efficiency is critical
- excellent support for bi-directional streaming. gRPC services can push messages in real-time without polling

### Features & fixes:
- Reduced the frequency of `sudo` mode password prompts when editing environment data
- Updated the “Getting started” documentations pages to reflect recent changes
- Fixed an issue where an addon could get into an unhealthy state when a disk restore applied an old version of the addon
- Various minor API example payload & schema improvements

]]>
  </content:encoded>
</item><item>
  <title>Configurable Ephemeral Storage, PostGIS, Public MySQL and Postgres</title>
  <link>https://northflank.com/changelog/configurable-ephemeral-storage-post-gis-public-my-sql-and-postgres</link>
  <pubDate>2022-03-14T14:00:00.000Z</pubDate>
  <description>
    <![CDATA[Configurable ephemeral storage for services and jobs, MySQL and Postgres public network feature flags are enabled for all users and more.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/ephemeral_storage_image_b9014d2eb2.png" alt="Configurable Ephemeral Storage, PostGIS, Public MySQL and Postgres" />We’re excited to release configurable ephemeral storage for services and jobs. This further expands the breadth and depth of workloads that can be run on Northflank. Simply select the available ephemeral storage you need from 1GB (default), 5GB, 10GB and 20GB. The increased storage above 1GB (included for free) will be billed at the volume rate on <a href="https://northflank.com/pricing" target="_new">our pricing page</a>.

This is a complementary capability to persistent volumes <a href="https://northflank.com/changelog/persistent-volumes-and-storage-for-deployments" target="_new">released earlier this year</a>.

Why would you use ephemeral storage over persistent volumes?

- Some workloads you want to fire and forget, not worry about state or persistence, and thus configuring ephemeral storage is optimal.
- PVs only provided increased disk at routes not inside the container workload. 
- PVs are billed for the lifetime of the disk whereas ephemeral disks are billed by the lifetime of the container during runtime by the second.


Type of workloads that require larger ephemeral storage?
- Non-trivial database migrations and backups
- Downloading and operating large ML models
- Downloading and transforming large media files (video/images)
- Large tmp directories or file-based cache

We’re expanding capabilities to provide more database addon configurations starting with PostGIS. PostGIS is a spatial database extender for PostgreSQL. It adds support for geographic objects allowing location queries to be run in SQL. 

Email support@northflank.com if you are interested in using PostGIS.

### Features & fixes 

- Exposing PostgreSQL and MySQL feature flag is now enabled by default (still restricted to 2 exposed addons of these types)
- Fixed an issue where repositories on self-hosted GitLab would sometimes not be detected correctly
- Fixed an issue with Logs which could prevent loading new logs if there was a large number of entries in a sub-second interval
- Fixed an issue where billing alerts would not be triggered
- Build service branch & PR restriction build restriction patterns now support more special characters
- Update the Docker registry permission names from `integrations` to `registries`
- Users will now be signed up automatically when no account is present while trying to login with OAuth buttons from the login page
- Improved the manual invoice payment option styling, including the component to add a new card while paying an invoice
- Added a scale job endpoint to allow modifying job resources

]]>
  </content:encoded>
</item><item>
  <title>Parallel builds for same SHA, unicode branches and PRs &amp; flexible job source</title>
  <link>https://northflank.com/changelog/parallel-builds-for-same-sha-unicode-branches-and-p-rs-flexible-job-source</link>
  <pubDate>2022-03-07T18:00:00.000Z</pubDate>
  <description>
    <![CDATA[Simply tigger multiple builds with the same Git commit with different build arguments in parallel. Added support for unicode in branch and PR names. More fixes and improvements.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/run_job_66bee4664e.png" alt="Parallel builds for same SHA, unicode branches and PRs &amp; flexible job source" />Welcome to another Northflank changelog, this week we're happy to dedicate it to our continued focus on platform stability and reliability. 

### Features & fixes 

- Enabled parallel builds for the same commit, branch or PR
- Hovering over environment keys now shows their full name
- Jobs can now be created without a configured deployment source 
- Added support for displaying unicode characters in branch and PR names 
- Fixed some documentation links leading to 404 errors
- Fixed an issue with password prompts on registry credentials 
- Fixed an issue where API would return error 500 on bad command override 
- Fixed Redis connection strings not working in the Redis CLI 
- Fixed an issue with accessing health checks of jobs in certain scenarios 
- Fixed an issue where Login with Google would sometimes take a long time to load 
- Added a section on configuring persistent volumes to the <a href="https://northflank.com/guides/deploying-ghost-cms" target="_new">Deploy Ghost</a> guide ]]>
  </content:encoded>
</item><item>
  <title>Updated API, CLI &amp; Documentation</title>
  <link>https://northflank.com/changelog/updated-api-cli-and-documentation</link>
  <pubDate>2022-02-28T11:00:00.000Z</pubDate>
  <description>
    <![CDATA[Released upgrades to JS client and CLI with several updated API endpoints, added ANSI escape sequence formatting on logs and more.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/ansi_logs_5117669af8.png" alt="Updated API, CLI &amp; Documentation" />This week we are introducing some improvements to the Northflank API & CLI together with updated documentation and several other new features.

### API & CLI

- Released <a href="https://www.npmjs.com/package/@northflank/js-client" target="_blank">JS client v0.5.5</a> and <a href="https://www.npmjs.com/package/@northflank/cli" target="_blank">CLI v0.7.5</a>
- Added `docker image` and `cmdOverride` settings on job run trigger
- Added build argument overrides for job and service build endpoints
- Fixed `cmdOverride` to be unified under `cmdOverride` inside deployment on jobs and services
- Added `environment-arguments` secret group type to API for runtime and buildtime inheritance
- Added `useCache` on API creation for build and combined services
- Fixed `create secret` API endpoint returning duplicate error incorrectly 

### Other features & fixes

- Added ANSI escape sequence formatting on logs
- Updated documentation with new content such as Persistent Volumes 
- Addon dashboard now clearly shows whether it is exposed publicly or not 
- Jobs now require to specify a branch of the selected build service 
- Fixed documentation tables overflowing on mobile devices 
- Fixed handling of branch names that required URL encoding 
- Fixed an issue where billing emails wouldn’t include the PDF invoice as an attachment 
]]>
  </content:encoded>
</item><item>
  <title>One Click Deployment &amp; New Project Starter UX</title>
  <link>https://northflank.com/changelog/one-click-deployment-and-new-project-starter-ux</link>
  <pubDate>2022-02-21T11:00:00.000Z</pubDate>
  <description>
    <![CDATA[Improved the user experience with one-click deployments of services, databases and jobs. Documentation is now linked throughout the platform so you can get started easily. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/header_oneclick_66da193de7.png" alt="One Click Deployment &amp; New Project Starter UX" />We’ve made several user experience and onboarding improvements to new projects. After creating a new project you can start with several one-click example deployments including databases, docker images, cron jobs and template repositories from your chosen version control provider. This provides a successful blueprint for resource creation and healthy deployments in seconds.

![one-click-deployments.png](https://assets.northflank.com/one_click_deployments_19d5cf74b5.png)

You can still tinker and customise your deployment from the suggested configuration. We’ve made this possible by supporting a shareable link which will update any Northflank resource creation form. As we continue to improve and expand this capability you will be able to create your own Deploy to Northflank buttons.

![addon-creation.png](https://assets.northflank.com/addon_creation_8772e762bb.png)

If you prefer to just get started yourself without using our examples or templates, we’ve added links to the documentation so the experience is a breeze. 

![empty-service.png](https://assets.northflank.com/empty_service_1b015b820e.png)

### Guides and Content 

We published several guides on how you can deploy NestJS on Northflank:
- <a href="https://northflank.com/guides/deploy-nest-js-with-typescript-on-northflank" target="_blank">NestJS with TypeScript</a>
- <a href="https://northflank.com/guides/deploy-nest-js-with-javascript-on-northflank" target="_blank">NestJS with JavaScript</a>
- <a href="https://northflank.com/guides/deploy-nest-js-with-typescript-and-mysql-on-northflank" target="_blank">NestJS with TypeScript and MySQL</a>
- <a href="https://northflank.com/guides/deploy-nest-js-with-typescript-and-postgresql-on-northflank" target="_blank">NestJS with TypeScript and PostgreSQL</a>
- <a href="https://northflank.com/guides/deploy-nest-js-with-typescript-and-mongodb-on-northflank" target="_blank">NestJS with TypeScript and MongoDB</a>

We also wrote new DBaaS and serverless pages for Northflank addons: 
- <a href="https://northflank.com/dbaas/mongodb-on-northflank" target="_blank">MongoDB</a>
- <a href="https://northflank.com/dbaas/managed-redis" target="_blank">Redis</a>
- <a href="https://northflank.com/dbaas/managed-mysql" target="_blank">MySQL</a>
- <a href="https://northflank.com/dbaas/managed-postgresql" target="_blank">PostgreSQL</a>
- <a href="https://northflank.com/dbaas/managed-rabbitmq" target="_blank">RabbitMQ</a>
- <a href="https://northflank.com/dbaas/managed-minio" target="_blank">MinIO</a>

### Other features & fixes

- Fixed an issue with Northflank Documentation theming occasionally rendering mixed colours 
- Added advanced example to cron jobs schedule help popover 
- Fixed an issue where the project billing dashboard would sometimes display values from other of your own projects
- Improved GitLab integration to handle very large repositories 
- Fixed an issue where changing a repository on an existing combined service wouldn’t work correctly for Bitbucket repositories
- Fixed an issue where you couldn’t deploy services from self-hosted team repositories on personal accounts although this option was enabled in your settings]]>
  </content:encoded>
</item><item>
  <title>Slack, Discord and Webhook Notifications</title>
  <link>https://northflank.com/changelog/slack-discord-and-webhook-notifications</link>
  <pubDate>2022-02-14T07:00:00.000Z</pubDate>
  <description>
    <![CDATA[Configure custom notifications to receive alerts about your account and services in your favourite tools or via a raw webhook. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/notifications_northflank_e136e8451e.png" alt="Slack, Discord and Webhook Notifications" />We are excited to release notifications and alerting in today's changelog. Enable notifications in your account settings with support for Slack, Discord and webhooks. Once enabled, alerts for builds, jobs and billing will be dispatched on success and failure.

Configure fine-grained event types and project scope for each integration. Quickly gain greater observability into how your deployments are performing as collaborators trigger builds and job runs.

Slack and Discord support is quick and familiar to set up. If you need something more advanced, webhooks will enable greater flexibility. For example, create a ticket in your bug tracking software upon a build failure or use the Northflank API to automatically upload a database backup to an S3 bucket when a job succeeds.

Read more about webhooks and see an example schema in our [documentation](https://northflank.com/docs/v1/application/notifications/webhooks).

![Notifications UI](https://assets.northflank.com/notifications_ui_1ec2cae1a3.png)

### API & CLI 

- Released CLI v0.7.3 and JSclient v0.5.3 
- Creating a combined service now also returns the initial build ID 
- Service creation endpoints now support attaching a custom domain and specifying command override 
- Added a new `get build` endpoint for jobs and services to retrieve details about a specific build  

### Other features & fixes

- Published guides for [Deno](https://northflank.com/guides/deploy-deno-on-northflank), [Flask](https://northflank.com/guides/deploying-flask-on-northflank) and [nginx](https://northflank.com/guides/deploy-nginx-on-northflank)
- Improved the onboarding experience after signup and when creating the first project
- Help popovers are now clearer in the explanations 
- Released an upgraded MinIO admin console interface 
- Fixed GitLab integration fetching branch data incorrectly 
- Fixed an issue where the platform wouldn’t always load correctly in Safari browsers
]]>
  </content:encoded>
</item><item>
  <title>Introducing Managed RabbitMQ - build and scale with queues and message brokers</title>
  <link>https://northflank.com/changelog/introducing-managed-rabbit-mq-build-and-scale-with-queues-and-message-brokers</link>
  <pubDate>2022-02-07T07:00:00.000Z</pubDate>
  <description>
    <![CDATA[Northflank expands DBaaS support to message queues with RabbitMQ, new addon versions and guides.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/rabbitmq_header_d2a4e1d6f8.png" alt="Introducing Managed RabbitMQ - build and scale with queues and message brokers" />We’re very excited to release Managed RabbitMQ support on Northflank. RabbitMQ is the most widely deployed open-source message broker. A number of Northflank users requested a DBaaS experience via Addons and we’re pleased to say it’s live in production now after a month of early access.

#### Why deploy RabbitMQ on Northflank

- Your first cluster deployed in minutes starting at $10 per month
- Horizontal and vertical scaling with more resources and replicas
- Serverless like experience with automatic updates and no infrastructure management
- Provision and manage via UI, API and CLI
- Out of the box backups and restore
- Observability and monitoring with real-time logging and metrics
- Collaborate with a team and configure important RBAC rules
- Protocols (AMQP, MQTT, HTTP, STOMP, Streams)
- The most simple, secure and scalable way to deploy RabbitMQ clusters on Kubernetes and DBaaS
- Automatically connect workloads with secure RabbitMQ connection details & secrets
- Configure private and public networking dynamically and add optional network security settings

#### Examples of where RabbitMQ can be used:

- Application with many components which need to exchange information in a granular and highly controllable way
- Applications that need a granular control over consistency guarantees or have complex routing requirements
- IoT applications can make use of the lightweight pub-sub MQTT protocol, optimised for heterogeneous devices in the Internet of Things scenarios

For a more detailed feature breakdown of RabbitMQ on Northflank, <a href="https://northflank.com/dbaas/managed-rabbitmq" target="_blank">visit this page</a>.

### Other features & fixes
- Published guides for [Laravel](https://northflank.com/guides/deploying-laravel-on-northflank), [Gatsby](https://northflank.com/guides/deploy-gatsby-on-northflank) and [Streamlit](https://northflank.com/guides/deploying-streamlit-on-northflank)
- Released upgrades for PostgreSQL v14 (major) and MongoDB v5 (major)
- Fixed an issue where a branch named the same as a previously deleted branch wouldn’t show in the Northflank UI 
- Fixed an issue with the environment variables editor not parsing true/false values correctly ]]>
  </content:encoded>
</item><item>
  <title>Auto Trigger Job Runs on Source Changes</title>
  <link>https://northflank.com/changelog/auto-trigger-job-runs-on-source-changes</link>
  <pubDate>2022-01-31T11:00:00.000Z</pubDate>
  <description>
    <![CDATA[Trigger a job when its source changes on a successful build, pipeline promotion or selecting a new build, new blog posts and deployment guides, other fixes.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/job_run_pipelines_f7bb89ff0c.png" alt="Auto Trigger Job Runs on Source Changes" />Working with multiple microservices, dependent jobs and databases can be complex. Today Northflank takes another step to simplifying rolling out interdependent workloads, migrations and workflows. It is now possible to automatically trigger jobs (either cron or manual) via source triggers. Trigger a job run when the source deployment images changes:

- On build completion with continuous integration and delivery
- On promotion through a pipeline stage
- On selection of a new build source to run

## New blog posts & guides

- [Adding themes to a React app using styled-components](https://northflank.com/blog/adding-themes-to-a-react-app-using-styled-components) 
- [Hosting a Shopify application on Northflank](https://northflank.com/blog/hosting-a-shopify-application-on-northflank)
- Deploying [Wordpress](https://northflank.com/guides/deploy-wordpress-on-northflank), [Next.js](https://northflank.com/guides/deploy-next-js-on-northflank), [Django](https://northflank.com/guides/deploying-django-on-northflank) and [Discord Music Bot](https://northflank.com/guides/deploying-a-discord-music-bot-on-northflank) 

## Other improvements & fixes

- Improved job runs messaging to display if job run terminated due to a time limit 
- Improved Buildpacks to always pull latest stack version 
- Fixed an issue with some log live tail queries 
- Fixed an issue with verifying some public GitHub registry images 
- Several frontend improvements with responsiveness and accessibility ]]>
  </content:encoded>
</item><item>
  <title>How (and Why) to Add Themes to your React App Using Styled-Components</title>
  <link>https://northflank.com/blog/adding-themes-to-a-react-app-using-styled-components</link>
  <pubDate>2022-01-25T09:00:00.000Z</pubDate>
  <description>
    <![CDATA[Implement a complete theming system using React and styled-components.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Group_377_05e227d217.png" alt="How (and Why) to Add Themes to your React App Using Styled-Components" />It’s become quite common for websites and applications to provide users with a choice of visual themes. This is often just a choice between light and dark mode options. However, some applications provide additional theming, or even allow users to put together their own custom themes. 

In this blog post, we’ll cover:

1. Why giving users a theme choice is a good idea
2. How to use the styled-components library
3. How to add UI themes to a React app using React and styled-components
4. How to persist theme preferences across sessions
5. How to use operating system theme preferences
6. How to implement user-customisable theming


By the end of this article, you should have everything you need to implement your own theming system, and a demo app to play around with. The source code for the demo app can be found <a href="https://github.com/northflank-examples/react-themes-demo" target="_blank">on GitHub</a>. There is also a live deployment <a href="https://site--react-themes-demo--internal-apps--nort-xjjq.code.run" target="_blank">on Northflank</a> that you can play around with.

### Why give users a choice of theme?

In the simplest terms, users have preferences. Some users might be used to reading from paper and prefer dark text on a white background. Some users might primarily use your application in the evening or at night, and want a dark background to go easy on their eyes. Whatever the reason for a user’s preference, you want to provide them with the best experience possible.

Another reason to provide a choice of themes is accessibility. Due to visual impairments, dyslexia, or a number of other conditions, some users might struggle to read text with a poor contrast ratio, or a specific background colour. Providing more than one theme option gives these users a chance to try out different visual combinations and use what works best for them. For ultimate accessibility, you can let users define their own custom themes that meet their precise needs.

At Northflank, we want our application to feel like an extension of our users’ own development environments. As well as our standard light & dark offerings, we provide a few themes that match with popular IDE colour schemes and seasonal designs, as well as letting users customise their own themes.

![Frame 259.png](https://assets.northflank.com/Frame_259_ce63487d79.png)

### How to use the styled-components library

<a href="https://styled-components.com" target="_blank">styled-components</a> is a JavaScript styling library. It allows us to write CSS within our JS files, rather than writing separate CSS/SASS etc. This is handy for a few reasons:

- **Everything is in one place:** now when we build a component, we can keep the component markup, functionality, and styling all in one place. No separate CSS files to keep track of or import.
- **We can use JS within our styles:** props passed to our styled component can be used to influence the resulting CSS.
- **Styles are scoped:** unlike CSS classes, which are global and affect your entire document, styled-components styles are specific to your component. This can save frustration with a polluted global class-space. Importantly, styled-components does allow us to create global styles if we really need to.

A very simple styled component might look something like this:

```jsx
import styled from 'styled-components'

const Text = styled.p`
  background-color: blue;
  color: white;
`

<Text>Hello world!</Text>
```

Which would render a `<p>` tag with a blue background and white foreground.

A slightly more advanced example could include a conditional style:

```jsx
import styled from 'styled-components'

const Text = styled.p(
  ({ inverse }) => `
    background-color: ${inverse ? 'white' : 'blue'};
    color: ${inverse ? 'blue' : 'white'};
  `
)

<Text inverse>Hello world!</Text>
```

Here, the background and foreground colours will switch depending on whether or not the component is passed the `inverse` prop.

### How to add UI themes using React and styled-components

To make styling inside an application easier, we will want to have some rules and variables that can apply to all components. For example, if you have a specific brand colour in your application, you don’t really want to be defining the hex value every time you need to use it within a component. What happens if the brand colour changes? Ideally we can define the value once and then reuse the same variable wherever needed.

To achieve this, we can put together some simple objects that will contain our theme.

`src/app/themes.js`

```javascript
export const base = {
  ​​breakpoints: ['768px'],
  space: ['0px', '2px', '4px', '8px', '16px', '32px', '64px', ...],
  fonts: {
    heading: 'Inter, system-ui, sans-serif',
    body: 'Inter, system-ui, sans-serif',
  },
  fontSizes: ['12px', '14px', '16px', '20px', '24px', ...],
  ...
}
export const light = {
  primary: '#4851f4',
  background: '#ffffff',
  nav: '#f8f8f8',
  border: '#deebf1',
  text: '#202224',
  ...
}
```

Here, `base` contains some rules that will apply across all of our themes: things like spacing values, fonts, breakpoints etc. Defining these values here means that we can keep all of our styling consistent across the whole application. `light` contains the colours that will make up our light theme.

Then, at the root of our application, we can make styled-components aware of this theme using a `ThemeProvider`.

`src/app/App.js`

```jsx
import React from 'react'
import { ThemeProvider } from 'styled-components'
import { base, light } from './themes'

const App = () => {
  const theme = { ...base, colors: light }

  return (
    <ThemeProvider theme={theme}>
      {/* rest of your app goes here */}
    </ThemeProvider>
  )
}

export default App
```

Now, when writing a styled component, you will always receive your theme object as a prop:

```jsx
import styled from 'styled-components'

const Text = styled.p(
  ({ theme }) => `
    color: ${theme.colors.primary};
  `
)

<Text>Hello world!</Text>
```

### Add a second theme and persist theme preference across sessions

Now that we’re using a theme in our application, it is not much extra work to add a second. Define another set of colours:

`src/app/themes.js`

```javascript
export const base = {
  ​​breakpoints: ['768px'],
  space: ['0px', '2px', '4px', '8px', '16px', '32px', '64px', ...],
  fonts: {
    heading: 'Inter, system-ui, sans-serif',
    body: 'Inter, system-ui, sans-serif',
  },
  fontSizes: ['12px', '14px', '16px', '20px', '24px', ...],
  ...
}
export const light = {
  primary: '#4851f4',
  background: '#ffffff',
  nav: '#f8f8f8',
  border: '#deebf1',
  text: '#202224',
  ...
}
export const dark = {
  primary: '#4851f4',
  background: '#1f2023',
  nav: '#27282b',
  border: '#303236',
  text: '#f8f8f8',
  ...
}
```

Now we need to allow the user to choose a theme. We will store the current value in a React state variable.

`src/app/App.js`

```jsx
import React, { useState } from 'react'
import { ThemeProvider } from 'styled-components'
import { base, light, dark } from './themes'

const themesMap = {
  light,
  dark
}

export const ThemePreferenceContext = React.createContext()

const App = () => {
  const [currentTheme, setCurrentTheme] = useState('light')

  const theme = { ...base, colors: themesMap[currentTheme] }

  return (
    <ThemePreferenceContext.Provider value={{ currentTheme, setCurrentTheme }}>
      <ThemeProvider theme={theme}>
        {/* rest of your app goes here */}
      </ThemeProvider>
    </ThemePreferenceContext.Provider>
  )
}

export default App
```

Now from anywhere within your application, you can use the `ThemePreferenceContext` context to get the `setCurrentTheme` function and call it to update the current theme. Your styled components will pick up on this change and update accordingly.

For example, you could do this with a select element:

```jsx
<select
  value={currentTheme}
  onChange={(e) => setCurrentTheme(e.target.value)}
>
  <option value="light">Light</option>
  <option value="dark">Dark</option>
</select>
```

From here, hopefully you can see that it is trivial to add as many themes as you like - simply add a new set of colours, import them into your app, and make them available to be selected and stored in the state.

Storing the current theme in React state is a start, but is only part of the solution. As it stands, this preference will not persist and will be reset every time the user refreshes a page or opens your application. 

Persisting the user's preference can be done in a couple of different ways:

- **Save to a cookie:** By saving to a cookie, the user's preference is available both to the browser and the server. This is ideal if you are doing server-side rendering and need to know the theme preference to render HTML. If you SSR without taking the user's theme preference into account, you can be left with an annoying flash when a page loads because the server is sending the ‘default’ theme which is then changed on the client when rehydration occurs.
- **Save to localStorage:** This is similar to using a cookie, but the value will only be available in the browser and not on the server. If you don’t need to know the theme preference on the server and everything is rendered on the client, this can be simpler.
- **Save to a database:** If you are already storing user accounts or profiles in a database of some sort, then it might make sense to store their theme preference as a part of their account information. This method has the added advantage of persisting a user's theme choice across different devices.

#### Server-side rendering

From our application, we can save the user's preference to a cookie using a library like <a href="https://www.npmjs.com/package/react-cookie" target="_blank">react-cookie</a>. Here, we also add a new prop `initialTheme` to our App component, which will be important in a second.

`src/app/App.js`

```jsx
import { useCookies } from 'react-cookie'

const App = ({ initialTheme = 'light' }) => {
  const [currentTheme, setCurrentTheme] = useState(initialTheme)
  const [, setCookie] = useCookies()

  const handleThemeChange = (theme) => {
    setCookie('themePreference', theme, {
      path: '/',
      expires: new Date(Date.now() + 1000 * 60 * 60 * 24 * 365) // 1 year
    })
  }

  ...
}
```

Then, in our server, we can use the value of this cookie when we server-side render the initial HTML:

`src/server/index.js`

```jsx
import ...

const app = express()

app.use(cookieParser())
app.use(express.static('dist'))

app.get('*', (req, res) => {
  const { themePreference } = req.cookies
  let app = ''
  let styles = ''
  const sheet = new ServerStyleSheet()
  try {
    app = ReactDOMServer.renderToString(
      sheet.collectStyles(
        <StaticRouter location={req.url}>
          <App initialTheme={themePreference} />
        </StaticRouter>
      )
    )
    styles = sheet.getStyleTags()
  } catch (e) {
    console.error(e)
  } finally {
    sheet.seal()
  }

  const html = `
    <!DOCTYPE html>
    <html lang="en">
      <head>
        <title>react-themes-demo</title>
        <script src="/app.js" async defer></script>
        ${styles}
      </head>
      <body>
        <div id="root">${app}</div>
      </body>
    </html>`

  res.send(html)
}
```

Here, we fetch the value of the `themePreference` cookie from the request. Then, using the `ReactDOMServer.renderToString` and `ServerStyleSheet` from styled-components, we build the HTML and CSS content of our application. Note that we are passing our theme preference value through to the `<App />` component, where it is consumed as the initial value for our React state theme variable. We then send the HTML and CSS back as the response from our request, where the user will receive a page styled with their theme preference.

For the full server source code, see the demo app repository linked at the end of the article.

### How to use the operating system theme preference

Most operating systems give the user a choice between a light and a dark theme. In the browser, we can access this choice via a media query - meaning that if a user has set their OS preference to dark, we can automatically do the same in our application, giving the user a cohesive and familiar experience.

![Frame 258.png](https://assets.northflank.com/Frame_258_7b667f5df0.png)

To achieve this, we can watch the `prefers-color-scheme` media query, and update our state when we see a change. 

`src/app/App.js`

```jsx
import React, { useState, useEffect } from 'react'
import { ThemeProvider } from 'styled-components'
import { base, light, dark } from './themes'

const themesMap = {
  light,
  dark
}

export const ThemePreferenceContext = React.createContext()

const App = () => {
  const [currentTheme, setCurrentTheme] = useState('light')

  useEffect(() => {
    const themeQuery = window.matchMedia('(prefers-color-scheme: light)')
    setCurrentTheme(themeQuery.matches ? 'light' : 'dark')
    themeQuery.addEventListener('change', ({ matches }) => {
      setCurrentTheme(matches ? 'light' : 'dark')
    })
  }, [])

  const theme = { ...base, colors: themesMap[currentTheme] }

  return (
    <ThemePreferenceContext.Provider value={{ currentTheme, setCurrentTheme }}>
      <ThemeProvider theme={theme}>
        {/* rest of your app goes here */}
      </ThemeProvider>
    </ThemePreferenceContext.Provider>
  )
}

export default App
```

Using React’s `useEffect` hook, we create a `matchMedia` listener with our media query, set our theme once on first load, and then update it again every time the OS preference changes. Here, you would probably want to add some additional logic to not change the theme based on OS preference if the user has already made a theme choice inside the application.

### How to implement user-customisable theming

In your application, you may also want to allow your users to create their own themes. This is more of a considered decision - you may want to only offer brand-specific themes and not allow users to deviate too far from your own meticulously designed colour schemes. Or, you may want to allow users total control, and let them change every aspect of the theme. Probably, you want to meet in the middle - you could allow users to customise a specific subset of theme colours while keeping the ones integral to your brand fixed.

To implement customisation, we can add another state variable to contain the custom theme, and conditionally apply that when the user selects ‘custom’.

`src/app/App.js`

```jsx
import React, { useState } from 'react'
import { ThemeProvider } from 'styled-components'
import { base, light, dark } from './themes'

const themesMap = {
  light,
  dark
}

export const ThemePreferenceContext = React.createContext()

const App = () => {
  const [currentTheme, setCurrentTheme] = useState('custom')
  const [customTheme, setCustomTheme] = useState({ primary: '#4851f4', ... })

  const theme = {
    ...base,
    colors: currentTheme === 'custom' ? customTheme : themesMap[currentTheme]
  }

  return (
    <ThemePreferenceContext.Provider
      value={{
        currentTheme,
        setCurrentTheme,
        customTheme,
        setCustomTheme
      }}
    >
      <ThemeProvider theme={theme}>
        {/* rest of your app goes here */}
      </ThemeProvider>
    </ThemePreferenceContext.Provider>
  )
}

export default App
```

Again, `setCustomTheme` is exposed via the `ThemePreferenceContext` and thus can be manipulated from anywhere in the application.

The UI to set the custom theme values could look something like this:

```jsx
<div
  style={{
    display: 'grid',
    gridTemplateColumns: 'repeat(3, 1fr)',
    gridGap: '8px',
  }}
>
  {Object.entries(customTheme).map(([key, val]) => (
    <label key={`custom-${key}`}>
      <p>{key}</p>
      <input
        type="color"
        value={val}
        onChange={(e) => {
          setCustomTheme((t) => {
            const current = { ...t }
            current[key] = e.target.value
            return current
          })
        }}
        style={{ display: 'block', width: '100%' }}
      />
    </label>
  ))}
</div>
```

Here, we render a colour input field for each value in our theme. When a value is changed, `setCustomTheme` is called, and thus the custom theme is updated in the state.

![Screenshot 2022-01-20 at 10.47.49.png](https://assets.northflank.com/Screenshot_2022_01_20_at_10_47_49_1d6064c92b.png)

Some applications also allow users to share their custom themes with other users by providing them a simple string representation of their colour scheme. Such a system is left as an exercise for the reader.

### Summary

That’s it! You should now have all of the knowledge you need to add a few themes to your React app, consume them via styled-components, and even allow your users to create their own custom themes.

#### Demo application

![Screenshot 2022-01-20 at 10.45.41.png](https://assets.northflank.com/Screenshot_2022_01_20_at_10_45_41_fb7a2e1fa9.png)

All of the ideas discussed in this blog post have been put together into a simple demo application. You can see the <a href="https://github.com/northflank-examples/react-themes-demo" target="_blank">GitHub repository here</a>, and an instance of the <a href="https://site--react-themes-demo--internal-apps--nort-xjjq.code.run" target="_blank">app deployed on Northflank here</a>.

#### Hosting React apps on Northflank

Northflank makes building and deploying React apps from GitHub or other version control really easy. In the demo repository above, you’ll find a simple Dockerfile, which doesn’t do much more than run the build command, expose a port and run the code. All you need to do is create a service on Northflank and select your GitHub repository. Northflank will parse your Dockerfile, build your app, and deploy it to the cloud with automatic Let’s Encrypt TLS and a code.run domain name.

> Check out our guide on <a href="https://northflank.com/guides/deploying-react-app-on-northflank" target="_blank">deploying a create-react-app application on Northflank</a>.

Northflank can build and deploy any code from version control with Dockerfiles or Buildpacks. Run all of your apps, backends, databases, and cron jobs in one place.

<InfoBox className="BodyStyle">
  <Text mb={7}>Get started with Northflank in minutes. Our generous free tier gives you 2 services, 2 cron jobs and a database to get you going.</Text>
  <a href="https://app.northflank.com/signup">
    <Button variant={['large', 'gradient']} width="100%">Get started now</Button>
  </a>
</InfoBox>

<p><em>Thanks to <a href="https://twitter.com/mxstbr" target="_blank">Max Stoiber</a>, creator of styled-components, for reviewing the draft of this blog post.</em></p>]]>
  </content:encoded>
</item><item>
  <title>Hosting a Shopify application on Northflank</title>
  <link>https://northflank.com/blog/hosting-a-shopify-application-on-northflank</link>
  <pubDate>2022-01-25T09:00:00.000Z</pubDate>
  <description>
    <![CDATA[Shopify App founders and developers have many aspects to take care of, DevOps shouldn't be one of them. Being able to count on a reliable and easy to use deployment platform is essential.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Group_379_350aa9d4fd.png" alt="Hosting a Shopify application on Northflank" />Creating a Shopify app from scratch has many aspects which can cause complexity. In this article we will cover a brief description of all these requirements and delve deeper into the hosting and deployment of your Shopify app.

## Shopify Application 
Shopify apps extend the existing functionality of Shopify and are created by third-party developers. Developers build apps to solve merchant problems at scale and expand the features of Shopify stores. Merchants use apps to help build their business, integrate with external services and add features to simplify their Shopify admin.

As a developer creating a Shopify app there are many aspects/layers you need to take care of: 
- Find a problem faced by many Shopify merchants, which your app will solve
- Understand Shopify as a platform 
- Build the front-end - using popular frameworks such as React
- Build the back-end - using languages and frameworks like NodeJS, Ruby on Rails, PHP
- Manage API calls - Shopify’s REST API or GraphQL can be used to communicate with the Shopify system to update and fetch data
- Create a database - many Shopify apps will have to store customer data and you’ll want it to be efficient at retrieving data so PostgreSQL (RDMS) or MongoDB (NoSQL) will be good options
- Learn about the basics of security - to avoid leaking customer data
- Host and deploy your app - once the app is ready, it will be hosted on a public web server so it’s accessible to merchants
- Marketing - selling your newly created app to merchants
- Customer support - handle support for the first customers directly
- Maintenance - fixing small bugs or deploying new features 


## Guides on how to create an app
If it’s your first time creating a Shopify app, you will find many resources from Shopify and certified Shopify developers covering the technology stack you need and the steps to follow to get your app running locally. 

Some of our favourite resources are: 
- [Shopify Getting Started Guide](https://shopify.dev/apps/getting-started ) 
- [Shopify, Building an app in one week](https://www.shopify.ie/partners/blog/building-a-shopify-app-in-one-week )
- [Jan's video on how to make a Shopify app](https://www.youtube.com/watch?v=A8YCxBTgsbI )

Following these resources you will learn about the front-end, back-end, API calls you need to make with the Shopify system and how Shopify works as a platform. This is all great, but you’ll find there is one very important piece of the puzzle missing, deployment and hosting. Once your app is ready and you want to move from local hosting to a public web server, there are many things which will be important to keep in mind as you scale, and Northflank is here to help.

Shopify apps have specific characteristics: 
- Development versions, staging and production environments
- Traffic peaks during sales, peak hours and events such as Black Friday
- Storing product, sales information, shop metadata and customer information in databases

Having a deployment platform which is reliable, allows CI/CD from Git, supports vertical and horizontal scaling, is price competitive and offers support with onboarding will be key for success. 

## Deployment features

### Development versions and application rollbacks - CI/CD from Git

Your application is likely to be in constant change, therefore having staging and production versions is something you will be familiar with. Pipelines allow you to manage the process of building, deploying and promoting images in different stages and versions. Northflank also gives you the flexibility to set up rules for deployment of the branches you decide on. Read more about pipelines in <a href= "https://northflank.com/docs/v1/application/getting-started/set-up-a-pipeline" target="_blank">our documentation</a>.  

Continuous Integration and Continuous Delivery from your selected Git provider allows you to rollout new code as it's ready to deploy and rollback to previous builds of your application in seconds. This means that if there is an issue with a new commit, like a bug or security issue, you can quickly rollback to a known working commit, giving you time to fix your issue whilst keeping your users happy.

### Multiple sources - Highly reliable networking

Your Shopify app communicates with merchants (your direct client), merchants’ customers and the Shopify platform. Globally accessible load-balancers and DNS are essential and allow traffic to be distributed amongst your running applications. Northflank can handle millions of requests without breaking a sweat ensuring your business and your merchant businesses keep operating, even under peak load. 

Requests and load can be increased by many actions like merchants loading your admin dashboard, webhooks that need to be received and processed as a result of updating Shopify stores (inventory updates, new orders, updated SKUs) and users requesting your application via product pages and checkout experience on Shopify stores.

### Traffic peaks - Horizontal and vertical scaling 

In certain situations there can be high demand and therefore high traffic, for example in seasonal periods like Black Friday, when customers are running sales or when a store becomes popular via social media. 

With Nothflank you can auto-scale either by number of containers (horizontally) or by size of the container (CPU/memory also known as vertically). Triggers can be requests per minute or min/max resource consumption per container. Of course, you can also scale your applications manually.

### Database persistence - DBaaS 

Many Shopify applications have to store SKUs, store metadata, order details, images, video and other information for the Shopify App. You can choose one or multiple datastores such as MongoDB, Redis, MySQL, PostgreSQL and Minio, so you always have the right tool for the job. Your users want the Shopify app to be fast and handle requests quickly, therefore having a high performance database with low latency to your backend APIs is very important. In the same way as with services, addons can scale horizontally, with multiple read replicas, and vertically, by adding more compute. A common requirement for Shopify apps is image storage, usually with an S3 compatible API, MinIO would be a good option here. Northflank also offers staging databases and the ability to fork a database from a backup. Read more about addons in [our documentation](https://northflank.com/docs/v1/application/databases-and-persistence/stateful-workloads-on-northflank).

### Environments and secrets

Securely inject your environment variables into your code and inherit values from addons. If you have a couple of services you can simply inherit the variables, for example a Shopify API key, Twitter key and other variables from a single source of truth. You are able to quickly link connection details from your databases into a secret group to make secure and fast connections between your code and database without manually copying connection details. 

### Zero downtime - Health checks

Health checks are set up to test the backend logic of your app by sending requests to containers in order to check their health. By conducting health checks you can ensure that requests are only sent to healthy containers, assist with rolling out new versions to prevent downtime and auto recover containers when they get in a broken state. Your users need the app to run 24/24 without downtime because when your Shopify app is down your customers could be losing money. [More information on health checks here](https://northflank.com/docs/v1/application/observe/configure-health-checks). 

### Observability - Logs and metrics

At Northflank we provide access to logs and metrics from builds, services, addons and job runs to analyse builds to make sure the code is performant and operating without errors. Logs and metrics are invaluable in helping you and your developers pinpoint production issues enabling you to fix and deploy changes in rapid time. [This page covers more details on logs](https://northflank.com/docs/v1/application/observe/view-logs-and-metrics). 

### Simplicity - Comprehensive UI, API and CLI 

Shopify App creators are sometimes solo-founders or small teams, you want to spend your time growing your business and helping your customers. You have little time to spare and you want your app to be running through traffic spikes and for updates to hit production in rapid time. Northflank's web interface is fast, with real-time updates, and is accessible on mobile devices. Otherwise, you can also interact with Northflank through our CLI client, API, or native JavaScript client. Going from zero-to-hero will be a fast straightforward experience, and we will also be available to support your onboarding and migration.

<InfoBox className='BodyStyle'>

### Using Northflank to deploy your Shopify application

Northflank allows you to deploy your code and databases within minutes. Sign up for a Northflank account and create a free project to get started. 

- Connect with your preferred VSC: Github, Gitlab or Bitbucket
- Observe & monitor with real-time metrics & logs
- Low latency and high performance
- Backup, restore and fork databases
- Private and optional public load balancing as well as Northflank local proxy

<div>
    <a href="https://app.northflank.com/signup">
        <Button variant={["large", "gradient"]}>Get started now</Button>
    </a>
</div>

</InfoBox>]]>
  </content:encoded>
</item><item>
  <title>Resources Search, New Guides &amp; MySQL Database Upgrade</title>
  <link>https://northflank.com/changelog/resources-search-new-guides-and-mysql-database-upgrade</link>
  <pubDate>2022-01-24T11:00:00.000Z</pubDate>
  <description>
    <![CDATA[Introducing search on all tables, new guides to deploy various CMS on Northflank, released new MySQL version and more.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/upgrade_search_header_9635648a93.png" alt="Resources Search, New Guides &amp; MySQL Database Upgrade" />Last week, we focused on writing guides, improving the developer experience with new table search across all resources tables (services, addons, jobs, roles, registries) and released upgrades for MySQL. It’s also an exciting opportunity for us to start offering a Student Developer Pack.

## Improvements & changes

- Added new guides to deploy  [Create React App](https://northflank.com/guides/deploying-react-app-on-northflank),  [Strapi with PostgreSQL & Volumes](https://northflank.com/guides/deploying-strapi-with-postgresql-and-volumes), [Strapi with PostgreSQL & S3](https://northflank.com/guides/deploying-strapi-with-postgresql-and-using-s3), [Ghost CMS](https://northflank.com/guides/deploying-ghost-cms) and [Payload CMS](https://northflank.com/guides/deploying-payload-cms) on Northflank
- Introduced [Northflank student developer pack](https://northflank.com/student-developer-pack)
- Added a search onto table headers so you can easily find your services, jobs, databases and more 
- Released Redis v6.2.6 (minor) and MySQL v8.0.27 (minor)
- Redesigned and improved platform alert popups which now appear in the bottom centre
- Improved SSO login & job run error messages 
- Hovering over environment variables or connection strings now expands their value
- Fixed an issue where completed jobs would show as running in some edge cases 
- Fixed an issue where some repositories appeared twice in the service repo dropdown 
- Fixed Dockerfile fetching for Bitbucket repositories in certain circumstances
- Fixed an issue where a build wouldn’t trigger automatically on creating a new branch in Bitbucket 
]]>
  </content:encoded>
</item><item>
  <title>Environment and Secret Templating</title>
  <link>https://northflank.com/changelog/environment-and-secret-templating</link>
  <pubDate>2022-01-17T18:30:00.000Z</pubDate>
  <description>
    <![CDATA[Create dynamic variables with secret templating, and new documentation and API.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/group_secret_templates_fd0540278a.png" alt="Environment and Secret Templating" />We are excited to release dynamic environment and secret templating. This enables developers to generate environment variables from multiple sources including defined variables and inherited secrets from managed databases. A good example is when your code requires a slightly different connection string or when you need to set a custom option as seen in the example below.

```
DATABASE_URL=mongodb://user:password@replica.mongodb.addon.code.run:27017/database?replicaSet=rs0&authSource=database&tls=true
DATABASE_URL=${DATABASE_URL}&readConcernLevel=majority&w=majority&wtimeoutMS=0
````

Templating has been added as a tooltip for table view and inline edit modes, and as a Monaco editor component for `JSON`, and `.env`. All UX flows have smart auto-completion to search across all available keys and aliases, allowing you to reference inherited values.

![northflank-screenshot-group-secrets-autocomplete.jpg](https://assets.northflank.com/northflank_screenshot_group_secrets_autocomplete_c725730758.jpg)

![northflank-screenshot-group-secrets-monaco-editor.jpg](https://assets.northflank.com/northflank_screenshot_group_secrets_monaco_editor_3d460d430d.jpg)

### CLI & API 
- Adds support for branch switching to API
- Fixed issue where service creation endpoints could fail to find a provided GitHub repository for certain configurations of GitHub links
- Fixed issue where creating a service through the API with a newly created repository could fail to build automatically when commits were made

### Documentation
- Added path ignore rule documentation
- Added multiple VCS account linking guide
- Added documentation for specific DBaaS (Redis, Postgres, MongoDB, Minio and MySQL)
- Updated secret group for new default `runtime and buildtime` secret group type
- Fixed typo in JS header

### Other features & fixes 
- Added specific RBAC permissions for viewing and configuring Tax ID status via the UI
- Improved dashboard list items and alignment for consistent readability
- Improved retry logic during multiple stages inside a Kaniko build
- Improved resume action in addon lists to be more consistent with real-time state
- Fixed external docker image verification when providing a custom image digest
- Fixed rendering of custom container entrypoint if items are greater than one
- Fixed when build engine options could be reset or overridden in certain circumstances
]]>
  </content:encoded>
</item><item>
  <title>Persistent Volumes and Storage for Deployments</title>
  <link>https://northflank.com/changelog/persistent-volumes-and-storage-for-deployments</link>
  <pubDate>2022-01-10T11:00:00.000Z</pubDate>
  <description>
    <![CDATA[Introducing volumes for persistent data storage and new versions of Northflank JS client &amp; CLI.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/volumes_ui_baf4863897.png" alt="Persistent Volumes and Storage for Deployments" />We hope you all enjoyed the Christmas break and wish you a successful & healthy New Year! 

We at Northflank kept ourselves busy shipping more features and bug fixes. Today, we’re announcing a highly requested feature - persistent container volumes.

Volumes are great for persisting data across service restarts and we make them incredibly simple and highly performant. For example, deploying container images from DockerHub such as `clickhouse`, `materialized` and `meilisearch` where persistance is as simple as adding a new volume to their data directory.

Northflank has developed DBaaS experiences for several databases, for those we have not reached yet, volumes and external images are the next best workflow.


- Multiple volumes can be attached to the same service
- Volumes can have multiple container mount locations 
- Volumes can be detached/attached and moved between different services 
- Volumes can be configured for deployment and combined services

### CLI & API 

- Released JS client v0.5.0 and CLI v0.7.0
- JS client (v0.5.0): improved support for CommonJS and ESM which allows optimal compatibility for different Node.js versions and configurations
- Removed default export of ApiClient in JS client so it can now be imported using `import { ApiClient, ApiClientInMemoryContextProvider } from '@northflank/js-client';`
- Volumes and path ignore rule endpoints are integrated in JS client and CLI 
- CLI (v0.7.0): fixed dynamic selector for plans and error messages for forwarding 

### Other features & fixes 

- Added a random encryption key generator to secrets where you input the desired length and your base64 key will get auto-generated 
- Added an autocomplete for address selectors 
- Updated NextJS and Gatsby templates to newer versions
- Improved the performance of database backups and restores 
- Improved mobile view responsiveness on VCS dropdown
- Fixed incorrect confirmation modal buttons and messages
- Fixed port option selectors where multiple health checks are used 
- Fixed an issue where secret groups could contain blank values
- Fixed `help and feedback` not clickable on mobile devices
- Fixed real-time rendering of health check status details on the container list 
]]>
  </content:encoded>
</item><item>
  <title>Version Control and API Enhancements</title>
  <link>https://northflank.com/changelog/version-control-and-api-enhancements</link>
  <pubDate>2021-12-21T09:00:00.000Z</pubDate>
  <description>
    <![CDATA[Support for multiple account links per Git provider, conditional builds based on what files changed, new Git API endpoints. ]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/multiple_git_f3f61ce109.png" alt="Version Control and API Enhancements" />We are excited to present the final changelog of 2021. Time certainly does fly when you're having fun and releasing weekly. Today's focus is on version control. We've increased flexibility with multiple provider links for teams and conditional builds based on what files have changed. In addition, powerful API endpoints have been added to make Northflank's Git integrations accessible via code.

### Multiple version control links per provider

- Teams can link multiple version control accounts from the same provider to their team account. 
- When creating a service or job from a version control repository, team members can access any of the repositories that any of the linked version control accounts can access.
- If multiple accounts can access the same repository, you will get the option to select which version control account Northflank should use to access that repo. This can be changed later in the service or job’s Build options.
- For Bitbucket, GitLab and self-hosted GitLab version control, each link can be configured individually to restrict access to specific contexts.
- When creating a service or job from version control via the API, Northflank will pick a linked account that has access to that repo. If you wish to use a specific account link to access that repository, you can pass in a new argument `accountLogin`.
- To support multiple version control links, the List VCS providers endpoint now returns an array of version control account links rather than each version control provider having a separate field.

![acme-git-multiple.png](https://assets.northflank.com/acme_git_multiple_0a9fae8812.png)

### Build path ignore rules

- Combined services, build services and jobs building from version control support path ignore rules, which allow you to configure which files Northflank should watch for changes when building commits automatically. This is useful if you have a large monorepo containing multiple services.
- Path ignore rules share the same syntax as a .gitignore file, allowing you to set up a list of file rules which Northflank should ignore.
- If all the modified files in a commit match the build rules, the commit will not be built automatically.

![build-path-ignore-rules.png](https://assets.northflank.com/build_path_ignore_rules_389551ee68.png)

### Version Control API

- Version control data that can be accessed via the UI is now accessible via the API.
- There is a new endpoint for listing available repositories that can be filtered by version control provider and account login.
- There is a new endpoint for listing the branches of a given repository.
- There are new endpoints for services and jobs building from version control for listing the branches and pull requests of the repository they are building from.

### Misc fixes

- Fixed an issue where repeatedly relinking a version control account could cause other team or user namespaces using the same account to have issues connecting to version control.


]]>
  </content:encoded>
</item><item>
  <title>Integrating with GitHub - GitHub Apps and OAuth</title>
  <link>https://northflank.com/blog/integrating-with-github-github-apps-and-oauth</link>
  <pubDate>2021-12-17T12:00:00.000Z</pubDate>
  <description>
    <![CDATA[Answering some of the common questions around integrating with GitHub, and discussing some of the quirks you should watch out for.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/Group_349_89af322dc0.png" alt="Integrating with GitHub - GitHub Apps and OAuth" />When creating a web application designed to improve the experience of software development, it might be useful to integrate with version control providers in order to access your users’ existing code. For example, here at Northflank, users can link their version control provider accounts and then can use Northflank to build and deploy their code. The most popular version control providers all provide a way to link their version control accounts with your platform, allowing you to access data about that user’s version control through an API. 

Here, we’ll be going into detail about GitHub, both because it is a very popular version control provider and also because its integration handling is more complex than many of the other providers. We’ll be discussing a number of quirks and pitfalls that you should be aware of when integrating with GitHub. Particularly, GitHub provides two ways to integrate with it - OAuth Apps, which are very similar to the way you integrate with GitLab and Bitbucket, and GitHub Apps, which is GitHub’s own, more powerful system.

In this article, we will answer some of the common questions about GitHub integration:

- What is the difference between a GitHub App and a GitHub OAuth App?
- How do I authenticate API requests using a GitHub App?
- How do I use OAuth linking with GitHub Apps?

### What is an OAuth App?

OAuth Apps allow a user to link their GitHub account to your service, which then allows you to perform actions on behalf of that user. You can define which scopes your application requires as part of the linking process and once the user has linked, you can use their access token to perform any action that the user would be able to (provided it is in the permission scope).

<img src="https://assets.northflank.com/image_20_e96a3e3754.png" alt="github oauth link process" style={{ maxWidth: "500px" }} />

For example, if a user links with the repo scope, you can use the access token to access any repository that the user could. This includes their own public and private repos, but also any repositories that they have been given access to by another user, or as part of an organization that they belong to. As all the actions are performed on behalf of the user, any commits you make will be made in the user’s name.

As the scopes are defined as part of the linking process, you can have multiple tokens with different scopes. For example, you could implement single sign-on with GitHub by having the user authenticate whilst providing only minimal account access. Then, you could have a second authentication flow with more scopes in order to integrate with their repositories. This could be useful if repository integration is only a small part of your application so you want users to be able to log in with GitHub without having to give you access to all of their repositories.

One thing to be careful of when generating OAuth tokens is that there is a limit to the number of valid tokens that can be generated at one time. Per user, each combination of scopes can only have ten active tokens. If you generate another OAuth token for this set of scopes, the oldest token will become invalid. It is important you don’t put yourself in a position where tokens you are using become invalidated. In the previous example with single sign-on, if you were to use the same scope for both SSO and a separate linking flow that you use when making API requests, a user repeatedly logging in could cause your API token to become invalid, so in this case you should make sure to use separate tokens. If your application allows users to make multiple accounts linked to the same GitHub account, a user who links their GitHub to more than ten accounts could have some of their tokens invalidated, so you should make sure to update your older tokens whenever a new one is generated.

OAuth tokens have a rate limit of 5000 API requests per hour. However, it is important to note that this rate limit is per user, and not per OAuth application. This means if a user has other OAuth applications linked to their account, or they are making API requests themselves with Basic Authentication, all these requests are coming out of the same limit. Therefore, it is important that you build in error handling for when you are being rate limited by GitHub, as well as to be respectful of the user’s rate limit in case they are using other OAuth applications. If your application performs regular polling on GitHub, you could allow the user to configure the frequency of the polling depending on [how much rate limit they have](https://docs.github.com/en/rest/overview/resources-in-the-rest-api#rate-limiting) to spare.

### What is a GitHub App?
GitHub Apps are a more powerful way to link with GitHub, though they are a bit more complicated. Rather than linking with a user, users ‘install’ your GitHub App onto one or more repositories that they own. When linking, users get the option to either install the app on their personal account or an organization that they are an administrator of. They then get to choose whether the app can access every repository on that user or organization, or just a specific subset. Each user account or organization has its own installation, each with its own installation ID. Installations on an organization do not belong to any specific user - it does not matter which GitHub user performed the installation, and there can only ever be one installation of your app per organization.

<img src="https://assets.northflank.com/image_21_d373809d31.png" alt="install github app" style={{ maxWidth: "500px" }} />

Unlike OAuth applications, you select the scopes your GitHub App requires in the App settings, where it is then fixed for every user who installs your GitHub App. If you need to change the scopes in the future, you need to modify the scopes in the settings, and GitHub will then send an email to each user telling them about the additional permissions required and requesting that they authorize the additional scope requested. GitHub allows you to write a custom message for this email. Your GitHub App will continue to work without the user authorizing the additional permissions, and you can verify the current permissions of an installation with the ‘[Get an installation for the authenticated app](https://docs.github.com/en/rest/reference/apps#get-an-installation-for-the-authenticated-app)' endpoint.

![github app permissions and events](https://assets.northflank.com/image_22_d5d2222fab.png)

### Authenticating with your GitHub App
When you wish to perform an action, you perform the action on behalf of your GitHub app. To do this, you first create a JWT signed with your app’s secret. You can then authenticate with this JWT and make an API request to GitHub along with one of the installation IDs to get an installation token for that installation. This installation token then lets you make API requests for repositories in that installation. As this installation token is not linked to any specific user, any commits you make will be made in the app’s name, and labelled as a bot. This is useful for automated commits since it makes it very clear that this was not a user making the commit.

```javascript
import jwt from 'jsonwebtoken';

const { GITHUB_APP_PEM, GITHUB_APP_ID_VALUE } = process.env;

// Tokens can have an expiry of at most 10 minutes
const expiryTime = 9 * 60;

// https://developer.github.com/apps/building-github-apps/authenticating-with-github-apps/#jwt-payload
const getGithubAppToken = () => {
  const pem = GITHUB_APP_PEM;
  const timestamp = Math.floor(Date.now() / 1000);

  const payload = {
    // Issued at time
    iat: timestamp,
    // JWT expiration time
    exp: timestamp + expiryTime,
    // Github app identifier
    iss: GITHUB_APP_ID_VALUE,
  };

  const token = jwt.sign(payload, pem, { algorithm: 'RS256' });
  return token;
};

export default getGithubAppToken;
```


```javascript
import fetch from 'node-fetch';
import getGithubAppToken from './get-github-app-token';

const getGithubInstallationToken = async (installationId) => {
  const uri = `https://api.github.com/app/installations/${installationId}/access_tokens`;
  const accessToken = getGithubAppToken();

  const options = {
    method: 'POST',
    headers: {
      Accept: 'application/vnd.github.v3+json',
      Authorization: `Bearer ${accessToken}`,
      'User-Agent': 'Northflank',
    },
  };

  const resp = await fetch(uri, options);

  const json = await resp.json();

  return json.token;
};

export default getGithubInstallationToken;
```

The interesting thing about installation tokens not being linked to a user is that you can make requests without storing anything other than the app’s secret. For example, a GitHub App designed to make a comment on every new pull request could wait to receive a pull request webhook, take the installation ID from the webhook and generate an installation token using the ID and the app secret, and then make the comment API request with that token. In some circumstances, the user might not even need to have gone on your website - they could install the app from the GitHub marketplace and perform the whole installation without leaving GitHub.

### Using OAuth with GitHub Apps

Of course, this kind of user-agnostic functionality is not always something you want. If you want a user to be able to link their account on your website with their GitHub account, you need a way of identifying who performed the linking, and we can do this with OAuth. Each GitHub app also has an OAuth app built in which you can link with - GitHub calls these App authorizations. You can perform the OAuth linking in two ways - you can either have it separate from the installation (e.g. the user performs the OAuth authorization flow, and then performs the installation flow, or vice versa) or you can select the ‘Request user authorization (OAuth) during installation’ option which will combine the two flows into a single one. Combining the steps avoids making the user navigate away from your site a second time, but it means users editing installation settings later will get redirected back to your OAuth callback endpoint, which you might not want. 

![github identifying and authorizing users](https://assets.northflank.com/image_24_65e6258470.png)

Whilst users can start the OAuth flow from GitHub if the steps are combined, it’s recommended that you only perform OAuth linking originating from your site, and validate that request via a token that you pass to the GitHub authorization flow through the state parameter - this helps to avoid cross-site request forgery attacks.

With that OAuth token, you can then perform a number of things including check which installations that user can access. These OAuth tokens can be a little bit awkward however, since unlike the installations OAuth tokens are bound to a specific user - so there’s a bit of a disconnect there. It’s important that your users understand what they’re linking and what you’re trying to access. For example, a user who is part of a shared team account on your website might install your GitHub App on their GitHub organization. If you then use the OAuth token to perform actions on behalf of their personal account, they might be quite confused!

These OAuth tokens have the same ten token limitation as the OAuth application tokens mentioned before. However, you do not need to provide any scopes during the OAuth token request - GitHub App OAuth tokens inherit the scopes and permissions of the GitHub App they are a part of.

### Using webhooks with GitHub Apps

GitHub Apps have support for webhooks. Whenever a user installs your GitHub app, you will start to receive the webhooks you have selected for all the repositories your app can access. You can also receive webhooks for things like repository creation and changes to an organization such as members being invited or removed.

## Improvements for GitHub
There are a few things we would love to see improved by GitHub in the future.

### GitHub App linking steps
Firstly, the linking process for GitHub Apps that need to authenticate with OAuth is awkward. It is simpler for developers to split the OAuth authentication and the GitHub App installation into two parts. However, doing it this way requires sending the user through two separate flows. This is confusing for users and provides even more friction in your onboarding process, making it more likely that your users don’t finish setting up their accounts.

If you join them together though, that introduces its own problem. If you want the user to go through the linking process again, such as if they unlinked their account from your site but not via GitHub, or if they want to link the same GitHub account to a second account on your site such as a team account, they are sent directly to the installation settings form with seemingly no option to finish the link as the save button is greyed out. If the user wants to finish the linking process a second time, they need to first change their Repository access setting so that the save option becomes available. Then, the user can switch back to their previous setting and click the now enabled save button.

<video controls autoPlay playsInline loop muted width="100%">
    <source src="https://assets.northflank.com/github_app_existing_installation_337389e1d2.mp4"/>
</video>

Also, the combined flow can be confusing to users. When installing a GitHub App, if you select a GitHub organization the App is installed on that organization but the OAuth link is performed on the user account who went through the linking process. That OAuth link can be used to fetch data about that specific user rather than the organization as a whole - particularly if the user has already installed the App on their personal account. If you are using the OAuth token in this way, you should make sure that your users understand that they are linking their personal account even though they selected an organization. You don’t want a user accidentally giving access to repositories they didn’t want to, especially if other users on your site have access to this information such as if you have team accounts or groups..

### Checking repository access

Working out which repositories a user can access can also be a pain. If you want list all the repos a user can access that have your app installed, you must first call the API endpoint that [lists all installations a user can access](https://docs.github.com/en/rest/reference/apps#list-app-installations-accessible-to-the-user-access-token) then for each of those, call the API endpoint that [lists the accessible repositories for that installation](https://docs.github.com/en/rest/reference/apps#list-repositories-accessible-to-the-user-access-token). Then, if you want to perform an operation on a repository that isn’t part of the user’s installation, you need to make sure that the user actually has access to perform that action. Since ideally you want to be using the GitHub App installation token whenever possible, and those tokens are user-agnostic, you need to either call the API endpoint to list a repository’s collaborators and verify that against the user performing the action, or get the user’s permissions by fetching the repo with their OAuth token. It would be useful to have some kind of endpoint that uses the installation token to verify which users can access that repo.

Similarly, whenever you receive a webhook, it can be difficult to know which users that webhook is relevant to, as there is not an easy way to know which users on your site have access to that repository.

### Creating new repositories
Creating new repositories can also be difficult. Firstly, repo creation requires the Administration scope, which can be a scary ask for users since that gives your App the ability to delete repositories, among other things. Secondly, once you have created a repository, your App does not have access to that repo unless the user has selected the option to install the App on all repos. This is frustrating since you have to call an API endpoint to check whether the installation can access all repos, and if not, request that the user modifies their installation settings to support this. It would be great if repos created by the GitHub App automatically had the App installed. 

### Permission selection
GitHub Apps provide some great granularity to users by allowing them to select which repositories to install the GitHub App on, but it would be useful to extend that functionality to permission scopes. It would be handy to allow users to connect with the minimum scopes needed for your site, and then allow them to optionally select further permissions if you have specific features that need permissions like Administration.
 

As you can see, integrating with GitHub gives you access to a number of powerful features, and once you begin to understand the quirks of GitHub and GitHub Apps, you can begin to do all sorts of things with your application. Hopefully this article has helped you to grasp some of the complexities of GitHub integration, and help you to avoid some of the common pitfalls.


Thank you for reading! Hopefully you find some use from some of the lessons I've learnt from working on version control integration here at Northflank. If you have any questions you can contact me at [first name] @northflank.com.
]]>
  </content:encoded>
</item><item>
  <title>Improved Health Checks and Networking</title>
  <link>https://northflank.com/changelog/improved-health-checks-and-networking</link>
  <pubDate>2021-12-13T11:00:00.000Z</pubDate>
  <description>
    <![CDATA[Improved network routing for new deployments and containers in a transitioning non-terminal state. Greater UX for health-check configuration across start-up, readiness and liveness probes.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/healthchecks_2bd72d0332.png" alt="Improved Health Checks and Networking" />We have been working hard on building tooling to alleviate the limitations of Kubernetes health-checks, Kubernetes endpoint slices and Istio. This increases the reliability during blue green deployments, free service continuous deployment and correctness of health-check routing for start-up, readiness and liveness probes.

Northflank has developed a Kubernetes admission controller to enable graceful termination of user workloads by forcing DNS propagation before service termination - allowing in-progress requests to complete and preventing new requests to soon-to-be terminated containers, solely routing to healthy containers.

Northflank’s service mesh will auto-retry three times before failing a request to maintain service uptime during redeployments. We have identified a number of factors that could interfere with request routing:
- Avoid transient failures - a client should not perceive a failed request if a service was momentarily unavailable. 
- Expect race conditions occurring in Kubernetes state vs DNS configuration - this often could be just a few milliseconds. 
- Workload stability - load-balance retries across available workload instances.

Further UX improvements have been made:
- Improved real-time health check propagation via Prometheus metrics into the Northflank UI
- When configuring an HTTP liveness probe a readiness probe will automatically be generated
- New health checks descriptions:
    - **Readiness Probe** - ensures a new container is ready to serve traffic before terminating an old container and handling new requests
    - **Liveness Probe** - will restart containers that are failing checks
    - **Startup Probe** - will delay readiness and liveness probes until checks are passed

![Health Checks Advanced](https://assets.northflank.com/health_checks_advanced_config_fe37a965cd.png)


### Other features & fixes

- Introduced a new instance selector that is now applied across the whole UI 
- Added a new secret group type `Build & Environment` which is fetched both during build and deployment phases 
- Fixed an issue where very large repositories would take a long time to build 
- Fixed an issue where an empty project dashboard would render strangely on Safari 
- Fixed an issue where API endpoint `assign service to subdomain` would error 
- Fixed an issue where full DNS entry on Networking would not display service name as slug 
- Fixed an issue where `heroku/buildpacks:20` with PHP and Laravel would not run due to an error with permissions
- Improved changing ports and custom domains RBAC
- Fixed rendering of the team member role selector
- Fixed rendering custom CMD override if quotes were used in the command
]]>
  </content:encoded>
</item><item>
  <title>Build Caching for Docker Container Builds</title>
  <link>https://northflank.com/changelog/build-caching-for-docker-container-builds</link>
  <pubDate>2021-12-06T11:00:00.000Z</pubDate>
  <description>
    <![CDATA[Introducing caching for Docker container builds, speeding up your build and release cycle. Support to change Git repo on existing services, updated documentation.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/docker_build_cache_a71a4f1266.png" alt="Build Caching for Docker Container Builds" />Northflank is pleased to introduce build caching for Docker container builds. Caching speeds up subsequent builds by reusing image layers from previous builds, allowing you to release and iterate faster. Build times can be reduced dramatically by skipping build steps that would produce the same output as in previous builds. Typical examples of build steps that would benefit from caching are download and compilation of dependencies.

- Uses the previous build image as a cache
- Image layers are cached per service allowing build services to use the same cache for all PRs and branches of a repository
- Caching is supported for the Kaniko and Buildpack backends
- Implemented with Kaniko’s and Buildpack’s native caching capabilities
- Distributed build caching, high performance, scalable and backed by Northflank’s global container registry

<table>
<thead>
  <tr>
    <th><small>Name</small></th>
    <th><small>Language</small></th>
    <th><small>Build without cache</small></th>
    <th><small>Build time with cache</small></th>
  </tr>
</thead>
<tbody>
  <tr>
    <td><a href="https://github.com/rancher/k3d" target="_blank">rancher/k3d</a></td>
    <td>Go</td>
    <td>7m 28s</td>
    <td>3m 37s</td>
  </tr>
  <tr>
    <td><a href="https://github.com/coredns/coredns" target="_blank">coredns/coredns</a></td>
    <td>Go</td>
    <td>8m 29s</td>
    <td>3m 31s</td>
  </tr>
  <tr>
    <td><a href="https://github.com/micro/micro" target="_blank">micro/micro</a></td>
    <td>Go</td>
    <td>9m 52s</td>
    <td>5m 19s</td>
  </tr>
  <tr>
    <td><a href="https://github.com/scalacenter/docker-scala" target="_blank">scalacenter/docker-scala</a></td>
    <td>Scala</td>
    <td>13m 40s</td>
    <td>5m 32s</td>
  </tr>
  <tr>
    <td><a href="https://github.com/northflank-examples/php-laravel-example" target="_blank">northflank-examples/php-laravel-example</a></td>
    <td>PHP</td>
    <td>3m 26s</td>
    <td>1m 9s</td>
  </tr>
  <tr>
    <td><a href="https://github.com/northflank-examples/create-react-app-example" target="_blank">northflank-examples/create-react-app-example</a></td>
    <td>JavaScript</td>
    <td>5m 40s</td>
    <td>2m 32s</td>
  </tr>
</tbody>
</table>

### API & CLI

- Unified response date types across the API and CLI 
- API now returns resource status on `get service` endpoint
- Added IP Policies for addons to documentation

### Other features & fixes 

- You can now modify the Git source repository on combined services and jobs, whilst maintaining the ability to roll back to previous builds. This enhances [previously announced branch source switching](https://northflank.com/changelog/switch-target-git-branch-for-continuous-integration-and-validation-build-status-improvements).
- Added support to download PDF invoices 
- Improved billing dashboard to support adding a VAT ID and address 
- Added documentation on how to write Dockerfiles for build caching
- Added documentation for addon upgrades 
- Updated Buildkit documentation to include adding a git URL (in addition to the CNB registry) 
- Updated documentation for authenticating GitHub Container Registry 
- Fixed an issue where the project dashboard wouldn’t show all addons in certain scenarios
- Fixed notification text on Continuous Deployment toggle 
- Fixed an issue where adding CMD override wouldn’t show on the dashboard immediately ]]>
  </content:encoded>
</item><item>
  <title>Database Snapshot Backups</title>
  <link>https://northflank.com/changelog/database-snapshot-backups</link>
  <pubDate>2021-11-29T13:30:00.000Z</pubDate>
  <description>
    <![CDATA[Introducing disk snapshot backups. Powered by the Kubernetes CSI Driver. Incremental disk backup and restores for MongoDB, Redis, Postgres, MySQL and MinIO.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/addon_backups_header_957ea7e022.png" alt="Database Snapshot Backups" />In addition to our existing native backups (backup dumps), we are excited to introduce snapshots (also called disk backups) for all our database offerings (MySQL, PostgreSQL, MongoDB, MinIO and Redis).

This was made possible and is powered by the Kubernetes CSI driver and works across currently supported providers: Northflank PaaS, GCP, AWS, bare-metal (K3s Longhorn) and Azure.

### Native vs Snapshot Backups

Native (Dump) backups make use of dumping tools for the datastore (e.g. MongoDB uses mongodump).
- Backup creates a full copy of the database 
- Well-suited if the backups need to be inspected or exported  
- Can be imported via URL or file upload 
- Restores do not lead to a database container restart but the database will be unreachable whilst restoring

Snapshot (Disk) backups take a snapshot of the whole volume at the point of the backup.
- If multiple backups are created, they rely on the previous backup as only the difference between snapshots is stored
- Great for recurring/scheduled backups as they only store the difference of the file system since the last snapshot
- Cannot be inspected or exported 
- Restoring a disk backup creates a new disk from the latest snapshot, once the database is restarted with the new disk your database is resumed exactly how it was when the snapshot was taken

We have already seen users implementing Northflank cron jobs to trigger regular snapshots and hourly native backups via the [Northflank API](https://northflank.com/docs/v1/api/addons/backup-addon), excited to see how this evolves!

![Addon Backups UI](https://assets.northflank.com/Addon_Backups_55a98f0f2e.png)


### Other features & fixes

- Added handling for rendering containers with unknown status and improved sorting of the container list to always list running containers first 
- Changing service template during creation no longer wipes previously entered data 
- Added support for comments on the environment editor 
- Fixed an issue where login page required a refresh if Google SSO was used with an invalid account 
- Fixed an issue where service environment editor would be stuck on load if a user was missing secret groups permissions 
- Fixed service header responsiveness
- Fixed an issue where sign up with GitLab would fail 
- Fixed an issue where team members missing the `billing read` permission could not create new resources 
- Fixed an issue where some builds configured with Buildkit would fail
- Fixed sorting of branches on the build selector 
- Fixed the environment editor lagging on some lower specification machines, or those under CPU throttling conditions
]]>
  </content:encoded>
</item><item>
  <title>Free Developer Projects</title>
  <link>https://northflank.com/changelog/free-developer-projects</link>
  <pubDate>2021-11-22T11:00:00.000Z</pubDate>
  <description>
    <![CDATA[Get started on Northflank with free developer projects enabling you to try the full Northflank experience.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/free_project_6da97e8501.png" alt="Free Developer Projects" />We are pleased to introduce free developer projects, removing barriers to entry with no risk of incurring cloud costs and providing access to the tools needed to build & release delightful software.

Everything is free within a developer project. Out of the gate you can create 2 services, 2 jobs and 1 database with generous resources. 

This is perfect for building an MVP, allowing you to experiment and try Northflank before you commit to the platform, pun not intended. Limits can be lifted at any time with a project upgrade to scale into production and beyond. 

<br />
<a href="https://app.northflank.com/signup"><button variant={ ["large", "gradient" ] }>Get started now</button></a>

### API & CLI

* `health check` endpoints now have an updated schema replacing some required parameters to be optional 


* [Northflank CLI version 0.6.1](https://www.npmjs.com/package/@northflank/cli) is available and can be installed using `npm i @northflank/cli -g` or `yarn global add @northflank/cli`.
    * Fixes dynamic selector for resource plans
    * Fixes error messages when using northflank forward 
    * Reflects recent API changes


* [Northflank JS Client version 0.4.1](https://www.npmjs.com/package/@northflank/js-client) reflects recent changes and can be added as a project dependency using `npm i @northflank/js-client` or `yarn add @northflank/js-client`. 

### Other features & fixes

- Added status fields to services and jobs to handle `creating` and `deleting` statuses
- Updated our internal CMS to use Strapi for more dynamic content types
- Improved search in teams and projects dropdown
- Improved Redis connection strings to include TLS flag
- Fixed an issue where some addons would get stuck in deletion state after billable resources had been removed due to networking configuration
- Fixed ordering of PRs in pipeline build selector so recent PRs appear at the top
- Fixed an issue where a repository would not link correctly to a service if created via API
- Fixed an issue where boolean values in secret groups would render incorrectly in inherited services & jobs

]]>
  </content:encoded>
</item><item>
  <title>Support for all Custom Buildpacks with Git Source</title>
  <link>https://northflank.com/changelog/support-for-all-custom-buildpacks-with-git-source</link>
  <pubDate>2021-11-01T12:30:00.000Z</pubDate>
  <description>
    <![CDATA[Build and run any custom buildpack supplied via Git repositories, search on the team selector and bug fixes.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/11/buildpacks-git-header.png" alt="Support for all Custom Buildpacks with Git Source" />It is now possible to build and run continuous integration on Northflank with any custom buildpack that can be supplied via a Git repository. Buildpacks are not always available in the CNB registries and being able to target a branch, release or a forked buildpack is very powerful! This feature builds on our recent release of buildpack support announced [here](https://northflank.com/changelog/cloud-native-buildpacks-on-northflank).

### Other features & fixes

* Added search to the team selector popover 
* Public repositories can now be deployed even without having a linked Git account 
* The build list on jobs now shows a deployed icon on a currently deployed build 
* Improved responsiveness of the invoice page table 
* API: [Update job deployment](https://northflank.com/docs/v1/api/jobs/update-job-deployment) endpoint now accepts internal build services 
* Fixed Ports & DNS form returning an error on custom domain & security rules in some edge cases
* Fixed an issue where promoting services/jobs without successful builds caused pipeline links to get stuck at promotion 
* Fixed an issue where two clicks were required to switch deployment types in creation forms under certain circumstances]]>
  </content:encoded>
</item><item>
  <title>Faster Image Pulls, Deployments and More</title>
  <link>https://northflank.com/changelog/faster-image-pulls-deployments-and-more</link>
  <pubDate>2021-10-25T10:00:00.000Z</pubDate>
  <description>
    <![CDATA[Improved workload performance with faster Docker image pulls and deployments, more documentation and fixes.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/10/help-popovers.png" alt="Faster Image Pulls, Deployments and More" />We are excited to announce faster Docker image pulls and deployments. As Northflank scales deployments, we continue to aim for sub-second image staging times. As nodes are created, updated, rolled or migrated to different zones the pod churn can be high as thousands of pods attempt to redeploy concurrently. This leads to a traffic jam as pods fight to pull images before one another. Northflank can regularly replace and resize Kubernetes node pools for peak performance & reliability with the guarantee that workloads are spun up in seconds rather than minutes.

### Other features & fixes 

* Node image pull queues were modified to allow multiple images to pull their layers in parallel
* Node image pull rate limits were doubled to allow for more efficient fetching when multiple are downloaded at once
* Added help popovers to resource types on new creation forms (service, job, addon, pipeline, service from template and secret group)
* Added help popovers across secret groups
* Improved validation of external image paths when creating jobs 
* Updated documentation JSON examples for API & CLI
* Fixed an issue where the pipeline UI would crash after some services are deleted
* Fixed issues with creating and restoring MySQL & PostgreSQL backups 
* Fixed an issue where Redis CLI forwarding wouldn’t work as expected in some cases 
* Fixed an issue where instances in `terminating` status would display an incorrect status icon for a couple of seconds
]]>
  </content:encoded>
</item><item>
  <title>Support for Public Repositories</title>
  <link>https://northflank.com/changelog/support-for-public-repositories</link>
  <pubDate>2021-10-18T13:27:30.000Z</pubDate>
  <description>
    <![CDATA[Build and deploy public repositories from all Git providers, several API changes and more.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/10/public-repos-header.png" alt="Support for Public Repositories" /><!--kg-card-begin: markdown--><p>Introducing public Git repository support. Simply paste any Git repository URL during creation to build and deploy in minutes. This means you can deploy any repository (public or private) from all version control platforms on Northflank regardless if the relevant App, OAuth or Webhooks have been set up.</p>
<p>Here's an example of deploying the <a href="https://github.com/minio/minio.git">minio</a> repository with configured CMD override:</p>
<video controls autoplay playsinline loop muted width=100%>
    <source src="https://assets.northflank.com/changelog/minio-public-repo.mp4"/>
</video>
<h3 id="api-changes">API changes</h3>
<ul>
<li>Improved error message if a specified branch doesn’t exist</li>
<li>Fixed URL schema not being correctly converted to OpenAPI specs</li>
<li>Build settings object of either Dockerfile or Buildpack is now a required field</li>
<li>Build endpoint now fetches and builds the latest commit if no commit SHA is specified</li>
</ul>
<h3 id="other-features-fixes">Other features &amp; fixes</h3>
<ul>
<li>Improved the visual appearance of build options, health checks and service search in pipelines</li>
<li>Billing emails now explicitly say if the amount was paid or carried over to the next period</li>
<li>Service header icon now stays consistent with the build or container status</li>
<li>Fixed linking addons to a secret group on new creation forms</li>
<li>Fixed an issue where you could input multiple variables with the same key on new creation forms</li>
<li>Fixed an issue where realtime container lists would be rendered inconsistently</li>
<li>Fixed an issue where updating a Dockerfile via the Northflank UI wouldn’t trigger a new build on repositories from self-hosted GitLab</li>
<li>Fixed an issue where certain builds could not be deployed if the service branch was changed</li>
<li>Fixed an issue where the <code>Import backup</code> button would stay disabled if there was an error importing the addon backup</li>
<li>Fixed an issue where secret groups could not be favorited from the project dashboard</li>
<li>Fixed some instances where long texts in components would overflow</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>New Resource Creation Forms</title>
  <link>https://northflank.com/changelog/new-resource-creation-forms</link>
  <pubDate>2021-10-11T08:27:09.000Z</pubDate>
  <description>
    <![CDATA[Forms to create services, jobs, addons and secret groups now share a unified style for an improved UX and allow you to configure everything in one go.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/10/creation-forms-header.png" alt="New Resource Creation Forms" /><!--kg-card-begin: markdown--><p>We are excited to release our new resource creation forms to unify the creation flow for all resources, enable advanced configuration during creation and allow for future growth in features and capability.</p>
<ul>
<li>Unified style for creating all Northflank resources</li>
<li>Select what resource you wish to create from the header</li>
<li>Review your configuration by collapsing/expanding all sections at once or individually</li>
<li>Configure all aspects on one page (including build type, environment variables, advanced network settings and more)</li>
<li>Improved form validation highlighting any misconfigured or missing fields</li>
</ul>
<p><Text>Creating a MongoDB addon</Text></p>
<video controls autoplay playsinline loop muted width=100%>
    <source src="https://assets.northflank.com/changelog/addon-create.mp4"/>
</video>
<p><Text>Creating nginx:latest deployment service</Text></p>
<video controls autoplay playsinline loop muted width=100%>
    <source src="https://assets.northflank.com/changelog/service-create.mp4"/>
</video>
<h3 id="otherfeaturesfixes">Other features &amp; fixes</h3>
<ul>
<li>Added button to <a href="https://calendly.com/northflank/demo">schedule a Northflank demo</a> to our landing page 🎉</li>
<li>Addon connection details now show full value on hover rather than a trimmed version</li>
<li>Custom domain TLS certificates through 3-7th October have been reapplied due to missing chain</li>
<li>Fixed an issue where some suspended GitHub installations would still appear in the repository selection dropdown</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>CLI Usage Improvements, More Documentation and Fixes</title>
  <link>https://northflank.com/changelog/cli-usage-improvements-more-documentation-and-fixes</link>
  <pubDate>2021-10-06T10:12:20.000Z</pubDate>
  <description>
    <![CDATA[Updated API, CLI and JS Client for a better user experience, RBAC is now included in API documentation, improved Docker image validation.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/10/api-perms-header.png" alt="CLI Usage Improvements, More Documentation and Fixes" /><!--kg-card-begin: markdown--><ul>
<li>API: <code>update service security rules</code> endpoint is now embedded in the <a href="https://northflank.com/docs/v1/api/services/update-service-ports"><code>update service ports</code></a> endpoint. JS-client (v0.4.0) and CLI (v0.6.0) have been updated accordingly.</li>
<li>JS-client (v0.4.0), CLI (v0.6.0): Simplification of <code>get &lt;resource&gt; details</code> endpoints. These endpoint can now be accessed without specifying <code>details</code>. E.g. <code>get service</code>, <code>get addon</code></li>
<li>JS-client (v0.4.0): Use plural for all list commands. E.g. <code>list.plan(..)</code> becomes <code>list.plans(..)</code>.</li>
</ul>
<h3 id="documentation">Documentation</h3>
<ul>
<li>Expanded <a href="https://northflank.com/docs/v1/application/build/build-code-from-a-git-repository#choose-a-build-type">Dockerfile and Buildpack build types</a> to describe different build engines</li>
<li>Improved guides to link custom domains with <a href="https://northflank.com/docs/v1/application/domains/domain-registrar-guides#cloudflare">Cloudflare</a>, <a href="https://northflank.com/docs/v1/application/domains/domain-registrar-guides#namecheap-dns">Namecheap</a>, <a href="https://northflank.com/docs/v1/application/domains/domain-registrar-guides#ovh">OVH</a> and <a href="https://northflank.com/docs/v1/application/domains/domain-registrar-guides#ns1">NS1</a> DNS providers</li>
<li>Expanded <a href="https://northflank.com/docs/v1/api/use-the-javascript-client">overview and example usage</a> of the <a href="https://www.npmjs.com/package/@northflank/js-client">Northflank JS Client</a></li>
<li>API Documentation now contains required RBAC permissions for all endpoints</li>
</ul>
<p><img src="https://assets.northflank.com/2021/10/api-documentation.png" alt="API Documentation" loading="lazy"></p>
<h3 id="otherfeaturesfixes">Other features &amp; fixes</h3>
<ul>
<li>Improved automatic external image validation on paste and manual path edits</li>
<li>Fixed an issue where you would have to manually redeploy a service after setting Basic Auth rules</li>
<li>Fixed an issue where deleting a secret group would prompt you to enter your password again</li>
<li>Fixed an issue where loading builds on a service with self-hosted GitLab would cause the page to crash</li>
<li>Fixed issues where resuming deployment services with only private ports would fail and crash related pipelines</li>
<li>Fixed an issue where branches wouldn’t load for self-hosted repositories if used in a team</li>
<li>Fixed an issue where Dockerfile couldn’t be updated via the Northflank UI on repositories started from our templates</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Proxy as Code - Dynamically Forward Private Workloads via the Northflank API Client</title>
  <link>https://northflank.com/changelog/proxy-as-code-dynamically-forward-private-workloads-via-the-northflank-api-client</link>
  <pubDate>2021-09-27T13:40:50.000Z</pubDate>
  <description>
    <![CDATA[Proxy as code enables developers to securely and rapidly connect to a range of private TCP and UDP workloads easily in their code using the Northflank API client.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/09/proxy-as-code.png" alt="Proxy as Code - Dynamically Forward Private Workloads via the Northflank API Client" /><!--kg-card-begin: markdown--><p>Northflank is pleased to release new versions of our <a href="https://www.npmjs.com/package/@northflank/js-client">JS Client</a> (0.3.5) and <a href="https://www.npmjs.com/package/@northflank/cli">CLI</a> (0.5.2). In this release, we have added support for programmatic proxy support. It is now easier than ever to connect to multiple TCP and UDP workloads running in a private VPC with Northflank. This enables similar capability as tools such as kubectl forward, kubefwd, and cloudsql-proxy but now via proxy as code.</p>
<ul>
<li>Works with all TCP and UDP traffic</li>
<li>Proxy multiple ports per service or addon</li>
<li>Bind traffic to local IP addresses</li>
<li>Automates local DNS to replicate production endpoints when relevant permissions are enabled</li>
<li>Forward as many services and addons as you need with automatic port selection if you have multiple ports are already in use.</li>
<li>Inherits existing Northflank RBAC permissions</li>
</ul>
<!--kg-card-end: markdown--><pre><code class="language-javascript">const forwardingInfo = await apiClient.forwarding.forwardAddon({ projectId, addonId });

const connectionConfig = {
      port: forwardingInfo[0].data?.port,
      host: forwardingInfo[0].data?.address,
      user: process.env.USERNAME,
      password: process.env.PASSWORD,
      database: process.env.DATABASE,
    };

const connection = await mysql.createConnection(connectionConfig);

await connection.connect();

const results = await connection.query("SHOW TABLES;");

await connection.end();

await apiClient.forwarding.stop();</code></pre><!--kg-card-begin: markdown--><h3 id="otherfeaturesfixes">Other features &amp; fixes</h3>
<ul>
<li>Added logic for importing database backups from different database versions</li>
<li>Added handling to prevent table pagination resetting to the first page after selecting an action item or when the table content was updated</li>
<li>Improved handling of renamed and deleted repositories</li>
<li>Fixed an issue where importing a MySQL database from an external backup would sometimes fail due to a permission error</li>
<li>Fixed addon version header alignment</li>
<li>Fixed formatting of <code>list builds</code> API endpoint where data would return an array rather than an object</li>
<li>Fixed service URL overflowing the service header on mobile</li>
<li>Fixed an issue where database backup could be deleted using the API whilst restore was still in progress</li>
<li>Fixed an issue where creating a new deployment service would show an error page and had to be reloaded in some edge cases</li>
<li>Fixed external image validation failing in some edge cases</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Switch Target Git Branch for Continuous Integration and Validation &amp; Build Status Improvements</title>
  <link>https://northflank.com/changelog/switch-target-git-branch-for-continuous-integration-and-validation-build-status-improvements</link>
  <pubDate>2021-09-17T12:44:17.000Z</pubDate>
  <description>
    <![CDATA[Change the target git branch of your services and jobs using build options, headers now display the current build status, several frontend improvements.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/09/build-options-branch.png" alt="Switch Target Git Branch for Continuous Integration and Validation &amp; Build Status Improvements" /><!--kg-card-begin: markdown--><p>We are excited to release support for switching the target Git branch in services via Build Options &gt; Version control source settings. You now do not need to create a new service, pipeline or build service when you wish to simply change the branch. This was a highly requested feature and will improve the flexibility of our most accessible feature. In the future you will be able to change the Git repository.</p>
<ul>
<li>Continuous integration and deployment settings will take effect on the new branch.</li>
<li>A new build will be automatically started for you if CI is enabled.</li>
<li>Builds from previous branches can still be deployed, however CI is limited to the active source branch.</li>
</ul>
<p><img src="https://assets.northflank.com/2021/09/build-options-branch-settings.png" alt="Switching Git Source Branch" loading="lazy"></p>
<h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Added handling for password managers to only suggest password saving and autofill when relevant</li>
<li>API documentation now lists all possible values for enums</li>
<li>Updated service and job headers to display the current build status</li>
<li>Port detection now returns partial results instead of an empty array if the request times out</li>
<li>Improved server time displayed on Job settings to be updated in real-time</li>
<li>Improved duplicate detection and validation on environment variables and port editor forms</li>
<li>Fixed commit icon on Latest commits disappearing after commits are loaded</li>
<li>Fixed API returning 500 when creating an internal deployment service</li>
<li>Fixed an issue where changing cron job from external image to an existing build service would pause the job</li>
<li>Fixed an issue with MongoDB multi replica startup probe health check</li>
<li>Fixed an issue where revoking self-hosted GitLab to allow personal use wouldn’t reflect in the existing services</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Dynamic Runtime Configuration for Jobs, Improvements to API,  Addons and Templates</title>
  <link>https://northflank.com/changelog/dynamic-runtime-configuration-for-jobs-improvements-to-api-addons-and-templates</link>
  <pubDate>2021-09-13T13:02:17.000Z</pubDate>
  <description>
    <![CDATA[Environment variables and CMD override can now be configured for each job run, added a new API endpoint to abort builds, updated databases connection strings and more.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/09/batch-job-run-1.png" alt="Dynamic Runtime Configuration for Jobs, Improvements to API,  Addons and Templates" /><!--kg-card-begin: markdown--><p>Today we are excited to announce dynamic runtime configuration for Jobs on Northflank. It is now possible to configure environment variables and container image arguments for unique invocations of your job runs.</p>
<ul>
<li>Every invocation spawns a container with its own resources (current maximum 2 vCPU &amp; 4 GB RAM)</li>
<li>Unlimited execution time billed by the second of execution - pay for a powerful and limitless elastic compute only when you need it</li>
<li>Optionally configure job retry limit and max execution time</li>
</ul>
<p>Deploy a diverse range of workloads to Northflank with the ability to choose the most suitable runtime environment to get the best capability, cost and performance for your workload:</p>
<ul>
<li><strong>Services:</strong> sustained runtime with elastic compute required to serve requests continuously</li>
<li><strong>Jobs:</strong> on-demand runtime with elastic compute to process background tasks as required</li>
</ul>
<h3 id="exampleusecases">Example use cases</h3>
<ul>
<li>Trigger a game server for a matchmade game</li>
<li>Convert batch media files to different formats</li>
<li>Generate and send PDFs to your users with configuration set via environment variables</li>
<li>Calculate statistics and process complex data from databases</li>
</ul>
<p>This feature is now supported in the Northflank API and CLI.</p>
<!--kg-card-end: markdown--><pre><code class="language-javascript">await apiClient.start.job.run({
  parameters: {
    projectId: "billing",
    jobId: "invoice-collection",
  },
  data: {
    runtimeEnvironment: {
      CUSTOMER_ID: "WhoR8gCYEF",
      CUSTOMER_EMAIL: "jane-doe@acme.corp",
    },
    deployment: {
      docker: {
        cmd: "node invoice.js $CUSTOMER_ID $CUSTOMER_EMAIL",
      },
    },
  },
});</code></pre><!--kg-card-begin: markdown--><h2 id="api">API</h2>
<ul>
<li>Added an example JS client code for <code>Assign service to subdomain</code> endpoint</li>
<li>Added an API endpoint to abort builds</li>
<li>Improved accessibility of the Northflank API Documentation</li>
<li>Fixed an issue where description was required in the <code>Create project</code> endpoint</li>
<li>Fixed an issue with <code>Assign service to subdomain</code> endpoint not updating the service if a certificate hasn’t been issued</li>
<li>Fixed <code>Get health checks</code> endpoint not returning any data even if health checks were configured</li>
</ul>
<h2 id="addons">Addons</h2>
<ul>
<li>UI now displays a clearer warning for addons where an upgrade is recommended  and addons that have an upgrade available</li>
<li>Updated databases connection strings to work with Compass and and support both TLS/SSL query parameters with their enabled/disabled state</li>
<li>Fixed an issue where backups wouldn’t work correctly for MongoDB with configured security rules</li>
</ul>
<h2 id="templaterepositories">Template repositories</h2>
<ul>
<li>Updated Rust template repository to bind to <code>0.0.0.0</code> so that the webserver is bound to both the <code>etho</code> and <code>loopback device</code> rather than only <code>127.0.0.1</code></li>
<li>Improved auto port detection when template repositories are used</li>
<li>Fixed an issue where GitHub wouldn’t create new repositories in some edge cases</li>
<li>Fixed an issue where GitLab repositories would create as internal despite public being selected</li>
</ul>
<h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Aborted job runs now display an explicit status saying <code>Aborted</code> rather than <code>Failed</code></li>
<li>Updated build identifiers to be the human-readable name across the UI and API for easier team collaboration when referencing builds</li>
<li>Updated Northflank GitHub actions so the link for <code>View deployment</code> takes you to your Northflank dashboard if there is no public service endpoint</li>
<li>Improved VCS error handling to display more informative messages to the user</li>
<li>Removed Auto DevOps on self-hosted GitLab instances so they don’t run default CI steps with GitLab runners</li>
<li>Fixed an issue where build type (Dockerfile / Buildpack) would appear as unselected on existing services</li>
<li>Fixed issues with environment diff editor showing local changes incorrectly and not displaying a warning when a team member made changes</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Cloud Native Buildpacks on Northflank</title>
  <link>https://northflank.com/changelog/cloud-native-buildpacks-on-northflank</link>
  <pubDate>2021-09-06T08:38:00.000Z</pubDate>
  <description>
    <![CDATA[Comprehensive Buildpack support with Paketo, Heroku, Google Cloud and CNB sample image builders are now available on Northflank's E2E DevOps platform. Get started today without a Dockerfile.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/09/buildpack-header-changelog.png" alt="Cloud Native Buildpacks on Northflank" /><!--kg-card-begin: markdown--><p>We are excited to release Buildpack support alongside our Buildkit and Kaniko Docker build runtimes. Northflank’s mission is to allow developers to release any code with simplicity across any cloud provider. Buildpack support fits directly into the Northflank developer tools sandbox enabling you to immediately use end-to-end DevOps capabilities without a Dockerfile.</p>
<ul>
<li>Full compatibility with the Buildpack API specification</li>
<li>Inspiration and building on best-in-class Buildpack implementations such as <a href="https://github.com/pivotal/kpack">KPack</a> and <a href="https://tekton.dev/">Tekton</a></li>
<li>Secure runtime environment with rootless build &amp; no requirement to mount a Docker socket</li>
<li>Real-time log output and stage tracking with 30 days retention</li>
<li>Choose from a number of Buildpack stacks with different capabilities that Northflank supports by default:
<ul>
<li>heroku/buildpacks:20 (suggested for most users)</li>
<li>heroku/buildpacks:18</li>
<li>gcr.io/buildpacks/builder:v1</li>
<li>cnbs/sample-builder:alpine</li>
<li>cnbs/sample-builder:bionic</li>
<li>paketobuildpacks/builder:tiny</li>
</ul>
</li>
<li>Supply custom Buildpacks which are applied within the selected stack by providing the URL or registry path</li>
<li>All official and community maintained Heroku Buildpacks are supported</li>
<li>Automatic DockerHub rate limit handling</li>
<li>Reduce build times with caching support</li>
</ul>
<h3 id="herokubuildpacksonnorthflank">Heroku Buildpacks on Northflank</h3>
<p>To supply Heroku Buildpacks, use the Buildpack registry URL followed by namespace and name:<br>
<code>https://buildpack-registry.heroku.com/cnb/&lt;namespace&gt;/&lt;name&gt;</code></p>
<p>For example, to supply <a href="https://elements.heroku.com/buildpacks/heroku/heroku-buildpack-chromedriver">Heroku's Chromedriver</a> in your service via the API include <code>https://buildpack-registry.heroku.com/cnb/heroku/heroku-buildpack-chromedriver</code> in the <code>buildpackLocators</code> array:</p>
<!--kg-card-end: markdown--><pre><code class="language-javascript">await apiClient.create.service.combined({
  parameters: {
    projectId: "default-project",
  },
  data: {
    name: "chromium-service",
    billing: {
      deploymentPlan: "nf-compute-10",
    },
    deployment: {
      instances: 1,
    },
    vcsData: {
      projectUrl: "https://github.com/acme-corp/chromedriver",
      projectType: "github",
      projectBranch: "master",
    },
    buildSettings: {
      buildpack: {
        builder: "HEROKU_20",
        buildpackLocators: ["https://buildpack-registry.heroku.com/cnb/heroku/heroku-buildpack-chromedriver"],
        buildContext: "/",
      },
    },
  },
});</code></pre><!--kg-card-begin: markdown--><p>When using the UI, specify the URL (such as <code>https://buildpack-registry.heroku.com/cnb/mars/create-react-app-buildpack</code> for <a href="https://elements.heroku.com/buildpacks/mars/create-react-app-buildpack">create-react-app</a> in your Build settings:<br>
<img src="https://assets.northflank.com/2021/09/buildpack-cra-custom.png" alt="Custom Buildpack with Create React App" loading="lazy"></p>
<h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Addons now display storage sizes in gigabytes by default</li>
<li>Added support to specify project color to the API</li>
<li>Disabled buttons to delete backups on addons that are restoring from a backup</li>
<li>Fixed an issue where clicking on the addon instance bar would display a 404 page</li>
<li>Fixed an issue where manual jobs could be saved as cron if you switched the job type tabs before saving</li>
<li>Fixed an issue where the <code>get addon details</code> API endpoint would error in cases where IP policies were not configured</li>
<li>Fixed an issue where the job data grid wouldn’t show the correct status for the first couple of job runs</li>
<li>Fixed an issue where some older MongoDB versions wouldn’t support TLS</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>New API Endpoints and Improved Jobs UI</title>
  <link>https://northflank.com/changelog/new-api-endpoints-and-improved-jobs-ui</link>
  <pubDate>2021-08-30T09:42:09.000Z</pubDate>
  <description>
    <![CDATA[Added new API &amp; CLI endpoints for configuring addon network settings, IP policies and upgrades, improved the jobs UI and more fixes.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/08/improved-jobs-ui.png" alt="New API Endpoints and Improved Jobs UI" /><!--kg-card-begin: markdown--><p>We are introducing a number of new endpoints available in the <a href="https://northflank.com/docs/v1/api/introduction">API and CLI</a> as well as the <a href="https://northflank.com/docs/v1/api/use-the-javascript-client">JS client</a>:</p>
<ul>
<li><code>Update network settings</code> updates the addon TLS and External Access settings as announced <a href="https://northflank.com/changelog/public-access-for-managed-databases-and-storage">last week</a></li>
<li><code>Update security rules</code> configures addon IP policy rules</li>
<li><code>Get addon version details</code> lists available upgrades and upgrade history</li>
<li><code>Upgrade addon version</code> upgrades an addon to a specified new version</li>
</ul>
<p>The jobs UI was improved to give a clearer overview of all your jobs and their statuses, as well as show useful notifications and confirmation modals:</p>
<ul>
<li>Cron expressions now have labels showing them in human readable formats, such as <code>Every 5 minutes</code> or <code>At 30 minutes past the hour</code></li>
<li>Jobs data grid shows real-time deployment source, status, rules (such as deploying latest commits and builds) and whether the job is active or paused with explanations on hover</li>
<li>Added confirmation modals for triggering job runs as well as pausing and re-activating a cron job</li>
<li>Cron job dashboards now show a notification with a button to resume job scheduling if it is currently paused</li>
</ul>
<h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Added a <code>createdAt</code> field to the API response for addons, services and jobs</li>
<li>Improved loading of job runs to be faster</li>
<li>Improved loading of containers to be faster</li>
<li>Service creation progress tracker now always sticks to the top to give you an overview of your configuration</li>
<li>Fixed an issue where builds would not be found if a repository was renamed</li>
<li>Fixed an issue where <code>Create new</code> dropdown menu would display unrelated items under <code>On this page</code></li>
<li>Fixed an issue where secret groups diff editor would suggest viewing differences after initial creation</li>
<li>Fixed an issue where an addon backup could not be restored straight after another restore without refreshing the page</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Public Access for Managed Databases and Storage</title>
  <link>https://northflank.com/changelog/public-access-for-managed-databases-and-storage</link>
  <pubDate>2021-08-23T14:12:49.000Z</pubDate>
  <description>
    <![CDATA[Public access to your Northflank addons via highly performant load-balancers is now supported. Connect to your databases and storage outside Northflank with secure TLS.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/08/security-rules-changelog.png" alt="Public Access for Managed Databases and Storage" /><!--kg-card-begin: markdown--><p>Today we are excited to announce public network support for Northflank Addons (databases such as MySQL, PostgreSQL, MongoDB &amp; Redis and S3 compatible MinIO storage).</p>
<ul>
<li>Connect to your databases outside of Northflank</li>
<li>Easily connect to popular tooling such as Retool, Segment and database UIs without needing to use Northflank CLI proxy</li>
<li>External access is routed via Northflank’s highly performant DNS and load-balancers</li>
<li>Traffic is encrypted end-to-end with TLS from client to the database</li>
<li>Fine-grained network access controls for increased security</li>
</ul>
<p>Northflank continues to level up the DBaaS experience across multiple popular OSS databases with consistent capability. You are now able to access your addons securely within the same project, via <code>northflank forward</code> and now for production workloads via load-balancers when workloads are deployed externally.</p>
<p>Public access can be enabled on addons deployed with TLS which can be configured in <code>Network Settings</code>. Once enabled, you will notice new external connection strings in <code>Connection Details</code>. You can configure them to be automatically inherited from your jobs and services.</p>
<p>Configurable security rules are also included in this release. Similar to IP and Basic Auth security rules that are available for services, you can specify certain IP addresses from which external ingress traffic will be allowed. We recommend always configuring IP policies as this brings another layer of security to your data.</p>
<p><img src="https://assets.northflank.com/2021/08/security-rules.png" alt="Security rules" loading="lazy"></p>
<!--kg-card-end: markdown--><!--kg-card-begin: markdown--><h2 id="exampleconnectionscripts">Example connection scripts</h2>
<!--kg-card-end: markdown--><pre><code class="language-javascript">// Connect to a publicly accessible MongoDB with inherited environment variables
// Inherit 'EXTERNAL_SRV' and 'DATABASE' via a Secret Group


import MongoClient from  'mongodb'

let db = {}
let client = {}

const MONGO_CONNECTION_STRING = process.env.EXTERNAL_SRV
const MONGO_DATABASE = process.env.DATABASE

const INIT_MONGODB = async () =&gt; {
    try {
        console.log('Connecting to database')
        client = await MongoClient.connect(MONGO_CONNECTION_STRING, { useUnifiedTopology: true });
        db = client.db(MONGO_DATABASE)
        console.log('Connected to database');
    } catch (err) {
        console.log(err);
    }
}

export default async () =&gt; {
    await INIT_MONGODB();
}

export { db, client }</code></pre><pre><code class="language-javascript">// Connect to a publicly accessible PostgreSQL with inherited environment variables
// Inherit 'EXTERNAL_POSTGRES_URI' via a Secret Group

import pg from 'pg'
const { Pool } = pg;

const POSTGRESQL_CONNECTION_STRING = process.env.EXTERNAL_POSTGRES_URI

let client = {};

const pool = new Pool({
    connectionString: POSTGRESQL_CONNECTION_STRING,
})

const INIT_POSTGRES = async () =&gt; {
    try {
        console.log('Connecting to database');
        client = await pool.connect();
        console.log('Connected to database');
    } catch (err) {
        console.log(err);
    }
};

export default async () =&gt; {
    await INIT_POSTGRES();
}

export { client }</code></pre><pre><code class="language-javascript">// Connect to a publicly accessible Redis with inherited environment variables
// Inherit 'HOST' and 'REDIS_MASTER_URL' via a Secret Group

import redis from 'redis'

const REDIS_CONNECTION_STRING = process.env.REDIS_MASTER_URL
const REDIS_HOST = process.env.HOST

let client = redis.createClient({url: REDIS_CONNECTION_STRING, tls: {
        servername: REDIS_HOST
    }});

const INIT_REDIS = async () =&gt; {
    try {
        console.log('Connecting to database');
        await client.on('connect', function() {
          console.log('Connected to database');
        });
    } catch (err) {
        console.log(err);
    }
};

export default async () =&gt; {
    await INIT_REDIS();
}

export { client }</code></pre>]]>
  </content:encoded>
</item><item>
  <title>Managed Upgrades for Databases and Storage</title>
  <link>https://northflank.com/changelog/managed-upgrades-for-databases-and-storage</link>
  <pubDate>2021-08-17T14:40:35.000Z</pubDate>
  <description>
    <![CDATA[Added support for upgrading between minor and major database and storage engine versions. The UI displays warnings on deprecated addon versions, and users can select from available upgrade options.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/08/managed-upgrades-header.png" alt="Managed Upgrades for Databases and Storage" /><!--kg-card-begin: markdown--><ul>
<li>Addons can be upgraded between supported minor and major versions</li>
<li>Guaranteed valid upgrade path between deprecated and latest supported versions</li>
<li>Addons on deprecated/discontinued versions now display a visual indicator with a prompt to upgrade</li>
<li>If there are multiple upgrades available the user can select the desired version</li>
<li>A history of previous upgrades is available on the new Upgrade page</li>
</ul>
<!--kg-card-end: markdown--><figure class="kg-card kg-image-card"><img src="https://assets.northflank.com/2021/08/addon-upgrade.png" class="kg-image" alt="Addon Upgrade" loading="lazy" width="2871" height="1587"></figure><!--kg-card-begin: markdown--><h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Added a redirect after a resource is deleted to prevent displaying 404 pages</li>
<li>Improved dashboard alignment on smaller screen sizes</li>
<li>Improved validation formatting on the invite team members form</li>
<li>Updated Job and Service creation headers to be consistent</li>
<li>Moved Single Sign On into Account Settings from its dedicated page</li>
<li>Fixed an issue where Create service button would not be disabled on uncompleted forms</li>
<li>Fixed an issue where pull requests wouldn’t automatically get marked as closed in the UI for Bitbucket repositories</li>
<li>Fixed an issue where the new repository name would persist after changing VCS provider on service creation from template</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Enhanced API Responses &amp; Build Rules</title>
  <link>https://northflank.com/changelog/enhanced-api-responses-build-rules</link>
  <pubDate>2021-08-09T10:26:26.000Z</pubDate>
  <description>
    <![CDATA[API endpoints return more details, added human-readable branches &amp; PRs build rules, links to VCS commits, improved UX.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/08/changelog-build-source.png" alt="Enhanced API Responses &amp; Build Rules" /><!--kg-card-begin: markdown--><ul>
<li>Added human-readable branch &amp; pull requests rules that are generated from your regex input</li>
<li>Build service creation now automatically configures your repository’s default branch as a build rule</li>
</ul>
<h2 id="apienhancements">API Enhancements</h2>
<ul>
<li>Responses now return detailed metadata so they can be handled without additional queries</li>
<li>Added consistent upper case for endpoint names</li>
<li>Added dedicated get deployment endpoint schema</li>
<li>Changed public endpoints to be in line with authenticated endpoint responses</li>
<li>Fixed deployment service creation failing to read a property correctly</li>
<li>Fixed some endpoint responses not being formatted correctly (get deployment details, list regions, list plans)</li>
</ul>
<!--kg-card-end: markdown--><pre><code class="language-json">// Sample 200 Response
{
  "data": {
    "external": {
      "imagePath": "nginx:latest",
      "registryProvider": "dockerhub",
      "privateImage": false
    }
  }
}</code></pre><pre><code class="language-json">// Sample 400 Response
{
    "error": {
        "status": 400,
        "message": "Request failed payload validation - see details.",
        "id": "nf-invalid-request-body",
        "details": {
            "name": [
                "\"name\" is required. Received \"undefined\""
            ]
        }
    }
}</code></pre><!--kg-card-begin: markdown--><h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Tweaked service creation form for better user experience</li>
<li>Improved the user experience of automatic image path verification where a wrong path is pasted in</li>
<li>Added external links to commits so you can compare file changes and see details in the VCS UI</li>
<li>Fixed container bar not visible in solarized light theme</li>
<li>Fixed max-width on service headers which caused the re-deploy button to be hidden behind a CD toggle</li>
<li>Fixed advanced dropdowns displaying incorrectly in Firefox</li>
<li>Fixed issues where configuring CMD override couldn’t be removed or it would disappear from the image settings page</li>
<li>Fixed an issue where builds from GitLab repositories would fail if there were multiple commits in a short period of time</li>
<li>Fixed GitHub container registry credentials not verifying correctly</li>
<li>Fixed job list crashing occasionally where items are deleted</li>
<li>Fixed an issue where an error would be displayed if service creation was submitted via hitting Enter rather than clicking a button</li>
<li>Fixed an issue where repositories wouldn’t load correctly for self-hosted GitLab</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Improved Pipelines, Teams and Jobs</title>
  <link>https://northflank.com/changelog/improved-pipelines-teams-and-jobs</link>
  <pubDate>2021-08-02T08:28:00.000Z</pubDate>
  <description>
    <![CDATA[External domains now displayed in pipelines, invite team members when creating your team, job runs highlight manual triggers and more.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/08/changelog-pipelines-jobs.png" alt="Improved Pipelines, Teams and Jobs" /><!--kg-card-begin: markdown--><ul>
<li>Pipelines now display linked custom domains in addition to generated Northflank domains on each service card</li>
<li>Invite team members during team creation flow to get started quickly with your colleagues</li>
<li>Hover on build services in the pipelines sidebar displays the linked repository</li>
<li>Job runs UI now displays if the run was triggered by the cron schedule or triggered manually by the user</li>
</ul>
<p><img src="https://assets.northflank.com/2021/08/pipeline-domains.png" alt="Custom domains on Pipelines" loading="lazy"></p>
<h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Added error codes and request body descriptions across all API calls</li>
<li>Environment variables are now always passed as strings to prevent accidental trimming/rounding when supplied as a number</li>
<li>Added support for displaying a company VAT ID in billing where applicable</li>
<li>Refund status is now reflected on refunded invoices in billing</li>
<li>The applicable VAT rate is displayed through current usage and invoice views</li>
<li>Saving service configurations now skips initiating a redeployment if no effective change was detected</li>
<li>Updated help popovers in addon connection details</li>
<li>Pressing Esc on help popovers now closes them</li>
<li>Improved cron job syntax checking to prevent multiple spaces</li>
<li>Improved handling when generating unique DNS identifiers preventing partial word generation</li>
<li>Fixed an issue where some GitHub organisation commits wouldn't load in the UI</li>
<li>Fixed an issue where the service list would crash after deleting some services</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>New Application Documentation</title>
  <link>https://northflank.com/changelog/new-application-documentation</link>
  <pubDate>2021-07-26T10:33:29.000Z</pubDate>
  <description>
    <![CDATA[Reworked documentation, added Rust to templates, start from template available for all Git providers.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/07/app-docs-header.png" alt="New Application Documentation" /><!--kg-card-begin: markdown--><p>Today, we are pleased to release our new <a href="https://northflank.com/docs">documentation</a> experience - making it easier for you to quickly understand Northflank features and how to get started step-by-step.</p>
<ul>
<li>Select between a dark and light theme to match your Northflank UI 🌓</li>
<li><a href="https://northflank.com/docs/v1/application/getting-started/">Getting started</a> is great when you're just starting out - it will guide you from setting up your account with Git and creating your first service, pipeline and all the way to deploying with custom domains</li>
<li><a href="https://northflank.com/docs/v1/application/overview">How-to guides</a> help you configure your workloads in all stages of the DevOps workflow (Build, Run, Release, Observe and Scale) with in-line explanation and sane defaults, expanding on advanced topics such as addons (databases and persistent storage) or network security and encryption</li>
<li><a href="https://northflank.com/docs/v1/api/introduction">API and CLI documentation</a> provides an API reference to build applications integrated with Northflank - example code snippets are available for the <a href="https://www.npmjs.com/package/@northflank/cli">Northflank CLI</a>, <a href="https://www.npmjs.com/package/@northflank/js-client">JS client</a> and API calls (<code>curl</code>, <code>JavaScript</code>, <code>Python</code> &amp; <code>Go</code>)</li>
<li>Updated help popovers in the UI to reflect the new documentation</li>
<li>Improved fast search across both App and API docs 🔎</li>
</ul>
<p><img src="https://assets.northflank.com/2021/07/api-docs.png" alt="API Documentation" loading="lazy"></p>
<h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Create template repository services with GitHub, Bitbucket and GitLab (including self-hosted instances) on both users and team accounts (previously only personal GitHub accounts were supported)</li>
<li>Added support for importing addon backups from links that require a redirect (e.g. Dropbox)</li>
<li>Added an explicit message if automatic port detection couldn't find any ports</li>
<li>Hovering on deployment services now highlights the linked build services on pipelines</li>
<li>Increased a feedback input limit to 1200 characters</li>
<li>Added a starter template for Rust</li>
<li>Added an option to add unlinked VCS via Git selection dropdowns rather than having to navigate to Account Settings</li>
<li>Fixed an issue where container icon and status sometimes wouldn't match</li>
<li>Fixed an issue where Docker Credentials couldn't be selected if the image path was missing or was copied and pasted into the modal</li>
<li>Fixed an issue where private images would sometimes be labeled as public in the UI</li>
<li>Fixed the instance selector not rendering on logs page while logs loading</li>
<li>Fixed pull requests showing 'invalid date' for the first couple of seconds in the UI</li>
<li>Fixed an issue where accepting a team invite via an email invitation link would fail</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Upgraded API Client, CLI and Example Code Snippets</title>
  <link>https://northflank.com/changelog/upgraded-api-client-cli-and-example-code-snippets</link>
  <pubDate>2021-07-19T12:41:15.000Z</pubDate>
  <description>
    <![CDATA[The Northflank API Client was updated for simpler usage and examples are now provided in documentation.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/07/apiclient-header.png" alt="Upgraded API Client, CLI and Example Code Snippets" /><!--kg-card-begin: markdown--><p>We are excited to release a new version of the <a href="https://www.npmjs.com/package/@northflank/js-client">Northflank JavaScript API client</a> alongside another release for the CLI. To install the API client in your Node JS projects setup with <code>npm i @northflank/js-client</code> or <code>yarn add @northflank/js-client</code> and enjoy full native IntelliSense &amp; type suggestion support in your IDE of choice.</p>
<p>The API client provides an easy way to provision and maintain infrastructure as code and integrate your applications with Northflank. Simply get started by <a href="https://northflank.com/docs/v1/application/secure/manage-api-tokens">generating an API key</a> in your team or account settings.</p>
<p>The new version simplifies the user experience and removes some previous complexity:</p>
<ul>
<li>
<p>Client no longer requires <code>.endpoints</code> and <code>.call</code></p>
<ul>
<li><code>apiClient.endpoints.create.project.call</code> becomes <code>apiClient.create.project</code></li>
</ul>
</li>
<li>
<p>Client parameters passed to the API call follow a new format</p>
<ul>
<li><code>{parameters: {..}, data: {..}, options: {..}}</code></li>
</ul>
</li>
<li>
<p><a href="https://northflank.com/docs/v1/api/introduction">API Documentation</a> now shows example JS client code snippets for all endpoints</p>
</li>
</ul>
<h3 id="exampleusage">Example usage</h3>
<!--kg-card-end: markdown--><pre><code class="language-javascript">import { ApiClient, ApiClientInMemoryContextProvider } from "@northflank/js-client";

(async () =&gt; {
  // Create context to store credentials.
  const contextProvider = new ApiClientInMemoryContextProvider();
  await contextProvider.addContext({
    name: "default-context",
    token: "&lt;api-token&gt;", // Use generated API token
  });

  // Initialize API client.
  const apiClient = new ApiClient(contextProvider);

  // Retrieve list of projects and log to console.
  const { response: { projects } } = await apiClient.list.project({});
  console.log(projects);

  // Create a new project.
  await apiClient.create.project({
    data: {
      name: "default-project",
      region: "europe-west",
      description: "Default project description",
    },
  });
})();</code></pre><!--kg-card-begin: markdown--><h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Changed layout of our blog &amp; changelog to display the most recent item at the top and added infinite scrolling</li>
<li>Added container statuses to influence overall service health, indicating in real-time if there is an issue with one or more containers</li>
<li>Limited maximum width of forms across the UI to make it a better experience on 4K or widescreen displays</li>
<li>Services now show an explicit error if the GitHub, Bitbucket or GitLab installation is missing</li>
<li>Fixed API addon backups endpoints not parsing path parameter correctly</li>
<li>Fixed an issue where editing security rules would sometimes crash the page</li>
<li>Added a warning if two or more IP security rules use the same IP address</li>
<li>Fixed an issue where a Dockerfile wouldn't get fetched correctly in some edge cases with Bitbucket repositories</li>
<li>Fixed an issue where you'd get redirected to a non-existing page after adding a new subdomain</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Platform Feedback, Improved Addon Inheritance</title>
  <link>https://northflank.com/changelog/platform-feedback-improved-addon-inheritance</link>
  <pubDate>2021-07-12T16:40:09.000Z</pubDate>
  <description>
    <![CDATA[Send us your feedback via a modal in the UI, enhanced addon connection details inheritance via secret groups.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/07/help-feedback-header.png" alt="Platform Feedback, Improved Addon Inheritance" /><!--kg-card-begin: markdown--><p>We added a feedback form to the UI which you can use to share feedback, suggestions or any issues. You can also upload attachments such as screenshots or screen recordings.</p>
<p>The addon connection details inheritance was improved to prevent adding duplicate aliases and the UI now displays all aliases in a nested way, so it becomes clearer which key names are used for which connection detail.</p>
<p><img src="https://assets.northflank.com/2021/07/addon-inheritance-nested.png" alt="Addon Connection Details Inheritance" loading="lazy"></p>
<h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Improved automatic port detection to detect ports declared as variables in the Dockerfile</li>
<li>Fixed an issue where updating port configuration would trigger two restarts</li>
<li>Fixed an issue where addon connection details wouldn't get inherited if other projects used the same addon name</li>
<li>Fixed an issue where the image settings page would crash after inputting quotes in the CMD override</li>
<li>Fixed an issue where pressing enter on some forms would disregard changes rather than saving</li>
<li>Fixed an issue where a service couldn't be created after switching between service types</li>
<li>Fixed a validation issue of the environment editor after removing invalid fields</li>
<li>Fixed issues with validating and switching external registry providers after they have been configured</li>
<li>Fixed Dockerfile validation preventing from creating service if Dockerfile was originally missing</li>
<li>Fixed job run durations displaying wrong timestamps for the first couple of seconds</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>High Availability Addons for Databases and Storage</title>
  <link>https://northflank.com/changelog/high-availability-addons-for-databases-and-storage-kubernetes-api-cli-ui</link>
  <pubDate>2021-07-05T16:08:50.000Z</pubDate>
  <description>
    <![CDATA[PostgreSQL, MySQL, MongoDB, Redis and MinIO addons are now available to all users.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/07/changelog-addons-1.png" alt="High Availability Addons for Databases and Storage" /><p>We are excited to announce Northflank Addons are now available to all users and teams in supported regions. Deploy highly available persistent workloads such as PostgreSQL, MySQL, Redis, MongoDB, and MinIO in seconds.</p><p>Northflank Addons is our DBaaS solution, bringing a consistent and comprehensive operator pattern across multiple popular databases and object stores to any Kubernetes cluster. Create, backup, scale, secure and observe stateful workloads via UI, CLI or programmatically via our API or Javascript Client.</p><pre><code class="language-javascript">// example code snippet provisioning a stateful MySQL workload
// with 1 replica using @northflank/js-client

const myAddon = await apiClient.create.addon({
  project: "my-amazing-project",
  payload: {
    name: "my-amazing-database",
    description: "my amazing database for my amazing project",
    type: "mysql",
    externalAccessEnabled: true,
    tlsEnabled: true,
    version: "8.0.25",
    billing: {
      deploymentPlan: "nf-compute-100",
      replicas: 1,
      storage: 1024,
    },
  },
});
</code></pre><!--kg-card-begin: markdown--><ul>
<li>Provision database read and write replicas in seconds</li>
<li>Select from a range of supported versions</li>
<li>Cost-effective vertical and horizontal scaling</li>
<li>Low latency internal networking with your micro-services and applications</li>
<li>Secure internal network isolation within your Northflank project</li>
<li>Easily provision with TLS for secure network traffic</li>
<li>Monitor and observe all addon containers via logs and metrics</li>
<li>Scale storage size rapidly so you never run out of disk space</li>
<li>Link addon connection details to secret groups for automatic inheritance of environment variables into jobs and services as announced <a href="https://northflank.com/changelog/managed-addon-connection-details-inheritance">here</a></li>
<li>Backup via UI or API and restore your databases from a Northflank backup or an external source</li>
<li>Manage and interact with addons via the <a href="https://northflank.com/docs/v1beta/api/introduction">Northflank API and CLI</a></li>
<li>Securely access databases on your development machines via northflank forward</li>
</ul>
<!--kg-card-end: markdown--><p><br></p>]]>
  </content:encoded>
</item><item>
  <title>Increased Observability</title>
  <link>https://northflank.com/changelog/increased-observability</link>
  <pubDate>2021-06-28T10:23:13.000Z</pubDate>
  <description>
    <![CDATA[Logs live tailing and search available across all containers, displaying offline logs if connection fails.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/06/changelog-logs-offline.png" alt="Increased Observability" /><p></p><!--kg-card-begin: markdown--><p>You are now able to tail and search logs across all containers within a Northflank service, job, build or addon. This levels up observability when deploying and debugging in a highly available production environment and applies to:</p>
<ul>
<li>Logs from containers deployed in a service</li>
<li>Logs from CI tracking build progress across parallel builds</li>
<li>Logs from parallel job runs triggered via cron schedule or API trigger</li>
<li>Logs from database masters and replicas</li>
<li>Logs from terminal or running containers (up to 30 days of retention)</li>
<li>Logs from backups and restores</li>
</ul>
<p>Due to increased load on our logging backend, we have implemented a fallback when WebSockets are exhausted. If Northflank is unable to establish a real-time connection to the queried log stream, it creates a static HTTP query &amp; returns the relevant log messages, putting your client into an offline state.</p>
<p>You will always be able to retrieve logs irrespective of load on our real-time logging infrastructure, and you can attempt to reconnect to the real-time stream via a button in the UI.</p>
<h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Addons header now show status, containers and meta information</li>
<li>Addon connection details inheritance now prevents setting duplicate names</li>
<li>Added addon port to the connection details</li>
<li>Added real-time updates to the project list when permissions are changed</li>
<li>Jobs with external deployments can now be created even without a linked Git account</li>
<li>Improved the UI of build deployment modals inside Pipelines</li>
<li>Fixed Northflank logo not loading on template repositories</li>
<li>Fixed a bug where service couldn't be created after deleting a custom port configuration</li>
<li>Fixed several line breaks and now showing abbreviated names of branches and PRs if they're too long</li>
<li>Fixed an issue where API Templates couldn't be deleted</li>
<li>Fixed favouriting container registry credentials</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Bug Fixes</title>
  <link>https://northflank.com/changelog/bug-fixes</link>
  <pubDate>2021-06-21T13:26:36.000Z</pubDate>
  <description>
    <![CDATA[Logs throw explicit errors for issues with Dockerfiles, tweaked the Billing and Resources UI, other fixes.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/06/chlog-commit.png" alt="Bug Fixes" /><!--kg-card-begin: markdown--><ul>
<li>Build logs now throw an explicit error if the Dockerfile couldn't be found</li>
<li>Small tweaks to the Billing and Resources UI</li>
<li>Fixed flickering of the Edit deployment modal when switching deployment source tabs</li>
<li>Fixed the project list not updating in real-time when restricting users from projects</li>
<li>Fixed real-time reloading of new branches, added refresh buttons for manual reload</li>
<li>Fixed an issue where some commits would be delayed by ca. 20 seconds before appearing in the UI</li>
<li>Fixed some Bitbucket commits not building automatically with CI enabled</li>
<li>Fixed commits showing as deployed before a build is completed</li>
<li>Fixed handling member removals if they linked their Git to the team</li>
<li>Fixed job durations and start times not displaying correctly sometimes</li>
<li>Fixed a form validation issue with the secrets dif editor</li>
<li>Fixed an error where backups could be created for addons that are still provisioning</li>
<li>Fixed crashing the instances page if all three health checks were added to the service</li>
<li>Fixed infinite loading on pages with deleted API tokens</li>
<li>Fixed issues with self-hosted VCS webhooks</li>
<li>Fixed occasional lagging of the Dockerfile editor</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Pipeline Service Overview &amp; Health Checks Previews</title>
  <link>https://northflank.com/changelog/pipeline-service-overview-health-checks-previews</link>
  <pubDate>2021-06-14T12:07:57.000Z</pubDate>
  <description>
    <![CDATA[See services status immediately via pipelines and preview health checks for all your containers.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/06/changelog-header-pipelines.png" alt="Pipeline Service Overview &amp; Health Checks Previews" /><!--kg-card-begin: markdown--><p>We have redesigned pipelines for increased accessibility and user experience. Each service now shows the status of its containers and the public URLs for easy navigation.</p>
<h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Unified wording across the UI</li>
<li>Added a link to open the selected Git repository during service creation</li>
<li>Confirmation modal to remove a team member now displays the member's name</li>
<li>Ports now use internal IDs to prevent removing security rules (Basic authentication and IP policies) when updating port names</li>
<li>Fixed an issue where Docker command override could not be cleared</li>
<li>Fixed an issue with updating ports on subdomains from Settings</li>
<li>Fixed wrong timestamps on the job runs list</li>
<li>Health checks are now displayed on the Containers page</li>
</ul>
<p><img src="https://assets.northflank.com/2021/06/health-checks.png" alt="Health Checks" loading="lazy"></p>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Profile Settings &amp; Docker Credentials</title>
  <link>https://northflank.com/changelog/profile-settings-docker-credentials</link>
  <pubDate>2021-06-07T12:44:10.000Z</pubDate>
  <description>
    <![CDATA[Avatars for user and team profiles, enhanced Docker credentials with auto verification and meta info.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/06/profile-changelog.png" alt="Profile Settings &amp; Docker Credentials" /><!--kg-card-begin: markdown--><p>Users and teams can now edit profiles and upload avatars on a newly designed profile page. It also displays any account limits such as the number of free services, instances per service or parallel builds.</p>
<p>The user experience of adding new Docker credentials for external registries has been enhanced, allowing you to get started with GitHub Container Registry (ghcr.io), GitLab Container Registry, Google Container Registry (GCR) and Amazon Elastic Container Registry (ECR) within seconds. The image path is automatically validated and relevant meta information is displayed.</p>
<p><img src="https://assets.northflank.com/2021/06/image-edit-retool.png" alt="Editing deployment image" loading="lazy"></p>
<h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Made the PRs and branches list consistent across the UI</li>
<li>Team members page now shows when each member joined the team</li>
<li>Fixed several UI issues causing strange behaviours on 13&quot; screens</li>
<li>Fixed an issue where secret name duplicates would be detected across the whole account rather than just projects</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Enhanced Services &amp; Jobs DX</title>
  <link>https://northflank.com/changelog/enhanced-services-jobs-dx</link>
  <pubDate>2021-05-28T07:20:19.000Z</pubDate>
  <description>
    <![CDATA[Create external Docker deployments without a linked Git, cancelling job runs, and improved real-time domains.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/05/chlog-service-cr.png" alt="Enhanced Services &amp; Jobs DX" /><!--kg-card-begin: markdown--><ul>
<li>Create deployment services without a linked Git account - this is handy when you only want to deploy images from external registries</li>
<li>Service creation now informs you if there are any missing or invalid fields for networking and ports</li>
<li>Domains in service headers now update in real-time if port names change</li>
<li>Custom domains show detailed errors and warnings if validation fails whilst configuring new domains or subdomains</li>
<li>Individual job runs can now be cancelled either via the UI or API</li>
</ul>
<h2 id="otherfixes">Other fixes</h2>
<ul>
<li>Fixed wrapping long branch names in pipelines</li>
<li>Fixed the team selector showing an empty space if there was only one team</li>
<li>Fixed an issue where you couldn't edit a GitHub installation if linked to a personal account</li>
<li>Fixed an issue where a combined job wouldn't display its repository in the resource data grid</li>
<li>Fixed an issue comparing local &amp; remote changes via the diff editor displaying some incorrect fields in project secrets</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Support for UDP Port Forward via the CLI</title>
  <link>https://northflank.com/changelog/support-for-udp-port-forward-via-the-cli</link>
  <pubDate>2021-05-21T09:45:22.000Z</pubDate>
  <description>
    <![CDATA[Added UDP port forwarding to the Northflank CLI for a secure local connection to your running services.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/05/changelog-forward.png" alt="Support for UDP Port Forward via the CLI" /><!--kg-card-begin: markdown--><p><a href="https://www.npmjs.com/package/@northflank/cli/" target="_blank">Northflank CLI</a> now supports UDP port forwarding in addition to existing TCP &amp; HTTP protocols. This enables secure forwarding of UDP traffic running within your project firewall to your local machine. UDP traffic is essential for real-time communication, media and game servers, VoIP, DNS and many other applications.</p>
<p>This unique capability has been added in conjunction with wide-ranging security and runtime improvements, prompting us to migrate from Kubernetes API port-forward due to its limitations in only supporting TCP traffic. In summary, Northflank can power many more applications, furthering our mission to deliver a sandbox of developer tools that get out of your way.</p>
<h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Cron jobs UI now shows a warning if the job is paused</li>
<li>Form headers are now consistent across different views</li>
<li>Advanced options inside forms are now hidden in a dropdown by default</li>
<li>The modal to link self-hosted VCS was changed to be on its own page, rather than a popup modal</li>
<li>Fixed an issue where invalid credentials could be saved</li>
<li>Fixed an issue where the online status of other team members wouldn't show correctly sometimes</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Configurable Resource Data Grids</title>
  <link>https://northflank.com/changelog/configurable-resource-data-grids</link>
  <pubDate>2021-05-14T11:45:54.000Z</pubDate>
  <description>
    <![CDATA[Resource data grids improve the observability of your projects and let you configure your Northflank experience.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/05/chlog-lists.png" alt="Configurable Resource Data Grids" /><!--kg-card-begin: markdown--><p>Lists across the Northflank UI are now user-configurable and have been upgraded to match the increased functionality of Northflank over the last 12 months. Resource data grids now give immediate observability of your projects and the status of your resources.</p>
<ul>
<li>Alphabetic sort of the table by a selected key, sort by date or status</li>
<li>Filter content by a string search or select dropdown</li>
<li>Set your preference of which columns to display, hide, or sort by</li>
<li>Lists continue to work across all devices including mobile</li>
</ul>
<h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Northflank now stores your last user/team context in local storage so when you open the UI again you can continue where you left off</li>
<li>Fixed an issue where a user/team switch popover would be inaccessible if viewport height is small</li>
<li>Fixed an issue where self-hosted VCS settings wouldn't display the application ID correctly</li>
<li>Fixed an issue where automatic port detection would display some private ports as public for some deployment services</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Managed Addon Connection Details Inheritance</title>
  <link>https://northflank.com/changelog/managed-addon-connection-details-inheritance</link>
  <pubDate>2021-05-07T07:57:30.000Z</pubDate>
  <description>
    <![CDATA[Addon environment variables can be injected to project secret groups and used across services and jobs easily.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/05/addon-inheritance-header.png" alt="Managed Addon Connection Details Inheritance" /><!--kg-card-begin: markdown--><p>Secret groups can now inherit environment variables from your addons. This enables easy and safe access to connection details in services and jobs without the need for error-prone manual copy and paste. Addon variables can be linked to a secret group via the UI on both the addon and secret group pages.</p>
<ul>
<li>Control which connection details get injected into your runtime from a list of available variables</li>
<li>Northflank suggests default variables to inherit if you're not sure which ones to select</li>
<li>You can define additional aliases for each variable to match your software requirements</li>
<li>On your services and jobs, you are able to see all inherited environment variables and which ones take effect</li>
<li>Control the order of inheritance between conflicting keys by defining a priority per secret group</li>
<li>Variables defined on a service or job level always take priority over inherited values (the UI also shows a warning to prevent accidental overrides)</li>
<li>Fine-grained control on which services and jobs will the inheritance apply</li>
</ul>
<video autoplay playsinline loop muted width=100%>
    <source src="https://assets.northflank.com/2021/05/addon-inheritance.mp4" />
</video>
<h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Updated addon offerings to the latest versions</li>
<li>Fixed an issue where connections between services in the pipeline view wouldn't update during promotion</li>
<li>Fixed an issue where VCS source wouldn't load on the 'edit deployment' modal for self-hosted services</li>
<li>Fixed an issue where the environment editor would convert arrays into objects</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Real-time Git Integration</title>
  <link>https://northflank.com/changelog/real-time-git-integration</link>
  <pubDate>2021-04-30T15:15:52.000Z</pubDate>
  <description>
    <![CDATA[Northflank UI now updates any Git changes in real-time for improved performance and UX.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/05/changelog-realtime-git.png" alt="Real-time Git Integration" /><!--kg-card-begin: markdown--><p>We improved our integration with GitHub, GitLab and Bitbucket to show updates in real-time as you commit your files, update your branches and manage pull requests. The UI automatically reflects any changes you make, such as changing a branch name or closing a PR.</p>
<p>Real-time Git integration rapidly improves the speed and performance of the UI and pages are still accessible even during Git outages and respect the permissions of the Git providers (e.g. revoking access to a repository will prevent new data from being ingested or viewable for removed accounts).</p>
<video autoplay playsinline loop muted width=100%>
    <source src="https://assets.northflank.com/2021/04/realtime-git.mp4" />
</video>
<h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Added an infobox to the billing dashboard that shows warnings for expiring cards and overdue invoices</li>
<li>Extended support for additional syntaxes for automatic port detection</li>
<li>Networking rules for deleted/renamed service ports are now removed immediately</li>
<li>Fixed an issue where a cursor and copy highlights wouldn't show on code editors</li>
<li>Fixed an issue where an error would sometimes show for correctly formatted JSON in environment editors</li>
<li>Fixed an issue where empty keyfiles would be accepted when creating environment credentials</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Moving Domains Between Regions &amp; Deployment Improvements</title>
  <link>https://northflank.com/changelog/moving-domains-between-regions-real-time-and-deployment-improvements</link>
  <pubDate>2021-04-23T15:47:53.000Z</pubDate>
  <description>
    <![CDATA[Switch domains between services across regions, easily deploy commits onto jobs, improved performance.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/04/ch-build-select.png" alt="Moving Domains Between Regions &amp; Deployment Improvements" /><!--kg-card-begin: markdown--><p>You can now easily switch domains between services deployed in different regions without having to update your DNS records with your provider. Once your domain is linked to Northflank, you have the full control to move it across services &amp; regions. All traffic gets redirected to the new region automatically.</p>
<p>We also made it easier to deploy specific commits from a builder onto a scheduled job, which now matches the experience with deployment services. You can use the Build Selection modal to select a specific build or commit from a branch/pull request.</p>
<h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Improved syncing between Kubernetes clusters and the Northflank control plane, increasing reliability and enabling automatic incident recovery for event streams</li>
<li>Added the ability to delete individual pods via the UI</li>
<li>Free services will now only have at most one running pod rather than completing a full rollout restart</li>
<li>Environment editor pages now display a warning if they are open in multiple tabs and allow you to compare changes between versions</li>
<li>Job run view page meta information is now displayed in a reactive manner</li>
<li>Optimised performance of Northflank’s multiplayer experience to reduce overly eager React re-renders which caused issues when a larger number of active tabs were in use</li>
<li>Fixed inconsistent websocket load-balancing in the UI when using the Brave browser. Thanks @Ben!</li>
<li>Fixed an issue where successful build and promotion events would trigger two parallel deployments in certain circumstances, this resulted in excess containers for a short time and increased duration of a completed deployment</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Extended GitHub Deployment Integration</title>
  <link>https://northflank.com/changelog/extended-github-deployment-integration</link>
  <pubDate>2021-04-16T14:55:02.000Z</pubDate>
  <description>
    <![CDATA[GitHub now displays your services deployed on Northflank, Docker credentials can be updated without a service restart, other features &amp; fixes.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/04/changelog-github.png" alt="Extended GitHub Deployment Integration" /><!--kg-card-begin: markdown--><p>We improved the GitHub integration to display Northflank combined services deploying your repositories in the GitHub UI. This is especially helpful when rapidly iterating and reviewing changes on feature branches. We will be evaluating this integration for deployment services over the coming weeks, too.</p>
<h2 id="otherfeaturesfixes">Other features &amp; fixes</h2>
<ul>
<li>Docker registry credentials can now be updated without triggering restarts in affected services and jobs</li>
<li>Job cron schedule editor now shows the server's current time to ease scheduling</li>
<li>Added a Rust Nickel.rs repository to service templates</li>
<li>Added a confirmation modal before deleting team roles</li>
<li>Added a copy to clipboard button on basic auth credentials in network security rules</li>
<li>Fixed an issue where the role permission selector would not correctly display granted permissions if the form was disabled</li>
<li>Fixed an issue where the team admin role wouldn't have access to update domains by default</li>
<li>Fixed an issue where deleted ports would still appear as part of security rules</li>
<li>Fixed an issue where a build wouldn't start automatically after service creation in certain scenarios</li>
<li>Fixed an issue where 1Password would suggest creating a new password instead of filling out the existing one on password confirmation modals in the UI</li>
<li>Fixed an issue where the user would be redirected to a non-existing route on secondary tabs when finalising their account</li>
<li>Fixed an issue with assigning subdomains to a port via the API</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Onboarding Achievements &amp; Other Features</title>
  <link>https://northflank.com/changelog/onboarding-achievements-other-features</link>
  <pubDate>2021-04-09T15:12:53.000Z</pubDate>
  <description>
    <![CDATA[Introduced onboarding achievements steps on the user and team dashboards to get started on Northflank easily.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/04/header-ch-dashboard.png" alt="Onboarding Achievements &amp; Other Features" /><!--kg-card-begin: markdown--><p>We redesigned the user and team dashboards to include account achievement steps, which guide developers to link their Git accounts, create projects and services, add pipelines and domains, as well as invite team members with various permissions.</p>
<h3 id="otherfeatures">Other features</h3>
<ul>
<li>Added links to documentation, changelog and feedback within the platform</li>
<li>Added global messaging for version control linking</li>
<li>Added support for <code>${}</code> syntax for port variables in Dockerfiles</li>
<li>Added connection details for Minio and Redis with example CLI commands</li>
<li>Increased support for APEX or flattened CNAMEs that are actually subdomains</li>
<li>Improved colours, contrast and code editors across all themes to make the appearance consistent</li>
</ul>
<h3 id="fixes">Fixes</h3>
<ul>
<li>Increased number of retries when environment fetching is unavailable</li>
<li>Fixed permissions for listing API key templates that the user can access</li>
<li>Fixed DockerHub and Quay pull rate limits</li>
<li>Fixed an issue where addon would move from 'deleting' to 'failed'</li>
<li>Fixed an issue with listing branches from self-hosted VCS</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Single Sign-On &amp; Dynamic Project Selector</title>
  <link>https://northflank.com/changelog/single-sign-on-dynamic-project-selector</link>
  <pubDate>2021-03-26T16:00:54.000Z</pubDate>
  <description>
    <![CDATA[New project selector for rapid resource creation, added support for SSO login with Google, GitHub, GitLab and Bitbucket.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/03/project-s-changelog.png" alt="Single Sign-On &amp; Dynamic Project Selector" /><!--kg-card-begin: markdown--><p>Login to your account using single sign-on for Google, GitHub, Gitlab and Bitbucket. You can enable and disable SSO for each provider in your Account Settings for increased security.</p>
<p><img src="https://assets.northflank.com/2021/03/sso.png" alt="sso" loading="lazy"></p>
<h3 id="userexperience">User Experience</h3>
<ul>
<li>'Create new' button was redesigned to work without a selected project when adding services, jobs, addons, pipelines or secret groups - it now shows a list of all your projects for you to choose from</li>
<li>Code editors in the UI now change syntax highlights based on the selected theme</li>
</ul>
<h3 id="otherfeaturesfixes">Other features &amp; fixes</h3>
<ul>
<li>We improved onboarding for first-time users - guiding them to configure their settings, link Git and create their first project</li>
<li>Fixed an issue where a service couldn't be scaled vertically if configured to use spot resources</li>
<li>Fixed an issue where addon storage scaling would return slightly different sizes on different providers (e.g. requested 20000Mi leads to 20Gi, which is 20480Mi)</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Northflank Themes</title>
  <link>https://northflank.com/changelog/northflank-themes</link>
  <pubDate>2021-03-19T14:29:26.000Z</pubDate>
  <description>
    <![CDATA[Customise your Northflank theme to match your favourite IDE across devices.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/03/header-changelog-themes.png" alt="Northflank Themes" /><!--kg-card-begin: markdown--><p>Customise your Northflank experience to match your IDE or preferred colour style. We are pleased to introduce Light, Blackout, Solarized Light, Solarized Dark and Dracula (JetBrains IDE) in addition to the default Northflank theme.</p>
<p>Other popular themes will be added over the coming months as we gather user feedback. Let us know if there are any specific ones you would like to see.</p>
<p>You can change your theme in user account settings, it applies in real-time to all your open Northflank tabs on all devices (including mobile) and will also affect your view across teams.</p>
<!--kg-card-end: markdown--><p></p><!--kg-card-begin: markdown--><video autoplay playsinline loop muted width=100%>
    <source src="https://assets.northflank.com/2021/03/themes-changelog-post.mp4" />
</video><!--kg-card-end: markdown--><!--kg-card-begin: markdown--><h3 id="otherfeaturesfixes">Other features &amp; fixes</h3>
<ul>
<li>Improved platform notifications to be more consistent and user friendly</li>
<li>Fixed an issue where addons backups list wouldn't show if all backups were deleted</li>
<li>Fixed an issue where creating a Docker credential secret would redirect back to the credentials list instead of the newly created secret</li>
<li>Fixed an issue where the commit list wouldn't update redeployment buttons on the deployed commit in certain scenarios</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Custom Docker Registries &amp; RBAC</title>
  <link>https://northflank.com/changelog/custom-docker-registries-rbac</link>
  <pubDate>2021-03-12T17:18:19.000Z</pubDate>
  <description>
    <![CDATA[Added support for self-hosted Docker registries, redesigned Role-Based Access Control for teams and APIs.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/03/custom-docker-header.png" alt="Custom Docker Registries &amp; RBAC" /><!--kg-card-begin: markdown--><h3 id="customdockerregistries">Custom Docker Registries</h3>
<p>You can now deploy images from any custom Docker registry on Northflank, no longer restricting you to GitHub, GitLab, Docker Hub and Google Container Registry.</p>
<ul>
<li>Bring your self-hosted registries and conveniently store credentials on Northflank for secure and rapid access</li>
<li>Automatic detection of public/private visibility &amp; registry authentication scheme, and login with username &amp; password or .docker.config</li>
</ul>
<h3 id="finegrainedrbac">Fine-Grained RBAC</h3>
<p>We have redesigned the Role-Based Access Control (RBAC) for teams and APIs.</p>
<ul>
<li>Teams can create varying permissions and apply them to specific projects or team members as required</li>
<li>Permissions update in real-time across the Northflank UI, removing or adding relevant components dynamically</li>
<li>RBAC provides fine-grained security for you and your team across the Northflank UI, API and CLI</li>
<li>RBAC also applies to the 'northflank forward' CLI command, giving you control of which services and addons can be forwarded for local development</li>
</ul>
<p><img src="https://assets.northflank.com/2021/03/rbac.png" alt="rbac" loading="lazy"></p>
<h3 id="otherfeaturesfixes">Other features &amp; fixes</h3>
<ul>
<li>Team owners can now transfer ownership to another team member</li>
<li>Jobs (cron or manual trigger) can now be created with a Northflank build service as the deployment source</li>
<li>Moved 'Recent builds' card higher in the dashboard for instant observability</li>
<li>Addons 'redeploy' and 'pause' buttons are now only enabled if the addon is running or scaling</li>
<li>Fixed an issue causing some build rules validations to fail</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Live Secrets Editor</title>
  <link>https://northflank.com/changelog/live-secrets-editor</link>
  <pubDate>2021-03-05T16:22:14.000Z</pubDate>
  <description>
    <![CDATA[Live secrets editor notifies you if a team member is editing the same secrets and you can compare and merge changes in a diff editor.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/03/changelog-diff-editor-header.png" alt="Live Secrets Editor" /><!--kg-card-begin: markdown--><p>We've added live notifications to warn you if a team member may be editing the same environment variables, build arguments or project secrets. If a change is made while you're editing the same settings you can compare differences between your edits and the remote version, and decide if you want to over-ride them. The difference editor also gets real-time updates, so you can see new changes straight away.</p>
<p><img src="https://assets.northflank.com/2021/03/diff-editor-update.png" alt="diff-editor-update" loading="lazy"></p>
<h3 id="otherfeaturesfixes">Other features &amp; fixes</h3>
<ul>
<li>New login and sign up pages</li>
<li>You can now manually pay a failed invoice with a new or saved card</li>
<li>Fixed an issue where log lines would be copied in reverse</li>
<li>Fixed an issue causing some team invitations to be rate-limited</li>
<li>Fixed an issue causing commits to not update remote VCS check status in real-time when build state changed on Northflank</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>New Marketing Site, Improved DX and API Responses</title>
  <link>https://northflank.com/changelog/new-marketing-site-improved-dx-and-api-responses</link>
  <pubDate>2021-02-26T12:50:18.000Z</pubDate>
  <description>
    <![CDATA[New site, improvements to various aspects of Developer Experience and new features &amp; fixes.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/02/chlog-website.png" alt="New Marketing Site, Improved DX and API Responses" /><p></p><!--kg-card-begin: markdown--><p>We are excited to introduce our <a href="https://northflank.com" target="_blank">new marketing site</a>! The experience is now more consistent with the Northflank platform, and it highlights Northflank's capabilities at a glance.</p>
<h3 id="developerexperienceimprovements">Developer Experience Improvements</h3>
<ul>
<li>Released a new version of the <a href="https://www.npmjs.com/package/@northflank/js-client" target="_blank">Northflank API client</a> with 8 new endpoints</li>
<li>Redesigned docker image settings overview to be clearer and compact</li>
<li>Added a 'copy to clipboard' button for API tokens with a highlight of the last 4 digits</li>
<li>Improved UX of adding self-hosted VCS and you can now link directly to specific VCS setting modals</li>
</ul>
<h3 id="otherfeaturesfixes">Other features &amp; fixes</h3>
<ul>
<li>Added an option to pay invoices manually if the automatic payment fails or requires additional verification</li>
<li>Various improvements to API error responses for easier debugging</li>
<li>Fixed an issue causing relinking Git from Pipelines to fail</li>
<li>Fixed an issue with JSON view in Secrets sometimes rendering incorrectly</li>
<li>Fixed an issue with container registry credentials sometimes not loading</li>
<li>Fixed an issue with GCR credentials verification failing in certain scenarios</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Stateful Workload Endpoints and Log Timestamp Filtering</title>
  <link>https://northflank.com/changelog/stateful-workload-endpoints-and-log-timestamp-filtering</link>
  <pubDate>2021-02-19T19:03:47.000Z</pubDate>
  <description>
    <![CDATA[Job duration runtimes, stateful workload API endpoints and platform enhancements.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/02/changelog-logs-api.png" alt="Stateful Workload Endpoints and Log Timestamp Filtering" /><!--kg-card-begin: markdown--><p>This week, we introduced some new features and fixed several bugs:</p>
<ul>
<li>Automatic port detection now suggests removing unused ports</li>
<li>When connecting domains to services, the service selection dropdown is now grouped by project</li>
<li>Added duration and container runtime for manual and cron job runs to the UI</li>
<li>Added new API for stateful addons (abort backup, abort restore, delete backup, download backup endpoints)</li>
<li>Added automatic retrigger of log search after timestamp ranges are updated</li>
<li>Added favouriting pipelines, secrets and Docker credentials</li>
<li>Fixed an issue where log lines would display as objects instead of the message string during log search between specific timestamps</li>
<li>Fixed an issue causing random name generation to exceed the maximum character limit</li>
<li>Fixed an issue causing a page to crash after accepting a team invite for the invitee</li>
<li>Fixed an issue causing the job restart modal to crash if another team member opened and interacted with it</li>
<li>Fixed an issue causing interactive deployment pipelines to crash when removing a service from a stage in certain circumstances</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Increased mTLS support and Mobile &amp; App UI Tweaks</title>
  <link>https://northflank.com/changelog/increased-mtls-support-and-mobile-app-ui-tweaks</link>
  <pubDate>2021-02-11T18:24:36.000Z</pubDate>
  <description>
    <![CDATA[Design tweaks, new features and several bug fixes.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/02/mobile-header.png" alt="Increased mTLS support and Mobile &amp; App UI Tweaks" /><!--kg-card-begin: markdown--><p>Here is a summary of what we worked on this week:</p>
<ul>
<li>UI design fixes and tweaks - Northflank now works well on mobiles and tablets</li>
<li>Enabled proxy and mTLS on jobs and cron jobs to enable communication with other services and addons in your project</li>
<li>Platform prompts automatic port detection after your Dockerfile has been edited via the Northflank UI</li>
<li>Updated <a href="https://northflank.com/docs/v1beta/api/domains" target="_blank">API</a> to handle new logic of <a href="https://northflank.com/changelog/subdomain-verification-and-billing-redesign" target="_blank">adding domains and subdomains</a>, improved error handling</li>
<li>Team members now have the option to leave teams of which they are not the owner</li>
<li>Fixed an issue that would sometimes display an error after creating services from template repositories</li>
<li>Fixed an issue when viewing logs for an addon would sometimes display logs for another addon in the same project</li>
</ul>
<!--kg-card-end: markdown--><p></p>]]>
  </content:encoded>
</item><item>
  <title>Subdomain Verification and Billing Redesign</title>
  <link>https://northflank.com/changelog/subdomain-verification-and-billing-redesign</link>
  <pubDate>2021-02-04T12:00:35.000Z</pubDate>
  <description>
    <![CDATA[Improved subdomain verification experience and redesigned resource configuration components.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/02/header-billing-domains-1.png" alt="Subdomain Verification and Billing Redesign" /><h3></h3><h3></h3><!--kg-card-begin: markdown--><h3 id="addingcustomsubdomains">Adding custom subdomains</h3>
<p>As we announced <a href="https://northflank.com/changelog/improved-custom-domains" target="_blank">recently</a>, we streamlined the process of adding and verifying custom domains. We have now also improved the <code>Add domain</code> modal that guides you through the process and automatically checks if the TXT verification record has been added.</p>
<video autoplay playsinline loop muted width=100%>
    <source src="https://assets.northflank.com/2021/01/txt-verify-domain.mp4"/>
</video>
<h3 id="improvedbillinguserinterface">Improved billing user interface</h3>
<ul>
<li>Compute plans are in a horizontal table view allowing you to easily compare resources and pricing</li>
<li>Resources page displays a price breakdown showing cost per hour, month and a selected number of replicas</li>
<li>The invoice list displays the status, date issued, invoice period and amount in a clearer layout</li>
<li>Other minor UI changes to improve the visibility of critical billing information</li>
</ul>
<!--kg-card-end: markdown--><figure class="kg-card kg-image-card"><img src="https://assets.northflank.com/2021/02/billing-overview-page.png" class="kg-image" alt="Billing page" loading="lazy" width="1672" height="940"></figure><!--kg-card-begin: markdown--><h3 id="otherimprovementsandfixes">Other Improvements and Fixes</h3>
<ul>
<li>Added TLS certificate status to Account Settings - Domains page</li>
<li>Account Overview page now also shows self-hosted VCSs under Git integrations</li>
<li>CLI upgrade now gives more control over addons (you can now use commands such as import backup or pause addon)</li>
<li>Added filtering of MongoDB addon logs to hide health check connections being created to reduce noise</li>
</ul>
<!--kg-card-end: markdown--><p></p><p></p>]]>
  </content:encoded>
</item><item>
  <title>Northflank API &amp; CLI Working in Unison</title>
  <link>https://northflank.com/changelog/northflank-api-cli-working-in-unison</link>
  <pubDate>2021-01-25T11:57:38.000Z</pubDate>
  <description>
    <![CDATA[Example of using the Northflank API &amp; CLI to deploy a game server and several bug fixes.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/01/header-api-game-deployment.png" alt="Northflank API &amp; CLI Working in Unison" /><p></p><!--kg-card-begin: markdown--><p>This week, we're excited to showcase the <a href="https://northflank.com/docs/v1beta/api/introduction" target="_blank">Northflank API &amp; CLI</a> working in parallel. We used our API to securely deploy a game server and connected to it using the Northflank CLI and TCP proxy - creating an example of infrastructure as data.<br>
Check the video below to see this in action:</p>
<ul>
<li>First, we create a new project in the Northflank UI and run an API call that creates a deployment service from an external Docker image <a href="https://github.com/itzg/docker-minecraft-server" target="_blank"><code>itzg/minecraft-server:latest</code></a>, exposing it on an internal port 25565</li>
<li>Once the service starts, we run a <code>northflank forward</code> CLI command that exposes the service locally</li>
<li>After the Minecraft server has spun up, we connect to it ready to start playing - you can see the logs coming up in real-time in the Northflank UI</li>
</ul>
<video playsinline muted controls width=100% >
    <source src="https://assets.northflank.com/2021/01/game-deployment.mp4"/>
</video><!--kg-card-end: markdown--><p></p><p></p><!--kg-card-begin: markdown--><p><strong>Other Improvements and Fixes</strong></p>
<ul>
<li>Domains Ports dropdown now only shows the relevant public entries</li>
<li>Improved handling of backup and restore of databases resulting in faster processing and fixed an issue with restoring database users and rights</li>
<li>Updated documentation context to cover newly added features</li>
<li>Updated MinIO connection strings to work for JS clients</li>
<li>Fixed a couple of frontend mobile view bugs</li>
<li>Fixed Edit Deployment modal crashing on internal services</li>
<li>Fixed a bug causing image verification to fail if it contained a numeric value</li>
<li>Fixed a bug causing log filtering by time range to fail</li>
<li>Fixed a bug causing docs search not loading found pages</li>
<li>Fixed a bug causing <code>northflank forward</code> CLI command to fail occasionally</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Improved Deployments, Pipelines and Ports Detection</title>
  <link>https://northflank.com/changelog/improved-deployments-pipelines-and-ports-detection</link>
  <pubDate>2021-01-18T11:28:09.000Z</pubDate>
  <description>
    <![CDATA[Greater visibility to deployments on internal and external images, improved port detection and more.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/01/header-port-detection.png" alt="Improved Deployments, Pipelines and Ports Detection" /><p><strong>Deployment Source Overview</strong></p><p>Deployment services now have an improved visual display of the deployment source, whether deploying Docker images from the Northflank container registry or a public/private external container registry.</p><figure class="kg-card kg-image-card"><img src="https://assets.northflank.com/2021/01/widget-deployment.png" class="kg-image" alt="Deployment Widget" loading="lazy" width="3344" height="1880"></figure><p><strong>Pipelines</strong></p><p>To configure your services using Pipelines, you can drag and drop deployment services from the side menu to Development, Staging or Production. To add a build service, select it from the menu and choose the build you want to link from the modal. We've improved the UI &amp; UX to make Pipelines more accessible.</p><figure class="kg-card kg-image-card"><img src="https://assets.northflank.com/2021/01/pipeline-modal.png" class="kg-image" alt="Pipelines" loading="lazy" width="3344" height="1880"></figure><p><strong>Improved Port Detection</strong></p><p>Northflank automatically detects ports from your Docker images. We've added a modal that lets you review and change the suggested ports before adding them to your services. You can detect ports during service creation or from the Ports &amp; DNS tab in your existing services.</p><figure class="kg-card kg-image-card"><img src="https://assets.northflank.com/2021/01/automatic-port-detection.png" class="kg-image" alt="Automatic Port Detection" loading="lazy" width="3344" height="1880"></figure><p><strong>Hostname in Addons Connection Details</strong></p><!--kg-card-begin: markdown--><p>In addition to connection strings and secrets for addons, we've added a connection detail with a hostname of the main replica that handles write operations. This can be used for local debugging and development in applications such as DataGrip, Robo3T and SQL Workbench. Use the <a href="https://northflank.com/docs/v1beta/api/introduction#about-the-cli" target="_blank">Northflank CLI</a> to securely forward addons for local access and development. Alternatively continue using the full admin or data connection strings to connect to your addons normally.</p>
<!--kg-card-end: markdown--><p><strong>Other Improvements and Fixes</strong></p><!--kg-card-begin: markdown--><ul>
<li>Added image path validation for versions of Docker images, shows a warning if you didn't add any version or if it doesn't exist</li>
<li>In addition to <code>us.gcr.io</code> and <code>eu.gcr.io</code>, we've added support for <code>gcr.io</code> and <code>asia.gcr.io</code> <a href="https://cloud.google.com/container-registry/docs/overview#repos" target="_blank">Google Cloud Container Registry regions</a></li>
<li>Following GitHub's new registry URL we've updated the hostname from <code>docker.pkg.github.com</code> to <code>ghcr.io</code></li>
<li>Fixed an issue causing Environment Variables not saving after switching between JSON and Table view</li>
<li>Fixed an issue causing build duration to display <code>unknown</code> if it was zero seconds</li>
</ul>
<!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>Improved Custom Domains</title>
  <link>https://northflank.com/changelog/improved-custom-domains</link>
  <pubDate>2021-01-11T12:54:26.000Z</pubDate>
  <description>
    <![CDATA[Rapidly deploy services on your external domains with automatic TLS certificate generation.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/01/custom-domains-header.png" alt="Improved Custom Domains" /><p>Northflank allows you to easily bring your custom domains to your account. We received feedback that connecting your domains and then adding them to a service was too difficult, so we revised this workflow to be more streamlined:</p><!--kg-card-begin: markdown--><ul>
<li>Add your domain in <em>Account Settings</em> - <em>Domains</em> and verify it with your provider by adding a TXT record</li>
<li>Create subdomains on Northflank and add appropriate CNAME records in the DNS settings</li>
<li>Start linking services &amp; ports from the dropdown or in the <em>Ports &amp; DNS</em> tab of your service</li>
</ul>
<p>Once your subdomains are set up you can change the linked ports and services without having to update your DNS records again. You can use custom domains across all projects.</p>
<p>Key features:</p>
<ul>
<li>Access your services deployed at <code>acme-domain.com</code>, <code>acme-domain.com/microservice</code> or <code>microservice.acme-domain.com</code></li>
<li>Northflank handles load balancing of services to the relevant region &amp; cloud provider to ensure availability</li>
<li>Northflank rapidly creates and regenerates Let's Encrypt certificates for free on all your domains, ensuring certificates are in date and connections stay secure</li>
<li>Domains can also be managed via the <a href="https://northflank.com/docs/v1beta/api/domains/list-domains" target="_blank">Northflank API and CLI</a></li>
</ul>
<!--kg-card-end: markdown--><p>Beside custom domains, Northflank now exposes your public services as a subdomain of <code>code.run</code> - the URL follows the structure of <code>[port-name]--[service-name]--[project-name]--[account-dns-identifier].code.run</code>.</p><p></p><!--kg-card-begin: markdown--><video autoplay playsinline loop muted width=100%>
    <source src="https://assets.northflank.com/2021/01/custom-domains.mp4"/>
</video>
&nbsp;<!--kg-card-end: markdown--><!--kg-card-begin: markdown--><h3 id="demotogitlab">Demo to GitLab</h3>
<p>Last week, we gave a demo to Sid Sijbrandij (Co-founder and CEO at GitLab) that was streamed live. Watch the recording on <a href="https://youtu.be/a-e06khPIz0" target="_blank">YouTube</a> or below.</p>
<div style="position: relative; padding-bottom: 56.25%; height: 0;">
    <iframe style="position: absolute; top: 0; left: 0; width: 100%; height: 100%;" src="https://www.youtube-nocookie.com/embed/a-e06khPIz0" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
</div><!--kg-card-end: markdown--><p></p>]]>
  </content:encoded>
</item><item>
  <title>Docker Registry Credential Management</title>
  <link>https://northflank.com/changelog/docker-registry-credential-management</link>
  <pubDate>2020-12-28T09:40:54.000Z</pubDate>
  <description>
    <![CDATA[Save Docker credentials to your account and reuse them across jobs and services.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2020/12/header-docker-cred-1.png" alt="Docker Registry Credential Management" /><!--kg-card-begin: markdown--><p>As <a href="https://northflank.com/changelog/templates-external-deployments" target="_blank">introduced recently</a>, Northflank supports deployments from external private registries such as Google Container Registry, Docker Hub, GitLab and GitHub.</p>
<p>To make the authorisation easier, you can now save Docker credentials in your account and reuse them across services and jobs.</p>
<!--kg-card-end: markdown--><p>When creating a new Docker credential, select your Registry provider and login using one of the following methods:</p><ul><li>Username and password</li><li>Username and API token</li><li>Username and Personal Access Token for GitHub</li><li><code>keyfile.json</code> for Google Container Registry</li><li>Directly supply your Docker <code>config.json</code> file</li></ul><p>Northflank will automatically encode your login details in base64 for registry authorisation.</p><p>Docker credentials can be reused across all projects or restricted only to selected ones. You can always manually override your credentials when creating jobs and services.</p><p>To add your credentials, navigate to your Account settings - Docker - Add credentials. </p><figure class="kg-card kg-image-card"><img src="https://assets.northflank.com/2020/12/new-credentials.png" class="kg-image" alt="Add new credentials" loading="lazy" width="3344" height="1880"></figure><p>You can then use your saved credentials when you create a new job or service with deployment from an external image.</p><figure class="kg-card kg-image-card"><img src="https://assets.northflank.com/2020/12/new-private-service.png" class="kg-image" alt="Create new service" loading="lazy" width="3344" height="1880"></figure><p></p><p></p>]]>
  </content:encoded>
</item><item>
  <title>Project Secrets</title>
  <link>https://northflank.com/changelog/project-secrets</link>
  <pubDate>2020-12-21T10:17:28.000Z</pubDate>
  <description>
    <![CDATA[Easily manage secrets for your services &amp; jobs on a project level.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2020/12/changelog-header-proj-secret.png" alt="Project Secrets" /><p>Environment variables provide a secure way to store your keys, tokens, paths and other secrets used in your applications. They can be added for services and jobs under the Environment tab. </p><p>Projects are groups of services, jobs and addons. With Project Secrets, you can now set environment variables on a project level which will save time and ease variable management if several of your applications depend on the same values.</p><!--kg-card-begin: markdown--><ul>
<li>Apply Project Secrets to the whole project or restrict to one or more services &amp; jobs</li>
<li>Create more Project Secrets and set priorities to define which ones take precedence</li>
<li>Setting variables directly in the service or job will always have priority over Project Secrets</li>
</ul>
<!--kg-card-end: markdown--><p>Secrets are encrypted at rest and only decrypted before being merged and injected into your runtime environment.</p><p>To create your Project Secrets, navigate to your project and select Secrets from the Dashboard or Sidebar. </p><!--kg-card-begin: markdown--><video autoplay playsinline loop muted width=100%>
    <source src="https://assets.northflank.com/changelog/finalkap.mp4"/>
</video><!--kg-card-end: markdown--><!--kg-card-begin: markdown--><p>Project Secrets are also fully supported via the <a href="https://northflank.com/docs/v1beta/api/secrets/list-secrets" target="_blank">Northflank API &amp; CLI</a> (introduced in <a href="https://northflank.com/changelog/northflank-cli-extended-api" target="_blank">this article</a>) with team RBAC included.</p>
<p>Here is an example of creating Project Secrets using the Northflank API:</p>
<!--kg-card-end: markdown--><pre><code class="language-javascript">const payload = {
  name: 'My Project Secret',
  description: 'Environment variables for my project',
  secretType: 'environment',
  priority: 10,
  restrictions: {
    restricted: false,
    nfObjects: [
      {}
    ]
  },
  data: {
    API_KEY: '****',
    API_TOKEN: '****'
  }
}

const response = await fetch(NORTHFLANK_API_ENDPOINT, {
  method: 'POST',
  headers: {
  	'Accept': 'application/json',
  	'Content-Type': 'application/json',
  	'Authorization': `Bearer ${NORTHFLANK_API_TOKEN}`,
},
  body: JSON.stringify(payload),
});

const json = await response.json()
console.log(json)

// =&gt; Created secret My Project Secret</code></pre>]]>
  </content:encoded>
</item><item>
  <title>Northflank API Client</title>
  <link>https://northflank.com/changelog/introducing-northflank-api-client</link>
  <pubDate>2020-12-14T13:46:32.000Z</pubDate>
  <description>
    <![CDATA[Introducing the Northflank API Client for JavaScript and Node.js.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2020/12/api-client-header-changelog.png" alt="Northflank API Client" /><p>You can now leverage the full capabilities of the Northflank API using our Node.js and JavaScript API client. Full typing support is available for TypeScript.</p><!--kg-card-begin: markdown--><p>Install the <a href="https://www.npmjs.com/package/@northflank/js-client" target="_blank">Northflank API client</a> using <code>npm i @northflank/js-client</code> or <code>yarn add @northflank/js-client</code>. You will need to create an <a href="https://northflank.com/docs/v1beta/application/api-keys" target="_blank">API token</a> in your user or team account. Full API documentation is available <a href="https://northflank.com/docs/v1beta/api/introduction" target="_blank">here</a>.</p>
<!--kg-card-end: markdown--><p><strong>Use the following import statement to include it in your project</strong></p><pre><code class="language-javascript">import ApiClient, { ApiClientInMemoryContextProvider } from '@northflank/js-client';</code></pre><p><strong>Initialise the API client</strong></p><pre><code class="language-javascript">(async () =&gt; {
    // Create context to store credentials
    const contextProvider = new ApiClientInMemoryContextProvider();
    await contextProvider.addContext({
        name: 'test-context',
        token: '&lt;api-token&gt;', // Use API token generated in the Northflank UI
    });
    
    // Initialize API client
    const apiClient = new ApiClient(contextProvider);
})();</code></pre><p><strong>Create a new project</strong></p><pre><code class="language-javascript">await apiClient.endpoints.create.project.call({
		quiet: true,
		payload: { name: 'test-project', region: 'europe-west' },
	});
// =&gt; New project test-project created</code></pre><p><strong>List all projects</strong></p><pre><code class="language-javascript">const projects = await apiClient.endpoints.list.project.call({ quiet: true });
console.log(
		'Projects:',
		projects.response.map((project) =&gt; `${project.name} ${project.internalId}`)
	);
// =&gt; Listed all projects successfully</code></pre><p><strong>Create a combined service</strong></p><pre><code class="language-javascript">const combinedServiceCreationResponse = await client.endpoints.create.combinedService
  .call({
    project: 'default-project',
    payload: {
      name: 'My awesome combined service',
      description:
        'A combined service created with the Northflank API Client',
      billing: { deploymentPlan: 'micro' },
      deployment: { instances: 1 },
      vcsData: {
        projectUrl: '&lt;https://github.com/northflank/my-project.git&gt;',
        projectType: 'github',
        dockerFilePath: '/app/Dockerfile',
        dockerWorkDir: '/app',
        projectBranch: '/feature/changelog',
      },
      ports: [
        {
          name: 'port-1',
          internalPort: 3000,
          public: true,
          protocol: 'HTTP',
        },
      ],
    },
  })
  .catch((err) =&gt; err.message);
// =&gt; Created a combined service with ID my-awesome-combined-service</code></pre><p><strong>Scaling a service</strong></p><pre><code class="language-javascript">const scaleServiceResponse = await client.endpoints.scale.service
  .call({
    project: 'default-project',
    service: 'my-awesome-combined-service',
    payload: {
      instances: 10,
      deploymentPlan: 'large',
    },
  })
  .catch((err) =&gt; err.message);
// =&gt; Service scaled successfully</code></pre>]]>
  </content:encoded>
</item><item>
  <title>Supporting Self-hosted GitLab</title>
  <link>https://northflank.com/changelog/supporting-self-hosted-gitlab</link>
  <pubDate>2020-12-07T13:08:22.000Z</pubDate>
  <description>
    <![CDATA[Teams can now add their self-hosted instances of GitLab to Northflank for build and deployment.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2020/12/header-gitlab-1.png" alt="Supporting Self-hosted GitLab" /><p>Teams can now add their self-hosted instances of GitLab to Northflank in addition to the SaaS offerings of GitHub, GitLab and Bitbucket.</p><!--kg-card-begin: markdown--><p>To get started, create a new application in your self-hosted GitLab instance. Enter the application name, select the API scope and paste the redirect URI as <code>https://app.northflank.com/api/integrations/git/finalize</code>.</p>
<!--kg-card-end: markdown--><p>Then navigate to your team on Northflank and select <em>Add a self-hosted VCS</em> under <em>Team Settings - Integrations - Git</em>. Choose your name, enter your GitLab URL and paste application ID and secret from the created application.</p><figure class="kg-card kg-image-card"><img src="https://assets.northflank.com/2020/12/gitlab-link-northflank.png" class="kg-image" alt="Link GitLab to Northflank" loading="lazy" width="3344" height="1880"></figure><p>Once added, you can use your self-hosted GitLab to create combined or build services and jobs:</p><ul><li>During service or job creation, select your self-hosted GitLab from the dropdown</li><li>Continuous Integration will build your code on every push</li><li>With build services, you can build merge requests and branches </li><li>You can add multiple self-hosted GitLab instances to your account</li></ul><figure class="kg-card kg-image-card"><img src="https://assets.northflank.com/2020/12/gitlab-create-combined-service.png" class="kg-image" alt loading="lazy" width="3344" height="1880"></figure><p>🎉 Here is an example of a successfully deployed and running NodeJS Express app:</p><figure class="kg-card kg-image-card"><img src="https://assets.northflank.com/2020/12/gitlab-express-app.png" class="kg-image" alt="NodeJS running on Northflank" loading="lazy" width="3344" height="1880"></figure>]]>
  </content:encoded>
</item><item>
  <title>New API &amp; CLI Documentation</title>
  <link>https://northflank.com/changelog/new-api-cli-documentation</link>
  <pubDate>2020-12-03T12:52:02.000Z</pubDate>
  <description>
    <![CDATA[Introducing new documentation with high resolution search.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/changelog/headers/header-changelog-documentation.png" alt="New API &amp; CLI Documentation" /><!--kg-card-begin: markdown--><p>Introducing improved API documentation which now includes documentation for the Northflank CLI <a href="https://northflank.com/changelog/northflank-cli-extended-api" target="_blank">covered last week</a>. To get started, create an <a href="https://northflank.com/docs/v1beta/application/api-keys" target="_blank">API token</a> for your user or team account.</p>
<!--kg-card-end: markdown--><h3 id="documentation-features">Documentation Features</h3><ul><li>Choose from light or dark theme 🌗 </li><li>Search is super fast across all content and powered by Algolia 🔎 </li><li>Customise your layout and see API code snippets in curl, JavaScript, Python and Go 🚀 </li></ul><!--kg-card-begin: markdown--><p>You can access the docs <a href="https://northflank.com/docs/v1beta/api/introduction" target="_blank">here</a>.</p>
<!--kg-card-end: markdown--><p></p><!--kg-card-begin: markdown--><video autoplay playsinline loop muted width=100%>
    <source src="https://assets.northflank.com/changelog/docs-search.mp4"/>
</video><!--kg-card-end: markdown-->]]>
  </content:encoded>
</item><item>
  <title>New UI &amp; Logs and Metrics</title>
  <link>https://northflank.com/changelog/new-ui-logs-and-metrics</link>
  <pubDate>2020-11-30T10:11:12.000Z</pubDate>
  <description>
    <![CDATA[We have redesigned the user interface and made improvements to logging &amp; metrics.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/changelog/headers/header-changelog-logs.png" alt="New UI &amp; Logs and Metrics" /><p>We have worked on a redesign and made significant improvements to the UI &amp; UX of the platform. The layout is more uniform making it even easier to navigate around your infrastructure on Northflank.</p><figure class="kg-card kg-image-card"><img src="https://assets.northflank.com/2020/11/dashboard-new-ui.png" class="kg-image" alt="Dashboard" loading="lazy" width="3344" height="1880"></figure><h3 id="logs-metrics">Logs &amp; Metrics</h3><!--kg-card-begin: markdown--><p>We have improved real-time logging and long-term storage for all jobs, services and addons resulting in better observability of your applications. This capability is perfect for debugging in production with a powerful include/exclude <code>REGEX</code> and text-based search that works in live-tail and over specified time periods.</p>
<!--kg-card-end: markdown--><figure class="kg-card kg-image-card"><img src="https://assets.northflank.com/2020/11/logs.png" class="kg-image" alt="Logs" loading="lazy" width="1280" height="720"></figure>]]>
  </content:encoded>
</item><item>
  <title>Northflank CLI &amp; Extended API</title>
  <link>https://northflank.com/changelog/northflank-cli-extended-api</link>
  <pubDate>2020-11-23T09:53:45.000Z</pubDate>
  <description>
    <![CDATA[Introducing the Northflank CLI and enhancements to the API.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2020/12/header-api-snippet.png" alt="Northflank CLI &amp; Extended API" /><p>In addition to the UI and API, you can now interact with Northflank using a Command Line Interface. Local deveploment becomes even easier with the ability to proxy your remote databases and services to your machine. The Northflank CLI comes with RBAC so you can securely share services with developers in your team. </p><!--kg-card-begin: markdown--><p>Install the <a href="https://www.npmjs.com/package/@northflank/cli/" target="_blank">Northflank CLI</a> with <code>npm i @northflank/cli -g</code> or <code>yarn global add @northflank/cli</code>. To log in, you will need to create an <a href="https://northflank.com/docs/v1beta/application/api-keys" target="_blank">API token</a> in your user or team account.</p>
<!--kg-card-end: markdown--><h3 id="available-commands">Available commands</h3><!--kg-card-begin: markdown--><pre><code>forward           Port-forwarding for Northflank services and addons
login [options]   Connect the CLI to your Northflank account
context|contexts  Retrieve and update local context settings
list              List Northflank resources
create            Create Northflank resources
get               Get information about Northflank resources
delete            Delete Northflank resources
scale             Scale Northflank resources
update            Update Northflank resource properties
restart           Restart Northflank resources
pause             Pause Northflank resources
resume            Resume Northflank resources
help [command]    display help for command
</code></pre>
<!--kg-card-end: markdown--><h3 id="creating-a-service">Creating a service</h3><!--kg-card-begin: markdown--><p>To create a deployment service, use the <code>create deploymentService</code> command. You can also create other service types or projects:</p>
<pre><code>project|projects [options]   Creates a new project.
combinedService [options]    Creates a new combined service.
deploymentService [options]  Creates a new deployment service.
buildService [options]       Creates a new build service.
</code></pre>
<!--kg-card-end: markdown--><!--kg-card-begin: markdown--><div>
<video autoplay playsinline loop muted width=100%>
    <source src="https://assets.northflank.com/changelog/video-cli-create-service.mp4"/>
</video>
</div><!--kg-card-end: markdown--><h3 id="forwarding-resources-for-local-development">Forwarding resources for local development</h3><!--kg-card-begin: markdown--><p>You can choose which services and addons to forward or select all project resources using the <code>forward all</code> command. This allows you to connect directly to your applications and databases without having to publically expose them.</p>
<!--kg-card-end: markdown--><!--kg-card-begin: markdown--><div>
<video autoplay playsinline loop muted width=100% height=auto>
    <source src="https://assets.northflank.com/changelog/video-cli-forward-all.mp4"/>
</video>
</div><!--kg-card-end: markdown--><h3 id="extended-api">Extended API</h3><!--kg-card-begin: markdown--><p>We have expanded the Northflank API so you can interact with your Northflank resources directly in your code. Documenation is available <a href="https://northflank.com/docs/v1beta/api/introduction" target="_blank">here</a>.</p>
<!--kg-card-end: markdown--><pre><code class="language-javascript">const payload = {
  name: 'My awesome service',
  description: 'Example deployment service',
  billing: {
      deploymentPlan: 'micro'
  },
  deployment: {
    instances: 1,
    external: {
      registryProvider: 'dockerhub',
      imagePath: 'nginx:latest',
      privateImage: false,
    },
  },
  ports: [{ name: 'default-port', internalPort: 80, public: true, protocol: 'HTTP' }],
};
const response = await fetch(NORTHFLANK_API_ENDPOINT, {
  method: 'POST',
  headers: {
  	'Accept': 'application/json',
  	'Content-Type': 'application/json',
  	'Authorization': `Bearer ${NORTHFLANK_API_TOKEN}`,
},
  body: JSON.stringify(payload),
});
const { serviceType, serviceId } = await response.json()
console.log(`Deployed a service of type ${serviceType} with the ID ${serviceId}!`)
// =&gt; Deployed a service of type deployment with the ID my-awesome-service</code></pre><p></p>]]>
  </content:encoded>
</item><item>
  <title>Templates &amp; External Deployments</title>
  <link>https://northflank.com/changelog/templates-external-deployments</link>
  <pubDate>2020-11-16T09:42:33.000Z</pubDate>
  <description>
    <![CDATA[Introducing template repositories and external Docker image deployment.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/changelog/headers/header-changelog-template.png" alt="Templates &amp; External Deployments" />Start new services rapidly from predefined templates via the Northflank UI. We support a collection of popular technology stacks that automatically configure Northflank Build, Deployment and Networking.

Get started in seconds with Create React App, Node.js Express, Angular.js, PHP Laravel, Python Flask, Gatsby.js, Python Django, Vue.js, Ruby on Rails and Next.js.

![Template Repositories](https://assets.northflank.com/2020/11/template-repos.png)

### Deploy images from external registries

In addition to deploying images from Docker Hub, we now support deployments from external private registries with GitHub, GitLab and Google Container Registry:

- Bring existing images to the Northflank Cloud Platform 
- The easiest way to deploy any private or public Docker image
- Release management of your images using Northflank Pipelines
- Pair Northflank with external CI platforms such as CircleCI, Github Actions or Gitlab CI

Start by creating a new Deployment service and select External Image as the deployment source.

![External Registries](https://assets.northflank.com/2020/11/deployment-from-external-registries.png)

![Pipelines](https://assets.northflank.com/2020/11/pipelines.png)]]>
  </content:encoded>
</item><item>
  <title>Health Checks, Port Detection &amp; Network Security</title>
  <link>https://northflank.com/changelog/health-checks-port-detection-network-security</link>
  <pubDate>2020-11-09T10:02:29.000Z</pubDate>
  <description>
    <![CDATA[Services now support health checks, automatic port detection, enhanced network security and jobs have advanced controls.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2021/01/header-port-detection.png" alt="Health Checks, Port Detection &amp; Network Security" /><h3 id="configurable-health-checks">Configurable health checks</h3><p>You can now configure health checks on Northflank, with support for readiness and liveness probes. Health checks ensure zero-downtime deployments and automatic replacement of unhealthy instances.</p><figure class="kg-card kg-image-card"><img src="https://assets.northflank.com/2020/11/health-check.png" class="kg-image" alt="Health Checks" loading="lazy" width="3344" height="1880"></figure><h3 id="automatic-port-detection">Automatic port detection</h3><p>Northflank fetches all ports exposed in a built Docker image automatically, or recursively from a Dockerfile added in your version control. We then configure your load-balancing automatically. This helps if you are unsure about the Dockerfile or if you don't know on which port(s) the application runs.</p><figure class="kg-card kg-image-card"><img src="https://assets.northflank.com/2020/11/automatic-port-detection.png" class="kg-image" alt="Automatic Port Detection" loading="lazy" width="3344" height="1880"></figure><h3 id="networking-security">Networking security</h3><!--kg-card-begin: markdown--><p>Secure your services with IP Policies &amp; Basic Authentication and configure them for one or multiple exposed ports in your deployment:</p>
<ul>
<li>Allow or block certain IP addresses or <code>CIDR</code> blocks</li>
<li>Configure username and password combinations to restrict access to your endpoints</li>
<li>Compose these security rules together to enable security in-depth, for example allowing access from a specific VPN and certain user credentials</li>
</ul>
<!--kg-card-end: markdown--><figure class="kg-card kg-image-card"><img src="https://assets.northflank.com/2020/11/security-popup.png" class="kg-image" alt loading="lazy" width="3344" height="1880"></figure><h3 id="docker-cmd-override">Docker CMD override</h3><!--kg-card-begin: markdown--><p>We have added the ability to override the <code>CMD</code> of the Docker image in your deployment with a helpful interface giving you more observability over how your containers start.</p>
<!--kg-card-end: markdown--><figure class="kg-card kg-image-card"><img src="https://assets.northflank.com/2020/11/cmd-override.png" class="kg-image" alt="Docker CMD Override" loading="lazy" width="3344" height="1880"></figure><h3 id="advanced-job-controls">Advanced job controls</h3><p>We have added advanced job settings so you now control the number of retries, the concurrency policy and the maximum runtime duration.</p><figure class="kg-card kg-image-card"><img src="https://assets.northflank.com/2020/11/cron-jobs.png" class="kg-image" alt="Advanced Job Controls" loading="lazy" width="3344" height="1880"></figure><h3></h3><h3></h3><h3></h3>]]>
  </content:encoded>
</item><item>
  <title>Northflank joins the fray</title>
  <link>https://northflank.com/blog/northflank-joins-the-fray</link>
  <pubDate>2020-07-07T08:00:00.000Z</pubDate>
  <description>
    <![CDATA[Northflank joins the fray, announcing the fullstack cloud platform and a $2.6m seed round of financing.]]>
  </description>
  <content:encoded>
    <![CDATA[<img src="https://assets.northflank.com/2020/09/meta100-1.jpg" alt="Northflank joins the fray" />
Northflank is pleased to announce its $2.6M seed round and the launch of a new full-stack cloud platform for DevOps. Northflank enables developers to easily build, run and operate their web applications, microservices and databases from a single real-time interface and API, all at a superior pace. Developers can sign up at [northflank.com](https://northflank.com/beta).

![alt text](https://assets.northflank.com/service-overview.jpg "Northflank Service Dashboard")


**Today, it is still difficult and expensive to manage a variety of web services**, taking important time away from the main focus of software engineers: *shipping great products*. To set up your services properly, you either need to invest into complex solutions and integrate them together or opt for a serverless framework, limiting the creativity of your developers.

Northflank improves the developer experience by combining many DevOps capabilities into *a single solution*. By merging integration, deployment and delivery into a seamless platform, you can connect an existing repository on GitHub, GitLab or Bitbucket and immediately deploy anything with a Dockerfile.

Northflank’s developer sandbox is accessible through a real-time UI and API that has been *built for speed and functionality*, with a variety of features such as Build, Run, Pipelines, Crons and Projects alongside a huge number of ancillary features coming together in a seamless, unified experience.

![alt text](https://assets.northflank.com/pipelines.jpg "Northflank Pipelines")


Northflank provides solutions for stateful workloads, with a quick and flexible add-on system supporting scalable instances of PostgreSQL, MongoDB and Redis.

Northflank uses a developer friendly pricing model based on resource consumption. Scale your services horizontally and vertically, along with simple one-click pause functionality. With a detailed breakdown of each resource, you can easily monitor your spending.

Teams of any size can collaborate together at no extra cost and all have access to secure role based access control, which provides a powerful and highly granular way of managing access to Northflank’s functionality on a per user and project basis.

**Northflank’s culture has revolved around flexible, remote working from its inception.** The founders Will and Fred first connected whilst playing multiplayer games as teenagers back in 2011. Eight years later, three of them building Northflank while at university, they met for the first time in early 2019 at an [accelerator weekend organised by The Family](https://salon.thefamily.co/northflank-is-doing-everything-a-startup-shouldnt-do-b54b8c6a4a51). Will and Fred then focused on development full-time, raising an angel round used to hire the fully distributed and remote team which has now grown to nine engineers and designers from across the UK, Switzerland, Ireland, Colombia and Latvia. 
Northflank has closed a $2.6M Seed round of investment led by [Kindred Ventures](https://kindredventures.com/) ([Steve Jang](https://www.linkedin.com/in/stevejang1)) in San Francisco, [Stride.VC](http://stride.vc/) ([Harry Stebbings](https://uk.linkedin.com/in/harry-stebbings-50b8b14b)) in London, and the global Amaranthine fund. The team are also supported by numerous Founder-CTOs as angel investors, including [Alexis Le-Quoc](https://www.linkedin.com/in/alexislequoc) ([Datadog](https://www.datadoghq.com/)), [Laurent Perrin](https://linkedin.com/in/laurent-perrin-9199505) ([Frontapp](https://frontapp.com/)), [Eric Nadalin](https://www.linkedin.com/in/enadalin/) ([Nexmo](https://developer.nexmo.com/)), [Louis Beryl](https://www.linkedin.com/in/louisberyl) (Earnest & Rocketplace), [Lawrence Chu](https://www.linkedin.com/in/lawrencechuattorney), [Cindy B](https://twitter.com/CindyBiSV), [Edward Lando](https://www.linkedin.com/in/edwardlando) and [Antoine Zins](https://www.linkedin.com/in/antoine-zins-82224b14/).

Northflank over the coming months will continue to make the experience lightning fast, expand on the existing supported regions, offer improved support for mono-repos and will add support for UDP services. Developers can sign-up now at [northflank.com](https://northflank.com/beta).

![alt text](https://assets.northflank.com/env-variables.jpg "Runtime Variables and Secrets")]]>
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